South African Journal of Business Management

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1 South African Journal of Business Management Volume 44 Number 2 June 2013 ISSN SAJMDC 44(2) 1 86 (2013) Contents An investigation of the efficiency of South Africa s sector education and training authorities (SETA s) M. Turner, A.K. Halabi, K. Sartorius & J. Arendse 1-9 Does race really matter? Consumer identity and advertising effectiveness in post-apartheid South Africa G.D. Johnson The influence of transformational policies on the operational competitiveness of South African businesses L.P. Krüger Community structure and centrality effects in the South African company network I. Durbach & D. Katshunga An investigation into the perceptions of business stakeholders on the benefits of enterprise architecture: The case of Telkom SA S.M. Lehong, E. Dube & G. Angelopoulos The influence of employees perceptions of organizational politics on turnover intentions in Zimbabwe s SME sector R. Chinomona & E. Chinomona Are bank loans to SMEs procyclical? Evidence from an analysis of the lending behavior of Korean banks J-Yong Seo Copyright 2001 by the Association for Professional Managers in South Africa. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, including electronic, mechanical, photographic, magnetic or other means, without the prior written permission of the copyright holder.

2 South African Journal of Business Management Produced in collaboration with the Association for Professional Managers in South Africa Scientific Editor/Wetenskaplike Redakteur E.v.d.M. Smit, University of Stellenbosch Business School, PO Box 610, Bellville 7535, South Africa Editorial Committee - national F. Ahwireng-Obeng, Wits Business School, University of the Witwatersrand N. Bhana, MANCOSA/Regent Business School D. Kotzé, Department Statistics, University of the Western Cape J. Luiz, Graduate School of Business, University of Cape Town J.A. Meyer, Independent Institute of Education (Pty) Ltd, Sandton J. Morrison, University of Stellenbosch Business School S. Nkomo, Human Resource Management, University of Pretoria H. Oosthuizen, Emeritus Professor, University of Stellenbosch Business School M. Sutherland, Gordon Institute of Business Science C.C. Theron, Department of Industrial Psychology, University of Stellenbosch S. van der Merwe, Potchefstroom Business School H. van Zyl, University of the Free State School of Management E. Venter, Nelson Mandela Metropolitan University Business School M. Ward, Gordon Institute of Business Science Editorial Committee - international R. Abratt, Huizenga School of Business and Entrepreneurship, Nova Southeastern University S. Davidson, School of Economics, Finance and Marketing RMIT Business, RMIT University M. Gruszczynski, Warsaw School of Economics, Poland A. Money, Emeritus Professor, Henley Management College in England S. Myasoedov, Institute of Business Studies (IBS-Moscow), Russia R. Ouellet, Reims Management School M.J. Page, McCallum Graduate School, Bentley College, Waltham, USA L.F. Pitt, Simon Fraser University, Vancouver, Canada G. Stockport, University of Western Australia, Australia Enquiries about membership of APM may be directed to the Secretariat, Association of Professional Managers in South Africa, PO Box 11937, Centurion, Republic of South Africa Members receive the Journal free of charge Published by the Association for Professional Managers in South Africa. Website: Publishing Editor: Marion Leurs University of Stellenbosch Business School PO Box 610, Bellville 7535, Republic of South Africa [email protected] Printed in the Republic of South Africa by US Printers: University of Stellenbosch, Stellenbosch 7600, Republic of South Africa Annual subscription (volume of four numbers): R SA private subscriptions; R SA institutional subscriptions; $ overseas subscriptions. Orders to the Association for Professional Managers in South Africa, PO Box 11937, Centurion, 0046 Republic of South Africa [email protected] SAJBM is also available online: This publication is indexed and abstracted in the following Thomson Scientific services: Social Sciences Citation Index Social Scisearch Journal Citation Reports/ Social Sciences Edition

3 S.Afr.J.Bus.Manage.2013,44(2) 1 An investigation of the efficiency of South Africa s sector education and training authorities (SETA s) M. Turner, A.K. Halabi*, K. Sartorius and J. Arendse Second author from Monash University, Gippsland Campus and School of Accounting, University of the Witwatersrand. First, third and fourth authors from School of Accounting, University of the Witwatersrand, Republic of South Africa [email protected] The performance of South African Sector Education and Training Authorities (SETA s) has been increasingly questioned. On this premise, the paper investigated the efficiency of the SETAs with respect to their utilization of funds in order to promote a range of education and training outputs was investigated. More specifically, the study investigated the quantity and quality of five training and education outputs, set by the National Skills Development Strategy (NSDS), in relation to the funding received. Furthermore, the study examined the amount of money spend on administrative expenditure by the various SETAs, as well as the SETAs management of financial reserves. In order to guide the study, as well as analyze the data, a conceptual framework to measure efficiency was based on an inputoutput model developed by Gupta and Verhoeven (2001). Data were obtained from the published accounting and annual reports for the period The results indicated only two of the SETA s were efficient with respect to their utilization of funds and that only five SETA s consistently met their own targets. The study also shows that if the SETA s funds had been applied to education and training outputs, rather than for investment purposes, training outputs could have been considerably increased. The paper has implications for the use of public funds with respect to the critical skills shortage confronting the economy *To whom all correspondence should be addressed. Introduction The success record of skills development proposals in South Africa since 2002 has been mixed (Prinsloo & Lategan, 2005). In many instances, moreover, there is an absence of coordination between various skills development initiatives. In this regard, the efficiency and alignment of South African Sector Education and Training Authorities (thereafter SETA s) operations has been increasingly questioned since 2008 (Anonymous, 2009a; Mahlong, 2009). Despite the fact that the SETA sector has been operational since 2000, South Africa still faces a major skills shortage. Grant Thornton's 2009 International Business Report, for example, stated that 41% of South Africa's privately held businesses cite the availability of a skilled workforce as the biggest constraint to growth. Some of the skill shortages in South Africa include engineers, technologists, accountants, artisans and a wide range of other technical skills (Webster, 2010; Garrun, 2009; SAICA, 2008; Letsoalo, 2007a; Letsoalo, 2007b; The Department of Labour, 2007). Further criticism of the SETA sector s performance indicates that in 2004, for example, only 14% of the registered SETA learners had completed their courses since the system was implemented. In 2007, only 19% of the registered learners completed their training between 2005 and 2007 (Mahlong, 2009). However, other research indicates that the SETA sector is on a positive trajectory but qualify this observation by stating that many problems still persist and that the sector should undertake a more defined set of key responsibilities (Marock, Harrison-Train, Soobrayan & Gunthorpe, 2008). There is also evidence that there is a wide disparity in the performance of the SETA s with some performing exceptionally well and others equally badly (Marock et al., 2008; Webster, 2010). Finally, there has been a deluge of criticism in the media with respect to the mismanagement of public funds, in particular, with respect to monies not spent to provide services or diverted for other purposes (Hamlyn, 2007; Boyle, 2009; Blain, 2009; Anonymous, 2010c). The objective of this paper is investigate the efficiency of the SETA sector with respect to the efficiency of their utilization of funds to promote a range of education and training outputs. More specifically, the study will evaluate the quantity and quality of five training and education outputs in relation to the funding received, as well as assess whether each SETA achieved its own targets. Finally, the paper will also comment on the SETA management of cash reserves. While there is literature on the operation and performance of SETA s, there is a dearth of in-depth research on their performance and a lack of application of a conceptual framework for examining efficiency. The study contributes to the performance measurement domain in the public sector by developing a conceptual framework to complement the work of Lee (2003) who suggested the use of decision theoretical utility analysis as a method to estimate SETA productivity. A limitation of our study,

4 2 S.Afr.J.Bus.Manage.2013,44(2) however, is that the interpretation of SETA performance on an input-output basis that ignores the quality of training provided. The outline of the balance of the report is as follows: Section 2 discusses the creation and background of SETA institutions. Section 3 develops a conceptual framework to measure the efficiency of government entities. Section 4 outlines the data and method and Section 5 examines the results. Finally, Section 6 reaches a conclusion and some recommendations are made. Sector education and training authorities (SETA) SETA s were established by section 9(1) of the Skills Development Act No. 97 (1998) and came into operation from 1 April 2000 (The Department of Labour, 2005). In this regard, the Department of Labour established 25 SETA s whose members include employers, trade unions, government, interested professional bodies and bargaining councils (Steyn, 2004). SETA s are responsible for the disbursement of training levies payable by all employers and are required to develop and implement an appropriate skills development plan for their sector. All SETA s have five principal objectives. The first is to prioritize critical skills for growth, development and equity. The second objective is to stimulate quality training for all in the workplace. The third objective is to promote employability and sustainable development through skills development. The fourth objective is to assist new entrants into the labour market and self employment. The fifth objective is to improve the quality and relevance of training and learning provisions. In particular, a crucial role of these organizations is to assist government implement the National Skills Development Strategy. Finally, SETA s are required to ensure that all training interventions adhere to the standards set out by the National Qualifications. (Skills Development Act No. 97 (1998)). SETA funding was previously received from the Department of Labour (from 1 November 2009, the control of the SETA s was taken over by the Department of Education, Anonymous 2009b) via the allocation of 80% of tax levy monies received from the respective sectors. In this regard, 10% is allocated for administration expenses and 70% to promote sector education and training programs. A proportion of these funds (50%) are applied to mandatory grants with the balance (20%) available for discretionary funds which can be used for projects designed to assist in the achievement of sector priorities, including the design and implementation of learnerships. Currently, the SETA s receive an annual budget of R5 billion to address the skills shortage in the economy (Boyle, 2009). The SETA s each renew a service level agreement on an annual basis (s10a of the Skills Development Act). This concerns the performance of the SETA s functions in terms of the Skills Development Act, the SETA s annual business plan and any assistance that the Director-General is to provide in order to enable the SETA to perform its functions. Conceptual framework Performance measurement in the public sector is complex and there has been much debate as to whether private sector practices can be successfully implemented (McAdam, Hazlett & Casey, 2005). The difficulty of developing performance measurement frameworks for the public sector has been complicated by the need to service the needs of a wide range of stakeholders that include various industry sectors and society, as well as motivate operations at business unit level (Neely, 2005; Johanson, Skoog, Backlund & Almquist, 2006; Johnsen, 2001; McAdam, Hazlett & Casey, 2005). Because of the need to reconcile the interests of a broad range of stakeholders, public sector performance measurement frameworks (PMF) are often compromised by an overload of performance measures (Brignall, 2002; Wisniewski & Steward, 2004; McAdam et al., 2005; Chang, 2007). In this regard, several approaches for measuring the efficiency of government expenditure have been attempted, however, these approaches do not allow for easy comparison or the use of simple proxies to gauge efficiency. A selective overview of studies examining the efficiency of government expenditure has been provided by Gupta and Verhoeven (2001) who indicate four principal approaches to measure government efficiency. Firstly, some studies have concentrated on gauging and enhancing efficiency in practical applications, often focusing on certain types of government spending in a specific country. Secondly, the efficiency of governments has been addressed in quantitative terms, using data on inputs of government spending but not on outputs. Thirdly, the efficiency of public spending has been based on using outputs but not inputs. Fourthly, a combination of both inputs and outputs has been used to assess efficiency. Finally, it has been proposed that the efficiency of Government spending can be further assessed by comparing the outputs, being the goods or services produced by the government, with the targets or goals that the government entity set with respect to the outputs (Scott, 1996). Gupta and Verhoeven (2001) state that the best way to assess government efficiently is on the basis of both inputs and outputs. The present study, therefore, concentrates on both the inputs and outputs of each SETA. The study primarily addresses the question of whether the same level of output could be achieved with less input or, equivalently, whether more output could be generated with the same level of input (Gupta & Verhoeven, 2001). With this in mind, the measures adopted in the SETA s case are based on comparisons or inputs (revenue) with measureable outputs (in objectives). Data and method One of the purposes of a SETA is to fully utilize its resources to promote training and education outcomes. In this regard, it has been assumed that a principal objective of SETA s is not to (unduly) increase its financial reserves but to utilize these for skills development. We have assumed, therefore, that SETA s that unduly increase their balance

5 S.Afr.J.Bus.Manage.2013,44(2) 3 sheet reserves are not acting according to their mandate and that an undue increase in cash reserves reflects inefficiency. The assumption that government departments do not unduly increase their cash reserves is supported in many past studies (Gupta & Verhoeven, 2001). The data for the study were both cross sectional and time series (panel data) largely of a numerical nature and were obtained from the published financial statements and annual reports of 21 SETA s for the period 2005 to 2009 (the reports of two SETA s could not be obtained). The data included a record of all income, expenditure and certain items from the statement of financial position. Furthermore, the data included a record of all the training and education outputs that had been achieved for each of the five objectives for the four year period. The objectives as listed in the annual reports were also captured and (where provided) costs were directly assigned to the objectives. The annual report was also analysed to capture information on each SETA s targets within the objectives, and whether these targets had been met over the four year period. All the data was captured on Excel spreadsheets in order for analysis purposes. The data were analyzed as follows: Firstly, two measures of efficiency were developed. The first measure was based on the conceptual framework of the Gupta and Verhoeven (2001) model. In this regard, the outputs per objective were recorded for each year and compared to each other on a percentage basis with 2005 being regarded as the base level. A percentage growth/reduction was then determined for each output for the three following years. Similarly, the input, namely, total revenue as reflected on the income statement, was compared over the four year period. Growth in the Input over the four year period was initially calculated by comparing the 2005/2006 (revenue) amount with the 2008/2009 figure. This growth (or reduction) was then compared to the list of outputs within each objective. If the percentage growth in output exceeded the percentage growth of the revenue input, this was considered efficient, however if the output was less than the revenue input this was considered inefficient (Gupta & Verhoeven, 2001). The strength of the relationships between the efficiency rating and the outputs, namely, the five objectives of each SETA was then estimated using two longitudinal models, namely, the fixed effects and random effects models. The suitability of the Random Effects model (more robust) was then determined using the Hausman Test (Hausman, 1978) to ensure if there were significant differences in the coefficients. Because the level of significance was slightly in excess of 10% the Random Effects Model was adopted. The statistical analysis and tests was performed using Stata 10 software. The efficiency of SETA operations was also evaluated from a cash management perspective. The cash reserves were analyzed over the four year period to see how much they had increased, or decreased. According to the assumption made by this study, an increase in cash reserves reflects inefficiency (Gupta & Verhoeven, 2001). Results and discussion The results first present the efficiency measures of the SETA sector before further analysis that presents the statistical significance of the relationships between the efficiency ratings of the SETA s (the outcome variable) and the achievement of objectives (predictor variables). Finally, the cash management performance of the SETA s are also presented in this section. The efficiency ratings of the SETA sector The efficiency of each SETA is illustrated in Table 1. The efficiency ratings were based on the input/output model developed as a conceptual framework, as well as whether they achieved their own targets with respect to a series of five outputs. In order to compare the results with the findings of the Marock et al. (2008) report, the efficiencies of the 21 SETA s were ranked. In order to do this, the scores for each efficiency were aggregated with a 2:1 weighting ratio in favor of the input-output model. Table 1 shows that only one SETA (FASSET) was efficient in all five objectives based on the computation developed for the input-output model. That is, it was only FASSET that showed greater increases in outputs compared to total revenue input for all five objectives. Conversely, five SETA s (ISETT, ETDP, CETA, CHIETA and SASSETA) were efficient in only one out of the five objectives. Table 1 also shows that six SETAs met all five objective targets (FASSET, FOODBEV, FIETA, BANKSETA, ISETT and ETDP), while seven SETA s (CTFL, MAPPP, LGSETA, THETA, AGRISETA, CETA and SASSETA) met only two objectives. Finally the table shows the highest ranking SETA was FASSET with an efficiency measure of 15, followed by FOODBEV with a score of 13. Two SETAs scored the lowest (4), being CETA and SASSETA. The efficiency ranking of the top performing SETAs concurs, to some degree, with the ranking of Marock et al. (2008) who also ranked FASSET, FOODBEV, CFTL, SERVICES SETA, MQA and BANKSETA amongst their top performers (see Table 1 column DPRU rating efficiency ). Conversely, the present study ranked CHIETA in the third last position, yet the Marock et al. (2008) gave this SETA an 84% efficiency rating. The present study found the worst performing SETAs were CETA and SASSETA. Again, the Marock et al. (2008) study concurs with CETA, and there is some difference in SASSETA. Interestingly, the Marock et al. report ranks ISETT as its worst performer yet the present study ranks this SETA in midrange. In this regard, the SETA achieved 1/5 for input output model and 5/5 for achieving all of its own targets. An explanation may be that this SETA (ISETT) could have set inappropriately easy targets thus inflating its efficiency. Interestingly, ETDP was also efficient in one objective for the first efficiency measure, while it met all five objective targets, and this would seem to suggest that the targets were easy to obtain.

6 4 S.Afr.J.Bus.Manage.2013,44(2) Table 1: Analysis of Efficiency of each SETA based on the Input/Output Model, and the Objective Targets Achieved. SETA# Objectives Efficient Objectives based on Input/Output model* Objective Targets Achieved Total Score for Both Efficiency Measures DPRU rating of SETAs in 2008 FASSET FOODBEV CTFL SERVICE FIETA MQA INSETA BANK SETA MERSETA W&RSETA ISETT HWSTA ETDP ESETA MAPPP LGSETA THETA AGRISETA CHIETA CETA SASSETA *2 x weighting for input/output efficiency measure. Total efficiency score e.g. Fasset 2 x = 15 # See Appendix 1 for a list of Abbreviations/Acronyms Random-effects GLS regression Both Fixed and Random Effects GLS regression models were investigated. The results, listed below, suggest Objective 3 was the only training variable that was significantly linked to the outcome efficiency variable (5% level). Group variable: SETA2 R-sq: within = 0,0507 between = 0,3884 overall = 0,2520 Random effects u_i ~ Gaussian corr(u_i, X) = 0 (assumed) Outcome Obj1 Obj2 Obj3 Obj5 Number of obs = 62 Number of groups = 21 Obs per group min = 2 avg = 3,0 max = 3 Wald chi2(4) = 12,71 Prob > chi2 = 0,012 Coef. Std.Err. z P> z [95% Conf. Interval], , , , , , ,006172, ,28 1,26 2,07 0,46 0,777 0,207 0,038 0,646 -, , , , , , , , A Hausman test indicated that no systemic significant differences (>10%) between the coefficients of the Fixed versus Random Effects models thus confirming the suitability of the Random Effects model demonstrated above. chi2(4) = 7,76 Prob>chi2 = 0,1006 The administration of cash reserves Analysis was then conducted on the cash reserves of each SETA. This analysis, illustrated in Table 2 presents cash reserves in 2006 and compares this with the cash reserves 2009 to work out the percentage increase. The results in Table 2 show that 18 of the 21 SETA s had increased their cash reserves since The three SETAs that had decreased their cash positions were THETA, MERSETA and MAPPP). Five SETAs had increased their cash position by over 100% during this time (SERVICE, FOODBEV; MQA; CETA and CHIETA). Table 2 also assumes that SETA s are inefficient if the Cash position has increased by more than 3 0%, and in this case, fifteen of the twenty one SETA s are inefficient and six are efficient. Five SETAs had increased their cash position by over 100% during this time (SERVICE, FOODBEV; MQA; CETA and CHIETA). Three SETAs that had decreased their cash positions were THETA, MERSETA and MAPPP).

7 S.Afr.J.Bus.Manage.2013,44(2) 5 Table 2: Cash reserves of SETA s SETA Cash Reserves in 2006 (000's) Cash reserves in 2009 (000's) Increase in Cash Held Inefficient if greater than 30% increase in cash FASSET 114, ,256 12,89% Efficient CTFL 32,001 50,505 57,82% Inefficient INSETA 93, ,107 80,48% Inefficient ISETT 135, ,494 43,11% Inefficient W&RSETA 431, ,605 78,25% Inefficient THETA 141,935 36, ,45% Efficient SERVICE 136, , ,42% Inefficient SASSETA 136, ,252 60,98% Inefficient FOODBEV 58, , ,31% Inefficient HWSTA 205, ,162 52,67% Inefficient MQA 217, , ,02% Inefficient MERSETA 514,583 6, ,12% Efficient MAPPP 179, ,749-3,73% Efficient LGSETA 237, ,584 46,21% Inefficient ETDP 294, ,222 0,09% Efficient ESETA 97, ,864 3,42% Efficient CETA 30, , ,25% Inefficient BANK SETA 90, ,535 66,43% Inefficient AGRISETA 121, ,231 39,45% Inefficient CHIETA 105, , ,44% Inefficient FIETA 53,274 57,877 8,64% Efficient TOTAL R3,427, 152 R5,045,180 Conclusion The objective of this study was to examine the efficiently of South Africa s SETAs. Prior research on SETA s had not developed or applied a conceptual framework with respect to measuring efficiency using an input/output model (Gupta & Verhoeven, 2001). Each SETAs targets were then examined and this lead to a ranking of the SETAs. A further measure of efficiency was examined being increases in cash reserves. Finally a Random Effects GLS regression model suggested that only Objective 3, namely, the promotion of employment and sustainable development, was significantly (5% level) linked to the outcome efficiency variable. Overall, the results showed that only one SETA s achieved all five objectives with respect to the input / output model while five achieved all five targets. The best and worst performers were fairly well supported by the limited prior research (Marock et al., 2008). The regression analysis supported these results. Further, there appears an excessive build up of cash reserves as 18 of the 21 SETA s had increased their cash position over the four years, and 15 had increased it by over 30%. The study has a number of implications for each SETA, the South African Government and the sectors that are in need of critical skills and growth. Firstly in respect of the SETA the study has shown that some are efficient and some are not efficient. While some are meeting their own targets, these might be too easy and so their inefficiencies are not being highlighted. The study has implications for the South African Government. Currently the SETA s are provided with funding of over R5 billion and yet many are not being efficiently managed. If the government was to put this funding into other areas (such as schools and universities) this may result in a better usage of funds. At the least the government should acknowledge that some SETAs are not efficient and close these down, and use these funds for those areas in critical needs. This study is not without its limitations, however these could be used to further research in this important area. Firstly, the study was essentially an exploratory exercise whose findings should be further tested by more intensive studies of the individual SETAs. It should be noted, that performance measurement in the public sector is extremely problematic given the wide range of stakeholders, as well as service outcomes (Gupta & Verhoeven, 2001). A further limitation is the fact that the data only examined four years of reports, rather than from the time the SETA s came into operation. Further studies could examine why some SETA s are much for efficient than others and provide indepth reasons for these variations.

8 6 S.Afr.J.Bus.Manage.2013,44(2) References SAinfo All at sea with SETA. [online] URL: pment/setas_overview.htm. Accessed on 12/02/2010. Dept of Education Statement by Minister Blade Nzimande on the transfer of the Skills Development and Training Sector to the Department of Higher Education and Training. [online] URL: ageid=310&id=9137. Accessed on 12/21/2009. BusinessReport 22 August Chieta row deals blow to training structures [online] on 10/25/2010. Blain, S SETA s to remain, but must be better aligned, Business Day [online] URL: Accessed on 12/21/2009. Boyle, B Blade vows to keep Setas But will change the way they work. URL: Accessed on 12/21/2009. Brignall, S The unbalanced scorecard: A social and environmental critique. In Neely, A., Walters, A. & Austin, R. (Eds.). Performance measurement and management: Research and action. Boston, MA: Performance Measurement Association, pp Chang, L The NHS performance assessment framework as a balanced scorecard approach : Limitations and implications, International Journal of Public Sector Management, 20(2): Garrun, T., Draughting a plan for SA amid the skills crisis, Workplace, February 10: 3. South Africa. Government publication Skills Development Act No 97 [online] URL: of%201998).pdf Assessed on 12/21/2009. South Africa. Government publication Skills Development Levies Act No 9 [online] URL: esact.pdf Accessed on 12/21/2009. Gupta, S. & Verhoeven, M., The efficiency of government expenditure: Experiences from Africa, Journal of Policy Modeling, 23: Hamlyn, M Seta s poorly managed, Fin24 [online] ArticleId= _ Accessed on 12/21/2009. Hausman, J.A Specification tests in econometrics, Econometrica, 46(6): Johanson, U., Skoog M Backlund, A. & Almquist, R Balancing dilemmas of the balanced scorecard, Accounting, Auditing & Accountability Journal, 19(6): Johnsen, A. 2001, Balanced scorecard: theoretical perspectives and public management implications, Managerial Audit Journal, 16(6): Lee, G J Evaluating sectoral training: A utility tool for Setas, South African Journal of Economics and Management Sciences, 6(3): Letsoalo, M. 2007a. Skills Development Act set to overhaul, Mail&Guardian [online] URL: Accessed on 12/21/2009. Letsoalo, M. 2007b. Seta results a big blow for government. Mail&Guardian [online] URL: Accessed on 12/21/2009. Mahlong, A, Shake-up for Seta system. [online] URL: icle&id=27769:shakeupforsetasystem. Accessed on 12/21/2009. Marock, C., Harrison-Train, C., Soobrayan, P. & Gunthorpe, J SETA review. Development Policy Research Unit, DPRU Working Paper 08/132. McAdam, R., Hazlett, S.A. & Casey, C Performance management in the UK public sector: Addressing multiple stakeholder complexity, International Journal of Public Sector Management, 18(3): Neely, A The evolution of performance based research in the last decade and a research agenda for the next, International Journal of Operations and Production Management, 20(12): Prinsloo, F.P.J & Lategan, A.H Identifying a National Leadership Skills Training and Development Strategy for leaders within Sector Education Training Authorities (SETAS), SA Journal of Human Resource Management, 3(1): Scott, G. C Government reform in New Zealand. IMF Occasional Paper, No Washington, DC: International Monetary Fund. SAICA SA searching in vain for accountants. [online] URL: nterfriendly.aspx?itemid=1012&portalid=0&tabid=1185.accessed on 12/02/2010.

9 S.Afr.J.Bus.Manage.2013,44(2) 7 Steyn, G. M How do professionals develop? Lessons for the effective implementation of the South African Skills Development Act, South African Journal of Education, 24(3) : The Department of Labour National Skills Development Strategy 1 April March 2010 [online] URL: 41 Accessed on 12/21/2009. The Department of Labour National Scarce Skills List 2007 [online] URL: %20Scarce%20Skills%20List% doc/view Accessed on 12/21/2009. The Department of Labour (n.d.). Sector Skills Plan Guide. [online] URL: Accessed on 15/01/2011. Webster, E Revamp set to streamline SETAs, Sunday Times, May 2: 6. Wisniewski, M. & Steward, D Performance measurement for stakeholders: The case of Scottish local authorities, International Journal of Public Sector Management, 17(3):

10 8 S.Afr.J.Bus.Manage.2013,44(2) Appendix 1: Acronyms for SETA s AgriSETA BANKSETA CETA CHIETA CTFL ESETA ETDP SETA FASSET FIETA FoodBev Seta HWSETA INSETA ISETT SETA LGSETA MAPPP SETA MERSETA MQA PSETA SASSETA SERVICES SETA SETA THETA W&RSETA Agriculture Sector Education and Training Authority Banking Sector Education and Training Authority Construction Education and Training Authority Chemical Industries Education and Training Authority Clothing, Textiles, Footwear and Leather Education and Training Authority Energy Sector Education and Training Authority Education, Training and Development Practices Sector Education and Training Authority Sector Education and Training Authority for Finance, Accounting, Management Accounting and Other Financial Services Forest Industries Education and Training Authority Food and Beverages Manufacturing Sector Education and Training Authority Health and Welfare Sector Education and Training Authority Insurance Sector Education and Training Authority Information Systems, Electronics and Telecommunications Technologies Sector Education and Training Authority Local Government Sector Education and Training Authority Media, Advertising, Publishing, Printing and Packaging Sector Education and Training Authority Manufacturing, Engineering and Related Services Sector Education and Training Authority Mining Qualifications Authority Public Service Sector Education and Training Authority Safety and Security Sector Education and Training Authority Services Sector Education and Training Authority Sector Education and Training Authority Tourism, Hospitality and Sport Education and Training Authority Wholesale and Retail Sector Education and Training Authority

11 S.Afr.J.Bus.Manage.2013,44(2) 9 Appendix 2: List of annual financial reports used to analyse sector education and training authorities (SETA s) Acronym of SETA Name of SETA Annual Financial Report - Year AgriSETA Agriculture Sector Education and Training Authority BANKSETA Banking Sector Education and Training Authority CETA Construction Education and Training Authority CHIETA Chemical Industries Education and Training Authority CTFL Clothing, Textiles, Footwear and Leather Education and Training Authority ESETA Energy Sector Education and Training Authority ETDP SETA Education, Training and Development Practices Sector Education and Training Authority FASSET Sector Education and Training Authority for Finance, Accounting, Management Accounting and Other Financial Services FIETA Forest Industries Education and Training Authority FoodBev Seta Food and Beverages Manufacturing Sector Education and Training Authority HWSETA Health and Welfare Sector Education and Training Authority INSETA Insurance Sector Education and Training Authority ISETT SETA Information Systems, Electronics and Telecommunications Technologies Sector Education and Training Authority LGSETA Local Government Sector Education and Training Authority MAPPP SETA Media, Advertising, Publishing, Printing and Packaging Sector Education and Training Authority MERSETA Manufacturing, Engineering and Related Services Sector Education and Training Authority MQA Mining Qualifications Authority PSETA Public Service Sector Education and Training Authority SASSETA Safety and Security Sector Education and Training Authority SERVICES SETA Services Sector Education and Training Authority THETA Tourism, Hospitality and Sport Education and Training Authority W&RSETA Wholesale and Retail Sector Education and Training Authority

12 S.Afr.J.Bus.Manage.2013,44(2) 11 Does race really matter? Consumer identity and advertising effectiveness in post-apartheid South Africa G.D. Johnson* Dauphine Recherches en Management (DRM), UMR CNRS 7088, Université Paris-Dauphine, Place du Maréchal de Lattre de Tassigny, Paris, France This study examines the effects of consumers multiple identities on advertising effectiveness. Based on the Ingroup Bias Theory, the study investigates how the race of an advertisement model, in comparison to another social identity (i.e. socioeconomic position), influences advertising effectiveness. Results indicate that, even though race matters, the socioeconomic position of the model also predicts advertising effectiveness depending on viewers racial group. Findings suggest ways to design successful cross-cultural advertising strategies in post-apartheid South Africa. *To whom all correspondence should be addressed. Introduction The demise of the apartheid regime has engendered considerable changes in the targeting practices of the South African advertising industry. While representing the cultural diversity of the country has become a new corporate responsibility, the emergence of a growing black middle class as a financially viable target has encouraged advertisers to increasingly feature black models in their advertisements (Cassim & Monteiro, 2001; Milner, 2007; North & Millard, 2003). Advertisers assume that black viewers will react positively to advertisements featuring black models. However, this assumption may be overly simplistic as other social identities may predict advertising effectiveness (Brumbaugh, 2009; Williams, 1992). Little South African advertising research has examined the impact of models race on advertising processing. Extant studies show that the racial group of a model has little to no effect on advertising effectiveness (Orpen, 1975; Grier & Brumbaugh, 2007; Grier & Deshpandé, 2001). Yet these studies overlook other social identities such as gender, marital status, and/or socioeconomic position as possible predictors of advertising effectiveness. Understanding the effect of a model s depiction, beyond his/her perceived racial group, is primordial for advertisers to design successful cross-cultural advertising strategies and to reach a growing non-white South African target market which remains highly unknown and misunderstood by marketers. The present study examines how the race of a model, in comparison to another social identity (i.e. socioeconomic position), influences advertising effectiveness. Model s race and advertising effectiveness Studies examining the role of a model s race on advertising effectiveness started in the mid-1960s in the United States (see Cui, 2001). These studies resulted from the emergence of a black middle class and the strategic imperative for marketers to target this segment without alienating white consumers (e.g. Barban & Cundiff, 1964; Cagley & Cardozo, 1970; Stafford, Birdwell & Van Tassel, 1970). The findings of these first studies were equivocal (see Whittler, 1991 for a review). Some of them suggested a positive reaction of white viewers towards advertising featuring black models (Barban & Cundiff, 1964), whilst some found a neutral reaction (Choudhury & Schmid, 1974) and others a negative reaction (Cagley & Cardozo, 1970). These inconsistent results were due to the variety of measures used (see Green, 1999), such as purchase intention (Schlinger & Plummer, 1972), brand recall (Choudhury & Schmid, 1974), pupil dilation (Stafford et al., 1970), or sales (Solomon, Bush & Hair, 1976). Nevertheless, the overall conclusion was that an advertisement featuring black models generates positive attitude amongst black respondents and not extremely negative attitude amongst white respondents (Whittler, 1991). This comforted advertisers about the use of black models to target black consumers without fearing a negative reaction from white consumers (e.g. Schlinger & Plummer, 1972; Stafford et al., 1970). However, these studies paid little attention to the underlying psychological processes explaining viewers reactions to black or white models (Whittler, 1989). Subsequently, the In-group Bias Theory (Brewer, 1979) has been used to provide a sound theoretical framework for understanding consumers reactions (Green,

13 12 S.Afr.J.Bus.Manage.2013,44(2) 1999; Qualls & Moore, 1990; Williams, Qualls & Grier, 1995). In-group bias theory This theory suggests that individuals, as members of a social group, have a positive bias towards the other members of their group (Brewer, 1979). When a social interaction occurs, individuals produce comparisons and/or evaluations that enable them to judge their level of social distance and similarity from the contact. As a function of this social distance/similarity, the bias towards their own group will influence their social behaviour. Applied to advertising research, this theory implies that black consumers express more favourable responses towards advertisements featuring black models, similarly for white consumers towards advertisements featuring white models and so on (Green, 1999; Qualls & Moore, 1990; Williams et al., 1995). However, race is not the only relevant social identity for predicting advertising attitude (Williams, 1992). Even though race-based identification tends to overshadow both gender- and role-based identification (see Brumbaugh, 2009), Williams (1992) contends that, although a black viewer may respond more favourably to advertisements featuring black models (versus advertisements without black models), a black middle-class professional may also respond more favourably to advertisements with black middle-class professional models (versus advertisements featuring black models from other socioeconomic segments). However, little research has tested this assumption and evaluated the effect of consumers multiple identities on advertising effectiveness. In South Africa, little research has examined the impact of a model s race on advertising effectiveness. A literature search reveals only three advertising studies examining the South African context (Orpen, 1975; Grier & Brumbaugh, 2007; Grier & Deshpandé, 2001). Orpen (1975) shows that coloured viewers are not influenced by a model s racial group (coloured versus white), whereas Grier and colleagues (Grier & Brumbaugh, 2007; Grier & Deshpandé, 2001) find that black and coloured viewers (relative to white viewers) give higher ratings to advertising regardless of the model s race. However, none of these studies has taken into consideration the socioeconomic position occupied by the model in the advertisement. What is missing is an understanding of the effect of the depiction of a model beyond his/her perceived racial group. Understanding such effects is critical for South African advertisers in order to design adequate and successful cross-cultural advertising strategies. Problem statement Since the 1930s advertisers have recognised the economic potential of African/black consumers (Bryce, 1990). However, some advertisers were reluctant to target the black community, afraid of the white consumers potential backlash, whereas others had reservations about the black consumers financial muscle and their intellectual capacity to understand the advertising message (for an illustration of the debate see Society of Advertisers, 1958:4-16). As a result, there was limited focus on the black community by advertisers until the acknowledgment of the black consumer as a power in the late 1970s (Iheduru, 2004). This trend was given increased impetus by the demise of apartheid. The demise of the apartheid regime engendered numerous changes in the South African advertising industry. While using multi-racial features has become a social responsibility (Johnson, 2009), the emergence of a growing black middle class as a financially viable target (Olivier, 2007) has encouraged advertisers to increasingly feature black models in their advertising strategy (Cassim & Monteiro, 2001; Milner, 2007; North & Millard, 2003). Advertisers assume that by featuring black models in their advertisements, black viewers will feel targeted and so will display positive attitude towards the advertisements and the brand due to an in-group bias. However, this may be an overly simplistic analysis of the situation in South Africa. Indeed, although the end of the apartheid regime accelerated the emergence of a black middle class and, in so doing, diminished interracial inequality, it also increased intraracial inequality (Hamann, Agbazue, Kapelus & Hein, 2005). Considering this growing inequality, the similarity perceived by the viewer with the model might not be simply a function of the perceived race of the model. Accordingly, and consistent with Williams (1992) argument described earlier, viewers can perceive the model as similar in terms of race but also in terms of gender, marital status, and/or socioeconomic position. This possibility has been overlooked by extant advertising literature. This study examines which social identity (race versus socioeconomic position) is the more relevant/salient in South African consumers identity to predict advertising effectiveness. Research objective This study examines if race, in comparison to another social identity (i.e. socioeconomic position), explains viewers advertising responses. The first step of this study determines the effect of a model s social identity (i.e. racial group versus socioeconomic position) on advertising effectiveness. The second step considers whether this reaction is explained by viewers felt targetedness, i.e. viewers cognitive beliefs that the advertiser is attempting to reach them (Aaker, Brumbaugh & Grier, 2000). The operationalisation of variables Three independent variables are considered in this study: models racial group, model s socioeconomic position, and participants racial group. The models racial group was operationalised by two advertisements varying in their racial composition: an advertisement with a white model and another one with a black model. To operationalise models socioeconomic position, both black and white models were represented either in a profession that matches the one of the participants (i.e. lecturer) or not (i.e. worker). For the purpose of this study, the independent variable of race considers only black and white demographics. The first group refers to individuals whose ancestors inhabited South African soil before the European settlement, whereas the second group consists of people of European origin

14 S.Afr.J.Bus.Manage.2013,44(2) 13 (Afolayan, 2004). However, it is acknowledged that race exists as a social construction with much meaning beyond the physical definition (Bhopal, 2004). Two dependent variables were used, namely advertising effectiveness and felt targetedness. Advertising effectiveness is operationalised as advertising attitude, which is defined as a predisposition to respond in a favourable or unfavourable manner to a particular advertising stimulus during a particular exposure occasion (Lutz, 1985: 53). On the other hand, felt targetedness represents viewers belief that they are the intended audience for the advertisement (Aaker et al., 2000). This belief has been found to be a key ad cognition variable in culturally-targeted advertising processing (Aaker et al., 2000). Research hypotheses The hypotheses tested in this study are: H 1 : Viewers advertising attitude is influenced by both a model s racial group and profession. Black (white) viewers express higher advertising attitude when the model depicted in the advertisement is black (white), but only when this model has the same profession as them. H 2 : Both a model s racial group and profession influence viewers belief that they are the intended audience for the advertisement. Black (white) viewers feel more targeted by an advertisement when the model depicted is black (white), but only when this model has the same profession as them. H 3 : The extent to which viewers believe that they are the intended audience for the advertisement influences their attitude towards the advertisement. Research design and methodology The design of the experiment involved a 2 (model s racial group: black versus white) 2 (model s profession: lecturer versus worker) 2 (respondent s racial group: black versus white) between subjects factorial design. A sample of 147 male university lecturers (79 black and 68 whites) ranging from 24 to 72 years-old (M black = 41,10, SD black = 9,48; M white = 42,79, SD white = 11,70) was recruited. This sample was selected to control for socioeconomic position and other extraneous variables that could have influenced viewers identity-based decision making (e.g. gender). The stimuli consisted of four full-colour photographic advertisements for a fictitious brand. The product advertised was life insurance, as it does not possess any cultural stereotype and is not race-related. The advertisement was embedded in a press article about the National Arts Festival in order to make the experiment as realistic as possible and to reach a better external validity. This study used digital techniques to control the vast majority of extraneous variables present in an advertisement. Firstly, it was decided that only male models should be used within the advertisements to match with our sample population and control for gender-based identity. Furthermore, the profession of the model in the advertisement was manipulated through the clothing and surroundings of the model. To represent the models with a profession similar to the demographic characteristics of the sample (i.e. lecturer), the models were depicted in an office, writing notes and with books in the background. On the other hand, the models with a profession different from the demographic characteristics of the sample (i.e. worker) were represented in a workshop wearing blue overalls. A pre-test sample evaluated correctly the profession of the models, i.e. lecturer versus worker. Moreover, another pre-test sample rated the photographs of the models separately to guarantee that models attractiveness was similar across the manipulations. Similarly, the age of the four models was perceived as close (ranging from 45 to 47 years-old) and congruent with the sample. The survey was initially presented as a research project about media. Participants completed the study individually. Participants were randomly assigned to one of the four advertisements (black lecturer, white lecturer, black worker, or white worker). After reading the article for about five minutes, participants were given the questionnaire booklet to complete. Advertising attitude was used to measure the effectiveness of the advertisement. Participants indicated their attitude toward the advertisement using two semantic differential scales anchored with unfavourable/favourable and boring/interesting (MacKenzie, Lutz & Belch, 1986). The internal consistency reliability of this measure was respectable, with the scale attaining Nunnally s (1978) suggested Cronbach s alpha of 0,70 or higher (α = 0,807). Participants indicated the extent to which they believed that the advertisement was intended for them (Aaker et al., 2000) with three items (i.e., I feel the advertisement was intended for people like me, I do not believe I was in the target market the company created the advertisement for [reverse coded], and the advertiser made that advertisement for people like me) which were averaged into a single measure of felt targetedness (α = 0,736). Finally, respondents racial group was measured by the emic self-report method of ethnic identification, i.e. participants ticked the racial group to which they belong, or wrote in one that was not listed (Grier & Deshpandé, 2001). Empirical results The results of the experiment are presented and discussed according to the hypotheses presented earlier. Series of three-way analyses of variance (ANOVA) for the two first hypotheses are given below. Only significant main effects and interactions are mentioned in the results. The results for hypotheses H 1 and H 2 are presented in Table 1. H1: Advertising attitude H 1 proposes that both a model s racial group and profession influence viewers advertising attitude. To test this hypothesis, a three-way ANOVA was conducted in which participants racial group, model s racial group and model s profession were used as independent variables and attitude towards the advertisement as the dependant variable. Results reveal a main effect of the model s profession on advertising attitude, F(1,139) = 13,305, p < 0,001. Regardless of their

15 14 S.Afr.J.Bus.Manage.2013,44(2) race and of the model s race, viewers express more favourable advertising attitude when exposed to a lecturer (M = 3,37) than to a worker (M = 2,54). Unexpectedly, the two-way interaction between participants racial group and the model s racial group is found not to influence viewers advertising attitude, F(1,139) = 2,292, p > 0,10. Examination of the means shows that the race of a model does not have the same impact for black and white viewers. The results are depicted in Figure 1. The results indicate that black viewers express higher advertising attitude when the model depicted in the advertisement is black (M = 3,31) than white (M = 2,60, p < 0,05), whereas white viewers attitude is not influenced by the race of the model (M black = 2,94; M white = 2,91, p > 0,90). Furthermore, the three-way interaction between participants racial group, model s racial group and model s profession is found not to be significant, F(1,139) = 0,716, p > 0,30. Consequently, the profession of the model does not moderate the influence of race on advertising attitude and H 1 is rejected. H2: Felt Targetedness H 2 assumes that both a model s racial group and profession influence viewers felt targetedness. To test this hypothesis, a three-way ANOVA was conducted in which participants racial group, model s racial group and model s profession were used as independent variables and felt targetedness as the dependant variable (see Table 1). Results reveal a significant main effect of the model s profession on felt targetedness, F(1,139) = 31,091, p < 0,001. Regardless of their race and of the model s race, viewers feel more targeted when exposed to a lecturer (M = 3,79) than to a worker (M = 2,58). A two-way interaction between participants racial group and model s racial group is also found to influence viewers felt targetedness, F(1,139) = 31,201, p < 0,001. The results are depicted in Figure 2. Table 1: Hypotheses H 1 and H 2 Three-way ANOVA Dependent Variables H 1 : Advertising Attitude H 2 : Felt Targetedness F Sig, F Sig, Corrected Model 2,880 0,008* 11,186 0,000* Intercept 719,691 0,000* 866,668 0,000* Model s racial group 2,257 0,135 0,026 0,871 Model s profession 13,305 0,000* 31,091 0,000* Respondent s racial group 0,069 0,793 0,111 0,739 Model s racial group Model s profession 0,000 0,992 3,070 0,082 Model s race Respondent s racial group 2,292 0,132 31,201 0,000* Model s profession Respondent s racial group 0,153 0,696 0,299 0,586 Model s racial group Model s profession Respondent s racial group 0,716 0,399 11,412 0,001* * = p < 0,05 Figure 1: H 1 Two-way interaction viewers racial group Model s racial group on advertising attitude

16 S.Afr.J.Bus.Manage.2013,44(2) 15 The results show that black viewers feel more targeted when the model depicted in the advertisement is black (M = 3,86) than white (M = 2,55, p < 0,001). Similarly, white viewers feel more targeted when the model is white (M = 3,72) than black (M = 2,57, p < 0,01). Finally, the three-way interaction between participants racial group, model s racial group and model s profession is found to be significant, F(1,139) = 11,412, p < 0,05. The results are depicted in Figure 3. The results indicate that white viewers feel significantly more targeted by the white model depicted as a lecturer (M = 4,85, p < 0,001) than by the white model depicted as a worker (M = 2,65), whereas the representation of the black model does not influence white viewers felt targetedness (M black worker = 2,58, M black lecturer = 2,56, p > 0,90). On the other hand, black viewers felt targetedness is not influenced by the interaction between the model s racial group and the model s profession, F(1,75) = 1,485, p > 0,231. However, a closer examination of the means reveals that black viewers feel more targeted by the black lecturer (M = 4,70) versus the black worker (M = 3,02, p < 0,001), and feel more targeted by the white lecturer (M = 3,09) than by the white worker (M = 2,12, p < 0,02). Hence, H 2 is accepted. H2: Regression linear felt targetedness on advertising attitude. The third and last hypothesis examines the influence of felt targetedness on advertising attitude. In other words, H 3 assumes that the extent to which viewers feel targeted by an advertisement influences viewers advertising attitude. A regression analysis, presented in Table 2, tests this hypothesis. Figure 2: H 2 Two-way interaction viewers racial group Model racial group on felt targetedness Figure 3: H 2 Three-way interaction viewers racial group Model s racial group Model s profession on felt targetedness Table 2: Hypothesis H 3 Linear regression * = p < 0,05 Dependent variable: Linear regression Advertising Attitude F Df Sig. Std. β R R² Felt Targetedness 32,437 1, 145 0,000* 0,428 0,428 0,183

17 16 S.Afr.J.Bus.Manage.2013,44(2) The result of the linear regression shows a significant relationship between the felt targetedness on advertising attitude (Std. β = 0,428, p = 0,000) and H 3 is accepted. Conclusion The results of this study provide some important insights in the field of targeted advertising in South Africa. Results indicate that both black and white viewers express higher advertising attitude when the model is depicted as a lecturer regardless of his racial group. Such findings highlight the role of socioeconomic position on advertising effectiveness as viewers have a more favourable attitude when exposed to a model that has the same position as them. Typically, while the results are consistent with previous US studies that shows that the racial group of the model matters for social minority viewers only (i.e. black viewers, see Aaker et al., 2000), findings show that a perfect interaction between consumers multiple identities is not necessary to influence advertising effectiveness. Simply put, the model does not need to be similar in terms of both race AND profession to influence advertising effectiveness. In this study, being depicted as a lecturer is enough to produce persuasion amongst black and white lecturers. Furthermore, despite the strong relationship between felt targetedness and advertising attitude (see H 3 ), the results associated with H 2 suggest that both constructs may work differently. In particular, findings indicate that although viewers correctly perceive that advertisers aim to target them based on both their race AND profession (see Figure 3), their final advertising response is determined mostly by the socioeconomic position of the model. Limitations of the study The main limitation of this study concerns sampling from a university male lecturer population. Although this restricted choice aimed to control for profession and gender, further research needs to investigate a larger sample (the study may have failed to find statistically significant results because of the small sample size), broader demographic groups (amongst both respondents and models) as well as additional social identities besides race and profession. Specifically, advertising responses from a sample of workers may be compared to the present results. In the same vein, more professions within the same socio-economic position may be investigated. Moreover, recruiting respondents at universities may have primed respondents as it makes their profession a more salient self-identity than it would otherwise be. Future research may examine more neutral environments. Further research can also include consumers responses to a broader panel of products from fictitious and real brands. Managerial implications The managerial implications resulting from this study are rich for South African advertisers. This study confirms that using black models has a positive effect on black consumers persuasion. However, the findings also suggest that a model s socioeconomic position may have a greater impact than race on predicting advertising effectiveness across viewers. As a result, advertisers should not merely take into consideration the racial composition of their target market but should also consider other social identities as critical. Thus, advertisers need to understand the entire range of social categories their target market belongs to, identify the more salient/relevant identity in function of the context and target accordingly and respectfully. Only then will advertisers be able to implement successful crosscultural marketing strategy in post-apartheid South Africa. References Aaker, J.L., Brumbaugh, A.M. & Grier, S.A Nontarget markets and viewer distinctiveness: The impact of target marketing on advertising attitudes, Journal of Consumer Psychology, 9(3): Afolayan, F Culture and customs of South Africa. Wesport: Greenwood Press. Barban, A.M. & Cundiff, E.W Negro and white response to advertising stimuli, Journal of Marketing Research, 1(4): Bhopal, R Glossary of terms relating to ethnicity and race: for reflection and debate, Journal of Epidemiology & Community Health, 58(5): Brewer, M In-group bias and the minimal intergroup situation: A cognitive-motivational analysis, Psychological Bulletin, 86: Brumbaugh, A.M Why do I identify with thee? Let me count three ways: How ad context influences race-based character identification, Psychology & Marketing, 26(11): Bryce, A A pictorial history of advertising in South Africa. Cape Town: Don Nelson. Cagley, J.W. & Cardozo, R.N White response to integrated advertising, Journal of Advertising Research, 10(April): Cassim, S. & Monteiro, M Black role portrayals in South African television advertising. Ecquid Novi, 22(1): Choudhury, P.K. & Schmid, L.S Black models in advertising to blacks, Journal of Advertising Research, 14(June): Cui, G Marketing to ethnic minority consumers: A historical journey ( ), Journal of Macromarketing, 21(1): Green, C.L Ethnic evaluations of advertising: Interaction effects of strength of ethnic identification, media placement, and degree of racial composition, Journal of Advertising, 28(1): Grier, S.A. & Brumbaugh, A.M Compared to whom? The impact of status on third person effects in

18 S.Afr.J.Bus.Manage.2013,44(2) 17 advertising persuasion in a South African context, Journal of Consumer Behaviour, 6(Jan.-Feb.): Grier, S.A. & Deshpandé, R Social dimensions of consumer distinctiveness: Influence of social status on group identity and advertising persuasion, Journal of Marketing Research, 38(2): Hamann, R., Agbazue, T., Kapelus P. & Hein, A Universalizing corporate social responsibility? South African challenges to the international organization for standardization s new social responsibility standard, Business and Society Review, 110(1): Iheduru, O.C Black economic power and nationbuilding in post-apartheid South Africa, Journal of Modern African Studies, 42(1): Johnson, G.D The social dimension of multi-racial advertising: Its impact on consumers attitude, South African Journal of Business Management, 40(2): Lutz, R.J Affective and cognitive antecedents of attitude toward the ad: A conceptual framework. In Alwitt, L. & Mitchell, A. (Eds.). Psychological processes and advertising effects. Hillsdale, NJ: Lawrence Erlbaum Associates. Solomon, P.J., Bush, R.F. & Hair Jr., J.F White and black consumer sales response to black models, Journal of Marketing Research, 13: Stafford, J.E., Birdwell, A.E. & Van Tassel, C.E Integrated advertising White backlash?, Journal of Advertising Research, 10(2): Whittler, T.E Viewers processing of actor s race and message claims in advertising stimuli, Psychology and Marketing, 6(4): Whittler, T.E The effects of actors race in commercial advertising: Review and extension, Journal of Advertising, 20(1): Williams, J.D Reflections of a black middle-class: Caught between two worlds or getting the best of both? In Sherry, J. & Sternthal, B. (Eds.). Advances in Consumer Research, 19. Provo; UT: Association for Consumer Research. Williams, J.D., Qualls, W.J. & Grier, S.A Racially exclusive real estate advertising: Public policy implications for fair housing practices, Journal of Public Policy and Marketing, 14: MacKenzie, S.B., Lutz, R.J. & Belch, G.E The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations, Journal of Marketing Research, 23: Milner, L.M Race portrayals in Ghana, Kenya, and South Africa television advertisements, Journal of African Business, 8(2): North, E. & Millard, S Children and race in South African magazine advertising: Pre- and post-apartheid, Ecquid Novi, 24(1): Nunnally, J Psychometric theory. New York: McGraw-Hill. Olivier, D South Africa poised to become a loyalty marketing gem, Journal of Consumer Marketing, 24(3): Orpen, C Reactions to black and white models, Journal of Advertising Research, 15(5): Qualls, W.J. & Moore, D.J Stereotyping effects on consumers evaluation of advertising: impact of racial differences between actors and viewers, Psychology & Marketing, 7(2): Schlinger, M.J. & Plummer, J.T Advertising in black and white, Journal of Marketing Research, 9(May): Society of Advertisers Report of the first advertising convention in South Africa. Johannesburg: Statistic Holding.

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21 S.Afr.J.Bus.Manage.2013,44(2) 19 The influence of transformational policies on the operational competitiveness of South African businesses L.P. Krüger Operations, Quality and Project Management, University of South Africa, PO Box 392, Unisa 0003, Republic of South Africa Since the dawn of democracy in South Africa in 1994, transformational policies such as black economic empowerment (BEE) and affirmative action (AA) have increasingly and inextricably become part of the everyday political, economic and social life of its populace. As a result, South African businesses are subject to a whole array of mandatory regulations which ostensibly influence their operational capabilities to effectively and efficiently compete in national and global markets. In a survey of the largest 500 (including the top 100 JSE listed) companies in South Africa, it appears that transformational policies are positively supported and endorsed, although their impact on the operational competitiveness of these companies is largely unclear and unknown. A number of warning signs, however, are now being detected from reports in the popular media and in the academic literature about the possible negative consequences of such policies. BEE malpractices, which basically result in the continuation of past injustices against the majority of poor and unskilled people of the country, are becoming increasingly evident. Even more alarming is the fact that the corruption, nepotism and self-enrichment that accompany most BEE transactions are attributed to the ruling ANC political elite. *To whom all correspondence should be addressed. Introduction The current South African government, formed by the African National Congress (ANC) following the first, allparty democratic elections in April 1994 (which the ANC won with a more than two-thirds majority), has done away with or altered previous legislation and enacted many new pieces of legislation laws that impact on the daily political, economic and social lives of the country s populace. Accompanying these changes is a full array of policies, procedures and legal requirements that have been introduced over the last 15 years, leading to widespread sweeping changes designed to transform South Africa into a nonracist, non-sexist, peaceful and prosperous society. In the business world, these transformational policies, which include black economic empowerment (BEE) and affirmative action (AA), have also directly impacted on the way businesses are managed, including their operations management. From an operations management perspective, Slack, Chambers and Johnston (2010) note that a business designs, plans and controls its operational processes in order to achieve certain performance objectives that its customers or clients regard as important. The performance objectives usually a combination of improved quality, lower costs, high speed, greater dependability and/or more flexibility represent the competitive operations priorities (COPs) that management will attempt to pursue. In this context (i.e. transformational policies and business) it is therefore necessary to consider the potential influence of BEE and AA on the operational capabilities and hence the competitiveness of South African businesses. Background: The 2009 empirical study competitive operations priorities In 2009 a study was commissioned 1 using a sample of the largest 500 (including the top 100 Johannesburg Securities Exchange [JSE] listed) companies in South Africa. This study focused on determining which of the specified COPs, such as low cost, high quality, speed, dependability and flexibility, were deemed to be the most important for competing in national (local) and/or international markets in the immediate and medium-term future. The primary purpose of this study was to compare the 2009 results with those of a previous study completed by Krüger (1997) in order to determine whether any shifts in priority and/or relative strengths had occurred over the past 13 years. The 2009 study was also broader its focus and included service providers (rather than only traditional manufacturers). The 2009 study had a further, explicit secondary objective, namely to investigate the influence of so-called transformational policies such as BEE and AA on the competitiveness of South African businesses. While the results for the main part of the 2009 study (i.e. the current and future importance of COPs in competing better both nationally and internationally) were reported in an earlier article by Krüger (2010), this article focuses on the results of part D of the survey questionnaire, namely the influence of transformational policies on the COPs and general competitiveness of South African businesses. It should be recognised from the outset that this particular topic is 1 Ms Ilja de Boer was appointed as the research consultant to assist with the design and development of the electronic questionnaire, the administration of the survey, data capturing and subsequent statistical analysis.

22 20 S.Afr.J.Bus.Manage.2013,44(2) generally perceived as a sensitive issue in the context of South Africa s new democracy, given its past history of political, economic and social injustices based on purely racial divisions. Literature review Introduction The theoretical foundation that provided the platform for the initial 1996 and the subsequent 2009 research projects is well documented in a comprehensive research report, and summarised in (Krüger, 1997: ). The following three main points of focus in the literature review were covered in that article: (1) the evolution of manufacturing-based strategies and their potential to improve business s competitive capabilities (Hayes & Wheelwright, 1984; (2) the use of various aggregated terms such as strategic manufacturing priorities (SMPs) and performance objectives (POs) to indicate a collective of required operations capabilities (i.e. high quality, low cost, high speed, greater dependability and more flexibility) (Slack et al., 2010); and (3) accepting the need to make trade-offs between the different SMPs owing to differences in customer requirements, competitor actions and the specific stage of the product or service in its life cycle (Hill, 1985). In an update of the literature from 1996 to 2009, Krüger (2010) noted a number of important developments in the context of operations strategies. These were as follow: (1) the continued interest in the research topic (operations strategy) (2) the improved sophistication of the research conducted in the field and the significant increase in empirical testing of various theoretical models of operations strategy for casual interrelationships (3) the enlarged scope of the research topic through a number of new linkages reported with other theoretical constructs, not only in the operations domain, but also in the context of financial, marketing and supply chain management (4) the reaffirmation of the main theoretical focus points (see previous section), from which the conceptual framework for the initial research was developed, which improves the support for the framework (5) the possible link of the specific research focus to some broader or more general concepts (including competiveness and sustainability), which indicate that certain niche competences and expertise can be developed by companies in specific industry clusters (6) the continued apparent lack of universal consensus on the use of terminology, specific performance factors or objectives, methods of measurement, levels of acceptable to superior performance and models of operations strategy note that the term, COPs, which was used in the 2009 survey, was derived from the concept of competitive priorities (Evans & Collier, 2007:122) and was adapted to reflect an operations management focus (7) the prevalence of similar studies being conducted in other areas of specialisation and certain new ideas directed to advancing research in operations strategy As indicated earlier, one of the new focus points (and also an explicit secondary objective of the 2009 research study) was to investigate the influence of so-called transformational policies such as BEE and AA on the operational competitiveness of South African businesses. The academic literature on the topic of transformation in South Africa is still relatively limited in depth, and includes many gaps (including BEE dealings, the challenges and the factors necessary for success) (Fauconnier & Mathur-Helm, 2008), but does not lack the intense and vibrant debate that is characteristic of the general media (Ponte, Roberts & Van Sittert, 2007). The accredited articles that have been published, however, do cover a wide array of topics in the transformation agenda. These topics range from specific industry experiences in implementation (e.g. the mining, agriculture and banking sectors) (Booysen, 2007) to much higher levels of academic endeavour (Vermeulen & Coetzee, 2006) and intellectual discourse (Du Toit, Kruger & Ponte, 2008). Another apt example of such a higher level of discourse may be found in a critique by Kruger (2010:76) of a South African television drama script that focuses on the impact of transformation in post-1994 society in which the... visual elements highlight the glamour of conspicuous consumption by the BEE elite and those who emulate them.... Clearly, South Africa in this context of transformation and the application of BEE and AA may be considered somewhat unique in comparison with other countries. Hipkin (2004:722) highlights two specific aspects of this uniqueness. Firstly, South Africa is classified as a developing country in terms of its economic position and competiveness in world markets; it finds itself in a dual world situation, because it exhibits some favourable attributes of a developed economy as well as the negative characteristics of the poorest countries, and lags behind many of its competitors. Secondly, South Africa has undergone major political changes since 1994 and must accommodate factors peculiar to the country and issues in the broader context such as affirmative action, employment equity, employee empowerment which have introduced additional dimensions to the management of South African firms. South Africa s competitive position in terms of the Institute for Management Development s (IMD s) world rankings Probably the best-known and frequently cited authoritative source of world competitiveness rankings is the IMD World Competitiveness Yearbook (WCY), which has been published by the IMD Competitiveness Centre annually since 1989 (IMD, 2010a). More than 50 countries are ranked on their overall competitiveness and on the following four leading indicators: economic performance, government efficiency, business efficiency and infrastructure. The

23 S.Afr.J.Bus.Manage.2013,44(2) 21 IMD s (2009) definition of competitiveness is as follows: How nations and businesses are managing the totality of their competitiveness to achieve greater prosperity. South Africa s overall position in 2009 (when the empirical part of this research was conducted) was 48 th out of the 57 nations ranked. This overall position improved to 44 th out of 58 in 2010, mainly owing to the improvement in government efficiency and infrastructure there was a slight decline in business efficiency and the country s economic performance remained unchanged. South Africa s overall competitiveness performance, according to the IMD ranking, including its performance in the four main indicators for the period 2006 to 2010, is illustrated in Figure 1. Most of the competing nations in the first five positions above and below South Africa s overall competitiveness ranking are also classified as developing countries (e.g. the Philippines, Peru, Hungary, Colombia and Mexico - see Table 1). South Africa s relative competitive performance in 2010 in terms of the 20 individual competitive areas is illustrated in Figure 2 and needs to be understood against the so-called competitive landscape on a scale of 1 to 5, where 5 = very good, 4 = good, 3 = average, 2 = poor and 1 = very poor. Figure 1: South Africa's competitiveness performance by IMD ranking 2006 to 2010 Key: Rank order between 1 (most competitive) and 58 (worst competitive) nation Figure 1: South Africa s competitiveness performance by IMD ranking 2006 to 2010

24 22 S.Afr.J.Bus.Manage.2013,44(2) Table 1: Developing countries by region/imd competitiveness ranking and score (2010) Region/IMD competitiveness ranking (out of 58) Africa IMD score (out of 100) Country/Nation* 44 54,092 South Africa Asia Europe including East European Middle East 87,228 80,182 76,249 73,233 64,567 60,745 56,526 65,443 64,482 63,418 62,641 54,124 54,098 51,119 51,092 49,318 47,756 47,481 40,056 39,948 Malaysia China (mainland) Korea Thailand India Indonesia Philippines Czech Republic# Poland# Kazakhstan# Estonia# Hungary# Lithuania# Turkey Slovak Republic# Russia# (though considered one of the G8 countries) Bulgaria# Romania# Croatia Ukraine 50 49,642 Jordan North & Central America 47 South America 51,481 Mexico ,669 56,531 54,178 53,890 46,935 27,970 Chile Brazil Peru Colombia Argentina Venezuela *Classification of developing countries based on the list by Australian Government for receiving overseas aid (AusAid) as updated July 2009 and the list of developing countries published by the American Mathematical Society (AMS)# for 2010.

25 S.Afr.J.Bus.Manage.2013,44(2) 23 Domestic economy International trade International investment Employment Prices Public finance Fiscal policy Intellectual framework Business legislation Societal framework Productivity and efficiency Labour market Finance Management practices Attitudes and values Basic infrastructure Tech. infrastructure Scientific infrastructure Health and environment Education Very good (1 10) Good (11 20) Average (21 30) Poor (31 40) Very poor (41 58) Represents hypothetical ranking Represents actual ranking Figure 2: South Africa s 2010 relative competitive performance in terms of the 20 IMD individual competitive factors 3.3 South Africa s economic transformation since 1994 Numerous dramatic changes have occurred in South Africa on many different fronts since the first, non-racial, all democratic elections held on 27 April The majority party (the ANC) in this and the next three elections in 1999, 2004 and 2009 formed successive governments that have enacted legislation measures aimed at South Africa s economic transformation and implementing a strategy for broad-based black economic empowerment (B-BBEE) (DTI, 2007a). For the purposes of this study, a collective concept or construct, namely transformational policies, was derived from a possible combination (theoretical proposition) of five emerging or dominant and contemporary political, social and economic philosophies. These included black economic empowerment (BEE), affirmative action (AA), Ubuntu, Afro-centralism and socio-protectionism. Note: the purpose of the research was not to investigate or analyse the construct transformational policies per se, but instead to observe the perceived influence of the collective construct on the competitiveness of businesses operating in South Africa. Of these five individual concepts, AA is probably the best known, given its introduction in the USA in the early 1960s by President John F. Kennedy under executive order While the intended beneficiaries in the USA were the socalled racial or ethnic minorities of the population, AA practice in South Africa applies to the 90%+ majority of the population (Black & Geletkanycz, 2006). Whilst AA is not legislated in South Africa per se, Kovacevic (2007) notes that probably the world s most rigorous form of it, namely BEE, has been legislated under the concept of broad-based

26 24 S.Afr.J.Bus.Manage.2013,44(2) black economic empowerment (B-BBEE). This wider encompassing form of AA has become the predominant and driving ideology of the ANC government (Hamann, Khagram & Rohan, 2008; Mohammed & Roberts, 2008). It is defined by the South African Department of Trade and Industry (DTI, 2007a) as a specific government policy to advance economic transformation and enhance the economic participation of black people in the South African economy ( Given the amount of information in terms of the rationale, codes of practice, and so forth that is available on the DTI s website ( it is quite apparent that BEE (under the legislative framework of the B-BEE Act 53 of 2003) and the 2007 B-BBEE Codes of Good Practice (DTI, 2007b) have experienced a rapid metamorphosis and become part and parcel of South Africa s everyday business life (Chabane, Goldstein & Roberts, 2006). It should be noted, however, that BEE is not without its critics. An example is Moeletsi Mbeki (brother of the former president of South Africa, Thabo Mbeki who, during his premiership from 1999 to 2008, was probably most instrumental in enacting legislation to formalise BEE) (Mbeki, 2009:61) who vehemently argues... it [BEE] strikes the fatal blow against the emergence of black entrepreneurship by creating a small class of unproductive but wealthy black crony capitalists made up of ANC politicians, some retired and others not, who have become strong allies of the economic oligarchy. He also cynically observes that BEE and its subsidiaries affirmative action and affirmative procurement have metamorphosed... they have become both the core black ideology of the black political elite and, simultaneously, the driving material and enrichment agenda which is to be achieved by maximising the process of reparations that accrue to the political elite (Mbeki, 2009:61). Other authors, such as Hamann et al. (2008:25), have noted with concern the apparent lack of progress BEE has made in rectifying the legacies of apartheid because... ten years later many of the challenges remain or have become even more acute in terms of poverty, unemployment, housing and basic services, inequality, HIV/AIDS. In addition, Kovacevic (2007:6) observes that... the program has achieved little success in eradicating poverty, increasing employment or fostering economic growth. The concept of Ubuntu is now acknowledged as also being part of South African leadership and managerial literature and practice (Smit, Cronjé, Brevis & Vrba, 2010). According to Black and Geletkanycz (2006:106), the concept of Ubuntu reflects the African spirit of humaneness that individuals display for one another encompassing caring, community, harmony, hospitality, respect and responsiveness. It further manifests in relationships with others that are reciprocal, an oral tradition of language and communication, decision-making by consensus, a broader concept of time as a healer rather that a finite commodity, the optimization of productivity through solidarity, social harmony, shared rewards, a respect for age and leadership, and a belief in a creator, Unkulunkulu, the powers of spiritual healing and an afterlife, the mesocosmos. The four salient principles of Ubuntu as summarised by Mfuniselwa Bhengu (1996), according to Black and Geletkanycz (2006:106), include the principles of morality, interdependence, the spirit of man and totality (i.e. the collective participation by all in the organisation) and may simultaneously affect the management issues of coordination, communication, competence, competitiveness, and compassion. Ubuntu thus upholds the value of a greater community (us), rather than the Western values of individualism (me), involvement and benefit. It recognises certain characteristics of African employees, such as the propensity for collaboration, a desire for demonstration of mutual respect, the necessity for oral communication, and an acknowledgement of the extended family. Finally, the last two of the five interwoven concepts defining the construct transformational policies were, by themselves, derived from informal observation and understanding of African-founded and prevalent philosophies, ideologies and socio-political and economic approaches. Afro-centralism is meant to describe an approach that is predominant in many African countries where political and economic control is centralised in the hands of the ruling party and government. The socioprotectionism concept refers to the African practice of being more socially responsive in pursuance of the inclusivity of communities. 4. Research design and methodology 4.1 Conceptual research framework The previous conceptual research framework used in Krüger s study (1997) was adopted as the basis for the 2009 study, although it was amended to allow for the enlarged scope. Figure 3 illustrates the amended conceptual research framework. This framework has five main focus points, of which point 5 is particularly relevant to this article (focus points 1, 2, 3 and 4 are discussed in a separate article). Focus point 5 deals with the impact of transitional policies on the competitive operations priorities of South African companies in terms of their general feeling towards such practices and the probable influence that such transformational policies could have on the South African companies ability to: attain targets; improve on their competitive operations priorities; and compete nationally and internationally. 4.2 Research population The research population consisted of individuals who were either the Chief Executive Officer or Managing Director the Director: Operations or who held a similar, senior managerial position in the company see table 2. The personal contact details (name, address and ) of the targeted individuals were obtained from the top 100 listed JSE companies (based on turnover) and a further selection of the top 500 of South Africa s best companies (2008 edition). The size of the companies ranged from those employing fewer than 250 people to those with more than 750 employees see Table 3. These companies operated in a wide variety of industries (see Table 4).

27 S.Afr.J.Bus.Manage.2013,44(2) 25 Competitive operations priorities (COPs) Major decision points 1-5 Operations contribution: Current strength/weakness Future importance Transitional policies impact/influence Presently: In future: 3-point scale: Quality Cost Speed Dependability Flexibility 5-point scale Nationally Quality Cost Speed Dependability Flexibility Internationally Quality Cost Speed Dependability Flexibility 5-point scale General attitude BEE AA Ubuntu Afro-centralism Socio-protectionism Ability to improve and attain targets Quality Cost Speed Dependability Flexibility Nationally Quality Cost Speed Dependability Flexibility Internationally Quality Cost Speed Dependability Flexibility 5-point scale (Yes/Not sure/no) From very weak to very strong From not important to very important 4 Areas for change in emphasis and/or improvement (compare 2 with 3) Figure 3: Conceptual research framework: 2009 From very strong negative to very strong positive 4.3 Data collection procedures While the 1996 study made use of printed questionnaires that were posted to the physical addresses of the CEOs of some 500 companies listed as manufacturers, the 2009 study was more progressive, and reflected recent IT advances. Online surveys have become popular owing to their many advantages, such as speed of delivery, quick response, convenience of respondent, simplicity in data capturing, more reliable data analysis, etc. (note, however, that there may inherent problem areas to consider as well). As with any survey (postal or on-line), measures must be put in place to ensure that the data obtained are reliable and valid. To improve reliability, the on-line survey was first sent electronically to ten of the envisaged 100 participants and these participants were asked (in addition to completing the questionnaire itself) to give their comments about the suitability, clarity, etc. of the survey instrument. Note: in all cases where electronic surveys were sent to respondents, the individual addresses were obtained and verified before the questionnaire was dispatched. A total of 144 s were sent out to the targeted research population. In the end, 104 completed responses were solicited (36 were on-line and a further 68 were telephonic interviews), which represents an excellent response rate of 72%. The telephonic interview route was later taken as a result of the low on-line response rate and because of the relative small sample size. The low on-line response rate is possibly due to time limitations (the respondents work in private companies where time is normally at a premium) and/or respondent fatigue (many complained that they were inundated with a number of on-line surveys at any point in time); alternatively, it may suggest a genuine lack of interest in the research topic. 5. Research results 5.1 Company position occupied The position occupied by the respondents in the company is shown in Table 2. The majority of respondents (43%) were senior operations managers and the second largest group (30%) consisted of respondents who were the actual managing directors or CEOs of the company. Combined, a total of 73 per cent of the respondents came from the top or upper-level positions in the company s management hierarchy. This may in fact be interpreted as a positive outcome, given the nature of the topic which is more strategic and thus the confidence in their ability to answer such questions with insight.

28 26 S.Afr.J.Bus.Manage.2013,44(2) 5.2 Company size In terms of company size, the majority (46%) employed more than 750 people, the second largest group (20%), however, consisted of companies that employed fewer than 250 people (see Table 3). Combined, probably more than 90 per cent of the companies would thus fall within the ambit of the BEE charter and would know about the requirements of this charter. They should also be fully aware of the impact that conforming to BEE regulations has on their companies operations. 5.3 Company industry involvement The industry involvement of the South African businesses employing the respondents covers some 20 different industries (Table 4). Respondents came from both traditional manufacturing companies and service providers. In short, the sample can be regarded as being representative of a wide range of industries. Table 2: Company position occupied Frequency Per cent CEO or MD 31 29,8 Director of operations or production manager 43 41,3 Other 30 28,8 Total Table 3: Company size Frequency Per cent Fewer than ,2 More than 250 but fewer than ,4 More than 500 but fewer than ,3 More than ,2 Total Table 4: Company industry involvement Frequency Per cent Food, beverage and tobacco 9 8,7 Textiles, clothing and footwear 12 11,5 Pharmaceuticals 5 4,8 Chemicals and petroleum 6 5,8 Hotel, catering and restaurants 1 1 Medical service, hospitals and clinics 1 1 Entertainment incl. sport, theatre 4 3,8 Basic metals products 6 5,8 Fabricated metals products 4 3,8 Transport equipment 2 1,9 Electronics and electrical equipment 10 9,6 Other machinery and equipment 4 3,8 Professional services 4 3,8 Trading, warehousing, wholesale 11 10,6 Property 1 1 Financial 11 10,6 Mining 1 1 Other 12 11,5 Total

29 S.Afr.J.Bus.Manage.2013,44(2) Respondents views on transformational policies The first question, in part D of the research questionnaire, tried to determine the respondents views on certain transformational policies and African managerial philosophies. The results are shown in Table 5 and the test for significance in Table 6. The majority (57%) of the respondents felt strongly positive and a further (24%) very strongly positive about BEE. Combined, therefore, this means that some 81 per cent of the respondents were supportive of BEE. In the case of AA, which is a broader concept than BEE because it also includes other minority groups irrespective of their race categorisation alone such as the disabled, the majority (45%) were strongly and a further 15 per cent very strongly positive. A large proportion (31%) indicated they had no firm opinion either positively or negatively. In the case of the African managerial philosophy of Ubuntu, the majority (54%) indicated they had no opinion, while a combined 40 per cent of respondents were strongly or very strongly positive towards the approach. Afro-centralism, a term derived from the practice of centralising all or most political and economic power in government, drew a no opinion response from the majority (70%). Otherwise, both strong negative (12%) and strong positive (17%) feelings were expressed. It thus appears that respondents were largely unfamiliar with the term. Finally, as far as socio-protectionism (which is derived from the African practice of being more socially responsive and pursuing the inclusivity of communities) was concerned, a large majority (64%) also had no opinion on the concept). Combined, 28 per cent of the respondents indicated they felt strongly or very strongly negative, with only about 8 per cent being positively inclined. Again, it seems that respondents were largely unfamiliar with the term. Table 5: General feelings toward transitional policies and African philosophies Very strong negative Strong negative BEE AA Ubuntu Afro-centralism Socio-protectionism Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent 6 5, , , , , , , ,100 No opinion 14 13, , , , ,500 Strong positive 59 56, , , , ,700 Very strong positive 25 24, , , , ,000 Total Mean 3,933 3,644 3,462 3,077 2,769 Std. deviation 0,948 0,913 0,787 0,569 0,700 Variance 0,898 0,833 0,620 0,324 0,490 Minimum Maximum Table 6: Significance test with test value = no opinion t df Sig. (2-tailed) Mean difference 95% Confidence interval of the difference Lower Upper BEE 10, ,000,93269,7484 1,1170 AA 7, ,000,64423,4667,8218 Ubuntu 5, ,000,46154,3084,6147 Afro- centralism 1, ,171, ,0338,1876 Socio- protectionism -3, ,001 -, ,3669 -,0946

30 28 S.Afr.J.Bus.Manage.2013,44(2) When compared with the median test value = no opinion, the strong positive feeling towards BEE showed the greatest significance, followed by significantly positive feelings towards AA and Ubuntu. However, the little significance that is similarly indicated for the general attitude towards Afro-centralism shows that this concept is not known to respondents and they therefore have no opinion about it. Regarding the respondents general feeling towards socioprotectionism, a significantly strong negative perception is indicated when compared with the medium test value = no opinion. 5.5 Influence of transformational policies on companies ability to attain targets in each of the competitive operations priorities (COPs) The second question in part D focused on what respondents perceived the probable impact or influence of BEE would be on the company s ability to attain its targets in each of the five COPs. The results are shown in Table 7 and the test for significance in Table 8. In most cases, the majority of respondents (above between 55 and 65%) offered no opinion of what might be the impact of such transformational policies on their competitive operations priorities. Only in the case of low cost did the majority (43%) express some negative sentiments about the probable impact of BEE and AA. When compared with the median test value = no opinion, the impact of BEE and AA on the company s ability to attain its quality targets showed some significant positive support. However, similarly compared, the respondents had no opinion about what the impact of BEE and AA would be on the company s ability to attain its targets in terms of cost, speediness, dependability and flexibility. 5.6 Influence of transformational policies on a company s ability to improve its performance in each of the COPs The third question in part D similarly focused on what respondents felt the probable impact or influence of BEE would have on the company s ability to improve its performance in each of the five COPs. Again, the results are indicated in Table 9 and the test for significance in table 10. In all cases, the majority (above between 40 and 73%) of the respondents indicated they had no opinion of what the impact could be. In the case of quality and speediness, the second largest group of respondents indicated some positive sentiments, while in the case of cost, dependability and flexibility, they were, however, more negatively inclined. Table 7: Impact/influence of BEE and AA on company to attain targets in terms of its competitive operations priorities Quality Cost Speediness Dependability Flexibility Ability to improve Ability to improve Ability to improve Ability to improve Ability to improve Freq % Freq % Freq % Freq % Freq % Very strong negative Strong negative 13 12, , , , ,5 No opinion 58 55, , , , ,5 Strong positive 28 26, , , , ,3 Very strong positive 5 4,8 4 3,8 5 4,8 5 4,8 5 4,8 Total , , , , ,0 Mean 3,240 2,846 3,125 3,115 3,144 Std. deviation 0,731 0,879 0,692 0,754 0,703 Variance 0,534 0,772 0,479 0,569 0,494 Minimum Maximum

31 S.Afr.J.Bus.Manage.2013,44(2) 29 Table 8: Significance test with test value = no opinion Ability to attain targets t Df Sig. (2-tailed) Mean difference 95% confidence interval of the difference Lower Upper Quality 3, ,001,24038,0983,3825 Cost -1, ,077 -, ,3247,0171 Speediness 1, ,068, ,0096,2596 Dependability 1, ,122, ,0313,2621 Flexibility 2, ,039,14423,0076,2809 Table 9: Impact/influence of BEE and AA on the company to improve on its competitive operations priorities Quality Cost Speediness Dependability Flexibility Ability to improve Ability to improve Ability to improve Ability to improve Ability to improve Freq % Freq % Freq % Freq % Freq % Very strong negative Strong negative 9 8, ,9 9 8, , ,6 No opinion 71 68, , , , ,8 Strong positive 18 17, , ,6 10 9, ,7 Very strong positive 5 4,9 5 4,9 5 4,9 5 4,9 Total , , , , ,0 Mean 3,185 2,874 3,136 3,058 3,069 Std. deviation 0,653 0,763 0,627 0,654 0,748 Variance 0,426 0,582 0,393 0,428 0,560 Minimum Maximum Table10: Significance test with test value = no opinion Ability to improve t df Sig. (2-tailed) Mean difference 95% confidence interval of the difference Lower Upper Quality 2, ,005,18627,0574,3151 Cost -1, ,122 -, ,2672,0320 Speediness 2, ,030,13725,0135,2610 Dependability, ,368, ,0703,1879 Flexibility, ,356, ,0783,2156

32 30 S.Afr.J.Bus.Manage.2013,44(2) When compared with the median test value = no opinion, the impact of BEE and AA on the company s ability to improve on its quality targets showed some significant positive support. However, similarly compared, the respondents had no opinion about what the impact of BEE and AA would be on the company s ability to improve on its targets in terms cost, speediness, dependability and flexibility. 5.7 Influence of transformational policies on companies ability to compete nationally in each of the COPs The fourth question specifically focused on the probable impact or influence that transformational policies such as BEE and AA could have on the company s ability to compete nationally. The results in terms of the company s ability to compete nationally are shown in Table 11 and the test for significance in Table 12. In all cases, the majority (above between 45 and 75%) of the respondents indicated that they had no opinion. Some negative sentiments were expressed about cost, while similarly, some positive sentiments were expressed about quality, speediness, dependability and flexibility. When compared with the median test value = no opinion, the impact of BEE and AA on the company s ability to compete nationally to meet its quality targets showed some significant positive support. However, similarly compared, the respondents had no opinion about what the impact of BEE and AA would be on the company s ability to compete nationally to meet its targets in terms of cost, speediness, dependability and flexibility. What is your view on this? Table 11: Impact/influence of BEE and AA on the company s ability to compete nationally Very strong negative Quality Cost Speediness Dependability Flexibility Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent Strong negative 12 11, , , , ,70 No opinion 63 60, , , , ,00 Strong positive 23 22, , , , ,50 Very strong positive 6 5,80 1 1,00 6 5,77 6 5,80 5 4,90 Total , , , , ,00 Mean 3,221 2,893 3,115 3,1827 3,155 Std. deviation 0,723 0,753 0,643 0,6793 0,668 Variance 0,523 0,567 0,414 0,461 0,446 Minimum Maximum Table 12: Significance test with test value = no opinion National t df Sig. (2-tailed) Mean difference 95% confidence interval of the difference Lower Upper Quality 2, ,004,20588,0668,3450 Cost -1, ,116 -, ,2647,0294 Speediness 1, ,114, ,0240,2201 Dependability 2, ,012,16667,0368,2966 Flexibility 2, ,020,15686,0250,2887

33 S.Afr.J.Bus.Manage.2013,44(2) Influence of transformational policies on companies ability to compete internationally in each of the COPs The fifth question specifically focused on the probable impact or influence that transformational policies such as BEE and AA could have on the company s ability to compete internationally. The results in terms of the company s ability to compete internationally are shown in Table 13 and the test for significance in Table 14. The majority (above between 44 and 70%) of the respondents again opted for the no opinion answer. However, the second largest percentage group expressed negative sentiments for all five COPs (i.e. quality, cost, speediness, dependability and flexibility). When compared with the median test value = no opinion, the respondents had no opinion about what the impact of BEE and AA would be on the company s ability to compete internationally on any of the targets set in terms of quality, cost, speediness, dependability and flexibility. Table 13: Impact influence of BEE and AA on the company s ability to compete internationally Quality Cost Speediness Dependability Flexibility Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent Frequency Per cent Very strong negative Strong negative 32 30, , , , ,2 No opinion , , , ,7 Strong positive 21 20, , , , ,3 Very strong positive 5 4,8 2 1,9 5 4,8 5 4,8 Total , Mean 2,894 2,817 2,990 3,077 3,048 Std. deviation 0,709 0,953 0,602 0,759 0,755 Variance 0,503 0,908 0,363 0,577 0,570 Minimum Maximum Table 14: Significance test with test value = no opinion International t df Sig. (2-tailed) Mean difference 95% confidence interval of the difference Lower Upper Quality -1, ,096 -, ,2542,0212 Cost -2, ,029 -, ,3862 -,0216 Speediness -, ,870 -, ,1274,1080 Dependability, ,425, ,0861,2026 Flexibility, ,688, ,1143, Conclusions and recommendations Remarkably, South Africa is the only country on the whole African continent that is included in the world competitiveness rankings conducted annually by the IMD (IMD, 2010b) under the top 50 to 60 nations. Whilst its overall position is still in the bottom quarter of nations (at 44 th ), its performance in two of the four main indicators measuring government and business efficiency are surprisingly much better (with 21 st and 31 st rankings respectively). However, and disappointingly, South Africa s performance in the other two main indicators, economic performance (56 th ) and infrastructure (51 th ), are among the lowest rank performances. South African businesses need to improve their operational competitiveness both nationally and internationally if they are to further contribute to the country s overall competitiveness. Following the major political events and transformation initiatives such as BEE and AA that have occurred in the country since 1994, South African businesses also need to comply with this legislation and the ensuing industry charters, quotas and preferential treatment of previously disadvantaged individuals or designated groups. It is therefore imperative that South African businesses determine the possible impact and/or influence this compliance has on their COPs.

34 32 S.Afr.J.Bus.Manage.2013,44(2) In the 2009 survey among the top 100 JSE listed and 500 best companies in South Africa, the majority of respondents (73%) who were senior managers of large companies (46%) employing more than 750 people over a range of some 20 different industries, the following results were evident: A significant majority were strongly or very strongly in support of BEE, while slightly fewer felt the same towards AA and the managerial philosophy emanating from Africa, namely Ubuntu. These respondents indicated they had no opinion about the new research postulated concepts of Afro-centralism or socioprotectionism. The majority of respondents did not offer an opinion about the potential influence of BEE and AA on their company s ability to attain its targets in terms of four of the COPs (cost, speed, dependability and flexibility). However, some significant positive support was indicated in terms of the influence of BEE and AA on companies ability to achieve their quality targets. Similarly, the majority of respondents did not offer an opinion about the potential influence of BEE and AA on their company s ability to improve on its targets in terms of these same four COPs. Again, some significant positive support was indicated in terms of the influence of BEE and AA on companies ability to improve on their quality targets. Disappointingly, the majority of respondents did not offer an opinion about the potential influence of BEE and AA on their company s ability to compete BOTH nationally and internationally in terms of their COPs. Only in the case of competing nationally was some significant positive support expressed in terms of the influence of BEE and AA. Given the results obtained from the survey, it is clear that, despite the fact that a significant majority of respondents (who are the senior managers in these companies) are seemingly positive about transformational policies such as BEE and AA, they do not seem prepared or able to offer an opinion about the potential influence of these policies on their company s ability to attain targets, improve on its performance or compete both national and internationally in terms of its COPs. The potential impact of respondents not actively or deliberately participating in research focusing on BEE and similar topics confirms the dilemma that Fauconnier and Mathur-Helm (2008:1) also set out to address in their study, namely... to contribute to the literature by attempting to address the gap in BEE dealings, the challenges and the factors for the achievement of success which are currently unavailable in the literature. However, accepting that research studies of this nature are bound to be considered sensitive to many, given the past history and legacy of apartheid for which these measures are ostensibly being implemented, the critical comments and damaging observations about BEE practices by prominent individuals and organisations in South Africa today, do indeed demonstrate the absolute moral imperative for much more in-depth and critical attention to this somewhat emotive but national issue by business management academics and practitioners alike. References Bhengu, M.J Ubuntu: The essence of democracy. Cape Town: Novalis Press. Black, S.S. & Geletkanycz, M.A The new breed of black South African senior managers: Helping South African businesses meet the challenge of a transforming economy, Organization Management Journal, 2(3): Booysen, L Barriers to employment equity implementation and retention of blacks in management in South Africa, South African Journal of Labour Relations, 31(1): Chabane, N., Goldstein, A. & Roberts, S The changing face and strategies of big business in South Africa: More than a decade of political democracy, Industrial and Corporate Change, 15(3): De Boer, I Competitive operations priorities for SA Businesses. CD received 11 August Updated report 9 September 2009 and figures 10 August Department of Trade and Industry (DTI). 2007a. South Africa s economic transformation: A strategy for broadbased economic empowerment. [online] URL: Accessed 24 February Department of Trade and Industry (DTI). 2007b. Broadbased Black Economic Empowerment Act (53/2003): Codes of Good Practice on Black Economic Empowerment. Government Notice 112. Pretoria: DTI. Du Toit, A., Kruger, S. & Ponte, S Deracializing exploitation? Black economic empowerment in the South African wine industry, Journal of Agrarian Change, 8(1):6-32. Evans, J.R. & Collier, D.A.R Operations management: An integrated goods and services approach. Mason, OH: Thomson. Fauconnier, A. & Mathur-Helm, B Black economic empowerment in the South African mining industry: A case study of Exxaro Limited, South African Journal of Business Management, 39(4): Hayes, R.H. & Wheelwright, S.C Restoring our competitive edge: Competing through manufacturing. New York: Wiley. Hamann, R., Khagram, S. & Rohan, S South Africa's charter approach to post-apartheid economic transformation: Collaborative governance or hardball bargaining? Journal of South African Studies, 34(1)

35 S.Afr.J.Bus.Manage.2013,44(2) 33 Hill, T Manufacturing strategy: The strategic management of the manufacturing function. Basingstoke: MacMillan. Hipkin, I Determining technology strategy in developing countries, The International Journal of Management Sciences, Omega 32: Ihedurua, O.C Black economic power and nationbuilding in post-apartheid South Africa, The Journal of Modern African Studies, 42(1):1-30. Smit, P.J., Cronjé, G.J. de J., Brevis, T. & Vrba, M.J Management principles: A contemporary edition for Africa. 4 th Edition. Cape Town: Juta. Vermeulen, L.P. & Coetzee, M Perceptions of the dimensions of fairness of affirmative action: A pilot study, South African Journal of Business Management, 37(2): IMD. 2010a. IMD World Competitiveness Yearbook, Lausanne, Switzerland. [online] URL: coreboard.pdf. Accessed 16 August IMD. 2010b. World competitiveness online for South Africa overall performance. [online]url: Accessed 25 May Kovacevic, N Righting wrongs: Affirmative action in South Africa, Harvard International Review, Spring: 6. Kruger, L Critique by stealth: Aspiration, consumption and class in post-apartheid television drama. Critical Arts: A South-North Journal of Cultural and Media Studies, 24(1): Krüger, L.P Developing operations strategies: Reassessing the strength and importance of competitive operations priorities for South African businesses. Submitted to South African Journal of Business Management as unpublished article 10/32 submitted May Krüger, L.P Strategic manufacturing priorities for South African manufacturers: The need to shift emphasis and improve on current performance levels, South Africa Journal of Business Management, 28(4): Krüger, L.P Strategic manufacturing priorities for South African manufacturers. Pretoria: Centre for Business Management, University of South Africa. Published research report. Mbeki, M Architects of poverty: Why African capitalism needs changing. Johannesburg: Picador. Mohamed, G. & Roberts, S Weak links in the BEE chain? Procurement, skills and employment equity in the metals and engineering industries, Journal of Contemporary African Studies, 26(1): Ponte, S., Roberts, S. & Van Sittert, L Black economic empowerment, business and the state in South Africa, Development and Change, 38(5): Slack, N., Chambers, S. & Johnston, R Operations management. 6 th Edition. Harlow: Pearson.

36 S.Afr.J.Bus.Manage.2013,44(2) 35 Community structure and centrality effects in the South African company network I. Durbach* and D. Katshunga Department of Statistical Sciences, University of Town, Private Bag, Rondebosch 7701, Republic of South Africa H. Parker Graduate School of Business, University of Cape Town, Private Bag Rondebosch 7701, Republic of South Africa This paper conducts a search for community structure in the South African company network, a social network whose elements are South African companies listed on the Johannesburg Stock Exchange. Companies are connected in this network if they share one or more directors on their respective boards. Discovered clusters, called communities, can be considered to be compartments of the network working relatively independently of one another, making their distribution and composition of some interest. We test whether the discovered communities of companies are (a) statistically significant, and (b) related to other attributes such as sector membership or market capitalization. We also investigate the relationship between the centrality of a company s position in the network and its market capitalization. *To whom all correspondence should be addressed. Introduction For many years it has been popular to consider the causes and consequences of companies interlocking i.e. having one or more directors in common (e.g. see Davis, Yoo and Baker (2003) for a review). In more recent times, the abundance of directorship information and new techniques for the analysis of complex systems has led to a focus on the entire system of partially interlocked companies within a corporate landscape (usually but not necessarily a country). In this mode of research, one views the collection of companies as a social network a graph in which a set of nodes (the companies) are linked together by edges indicating the presence of some kind of social relationship, in this case the presence of one or more common directors on both boards i.e. an interlock. This type of analysis is unconventional in two respects. Firstly, it focuses on the relationships between entities rather than on the attributes of independent sampling units. Secondly, it aims to describe the structure of the system as a whole rather than assess the individual entities. Taken together this allows one to describe the extent and nature of interconnectedness in a corporate system using a small number of summary statistics. Analyses of corporate board networks have been conducted for the US (Newman, Strogatz & Watts, 2001; Davis et al., 2003; Conyon & Muldoon, 2006), UK (Conyon & Muldoon, 2006), Germany (Conyon & Muldoon, 2006; Kogut & Belinky, 2008), Switzerland and the Netherlands (Heemskerk & Schnyder, 2008), Denmark, Sweden and Norway (Sinani et al., 2008), and South Africa (Durbach & Parker, 2009). New analytical methods have made it possible to consider networks in ever greater detail. An important development is the assessment of whether the nodes making up a social network can be organised into clusters, such that many relationships exist between members of the same cluster and comparatively few exist between members of different clusters. Such clusters, also called communities, can be considered to be compartments of the network working relatively independently of one another (Fortunato, 2010). An example of a network with strong community structure is shown in Figure 1. Community detection has found application in many areas: networks of interacting proteins (Rives & Galitski, 2003), gene expression networks (Wilkinson & Huberman, 2004), metabolic networks (Holme, Huss & Jeong, 2003), mobile phone communications (Blondel et al., 2008), and collaboration networks between academics (Girvan & Newman, 2002). In this paper, we conduct a search for community structure in the South African company network (shown in Figure 1), a social network in which the nodes are South African companies listed on the Johannesburg Stock Exchange (at March 2008), and two companies are connected if they share one or more directors on their respective boards. We also test whether the discovered communities of companies are related to other attributes such as sector and market capitalization, and investigate the relationship between the centrality of a company s position in the network and its market capitalization.

37 36 S.Afr.J.Bus.Manage.2013,44(2) Figure 1: On the left, an example of a network with a strong community structure (there are three clear clusters or communities indicated by the dashed ellipses). On the right, the South African company network as of March The paper is structured as follows: we begin by giving a brief introduction to network statistics, followed by a summary of previous research on interlocking corporate boards. We then describe the company network used for this study and the algorithm used to detect community structure respectively. The main results are then presented: first basic descriptive results; then community detection results; and finally results obtained from an additional investigation into the relationship between network centrality and market capitalization. A final section contains conclusions. Network basics In this section the example network in Figure 1 is used to illustrate various quantities of interest. The network shows the existence of some form of relational tie (edges) between entities (nodes). Edges may be undirected or directed, although here our interest is limited to the undirected case. If two nodes are connected by a single edge they are known as adjacent, with nodes B and C being an example of a pair of adjacent nodes. Adjacencies can be collected into an adjacency matrix A with elements if nodes and are connected and 0 otherwise. Higher values for are possible if multiple edges are allowed between nodes, but this will not concern us here. The adjacency matrix plays a prominent role in many network computations, including ones we consider later. In discussing the connectivity of a network and its constituent nodes, two measures are of fundamental importance: degree and distance. The degree of a node is simply the number of edges leaving that node. The degrees of nodes A, B, and C in Figure 1 are 7, 5, and 4 respectively. The geodesic distance (or just distance ) between a pair of nodes is given by the smallest number of edges that must be traversed to get to one node from the other. The distance between nodes A and B is 3 while nodes B and C are separated by a distance of 1. Intuitively it is clear that nodes within the same community should tend to be separated from each other by smaller distances than nodes in different communities. Causes and consequences of interlocks between companies Previously hypothesised causes of interlocks include collusion, coopting sources of environmental uncertainty, monitoring, enhancing reputation and legitimacy, career advancement, and elite social ties (Mizruchi, 1996). With the exception of collusion (Pennings, 1980), evidence exists in favour of all these hypothesised causes, but this evidence tends to vary from study to study. Both Thompson and McEwen (1959) and Burt (1983) found evidence suggesting cooptation as a source of interlocks, but Ornstein (1980) and Palmer (1983) found that ties broken by death or retirement are not re-established, mitigating against cooptation. Monitoring explanations have been supported in Dooley (1969) and Mizruchi and Stearns (1988), with unprofitable companies found to be more likely to form interlocks, especially with banks (Richardson, 1987; Mizruchi & Stearns, 1988). Mace (1971) and Useem (1984) found that directors are often chosen on the basis of their own reputation, and these may be used to signal the reputation of the company on whose board they sit (Selznick, 1984). As a consequence, directors are more likely to be nominated to new boards if they are already a member of several boards (Davis, 1993). The link between reputation and membership of the upper social stratum has been supported by Zeitlin (1974) and Useem (1984). The research into the consequences of interlocks for company behaviour is well summarised by Mizruchi (1996), while Davis, Yoo and Baker (2003) and Di Pietra et al. (2008) present more recent evidence. To summarise, interlocking directorates have been shown to facilitate the adoption of executive compensation practices such as golden parachutes (Cochran, Wood & Jones, 1985), greenmail (Kosnik, 1987), and poison pills (Davis, 1991; Davis & Greve, 1997). Others have found that the amount of external financing a company receives is related to bank representation on its board (Mizruchi & Stearns, 1988). Interlocks also serve to facilitate contributions to political candidates and congressional testimony (Mizruchi, 1992), as well as switching behaviour between stock

38 S.Afr.J.Bus.Manage.2013,44(2) 37 exchanges (Rao, Davis & Ward, 2000). Di Pietra et al. (2008) find that the number of additional directorships held by a board of directors (expressed as a proportion of board size) has a positive association with the market value of a company. Interlocked directors tend to be less effective at monitoring (Fich & Shivdasani, 2006) and more likely to be absent from board meetings (Jiraporn, 2007). Data The network that we investigate comprises the boards of directors of all JSE-listed South African companies as at 1 March This information was obtained from the McGregor BFA database and checked manually for consistency. One problem that arises is that companies may provide different levels of detail in the names of their directors, for example in the number of initials that are specified. In some cases it is clear that the director is in fact the same person (for example, NJM Canca and NJMG Canca are presumably the same person), but in other cases the correct decision is not clear. Our approach has been to treat any names which are identical in surname and first initial as belonging to the same person. The full dataset consists of 2653 directors and 397 companies, but the community detection algorithm is run on the largest connected component of the network consisting of 2048 directors and 294 companies. Methods Communities generally refer to subsets of nodes that are more densely interconnected among one another than with nodes in the rest of the network i.e. outside their community. The search for community structure thus becomes the search for a statistically surprising arrangement of edges (Newman, 2006). Most current algorithms for detecting community structure assign nodes to communities so as to optimize some pre-specified quality function. Reichardt and Bornholdt (2006) suggest the following quality function, to be maximized: where ( ) ( ) ( ) [ ( )] ( ) are elements of the adjacency matrix, ( ) if nodes and are in the same community and is 0 otherwise, and are weights of the relative contributions made by present within-community edges, missing withincommunity edges, present between-community edges, and absent between-community edges respectively. The four summation terms in the above equation correspond respectively to (a) rewarding existing edges between nodes of the same community (since ( ) if and only if nodes of the same community are connected); (b) penalizing missing edges between nodes of the same community (since ( ) if and only if nodes of the same community are not connected); (c) penalizing existing edges between nodes of different communities (since ( ) if and only if nodes in differing communities are connected); and (d) rewarding missing edges between nodes of different communities (since ( ) if and only if nodes in differing communities are not connected). Different authors have used a number of approaches to optimise the equation above; see Fortunato (2010) for a review. Reichardt and Bornholdt (2006) begin by assigning the same importance to connections between nodes in the same community as to those between nodes in different communities i.e. setting and. This means that it is only necessary to consider present and absent connections between nodes in the same community (i.e. the last two terms in the above equation can be ignored). They then select weights and, where denotes the expected number of edges between nodes and and is a parameter giving the importance of present edges relative to absent edges. The choice for means that greater rewards accrue if two nodes with an existing but statistically surprising connection are assigned to the same community. Similarly, the choice for means that greater penalties result if two nodes that are expected to be connected but in reality are not, are assigned to the same community. The form taken by can be tailored for specific types of networks. In most cases (including ours) the are set to, where is the degree of node and is the total number of nodes. This indicates that edges are more probable between nodes which themselves have many edges, but other choices for can be used to indicate, for example, assortativity (degree-degree correlation), random graphs, or bipartite graphs. These choices of coefficients mean that the resulting quality function simplifies to: Optimizating the above function for large networks is computationally very intensive. Reichardt and Bornholdt (2006) use methods from statistical mechanics to reformulate the optimization problem in terms of finding the ground state configuration that minimizes the energy of an infinite range Potts spin glass. Full details of the method can be found in the original reference, and need not concern us here, although it is worth noting some of its advantages. Firstly, the reformulated quality, given by where ( ) the first sum runs over the set of all edges and is the sum of degrees of nodes in community, is computationally easier to optimize: indeed this can be achieved using

39 38 S.Afr.J.Bus.Manage.2013,44(2) standard approaches like simulated annealing (Kirkpatrick, Gelatt & Vecchi., 1983). Secondly, a single, easily interpreted parameter governs the relative weight assigned to present and missing edges; when the total contribution that can be made by present and missing edges is equal. Thirdly, it is a general model that, for example, includes the most popular quality function (Fortunato, 2010) based on modularity (Newman & Girvan, 2004) when. Finally, the method can be used to compute the expected modularity for a null model i.e. a random graph with the same number of nodes and average degree; the modularity of a discovered community structure must exceed that of the null model in order to be considered statistically significant. We used the implementation of Reichardt and Bornholdt (2006) available in the statistical software package R (version 2,13) via its igraph package. Results Basic network results Table 1 gives a brief overview of the South African company network. The average number of directors sitting on the board of a JSE-listed company is 8,56, ranging greatly from just two directors to 27. This is comparable to values reported by Conyon and Muldoon (2006) for the USA (9,97 members), the UK (6,51 members) and Germany (6,33 members). The average number of directorships held is 1,28, and the overwhelming majority of directors (83%) are members of just a single board. This is marginally lower than those reported by Conyon and Muldoon for the USA (1,63 directorships), UK (1,84 memberships) or Germany (1,45 memberships). There are just 32 directors (1,2%) who hold five or more board memberships. A JSE-listed company is directly connected to an average of 5,2 other companies, although this average increases to 6,9 if companies that are not connected to the largest component are excluded. Table 1: Basic descriptive and network statistics for the South African company network as at 1 March 2008 Full network Largest component Number of firms Average board size 8,56 (0,19) 9,40 (0,22) Director seats Unique directors % of one-board directors 83% 79% Average company degree 5,23 (0,29) 6,92 (0,34) Average distance between companies 3,65 (0,06) 3,65 (0,06) Community structure results The essential features of the South African company network as shown in Figure 1 appear to be that (a) a relatively large number of highly interconnected companies appear in the center of the network, and (b) other companies are peripheral to this central cluster of companies, being connected to it only by a distance of a few degrees. This structure presents a clear difficulty for community detection. Nevertheless, the communities detected by the algorithm of Reichardt and Bornholdt (2006) do exhibit some statistically significant associations. Figure 2 shows the communities detected by a neutral application of the algorithm, in which absent edges between members of a community are viewed as equally important as present edges. Nodes represent companies, with edges denoting membership to a common cluster. Nodes are coloured according to their sector and the size of a node is proportional to its market capitalization. For comparative purposes, the community structure detected using the algorithm in a conservative mode (with ) is shown in Figure 4 in the appendix. The former identifies 14 communities and the size of these communities decreases in an approximately linear fashion; the latter identifies 12 communities, one of which is much larger than all the others (an in fact contains some 50% of all companies). It must be acknowledged that a direct qualitative interpretation of these system-wide summary statistics is still lacking it is not yet known how, or even whether, network statistics like the number of communities or the distribution of cluster sizes affect the performance of the economy. Investigating these important topics would require longitudinal data, ideally for a number of countries, and as such is beyond the scope of the current study. We set ourselves the more modest task of assessing whether the fact that two firms belong to the same economic sector, or have similar levels of market capitalization, make them significantly more likely to belong to the same community (which, recall, is dictated only by arrangements of directorships). The modularity of the detected configuration of nodes into communities is 0,53. This is well above the null model modularity of 0,36, suggesting that the communities found have at least some statistical relevance over and above what would have been expected from a random graph. Moreover, there is a significant association between cluster membership and each of sector membership and market capitalization, two exogenous variables not used in the clustering process (sector: chi 2 = 170,0, DoF = 96, p < 0,001; log(market capitalization): F = 2,66, DoF = 12, 280, p = 0,002), although because of the small sizes of some clusters these p-values cannot be trusted entirely. From Figure 2 itself, it is clear that there is some tendency for companies of a similar market capitalization and sector to appear in the same cluster. For example, 67% of all industrial companies are in just three communities, and in

40 S.Afr.J.Bus.Manage.2013,44(2) 39 one community 14 out of 31 companies are based in the industrial sector. In one relatively small community of 15 companies, 7 are in the real estate sector. Other sectors are somewhat more evenly dispersed between communities. Significant market capitalization clustering occurs mainly between companies with high market capitalizations 27 of the 56 companies in the upper quintile of market capitalization belong to just 2 of the 13 communities. Our conclusions remain the same using either of the community structures in Figure 2 or 4. Taken together, our results suggest that companies of the same sector (particularly industrial and real estate) and companies with high market capitalization exhibit a greaterthan-expected tendency to form inter-relationships with one another through common directors on their boards. As indicated by the previous research summarized above, such a tendency can be expected to play a significant role in the sharing of information and some elements of company culture and behaviour. Centrality and market capitalization In this section we consider the effect of the centrality of a company s position in the company network on its market capitalization. That is, we ask whether companies that occupy more central positions in the network tend to have higher or lower market capitalization values. In doing so, it is important to control for the effect of board size. Companies with large market capitalizations will tend to require larger boards to manage them, so that one would expect a positive relationship between board size and market capitalization (e.g. Lincke, Netter & Yang, 2008). Since, as we have seen, larger boards also tend to possess higher centrality (Durbach & Parker, 2009), there is an obvious need to control for board size when examining the relationship between centrality and market capitalization. We do this by fitting a series of quantile regression models (using the quantiles q = 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%) to the 378 companies for which we were able to obtain market capitalization information. Models were fitted using degree as a measure of centrality, while controlling for board size. Board sizes and degree centralities were first centered around their means so that the intercept term can be more easily interpreted (Koenker & Hallock, 2001). Figure 3 shows the results obtained from the model using board size and degree centrality as independent variables. The solid line plots the parameter estimates obtained at various quantiles, while the dashed lines indicate 95% confidence intervals around those estimates. The parameter values can be interpreted as the effect of a oneunit increase in the independent variable on market capitalization. Figure 2: Communities in the South African company network, with the inset histogram showing community sizes

41 Effect size Effect size Effect size S.Afr.J.Bus.Manage.2013,44(2) Intercept Board size Degree centrality Quantile Quantile Quantile Figure 3: Quantile regression estimates for the effect of board size and degree centrality on market capitalization (in millions of Rands) The models verify the positive relationship between board size and market capitalization, though as indicated by the positive slope of the solid line in the Board size plot, this effect tends to be considerably larger in the upper quantiles of the distribution. For example, a company with 1 more director than another company has an approximately R50 million greater market value at the 5% quantile (i.e. at very low levels of market capitalization), but a R424 million greater value at the 0,50 quantile (i.e. median market capitalization) and over R1 billion greater value at the 80% quantile (i.e. at higher levels of market capitalization). Thus, while it appears true to say that in general companies with bigger boards tend to have bigger market capitalizations, the difference between a company with a small board and one with a big board tends to be far more pronounced at higher levels of market capitalization than at lower levels. Our models also find a strong positive relationship between degree centrality and market capitalization, even after controlling for the effect of board size. The coefficient of the degree centrality effect is positive and significant at the 1% level over all of the quantiles, indicating that companies having higher numbers of connections to other companies tend to have higher market capitalization. Similarly to the effect of board size, the degree centrality effect is smaller in the lower quantiles of the distributions, increasing in magnitude as the market capitalization quantile increases. At the median quantile, companies with a single additional connection have an approximately R180 million greater market capitalization. At the 5% and 80% quantiles, this figure is R24 million and R954 million respectively. Simply put, the difference in the market capitalization of a highlyconnected company and a poorly-connected company whose market values are both in the lower quantiles of their respective conditional distributions is not that large. But that same difference can be a full order of magnitude greater when those companies are both in the upper quantiles of their respective conditional distributions. Thus both centrality and the size of a company s board have larger effects for companies with relatively large market capitalizations. This suggests that limited connectivity and a small board can constrain market capitalization, but that the converse does not necessarily apply to the same degree being central or having a large board does not ensure high market capitalization. Interestingly, there is a large jump in the magnitude of the effect which occurs around the 60% quantile. At smaller quantiles, the centrality effect increases slightly as the market capitalization quantile increases. Beyond the 60% quantile, however, the size of the effect increases dramatically. This further suggests that there may be some sort of critical mass beyond which inter-firm connectivity exerts its full effect. Finally, and predominantly for completeness, the intercept may be interpreted as the estimated conditional quantile function of the market capitalization distribution for a typical company (one with 8,65 board members and 5,2 connections to other companies, these figures reflecting the sample mean board size and degree respectively). Thus the 10% quantile for such a company s market capitalization is estimated to be R406 million; the 80% quantile is estimated to be R11,5 billion. Conclusion This paper provides a coherent framework of (1) assessing whether companies in the South African company network can be organised into communities such that dense connections exist between members of the same community and relatively fewer connections exist between members of different communities; (2) investigating the relationship between identified communities within the South African company network and each of the attributes sector membership and market capitalization ; and (3) how the statistical tool of quantile regression can be used to both measure the effect of board size as well as of degree centrality in the South African company network on market capitalization, and control for board size while measuring the effect of degree centrality on market capitalization. Though the South African Company network s structure presents some difficulty for community detection, an application of the Reichardt and Bornholdt algorithm resulted in communities that are statistically significant

42 S.Afr.J.Bus.Manage.2013,44(2) 41 regardless of whether γ is set to 1 (a neutral value) or 0,5 (a more conservative value favouring the formation of larger communities). A statistically significant relationship has been detected between community membership and each of sector membership and market capitalization. Most companies with similar market capitalizations and sectors tend to regroup through common directors under the same community. Since edges are formed by common membership on a board of directors, this indicates that a director on the board of a company in one community is more likely to be on the board of another company in the same community. The presence of companies from the same sector in the same community may perhaps raise some warning signs for corporate governance, but we do not have any data to test this and a deeper investigation of this issue goes beyond the scope of the current paper. The constructed quantile regression models reveal a positive relationship between board size as well as degree centrality and market capitalization. These results show that companies that are more central (as measured by degree centrality) tend to have larger market capitalizations, even after controlling for board size. The results also showed that the magnitude of these effects increases at higher quantiles of the conditional market capitalization distribution. That is, centrality and board size both have larger effects for companies with relatively large market capitalizations. This suggests that limited connectivity and a small board can constrain market capitalization, but that the converse does not necessarily apply being central or having a large board does not ensure high market capitalization. Appendix: Community structure results with more conservative clustering Figure 4 and Table 2 respectively show the discovered communities and statistical associations between communities and sector membership and market capitalization, using a more conservative application of Reichardt and Bornholdt (2006) in which is set to 0,5 i.e. existing edges are viewed as more important than missing edges. The modularity of the detected configuration is 0,59, which is (as for the more aggressive clustering) well above the null modularity score of 0,36, suggesting a statistically significant arrangement of nodes into communities. Conclusions are as for the communities detected with γ set to 1 i.e. there is a significant tendency for companies of a similar market capitalization and sector to appear together in the same cluster. Figure 4: Communities in the South African company network, obtained with a more conservative clustering parameter than that used in Figure 2

43 42 S.Afr.J.Bus.Manage.2013,44(2) Table 2: Association between the communities found in Figure 4 and sector membership and market capitalization Test used Test stat DoF p Sector Chi-square 116,5 72 <0,001 Market capitalization ANOVA 5,34 9, 284 <0,001 References Blondel, V., Guillaume, J-L., Lambiotte, R. & Lefebvre, E Fast unfolding of communities in large networks, Journal of Statistical Mechanics, P Burt, R Corporate profits and cooptation. Academic Press New York. Cochran, P., Wood, R. & Jones, T The composition of boards of directors and incidence of golden parachutes, Academy of Management Journal, 28(3): Conyon, M. & Muldoon, M The small world of corporate boards, Journal of Business Finance & Accounting, 33(9-10): Davis, G Agents without principles? The spread of the poison pill through the intercorporate network, Administrative Science Quarterly, 36: Davis, G Who gets ahead in the market for corporate directors: The political economy of multiple board memberships, Academy of Management Proceedings, August: Davis, G. & Greve, H Corporate elite networks and governance changes in the 1980s, American Journal of Sociology, 103(1): Davis, G., Yoo, M. & Baker, W The small world of the American corporate elite, , Strategic Organization, 1(3): Di Pietra, R., Grambovas, C., Raonic, I. & Riccaboni, A The effects of board size and busy directors on the market value of Italian companies, Journal of Management & Governance, 12: Dooley P The interlocking directorate, American Economic Review 59(3): Durbach, I. & Parker, H An analysis of corporate board networks in South Africa, South African Journal of Business Management, 40(2): Fich, E. & Shivdasani, A Are busy boards effective monitors?, Journal of Finance, LXI(2): Fortunato, S Community detection in graphs, Physics Reports, 486: Girvan, M. & Newman, M Community structure in social and biological networks, Proceedings of the National Academy of Sciences of the USA, 99(12): Heemskerk, E. & Schnyder, G Small states, international pressures, and interlocking directorates: The cases of Switzerland and the Netherlands, European Management Review, 5(1): Holme, P., Huss, M. & Jeong, H Subnetwork hierarchies of biochemical pathways, Bioinformatics, 19(4): Jiraporn, P Too busy to show up? An analysis of directors absences. Great Valley School of Graduate Professional Studies Working Paper. Kirkpatrick, S., Gelatt, C. & Vecchi, M Optimization by simulated annealing, Science, 220: 671. Koenker, R. & Hallock, K Quantile regression, The Journal of Economic Perspectives, 15(4): Kogut, B. & Belinky, M Comparing small world statistics over time and across countries: An introduction to the special issue Comparative and Transnational Corporate Networks, European Management Review, 5(1): Kosnik, R Greenmail: A study of board performance in corporate governance, Administrative Science Quarterly, 32: Lincke, J., Netter, J. & Yang, T The determinants of board structure, Journal of Financial Economics, 87(2): Mace, M Directors: Myth and reality. Harvard Business School Press, Boston. Mizruchi, M The structure of corporate political action. Harvard University Press, Boston. Mizruchi, M What do interlocks do? An analysis, critique, and assessment of research on interlocking directorates, Annual Reviews in Sociology, 22(1): Mizruchi, M. & Stearns, L A longitudinal study of the formation of interlocking directorates, Administrative Science Quarterly, 33: Newman, M. & Girvan, M Finding and evaluating community structure in networks, Physical Review E, 69(2): Newman, M., Strogatz, S. & Watts, D Random graphs with arbitrary degree distributions and their applications, Physical Review E, 64(2):

44 S.Afr.J.Bus.Manage.2013,44(2) 43 Newman, M Modularity and community structure in networks, Proceedings of the National Academy of Sciences of the USA, 103: Ornstein, M Assessing the meaning of corporate interlocks: Canadian evidence, Social Science Research, 9: Palmer, D Broken ties: interlocking directorates and intercorporate coordination, Administrative Science Quarterly, 28: Pennings, J Interlocking directorates. Jossey-Bass Inc, San Francisco. Rao, H., Davis, G. & Ward, A Embeddedness, social identity and mobility: Why firms leave the NASDAQ and join the New York Stock Exchange, Administrative Science Quarterly, 45: Reichardt, J. & Bornholdt, S Statistical mechanics of community detection, Physical Review E, 74(1): Richardson, R Directorship interlocks and corporate profitability, Administrative Science Quarterly, 32: Rives, A. & Galitski, T Modular organization of cellular networks, Proceedings of the National Academy of Sciences of the USA, 100: Selznick, P Leadership in administration: A sociological interpretation. University of California Press. Sinani, E., Stafsudd, A., Thomsen, S., Edling, C. & Randoy, T Corporate governance in Scandinavia: Comparing networks and formal institutions, European Management Review, 5(1): Thompson, J. & McEwen, W Organizational goals and environment: Goal-setting as an interaction process, American Sociological Review, 23: Useem, M The inner circle: Large corporations and the rise of business political activity in the US and UK. Oxford: Oxford University Press. Wilkinson, D. & Huberman, B A method for finding communities of related genes, Proceedings of the National Academy of Sciences of the USA, 101: Zeitlin, M Corporate ownership and control: The large corporation and the capitalist class, American Journal of Sociology, 79:

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47 S.Afr.J.Bus.Manage.2013,44(2) 45 An investigation into the perceptions of business stakeholders on the benefits of enterprise architecture: The case of Telkom SA S.M. Lehong* School of Business Leadership, PO Box 392, UNISA 0003, Republic of South Africa E. Dube Council for Scientific and Industrial Research (CSIR) Modelling and Digital Science (MDS), Pretoria 0001, Republic of South Africa G. Angelopoulos CENTRUM Católica, Pontificia Universidad Católica de; Perú, Calle Alomia Robles , Urb. Los Alamos de Monterrico, Surco, Lima 33, Perú This study identifies the perceived benefits of Enterprise Architecture (EA) among business stakeholders who are aware of EA but are not specialists in the field of ICT, and proposes a clearly differentiated and comprehensive cluster of stakeholder groups based on these perceptions. It provides an understanding of the benefits of EA from the perspective of business beneficiaries, not the perspective of its practitioners. The study was conducted using Q-methodology, which was developed specifically to uncover subjective perceptions and preferences. Q-methodology correlates people in respect of their perception of a sample of tests, and is particularly useful in bridging the sharp dichotomy between qualitative and quantitative methodologies. The research identifies and describes 9 EA stakeholder types: Transformational Task-Driven Strategist, Universal Risk- Tolerant Pragmatist, Evolutionary Reuse Overseer, Technology Leverager, Simplicity Valuer, IT Commoditiser, Operational Stability Maintainer, Organisational Change Manager, and Siloed Product Manager. The study demonstrates an increasing understanding of EA benefits, and points to strategies that may be deployed when engaging each stakeholder class. *To whom all correspondence should be addressed. Introduction This study delineates the benefits of Enterprise Architecture (EA) to organisations, identifies dominant perceptions of the benefits of EA amongst a particular company s business stakeholders, and develops a typology of business stakeholder sub-sections as they relate to the perception of EA benefits. The focus of the study is the identification of the range of attitudes that exist amongst a particular set of business stakeholders towards EA benefits. Attitudes are generally viewed as a person s subjective evaluation of a particular topic (Wilkins, 2003). In order to identify attitudes towards EA, a methodology that is capable of identifying the subjectivity and complex constituent viewpoints inherent in attitudes must be utilised. In this study Q-methodology has been used for the purpose. Q-methodology was developed by William Stephenson in 1935 to uncover people s subjective perceptions of any issue towards which different points-of-view can be expressed. Its prime emphasis is on exploration rather than hypothesis testing. The focus of Q-methodology is the internal reference framework used by an individual to make decisions about the relative significance of individual stimuli (Sexton, Snyder, Wadsworth, Jardine & Ernest, 1998). Q-methodology is based on an individual participant s viewpoint and not the researcher s viewpoint, each of the responses is taken as valid and as a valuable source of knowledge (Amin, 2000: 411). This study provides an understanding of the benefits of EA from the perspective of business stakeholders and the segments that they fall into. The assumption that underlies segmentation is that specific groups of stakeholders can be clustered into segments according to their unique needs, behaviours and other relevant characteristics (Best, 2005). Segmentation can ensure that appropriate communication and EA products are developed for particular stakeholder groups (Van der Raadt, Schouten & Van Vliet, 2008). The

48 46 S.Afr.J.Bus.Manage.2013,44(2) value proposition for a segment should be built around benefits sought by the target stakeholder grouping (Best, 2005: 147). The study was undertaken at Telkom South Africa, a company listed on the Johannesburg Securities Exchange, amongst non-ict business stakeholders with an awareness of EA. Telkom is Africa's largest integrated communications company, providing integrated communications solutions to a wide range of customers. The company s stated objective is to become world-class. At the time of this study and to this end, the company has undergone a transformation process driven by the following thrusts of strategic intent: to offer customers enhanced bundled packages and tailored calling plans; to use fixed-mobile capability as a platform for future growth; to utilise the strength and reliability of its infrastructure to bring high-quality broadband products to market; and to become a Pan-African integrated service provider (Telkom, 2010). Telkom s intentions cannot be achieved without undergoing a comprehensive transformation exercise that constitutes an overhaul of its products and services and the dependent Information and Communications Technologies, including EA, that support a forward-looking business model. Stakeholder perception of enterprise architecture Typical modern enterprise is continuously evolving in order to adapt to an ever changing external environment of, amongst others, the market, customer, competition, regulatory landscape and new market segments (Strano & Rehmani, 2007; Zachman, 1987). This has created tremendous challenges for business leaders and has led, in response, to the implementation of divergent goals by many business units within individual enterprises (Zachman, 1987). The EA role was initially introduced to address this specific challenge. EA is still a maturing discipline with varied views of what it entails. Weill and Ross (2009: 91) define Enterprise Architecture as the design of the firm s digitised platform. Van der Raadt and Van Vliet (2008) define it as a holistic view of the organisation s current state, a clear description of the target situation, and a roadmap to an integrated, well structured organisation. Hilliard (2000: 10) on the other hand defines it as the fundamental organisation of a system embodied in its components, their relationships to each other and to the environment and the principles guiding its design and evolution. It is clear that a universally acceptable definition of the discipline is some way off, and still a matter of debate amongst practitioners and academics. For the purpose of this study we stay clear of the argument around definition and focus on what enterprise architects should provide to the organisation. We have adopted Kappelman s (2010: 2) view that EA provides a clear and comprehensive picture of an entity, whether an organisation or a functional or mission area that cuts across more than one organisation... the end object is not to build and run information systems. The end object is to engineer and manufacture the enterprise. In effect, therefore, EA provides a holistic set of descriptions about the enterprise over time. Chung, Song, Song and Subramanian (2009) classify Enterprise Architecture benefits as ICT-specific and business-specific. It appears that most EA benefits research has been designed to describe the typical pattern and content of observed EA initiatives, not to directly answer the question of which EA benefits are necessary and effective. Uncovering what most EA initiatives identify as EA benefits does not tell us how these benefits were derived or whether business stakeholders consider them essential or beneficial. Therefore, of the many EA benefits that are proffered by the ICT profession, many may not be perceived as the most effective by other business stakeholders - such perception has scarcely been tested. Most research identifies and attempts to resolve EA implementation issues from a technical perspective (Lindström, Johnston, Johansson, Ekstedt & Simonsson, 2006). In such research the focus is primarily on the production of architectural artefacts and the quality and maintenance of those artefacts, with little focus on stakeholder-related challenges. Kluge, Dietzsch and Rosemann (2006), however, find that stakeholder-orientation is a necessary condition of EA value and that EA initiatives have to be stakeholder-oriented for them to realise any sort of success. As for the challenge of classifying EA stakeholders by type, there is evidence of a growing awareness that EA stakeholders are not homogeneous and that the categories that have been proposed are still very broad and non-specific (Van der Raadt et al., 2008). Q-Methodology Nonaka and Toyama (2005) have intimated that new knowledge is created through a circular process where subjective tacit knowledge held by an individual is externalised into objective explicit knowledge which is then used and embodied by individuals to enrich their subjective tacit knowledge. Gremy, Fessler and Bonnin (1999) argue that in any information system that includes human subjects as part of the system, any evaluation of the system must take into account those subjects feelings, reactions, and behaviour. Instead of treating knowledge as objective and static truth, Nonaka and Toyama (2005) argue that knowledge is created through a dynamic interaction between subjectivity and objectivity. Viewing the knowledgecreation process in this manner implies that the value of subjectivity in knowledge expansion is immeasurable. It further means that in attempting to understand that process and the knowledge thus created we should strive to uncover its subjective dimension as effectively as possible. The methodology used in this study is Q-methodology, which holds special promise for those seeking to make more intelligible and rigorous the study of human subjectivity (McKeown & Thomas, 1988: 12). According to Brown (1993), Q-methodology provides a foundation for the systematic study of subjectivity, a person s viewpoint, opinion, beliefs, attributes, and the like. Q-methodology was developed as a variation of factor analysis by William Stephenson in the 1930 s following his work in factor analysis as a student of Charles Spearman and colleague of Cyril Burt. Q-methodology is, however, not well known and cannot be considered a mainstream methodology

49 S.Afr.J.Bus.Manage.2013,44(2) 47 (Angelopulo, 2009), but has nonetheless been used in behavioural research in fields including politics, psychology, sociology, marketing, and more recently Information and Communication Technology. Q-methodology is based on the two-fold premise that subjective points-of-view are communicable and always advanced from a position of self-reference. It therefore follows that subjective communication is amenable to objective analysis and understanding provided that the analytical means for studying such communication does not in the process destroy or alter its self-referent properties (McKeown & Thomas, 1988). Rather than administering tests to a sample of people, Q-methodology administers people to a sample of test items. Q-methodology attains validity by posing many questions to a few subjects rather than by posing a few questions to many people (Kowert, 1996). Q-methodology utilises Q-samples and Q-sorting as data gathering instruments and Q-factor analysis as its statistical data analysis tool. The successful application of Q- methodology requires a research topic, a research question and a list of statements regarding the research topic. The methodology requires a specific level of rigour, process and method. In the succeeding sub-section these are discussed. Q-methodology is typically used where there are fewer persons than tests to be correlated, and when it appears that relations among persons could be interpreted better than relations among tests. It uncovers the nature of the correlation among persons as opposed to the extent of the correlation, and focuses on interrelations among persons with respect to a population of tests. The research process comprises a number of steps: collection of opinion statements, development of representative statements, rank ordering of the statements, sorting of statements with a Q- sort, factor analysis and interpretation. Process and application The process begins with the collection of opinion statements. The collected set of opinion statements is referred to as the Concourse, which can be derived from a variety of sources that may include interviews, newspaper articles, academic articles and personal interviews. The concourse is ideally a comprehensive set of opinions on the topic under consideration. From this broad and often very extensive set of opinions, a smaller, representative, and more manageable set called the Q-sample is derived. The smaller Q-sample is drawn from the concourse to represent the main themes contained in the concourse and by extension, the topic of discourse (Amin, 2000; Steelman & Maguire, 1999). The sample of statements in the Q-sample is developed to represent the topic of discourse. In the process the raw data is redefined, clarified, and combined into more meaningful statements. It should be noted that one of the most important aspects of this form of research is the identification of the tacit, underlying criteria and perceptions that people use to consider an issue the opinion statements themselves are of secondary importance (Donner, 2001). The Q-sample is then generally piloted with a small group that may represent the participants, a sample of users, or experts on the topic under consideration. The pilot test ensures that breadth and comprehensiveness are achieved in order to maximise confidence that the major factors at issue have been manifested in the set of items comprising the Q-sample (Van Exel & De Graaf, 2005). The Q-sample is then submitted for assessment to the final group of participants. These participants (known as the P- sample or more commonly the person sample ) are selected according to the principles of theoretical sampling as opposed to more commonly used sampling techniques. The researcher attempts to include among the participants an array that will represent the full spectrum of perceptions that are held on the topic under consideration. It is less important to select participants according to the degree to which they represent the population or its demographics. This is due to the fact that the methodology is based on the principle of identifying person types: the subjects must be chosen for the study depending on known characteristics that they possess (Frederick, 1999: 14). Q-sorting, as defined by Brown (1980) is the means whereby data are obtained for factoring. The Q-sorting process is achieved by participants ordering the Q-sample statements in a ranked matrix according to some pre-defined instruction (e.g. most important to least important). Participants simply rank statements in accordance with how they view the issue at hand. The ranked statements of each participant is termed a Q-sort. Due to the fact that the same opinion statements are assessed in the same assessment format by different participants who more than likely have different perspectives, more rigour can be obtained in the comparison of their subjective preferences. There are two primary conditions of instructions which can be followed in the Q-sort process: forced-choice and freesort conditions of instruction. The forced-choice condition of instruction requires participants to sort the Q-sample statements according to a pre-defined matrix (McKeown & Thomas, 1988). Participants determine the meaning of their own sort as they are free to place any of the Q-sample statements at any position across the continuum. The restriction on the forced-choice condition of instruction is that the range and number of statements allocated are predetermined by the researcher (Table 1). The free-sort condition of instruction, on the other hand, imposes no restrictions on the participants (see Table 2 below as an example). Brown (1980) is of the opinion that when participants are not knowledgeable about the subject under consideration, the free-sort condition of instruction is suitable. He further argues that in such a study, participants are not expected to have opinions about most statements and as such more freedom is given on the Q-sort.

50 48 S.Afr.J.Bus.Manage.2013,44(2) Table 1: Forced-choice condition of instruction Table 2: Free-sort condition of instruction Lafrenz (2006), Chung et al. (2009), Cardwell (2008), Kamogawa and Okada (2009), Ross and Weill (2005), Kluge et al. (2006), Liimatainen (2008), Harris (2008), De Vries and Van Rensburg (2008), and Harmon (2005). A number of benefits have also been drawn from general, nonspecific discussion with EA practitioners and EA experts. These appear to be commonly held but are unidentified in the literature, and are indicated as Expert Opinion. The analysis of the Q-sort matrix was undertaken with the PCQ for Windows software package. The factor loading of each Q-sort represents its correlation with the factor. Positive factor scores indicate agreement with the statement and are therefore viewed as an important benefit of Enterprise Architecture whereas negative factor scores indicate disagreement with the importance of the benefit. The higher the score (positive or negative), the more strongly the benefit is deemed important, and vice versa. Each of the derived factors has at least one defining sort, which indicates the responses of the participants who loaded heavily on the factor. All factors are created by ranking the entire sample and not just the participants who most identify with the factor. Of the 23 participants in the study 16 were female and 7 male, the average years of service in their current organisation was 6,4, and all had at least a 3-year diploma qualification. Roughly 60% were in management positions. Once all participants have completed their Q-sorts, the arrangement of their statements is formally compared by means of factor analysis. Factor analysis reveals deep structures implicit in subjectivity, but about which neither the subject nor the investigator may be aware (Amin, 2000). Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables (DeCoster, 1998). The factor analysis used in Q-methodology differs from other variations of factor analysis because it correlates people the participants as opposed to variables, as is the case in normal factor analysis. Q-based factor analysis factors subjects and attempts to identify different types of people, depending on their responses to certain variables (Frederick, 1999). In this study the forced-choice condition of instruction was selected in order to crystallise the subjective hierarchies that govern stakeholder perceptions of EA benefits. The Q- sample was drawn from a number of sources. Statements were derived from Niemi s (2006) research on EA benefits, adapted to reflect a number of perspectives that are more recent than those expressed in Niemi s original research (refer to Table 3); and from Ross (2003), Strano and Rehmani (2007), Van der Raadt et al. (2008), Jonkers, Lankhorst, Ter Doest, Arbab, Bosma and Wieringa (2006), Plazaola, Flores, Vargas and Ekstedt (2008), Pereira and Sousa (2005), Buchanan and Soley (2002), Ross, Weill and Robertson (2006), Malan, Bredemeyer, Krishnan and Table 3: Enterprise architecture benefits The nine derived factors accounted for 58% of the variance, with 17 participants sorts being accounted for in these factors. The naming of the factors was meant to capture the core characteristics of each factor. Loading significance was set at equal to or greater than 0,40. Four participants (17,4% of participants) loaded on Factor 1. The factor has an eigenvalue of 3,67 and accounts for 16% of the total variance (see Table 5). The loadings on Factor 1 have the second highest values, all being above,70. Two of the participants (8,7% of participants), 9% of the total variance and an eigenvalue of 2,02 loaded on Factor 2 (see Table 6). The loadings on Factor 2 have the highest values, all above,80. Only one participant (4,35% of participants) loaded significantly on Factor 3, with 5% of the total variance and an eigenvalue of 1,25 (see Table 7). This factor is negatively loaded and as a result the statements were switched in order to identify the factor s true values. Two (8,7%) of the participants loaded on Factor 4 (see Table 8), with 4% of the variance and an eigenvalue of 0,87. One participant (4,35%) loaded on Factor 5 (see Table 9) with 4% of the total variance and an eigenvalue of 0,84 Two of the participants (8,7%) loaded on Factor 6 (see Table 10), with 7% of the variance and an eigenvalue of 1,65.

51 S.Afr.J.Bus.Manage.2013,44(2) 49 EA Benefits Statement Sources 1. Shortened cycle times. Niemi (2006); Van der Raadt & VVliet (2008) Niemi (2006); Brown (2004); Malan, Bredemeyer, Krishnan & Lafrenz 2. Improved business-it alignment. (2006); Plazaola, Flores, Vargas & Ekstedt (2008); Buchanan & Soley (2002); Pereira & Sousa (2005); Matthee, Tobin & Van der Merwe (2006) 3. Improved risk management. Niemi (2006); Ross & Weill (2006); Van der Raadt & Van Vliet (2008) 4. Provides a holistic view of the enterprise. Niemi (2006); Harmon (2005) 5. Improved communication. Niemi (2006) 6. Increased economies of scale. Niemi (2006) 7. Improved change management. Niemi (2006); Kamogawa & Okada (2009); Van der Raadt & Van Vliet (2008); Matthee, Tobin & Van der Merwe (2006) 8. Increased quality. 9. Increased market value. Niemi (2006) 10. Improved alignment with partners. 11. Increased interoperability and integration. Niemi (2006); Ross, Weill & Robertson (2006) 12. Increased standardisation. 13. Improved business processes. Niemi (2006); Brown (2004) 14. Improved alignment to business strategy. Niemi (2006); Harris (2008) 15. Improved asset management. 16. Evolutionary EA development & governance. Niemi (2006) 17. Improved customer orientation. 18. Reduced complexity. Niemi (2006); Chung, Song, Song & Subbramanian (2009) 19. Reduced costs. Niemi (2006); Ross & Weill (2006); Liimatainen (2008) 20. Increased stability. Niemi (2006) 21. Increased reusability. Niemi (2006), Brown (2004); Harris (2008) 22. Increased efficiency. Niemi (2006); Kamogawa & Okada (2009) 23. Improved strategic agility. Niemi (2006); Ross & Weill (2006); Kamogawa & Okada (2009); Brown (2004); Chung, Song, Song & Subbramanian (2009) 24. Improved staff management. Niemi (2006) 25. Improved management of IT investment. Niemi (2006); Harris (2008) 26. Improved decision making. Niemi (2006); Chung, Song, Song & Subbramanian (2009) 27. Improved innovation. Niemi (2006) 28. Improved IT governance. Harris (2008) 29. Improving integration of mergers and acquisitions. Liimatainen (2008); Matthee, Tobin & van der Merwe (2006) 30. Compliance to legislation. Cardwell (2008); Malan, Bredemeyer, Krishnan & Lafrenz (2006); Harmon (2005); Jonkers, Lankhorst, Ter Doest, Arbab, Bosma & Wieringa (2006) 31. Improve business use of technology. Kamogawa & Okada (2009) 32. Shared business platforms. Ross & Weill (2006); Ross, Weill & Robertson (2006) 33. Managerial satisfaction. Ross & Weill (2006); Van der Raadt & van Vliet (2008) 34. Accelerate IT system implementation. Expert Opinion 35. Readily available documentation of the enterprise. Brown (2004) 36. Ability to unify and integrate data across the enterprise. Brown (2004); Ross, Weill & Robertson (2006) 37. Improved IT visibility. Chung, Song, Song & Subbramanian (2009) 38. Reduce the impact that staff turnover has on business. Chung, Song, Song & Subbramanian (2009) 39. Closer partnership between business and IT. 40. Enable business intelligence. 41. To keep the business from disintegrating. Expert Opinion 42. Improve IT Security. Table 4: Factor variance & eigenvalues Factors Totals Eigens 3,67 2,02 1,25 0,87 0,84 1,65 1,33 0,82 0,68 13,13 % Variance

52 50 S.Afr.J.Bus.Manage.2013,44(2) Table 5: Factor 1. Transformational task-driven strategist, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 4: Provides a holistic view of the enterprise ,50% Statement 14: Improved alignment to business strategy ,09% Statement 2: Improved business-it alignment ,80% Statement 23: Improved strategic agility ,40% Statement 36: Ability to unify and integrate data across the enterprise Table 6: Factor 2. Universal risk-tolerant pragmatist, key items ,80% Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 4: Provides a holistic view of the enterprise ,50% Statement 10: Improved alignment with partners ,70% Statement 12: Increased standardization ,40% Statement 13: Improved business processes ,40% Statement 21: Increased reusability ,04% % % Table 7: Factor 3. Evolutionary reuse overseer, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 4: Provides a holistic view of the enterprise ,50% Statement 21: Increased reusability ,04% Statement 16: Evolutionary EA development & governance ,35 % Statement 31: Improved business use of technology ,04% Statement 37: Improved IT visibility ,35% % Table 8: Factor 4. Technology leverager, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 31: Improve business use of technology ,04% Statement 39: Closer partnership between business and IT ,40% Statement 8: Increased quality ,70% Statement 13: Improved business processes ,70% Statement 40: Enable business intelligence ,04% % Table 9: Factor 5. Simplicity valuer, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 6: Increased economies of scale ,35% Statement 18: Reduced complexity ,04% Statement 5: Improved communication ,35% Statement 24: Improved staff management ,35% Statement 40: Enable business intelligence ,04% %

53 S.Afr.J.Bus.Manage.2013,44(2) 51 Table 10: Factor 6. IT commoditiser, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 12: Increased standardization ,40% Statement 14: Improved alignment to business strategy ,10% Statement 2: Improved business-it alignment ,80% Statement 28: Improved IT governance ,40% Statement 39: Closer partnership between business and IT ,40% % Two (8,7%) of the participants loaded significantly on Factor 7, which accounts for 6% of total variance and an eigenvalue of 1,33 (see Table 11). This factor is negatively loaded and as a result the statements were switched in order to identify the factor s true values. Factor 8 (see Table 12) represents two participants (8,7%), 4% of total variance and an eigenvalue of 0,82. This factor is negatively loaded and as a result the statements were switched in order to identify the factor s true values. Two of the participants, making up 8,7% of all participants, 3% of the variance and an eigenvalue of 0,68, were loaded on Factor 9 (see Table 13). This factor is negatively loaded and as a result the statements were switched in order to identify the factor s true values. Three participants did not load significantly on any of the 9 factors, while 3 more participants were confounded as they loaded on more than 1 factor. Each of these groups represents 13,04% of all participants. The perceptions of these two groups did not coincide with those of other participants in the study to a level that could be considered significant, or their viewpoints were confused amongst the perceptions represented by more than one factor. Factor interpretation Factor 1 has been named Transformational Task-driven Strategists. The participants loading significantly on this factor believe in adjusting all parts of the organisation so that they fall into the appropriate relative positions. The belief is that once functional areas and IT (through EA) are congruent to the organisational strategy (statement 14, position +5), strategic agility (statement 23, position +4) is improved. EA should provide overall planning in which processes, information and technology interoperate seamlessly to achieve the objectives of the business. Furthermore the right data should be made available to the right people at the right time (statement 36, position +4). The benefits of such a state are transformational, and can be considered to be so because these stakeholders articulate a direction which seeks to change the current situation. It provides the insights needed to address business requirements as well as facilitating the transition from strategy to execution. Transformational Task-driven Strategists bring order out of chaos; they are proactive; and they help determine how EA should be used. They view EA as an evolution of current and future operations in order to achieve organisational goals. EA exists at the level of abstraction and hides the complexities of the various components of the organisation (statement 18, position +3). Factor 2 has been named Universal Risk-tolerant Pragmatist. The Universal Risk-tolerant Pragmatist believes in the universal applicability of EA in impacting the enterprise and its external environment in the form of its partners (statement 10, position +5). It is believed that it is easier for ICT to show the value that it adds by improving alignment with partners. A holistic view (statement 4, position +5) combined with standardisation (statement 12, position +4) and reuse (statement 21, position +4) centre around the belief that with best practice, a universally applicable approach can be found. EA is viewed more as an instrument used by management, and is far broader than mere staff management. It is not viewed as a means of improving customer orientation as this is seen as the responsibility of business itself (statement 17, position -4). The universalistic approach seems to emphasis rules above relationships. Statement 10 distinguishes this factor from all others. Factor 2 is the only factor that identifies improved alignment with partners as a critical benefit of EA. Factor 3 is named Evolutionary Reuse Overseer. The Evolutionary Reuse Overseer uses the holistic view of the enterprise (statement 4, position +5) to increase reusability of processes (statement 21, position +5). He or she believes that EA gives a high level overview of the enterprise and each unit s responsibilities. Furthermore, processes can be used to adhere to new operations. EA is developed in an evolutionary manner (statement 16, position +4). It is believed that the need to reduce complexity increases as the understanding of the enterprise increases (statement 18, position -5). Factor 4 has been named Technology Leverager. Technology Leveragers believe in leveraging technology to improve the business. They are most likely to enquire how any technology is likely to improve business outcomes (statement 39, position +5), including business process (statement 13, position +4). Quality is likely to feature dominantly in such discussions (statement 8, position +4). The Technology Leverager least associates Shortened cycle times (statement 1, position -5), Managerial satisfaction (statement 33, position -5), and Improving integration of mergers and acquisitions (statement 29, position -4) with EA benefits. This enforces the view that EA is primarily about leveraging technology (statement 31, position +5).

54 52 S.Afr.J.Bus.Manage.2013,44(2) Table 11: Factor 7. Operational stability maintainer, key items Statement 20: Improved stability Statement 28: Improved IT governance Statement 2: Improved business-it alignment Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Statement 4 : Provides a holistic view of the enterprise Statement 18: Reduced complexity Total No. Respondents ,70% ,70% ,80% ,50% ,04% % Table 12: Factor 8. Organisational change manager, key items Statement 7: Improved change management Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Statement 38: Reduced the impact that staff turnover has on business Statement 11: Increased interoperability and integration Statement 26: Improved decision making Statement 34: Accelerate IT system implementation Total No. Respondents ,70% ,70% ,70% ,70% ,70% % Table 13: Factor 9. Siloed product manager, key items Statement F1 F2 F3 F4 F5 F6 F7 F8 F9 Total No. Respondents Statement 4: Provides a holistic view of the enterprise ,50% Statement 36: Ability to unify and integrate data across the enterprise ,80% Statement 1: Shortened cycle times ,35% Statement 15: Improved asset management ,35% Statement 27: Improved innovation ,35% % Factor 5 has been named Simplicity Valuer. Simplicity Valuers consist of enthusiasts who fundamentally believe that complexity should be eliminated (statement 18, position +5). They are focused on bottom-line effects (statement 6, position +5). Increased economies of scale make business sense to them, and they are likely to view EA purely as a support function, similar to the rest of IT, with no possibility of offering any competitive edge (statement 23, position -5; statement 25, position -5; statement 9, position -4). They typically consider IT as a black-hole where money gets thrown in with the generation of little value (statement 15, position -4). They are likely to use EA as a vehicle in ensuring that value is ultimately derived from IT investments (statement 5, position +4). Factor 5 is the only factor which rates Item 2 as heavily negative while at the same time rating statements 6 and 24 as heavily positive. The combination of statements 6 and 24 emphasises the bottom-line focus of the participants. Improved staff management implies the drive for efficiency. Factor 6 has been named IT Commoditiser. The IT Commoditiser believes that EA should be used to not only drive IT towards commoditisation through standardisation (statement 12, position +5) but that this should be done within confines that ensure that deployment is aligned to business imperatives (statement 14, position +5; statement 2, position +4; statement 39, position +5). Governance is viewed as a vehicle to ensure that all is undertaken in a transparent and visible manner (statement 28, position +4). EA cannot guarantee organisations Increased market value (statement 9, position -5). Furthermore, current EA approaches are not considered to contribute to Improved communication (statement 5, position -4). Improved staff management (statement 24, position -4) is considered to be a highly specialised function which is more suited to dedicated units such as Human Resources and as such no improvement can be achieved through any successful implementation of EA. Factor 7 has been named Operational Stability Maintainer. The Operational Stability Maintainer utilises the holistic view (statement 4, position +4) to find the flaws of the whole, not just the flaws of the parts. IT Governance (statement 28, position +5) is the primary vehicle used to

55 S.Afr.J.Bus.Manage.2013,44(2) 53 ensure that this is achieved and that complexity is reduced (statement 18, position +4). The high positive rating of Improved IT governance (statement 28, +5) and Reduced complexity, (statement 18, +4), when combined with Provides a holistic view of the enterprise (statement 4, position +4), could be a reflection of the maintenance-based mode and attitude of such participants towards risk avoidance. Increased stability (statement 20, +5) is the only statement that distinguishes factor 7 from all other factors. Business intelligence (statement 40, position -4) is not associated positively with EA benefits, largely because business intelligence is considered a practice that should be entrenched through management practices. The organisation s market value (statement 9, position -4) is considered the responsibility of all functions within business and not purely that of EA. Factor 7 rates statement 20 as highly positive, and it is this statement ranking that makes factor 7 the most risk-averse ( stability maintainer ) of the factors. Factor 8 has been named Organisational Change Manager. An Organisational Change Manager views change as an integral part of the organisation that needs to be properly managed (statement 7, position +5). This could be done in a manner which reduces any impact that staff turnover might have on the business (statement 38, position +5). IT systems implementation has to be accelerated (statement 34, position +4) so that interoperability and integration can be accelerated. This in turn is expected to result in Improved decision making (statement 26, position +4). Improved change management (statement 7, position +5) is the only statement that distinguishes factor 8 from all other factors. Factor 8 rates statement 7 far more positively that all the other factors, which is indicative of the importance and benefit associated with change management by participants who loaded on this factor. Factor 9 has been named Siloed Product Manager. The Siloed Product Manager focuses on the delivery of products as indicated by Shortened cycle times (statement 1, position +4) without any consideration given to strategic agility (statement 23, position -5); or compliance to legislation (statement 30, position -5). The holistic view of the enterprise (statement 4, position +5) could be considered to be achievable through the ability to unify and integrate data across the enterprise (statement 36, position +5). This in turn could be expected to shorten cycle times (statement 1, position +4), improve innovation (statement 27, position +4) and improve asset management (statement 15, position +4).The perspective of the Siloed Product Manager is narrowed down to the delivery of a specific product only. Statements 1 and 19 distinguish factor 9 from all the other factors. Factor 9 rates statement 1 highly whereas the other factors do not. Siloed Product Managers are focused on ensuring that their products are taken to market far quicker than those of competitors. The low rating of statement 19 in this factor indicates the weak link between the need for a holistic view relative to its impact on costs. This is the very reason why the factor is considered to be siloed. Discussion This study has explored the subjective perceptions of business stakeholders regarding the benefits of EA. There are no perceived EA benefits that are consistent across the different stakeholder segments, reinforcing two widely held views. The first is that EA benefits different stakeholders in different ways and the second is that stakeholder segmentation will contribute substantially towards entrenching EA. The study further contributes to the provision of an initial insight into the subset of EA stakeholders, as identified in the 9 factors that emerged in the research. Lastly, by identifying clearly defined stakeholder segments, the research offers a method for targeted EA benefits communications. EA initiatives can begin with the customisation of benefits to specific target stakeholders in order to ensure higher levels of success. The study identifies a hierarchy of perceived EA benefits to an organisation. In terms of response, the following are the dominant perceptions that are seen to contribute significantly (+5, +4): Statement 4 Provides a holistic view of the enterprise (43.5% of participants across all factors). Statement 2 Improved business-it alignment (34.8% of participants across all factors). Statement 14 Improved alignment to business strategy (27% of participants across all factors). Statement 36 Ability to unify and integrate data across the enterprise (21.8% of participants across all factors). Five factors were jointly ranked 5 th. Statement 23 Improved strategic agility; Statement 12 Increased standardisation; Statement 13 Improved business processes; Statement 39 Closer partnership between business and IT; and Statement 28 Improved IT governance (17.4% of participants across all 9 factors). This ranking does not reflect any shared meaning across factors or the shared contexts of these statements or the participants who hold them, but merely indicates the salience of these statements amongst the participants in this study. It should be noted that Q-methodology focuses on interrelations among persons with respect to a population of tests therefore the salience of individual statements is not critical to the study or its overall conclusions (Donner, 2001). What can be drawn from the relative salience of these items is that EA benefits cannot be thought of in absolute terms. Their importance is very much dependent on the perspectives of the stakeholders under observation. The question Which benefits do you consider most significant for EA? has multiple answers. The answer is dependent on the stakeholder/participant being asked, and the relative position held by the stakeholder/participant relative to others. The study has clearly identified nine stakeholder categories: Transformational Task-driven Strategist, Universal Risktolerant Pragmatist, Evolutionary Reuse Overseer,

56 54 S.Afr.J.Bus.Manage.2013,44(2) Technology Leverager, Simplicity Valuer, IT Commoditiser, Operational Stability Maintainer, Organisational Change Manager, and Siloed Product Manager. The categories identified in the study may be evident in the broader population of EA business stakeholders but further research is required to test the validity of the assumption. Recommendations, contributions and limitations The contribution of this study is primarily the identification of hitherto undefined EA stakeholder segments and their perceptions of EA benefits. The study clarifies the perceptions of EA by Telkom SA s different business stakeholders, and the contexts and logic of these perceptions. The findings represent dominant perceptions of EA at Telkom and cannot be extrapolated to industry as a whole. The categories may, however, be used as starting points in further research into EA stakeholder perceptions with the use of methodologies that are suited to the generalisation of findings. Improved stakeholder knowledge will provide EA practitioners with the necessary tools required in ensuring that targeted EA artefacts are developed, and further that more targeted communication messages are developed in the realm of EA. The question then arises whether marketing concepts will be more vigorously applied in the domain of EA than is currently the case. Q-methodology is a powerful tool for the investigation of convergent subjective perceptions. While it has been in existence for close to 80 years, it has never been widely used because of the technical and mathematical difficulties in doing so. With recent developments in technology and the software to support it, its use may be expected to increase. Q-methodology offers the scientific rigour to test stakeholders perceptions, and to cluster them so that rich groupings of perceptions and the people who hold them can emerge. The following recommendations are drawn from the findings of this study: EA practitioners mindsets must change: The technically led development of EA will limit its application. An all-inclusive approach to EA is now required with external stakeholders of ICT playing a central role in directing EA efforts. Organisations should build initiatives to incorporate the voice of the customer. An ICT-centric approach to service delivery is likely to fail without a change in mindset. Benefit Segmentation must be incorporated into EA stakeholder definition: EA benefit needs should be used to segment EA stakeholders. It is evident in the study that different segments have significantly different expectations of the benefits sought from EA. Thus benefits sought should form segmentation variables for EA. EA practitioners can focus on those segments that are likely to tilt EA initiatives towards success. Application of Q methodology. The use of Q- methodology should be encouraged, particularly in the African leadership paradigm which encourages communalism, a high humane-orientation and emphasis on the value of the collective. Q- methodology ensures that subjective views are uncovered and grouped so that collective perspectives can emerge. The methodology can focus on the singular ( my preference ) and the collective ( our preference ) at the same time, an extremely useful attribute when attempting to develop a collective perspective. The study has a number of limitations. The sample was too limited to allow extrapolation to the wider EA community, and while this was not an objective of the study, it should be pursued in further research. The Concourse in this study was primarily derived from the academic literature, opinion articles and EA practitioners, and not stakeholders, whose views are essential and may not yet be fully evident in academic sources. Limited researcher-participant interaction may have restricted the rich qualitative information that could have been derived through the opportunities afforded by Q-methodology. Additional insights may, for example, have been derived with follow-up interviews or focus groups to derive a greater understanding of the perceptions that were identified and the contexts in which they apply. Conclusion This study serves as an initial step in generating knowledge and understanding of Enterprise Architecture s stakeholder segments and their perception of EA. Currently the links between EA benefits and stakeholders is limited and a relatively new topic of study, but one that is vital in improving the adoption of EA within organisations. This study s identification of EA stakeholder segments should go a long way towards ensuring that greater understanding and better targeted communication occurs. The 9 factors identified in this study represent subjective perceptions of EA benefits that are the most important, albeit in one organisation. More generally, the study identifies a methodology that is suitable for the identification of EA benefits and the value in doing so. It also offers a range of critical benefits and the stakeholder segments that may be most closely associated with these benefits. The feelings that stakeholders have of Enterprise Architecture are unique, and as Stephenson (1980:356) puts it, feelings are not a matter of mere words put into a certain order like pieces of a jigsaw puzzle, but far more, a new idea that cannot be described by any general formula. Perceptions of EA are new ideas, and it is hoped that this study contributes in describing them in the rich context that is made possible by the methodology of the study. References Amin, Z Q methodology A journey into the subjectivity of human mind, Singapore Medical Journal, 41(8):

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58 S.Afr.J.Bus.Manage.2013,44(2) 57 The influence of employees perceptions of organizational politics on turnover intentions in Zimbabwe s SME sector R. Chinomona* Department of Logistics Management, Vaal University of Technology, Republic of South Africa [email protected] E. Chinomona Department of Human Resources Management, Vaal University of Technology, Republic of South Africa Despite increasing awareness of the importance of managing the negative effects of organizational politics at the workplace, research on consequences relating to employees perceptions of the same in small and medium enterprises (SMEs) in Africa has received little attention. Therefore, using data from 250 SMEs in Zimbabwe, this study examines the effects of employees perceptions of organizational politics on turnover intentions and the mediating influence of employees perceptions of equity and organizational commitment. All the posited six hypotheses were supported by the sample data. Managerial implications of the findings are discussed and limitations and future research directions are indicated. *To whom all correspondence should be addressed. Introduction Organizational politics has intrigued academicians and practitioners for decades. Yet, serious scholarship on politics in organizations has emerged as a viable body of scientific inquiry just within the past twenty years (Ferris, Adams, Kolodinsky, Hochwarter & Ammeter, 2002; Drory & Vigoda-Gadot, 2010). Studies have long argued that politics is an epidemic phenomenon ubiquitous in organizations and that it deserves more attention and empirical examination (e.g., Vigoda, 2000; Pfeffer, 1992; Andrews, Witt & Kacmar, 2003). The importance of organizational politics (OP) lies in its potential negative consequences and effects on work outcomes. Accumulating empirical research has provided considerable evidence that organizational politics among others breeds negative perception of equity, diminish organizational commitment and eventually necessitates turnover intentions or turnover (see Ferris et al., 2002 and Rosen, Chang, Johnson and Levy, 2009). Moreover, the current received wisdom is that turnover intentions or turnover should be reduced or avoided by organizations since it is more costly to replace an employee than to retain one (Carson & Gilmore, 2000). In view of that, research interests on the effects of organizational politics on turnover intentions are quite substantial and growing (Andrews et al., 2003). Regrettably, these studies are focused on large size firms and with little regard to the small and medium enterprises (SMEs) sector. Given that the SMEs sector is regarded as the engine of economic growth and major instrument for employment generation in both developed and developing countries (Chinomona, Lin, Wang & Cheng, 2010), it is surprising that, this important matter in such a vital sector has been largely neglected by researchers. More so, the charecteristics of the large sized firms and the SMEs are different (Sieger, Bernard & Frey, 2011) and therefore, the findings might be expected to be different. Besides, these prior studies on large firms have been largely from developed countries (Chinomona & Pretorius, 2011) and thus, given the uniqueness of developing countries from Africa such as Zimbabwe, the results may be different. By and large, perhaps research focused on the effects of employees perceptions of organizational politics on turnover intentions in the context of SMEs in Africa is long overdue and imperative to undertake. The present study seeks to address the aforementioned gap in literature; hence the focal purpose of this research is three-fold. Firstly, from the perspective of employees in the SMEs sector, this research mainly seeks to investigate the extent to which perceptions of organizational politics directly influence their turnover intentions in Zimbabwe. Secondly, the current study also seeks to explore the mediating influence of employees perceptions of equity and organizational commitment in this relationship. Thirdly, an attempt is made to apply the justice judgment theory in this research context. This endeavor is considered to provide a strong theoretical grounding to the current research. On the

59 58 S.Afr.J.Bus.Manage.2013,44(2) whole, the findings of this study are expected to contribute new knowledge to the existing body of human resources management literature on organization politics effects on turnover intentions, as well as providing practical implications to practitioners in the SMEs sector, particularly in the context of developing countries in Africa. The remaining sections of this article will provide a literature review, propose a conceptual research model and develop the research hypotheses, provide the research methodology, analyze data and present results. Finally, results are discussed, implications provided and limitations and future research directions highlighted. Justice judgement theory The study of justice or fairness has been a topic of philosophical interests that extend back to as far as Plato and Socrates (Colquitt et al., 2001). Research consistently finds that people care about fair treatment. When individuals perceive that they are treated fairly, they express greater satisfaction with social relationships (Warner, Hegtvedt & Roman, 2005). A proliferation of studies in organizational justice suggests that Justice Judgment theory assumes that an individual s perception of fairness is based on justice rules (Colquitt et al., 2001; Walumbwa, Cropanzano & Hartnell, 2009). Justice rule is defined as an individual s belief that a distribution of outcome, or procedure for distribution of outcomes, is fair and appropriate when it satisfies certain criteria (Sieger et al., 2011). This definition presupposes two categories of justice rules, namely distribution rules and procedural rules. A distribution rule is defined as an individual s belief that it is fair and appropriate when rewards, punishments, or resources are distributed in accordance with certain criteria (Colquitt et al., 2001; Walumbwa et al., 2009). A specific criterion might require the matching of rewards to contributions, or matching rewards to needs or dividing rewards equally. Thus, a contributions rule, needs rule and equality rule are among the major distributive rules that can influence an individual s perception of distributive fairness (Warner et al., 2005). A procedural rule is defined as an individual s belief that allocative procedures which satisfy certain criteria are fair and appropriate (Walumbwa et al., 2009). Every group, organization or society has procedures that regulate the distribution of rewards and resources. There is a network of regulatory procedures that guides the allocative process. The concept of procedural fairness refers to an individual s perception of the fairness of procedural component of the social system that regulates the allocative process (Sieger et al., 2011). An individual uses justice rules to evaluate the fairness of allocative procedures (Ferris et al., 2002). Thus in this case justice rule is viewed as a belief that allocative procedures are fair when they satisfy certain criteria. Six procedural justice rules are postulated that define criteria which allocative procedures must satisfy to be perceived as fair. These are the consistency rule, bias-suppression rule, accuracy rule, correctability rule, representativeness rule and ethicality rule (Colquitt et al., 2001). While the presence of distributive or procedural justice in an organization makes employees anticipate equity at their workplace, be committed to the organization and reduce inclination towards turnover intentions (Loi, Hang-yue & Foley, 2006) organizational politics mostly goes against the spirit of justice theory. In a politically charged organization, employees feel insecure because of high job ambiguities, unfairness, unjust treatment and perceived inequity (Lind, 2001). For instance, where perceived inequity is high, employees are likely to be dissatisfied and consequently, this lead to low organizational commitment and high turnover intentions or ultimately job quit (Lowe & Vodanovich, 1995; Loi et al., 2006). On the contrary, the absence of negative organizational politicking might create a just and fair working environment that makes employees have a perception of equity, which might culminate in high organizational commitment and low turnover intentions (Sieger et al., 2011). Drawing from these aforementioned assertions a conceptual model in Figure 1 is developed. Conceptual research model In this conceptual model, employee perception of organizational politics is the sole predictor, directly influencing turnover intentions, employee perceptions of equity and organizational commitment. Employee perceptions of equity and organizational commitment are mediators while employee turnover intentions is the outcome variable. Hypothesis development Employee perception of organizational politics and employee turnover intention Given that people tend to view organizational politics as undesirable because of the unfair and unjust perceptions and treatment associated with it at workplace environment, they are unlikely to stay permanently in a politically charged organization (Ferris et al., 2002). It follows therefore, in concurrence to justice judgment theory that as a result of high organizational politics perceived in the form of unfair and unjust treatment, employees will likely choose to withdraw physically or psychologically from their jobs. One physical form of withdrawal is to quit the job. Not all employees, however, have the immediate luxury of such an option. The likely short-term option for those with less job mobility is psychological withdrawal, such as intentions to quit the job. Ferris and Kacmar (1992) showed that organizational politics cause inefficiency and ineffectiveness in organizations.

60 S.Afr.J.Bus.Manage.2013,44(2) 59 Employee Perception of Organizational Politics H1 H2 H3 Employee Organizational Commitment H5 Employee Turnover Intention H4 Employee Perception of Equity H6 Figure 1: Conceptual framework Previous empirical evidence has found perceived organizational politics to be positively related to turnover intentions in a significant way (Cropanzano, Howes, Grandey & Toth, 1997; Randall, Cropanzano, Bormann & Birjulin, 1999). Conceivably, when SMEs employees perceive their organization to be politically charged, they anticipate unfair treatment, manipulative decisions and actions at their workplace. As a result of this, their turnover intentions are likely to increase. Therefore, based on the abovementioned theoretical reasoning and prior empirical evidence, this study posits that: H1: Employees perceptions of organizational politics will have a significant positive effect on their turnover intentions in Zimbabwe s SME sector. Employee perception of organizational politics and organizational commitment From a theoretical perspective, justice judgment theory enumerate that when distributive and procedural rules are twisted to give some individuals unfair advantages or rewards, dissatisfaction sets in, morale is dampened because of perceived unfairness and consequently commitment to that organization is reduced (Randall et al., 1999). Empirical evidence also points to the fact that when political behavior within organization rises and workers feel that they have been treated unfairly, their organizational commitment decreases (Ferris et al., 2002; Valle & Witt, 2001). A sizable number of recent studies seem to support the inverse relationship between employees perceptions of organizational politics and organizational commitment (Vigoda-Gadot, Vinarski-Peretz & Ben-Zion, 2003; Cater & Zabka, 2009; Sieger et al., 2011). Conceivably, when SMEs employees perceive unfair treatment as a result of organizational politics at their workplace, they are likely to reduce their organizational commitment. Therefore, drawing from the theoretical reasoning and prior empirical evidence this study hypothesised that: H2: Employees perceptions of organizational politics will have a significant negative effect on their organizational commitment in Zimbabwe s SME sector. Employee perception of organizational politics and employee perception of equity Employees perceptions of equity suggest that perceptions of fairness are a job related motivational base that can influence the behavioral and affective responses of job performers (Colquitt et al., 2001). Equity plays a very important role of motivating employees at workplace (Loi et al., 2006; Sieger et al., 2011). Employees make comparisons of their job inputs and outcomes. If employees perceive the ratio to be equal to that of others then a state of equity is said to exist. According to equity theory, if the ratio is unequal then the employees experience equity tension (Chan, Moir, Mestelman & Muller, 1996).When employees see themselves as under rewarded, the resultant tension creates anger and when they see themselves as over rewarded then the consequential tension creates guilty (Janssen, 2001). In essence, perception of equity is related to the contribution rule of Justice Judgment theory. According to contribution rule of Justice Judgment theory, employees expect the ratio between effort spent and rewards received at work to be equitable. Job rewards on the other hand refer to inducements an employee may receive from an organization including money, desired job related responsibilities, esteem, status and social identity (Ranida, 2005). Inequity can be reflected as underrewarded or over-rewarded unfairness. According to the extant literature, a politically charged organization is characterized by, for instance, individuals manipulatively influencing decisions on rewards in their favor at the expense of others (Vigoda, 2000). Those unfairly disadvantaged will perceive inequity as they view their rewards to be not worthy their effort. In such a case organizational politics will be negatively related to

61 60 S.Afr.J.Bus.Manage.2013,44(2) employees perceptions of equity. Prior empirical studies (e.g. Loi et al., 2006; Sieger et al., 2011) on large size organizations have supported the existence of a negative relationship between organizational politics and employees perceptions of equity. Similarly, organizational politics in the SMEs sector is to be expected to be negatively related to employees perceptions of equity. Therefore, deducing from Justice Judgment theory and prior studies the following hypothesis is postulated: H3: Employees perceptions of organizational politics will have a significant negative effect on their perceptions of equity in Zimbabwe s SME sector. Employee perception of equity and organizational commitment When employees put effort at their workplace and that effort is fairly rewarded, commitment to that organization will result (Loi et al., 2006; Sieger et al., 2011). Employees regard an outcome reward to be equitable when there is a balance between effort input and output (Almar, 2005). Such employees perceptions of equity are likely to motivate and induce them to be loyal to that organization and eventually, long term organizational commitment result. The issue of equity which forms one of the basis of Justice Judgment theory has been used in numerous human resources studies to substantiate this reasoning (Sieger et al., 2011). In addition, prior empirical studies have supported a positive linkage between employees perceptions of equity and organizational commitment (Lemons & Jones, 2001; N Goala, 2007). Similarly, when SME employees perceive their rewards to be fair or equitable when compared to their effort input or that of others, they are likely to be committed to that organization. Therefore, based on the aforementioned theoretical reasoning and empirical evidence, this study proposes that: H4: Employees perceptions of equity will have a significant positive effect on their organizational commitment in Zimbabwe s SME sector. Organizational commitment and employees turnover intentions The relationship between organizational commitment and turnover intentions has been reviewed and examined extensively by researchers and academics over the decades (Meyer & Allen, 1991; Cater & Zabkar, 2009). Most of these studies argue that organizational commitment is an indication of employees satisfaction with their workplace and as a result of this satisfaction, their turnover intentions are reduced. Drawing from Justice Judgment theory, if an employee perceives fairness at the workplace, he or she is unlikely to contemplate leaving the current organization to another. Furthermore, substantial empirical evidence has supported the negative linkage between organizational commitment and employee turnover intentions (Meyer, Stanley, Herscovitch and Topolnytsky, 2002; Meyer & Herscovitch, 2001; Rhoades, Eisenberger & Armeli, 2001). Accordingly, when SME employees are committed to their organization, because they feel motivated and fairly treated at their workplace, their turnover intentions are likely to be reduced. Therefore, drawing from the aforementioned arguments, this study hypothesizes that: H5: Employees organizational commitment has a significant negative effect on their turnover intentions in Zimbabwe s SME sector. Employee perception of equity and employee turnover intention A cross examination of the existing empirical human resources literature indicates that employees perceptions of equity leads to low turnover intentions (Ranida, 2005). The reasoning is that when employees feel fairly treated, they feel motivated to stay in an organization and therefore, reduce their intention to leave (Griffeth, Hom & Geatner, 2000; Price, 2001). This argument is also supported by the equity reasoning in the justice judgment theory. Accordingly, when SME employees perceive equity in their organization, they are likely to be pleased and become loyal to that organization (Solinger, Olffen & Roe, 2008). In such a case they have no motivation to leave the organization hence, their turnover intention is reduced. Therefore, drawing from the aforementioned theory and empirical evidence, this study posits that: H6: Employees perceptions of equity will have a significant negative effect on their turnover intentions in Zimbabwe s SME sector. Research design and methodology Sample and data collection The research was conducted with a survey method. A simple random probability sampling was used to select 300 SMEs because it gives all the subjects equal opportunity to be selected and hence avoiding bias. The Ministry of Small and Medium Enterprise in Zimbabwe provided the database because they have a list of all the Zimbabwean registered small and medium enterprises. Research assistants were engaged from the University of Zimbabwe to distribute and collect the questionnaires. The researchers distributed 300 questionnaires after making appointments with the respective SMEs owners or managers. SMEs employees who are not in the managerial positions were targeted to fill in the questionnaires. Non-managerial employees are those who are at the operational floor and who do the actual work such as welding, molding, painting and cooking food. This study sought to get information from non-managerial employees because turnover intention is reported to be high among them unlike their counterparts in managerial positions (Beauchamp & Bowie, 2004; Carroll & Buchholtz, 2006; Ferrell, Fraedrich & Ferrell, 2008). Measurement instrument A questionnaire containing 42 measurement items was designed based on previous work for the current study. Adjustments were made in order to fit the purpose of the reflective scales used in the current research context. A fifteen-item scale used to measure employees perceptions of organizational politics was adapted from the previous

62 S.Afr.J.Bus.Manage.2013,44(2) 61 study by Kacmar, Bozeman, Carlson and Anthony (1999), while a seven-item scale to measure employees perceptions of equity was adapted from Janssen (2001). Organizational commitment used a fifteen-item scale measure adopted from Meyer, Allen and Smith (1993) and Chinen and Enomoto (2004), while a five-item scale adapted from Wayne, Shore and Liden (1997) was used to measure employees turnover intentions. All the measurement items were measured on a 5-point Likert-type scales that was anchored by 1= strongly disagree to 5 = strongly agree to express the degree of agreement. Data analyses and results The research sample is described below. Then, the two-step procedure suggested by Anderson and Gerbing (1988) was applied to analyze the research data. That is, the accuracy of multi-item construct measures was assessed, followed by a test of the research model and hypotheses. In both data analysis stages, the current study mainly used Structural- Equation-Modeling (SEM) technique. The computation SEM software was AMOS 5. Sample description The study distributed questionnaires to different respective SMEs in Zimbabwe. Of the total of 300 questionnaires which were distributed, 253 were returned and out of these 253 questionnaires, only 250 were usable. This yielded a valid response rate of about 83.33%. Descriptive statistics in Table 1 show the gender, age, marital status, and the number of employees in the company, respondents working experience, monthly salary, type of industries and the type of products produced. Table 1: Sample demographic characteristics Gender Frequency Percentage Male % Female 60 24% Total % Age Frequency Percentage ,4% ,4% ,2% Total % Marital status Frequency Percentage Married % Single % Total % Number of employees Frequency Percentage ,6% % % Total % Participants working experience Frequency Percentage 5 years ,6% 5-10 years 86 34,4% 10 years 55 22,0% Monthly salary in US dollar Frequency Percentage US$ ,6% US$200-US$ ,2% US$ ,2% Total % Industry Frequency Percentage Manufacturing ,6% Service 91 36,4% Total % As indicated in Table 1, this study shows that males dominate the SMEs sector and constitute 76% of the workforce. The most active age group in SMEs is that between years which constitute 60.4% of the total workforce, followed by those below 30 years and then above 60 years, constituting 24.4% and 15.2% respectively. Employees who are single occupy 60% and the remainder is married. The profile indicates that more than three fifths of the participating SMEs employed 20 or fewer workers, while one fifth had a workforce between employees and a minority of them had between employees. Participants with less than 5 years work experience constituted 43. 6% followed by those with between 6-10 years work experience who constituted 34.4% and the

63 62 S.Afr.J.Bus.Manage.2013,44(2) remainder had above 10 years work experience. The majority of the participants consisting of 77,6% earned below US$200 a monthly, while 21,2% earned between US$200 US$400 and the remainder earned above US$400. The study also indicated that the majority of the participants belonged to the manufacturing sector which occupied 63,6%, while service sector occupied the remainder Measurement accuracy assessment Confirmatory factor analysis (CFA) was performed to examine the reliability, convergent and discriminant validity of the multi-item construct measures. Initial specification search led to the deletion of some of the items in the constructs scale in order to provide acceptable fit. Overall acceptable CFA model fit indices used in this study included: the χ 2 /(df) (Chi-Square/Degree of Freedom) value equal to or less than 3.00, the CFI (Comparative Fit Index) value equal to or higher than 0,90, Tucker and Lewis Index (TLI) value equal to or higher than 0,90, the Incremental Index of Fit (IFI) value equal to or higher than 0.90, and the Root Mean Square Error of Approximation (RMSEA) value equal to or less than Recommended statistics for the final overall model assessment showed an acceptable fit of the measurement model to the data, that is: χ 2 /(df) = 2,201, CFI = 0,923, TLI = 0,917, IFI = 0,923 and RMSEA = 0,069. Loadings of individual items on their respective constructs are shown in Table 2. The lowest value for individual item loadings for the research constructs is 0,692. Therefore, all the individual item loadings exceeded the recommended value of 0,5 (Anderson & Gerbing, 1988). This indicates that all the measurement instruments are acceptable and reliable since all the individual items converged well and with more than 60% of each item s variance shared with its respective construct. Composite reliabilities (CR) and average variance extracted (AVE) for each construct were also computed using the formulae proposed by Fornell and Lacker (1981) i.e. CRη=(Σλyi)2/[(Σλyi)2+(Σεi)] where CRη = Composite reliability, (Σλyi)2= Square of the summation of the factor loadings; (Σεi)= Summation of error variances. Vη=Σλyi2/(Σλyi2+Σεi) where Vη= Average Variance Extracted (AVE); Σλyi2= Summation of the squared of factor loadings; Σεi= Summation of error variances. As indicated from the results shown in Table 2, the lowest obtained composite reliability (CR) value of 0,916 is well above the recommended 0.6 (Hulland, 1999), while the lowest obtained average variance extracted (AVE) value of 0,631 is also above the recommended 0.5 (Fraering & Minor, 2006). This indicates that convergent validity was achieved and also this further confirms an excellent internal consistency and reliability of the measurement instruments used. Discriminant validity was established by ensuring that the average variance extracted (AVE) for each multi-item construct was greater than the shared variance between constructs (Nunnally & Bernstein, 1994). As such, all pairs of constructs revealed an adequate level of discriminant validity (see Table 2). By and large, these results provided evidence for acceptable levels of research scale reliability. Research model assessment and hypothesis testing research The research model was estimated and the hypotheses testing done. All the research model fit statistics were within the acceptable ranges, i.e., χ 2 /(df) = 1,872, CFI= 0,944, TLI = 0,940, IFI = 0,945, and RMSEA = 0,059. The individual hypothesis testing results are also shown in Table 3. The path coefficients for H1, H2, H3, H4, H5 and H6 are 0,290, -0,811, -0,607, 0,284, -0,416 and -0,223 respectively. All hypothesis coefficients are significant at a confidence level (p value) of 0,001. Therefore, these results provide support for all the proposed six hypotheses. Discussion and conclusions This study utilizes the Justice Judgment theory to provide a theoretical grounding for the conceptual framework that seeks to expound the effects of organizational politics on employees turnover intentions in the SME business environment. More specifically, this study postulates that employees perceptions of organizational politics significantly affect their turnover intentions in a positive way and negatively their organizational commitment and perceptions of equity while organizational commitment and perceptions of equity negatively impact on their turnover intentions. To confirm the proposed hypotheses of the research framework, data are collected from Zimbabwe s SME sector. The data analysis results support all the research hypotheses in this study. Overall, this provides support to the research propositions that employees perceptions of organizational politics positively influence their turnover intentions and also negatively influence their organizational commitment and perceptions of equity in the SME setting. Some interesting findings from this study are that, employees perceptions of organizational politics influence organizational commitment more than they do on their perceptions of equity and turnover intentions. Furthermore, organizational commitment negatively influences turnover intentions more than employees perceptions of equity and organizational politics do. This means that employees perceptions of organizational politics have more influence on turnover intentions via organizational commitment than they directly do on the same or when mediated by employees perceptions of equity.

64 S.Afr.J.Bus.Manage.2013,44(2) 63 Table 2: Accuracy analysis statistics Research Construct Perception of Organizational Politics (POP) Organizational Commitment (OC) Perception of Equity (POE) Turnover Intention (TI) POP 1 Cronbach s Test Item-total value,811 C.R. Value AVE Value Shared Variance Factor Loading POP 2,845,856 POP 3,856,866 POP 4,843,859 POP 5,812,828 POP 6,785,801 POP 7,830,967,847,967,694 POP 9,816,827 POP 10,837,856 POP 11,785,801 POP 13,806,819 POP 14,831,846 POP 15,777,451,796 OC 1,733,753 OC 2,772,784 OC 3,801,821 OC 4,794,810 OC 5,754,773 OC 6,787,804 OC 8,793,819 OC 9,795,812 OC 10,772,790 OC 11,756,772 OC 12,791,809 OC 13,810,829 OC 14,792,959,960,631,803 OC 15 POE 1,235,826,724,736,641 POE 5,859,909,916,735,920 POE 6,832,908 POE 7,856,434,889 TI 1,762,803 TI 2,863,918,921,746,932 TI 3,866,912 TI 4,515,767,799 Note: C.R.: Composite Reliability; AVE: Average Variance Extracted; S.V.: Shared Variance; * Scores: 1 Strongly Disagree; 3 Neutral; 5 Strongly Agree Measurement CFA model fits: χ 2 /(df) = 2,201, CFI = 0,923, TLI = 0,917, IFI = 0,923 and RMSEA = 0,069,692

65 64 S.Afr.J.Bus.Manage.2013,44(2) Table 3: Analysis results of the research structural model and related hypotheses Path Coefficients Hypothesis Factor Loading Organizational Politics (POP) Turnover Intention (TI) H1 0,290 c Organizational Politics (POP) Organizational Commitment (OC) H2-0,811 c Organizational Politics (POP) Perception of Equity (POE) H3-0,607 c Perception of Equity (POE) Organizational Commitment (OC) H4 0,284 c Organizational Commitment (OC) Turnover Intention (TI) H5-0,416 c Perception of Equity (POE) Turnover Intention (TI) H6-0,223 c Note: a significance level <0,05; b significance level <0,01; c significance level <0,001; Research structural model fits: χ 2 /(df) = 1,872, CFI= 0,944, TLI = 0,940, IFI = 0,945, and RMSEA = 0,059. Implications This study has academic and practical implications. On the academic front, two contributions are made. Firstly, an attempt was successfully made to apply the Justice Judgment theory in order to explicate important human resources management phenomena in the small business field. Secondly, this study examined an important issue in a neglected research context, that is, SMEs in a developing country of Southern Africa. Therefore, this study has expanded the existing literature on SME human resources management to developing countries. As a practical contribution, the current study provides two strategic implications for owners or managers in the SMEs sector that focus on reducing the negative effects of organizational politics. Firstly, given that the employees perceptions of organizational politics adversely impact on their organizational commitment, perceptions of equity and consequently increase their turnover intentions, managers in the SMEs sector are encouraged to adopt measures that are targeted at curbing the negative impact of organizational politics in order to reduce the consequential negative feelings and behavioral intentions from job strain suffered by employees. Such measures might include instituting a strict code of conduct at the workplace and ensuring that the code of conduct is understood, observed and enforced. Managers are also encouraged to reward desired behaviors that follow the code of conduct while punishing undesirable behaviors that promote negative organizational politicking at the workplace. Secondly, managers are encouraged to promote increased cooperation and team spirit among employees. Cooperation and teamwork tend to reduce the negative influence of organizational politics at workplace. Perhaps managers might consider rewarding cooperative behaviors and teamwork in order to motivate and strengthen employees organizational commitment, perceptions of equity and consequently reducing their turnover intentions. Limitations and future research Although this study makes significant contributions to both academia and practice and also that due care was taken to achieve rigor, there are some limitations which open up avenues for further research. Firstly, the data were gathered from non-managerial employees in the SMEs sector. The results might be more informative if data from employees who hold managerial and non-managerial positions are to be compared. Therefore, subsequent studies might consider collecting data from these two sides for empirical investigation. Secondly, while this study focused on Zimbabwe, extending this study to other African countries is also another possible future research direction that might enable comparisons of results with the current study findings. Future research might also consider investigating the effects of employee perceptions of organizational politics on outcomes such as organizational citizenship behaviors and the actual employee turnover. References Almar, M.W Equity sensitivity and negotiation behaviors: A look at Mexican exporters, Academy of Management Journal, 4(3): Anderson, J.C. & Gerbing, D.W Structural equation modelling in practice: A review and recommended two step approach, Psychological Bulletin, 103(3): Andrews, M.C., Witt, L.A. & Kacmar, K.M The interactive effects of organizational politics and exchange ideology on manager ratings of retention, Journal of Vocational Behavior, 62(2): Beauchamp, T.L. & Bowie, N.E Ethical theory and business. Upper Saddle River, NJ: Pearson Prentice Hall. Carroll, A.B. & Buchholtz, A.K Business and society. Mason, OH: South-Western Thomson. Carson, D. & Gilmore, A Marketing at the interface: Not "what" but "how", Journal of Marketing Theory and Practice, 30(5): Cater, B. & Zabka, C Antecedents and consequences of commitment in marketing research services: The client s perspective, Industrial Marketing Management, 38(7):

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69 S.Afr.J.Bus.Manage.2013,44(2) 67 Are bank loans to SMEs procyclical? Evidence from an analysis of the lending behavior of Korean banks Ji-Yong Seo Department of Business Administration, Sangmyung University, 7 Hongji-dong, Jongno-gu, Seoul , Korea [email protected] This paper investigates the procyclicality of bank loans to Small and Medium Enterprises (SMEs) and to Large Enterprises (LEs) using aggregated and cross-sectional data from major private, foreign, and state-owned banks in Korea in the period from 1999 to Based on previous studies, it is hypothesized that compared to LEs, banks loans to SMEs may be more vulnerable to external economic shock. Berger and Udell (1994) suggested that bank loans to SMEs are comparatively risky due to their relatively low collateral and heavy dependence on banks for raising funds. In this study, empirical tests are verified by applying the rolling vector error correction Model (VECM), panel generalized least squares model (GLS), and the Clustering Fixed Effect Model. Findings include robust support for the procyclicality of bank loan to SMEs, but not for LEs. The review of short-term dynamics among first differential variables such as loans and GDP provides evidence to support a related hypotheses: the profit-oriented motivation of commercial banks in enhancing relationships with SMEs, the characteristics of governance structure in three types of banks (private, state-owned, and foreign owned banks), and the large-bank barriers assumption. *To whom all correspondence should be addressed. Introduction Global financial difficulties caused by the sub-prime mortgage crisis in the latter half of 2008 triggered the current global economic recession. Many financial institutions that had invested in derivatives linked to subprime mortgages faced insolvency, and the international credit crunch and fluctuating exchange rates resulted in a drastic reduction in both consumption and investment. The Korean economy, which is heavily dependent on export, has faced serious difficulties due to the worsening domestic market conditions experienced by its major trading partners, including the U.S. However, the global economic recession has also had a direct effect on Korean companies and financial institutions. It is assumed that during a recession, small and medium Enterprises (SMEs) are more likely to suffer a lack of funds and reduced achievements compared to Large Enterprises (LEs). As large companies reduce production, SMEs are expected to receive fewer orders, resulting in decreased sales and decreased profits. It is also expected that financial institutions will reduce loans to SMEs in order to counter rising insolvencies, and therefore, even some profit-making SMEs are expected to become bankrupt. Furthermore, in the wake of the introduction of the new Basel Accord (2008), in which weighted risk is applied according to borrowers credit rating, financial institutions are expected to reduce drastically loans to SMEs. Since SMEs do not have sufficient collateral relative to LEs, financial institutions such as commercial banks are expected to collect loans and refrain from extending new ones. Thus, it is expected that SMEs, which are heavily dependent on loans extended by financial institutions, will face increasingly difficult managerial challenges due to a lack of funds. Commercial banks in Korea have had difficulties in managing assets because of reduced profit margins and intensified competition among banks, which have drastically increased loans to SMEs in an effort to expand business. In the first half of 2005, the net amount of loans extended to SMEs increased by only 3,0 trillion won (about USD 2,5 billon) from the year before. In the first half of 2006, the amount increased by 19 trillion won (about USD 15,8 billon) and in the first half of 2008, by 68 trillion won (about USD 56,5 billon). However, since early 2009, the net amount of these loans has been gradually decreasing. In the beginning of 2010, the net increase was only 14 trillion won (about USD 11,4 billion). The global economic recession caused by financial crises in the U.S. has resulted in the reduction of the fund raising capacity of SMEs. This implies that bank loans to SMEs seem to have responded to the volatility in business fluctuation before the global economic crisis. Thus, it seems to indicate that there has been fundamental change in the SMEs loan market in Korea. However, it also seems that the capital adequacy ratios of Korean commercial banks are still good judging by the average of Bank for International Settlement (BIS) ratio of 11,27%. In recent years, financial authorities have asked the banking sector to finance SMEs proactively in an effort to help them resolve their lack of funds. The authorities promoted this action by demonstrating their intention to operate flexibly the standard of financial soundness for banks. Specifically, they reduced the BIS ratio to as low as

70 68 S.Afr.J.Bus.Manage.2013,44(2) 10%, but the banks still felt that extending loans to SMEs would be a burden. In the light of the fluctuations in SMEs financing, the relationship between economic conditions and bank loans to SMEs has emerged as an important issue in the financial sector of Korea. This paper aims to examine whether a comovement between business cycles and bank loans exists through an empirical analysis of quarterly aggregated data and panel data on Korea. In other words, the objective of this paper is to explain the relationship between the lending behaviors of banks to SMEs or LEs and business cycle, which is called the hypothesis of procyclicality. It argues that bank loans are affected over business cycles. It insists that the lending behavior of banks toward enterprises, especially SMEs, differs during times of economic boom and recession, and holds that loans increase during economic expansion and decrease during economic recession. If procyclical of bank loans exists, it is expected to affect struggling Korean SMEs negatively. Thus, this result could contribute to urging the financial authorities to take steps to alleviate the procyclicality. This result would also contribute to the literature on financial institutions and shed light on the theme of SME-related financing. In order to verify research question, the study is conducted in the following order. In Chapters 2 and 3, related studies are reviewed and appropriate hypotheses are suggested. In Chapter 4, the data and verification models used in the study are examined. In Chapter 5, the results of the analyses and their interpretations are discussed. Finally, the summary and conclusion are presented in Chapter 6. Related literature Prior to the examination of the existing literature, detailed study of the subject of this research is needed. The study analyses the lending behavior of commercial banks during changes in business cycles. Lending behavior is examined for key factors affecting loans to enterprises, such as aggregated economic variables and bank characteristics. The method of analysis is classified into two parts. As the first part, a dynamic methodology is adopted to determine the relationship between bank loans made on an aggregated basis and real GDP variables as a proxy for business cycles or economic fluctuation. In this step, in order to confirm the effect of lending behavior over changes in business cycles, effects on lending behavior during changes in business cycles are reviewed. These effects are examined dynamically in accordance with the flow of time. The effect means the relationship of balance between the first differentials of the bank loan as a dependent variable and the business cycle level as an explanatory variable if cointegration vectors among variables in time series statistically exist. The test also catches dynamic relationships among first differential variables. The second part tests panel data to resolve some biases 1 in the time series data on an aggregated basis. Analysis of the 1 Baltagi (1996) suggested that the efficiency of estimation of parameters through panel data analysis could be improved by controlling the heterogeneity of each variable. cross-sectional bank loan data of three types of banks private, state-owned, and foreign-owned and other bank characteristics, will re-confirm the procyclicality of bank loans to SMEs, the dynamic relationships among first 1 st differential variables, and verify other banking hypotheses, such as the governance structure issue and large-bank barriers. With regard to the topic of the study, the procyclicality hypothesis is examined in the light of mainly academic interest. Let us review the previous research on the procyclicality issue. Ayuso, Perez and Saurina (2004) analyzed the relationship between Spanish business cycles and capital buffers, which he defined as the bank s capital less the requirements divided by the requirements that cover the period comprising a complete cycle. Instead, this research considers the capital buffers on assumption that an increase in loans implies an increase in capital requirements. The study concluded that rising credit risks caused by increasing loans led to increased capital requirements, eventually reducing surplus capital. It argued that bank loans have procyclicality, and relatively risky loans to SMEs go through drastic reductions in surplus capital during economic expansion, so they have stronger pro-cyclicality than loans to LEs. Ayuso et al. (2004) found a negative relationship between capital buffers and business cycles. This finding supports the view that banks may behave in an excessively lax manner in managing capital buffers during economic upturns, and vice versa. As evidence supporting procyclicality, the study found that surplus capital reduced by 17% whenever the economy increases by 1%. Jokipii and Milne (2008) analyzed banks in 15 EU countries in order to determine procyclicality of capital buffers of banks similar to bank loans. Using an unbalanced panel of accounting data from 1997 to 2004, their study showed that capital buffers of banks in the EU 15 have a significant negative co-movement with business cycles. The study insisted that capital buffers of commercial and saving s banks, especially large financial institutions, exhibit negative co-movement and interpreted that negative comovement of capital buffers means the impact of procyclicality. Estrella (2004), Lindquist (2004), and Micco and Panizza (2006) analyzed the lending behavior of banks according to business cycle to explain the procyclicality of banks. Peek and Rosengren (1995) and Gambacorta and Mistrulli (2004) argued that low-capitalized banks are forced to cut their loan supply during a recession. Thus, they suggested that the banks having weak capital adequacy are procyclical to business cycle fluctuation. Benford and Nier (2007) supported Gambacorta and Mistrulli (2004). However, Nier and Zicchino (2005), Benford and Nier (2007), and Francis and Osborne (2009) asserted that banks tend to cut lending when the capital adequacy ratio falls below desirable levels.

71 S.Afr.J.Bus.Manage.2013,44(2) 69 However, there have been some interesting researches on the relationship between procyclicality and bank regulations under Basel Accord II. Kashyap and Stein (2004) and Saurina and Tucharte (2006) reported that bank regulations may amplify procyclicality inherent in the lending behavior of banks. Because it aims at making minimum capital requirements, lending behavior of banks is more sensitive to the underlying risk of banks operations than the formal framework set out in Basel Accord I. In the light of previous studies, it is assumed that Korean banks may have the possibility of procyclicality in loans to SMEs loan under Base Accord II, which sets out the current capital regulation framework. This study analyzed mainly whether state ownership of banks is correlated with lending behavior over business cycles, finding that their lending behavior is less responsive to macroeconomic shocks than that of private banks(domestic and foreign-owned). It is implied that stateowned banks could play a useful role in the transmission of financial policy. However, the study also showed the interesting finding that the lending of public banks located in developing countries seems less procyclical than the lending of public banks located in industrial countries. Previous research also focused how monetary policies affect bank loans instead of business cycles. The strong relationships between business cycles and monetary policies need to be understood in the context of the analysis of relationships between business cycles and lending behavior. According to Bernanke and Blinder (1988), an excessive economic expansion results in tight monetary policy by the financial authorities. During tight monetary expansion, SMEs having difficulty accessing the capital market may face difficulties in obtaining financing from banks. In other words, changes in monetary policy lead to changes in the capacity of banks to provide loans. Moreover, business cycles have a relatively greater effect on SMEs than banks do in terms of fund raising. Regarding monetary policy, Kashyap and Stein (2000), Kishan and Opiela (2006) focused on the difficulties in distinguishing shifts in bank loan demand from shifts in loan supply. These shifts prompted the researchers to focus on panel data to test for the existence of a loan supply function. They looked at the importance of bank characteristics for individual bank lending following a monetary policy change. They insisted that smaller and least capitalized banks are the most responsive to a monetary policy change. Another requirement is study of how bank characteristics, such as standards of capital adequacy, loan soundness, and profitability, affect lending behavior. In other words, the research theme concerns whether the managerial conditions of banks have a great effect on loans to enterprises. There have been many empirical studies on how the managerial conditions of banks affect loans to SMEs. Chiou (1999), Claessens, Djankov and Ferri (1999), Djankov, Jindra and Klapper (2000), Kang and Stulz (2000) and Ongena, Smith and Michalsen (2000) found that banks vulnerable to capital adequacy had negative effects on corporate fund raising. In particular, Berger and Udell (1994), Peek and Rosengren (1995), Hancock, Laing and Wilcox (1995), Shrieves and Dahl (1995), and Wagster (1999) thought a low BIS ratio of banks as proxy of capital adequacy has made government supervisors strengthen regulations, which results in a negative impact on loan to SMEs. These studies examined the issue that interested parties such as government supervisors, depositors, investors, and riskaverse managers of banks may affect lending behavior caused by bank characteristics. However, Berger et al. (2001) argued that the characteristics of Argentinean banks, such as standards of loan soundness, had nothing to do with loans to SMEs relative to LEs. This study analyzed the relationship between the managerial conditions of banks and changes in loan to SMEs by using data on proxy variables related to capital adequacy, loanextension soundness, and profitability. However, noting that bank ownership affects lending behavior regarding SMEs, the study insists that large and foreign-owned banks may have difficulty extending relationship loans to opaque small firms. Previous research also refutes the co-movement between bank loans and business cycles. In a typical study, Dell'Ariccia and Marquez (2001) observed that some banks enhanced relationships with SMEs, tending to increase loans to them in order to generate future profits despite economic recessions. Hypotheses and the variables that affect bank loans To analyze the procyclicality issue, the present study proposes hypotheses that based on the previous research discussed above. The first hypothesis aims to verify procyclicality of bank loans to SMEs and LEs under standard controlling bank characteristics such as the Bank for International Settlement (BIS) ratio, the amount of the Allowance for Bad Debts (ABD), and the level of the Net Interest Margin (NIM). It aims to control for the fact that the managerial conditions of banks may have a great effect on lending behavior. It is hypothesized that bank loans to SMEs are determined by changes in the business cycle under controlling bank characteristics, such as proxy variables representing the degree of capital adequacy, soundness, and profitability. Based on Micco and Panizza s study (2006), the second hypothesis states that state-owned banks are less responsive to shocks in the business cycle because of the characteristics of government structures. It is considered that state-owned banks tend to increase or decrease promptly bank loans over changes in business cycles because of the role of financial policy in credit stabilization. Third hypothesis is as follows. Smaller and least capitalized banks are the most responsive to changes in the business cycle regarding large bank barriers (Kashyap & Stein, 2000; Kishan & Opiela, 2006). This assumption is based on the idea that smaller banks that lack a capital buffer tend to be affected by interested parties and make lending behavior

72 70 S.Afr.J.Bus.Manage.2013,44(2) changes in response to changes in economic conditions. Thus, the third hypothesis examines whether changes in bank loan divided into SMEs loans and LEs loans are significantly influenced by business cycles in proportion to economic conditions. These hypotheses are first verified using the rolling Vector Error Correction (VECM) model consisting of aggregated variables because of the need to resolve biases in the sample selection bias 2. Furthermore, they are also empirically tested using panel data analysis in order to check the results of analysis of aggregated variables. Let us now review the variables used in this study. GDP: (+ or in case of first differentials and + in case of level variables) It is assumed that lending behavior is related to the business cycle. Domestic GDP growth is used as a proxy variable (Jokipii & Milne, 2008). The reason that bank loans to enterprises respond to macroeconomic shocks is that bank failures are more likely during recessions. Thus, the banks are reluctant to increase lending or they may reduce the amount of loans to companies. However, the relationship between GDP and first differentials of bank loans might differ according to the type of bank ownership and the size of the borrower, despite the instability of the relationship between the economic growth rate and first differentials of bank loans. 3 To investigate procyclicality between bank loans and GDP, it is desirable for us to isolate the relationship between GDP and the change in loans. As the literature review indicated, it is expected that GDP comoves with the trend in bank loans. However, with regard to the dynamics of the relationship between business cycles and type of bank ownership, it is assumed that the lending behavior of state-owned banks is relatively less responsive to economic conditions (Micco & Panizza, 2006). That is, the lending behavior of state-owned banks is less responsive to macroeconomic shocks than that of non-public banks because credit stabilization is their main role and objective function. Therefore, the first differential variable associated with bank loans in state-owned banks is less responsive to changes in the business cycle than that of private or foreign-owned banks. Furthermore, it is expected that lending behavior of foreign-owned banks in relation to the business cycle might differ from that of domestic banks. With regard to the type of borrower, it is expected that lending behavior is different to SMEs or LEs in relation to the business cycle. Hence, commercial or private banks are less likely to lend to informationally opaque SMEs than to LEs that have the public s confidence. Specifically it is 2 Guirguis et al. (2005) employed the rolling VECM technique, which does not affect the sample period selection bias as well as examining th e dynamic relationship among aggregated economic and financial varia bles. 3 In a short-term period, bank loans to companies tend to increase or de crease regardless of the business cycle. Aggregate bank loans may incr ease or decrease during recession or expansion due to the active market ing strategy of commercial banks to establish relationship (Dell Ariccia & Marquez, 2001) lending or precautionary steps as risk management. found that foreign-owned banks or organizations tend to lend to the large corporate affiliates of their customers (Grosse & Goldberg, 1991). Therefore, in the light of procyclicality, which analyzes the relationship between GDP and the change of bank loans, it is assumed that bank loans to SMEs correspond more to the business cycle than bank loans made to LEs. Control variables Prior studies showed that control variables, such as typical bank characteristics representing capital regulatory ratio (CRR). The proxies for risk are allowance for bad debts (ABD) and the non-performing loans (NPL) to total loans (TL) ratio. The cost of holding such a surplus or profitability like return on equity (ROE) or net interest margin (NIM) need to be included in empirical models verifying the relationship between bank loans and the business cycle (Jokipii & Milne, 2008). Previous studies have indicated why these variables should be used when confirming procyclicality. Ayuso et al. (2004) argue that a negative relationship exists between the capital buffers of banks and GDP growth rate. Therefore, they must be controlled in an estimated model. As a proxy variable for the measurement of risk, Ayuso et al. (2004) used the NPL ratio to estimate the relationship between the Spanish business cycle and the capital buffers held by commercial and savings banks. Aggarwal and Jacques (2001) also adopted the NPL ratio as a proxy variable representing financial soundness. They reported that change in the NPL ratio may affect lending behavior, cutting the supply of credit. Albertazzi and Gambacorta (2009) used provisions or allowance for bad debts as a component that affects the business cycle. Various authors have shown that ABD and GDP are negatively correlated (Salas & Saurina, 2002; Laeven & Majoni, 2003). In particular, Beatty and Liao (2011) adopted ABD as a proxy variable representing financial soundness and suggested that this variable may affect the lending behavior of banks. They reported that forward-looking provisioning mitigates procyclicality in lending. Furthermore, Barth, Caprio and Levine (2006) adopted bank regulatory variables such capital adequacy ratio to isolate economic consequences of discretion in loan loss provisioning affect the lending behavior of banks. Furthermore, it is considered that NIM is related to longterm interest rates because the latter are closely related to GDP growth rates. In Albertazzi and Gambacorta (2009), the nominal value of the net interest margin is expected to increase by almost 4% in the long run if the long-term interest rate rises by 1 percentage point. In addition, Milne and Whalley (2001) argued that the lending behavior of banks can be affected by banks profitability levels because banks with high profitability have a sufficient financial buffer to meet the minimum capital requirement resulting in an increasing supply of credit. Stolz and Wedow (2011) also reported that banks profitability may affect the lending behavior of banks via changing the level of capital buffer in the banks.

73 S.Afr.J.Bus.Manage.2013,44(2) 71 To investigate the procyclicality of bank loans to SMEs and LEs, in terms of the relationship between GDP and the change of loans, aggregated data and proxy variables representing typical bank characteristics as control variables were used. In this paper, three control variables that may affect the lending behavior of banks are adopted in the empirical test model. BIS ratio as capital adequacy or capital regulation level, ABD as financial soundness, and NIM as profitability, which are generally used in banking studies. Data and sample statistics Data The following data is used in the study. First, real GDP are used as an economic indicator. Bank loans to SMEs and LEs are used. Regarding amounts of loans to SMEs and LEs, the average remainder of loans to companies was used. To control for the managerial conditions of banks, such as capital adequacy, soundness of loan, and profitability in aggregated analysis using Rolling VECM, we use the BIS ratio, ABD, and NIM as the proxy variables. Regarding the panel data analysis, bank loans to SMEs and LEs including the lagged BIS ratio, NPL ratio, and NIM of nine banks (four major private banks, three foreign banks, and two state-owned banks) in Korea were used to check whether bank characteristics affect lending behavior. The period analyzed is fourth quarter of 1999 to fourth quarter of Data was obtained from Fnguide.com, Financial Statistics Information System in Financial Supervisory Service and statistical information in the Korea n Institute of Finance. Variables and summary statistics Table 1 presents the definition of variables used in the sample. Among the key variables, the dependent variables used in this paper are applied in two ways: 1) as aggregated analysis using rolling VECM; and 2) as panel data analysis. Table 1: Definition of the variables Analysis way Variables Definitions The aggregated analysis GDP as a level (lngdp) Real Gross Domestic Product quarterly basis standardized using natural logarithmic scale GDP lngdp(t)-lngdp(t-1) Loan SMEs or LEs, t (lnloan SMEs or LEs, t ) The amounts of bank loans to SMEs or LEs standardized using natural logarithmic scale Loan SMEs or LEs, t lnloan(t)-lnloan(t-1) BIS capital ratio (BIS) Capital to risk-weighted asset 100 Allowance for Bad Debts(ABD) Allowance for Bad Debts on quarterly basis standardized using natural logarithmic scale Net Interest Margin (NIM) Net interest margin is a measure of the difference between the interest income generated by banks and the amount of interest paid out to their lenders, relative to the amount of their interest-earning assets The panel data analysis Co-integration Vector(CIV. SMEs or LEs, t ) Co-integration vector between each bank loans to SMEs or LEs and GDP Bank Ownership Dummy1(BO 1 ) Managerial ownership of banks is equal to -1 or 1 if the banks are State owned or private. It is a proxy for banks ownership constraints Bank Ownership Dummy2(BO 2 ) Managerial ownership of banks is equal to -1 or 1 if the banks are foreign or domestic. It is a proxy for banks ownership constraints Bank size Dummy(ln size) Bank s asset size = ln(asset) ΔLoan SMEs, t or LEs, t The rate of state-owned banks loans to SMEs or Les standardized using natural logarithmic scale for panel data analysis BIS ratio of each bank(bis i, t) Capital to risk-weighted asset 100 in each bank NPL ratio of each bank(npl i, t) NIM of each bank(nim i, t ) Non Performing Loans / Total loans 100 in each bank The difference between the interest income generated by each bank and the amount of interest paid out to their lenders, relative to the amount of their interest-earning assets Note: ( ) indicates the sign used in estimated model.

74 72 S.Afr.J.Bus.Manage.2013,44(2) Table 2 reports the summary statistics for the main variables of SMEs and LEs used in this study. Real GDP is about 208 trillion won (about USD 173 billion) on average, and up to 256 trillion won (about USD 213 billion) were recorded during the analysis period. In addition, the average loans to SMEs and LEs as the aggregated variables is approximately 238 trillion won (about USD 198 billion) and 36 trillion won (about USD 30 billion) respectively. In addition, regarding the control variables, the BIS ratio, ABD, and NIM are 11,3%, 12 trillion won (about USD 10 billion) and about 2,6% to SMEs and LEs, respectively. All variables except for real GDP in the aggregated variables are distributed positively according to the degree of skewness. The degree of skewness of real GDP is -0,06. Regarding the statistics of the cross-sectional data, the sized of private bank loans among the three categorizes of banks is bigger than any other type of bank. Moreover, the BIS ratio is better in state-owned banks (13,77%) than private banks (12,88%) and foreign banks (12,68%). In the case of NPL, foreign banks (1,04%) are the best followed by private banks (1,06%) and state-owned banks (1,20%). These results are consistent with the previous study by Berger et al. (2005), which found that state-owned institutions have relatively high numbers of nonperforming loans. Moreover, the foreign banks average NIM of 2,66 is much higher than that of private and the state-owned banks. Hence, foreignowned banks have a much bigger cost of holding a surplus and are better positioned to make a profit. Table 3 shows the basic test of difference in means for main variables used in this analysis to compare LEs and SMEs. In the mean difference test between LEs and SMEs, all variables are different and statistically based on the t-test and Van der Waerden test 4 except the NPL ratio. Thus it is understood that the variables for SMEs are distinct from those of LEs. Empirical test results In this section, results for the rolling VECM and panel data analysis are presented. As mentioned earlier, the rolling VECM is used to verify the existence of procyclicality by identifying the relationship between GDP and the first differentials of bank loans to SMEs and LEs. A panel GLS and Clustering Fixed Effect Model are then used to confirm the existence of the relationship between GDP and the change in bank loans. The rolling VECM and results The rolling VECM suggested above is as follows: Loan (ln Loan ln GDP LEs(orSMEs),t 0 LEs(orSMEs),t 1 0 t trend ) Loan 1 t 2 LEs(orSMEs),t 1 GDP Control(BIS ) Control(ABD ) 3 t 1 4 t 5 t Control(NIM ) 6 t 4 This test is based on the same general idea as the Wilcoxon test, but it uses smoothed ranks. The signed ranks are smoothed by converting them to quantiles of the normal distribution. where each window period 5 for analysis is applied to t- 16quarters t+16quarters. In this model, control variables include the BIS ratio, ABD, and and γ are time trend and intercept respectively within the cointegration vector in parenthesis. δ indicates intercept in whole model. In Table 6 and Table 7, (1) ~ (6) means that each window period increased by 1-quarter from q ~2007.3q to q~2008.4q. This model is designed for the verification of the procyclicality hypothesis under controlling bank characteristics. The Rolling VECM was used to verify the hypothesis that loans to SMEs and LEs are determined by changes in the business cycle under the controlling standards of capital adequacy ratio, ABD as buffer of credit risks, and NIM as the pivotal variable for profit-making conditions. Regarding the method of analysis method, this model means that VECM based on loan to SMEs or LEs and GDP was consecutively applied to each data set by increasing 1-quarter from t-16quarters to t+16quarters in order to acquire estimated coefficients. In addition, this model shows the short-term dynamics between change of bank loan and lagged change of GDP growth rate. Namely, it aims to confirm relationships among pertinent variables of level and first difference as time progressed. The model (ln Loan LEs(orSMEs),t 1 0 ln GDP t 1 trend t ) is a co-integration vector between a level variable of bank loan to SMEs (or LEs) and GDP, including the time trend and intercept. In this model, the co-integration vector will affect to the change rate of bank loan (the first differential of bank loan) to adjust to the equilibrium if the balance between loan and GDP is collapsed. Therefore, the existence of procyclicality between the first differentials of bank loan and GDP is determined by the signs α 0 and ß 0. If procyclicality exists in the bank loan, ß 0 is expected to be a significantly positive sign. Furthermore, α 0 shows as a negative sign with significance if ß 0 is expected to be a significantly negative sign due to the unbalanced relationship among the first differentials. In other words, a negative sign of coefficient of adjustment to equilibrium with significance implies that ß 0 changed to a positive sign. In addition, this model also shows the dynamic relationship between the change rate of the loan and independent variables of the first differentials. The model Loan, LEs(orSMEs),t Loan and LEs(orSMEs),t 1 GDPt of 1 first differential variables shows the dynamics between business cycle and loan growth rate. However, the relationship may be adjusted by the co-integration vector shown above. This implies that α 0, the adjustment coefficient, is statistically significant. 5 The determination of the window period for dynamic analysis of VECM is attributed to the period of business cycle published by the Bank of Korea. It is analyzed in the data for each window period before 16-quarter and after 16-quarter as of this time 1-cycle period which is a four-year average in the 2000s.

75 S.Afr.J.Bus.Manage.2013,44(2) 73 Table 2: Summary statistic The aggregated variables The variables of cross-sectional data Variables Mean Std. Dev Degree of skewness Kurtosis The maximum value The minimum value Real GDP(trillion won) 207,53 27,16-0,06 2,13 256,00 150,27 Loans to SMEs(based on average 238,46 79,71 0,54 2,36 400,32 131,22 remainder, trillion won) Loans to Les(based on average 35,74 92,03 0,99 3,08 594,30 246,48 remainder, trillion won) BIS ratio (%) 11,27 0,88 0,56 1,80 12,78 9,95 Allowance for Bad Debts(trillion won) 12,00 3,70 1,17 3,20 21,42 8,24 ln(loans to LEs by Private Banks) 17,12 0,37-0,30 1,36 17,54 16,58 ln(loans to LEs by State-owned 16,67 0,30-0,10 1,39 17,03 16,28 Banks) ln Loans to LEs by Foreign Banks 15,75 0,31-0,07 1,34 16,12 15,30 ln (Loans to SMEs by Private 18,98 0,19-0,66 1,95 19,16 18,63 Banks) ln (Loans to SMEs by State-owned 18,19 0,18-0,09 1,76 18,43 17,90 Banks) ln (Loans to SMEs by Foreign 17,27 0,11-0,76 2,34 17,41 17,06 Banks) BIS of Private Banks (%) 12,88 1,33 0,25 2,21 15,27 10,74 BIS of State-owned Banks (%) 13,77 0,89-0,46 1,76 14,94 12,22 BIS of Foreign Banks (%) 12,68 1,53 0,22 2,23 15,33 10,34 NPL of Private Banks (%) 1,06 0,33 0,70 2,00 1,61 0,72 NPL of State-owned Banks (%) 1,20 0,40 1,37 4,63 2,31 0,77 NPL of Foreign Banks (%) 1,04 0,25 0,57 2,12 1,50 0,74 NIM of Private Banks (%) 2,39 0,33-0,62 1,98 2,78 1,82 NIM of State-owned Banks (%) 1,51 0,21 2,10 7,65 2,18 1,34 NIM of Foreign Banks (%) 2,66 0,22-0,36 2,42 3,02 2,27 Table 3: Mean different test of variables between LEs and SMEs Major private State owned Foreign Van der Waerden Variables t-test Bank Bank owned Bank Test Loans to LEs 17,12 16,67 15,75 72,31 *** 33,17 *** Loans to SMEs 18,98 18,19 17,27 454,02 *** 38,62 *** BIS ratio (%) 12,88 13,77 12,68 3,35 ** 4,44 * NPL ratio (%) 1,06 1,20 1,04 1,11 2,14 NIM (%) 2,39 1,51 2,66 86,00 *** 31,06 *** Note: ***, **, and * denote significant 1%, 5%, and 10% respectively In this dynamic model, the coefficients related to the cointegration vector are ß 0, ß 1, and γ, and the coefficients of ß 2 ~ß 6 indicate the dynamics. Pre-test on time series variables: unit root, cointegration tests etc. It is verified whether time series variables, such as GDP and bank loans, are non-stationary in an autoregressive model. The well-known augmented Dickey-Fuller (ADF) test is used here. The null hypothesis is that a unit root exists in these variables. The unit root test should be conducted first to determine whether the individual series are non-stationary in the levels, and whether they are stationary in the differentials. Table 4 presents the results from the ADF and Phillips- Perron tests, which show that all three variables are nonstationary in levels because the null hypothesis is not rejected. The test was then conducted again for the differentials, and the results show that all individual series are stationary, thus rejecting the null hypothesis at minimum 5% significance level of 5%. The test also adopts one, the number of the lagged level terms, as an optimal lag chosen by minimizing the Schwarz Bayesian criteria (SBC). Table 5 shows the results from the co-integration test, which are based on the Johansen test. A system of two or more time series that are non-stationary in levels can share common stochastic trends that are co-integrated. If a linear combination of these variables is stationary, the nonstationary time series is cointegrated, and it is interpreted as long-run equilibrium relationships among GDP and bank loans. According to Table 5, cointegration between GDPs

76 74 S.Afr.J.Bus.Manage.2013,44(2) and bank loans is evident. A single co-integration relation exists for GDP and bank loans to SMEs (or bank loans to LEs). In co-integration analysis, the trace and maximum eigenvalue statistics reject the null hypothesis at a minimum of 5% because one co-integration vector exists between GDPs and bank loans to SMEs (or bank loans to LEs). Table 5 presents these estimated co-integration vectors, which are normalized so that the coefficient of each variable is unified. Figure 1 and Figure2 show the effects of GDP shock on bank loans to SMEs and LEs. The response of bank loans to SMEs from GDP shock is gradually decreasing. However, the response of LEs drops sharply. The results of the impulse-response test show that the impact of GDP shock on SME loan is more persistent that that of LEs. Table 6 and Table 7 show the results of the variance decomposition test. According to the test results, the variance of SMEs is more susceptible to GDP variance than that of LEs. These results are in line with the results of the impulse-response test. Table 4: The result of unit root test among aggregate variables Variables Optimal time lag level Augmented DF The differential. Bank loans to SMEs 1-0,051-3,017 ** Bank loans to LEs 1-1,418-13,238 *** GDP 1-1,722-11,833 *** Variables Optimal time lag level Phillips-Perron The differential. Bank loans to SMEs 1 0,015-3,017 ** Bank loans to LEs 1-1,438-5,435 *** GDP 1-2,217-18,485 *** Note: ***, **, and * denote significant 1%, 5%, and 10% respectively Each critical value per level of significance of Augmented DF and Phillips-Perron is -2,62(10%), -2,96(5%), and -3,67(1%) respectively The optimal time lag of each time series is determined to minimize Schwarz criteria Table 5: The result of co-integration test among aggregate variables Trace statistic 5percent critical value Co-integration between bank loans to SMEs and GDP 1percent critical value Hypothesized number of Cointegrating equations(r) Max-eigen statistic 5percent critical value 1percent critical value H 0 H 1 34,48*** 25,32 30,45 r=0 29,10** 18,96 23,65 r=0 r 1 5,38 12,25 16,26 r 1 5,38 12,25 16,26 r=1 r 2 Trace statistic 5percent critical value Co-integration between bank loans to LEs and GDP 1percent critical value Hypothesized number of Cointegrating equations(r) Maxeigenvalue statistic 5percent critical value 1percent critical value H 0 H ** 18,17 23,46 r=0 25,71** 16,87 21,47 r=0 r ,74 6,40 r 1 0,07 3,74 6,40 r=1 r 2 Note: * (**) denotes rejection of the hypothesis (H 0 =co-integrating equations exist) at the 5%(1%) level. The optimal time lag of each time series is one that minimizes Schwarz criteria, and r means rank.

77 S.Afr.J.Bus.Manage.2013,44(2) Response of LOANSMALL to Generalized One S.D. REALGDP Innovation Note: A generalized impulse method that does not depend on VAR ordering is adopted. The generalized impulse responses from an innovation to the j-th variable are derived by applying a variable specific Cholesky factor computed with the j-th variable at the top of the Cholesky ordering Figure 1: Effect so of result of responses to GDP shock on bank loans to SMEs Response of LOANLARGE to Generalized One S.D. REALGDP Innovation Note: A generalized impulse method that does not depend on VAR ordering. The generalized impulse responses from an innovation to the j-th variable are derived by applying a variable specific Cholesky factor computed with the j-th variable at the top of the Cholesky ordering <Figure 2> Effects of responses to GDP shock on bank loans to LEs Table 6: Variance decomposition analysis of bank loans to SMEs and GDP Period S.E. LOANSMALL REALGDP 1 0, ,0000 0, , , , , , , , , , , , , , , , , , , , , , , , ,

78 76 S.Afr.J.Bus.Manage.2013,44(2) Table 7: Variance decomposition analysis of bank loans to LEs and GDP Period S.E. LOANLARGE REALGDP 1 0, ,0000 0, , , , , , , , , , , , , , , , , , , , , , , , , , , , Table 8 and Table 9 show the result of the causality test of the relationship between bank loans and GDP. According to the results, bank loans to SMEs affect GDP and are affected by GDP. However, bank loans to LEs are not influenced by GDP. In the light of the test results, we presume that bank loans to SMEs are influenced by fluctuation in business cycle. The rolling VECM s test results for bank loans GDP as level variable Table 10 presents coefficients of bank loans to SMEs, ß 0, and GDP at time t-1. They are significantly positive in the window period of (1) ~ (3) at shows 10,32, 4,55, and 44,37, respectively. This suggests that a positive relationship exists between lending behaviors to SMEs and the business cycle as the level variable, which is called the hypothesis of procyclicality. Hence, the results shows that bank loans to SMEs tend to increase in a booming economy. However, coefficients of bank loans to SMEs in the window period of (4) ~ (6) are significantly negative registering as - 15,88, -11,24, and -16,12, which shows a positive relationship between the level of GDP and the change in the number of bank loans to SMEs. At that time, the balance between change in loans to SMEs and level of GDP is collapsed; α 0 represents error correction and shows negative coefficients in the window period of (4) ~ (6); it is statistically significant at a 1% or 5% level at -0,03, -0,05, and -0,03. As the product of the speed of adjustment and the coefficient to GDP, α 0 ß 0, which is the strength of dynamic stability for equilibrium between the first differentials of bank loans to SMEs and GDP level, is positive, and the change rate of SMEs loans at time t, ΔLoan SMEs,t is adjusted to balance. In other words, the balance error is adjusted for GDP level and the first differentials of bank loans to SMEs. If α 0 is 0, there is no disequilibrium error between GDP and bank loans to SMEs occurring in the former period. Accordingly, if analysis is conducted based on a vector autoregressive (VAR) model without adjustment of balance errors, a positive linear relationship is not considered between the first differentials of bank loans to SMEs and the GDP level. Because ß 0 is a significant positive in all window periods, considering that α 0 is statistically significant to make α 0 ß 0 directed positively, it confirms that the change in bank loans to SMEs tend to co-move depending on what the business cycle is. Thus, bank loans to SMEs are procyclical. Table 11 shows whether or not bank loans to LEs have procyclicality. ß 0 is significantly negative in the window period of (1) ~ (4) except for (5) and (6). This suggests that the more business cycle upturns (or downturns), the smaller (or larger) the change in loans to LEs become. However, ß 0 is unstable because it is positive in the window periods of (5) and (6). Based on the sign of the window period (1) ~ (4), it might be negative in the periods of (5) and (6). If the negative relationship in the case of the window periods (5) and (6) is collapsed, α 0 should be worked to make α 0 ß 0 directed to negative. However, there is no significance in the coefficients of α 0. In other words, there is no adjustment to balance in the case of the window periods (5) and (6), which show positive coefficients of LEs loan at time t-1. Therefore, the change in bank loans to LEs has very little to do with the business cycle, and the procyclicality hypothesis is not confirmed in the case of bank loans to LEs. ΔGDP t-1 and ΔBank loan t-1 as differential variables Table 10 also presents the short-term dynamics among differential variables. In all window periods, ß 2 is statistically and positively significant at a level of 1%. Thus, the change in loans in the former period provides sufficient explanation with regard to expected changes in loans to SMEs. However, ß 2 is insignificant and negative in Table 11, showing the short-term dynamics among differential variables. In contrast to the case of bank loans to SMEs, the change in loans to LEs does not provide information to expect the change in bank loans to LEs at time t. Let us review the change of GDP in the previous period. Table 10 shows that ß 3 in window period (4) ~ (6) is statistically significant, but not in (1) ~ (3). This suggests that the short-term relationship between ΔGDP t-1 and loan to SMEs t is excessive in a negative direction during the period of (4) ~ (6). Thus, it should be adjusted to a positive direction by working the strength of dynamic stability for balance between the first differential of bank loans to SMEs and the GDP level. As reviewed earlier, α 0, the speed of the adjustment factor is statistically significant at a minimum level of 5% in the period (4) ~ (6).

79 S.Afr.J.Bus.Manage.2013,44(2) 77 Table 8: Granger causality test between bank loans to SMEs and GDP Null Hypothesis F-Statistic Probability GDP does not Granger Cause SMEs loan 7, ,00063 SMEs loan does not Granger Cause GDP 2, ,04681 Table 9: Granger causality test between bank loans to LEs and GDP Null Hypothesis F-Statistic Probability GDP does not Granger Cause LEs loan 1, ,39822 LEs loan does not Granger Cause GDP 2, ,07247 Table 10: Analysis of lending behavior on bank loans to SMEs Loan (ln Loan ln trend ) Loan SMEs,t 0 SMEs,t 1 0 t 1 1 t 2 SMEs,t 1 GDP Control(BIS ) Control(ABD ) Control(NIM ) 3 t 1 4 t 5 t 6 t (1) (2) (3) (4) (5) (6) Coefficient of speed of adjustment (α 0) 0,01 0,02 0,00-0,03 ** -0,05 *** -0,03 *** (0,62) (0,37) (0,96) (-2,10) (-2,57) (-2,61) lngdp t-1 10,32 *** 4,55 *** 44,37 *** -15,88 *** -11,24 *** -16,12 *** (5,80) (5,86) (5,46) (-4,71) (-4,61) t (time trend) -0,15 *** -0,09 *** -0,55 *** 0,15 *** 0,10 *** 0,16 *** (-6,20) (-8,69) (-5,02) (3,44) (3,12) (4,23) Intercept(γ) -138,0-68,6-547,3 176,9 121,1 179,6 ΔLoan SMEs,t-1 0,53 *** 0,53 *** 0,55 *** 0,62 *** 0,63 *** 0,65 *** (-0,66) (-0,58) (-0,36) (-0,22) (-0,49) (-0,37) ΔGDP t-1-0,09-0,07-0,11-0,25 ** -0,31 *** -0,32 *** (-0,80) (-0,60) (-1,01) (-2,18) (-2,68) (-2,71) Constant (δ) 0,03 0,07 0,01 0,30 0,75 0,75 (0,06) (0,15) (0,01) (0,69) (1,36) (1,36) BIS t 0,00 0,00 0,00 0,00-0,00 0,00 (0,44) (0,39) (0,47) (0,14) (-0,29) (0,31) ABD t -0,01-0,01-0,01-0,02-0,04-0,04 (-0,39) (-0,51) (-0,31) (-0,88) (-1,49) (-1,46) NIM t 0,04 0,03 0,03 0,01-0,02-0,03 (1,14) (1,08) (1,12) (0,35) (-0,58) (-0,91) Adj.R 2 0,15 0,15 0,17 0,27 0,33 0,33 H.S.(df=36) 46,10 44,25 44,79 44,70 44,93 46,52 (0,12) (0,16) (0,15) (0,15) (0,15) (0,11) Note: *** is statistically significant at 1%, ** at 5% and * at 10%. is referred to as loans to SMEs, GDP is real Gross Domestic and γ mean time trend and intercept within co-integration vector. BIS, ABD, NIM mean BIS ratio, allowance for bad debts, and net interest margin, respectively. In (ln Loan ln trend ) 0 SMEs,t 1 0 t 1 1 t, the parenthesis is an equilibrium error in the previous time; this co-integration vector is standardized as one of ln Loan SMEs,t 1, and 0 is an adjustment coefficient representing the speed of adjustment. The number of co-integrations between ln Loan SMEs and lngdp is confirmed with Max-eigenvalue statistics. Optimal time difference within the model is 1, which is the result of determination of the time difference based on SC (Schwartz Bayesian criteria). H.S. means heteroskedasticity chi square statistic based on a quadratic trend model. The parenthesis represents t-statistics. (1) ~ (6) means window period increases by 1-quarter from q ~2007.3q to q~2008.4q.

80 78 S.Afr.J.Bus.Manage.2013,44(2) Table 11: Analysis of lending behavior on bank loans to LEs The rolling VECM Model (Data for bank loans to LEs) : Loan (ln Loan ln trend ) Loan LEs,t 0 LEs,t 1 0 t 1 1 t 2 LEs,t 1 GDP Control(BIS ) Control(ABD ) Control(NIM ) 3 t 1 4 t 5 t 6 t (1) (2) (3) (4) (5) (6) Coefficient of speed of adjustment (α 0) 0,02 0,03 0,02 0,00 0,00 0,00 (0,66) (0,73) (0,77) (0,20) (0,54) (0,55) lngdp t-1-15,62 *** -12,02 *** -21,44 *** -66,86 *** 110,19 *** 304,88 *** (-6,17) (-6,32) (-6,12) (-5,64) (5,66) t (time trend) 0,20 0,15 0,27 0,75-1,22-3,39 Intercept(γ) 174,8 131,6 244,7 791,4-1,338,2-3,679,8 ΔLoan LEs,t-1-0,15-0,13-0,08-0,05-0,10-0,08 (-0,66) (-0,58) (-0,36) (-0,22) (-0,49) (-0,37) ΔGDP t-1 0,50 * 0,52 ** 0,59 ** 0,42 0,10 0,12 (1,65) (1,77) (1,93) (1,13) (0,27) (0,32) Constant (δ) -1,02-0,95-0,97-0,71 0,93 0,80 ** (-0,71) (-0,66) (-0,67) (-0,47) (0,51) (1,76) BIS t -0,01-0,01-0,03-0,03-0,03 * -0,03 (-0,19) (-0,41) (-1,19) (-1,38) (-1,45) (-1,26) ABD t 0,04 0,04 0,05 0,04-0,04-0,03 (0,50) (0,51) (0,69) (0,54) (-0,42) (-0,35) NIM t 0,14 * 0,13 * 0,12 0,09-0,03-0,05 (1,42) (1,39) (1,25) (0,85) (-0,27) (-0,34) Adj.R 2 0,17 0,20 0,31 0,35 0,46 0,42 H.S.(df=36) 48,39 48,45 48,71 48,90 48,61 44,89 (0,23) (0,23) (0,22) (0,22) (0,22) (0,35) Note: *** is statistically significant at 1%, ** at 5% and * at 10%. is referred to as loans to LEs, GDP is real Gross Domestic and γ mean time trend and intercept within co-integration vector. BIS, ABD, NIM mean BIS ratio, allowance for bad debts, and net interest margin, respectively. In (ln Loan ln trend ) 0 LEs,t 1 0 t 1 1 t, the parenthesis is an equilibrium error in the previous time; this co-integration vector is standardized as one of ln Loan LEs,t 1, and 0 is an adjustment coefficient representing the speed of adjustment. The number of co-integrations between ln Loan LEs and, lngdp is confirmed with Max-eigenvalue statistics. Optimal time difference within the model is 1, which is the result of determination of the time difference based on SC (Schwartz Bayesian criteria). H.S. means heteroskedasticity chi square statistic based on a quadratic trend model. The parenthesis represents t-statistics. (1) ~ (6) means the window period increase by 1-quarter from q ~2007.3q to q~2008.4q. Therefore, the short-term dynamics between Δbank loans to SMEs t and ΔGDP t-1 have significance. Namely, the change of GDP in former periods affects significantly and negatively current Δbank loans to SMEs. Table 11 shows that all ß 3 of window period of (1) ~ (3) are positive and statistically significant at a 5% level of 0.50 and 0.59 respectively. This means that the change in the lagged GDP contributes to a positive relationship with the change in LEs loans at time t. However, it also shows that there is no relationship between Δbank loan to LEs t and ΔGDP t-1 in the window period of (4) ~ (6). In particular, all α 0, the speed of adjustment factor in the window period (1) ~ (3) do not work to balance the first differentials of bank loans to LEs and GDP level as in the case of bank loans to SMEs. They are not significant; thus, it is suggested that Δbank loans to LEs t have little do with ΔGDP t-1. The rolling VECM s test results for control variables BIS ratio In Table 10 shows that all ß 4 are 0.00 in all window periods. The BIS ratio was included as a control variable in order to analyze procyclicality as a proxy of capital adequacy. The BIS ratio is insignificantly affected by changes in bank loans to SMEs. At first, it was expected that the higher (or lower) the BIS ratio, the smaller (or larger) the loan to SMEs becomes because the stronger the requests for capital adequacy, the more efforts banks make to reduce risky assets, is not true in this study. In Table 11, all ß 4 are also generally insignificant except for the window period (5), which shows a negative sign (-0.03), meaning that the higher (or lower) the BIS ratio, the smaller (or larger) the loans to LEs become. However, all window periods except for (5), show that the BIS ratio is not related

81 S.Afr.J.Bus.Manage.2013,44(2) 79 to the lending behavior of Korean banks over the business cycle. In light of the above results, it is understood that Δbank loan to SMEs t and to LEs t does not react steadily in all periods when the BIS ratio is control variable in a change of business cycle. ABD The amount of ABD was considered a control variable in existing studies (Albertazzi & Gambacorta, 2009; Salas & Saurina, 2002; Laeven & Majnoni, 2003), but in the present study changes in ABD did not affect changes in loan to SMEs. Table 10 and Table 11 show that the estimated coefficients (ß 5 ) had no statistical significance. NIM NIM was also included in the analysis model as a control variable that is affected by commercial interest rates and inducement of capital, but it was not statistically significant in the case of analysis of bank loans to SMEs. Table 10 shows that ß 6 is not significant. Therefore, the current NIM does not have a significant effect on the decision behavior in the financing of SMEs by Korean banks. Table 11 also shows that ß 6 is not significant in the window periods considering the current time even though it presents positive signs, 0,14 and 0,13 respectively at the 10% level in window periods (1) and (2). Robustness checks To confirm the robustness of this rolling VECM, heteroskedasticity test results are presented in the last column of Table 10 and Table 11. In general, heteroskedasticity has serious consequences for time series analysis. Although the estimator remains unbiased, the estimated standard errors may be wrong, thus estimator is no longer BLUE. It suggests that all H.S. (heteroskedasticity chi square statistic) do not reject the null hypothesis (H 0 means that there is no heteroskedasticity in the residual term.) in all window periods. Therefore, the estimated coefficients derived from this model are reliable. Panel data analysis results Panel GLS model This model is considered a first-order autoregressive model in which random errors have a structure of heteroskedasticity, the contemporaneously correlated, and autoregression. This model is as follows. Banks are denoted as subscript i=1,,n, and time as t=1,,t; K is the number of exogenous or independent variables. The detailed description of this model is as shown. Loan X K SMEs(or _ LEs),i,t k i,t 1,k i,t k 1 (i,..., N; t,..., T; k..., K; i,t i i,t ui,t ) (1) In this model, the covariance matrix of vector of random residuals is as follows. To determine the beta, calculate the covariance matrix V as shown below. 11P11 1NP1N E( _ ') Variance, N1PN1 NNP NN where P ij T 1 j T 1 0 j (2) The covariance matrix is estimated by a two-stage procedure leading to the estimation of model regression parameters by the generalized least squares (GLS) method. The variance is estimated in a two-step procedure. The first step involves the use of ordinary least squares to estimate ß, and the obtained fitted residuals are as follows. ˆ Loan SME(or _ LE) Xˆ OLS (3) A consistent estimator of the first-order autoregressive parameter is then obtained as follows. T T 2 i i, t _ i, t 1 i, t 1 (i 1,..., N) ˆ ( ˆ ˆ ) /( ˆ ) t 2 t 2 (4) To remove the autoregressive characteristic of the data, (6) is derived from (1) less (5) plus the estimated autoregressive parameter. P Loani, t ˆ i Loan i, t 1 (1) (X i, t, k) k P k 1 (5) ( ˆ X ) ˆ i, t i i, t 1, k k i, t 1 i k 1 P Loani, t ˆ i Loan i, t 1 (X i, t, k) k ˆ X ) ˆ i i, t 1, k k i, t i i, t 1 where k 1 (5) (6) * (6) replaces Loan i, t ˆ i Loan i, t 1 by Loani, t, * Xi, t, k ˆ ixi, t 1, k by X i, t, k, and i, t ˆ i i, t 1 by respectively, and thus is called (7). P * * * Loan i, t = X i, t, k k i, t k 1 * i, t (7) The second step in estimating the covariance matrix is to apply ordinary least squares to the preceding transformed P * * * * model, (7) Loan i, t = X i, t, k k i, t to obtain ˆ OLS. By using * ˆ OLS, calculate k 1 * ˆ as shown below (8).

82 80 S.Afr.J.Bus.Manage.2013,44(2) ˆ * * * * Loan X OLS (8) * (9) is derived as ˆ i, j by using ˆ *. Finally (4) into (10), and i, j with i, j is estimated. T * * i, j i, t j, t t 1 * ˆ i, j and ˆ i of can be estimated. Therefore, E( _ ') ˆ 1/ (T p) ˆ ˆ (9) ˆ i, j i, j / (1 ˆˆ i j) (10) Clustering fixed effect model This fixed effect used to determine the relationship between GDP, the change in bank loans, and the short-term relationship among differential variables. In this study, the model is used to measure differential bank loans to SMEs or LEs as defined in Table 12 and Table 13. The fixed effect used here is Loan X Z, where SMEs(or _ LEs)i,t 0 i,t i i i,t Loan SMEs(or _ LEs)i,t is the dependent variable observed for individual i at time t, X i,t is the time-variant regressor, Zi is the time-invariant regressor, i is the unobserved bankspecific effects, and i,t dealing with, it is assumed that i,t i is the error term. However, in i is not independent of X and Z. The fixed effect model is as follows: i Loan GDP GDP SMEs(or _ LEs)i,t 0 1 t 1 2 t Loan CIV 3 SMEs(or _ LEs)i,t 1 4 SMEs(or _ LEs)t GDP BIS GDP NPL 5 t 1 i, t 1 6 t 1 i, t 1 GDP NIM GDP BO 7 t 1 i, t 1 8 t 1 1 In this model, α i and Z i γ represent time-invariant bankspecific effects and bank constant time effects respectively. It is assumed that bank-specific effects, α i are unobservable but have a significant impact on bank loans. These effects change across banks but are fixed for a given bank over time. In contrast, Z i γ varies over time but is the same for all banks in a given year. The result of panel data analysis with panel GLS and the clustering fixed effect model CIV(Co-Integrating Vector) This analysis focuses on the question of whether or not positive relationship exists between the business cycle and the first differentials of bank loans. As the rolling VECM to aggregated variables shows, bank loans to SMEs are procyclical. suggests that the excessively increased Δbank loans to SMEs t compared with the lagged ΔGDP are adjusted for balance in the negative direction. The signs of all lagged ΔGDP in all models are negative in the case of panel GLS and the Fixed Effects model even though they are not statistically significant in the Fixed Effects model. In Table 12, the significance of CIV factors with negative signs suggests that CIV factors work to adjust the balance between the 1 st differential of bank loans to SMEs and the GDP level, which is attributed to the break of the short-term relationship between the lagged ΔGDP and ΔLoan SMEs at time t. Although the lagged ΔGDP is insignificant in models 3 and 4 using the Clustering Fixed Effect model, their signs are definitely negative and co-integrating vector (CIV) factors are statistically significant at 10%. Table 13 presents evidence of whether or not there exists a positive relationship between the business cycle and the first differential of bank loans to LEs. As expected, all coefficients of CIV factors are not statistically significant in all models. These results are in line with those of the rolling VECM, which shows insignificant error correction, α 0 in all window periods. In light of this evidence, it is understood that bank loans to LEs over the business cycle are not comoved, and it is suggested that bank loans to LEs are not procyclical, which is consistent with the results of the rolling VECM. ΔGDP variables Table 12 shows that the relationship between Δloan to SMEs at time t and lagged ΔGDP is negative regardless of models and estimation methods. According to the dynamic model, the rolling VECM, short-term dynamics between lagged ΔGDP and current change in loans to SMEs have a negative relationship even though the relationship between the first differential of bank loan to SMEs and GDP level is positive. In the panel analysis, all coefficients of lagged ΔGDP are negative, which is in line with the results of the rolling VECM. Moreover, the short-term relationship between lagged ΔGDP and current ΔLoans to SMEs is definitely negative and significant at a minimum level of 5%. The short-term relationship among differential variables of GDP and bank loans may be influenced by the marketing strategy of the bank 6 to make a profit or the credit stabilization of state-owned banks by enhancing relationships with SMEs (Dell Ariccia & Marquez, 2001). Thus, despite economic recession, banks tend to increase their loans to SMEs, which are heavily dependent on banks for raising funds. Table 12 shows that as expected, the signs are significantly negative. It provides evidence of a positive relationship between the business cycle and the first differentials of bank loans to SMEs. The coefficients of CIV factors in all models are statistically significant, and their signs are negative. This 6 According to Dell Ariccia and Marquez (2001), bank may hold onto captured : relationship borrowers during distress periods to reap future benefits from them.

83 S.Afr.J.Bus.Manage.2013,44(2) 81 Table 12: The Result of regression Bank loans to SMEs to other independent variables This table reports Panel GLS and Clustering Fixed Effect model s estimation to bank loans to SMEs. These panel data analysis methods use as major variables such as GDP t-1, GDP t, Loan SMEs,t,I, and CIV SMEs,t,(CIV factors are derived from cointegration analysis among cross-sectional data of bank and GDP variable) including interaction terms like GDP t-1 BIS t-1, i, GDP t-1 NPL t-1, i, GDP t-1 NIM t-1, i, GDP t-1 BO(Bank Ownership) 1, GDP t-1 BO(Bank Ownership) 2, GDP t-1 ln size t-1, i Panel GLS Model Clustering Fixed Effect Model (Clustering: Firm, Year) Model 1 Model 2 Model 3 Model 4 Constant 0,019 *** 0,018 *** 0,014 ** 0,014 ** (7,09) (8,13) (2,11) (2,18) GDP t-1-6,173 * -5,286 ** -4,110-4,224 (-1,90) (-2,28) (-0,65) (-0,72) GDP t -0,222 *** -0,212 *** -0,274 *** -0,262 ** (-6,39) (-7,83) (-2,84) (-2,80) Loan SMEs, t-1, i 0,383 *** 0,346 *** 0,562 *** 0,521 *** (5,00) (5,08) (4,16) (3,95) CIV. SMEs, t -0,013 *** -0,013 *** -0,012 * -0,011 * (-3,64) (-3,91) (-1,69) (-1,68) GDP t-1 BIS t-1, i -0,033-0,038 (-1,63) (-0,79) GDP t-1 NPL t-1, i 0,018 0,038 (0,24) (0,17) GDP t-1 NIM t-1, i -0,154 *** -0,119 (-3,38) (-0,92) GDP t-1 BO 1-0,110 ** -0,154 *** -0,105-0,137 (-2,33) (-3,34) (-0,91) (-1,32) GDP t-1 BO 2-0,281 *** -0,229 *** -0,277-0,251 (-2,57) (-3,03) (-1,35) (-1,34) GDP t-1 ln size t-1, i 0,373 ** 0,287 ** 0,260 0,229 Log likelihood 283,36 280,72 Wald chi square 91,73 *** 94,41 *** (2,03) (2,26) (0,74) (0,71) R 2 0,21 0,19 F-Test 2,38 ** 3,08 *** Note: ***, **, and * denote significant 1%, 5%, and 10% respectively Null hypothesis is all coefficients are same in Wald coefficient & F- Test.

84 82 S.Afr.J.Bus.Manage.2013,44(2) Table 13: The Result of regression Bank loans to LEs to other independent variables This table reports Panel GLS and Clustering Fixed Effect model s estimation to bank loans to LEs. These panel data analysis methods use as major variables such as GDP t-1, GDP t, Loan LEs,t, i, and CIV LEs,t, (CIV factors are derived from cointegration analysis among cross-sectional data of bank and GDP variable) including interaction terms like GDP t-1 BIS t-1, i, GDP t-1 NPL t-1, i, GDP t-1 NIM t-1, i, GDP t-1 BO(Bank Ownership) 1, GDP t-1 BO(Bank Ownership) 2, GDP t-1 ln size t-1, i Panel GLS Model Clustering Fixed Effect Model (Clustering: Firm, Year) Model 1 Model 2 Model 3 Model 4 Constant 0,031 *** 0,029 0,083 *** 0,081 *** (2,73) (0,85) (5,70) (5,67) GDP t-1-1,983-13,85-9,721-12,329 (-0,37) (-1,24) (-0,73) (-0,97) GDP t 0,026 0,122 0,208 0,210 (0,15) (0,52) (0,74) (0,79) Loan LEs, t-1, i 0,120-0,353 *** -0,344 *** -0,351 *** (1,25) (-3,35) (-2,70) (-2,89) CIV. LEs, t -0,021-0,028-0,015-0,021 (-0,97) (-1,56) (-0,55) (-1,01) GDP t-1 BIS t-1, i -0,031-0,075 (-0,52) (-0,69) GDP t-1 NPL t-1, i 0,254 0,328 (0,70) (0,62) GDP t-1 NIM t-1, i -0,218-0,242 (-0,81) (-0,69) GDP t-1 BO 1 0,245-0,030 0,134 0,011 (1,15) (-0,14) (0,41) (0,05) GDP t-1 BO 2-0,309-0,602 * -0,551-0,564 (-1,64) (-1,77) (-1,34) (-1,45) GDP t-1 ln size t-1, i 0,152 0,782 0,614 0,698 Log likelihood 129,57 69,00 Wald chi square 16,96 * 56,55 *** (0,50) (1,28) (0,83) (1,00) 2 R 0,32 0,30 F-Test 4,20 *** 5,75 *** Note: ***, **, and * denote significant 1%, 5%, and 10% respectively Null hypothesis is all coefficients are same in Wald coefficient & F- Test Next let us review the ΔGDP variables in Table 13. The estimated coefficients are negative to lagged ΔGDP, but are not significant in all models. Furthermore, the relationship among the first differential variables of GDP and dependent variables and the first differential bank loans to LEs is positive but insignificant regardless of models or estimation method. This suggests that the motivation for enhancing the relationship by banks is weak in the case of loans to LEs compared to SMEs. ΔLoan variables Considering the motivation of banks to enhance their relationship with SMEs, as mentioned above, we expect that a positive relationship between ΔLoan and lagged ΔLoan to SMEs. It is assumed that the motivation of banks to enhance their relationship with SMEs tends to skew financing policy towards continuity. As discussed in the analysis of the rolling VECM, all the coefficients of the lagged ΔLoan to SMEs shown in Table 12 have positive values with strong significance. Regardless of the model, information from former ΔLoans to SMEs may provide us with lending behavior. These results are consistent with those in Table 10, which shows ß 2 with significant and positive signs. However, we cannot find any evidence to suggest that banks motivated to enhance relationships with LEs tend to skew financing policy towards continuity. Table 13 shows that the coefficients in all models except model 1, have negative signs with a significance of 1%. The estimated coefficients are 0.120, , , and respectively. Estimated by using panel GLS, is insignificant despite positive signs. These results are consistent with those in Table 11, which show ß 2 with negative signs in all window periods.

85 S.Afr.J.Bus.Manage.2013,44(2) 83 This result suggests that the financing policy of banks tends to be relationship-oriented towards SMEs for shortterm periods irrespective of the business cycle. The interaction terms This study incorporates interaction terms (ΔGDP t-1 BIS t- 1,i, ΔGDP t-1 NPL t-1,i, ΔGDP t-1 NIM t-1,i, ΔGDP t-1 BO 1, ΔGDP t-1 BO 2, and ΔGDP t-1 ln size t-1,i ) in the panel data analysis to investigate the interaction effects of GDP and capital adequacy, loan soundness, profitability, governance structure, and the asset size of banks. First, in the case of capital adequacy, soundness, and profitability, the purpose of the analysis is to determine whether or not the interaction of banks with solid managerial conditions affects changes in loan policies at the current time. Second, it investigates assumptions related to governance structures, such as the idea that state-owned banks may stabilize credit, whereas, compared to domestic banks, foreign banks with low funding costs tend to concentrate on enhancing their relationship with SMEs. Third, it investigates whether or not bank size is correlated with lending behavior given that SMEs carry more business risk than LEs. Table 12 and Table 13 shows that the estimated coefficients of ΔGDP t-1 BIS t-1,i are negative in the panel GLS and the clustering Fixed Effect model. The negative sign means that banks with a high BIS ratio increase current loans to SMEs or LEs despite economic recession because the sign of ΔGDP t-1 to current Δloan is definitely negative. This result is consistent with Jokipii and Milne (2008) who demonstrated that the capital buffers of 15 EU banks have negative co-movement with the business cycle. However, the relationship between capital adequacy and lending behavior is not unique to bank loans to SMEs. Furthermore, because of statistical insignificance it is not strongly confirmed that capital adequacy is a definite determinant in decisions to lend to enterprises. The estimated coefficients of ΔGDP t-1 NPL t-1,i shown in Table 12 and Table 13 have positive signs but are insignificant. It can be cautiously suggested that under economic recession, banks with a low NPL ratio may increase loans to SMEs or LEs in the next time frame. Based on previous studies, it is understood that interested parties may cause banks with high NPL ratios to reduce their issuance of risky loans. Specifically, government supervisors or regulators, depositors and other capital market investors, and risk-averse managers in banks may encourage or require distressed banks to reduce the number of these loans. In their study of the banking sectors in Argentina, Chile, and Mexico, Martinez, Peria and Schmukler (2001) asserted that depositors disciplined risky banks by withdrawing their deposits. Although the relationship between the business cycle and NPL is positive in this analysis of panel data, because of its statistical insignificance, it deemed that the NPL ratio is not a critical determinant for lending behavior. On the other hand, the estimated coefficients of ΔGDP t-1 NIM t-1,i shown in Table 8 are negative in all models. In particular, the sign of model 1 using panel GLS to include all managerial conditions of banks shows a significance of 1%. In other words, banks with advantages in generating profits increase loans to SMEs, the margin of which is generally bigger despite economic recession. In the light of this evidence, this result supports the above explanation that banks tend to increase risky loans to SMEs to reap future benefits. Unfortunately, this statistical significance with a negative sign does not appear in model 3, as shown in Table 12. Neither did the results indicate that NIM might explicitly affect lending behavior to LEs, as shown in Table 13, because all coefficients are insignificant although they all have negative signs. Like most of the existing literature on governance structure such as role of state-owned banks and so called credit stabilization, this study tests whether state ownership of banks is related to lending behavior over the business cycle. Table 12 shows that the estimated coefficients of ΔGDP t-1 BO 1 (banking ownership 1: dummy variable 1 or -1 if it is a private bank or a state-owned bank) are consistently negative particularly if they are significant at 5% and 1%, respectively, in model 1 and model 2 using panel GLS. However, they are insignificant in the clustering fixed effect model. It is understood that private banks more positively increase SMEs loan than do state-owned banks in economic recession. This result is inconsistent with that of Micco and Panizza (2006) who insisted that state-ownership of banks is correlated with lending behavior over the business cycle. They found that the lending of these banks is less responsive to macroeconomic shocks than is the lending of private banks. Actually, as private banks, Korean commercial banks are eager to increase SMEs loans completely because of the maturation of other lending businesses such as household loans and LEs loans. Table 13 shows that estimated coefficients are generally positive and insignificant. This result confirms that private banks do not attract lending businesses, such as the abovementioned LEs loans. In addition to reviewing governance structure, such as the credit stabilization of state-owned banks, it is assumed that foreign-owned banks are less likely to lend to small companies that have opaque information than they are to lend to domestically owned banks (Berger, Klapper & Undell, 2001). Hence, the interaction term, ΔGDP t-1 BO 2 (banking ownership 2: dummy variable 1 or -1 if it is a domestically owned bank or foreign-owned bank), should be checked, as shown in Table 12. This result shows that domestically owned-banks increase SMEs loans more positively than do foreign-owned banks over economic recession because the estimated signs are all negative. Using Panel GLS, the signs are statistically significant at 1%. However, clustering fixed effect model shows no significance. The evidence that the signs are all negative regardless of estimation method, partly showing strong significance in the estimation results, is the similar to the prediction. Similar results of the analysis of LEs loans are shown in Table 13 even though the statistical significance is very

86 84 S.Afr.J.Bus.Manage.2013,44(2) weak. The estimated signs are all negative, which means that domestically owned-banks tend to increase LEs more than do foreign-owned banks over economic recession. Model 2 in Table 13 shows a significance of only 10%. Therefore, domestically owned banks tend to increase loans regardless of whether they lend to SMEs or LEs despite economic recession. As the previous literature observed, it is presumed that domestically owned banks gather information on companies at expense and are likely to information on locally based relationships. There is another assumption related to banking hypotheses the large-bank barriers hypothesis that large banks tend to have difficulty in extending relationship loans to SMEs (Berger et al., 2001). This hypothesis is based on the assumption that large banks generally hold that it is too costly to provide relationship services to small businesses in addition to their services to large corporate customers. Table 12 shows evidence in line with the large-bank barriers hypothesis as far as the result of panel GLS is concerned, in which the coefficients are all positive, indicating that smaller a bank s assets are, the larger the increase in bank loans to SMEs despite depression n the business cycle. This means that smaller bank loans to SMEs are less responsive to economic shock than are bigger bank loans. In other words, bigger bank loans to SMEs tend to have comparative procyclicality. In the case of analysis of LE loans shown in Table 13, there is no evidence on large-bank barriers. All estimated coefficients have no significance. To confirm the robustness of these coefficients in panel GLS and the clustering Fixed Effect model, they were tested by the Wald coefficient and F-test. All results strongly rejected null hypotheses, indicating that all the coefficients estimated are the same. Conclusion This study primarily examines the relationship between the first differentials of bank loans and business cycles to confirm whether procyclicality exists in bank loans to SMEs and LEs. In addition, it reviews the dynamics among first differential variables such as GDP, bank loans, and other bank characteristics based on hypotheses related to governance structures, large-bank barriers, and so on. Much of the empirical literature in this field has focused on examining the determinants of bank loans or the relation with business cycle using panel data analysis. This study takes the unique approach of evaluating previous hypotheses by using the analysis of both aggregated and panel data. The study s findings are summarized below. First, it was found that for the period from 1999 to 2008, procyclicality existed in bank loans to SMEs, but not in those made to LEs. This finding suggests that a positive relationship exists between changes in lending behavior to SMEs and the business cycle. The rolling VECM and panel data analysis found that the business cycle does play a pivotal role in determining lending behavior to SMEs. Second, the motivation of banks to enhance relationships with enterprises is especially strong in the case of loans to SMEs. The results of the dynamics among first differential variables such as lagged ΔGDP and ΔLoans support Dell Ariccia and Marquez (2001) who referred to future benefits as being captured. The negative relationship between ΔGDP t-1 and ΔLoans t is attributed to the fact that the banks motivation for making a profit despite economic recession increases bank loans to SMEs for short-term periods. This motivation is attributed to the banks need for more profitability rather than the interested parties demand for soundness or capital adequacy. Third, regarding the governance structure hypotheses, private banks in Korea tend to be more eager than stateowned banks to increase loans during a recession, which is the opposite of the expected lending behavior according to Micco and Panizza (2006). It is possible that the opposite result is attributable to the competitive nature of the banking industry in Korea. In addition, domestically owned-banks concentrate more on SMEs financing than do foreign-owned banks, despite economic recession. This finding supports the conclusions of Berger et al. (2001). Finally, it is suggested here that the loans of smaller banks to SMEs tend to be less responsive to economic shock, which is in line with the large-bank barriers hypothesis. This study concludes that changes in bank loans to SMEs are vulnerable to external economic conditions, whereas privately held commercial banks continue to enhance their SMEs financing business in order to make short-term profits. 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89 86 S.Afr.J.Bus.Manage.2013,44(2) 3501 North Fairfax Drive, C-3050, Arlington, Virginia 22226, U.S. Shrieves, R.E. & Dahl, D Regulation, recession, and bank lending behavior: The 1990 credit crunch, Journal of Financial Services Research, 9: Stolz, S. & Wedow, M Banks regulatory capital buffer and the business cycle: Evidence for Germany, Journal of Financial Stability, 7: Wagster, J.D The Basel Accord of 1988 and the international credit crunch of , Journal of Financial Services Research, 15: