Economic benefits of enterprise resource planning systems: some empirical evidence

Accounting and Finance 45 (2005) 439 456 Economic benefits of enterprise resource planning systems: some empirical evidence Zoltan P. Matolcsy a, Peter Booth b, Bernhard Wieder a a School of Accounting, University of Technology, Sydney, 2007, Australia b Chancellery, University of Technology, Sydney, 2007, Australia Abstract The present study provides empirical evidence on the economic benefits of enterprise resource planning (ERP) systems. We use a modified value chain approach and identify several ratios for each component of the value chain to reflect improvements as a result of the adoption of ERP systems. These financial ratios are tracked for 2 years for a group of companies that adopted ERP systems versus a group of companies that did not adopt ERP. Both univariate and multivariate statistics are used to test for differences. The key result of the present study is that the adoption of ERP systems leads to sustained operational efficiencies and improved overall liquidity. In addition, some support is found for increased profitability 2 years after the adoption of ERP and for improvements in accounts receivable management. Key words: Enterprise resource planning; Value of information technology; Benefits of information systems JEL classification: M49 doi: 10.1111/j.1467-629X.2005.00149.x 1. Introduction One of the key information systems innovations for business was the development of enterprise resource planning (ERP) systems, which both underpin and facilitate globalization. Davenport (1998, p. 122) argues that the business world s embrace of enterprise systems may in fact be the most important development in the corporate use of information technology in the 1990s. The business community has The comments of P. O Brien, W. F. Chua, L. Klott, P. Luckett, T. Malmi, P. Robinson, the two anonymous referees and participants of University of Technology, Sydney, Research Seminars and the Accounting Association of Australia and New Zealand Conference (2002, Perth, Australia) are gratefully acknowledged. Received 18 August 2003; accepted 3 December 2004 by Stewart Leech (Associate Editor). Published by Blackwell Publishing.

440 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 recognized the importance of ERP systems and made enormous investments into this new information technology (Intellibusiness, 2002; Scott and Shepherd, 2002). Despite the high number of ERP-implementations around the world and the large amount of ERP-related articles in professional journals, the academic world almost neglected the topic until the second half of the 1990s. Since then, several academics have highlighted issues associated with and the potential benefits of ERP investments (Parr et al., 1999; Grabski et al., 2000; Poston and Grabski, 2001; Granlund and Malmi, 2002; Hitt et al., 2002; Hunton et al., 2003; Nicolaou et al., 2003) and many information systems and accounting journals have run special editions and/or sections on issues related to ERP. 1 The objective of the present study is to provide further systematic evidence on the economic benefits of ERP investments. Our motivation for this research is the fact that leading ERP-vendors continue to claim that their ERP-solutions increase the performance of their customers. Oracle, for example, claims on its website that the E-Business Suite helps you make more informed decisions and improve your business operations while reducing expenses (Oracle, 2003a), and publishes customer case studies to support its claim that the E-Business Suite increases its customers return on investment (Oracle, 2003b). Similar claims are made by SAP AG, which promises faster return on investment (SAP AG, 2003a,b) with its mysap ERP solution. PeopleSoft offers a similar line of analysis (Peoplesoft, 2003). We contribute to the existing research in this field in several important ways. First, we develop a conceptual/theoretical framework based on Porter s (1985) value chain model to derive our performance and benefits measures. We adopt a modified value chain model (or an economic input/output model) that identifies three independent components of business activity: inbound logistics, operations and marketing, and sales and distribution. Then, we map the performance enhancing consequences of ERP systems for each of these three components. This mapping enables us to identify the appropriate ratios to measure different aspects of the benefits of ERP systems and provides a sound basis for the inclusion of ratios other than profitability ratios. Our other major contribution is that our evidence is based on Australian companies. The Australian economic/institutional setting is significantly different from the US and European ones. Australian companies are typically smaller, have less multinational subsidiaries and their management is more likely to be more homogenous in terms of cultural and educational background. Furthermore, the Australian roll out of ERP systems peaked in the late 1990s and it was led by European consulting firms. Hence, the consultants themselves have had a greater experience in identifying the key success factors in ERP installations. Given the above differences, the Australian setting provides an environment where the benefits of ERP systems, if any, are likely to be more pronounced and to be measurable in a shorter period of time. Hence, there is less concern about other confounding factors over time, such as changes in macroeconomic conditions and/or company-specific decisions. Furthermore, our 1 See, for example, Journal of Management Information Systems, Spring 2000; the Australian Accounting Review, November 2000; and the European Accounting Review, No. 1, 2003.

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 441 results provide a useful contrast and/or confirmation to the limited existing overseas (mainly US-based) evidence (Poston and Grabski, 2001; Hunton et al., 2003; Nicolaou et al., 2003). Our key results indicate that the adoption of ERP systems leads to more efficient operations as measured by inventory turnover (IT) and fixed assets turnover (FAT) and, to a lesser extent, efficiencies in marketing, sales and distribution as measured by accounts payable (AP) management. Furthermore, these improvements lead to increases in overall performance as measured by profitability and liquidity 2 years after the adoption of ERP systems. Our results are robust with respect to several different tests. Our empirical results differ from previous studies in several important ways. Earlier research either provides some case study evidence on the benefits or other aspects of ERP (Grabski et al., 2000; Granlund and Malmi, 2002) or it provides evidence on general information technology investments either using a market setting (Brynjolfsson, 1993; Brynjolfsson and Yang, 1998) or at the industry level (Berndt and Morrison, 1995). Only recently, empirical studies have emerged which investigate the financial benefits of ERP systems (Poston and Grabski, 2001; Hunton et al., 2003; Nicolaou et al., 2003). We build on and extend the evidence provided in those studies. Poston and Grabski (2001) look at four financial characteristics before and after ERP adoption using univariate tests. Their results indicate that ERP adoption leads to a reduction in employee numbers and in the ratio of employees to revenues for each year following the ERP implementation. However, their control firms have a greater reduction in employee numbers; hence, their overall results are counterintuitive. Hunton et al. (2003) provide evidence on overall firm performance by comparing return on assets, return on investments and asset turnover for ERP adopters and non-adopters. Their key results do not indicate a performance improvement for ERP adopters. Rather, they find that the financial performance of adopters have not declined during their test period, whereas the performance of non-adopters has declined during the same period. Our evidence indicates that the overall performance of ERP adopters has improved subsequent to adoption and provides an insight into the areas of improvement. Finally, Nicolaou et al. (2003) compare financial data of companies adopting enterprise-wide systems and of a matched control group of firms. The results from a univariate analysis of performance differences across time periods show that firms adopting enterprise systems have significantly higher differential performance in their second year after the completion of the system than the control group. Our results corroborate their overall findings on profitability ratios. However, we provide further detailed evidence on the benefits of ERP systems by including additional nonprofitability measures, such as accounts payable days (APD), sales days outstanding (SDO), FAT, IT and the current ratio (CR). The remainder of the present paper is organized as follows. In the next section, we develop our conceptual framework and predictions. In Section 3, the research design is described, and the results are reported in Section 4. The final section concludes the paper and includes suggestions for future research directions.

442 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 1 Information technology measures Business 2 process performance 3 measures Firm performance measures 4 Contextual factors 5 Figure 1 Framework for evaluating research on the effects of IT investments (based on Dehning and Richardson, 2002). 2. Conceptual framework and predictions The starting point of our conceptual framework is a study by Dehning and Richardson (2002). Based on a detailed review of prior research on the return on investment of information technology, they develop a framework that aims to explain differences in firms performance in terms of differences in information technology investments. They identify five approaches to research on the benefits of information technology investments (see Fig. 1) and analyse the gaps in prior research on this topic. We use this conceptual framework and focus on the direct effects of information technology investments by operationalizing the causal relationship between information technology investments and performance by way of a generic value chain model (Porter, 1985). This enables us to focus on the nature and the source of performance gains, and to distinguish between the impacts of information technology investments on business processes (path 2) captured by operational performance measures and the impact on the firms, overall performance (path 3). Our conceptual framework is based on a modified value chain model and within each component of the value chain we identify how ERP systems could add value, which in turn should be reflected in an appropriate financial performance measure or ratio. 2 2 Given that all of our financial data are based on publicly-available information and not on private data, the number and types of ratios that we are able to estimate is limited by the Australian disclosure requirements. Hence, we cannot adopt and measure all components of a full value chain model such as proposed by Porter (1985).

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 443 Inbound logistics Operations Marketing, sales and distribution Supported by: Firm infrastructure Human resource management Technology (development) Procurement Figure 2 A modified value chain model. Our value chain model has only three key components, as depicted in Figure 2. Enterprise resource planning systems, which provide both a horizontal integration (i.e. the components of the value chain) and a vertical integration (i.e. the supporting functions), are expected to add value right across all the elements of the value chain. 3 They automate business processes and enable (and often even impose) process changes (re-engineering), which is said to introduce best practices into organizations and is, therefore, expected to improve operational performance (e.g. cycle time reduction, customer service improvements, quality improvements and throughput increases) (Davenport, 1998; Shang and Seddon, 2000; Wieder and Davis, 2003). In the area of inbound logistics, ERP systems have been argued to add value in terms of better communication and integration with suppliers, better management of payables and more efficient raw material management (Davenport et al., 2002). ERP systems integrate all functions involved in the procurement process; namely, purchasing, production, sales (in the case of make-to-order production), inventory management and accounting. It has been proposed that the full integration of these functions provides more transparency across the whole business process, allowing better tracking of raw materials and monitoring of storage conditions, integrated planning of lot sizes and facilitating the matching of documents involved in the whole business process (e.g. purchase requisitions, purchase orders, delivery notes and vendor invoices). All these integration improvements are 3 This is not only an explicit claim made by major ERP vendors (e.g. SAP AG, 2004a) and supported by case studies (e.g. Oracle, 2004; SAP AG, 2004b), but an intrinsic benefit of systems integration (Markus, 2000).

444 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 expected to reduce raw material (and service) prices and consumption (including wastage). 4 Furthermore, ERP systems offer automation tools that allow accounts payable departments to optimize payments to vendors; that is, to configure the system in a way that it automatically clears open items with vendors at the most economic point of time (usually at the end of the first cash discount period). This ensures that no cash discounts are foregone and no payments are simply forgotten. Accordingly, we predict a lowering of accounts payables and accounts payable days (APD) for ERP adopters. 5 The expected improvements in inbound logistics will also have positive impacts on a firm s overall performance. We expect not only the raw material costs to be reduced, but also personnel costs in accounts payable, purchasing and inventory management. Toyota Financial Services (Australia), for example, achieved significant savings in accounts payable processing, because their new ERP solutions automated most of their formerly manual data capture procedures (SAP AG, 2004c). During the operations phase, inputs are transferred into outputs through valueadding activities, such as machining, assembly, testing, printing, packaging and other activities. Again, ERP systems aim to enhance these activities by integrating marketing, sales and other information into production schedules and by providing direct support for production planning and control (Rizzi and Zamboni, 1999; Davenport et al., 2002; Shang and Seddon, 2000). In particular, the improved scheduling of production runs helps to improve fixed asset utilization, reduce idle time and support higher levels of sales. The potentially more frequent reporting of production and quality performance indicators helps to identify waste and improve total quality management, leading to output performance improvements and better asset utilization. Many ERP vendors also provide special industry solutions that are proposed as improving the operations for a variety of industries in all sectors of the economy. In addition, ERP systems usually include modules that provide special support for the maintenance of fixed assets leading to better maintained fixed assets and lower downtime. All this would lead to more cost-effective utilization of equipment, machinery and other resources within an organization. 6 Accordingly, 4 The better handling of new material should affect also the cost of goods sold (COGS). However, we could not estimate a ratio of COGS/sales for all of our sample companies, because until recently the Australian Stock Exchange (ASX)-list requirements did not mandate the disclosure of COGS. 5 The reduction of APD is considered to be a positive effect, because suppliers tend to identify poor payers, rate them accordingly and grant less favourable conditions than for customers with high credit ratings. 6 This is in line with the expectations associated with the implementation of ERP systems in companies in Europe, the USA and Australia (Davenport et al., 2002).

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 445 we predict higher FAT ratios for ERP adopters as opposed to non-adopters. Furthermore, the better integration of information on sales/marketing and operations leads to improved inventory levels as the production runs better match orders received. This in turn leads to lower levels of wastage and obsolescence in inventories. Hence, we predict a higher IT for ERP adopters. 7 The marketing, sales and distribution phase of the value chain would also be enhanced by ERP systems (Gardiner et al., 2002). The promotion and advertising activities are integrated with production schedules and product inventory levels and available to promise quantities are available to all stakeholders, not just to inventory and production managers. Integrated planning of sales, production and purchasing increases the likelihood that delivery schedules are met and integrated quality management features are expected to increase the quality of both goods and services provided to the customers. Better management of customer information and real-time information on business processes associated with customers is expected to have a positive impact on customer retention. Hence, we predict greater sales increases for ERP adopters than for non-adopters. ERP systems not only enhance sales, but improve accounts receivable management by providing advanced tools for credit management, dunning and debt collection schedules. Better receivables management enhances cash flows, which is a key consideration for all organizations. Hence, we predict a reduction in the sales days outstanding (SDO) for ERP adopters. Finally, if components of the value-adding activities of the value chain are enhanced by ERP systems, then the overall performance of organizations would also improve. Furthermore, overall benefits of ERP systems, such as timelier reporting, broaderbased quantitative and qualitative performance indicators would also lead to better decisions by boards and senior management (Shang and Seddon, 2000). Hence, we predict that the overall profitability and liquidity of an organization would improve, and we measure overall performance improvement by the net profit margin (NPM) and liquidity by the CR. 8 Our performance measures for each component of the value chain are depicted in Figure 3. We test our predictions by using publicly-available information based on the annual reports of ERP adopters (treatment group) and non-adopters (control group). 7 In the absence of COGS-data (see footnote 4), we used the following formula to calculate inventory turnover: sales/inventory. 8 Although the CR is typically referred to as a liquidity ratio in accounting and finance textbooks, it might not be the best measure of liquidity. For example, improvements in inventory management could reduce this ratio if all other things remain the same and at the same time would be a positive outcome from a company point of view. It is only a reasonable proxy for cash flow if all components are optimized simultaneously.

446 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 Inbound logistics Operations Marketing sales and distribution Accounts payable days Fixed asset turnover Invertory turnover Change in sales Sales days outstanding Net profit margin current ratio Figure 3 The generic value chain model and key performance measures. 3. Research design 3.1. Sample and data The base sample of 20 companies was identified from a previous survey undertaken by Booth et al. (2000). We also directly contacted the Financial Controller or Finance Manager of several companies and added 6 more companies through this process. Finally, an additional 9 companies were obtained from the published client lists of SAP Australia and New Zealand (ANZ). As SAP ANZ is clearly dominating the Australian ERP-market, this SAP bias is deemed to be negligible. 9 The total final treatment sample is 35 companies. For each treatment company, we identified a control company from the same industry group. We aimed to match on size as well, as much as possible, although the thinness of the Australian market did not enable us to match on size alone. The sample was also taken from the preceding study (Booth et al., 2000). We confirmed with these companies that they neither operated nor installed an ERP system during the test period. Details of our sample companies are depicted in Table 1. 9 In 2001, SAP ANZ s total license revenue in Australia was $US15.6m, which is more than the total revenue of its two main competitors at that time, PeopleSoft ($US10.2m) and JD Edwards (JDE) ($US3.8m) (Foo, 2003). Despite the merger of Peoplesoft and JDE, SAP remains the leading ERP vendor in Australia and worldwide.

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 447 Table 1 Descriptive statistics of treatment and control firms Panel A: Industry profile of the sample pairs by ASX code Industry description Number of firms Energy 3 Gold 1 Diversified resources 2 Other metals 3 Alcohol and tobacco 2 Building materials 3 Chemicals 3 Developers and contractors 1 Diversified industrials 2 Engineering 1 Food and household goods 6 Health care and biotechnology 1 Infrastructure and facilities 1 Media 1 Miscellaneous industrials 2 Paper and packaging 1 Retail 2 Total 35 pairs Panel B: Yearwise distribution of ERP adoption by firms in treatment sample Year of adoption Number of firms 1993 1 1994 0 1995 3 1996 6 1997 8 1998 14 1999 3 Total 35 firms Panel C: Base year sales revenue of treatment and control firms Base year sales revenue Treatment firms Control firms <$A250m 12 20 $A250m 500m 5 6 $A500m 1000m 9 3 $A1000m 2000m 3 1 $A2000m 3000m 3 2 >$A3000m 3 3 Total 35 firms 35 firms ASX, Australian Stock Exchange; ERP, enterprise resource planning. The final sample of firms comes from 17 different industry groups (panel A, Table 1) and none of this group dominated the sample. The largest industry group is food and household goods, with 6 companies.

448 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 The years of adoption of ERP systems by the sample companies are from 1993 to 1999 (panel B, Table 1); notably, for the year 1998, we have 14 adopters. 10 We do not believe that this would introduce any bias into our results. Finally, the spread of companies is from small to large as measured by sales revenue in panel C of Table 1. For both the treatment firms and the control firms, the following financial ratios have been estimated for 3 years before adoption (T = 3, T = 2, T = 1), in the year of adoption (T = 0) and 1 and 2 years after adoption of ERP system (T =+1, T =+2): 11 NPM, IT, FAT, SDO, APD, CR and sales change (SC). 12 The mean, median, standard deviation and the minimum/maximum values of these ratios are depicted in Table 2. 13 The descriptive statistics in panel A of Table 2 seem to suggest no significant differences among the ratios for the treatment and control groups before the adoption of the ERP system. For example, in the year of adoption (T = 0), the median of the NPM for the treatment group is 4.8 per cent (0.048) and for the control sample is 5.2 per cent (0.052); ITs are 7.93 and 5.74, respectively; FATs are 1.83 and 1.075; SDO are 45.58 and 39.81 days; APD are 36.51 and 39.85 days; and the CRs are 1.28 and 1.57. Finally, the change in sales (SC) seems higher for the treatment sample (0.125) than that of the control group (0.084). Panel B of Table 2 depicts the t-statistics for the differences of the means of the financial ratios for the treatment companies and the control companies. The results are consistent with the proposition that there is no difference between the financial characteristics of these companies 1, 2 and 3 years before the adoption of ERP systems and in the year of adoption. These results are confirmed by and large by the Wilcoxon signed rank test (panel C). The only significant results in this panel are the differences in SC at T = 1 and in the CR at T =+1. 3.2. Statistical analysis We provide both univariate and multivariate analysis of the financial performance indicators for the following periods: time 3 to time 0; time 2 to time 0; 10 There is strong evidence that this sharp increase is a result of the YK2-problem and the introduction of the Australian goods and services tax (Power, 2002). 11 The selection of the timeframe was based on the following assumptions and restraints: (i) the 3 years before the adoption adequately reflects the pre-implementation stage ; (ii) the 2 years after the implementation are most representative for the post-implementation period ; and (iii) in many cases, financial data were only available up to the second year after the implementation. Assumption 2 is supported by empirical evidence showing that the majority of ERP benefits are realized within the first 2 years; after these 2 years, the perceived additional benefits from ERP systems are very low (Davenport et al., 2002). 12 We could not estimate the COGS/sales ratio as companies do not have to publish their costs of goods sold under the Australian Stock Exchange-list requirements. 13 The ratios have been winsorized at the 0.02 level.

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 449 Table 2 Statistics of treatment and control samples Panel A: Descriptive statistics of treatment and control firms Treatment sample Control sample Item Mean Median SD Minimum Maximum Mean Median SD Minimum Maximum T = 3 NPM 0.080 0.060 0.082 0.008 0.373 0.087 0.049 0.095 0.097 0.419 IT 15.504 6.446 24.380 0.000 129.488 8.584 6.317 8.423 0.000 44.050 FAT 2.730 1.964 2.728 0.220 13.205 2.329 1.022 2.896 0.109 11.912 SDO 42.038 38.110 24.402 0.185 114.593 56.126 37.931 58.049 0.947 276.515 APD 46.517 42.054 26.997 0.000 134.570 51.959 43.310 32.900 17.279 189.529 CR 1.491 1.249 0.696 0.770 4.515 1.698 1.434 1.118 0.293 7.027 SC 0.081 0.053 0.149 0.105 0.705 0.046 0.032 0.214 0.476 0.746 T = 2 NPM 0.070 0.050 0.063 0.004 0.253 0.111 0.073 0.166 0.125 0.889 IT 13.170 6.838 17.580 0.000 84.932 9.156 5.492 9.654 0.000 42.797 FAT 2.509 1.935 2.179 0.279 9.175 2.296 0.856 3.136 0.002 12.325 SDO 47.577 39.939 35.102 2.558 198.075 45.160 38.806 33.724 1.116 144.683 APD 42.122 36.606 23.139 0.000 114.620 61.025 44.795 68.077 0.000 412.680 CR 1.499 1.338 0.642 0.646 3.580 1.651 1.396 0.870 0.412 5.514 SC 0.110 0.079 0.227 0.142 1.231 0.052 0.049 0.209 0.408 0.621 T = 1 NPM 0.069 0.059 0.061 0.015 0.304 0.076 0.061 0.124 0.453 0.333 IT 15.502 6.742 23.284 2.470 111.693 10.456 6.036 10.918 0.228 42.027 FAT 2.495 1.739 2.212 0.282 8.924 2.121 0.833 2.655 0.208 10.310 SDO 46.303 40.411 26.888 3.115 130.199 47.097 43.151 31.887 1.321 134.420 APD 44.135 35.983 27.004 3.656 155.323 42.211 39.011 21.420 4.656 119.571 CR 1.396 1.264 0.600 0.492 3.478 1.678 1.455 1.249 0.444 8.250 SC 0.115 0.066 0.261 0.386 1.201 0.268 0.114 0.604 0.168 3.229 T = 0 NPM 0.066 0.048 0.060 0.015 0.298 0.074 0.052 0.077 0.063 0.260 IT 13.638 7.931 18.129 2.123 78.560 8.836 5.744 8.106 0.821 36.518 FAT 2.487 1.834 2.304 0.255 10.804 2.047 1.075 1.979 0.120 6.600 SDO 47.908 45.589 34.031 2.274 178.756 47.006 39.814 37.186 0.000 167.056 APD 42.904 36.516 20.537 3.188 101.021 45.244 39.857 24.145 14.254 119.488 CR 1.759 1.283 2.233 0.587 14.243 1.594 1.517 0.824 0.265 4.960 SC 0.148 0.084 0.189 0.131 0.689 0.200 0.125 0.330 0.253 1.468 T =+1 NPM 0.066 0.043 0.080 0.006 0.444 0.070 0.045 0.078 0.022 0.326 IT 14.366 7.117 21.254 1.781 98.896 11.524 7.734 10.508 2.509 51.108 FAT 2.383 1.958 2.137 0.252 9.873 2.160 1.594 2.040 0.199 7.852 SDO 47.897 44.965 29.580 2.645 162.893 41.016 38.842 24.510 0.836 94.180 APD 45.725 40.210 28.481 3.704 160.086 45.420 38.859 27.099 12.719 136.629 CR 1.317 1.153 0.582 0.146 2.854 1.619 1.572 0.614 0.618 3.258 SC 0.086 0.086 0.119 0.226 0.413 0.369 0.102 0.763 0.329 3.039 T =+2 NPM 0.071 0.043 0.082 0.028 0.398 0.070 0.034 0.092 0.056 0.390 IT 16.977 7.451 28.788 1.721 123.780 10.681 8.341 7.445 2.498 28.966 FAT 2.435 1.744 2.246 0.348 10.011 1.959 0.862 1.867 0.243 6.929 SDO 46.562 44.028 30.092 2.772 154.796 45.631 44.377 26.763 0.914 110.140 APD 39.555 39.940 16.250 4.253 65.823 41.820 35.519 26.112 10.091 153.742 CR 1.461 1.142 0.717 0.626 3.160 1.522 1.458 0.539 0.754 3.292 SC 0.160 0.035 0.311 0.058 1.321 0.206 0.121 0.388 0.667 1.377

450 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 Table 2 (continued) Panel B: Paired sample t-tests of absolute differences in means of treatment and control firms (time 3 to time +2) T = 3 T = 2 T = 1 Mean Mean Mean Item difference t-statistic p-value difference t-statistic p-value difference t-statistic p-value NPM 0.01 0.32 0.75 0.01 0.53 0.60 0.0002 0.02 0.99 IT 7.80 1.62 0.12 0.60 0.32 0.75 0.27 0.14 0.89 FAT 0.27 0.50 0.62 0.10 0.20 0.85 0.17 0.34 0.74 SDO 14.23 1.49 0.15 5.95 1.15 0.26 5.25 1.00 0.32 APD 5.02 0.76 0.45 3.86 0.83 0.41 3.78 0.94 0.35 CR 0.22 0.91 0.37 0.11 0.60 0.55 0.32 1.36 0.18 SC 0.04 0.89 0.38 0.05 0.30 0.77 0.10 0.54 0.60 T = 0 T =+1 T =+2 Mean Mean Mean Item difference t-statistic p-value difference t-statistic p-value difference t-statistic p-value NPM 0.0002 0.02 0.98 0.00 0.14 0.89 0.00 0.21 0.84 IT 0.46 0.23 0.82 3.21 0.77 0.45 7.39 1.36 0.19 FAT 0.49 1.17 0.25 0.04 0.08 0.94 0.21 0.53 0.60 SDO 4.94 1.02 0.32 6.72 1.23 0.23 0.74 0.13 0.90 APD 3.66 0.89 0.38 0.66 0.11 0.91 1.40 0.25 0.81 CR 0.39 2.19 0.04 0.62 5.88 0.00 0.13 0.99 0.33 SC 0.14 0.91 0.37 0.29 2.17 0.04 0.04 0.43 0.67 Panel C: Wilcoxon signed rank test of difference between treatment and control firms (time 3 to time +2) Wilcoxon signed rank test (z-values) Item T = 3 T = 2 T = 1 T = 0 T =+1 T =+2 NPM 0.778 1.376 1.176 0.374 0.059 0.701 IT 1.215 1.253 0.442 1.080 0.339 0.114 FAT 1.402 1.589 1.274 0.843 0.088 0.216 SDO 0.402 0.243 0.278 0.241 0.849 0.299 APD 0.880 1.921 0.072 0.027 0.654 0.542 CR 1.547 1.117 1.547 1.145 4.590 0.947 SC 0.607 0.689 2.149 0.184 1.851 0.378 The variables are defined as: APD, accounts payable days; CR, current ratio; FAT, fixed asset turnover; IT, inventory turnover; NPM, net profit margin; SC, sales change; SDO, sales days outstanding. p-values are based on a two-tailed test of significance. Significant at the 0.05 level (two-tailed). SD, standard deviation. time 1 totime0;time0totime+1; and time 0 to time +2. We estimate both the absolute changes and the percentage changes in these financial ratios and test for the differences using paired sample t-statistics. We group these ratios into: (i) measures of operation (IT and FAT); (ii) measures of inbound and outbound logistics including marketing and sales (SDO, APD and SC); and (iii) measures of overall performance (NPM and CR).

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 451 We use a multiple analysis of variance (MANOVA) test to determine whether the differences in the changes in the grouped ratios for the treatment group and the control group came from the same population. MANOVA is a multivariate procedure that can assess group differences across multiple metric-dependent variables simultaneously (Hair et al., 1998). This test is particularly suitable for categorical independent variables (here ERP adoption ) and multiple associated non-categorical variables (here performance measures ). We tested the dependant variables for normality and removed outliers (which have a particularly strong impact on MANOVA results). 4. Results 4.1. Univariate results The results of the paired sample t-statistics are depicted in Table 3. Panels A, B and C depict the results before adoption. These results suggest that none of the financial ratios are different for the treatment and control groups before the adoption of an ERP system, with the exception of SDO absolute change in the period of T = 1to T = 0. These results, as measured by absolute and percentage changes, are consistent with our expectation that before the adoption of ERP systems, there is no difference between these groups of firms. It is also consistent with the view that the control group and the treatment group come from the same population. Panel D indicates that 1 year after the adoption of ERP systems there are significant changes in the internal operation activities of the adopters. Both the IT and their FAT is significantly different for the treatment group from the control group, as measured by absolute and percentage changes. Furthermore, the SDO has significantly improved (by reducing the number of days sales outstanding), as measured by absolute changes. These results are consistent with our predictions and suggest that ERP systems enhance both the operations and the marketing, sales and distribution components of our modified value chain model. Our other predictions with respect to other aspects of the value chain and, in particular, with the inbound component, have not been confirmed by the univariate analysis. The results in panel E indicate that the improvements in operations have been sustained 2 years after the adoption of ERP systems, as indicated by the ratios of IT and FAT. Furthermore, both the absolute and percentage change for the variable CR indicate that the liquidity of the adopters has improved 2 years after adoption, indicating an overall improvement in the liquidity of adopting firms. Other predictions, again, have not been confirmed. 4.2. Multivariate analysis The results of the multivariate analysis depicted in Table 4.

452 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 Table 3 Paired sample t-tests of the difference in means in the treatment and control samples a Absolute change Percentage change Mean Mean Mean Mean Item treatment control t-statistic treatment control t-statistic Panel A: Tests for significance of variables, from time 3 totime0 NPM 0.02 0.01 0.25 0.07 0.73 1.10 IT 1.45 0.07 0.62 0.09 0.25 0.89 FAT 0.29 0.29 0.01 0.02 0.22 1.54 SDO 5.43 9.79 1.25 30.23 0.15 0.99 APD 5.18 7.06 0.22 0.03 0.05 0.51 CR 0.28 0.10 0.93 0.31 0.04 0.80 SC 0.05 0.16 1.25 5.19 6.45 0.21 Panel B: Tests for significance of variables, from time 2 totime0 NPM 0.01 0.03 0.86 0.28 0.51 0.38 IT 0.50 0.65 0.89 0.08 83.02 1.00 FAT 0.01 0.26 0.57 0.04 90.79 1.00 SDO 1.65 2.18 0.55 0.03 0.13 0.59 APD 0.57 16.65 1.20 0.04 0.003 0.25 CR 0.27 0.16 1.02 0.26 0.02 0.91 SC 0.04 0.13 1.12 3.35 7.54 0.77 Panel C: Tests for significance of variables, from time 1 totime0 NPM 0.004 0.003 0.07 0.09 0.16 0.32 IT 1.89 0.99 0.99 6.27 0.01 0.57 FAT 0.33 1.24 1.01 0.08 0.18 0.47 SDO 8.25 1.93 2.25 1.34 0.07 1.50 APD 1.71 1.84 0.05 0.09 0.09 0.10 CR 0.03 0.10 0.64 0.06 0.06 0.78 SC 0.38 0.09 0.24 0.57 1.24 0.52 Panel D: Tests for significance of variables, from time 0 to time 1 NPM 0.001 0.002 0.16 0.23 2.01 0.15 IT 2.17 0.73 2.81 0.53 0.04 2.43 FAT 0.11 0.21 2.05 0.36 0.06 2.06 SDO 5.91 3.61 2.44 0.02 0.14 1.01 APD 6.87 3.47 1.00 0.08 0.02 0.88 CR 0.16 0.05 0.47 0.10 0.04 0.54 SC 0.16 0.09 1.78 0.18 0.61 0.96 Panel E: Tests for significance of variables, from time 0 to time 2 NPM 0.02 0.004 0.24 0.47 0.17 0.88 IT 1.42 1.35 1.85 0.38 0.19 1.99 FAT 0.04 0.43 1.47 0.17 0.12 2.40 SDO 0.05 2.40 0.61 0.14 0.02 1.26 APD 0.09 0.53 0.18 0.004 0.01 0.14 CR 0.23 0.11 2.48 0.07 0.15 2.81 SC 0.05 0.04 0.15 0.77 0.83 0.68 a Differences in means slightly vary from the means shown in Table 2, Panel A, because the pairwise exclusion of samples with missing values result in lower n-values in Table 3 as opposed to Table 2. Statistical significance of the t-tests are indicated by: <0.10, <0.05, <0.01. The variables are defined as: APD, accounts payable days; CR, current ratio; FAT, fixed asset turnover; IT, inventory turnover; NPM, net profit margin; SC, sales change; SDO, sales days outstanding.

Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 453 Table 4 MANOVA test results for the treatment and control samples Absolute change Percentage change Pillais criterion, Univariate, Pillais criterion, Univariate, Item significance of F significance of F significance of F significance of F Panel A: MANOVA tests for significance of subsets of variables, from time 2 totime0 Group 1: IT and FAT IT 0.68 0.58 0.83 0.90 FAT 0.68 0.42 0.83 0.56 Group 2: SDO, APD and SC SDO 0.64 0.28 0.20 0.18 APD 0.64 0.76 0.20 0.64 SC 0.64 0.53 0.20 0.10 Group 3: NPM and CR NPM 0.57 0.52 0.10 0.21 CR 0.57 0.46 0.10 0.07 Panel B: MANOVA tests for significance of subsets of variables, from time 1 totime0 Group 1: IT and FAT IT 0.65 0.56 0.53 0.73 FAT 0.65 0.38 0.53 0.54 Group 2: SDO, APD and SC SDO 0.13 0.03 0.49 0.14 APD 0.13 0.67 0.49 0.69 SC 0.13 0.78 0.49 0.53 Group 3: NPM and CR NPM 0.77 0.75 0.68 0.78 CR 0.77 0.54 0.68 0.42 Panel C: MANOVA tests for significance of subsets of variables, from time 0 to time 1 Group 1: IT and FAT IT 0.01 0.00 0.06 0.02 FAT 0.01 0.09 0.06 0.03 Group 2: SDO, APD and SC SDO 0.12 0.05 0.59 0.88 APD 0.12 0.36 0.59 0.29 SC 0.12 0.09 0.59 0.38 Group 3: NPM and CR NPM 0.94 0.88 0.89 0.88 CR 0.94 0.73 0.89 0.64 Panel D: MANOVA tests for significance of subsets of variables, from time 0 to time 2 Group 1: IT and FAT IT 0.08 0.04 0.02 0.02 FAT 0.08 0.02 0.02 0.02 Group 2: SDO, APD and SC SDO 0.86 0.68 0.69 0.40 APD 0.86 0.99 0.69 0.98 SC 0.86 0.48 0.69 0.25 Group 3: NPM and CR NPM 0.08 0.81 0.10 0.46 CR 0.08 0.03 0.10 0.05 Statistical significance of the t-tests are indicated by: <0.10; <0.05; <0.01. The variables are defined as: APD, accounts payable days; CR, current ratio; FAT, fixed asset turnover; IT, inventory turnover; NPM, net profit margin; SC, sales change; SDO, sales days outstanding. MANOVA, multivariate analysis of variance.

454 Z. P. Matolcsy et al. / Accounting and Finance 45 (2005) 439 456 The results in Panels A and B of Table 4 confirm the results of the univariate analysis that none of the groups of ratios are statistically different from the two groups at the time of the adoption of an ERP system. The only outliers are SDO (Panel B) and the CR (Panel A). The difference in the CR, however, is only significant at the 10 per cent level and could be a result of other factors occurring in the period from 2 to0. Panel C confirms the results of the univariate analysis. Group 1 ratios (IT and FAT), representing operations of the value chain, show statistically significant improvements by both statistics (Pillais criterion and F-statistics) and for both absolute and percentage changes from T = 0 to T = +1. Furthermore, for group 2 measures (SDO, APD and SC) representing inbound logistics and sales and marketing of the value chain, only the SDO variable is significant and only by one measure. None of the other ratios are significant. The results for T = 0toT =+2 in panel D of Table 4 confirm our previous findings. The group 1 ratios are significant by all measures and statistics and confirm the predictions of operational improvements as a result of ERP. Furthermore, the CR in group 3 ratios is significant by all measures and statistics and the NPM measures are significant for both measures using the Pillais criterion. These results suggest overall liquidity and profitability improvements associated with the adoption of ERP systems. In summary, both our univariate and multivariate results indicate that ERP systems primarily add value to the operations component of a firm s value chain; they lead to limited improvements in the marketing, sales and distribution components. These improvements lead to an overall increase in performance of firms as measured by their CR and NPM. These observed differences could, of course, also be driven by other factors, such as changes in ownership, market size and other confounding effects. Our matched sample design controls for these factors by assuming that these confounding effects randomly impact on both the treatment and control samples. Furthermore, we have checked our companies annual reports for significant ownership changes, for corporate restructuring and other significant changes in their economic environment. We have not found any evidence of such changes. The above findings are consistent with Nicolaou et al. (2003), who also report improved performance for ERP adoption. Our results are in contrast to the results of Hunton et al. (2003), who do not find an improvement in the performance of ERP adopters. These differences could be a result of differences in the US versus Australian institutional settings, such as more aggressive competition in the US focus firms to pass on the benefits of ERP systems to customers. Also, US firms, on average, are larger and more complex, and/or vendors, on average, rolled out their ERP systems in the US market first; hence, they themselves became more efficient in implementing these complicated systems. We did not test for any of these differences. 5. Conclusion and future research The objective of the present study was to provide evidence on the economic benefits of ERP systems. We adopted a modified value chain approach and identified several

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