International Competitiveness in the ICT Industry: Evaluating the Performance of the Top 50 Companies

Transcription

1 Global Economic Review Vol. 36, No. 2, , June 2007 International Competitiveness in the ICT Industry: Evaluating the Performance of the Top 50 Companies GEORGE EMM. HALKOS & NICKOLAOS G. TZEREMES Department of Economics, University of Thessaly, Greece ABSTRACT Given the importance of global competition in the information and communication technology (ICT) industry and its contribution to national economies, this paper examines the competitive structure of the global ICT market. By examining the effect of global strategies of the top 50 ICT multinationals, benchmarks, performances and key characteristics of multinational strategic behaviour in the ICT industry is established. The results indicate that there is higher rivalry among the competitors in communication and electronics/equipment segments. Additionally, the most efficient global strategies are adopted from the US and Japanese multinationals imposing a policy gap on corporate governance between US, Japan and the European countries. KEY WORDS: International competitiveness, ICT, market structure, multinational corporations, DEA Introduction The economic importance of information and communication technology (ICT) continues to grow. In the Organization for Economic Cooperation and Development (OECD) countries ICT intensity, defined as ICT expenditures gross domestic product (GDP), is rising. It is particularly high in English-speaking countries, Sweden, Switzerland, Japan and the Netherlands. High-tech products and highly innovative service sectors are the key elements for national productivity and competitiveness (Windrum & Tomlinson, 1999). The strong growth of ICT is mainly driven by the growth in telecommunications equipment and services. Firm-level data suggest that the information technology (IT) industry (excluding telecommunications) continues to undergo rapid restructuring; although hardware is still the largest segment, data communications play an increasing role (OECD, 2000). Many traditional IT hardware firms (like IBM) are moving towards services. However, Asian firms are major players in many hardware segments. The rapid consolidation in the ICT industry, particularly telecommunications, is having an impact on the competitive structure of this sector. Due to the fact that the ICT sector generates innovation, foster entrepreneurial behaviour and increasing the size of relevant markets, it is Correspondence Address: George E. Halkos, Department of Economics, University of Thessaly, Korai 43, 38333, Volos, Greece. Tel: ; halkos@econ.uth.gr X Print/ Online/07/ # 2007 Institute of East and West Studies, Yonsei University, Seoul DOI: /

2 168 G. E. Halkos & N. G. Tzeremes unlikely to reduce rivalry among competitors. In turn it intensifies dynamic competition with the creation of new markets and products (Hemphill, 2003). OECD countries account for more than 80% of the world ICT production, a share that has remained stable. Within the OECD region and in market terms, the US (36%) continues to drive growth. However, in non-oecd countries, ICT market are growing at more than twice the OECD, average, and the largest markets, Brazil and China, are growing rapidly (OECD, 2000). The ICT market is a paradigm of a global market place which drives the firms to compete globally in order to survive. Buckley and Ghauri (2004) describe this phenomenon as a conflict between markets and management policies. Globalization in ICT industry is driven by various factors. These include the development and introduction of new and improved products through firm-level investments in R&D and innovation, the ready availability of venture capital funds for investments in ICT, the development and rapid growth of new products/services segments and the general shift towards services. In that sense issues like competitive structures and global business performance is essential to determine the key players of the ICT sector. The US and the European Union (EU) in their main concern regarding competition policies must recognize the new reality of the global business environment and the ongoing innovation on the ICT sector (Hemphill, 2003). This paper taking into consideration the importance of the ICT sector and its contribution to national economic growth tries to evaluate the competitive structure of the ICT sector by evaluating the performance of the top 50 largest companies. 1 Using Data Envelopment Analysis (DEA) it discusses the impact of the multinationals global strategies by measuring their efficiencies. Moreover, the study determines the efficiency scores of the companies considered, the target values of inputs, giving attention to the source(s) of inefficiency. Global Competition and Internationalization Strategies For many years, the ICT-industry has been one of the fastest growing industrial sectors. For example, the European Information Technology Observatory (EITO) recorded double digit growth numbers in 1999 for Europe and they expected a similar trend to continue (EITO, 1999). In this industry, all the major globalization drivers (macro-environment, industry globalization and pressure for increased internal efficiency) point in only one direction: global competition (Yip, 1989). Bartlett and Ghoshal (1987) have found that multinational companies face a need for new multidimensional strategic capabilities and suggested that many companies in the ICT sector seek simultaneous global efficiency, national responsiveness, and worldwide learning. Yip (1989), pp. 2930) has argued that globalization proceeds in three stages, which are development, internationalization and globalization of the strategy. Theories of foreign direct investment (FDI) aimed at justifying foreign expansion and global competition strategies have constituted the core conceptual foundations of internationalization-performance inquiry. Early FDI concepts focused on beneficial foreign factors located outside the organization, arguing particularly that imperfections in world factor and financial markets benefit the internationally expanding company. At the same time authors with backgrounds in international

3 International Competitiveness in the ICT Industry 169 business and industrial economics emphasized cost advantages (transaction costs, labour costs, material costs) and economies of scale or scope (monopoly advantage and risk diversification, respectively) stemming from internationalization (e.g. Buckley & Casson, 1976; Caves, 1971; Dunning, 1979; Hymer, 1976; Porter, 1985; Vernon, 1966). Specifically, scholars of financial economics underlined portfolio diversification theory (exploitation of national or regional divergence of factor price levels, tax systems, and capital sources) and its implications on risk-return performance (e.g. Errunza & Senbet, 1984; Hirsch & Lev, 1971; Lessard, 1973, 1976; Levy & Sarnat, 1970; Morck & Yeung, 1991). However, this focus on reactive exhaustion of external opportunities as the sole source of benefit shifted once FDI and international management researchers began to examine the organization itself and take into account the proactive development and exploitation of internal advantages. For example, Fayerweather (1978) suggested that the transfer of managerial and technical know-how on a worldwide scale can equip multinational companies (MNCs) with comparative advantages not available to firms operating purely domestically. Likewise, the resource-based view of the firm (Prahalad & Hamel, 1990; Wernerfelt, 1984) underlined intra-company global competencies as promoters of superior performance development. For instance, cultural heterogeneity in the workforce and at management level was argued to trigger innovation and technological advancement (Barkema & Vermeulen, 1998; Hitt et al., 1997). Finally, the theory of operational flexibility (Kogut, 1985) proposed that firms operating in multiple environments are flexible enough to generate both arbitrage (e.g. information transfer) and leverage (e.g. cross-subsidization) opportunities. ICT Industry and Multinational Performance In the high technology industry in general and in the ICT companies in particular, it may be assumed that the globalization drivers are especially strong for a number of reasons. Firstly, the market needs become more similar across countries, and global or at least regional customers and channels are often present. Secondly, in high technology and in the ICT field particularly, competition is very intense and companies often operate on a worldwide scale. Finally, the latest development in the ICT industry has meant that global competition has increased both vertically and horizontally. This has increased the importance of cooperation in the horizontal direction (Gabrielsson & Gabrielsson, 2003, p. 131). Andras and Srinivasan (2003) argue that generally, manufacturing firms in order to compete in such a competitive global market need to optimally utilize their inputs (such as R&D activities), which in turn will have a positive impact on their performance. Firm performance is often seen to relate to the match between the firm and its environment (Johnson & Scholes, 1993; Powell, 1992; Hrebiniak & Joyce, 1985; Thompson, 1999). The environment carries needs and expectations, i.e. market opportunities, which the firm tries to respond to with its resources and capabilities. The better the matching the better the success (Kay, 1995, p. 271). For example, according to contingency theory (Burns & Stalker, 1961), firm performance is the result of a proper alignment of firm design with the context it operates in. There is no better way to organize and the contextual factors should be taken into account

4 170 G. E. Halkos & N. G. Tzeremes (Pfeffer, 1982). In the configurational approach (e.g. Miller & Friesen, 1984) successful firms are considered to be aligned in a small number of typical patterns. However, as the environment of many firms is changing all the time, there is a continuous need for adjustment of the fit between the firm and its environment. Firm performance in the market is based on its competitive advantage. The interaction between firms and their competitive environment can be seen as market- and resource-dependency. Sources of competitive advantage are often bound with the firm s environment. A firm can attain competitive advantage by satisfying the needs of customers of some market segments better than its competitors do. More intensively in the ICT sector (as in any industry), the rules of competition are embodied in five competitive forces: (1) the entry of new competitors; (2) the threat of substitutes; (3) the bargaining power of buyers; (4) the bargaining power of suppliers; (5) the rivalry among existing competitors (Porter, 1985, pp. 45). The external forces of industry influence firms relatively, because they influence all firms in the industry. Firms abilities to get on with the factors influencing the industry are not the same in all firms. The strength of competitive forces influences the industry concentration. The number of firms and business size structure indicate the concentration or fragmentation of industry. Industry structure consists of several factors, such as entry and exit barriers, changes in industry growth, innovations, etc. (Porter, 1980, pp ). In that respect, it is essential to measure the performance of all the major players constituting such a dynamic sector. Berger et al. (1993) claim that there is a need for use of profit functions, DEA and other methodological techniques in order to study and evaluate the determinants of efficiency. This investigation will allow the prediction of groups or individual firms which are likely to survive and cope with the increased future competition. Adopting this methodological view, the DEA technique is used here in order to determine the performance of the largest companies competing in the ICT sector. Measuring the Efficiency of Global ICT Corporations DEA is a non-parametric method of measuring efficiency that relies on mathematical programming. Charnes et al. (1978) introduced the method of DEA to address the problem of efficiency measurement for decision-making units (DMUs) with multiple inputs and outputs in the absence of market prices. They denoted the phrase decision-making units in order to include non-market agencies like schools, hospitals and courts, which produce identifiable and measurable outputs from measurable inputs but generally lack market prices of outputs (and often of some inputs as well). In this paper, DMU is regarded as synonymous with ICT companies. DEA can be either input- or output-orientated. In the input-orientated case, the DEA method defines the frontier by seeking for the maximum possible proportional reduction in input usage, with output levels held constant, for each enterprise. However, in the output-orientated case, the DEA method seeks the maximum proportional increase in output production with input levels held fixed. The two measures provide the same technical efficiency scores when a constant returns to scale (CRS) applies, but are unequal when variable returns to scale (VRS) is assumed. In this paper an output orientation has been selected as it is believed that it would be fair to assume that, companies usually attempt to maximize output from a given set

5 International Competitiveness in the ICT Industry 171 of inputs rather than the converse. DEA has a number of advantages namely: it can incorporate multiple inputs and outputs and it can calculate technical efficiency, requiring only information on output and input quantities (not prices). This makes it particularly suitable for analysing the efficiency of global strategies and possible sources of inefficiency as well as efficiency levels. By identifying the peers for development which are not observed to be efficient, it provides a set of potential role models (reference ICT companies) that a firm can look to, in the first instance, for ways of improving its operations. This makes DEA a potentially useful tool for corporate strategy, benchmarking and change implementation programmes. However, like any empirical technique, DEA is based on a number of simplifying assumptions that need to be acknowledged when interpreting the results of DEA studies. DEA s main limitations include the following. Being a deterministic rather than statistical technique, DEA produces results that are particularly sensitive to measurement error. If a company s inputs are understated or its outputs overstated, then that company can become an outlier that significantly distorts the shape of the frontier and reduces the efficiency scores of nearby companies. In regression-based studies, the presence of error terms in the estimation tends to discount the impact of outliers, but in DEA they are given equal weight to that of all other enterprises. DEA only measures efficiency relative to best practice within the particular sample. Thus, it is not meaningful to compare the scores between two different studies because differences in best practice between the samples are unknown. DEA scores are sensitive to input and output specification and the size of the sample. Increasing the sample size will tend to reduce the average efficiency score, because including more companies provides greater scope for DEA to find similar comparison partners. In order to see how the efficiency frontier works a simplified case of a company is considered which uses one input and a single output. Figure 1 indicates such a case, where points A, B, F, L, and C are the locations of companies in input (X) and output (Y) space. Relying on the available data, the unit with the highest efficiency is regarded, i.e. the maximum output to input ratio. In Figure 1 company at point B has the highest efficiency as a line drawn from the origin to the observed points has the greatest slope in B. In this case line OE passing through point B determines the efficiency frontier. All the other points are inefficient because their efficiency (productivity) is lower. Assuming the observation(s) on the efficiency frontier (in this case B) to be fully efficient, it is given an efficiency score of 1 (or 100%). Observations below the efficiency frontier are inefficient and the degree of inefficiencies depends on the extent to which their productivity is below that of point B. The efficiency score for company B can be determined as the ratio of the efficient use of input X to the actual use of input X, i.e. X D /X C, keeping output constant. Assuming that variable returns to scale holds, the efficiency frontier takes the form of a curve passing through points A, B, F, and L. Taking output as given efficient use of input X for company C is obtained at point N at the efficiency frontier. Accordingly the efficiency score for company C is obtained as X N /X C. Mathematically, the efficiency score of company c, assuming that companies minimize the use of inputs given outputs, is determined by solving a linear optimization problem (Charnes et al., 1978). Considering n companies where company f uses the amount of X if of input i and produces the amount of y of of

6 172 G. E. Halkos & N. G. Tzeremes Output Y E L Y C B D N F C A X D X N X C Input X Figure 1. Efficiency frontiers under the assumption of CRS and VRS output o. It is assumed that x if ]0, y of ]0 and that each company uses at least one input to produce at least one output. By denoting the input weights by b i (i1,..., m) and output weights by m o (o1,..., s) the optimization problem, assuming constant returns to scale, can be formulated as follows: X s o1 m o y of Xm max w c Xs m o y oc (1) m:b 01 s:t: Xm i1 i1 b i x ic 1 (2) b i x if 50; f 1;...; n (3) m o ; b i ]f; o1;...; s; i1;...; m (4) where f is a small positive constant. The maximizing problem is called the multiplier problem and determines the efficiency score of a company c by maximizing the sum of its weighted outputs (equation (1)) so that the sum of its weighted inputs equals one (equation (2)) and so that the weighted outputs minus the weighted inputs of all companies is less than or equals zero (equation (3)). This setting implies that companies are either at the efficiency frontier or below it and the efficiency scores vary between 0 and 1 (or between 0 and 100 in percentages). By denoting the input weights of a company c by u and the input and output weights of other companies by l f (f1,..., n) the dual of the maximizing problem can be written when constant returns to scale hold as follows: min t c uf Xs s o;l;s o ;s o 1 o1 f Xm i1 s i (5)

7 International Competitiveness in the ICT Industry 173 s:t: Xn f 1 ux ic Xn l f y of s o y c ; o1;...; s (6) f 1 l f x if s i 0; i;...; m (7) l f ; s o ; s i ]0 (8) The variables s0, si are called slack variables measuring the excess of inputs and outputs. The small positive constant f guarantees that inputs and outputs are positive and that the slack variables do not influence the target function t c. The minimizing problem is called the envelopment problem and determines the efficient use of inputs for company c (equation (5)) so that the outputs of company c equal to the sum of weighted outputs of other companies (equation (6)). In addition, the weighted inputs of company c must equal the weighted inputs of other companies (equation (7)). The optimal value of parameter u in equation (7) determines the amount company c should reduce its use of inputs in order to be at the efficiency frontier. Positive values of l f determine those companies that dominate company c, i.e. form its comparative set. If it is assumed that variable returns to scale exist in the production, another restriction is needed to be added to the envelopment problem, which will ensure that the efficiency frontier is a convex hyperplane: X n f 1 l f 1 (9) In Figure 1, pure technical efficiency of company c is given by the ratio X N /X C K C. The degree of scale efficiency is computed as z C u C /K C, where u c X D /X C and as stated earlier represents the pure technical efficiency. By construction K C exceeds u C. If the value of z C is 1 the company is scale efficient. If scale inefficiency exists, it can be due to either increasing returns to scale (IRS) or decreasing returns to scale (DRS). To differentiate IRS from DRS, the same linear programming problem is solved again with the additional restriction of: X n f 1 l f 51 (10) which allows for non-increasing returns to scale (NIRS). In Figure 1, this case is represented by the ABFL frontier. For company c located at point C, the efficiency is given by f C X D /X C, which also equals u H. By construction, 8 C ] u C and 8 C 5K C, if 8 C K C and scale inefficiency exists, then it is due to DRS. If K C "8 C, then the scale inefficiency is due to IRS. If the results indicate CRS then a company will be overall efficient i.e. its combination of inputs and outputs is scale efficient. If a company is observed to have DRS, then inefficiency is caused by a too big an output, whereas when there is IRS it means that a firm is scale inefficient because according to its capabilities it can achieve a bigger level of output.

8 174 G. E. Halkos & N. G. Tzeremes Variables and Empirical Results This study analyses 50 ICT companies as has been provided from the dataset of OECD (2004) for the year Moreover, in order to calculate efficiency scores and determine the performances of the companies, five variables in the form of inputs and outputs have been used. Firstly the three inputs that have been used are the number of employees, the R&D expenditure and the market capitalization. The latter is defined as the total dollar value of all outstanding shares and it is calculated by multiplying the number of shares times the current market price. Finally, the two outputs that have been used are the revenues and the net income. Looking at the chart of Figure 2 it can be observed that the largest amount of companies (36% and 34%) are operating in the telecommunication and in the electronic and components industries, whereas IT equipment consists of only 12% and communication equipment 10% of the 50 largest companies. Looking at the graph of Figure 3 the country of origin of the top 50 ICT companies can be observed. Specifically, 18 of the companies are from the US, whereas 13 are from Japan. Korea has only three companies, whereas France, the UK and Germany have two companies each. Porter et al. (2001) in their study investigating the changes in national technological competitiveness indicate that US and Japan are remaining the leading countries of technological competitiveness. In this study (as indicated previously), this can be observed by the fact that the majority for the ICT companies are in fact US and Japanese. According to the derived results from the solution of the model, it emerges that the efficiency ratios of the ICT companies under consideration range from (or 17.64%) to 1 (or 100%). Twelve companies are considered to be efficient for the case of overall efficiency (OE) and 14 for the case of pure technical efficiency (PTE) (see Table 1). Specifically, as can be seen from Table 1, efficient companies are considered to be those with efficiency ratio equal to one (U*1 or 100). The most efficient companies are NTT, Hitachi, Vodaphone, Dell Comp., Mitsubishi El., BT, Sprint, Comms equipment 10% Services 6% Software 2% ICT industries Telecommunications 36% Telecommunications Electronics & components IT equipment 12% IT equipment Services Comms equipment Software Electronics & components 34% Figure 2. The industries in which the largest ICT companies are operating

9 International Competitiveness in the ICT Industry ICT companies countries Japan France UK USA Singapore Australia Bermuda Korea Germany China Canada Netherlands Finland Italy Sweden Spain Countries Figure 3. Country of origin of the 50 largest ICT companies KDDI, Tech Data, Qwest, Flextronics, Telstra for the case of overall efficiency and all the previous plus Emerson El. and HP for the case of pure technical efficiency. The first columns in Table 1 represent the rankings of the companies according to their overall efficiency, the second, third and fourth columns represent the company names, countries, of origin and industry characteristics, respectively. The fifth, sixth and seventh columns represent the overall, pure technical and scale efficiencies scores. Finally the last column represents the reference set for the inefficient companies compared to the efficient ones. The same column shows us how many times the efficient companies constitute a reference and comparison criterion for the inefficient companies. That is, how many times the specific company appears to be a member of the reference set. At this point it is worth mentioning that a company which appears to be the most times in the efficient frontier for the less efficient companies is considered to be the global leader. By counting how many times each company appears to be in the reference set (Table 1, column 9), it is noticed that Hitashi is the most efficient in the case of OE and PTE because it appears 34 times (more than all the other efficient companies) to be part of the reference set. As it can be seen from the mathematical formulation, the feasible target for the improvement of every ratio is achieved by summing up the products of the weights and the respective ratios. The financial ratios that are used for each company s efficiency as well as the feasible target for improving any ratio are shown in Table 2. It is noticed that for the companies that form the efficient frontier, there is no difference between the real ratios and the feasible targets. However, there is a possibility of improvement for all companies whose efficiency, according to Table 1 is less than 1. It is worth mentioning that Table 1 must be read along with Table 2, as both tables refer to the case of OE. For instance, the company IBM is examined looking first at Table 1 it is noticed that the reference set of IBM is Hitashi, Dell and Mitshubishi. So, as is shown in Table 2, the feasible target for the respective IBM s ratios will be given as the sum of the products of the respective weights for the reference set (Hitashi, Dell and Mitshubishi) multiplied by the matrix-columns that include the

10 Table 1. Rankings, company names, county of origin, industry and efficiency scores of the companies Rankings Companies Country Industry OE PTE SCALES Benchmarks 1 Hitachi Japan Electronics & components CRS 34 1 BT UK Telecommunications CRS 19 1 Vodafone UK Telecommunications CRS 11 1 Flextronics Singapore Electronics & components CRS 11 1 Mitsubishi Electric Japan Electronics & components CRS 10 1 NTT Japan Telecommunications CRS 8 1 Del Computer USA IT equipment CRS 8 1 Tech Data USA Services CRS 5 1 Telstra Australia Telecommunications CRS 5 1 Sprint (FON & PCS USA Telecommunications CRS 2 consolidated) 1 KODI Japan Telecommunications CRS 2 1 Qwest USA Telecommunications CRS 0 2 Emerson Electric USA Electronics & components IRS BT, Flextronics, Telstra 3 Hewlett-Packard USA IT equipment DRS Hitachi, Del, Mitsubishi 4 Canon Japan Electronics & components DRS Hitachi, Mitsubishi, Sprint 5 Bel South USA Telecommunications IRS Hitachi, Mitsubishi, Sprint 6 Fujistu Japan IT equipment DRS Hitachi, Flextronics 7 Toshiba Japan IT equipment DRS Hitachi, Flex Tronics 8 Accenture Bermuda Services IRS Vodaphone, BT, Flextronics, Telstra 9 IBM USA IT equipment DRS Hitachi, Dell, Mitsubishi 10 NEC Japan IT equipment DRS Hitachi, Flextronics 11 Korea Telecom Korea Telecommunications IRS Vodaphone, BT, Flextronics, Telstra 12 Sony Japan Electronics & components DRS NTT, Hitachi, BT 13 Matsushita Electric Japan Electronics & components DRS Hitachi, Flextronics 14 Xerox USA Electronics & components IRS Hitachi, Flextronics 15 Samsung Electronics Korea Electronics & components DRS NTT, Hitachi, BT 16 Ricoh Japan Electronics & components IRS NTT, BT, Flextronics 176 G. E. Halkos & N. G. Tzeremes

11 Table 1. (Continued) Rankings Companies Country Industry OE PTE SCALES Benchmarks 17 AT & T USA Telecommunications DRS Kitachi, Mitsubishi, KDDI, Tech Data 18 France Telecom France Telecommunications DRS Hitachi, Mitsubishi, Tech Data 19 Verizon Communications USA Telecommunications DRS Hitachi, Vodaphone, BT 20 Sharp Japan Electronics & components IRS NTT, Hitachi, BT 21 Deutsche Telecom Germany Telecommunications DRS Hitachi, Mitsubishi, Tech Data 22 SBC Communications USA Telecommunications DRS Hitachi, Vodaphone, Dell, BT 23 Sanyo Electric Japan Electronics & components DRS Hitachi, Mitsubishi, KDDI, Tech Data 24 Siemens Germany Electronics & components DRS NTT, Hitachi, Flextronics 25 China Mobile China Telecommunications DRS Hitachi, BT, Telstra 26 BCE Canada Telecommunications IRS Hitachi, Vodaphone, BT 27 Microsoft USA Software DRS Hitachi, Telstra 28 3M USA Electronics & components DRS Hitachi, Vodaphone, Dell, BT 29 EDS USA Services DRS Hitachi, Tech Data 30 Philips Electronics Netherlands Electronics & components DRS NTT, Hitachi, BT 31 Nokia Finland Comms equipment DRS Hitachi, Dell, Mitsubishi 32 Alcatel France Comms equipment IRS NTT, Hitachi, KDDI, 33 LG Electronics Korea Electronics & components DRS Hitachi, Vodaphone, Dell, BT 34 Motorola USA Comms equipment DRS NTT, Hitachi, Flextronics 35 Cisco Systems USA Comms equipment DRS Hitachi, Dell, Mitsubishi 36 Intel USA Electronics & components DRS Hitachi, Vodaphone, Dell, BT 37 Telecom Italia/Olvetti Italy Telecommunications DRS Hitachi, Vodaphone, BT 38 Ericsson Sweden Comms equipment DRS Hitachi, Vodaphone, BT 39 Telefonica Spain Telecommunications DRS Hitachi, Vodaphone, BT Note: OE Overall efficiency; PTE pure technical efficiency. International Competitiveness in the ICT Industry 177

12 178 G. E. Halkos & N. G. Tzeremes ratios of the reference set companies. Specifically, the feasible target for every inefficient company can be calculated as: Y i XN n1 l + n R i (11) In a similar way, for each company the rest of the feasible efficient target ratios can be calculated as shown in Table 2. For instance IBM in order to be competitive and thus to increase its performance, needs to efficiently use its resources. But due to scarce resources and inefficiencies it has to reduce the employees by 49%, reduce its R&D expenditure by 52.49% and to increase its market capitalization by %. As can be seen the methodology provides the competitive posture of the companies (top 50) competing globally in the ICT sector. Moreover, as can be observed from Table 1, most of the efficient companies (7 out of 12) are operating in the telecommunication industry. Furthermore, four of the efficient companies are US companies, four are Japanese and only two are from a European country, the UK. The other two efficient companies are from Australia and Singapore. Conclusions and Policy Implications In this study applying DEA to the top 50 companies operating in the ICT sector, the efficiency scores and the optimal output (ratios) levels for the inefficient companies are obtained. DEA provided an overall objective numerical score, ranking, and efficiency potential improvement targets for each one of the inefficient units. The results provide a clear view of the efficiency and the performance of the major competitors in the ICT sector. This study is an important tool for strategic planning, comparisons between the main competitors in the ICT competitive structure and the effect of different control systems on companies performance and competitive posture. In strategic planning the managers of the corporate centre believe that they should participate in and influence the development of business unit strategies. They use flexible performance targets, which are reviewed within the context of the longterm strategic process. DEA provided such performance targets by measuring the DMUs efficiencies giving the competitive position of every DMU. However, it is important to recognize that the scope of decision-making defines what can be regarded as choice variables and the criterion function has to be appropriately formulated. The obvious payoff from efficiency measurement is that it provides an objective basis for evaluating the performance of a DMU. The outcome at the highest level of efficiency provides an absolute standard for management by objectives. Furthermore, comparison of efficiency across DMUs at the same level provides a basis for differential rewards. In this way, one can assess the impact of various institutional and/or organizational changes or new product/service development by analyzing how it affects the efficiency. The results indicate clearly that most of the companies are US and Japanese. Telecommunications and electronic components are the main ICT segments with intense global competition. However, the huge segments created by ICT markets raise the challenges for new firms to compete globally. Furthermore, they raise the challenge for new firms to compete globally. Furthermore, they raise the challenge

13 Table 2. Actual, targeted and percentage changes of input values of the inefficient companies Companies Employees R&D Market cap Companies Employees R&D Market cap Actual values IBM Telefonica Targeted Percentage change Actual values Siemens Motorola Targeted Percentage change Actual values Hewlett-Packard Canon Targeted Percentage change Actual values Verizon Communications Bell South Targeted Percentage change Actual values Sony EDS Targeted Percentage change Actual values Matsushita Electric Sanyo Electric Targeted Percentage change Actual values France Telecom Cisco Systems Targeted Percentage change Actual values Deutsche Telekom Alcatel Targeted Percentage change Actual values Toshiba LG Electronics Targeted Percentage change Actual values Samsung Electronics M Targeted Percentage change Actual values SBC Communications Emerson Electric Targeted International Competitiveness in the ICT Industry 179

14 Table 2. (Continued) Companies Employees R&D Market cap Companies Employees R&D Market cap Percentage change Actual values NEC Sharp Targeted Percentage change Actual values Fujitsu Xerox Targeted Percentage change Actual values Nokia China Mobile Targeted Percentage change Actual values AT & T BCE Targeted Percentage change Actual values Telecom Italia/Olivetti Ericsson Targeted Percentage change Actual values Microsoft Ricoh Targeted Percentage change Actual values Philips Electronics Accenture Targeted Percentage change Actual values Intel Korea Telecom Targeted Percentage change G. E. Halkos & N. G. Tzeremes

15 International Competitiveness in the ICT Industry 181 for governments and public policy to capture important value added segments for local producers. EU countries policy need to be restructured and frameworked using basic elements of the US and Japanese models. They need to be effective in shaping the patterns of demand in ways that advantage local suppliers over their global competitors. Two factors can be the fundamental pillars for policy evaluation and restructuring for domestic demand: the regulatory policies for entry or competition and the structure of demand in domestic markets. Especially the latter involves the effect of government ownership, legal requirements and the general pressure for buyers to buy from national suppliers. Thus it is expected that governments from EU countries and local firm(s) to pursue joint strategies of entry into global information and communication technology or market preservation in local ICT. Such private/public partnership will draw on the firm(s) s technological capabilities on connection to local complementors or customers and on linkages to international complementors or customers. Note 1 The justification and classification of the top 50 worldwide companies has been conducted by OECD based on their revenue levels. References Andras, T. L. and Srinivasan, S. S. (2003) Advertising intensity and R&D intensity: differences across industries and their impact on firm s performance, International Journal of Business and Economics, 2(2), pp Barkema, H. G. and Vermeulen, F. (1998) International expansion through start-up or acquisition: a learning perspective, Academy of Management Journal, 41, pp Bartlett, C. A. and Ghoshal, S. (1987) Managing across borders: new strategic requirements, Sloan Management Review, Summer, pp Berger, A. N. et al. (1993) The efficiency of financial institutions: a review and preview of research past, present, and future, Journal of Banking and Finance, 17, pp Buckley, P. J. and Casson, M. C. (1976) The Future of the Multinational Enterprise (London: Macmillan). Buckley, P. J. and Ghauri, P. J. (2004) Globalisation, economic geography and the strategy of multinational enterprises, Journal of International Business Studies, 35, pp Burns, T. and Stalker, G. (1961) The Management of Innovation (London: Tavistock). Caves, R. E. (1971) Industrial corporations: the industrial economics of foreign investment, Economica, 38, pp Charnes, A. et al. (1978) Measuring the efficiency of decision making units, European Journal of Operational Research, 2, pp Dunning, J. H. (1979) Explaining changing patterns of international production: in defence of the eclectic theory, Oxford Bulletin of Economics and Statistics, 41(4), pp Errunza, V. R. and Senbet, L. W. (1984) International corporate diversification, market valuation, and sizeadjusted evidence, Journal of Finance, 34, pp EITO (1999) European Information Technology Observatory (Frankfurt) Available at Fayerweather, J. (1978) International Business Strategy and Administration (Cambridge, MA: Ballinger). Gabrielsson, M. and Gabrielsson, P. (2003) Global marketing strategies of born globals and globalizing internationals in the ICT field, Journal of Euromarketing, 12(3/4), pp Hemphill, T. H. (2003) Cooperative strategy, technology innovation and competition policy an the United States and the European Union, Technology Analysis and Strategic Management, 15(1), pp Hirsch, S. and Lev, B. (1971) Sales stabilization through export diversification, Review of Economics and Statistics, August, pp

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