International Journal of Accounting Information Systems 4 (2003) 165 – 184 Enterprise resource planning systems: comparing firm performance of adopters and nonadopters James E. Huntona,*, Barbara Lippincottb, Jacqueline L. Reckb a Department of Accounting, Bentley College, 175 Forest Street, Waltham, MA 02452 4705, USA b School of Accountancy, University of South Florida, Tampa, FL, USA Received 15 April 2002; received in revised form 10 October 2002; accepted 18 October 2002Abstract The current study examined the longitudinal impact of ERP adoption on firm performance bymatching 63 firms identified by Hayes et al. [J. Inf. Syst. 15 (2001) 3] with peer firms that had notadopted ERP systems. Results indicate that return on assets (ROA), return on investment (ROI), andasset turnover (ATO) were significantly better over a 3-year period for adopters, as compared tononadopters. Interestingly, our results are consistent with Poston and Grabski [Int. J. Account. Inf.Syst. 2 (2001) 271] who reported no pre- to post-adoption improvement in financial performance forERP firms. Rather, significant differences arise in the current study because the financial performanceof nonadopters decreased over time while it held steady for adopters. We also report a significantinteraction between firm size and financial health for ERP adopters with respect to ROA, ROI, andreturn on sales (ROS). Specifically, we found a positive (negative) relationship between financialhealth and performance for small (large) firms. Study findings shed new light on the productivityparadox associated with ERP systems and suggest that ERP adoption helps firms gain a competitiveadvantage over nonadopters.D 2003 Elsevier Science Inc. All rights reserved.Keywords: Enterprise resource planning, ERP; Firm performance; Productivity paradox; Longitudinal study * Corresponding author. Tel.: +1-781-891-2422. E-mail address: firstname.lastname@example.org (J.E. Hunton).1467-0895/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved.doi:10.1016/S1467-0895(03)00008-3
166 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–1841. Introduction Enterprise resource planning (ERP) reflects an innovative business strategy, as ERPadoption involves business process improvement, best practices implementation, intra-enterprise integration and inter-enterprise coupling. ERP systems are designed to facilitatethe ERP concept by replacing disparate patchworks of legacy systems across businessorganizations with synchronized suites of enterprise-wide applications. Potential benefits ofan ERP system include productivity and quality improvements in key areas, such as productreliability, customer service, and knowledge management. As a result, ERP systems areexpected to enhance market value and firm performance through efficiency and effectivenessgains. Hayes et al. (2001) offered evidence that the capital market placed incremental value onfirms that adopted ERP systems, as investors reacted positively to ERP implementationannouncements. Similarly, a behavioral study by Hunton et al. (submitted for publication)found that financial analysts significantly increased mean earnings forecast revisions when afirm announced plans to implement an ERP system. While both studies indicated that capitalmarket participants believed ERP adoption would improve future firm performance, theextent to which expected returns eventually materialize remains unknown. To investigate this issue, Poston and Grabski (2001) examined the effect of ERP systemson firm performance over a 3-year period. They found a significant decrease in the ratio ofemployees to revenues in all 3 years, and a reduction in the ratio of cost of goods sold torevenues in year 3. However, they reported no significant improvement in the ratio of selling,general and administrative expenses to revenues, or residual income (net operating incomeminus imputed interest). Hence, they suggested a contradiction—while ERP systems appearto yield efficiency gains in some areas, higher offsetting cost-to-revenue increases elsewhereleave residual income unaffected. Other researchers have also observed little or noperformance effects associated with increasing information technology (IT) expenditures, aphenomenon that is often referred to as the productivity paradox (e.g., Grover et al., 1998;Harris 1994; Pinsonneault, 1998). Robertson and Gatignon (1986) and Hitt and Brynjolfsson (1996) suggested another wayto look at the productivity paradox; that is, to the extent that increased spending on IT yieldsefficiency and effectiveness improvements, firms will pass on financial gains to consumersthrough decreased prices in a competitive marketplace. To investigate this possibility, weexamined the longitudinal impact of ERP adoption on firms by comparing financialperformance indicators of adopters and nonadopters. To the extent that adopters realizeand transfer financial rewards, performance of adopters might not change using a pre- to post-adoption analysis; however, the performance of nonadopters would be expected to decline bycomparison. As expected, our results indicate that ERP adopters performed significantlybetter than non-ERP adopters, primarily due to declining performance of non-ERP adopters. Further, we examined whether the financial performance of ERP-adopting firms wasaffected by the interaction of firm size and financial health. Theory and research evidenceindicates that the performance of small/healthy firms will be greater than small/unhealthyfirms, and large/unhealthy firms will be greater than large/healthy firms (Hayes et al., 2001;
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 167Hunton et al., submitted for publication). In accordance with the hypotheses, we found asignificant interaction between firm health and size. Support for these propositions sheds newlight on the productivity paradox by suggesting that performance gains and losses resultingfrom increased IT expenditures may be masked in aggregate, but such effects could be moreprecisely identified when firms are discriminated along key dimensions. The next section provides theory and develops the study hypotheses. Section 3 presents thesample selection technique and research method. The final two sections analyze study resultsand discuss the research findings.2. Hypotheses formulation2.1. ERP systems and innovation While many studies have attempted to find a positive relationship between IT investmentsand firm performance, for the most part, research findings have yielded nonsignificant results(e.g., Weill, 1992; Mahmood and Mann, 1993; Hitt and Brynjolfsson, 1996). As a result,researchers have yet to provide compelling evidence that IT investments equate to meas-urable, positive value for business organizations. However, Dos Santos et al. (1993)suggested that a more refined analysis of IT investments could provide clarity in this regard. Dos Santos et al. (1993) argued that non-innovative technologies (those that maintain thestatus quo) are not likely to improve a firm’s market value or financial performance, whereasinnovative technologies (those that improve business processes) are expected to enhancevalue and performance. To empirically test their proposition, they observed the marketresponse to IT investment announcements and found no overall effect; however, further sub-analysis revealed that the market reacted positively to innovative investment announcements(Dos Santos et al., 1993). Similarly, Peffers and Dos Santos (1996) reported a positiverelationship between innovative IT investments and firm performance. Hence, the lack ofmarket and performance effects in prior studies may be due to a failure to discriminatebetween innovative and non-innovative investments. Drucker (1988) and Huber (1990) suggested that information technologies are consideredinnovative if they facilitate key business process improvements, such as1. more accurate, comprehensive, timely, and available organizational intelligence from internal and external information sources at greatly reduced costs,2. greater speed and accuracy in identifying problems and opportunities,3. fewer intermediate human nodes within the organizational information-processing network,4. reduced number of organizational levels involved in authorizing and making decisions, and5. less time being consumed in the decision-making process. According to O’Leary (2000), ERP systems are designed to support business processimprovements of this nature, thereby enhancing information quality, decision making and
168 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184firm performance. In concept, the realization of such business process improvements isfacilitated by ERP systems due to the integrated nature of enterprise-wide information viarelational databases. To the extent that such improvements are not realized, much of theresponsibility rests with inadequate planning and implementation of the ERP system, as thetechnology is capable of supporting the innovative process improvements mentioned above.Hence, while we recognize that some ERP system implementations have not coincided withbusiness process improvements, we nevertheless categorize an ERP system as an innovativetechnology based on its potential in this regard. Accordingly, we expected to find significantpositive effects of ERP adoption on market value and firm performance. Research evidence offered by Hayes et al. (2001) reinforces the notion that ERP systemsare perceived to be innovative IT investments, as they observed a positive reaction frominvestors when firms announced that they were planning to implement an ERP system. In asimilar study by Hunton et al. (submitted for publication), financial analysts significantlyincreased mean earnings forecast revisions when they learned that a firm was planning toimplement an ERP system. The positive reaction of capital market participants to ERPadoption announcements reflects initial beliefs about the potential impact of ERP systems onfuture firm performance. However, whether ERP systems positively impact performance inthe long run remains largely unanswered.2.2. ERP systems and firm performance A recent study by Poston and Grabski (2001) investigated the impact of ERP systemimplementation on firm performance. They examined the post-implementation performanceof 50 ERP-adopting firms over a 3-year post-implementation time horizon, after controllingfor pre-implementation performance. They found no significant improvement in residualincome (net operating income less imputed interest for cost of capital) or in the ratio ofselling, general and administrative expenses to revenue throughout the 3-year window.However, they reported a significant decrease in the ratio of employees to revenue in each ofthe 3 years and a significant improvement in the ratio of cost of goods sold to revenue in year3. Overall, they noted that ERP firms exhibited efficiency gains in some areas, but increasedcosts elsewhere seemed to offset such gains. Other researchers have also indicated little or no relation between IT investment andfinancial performance, which is often referred to as the productivity paradox (Harris, 1994).However, as suggested by Dos Santos et al. (1993), delineating between innovative and non-innovative uses of IT could offer clarity in this regard. How then could it be that Poston andGrabski (2001) examined the performance of companies that adopted an innovative ITinvestment (ERP system), yet found no significant gain in financial performance? While there are likely many answers to this question, one possible explanation suggestedby Hitt and Brynjolfsson (1996) is that any financial gain associated with ERP adoption ispassed through to customers in the form of lower prices. Robertson and Gatignon (1986)offered a similar explanation when they examined the impact of competitive factors oninnovative technology diffusion. Through analytic modeling, Eliashberg and Jeuland (1986)discussed and Eliashberg and Chatterjee (1985, 1986) demonstrated that prices drop
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 169immediately after the adoption of innovative technologies and demand increases as a result ofprice sensitivity. They further indicated that the financial performance of adopters might ormight not improve significantly, depending on a host of exogenous factors such ascompetitive intensity, industry heterogeneity, demand uncertainty, and adoption rate ofcompetitor firms; nevertheless, the performance of nonadopters would be expected todeteriorate by comparison in a competitive marketplace. If we view ERP adoption through this lens, we would not necessarily expect to see pre- topost-adoption financial gains for ERP firms. Instead, we would anticipate the financialperformance of nonadopters to decline relative to adopters. Hence, we offer the followinghypothesis (alternate form):H1: Longitudinal financial performance of firms that have not adopted ERP systems will besignificantly lower than ERP-adopting firms.2.3. Financial performance indicators One way to evaluate firm performance is through financial statement analysis, which usestraditional accounting measures that are based on relationships among financial statementitems. In the current study, we used four measures of performance. The first measure, returnon assets (ROA), is frequently used by researchers as a measure of firm performance (e.g.,Balakrishnan et al., 1996; Barber and Lyon, 1996; Barua et al., 1995; Bharadwaj, 2000; Hittand Brynjolfsson, 1996; Weill, 1992). Since ROA incorporates both firm profitability andefficiency (Skousen et al., 1998), it tends to be a useful overall performance indicator. Wepurposely focused on ROA because it has been proposed that the benefits of ERP systemsinclude improved efficiency and profitability (Brakely, 1999; Schaeffer, 1996; Stein, 1998;Vaughan, 1996; Wah, 2000). The combined effects of profitability and efficiency represented by ROA can be separatedinto return on sales (ROS) and asset turnover (ATO)—two secondary measures of perform-ance used in the current study. ROS, represented as income per dollar of sales, is a measure ofthe firm’s profitability or margin. ATO, represented by the sales generated per dollar of assets,is a measure of asset efficiency. The last performance measure used in this study, return on investment (ROI) is included asa check on the robustness of the results using ROA, and because it has been cited as a keyperformance measure in the ERP implementation literature (e.g., Mabert et al., 2000;Stedman, 1999; Stein, 1998). We next examine the interaction of firm size and financialhealth on the financial performance of firms that adopt ERP systems.2.4. Interaction of firm size and health: large firm effect The adoption of an ERP system by small firms is a significant undertaking, particularlywith respect to the consumption of financial resources. For instance, Mabert et al. (2000)found that implementation costs, as a percent of revenue, range from 0.82% for very largefirms (revenues > $5000 million) to 13.65% for very small firms (revenues <$50 million).
170 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184Some small firms reported that total ERP implementation costs were nearly half of theirannual revenues. Based on this analysis, Mabert et al. (2000, p. 56) stated ‘‘. . .althoughlarger firms incur a greater implementation cost because of their operating size, acomparison of revenues shows that there are economies of scale working in favor of thelarger firms. . .[T]he move to ERP by smaller firms represents a major commitment in timeand money.’’ Additionally, ERP implementations can span a considerable amount of time. For instance,implementation periods from 1 to 2 years are common, and some implementation periods canstretch to 4 or 5 years (Cooke and Peterson, 1998; Mabert et al., 2000). Further, considerablylarge cost overruns are not unusual throughout extended ERP implementation periods.Overall firm performance can also decrease during implementation, due to temporarycoordination and control disruptions (Koch, 1996; Wah, 2000). Thus, it can take severalyears to achieve a positive payback and desired ROI (Davenport, 2000; Stedman, 1999). As aresult, a firm must have sufficient resources, or access to resources, to withstand the strain ofERP implementation. While firm size is a key contextual factor to consider when postulatingthe impact of ERP on firm performance, financial health is also important since healthinesscan affect a company’s access to additional resources from external stakeholders, such ascreditors and investors (Hayn, 1995). In general, large firms possess higher levels of resources and hold greater ability to attractadditional resources than small firms. Thus, large firms can more easily absorb and withstandERP implementation costs. However, research evidence offered by Khurana and Lippincott(2000) indicated that potential performance improvements are greater for relatively unhealthy,as compared to healthy, large firms. Consider a large financially healthy company that isefficient (e.g., high employee productivity and low cost producer) and effective (e.g., largemarket share and high-quality products) in its industry. Expected performance gains due toERP adoption for this firm are less than anticipated gains of a relatively unhealthycounterpart, as the latter has greater potential for further improvement. Hayes et al. (2001) and Hunton et al. (submitted for publication) offered similararguments with respect to the reaction of capital market participants to large firm ERPimplementation announcements. Hayes et al. (2001) reported that the market reacted morestrongly to ERP implementation announcements from large/unhealthy firms (standardizedcumulative abnormal returns=0.272), as compared to large/healthy firms (standardizedcumulative abnormal returns=0.061); however, the difference was not significant due, inpart, to the low power of the small sample. Hunton et al. (submitted for publication)indicated that the mean earnings forecast revision (for 2001) was greater for large/unhealthyfirms ($0.18) than large/healthy firms ($0.06). While they did not report the significance ofthis comparison, we analyzed their data and found that the difference was significant(t=3.01, P=.02). Based on extant theory and research evidence, for large firms, we expect anegative relationship between firm health and performance due to ERP adoption, as positedbelow (alternate form):H2a: For relatively large ERP-adopting firms, there will be a significant negativeassociation between firm health and performance.
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 1712.5. Interaction of firm size and health: small firm effect Small firms also stand to benefit from ERP adoption, particularly if the firms use ERPsystems to facilitate business process improvements. In this manner, small/healthy firms canbecome larger, and small/unhealthy firms can become healthier. With respect to relativehealth, on the surface, one would expect the logic just presented to hold for small firms,thereby suggesting that ERP-related performance gains should increase as financial healthdeclines. However, small firms have greater variability than large firms with respect toliquidity and solvency (Huff et al., 1999). Such variability, which equates to increased loanand investment risk, coupled with an economy of scale disadvantage, means that small/unhealthy firms may run into considerable obstacles with regard to acquiring needed financialand intellectual resources to complete full ERP integration. This situation places small/healthy firms in a better position to have or obtain imple-mentation resources than small/unhealthy firms. Another issue facing small/unhealthy firms isthat they might be forced into a partial implementation of the ERP system (due to costconsiderations and resource acquisition constraints), thereby foregoing the acclaimed innov-ative benefit of using a fully integrated system. Hayes et al. (2001) and Hunton et al. (submitted for publication) also posited that capitalmarket participants would react more positively to ERP announcements from small/healthyfirms, as compared to small/unhealthy firms. In support of this proposition, Hayes et al.(2001) indicated a significantly stronger market reaction to ERP implementation announce-ments from small/healthy firms (standardized cumulative abnormal returns=0.283) thansmall/unhealthy firms (standardized cumulative abnormal returns=À0.270), and Hunton etal. (submitted for publication) indicated that the mean earnings forecast revision (for 2001)was significantly greater for small/healthy firms ($0.08) than small/unhealthy firms ($0.02).Hence, we posit that firm performance will be positively related to firm health for small ERP-adopting firms (alternate form):H2b: For relatively small ERP-adopting firms, there will be a significant positive associationbetween financial health and performance.3. Sample selection and method3.1. Sample selection We obtained data on ERP-adopting firms from Hayes et al. (2001)1 and informationconcerning firm performance from Compustat. Since it may take several years for the benefitsof ERP adoption to accrue, we constrained our sample to include ERP announcing firms for 1 Hayes et al. (2001) included in their sample all firms announcing ERP implementation in the time periodJanuary 1, 1990 through December 31, 1998. Announcing firms were identified through Lexis/Nexis AcademicUniverse’s (News) Wire Service Reports.
172 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184which at least 3 years of financial data were available on Compustat subsequent to the ERPadoption announcement. As a result of this constraint, the sample included no ERP adoptionannouncements after 1996.2 For the inter-temporal comparisons, 3 years of pre-announce-ment performance information and 3 years of post-announcement performance informationhad to be available on Compustat. As a result of the constraints, the final sample resulted in63 firms that announced ERP adoption. For tests of hypothesis 2 (H2a and H2b), we lost anadditional three observations due to missing information on financial health (z-score). In our tests of the effect of ERP adoption on firm performance, we controlled formacroeconomic conditions that could influence test results. This was accomplished bymatching ERP firms to a control sample of non-ERP firms.3 As has been done in priorstudies of firm performance (e.g., Balakrishnan et al., 1996; Barber and Lyon, 1996), wematched firms on SIC code and firm size. The total sample of ERP and non-ERP firms was126 for the full sample (H1) and 120 for the reduced sample (H2a and H2b). To ensure that no ERP-adopting firms were included in the control sample, we conducted atelephone survey to determine whether the identified firms had indeed implemented an ERPsystem. A total of 21 of the 63 firms (33.33%) responded to the survey. With respect to thenonresponding firms, we searched through Lexis-Nexis and found that none of the controlfirms had a news wire disclosure concerning ERP adoption (see Hayes et al., 2001 for thesearch technique). Additionally, through Lexis-Nexis, we searched the SEC database andannual reports using the name of the firm and the following search string: ERP or enterpriseresource planning or QAD or SAP R/3 or Oracle or Peoplesoft or J. D. Edwards or SSA orBaan or Geac or Lawson or Hyperion. With regard to foreign firms, we conducted anadditional search of Lexis-Nexis using International Company Reports. One telephone survey respondent indicated ERP adoption. The Lexis-Nexis search resultedin three firms that possibly had adopted ERP. As a result, we selected four new control firms.The new control firms were subjected to the Lexis-Nexis search procedure to ensure no ERPdisclosures, and none were found.3.2. Measuring performance As mentioned earlier, we used four measures of performance (ROA, ROS, ATO, and ROI).Descriptions of the performance variables, along with descriptions of the metrics we used todetermine firm size and health, are shown on Table 1. Performance was divided into two time periods—pre-adoption and post-adoption. Thefiscal year of the ERP announcement, identified as year zero (t0), served as the baseline yearfor aligning the ERP-adopting and non-adopting firms. The pre-adoption period encompassed3 years (tÀ3 to tÀ1), and the post-adoption period covered 3 years (t+1 to t+3). 2 The sample size for firms having 4 years of post-adoption information was too small to allow for testing. 3 When industry averages were calculated for the sample firms, it was found that the industry size variedconsiderably from the sample firms. In some instances, the industry average was much smaller than the samplefirm, and in other instances, the industry average was much larger than the sample firm. As a result, a matched-pairdesign was used rather than an industry average control variable.
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 173Table 1Description of variablesVariable DescriptionROA Return on assets is income before extraordinary items (available for common stockholders), divided by the average of the beginning and ending total assets. Compustat then multiplies the ratio by 100ROS Return on sales is income before extraordinary items (available for common stockholders), divided by net sales for the periodATO Asset turnover is net sales for the period, divided by the average of the beginning and ending total assetsROI Return on investment is income before extraordinary items (available for common stockholders), divided by the sum of total long-term debt, preferred stock, minority interest and total common equity. Compustat then multiplies the ratio by 100Size The natural log of total assetsHealth Altman’s z-score, as represented by the Compustat variable ZSCORE For H1, we tested for differences between pre- and post-adoption for ERP firms and non-ERP firms. Additionally, we conducted parametric and nonparametric tests and a regressionanalysis of performance differences between ERP and non-ERP-adopting firms. Theregression model allowed us to control for the firms’ pre-adoption performance. In theregression model, we regressed performance measures on pre-adoption financial performanceand a dummy variable representing ERP versus non-ERP adoption as follows: Financial Ratio ¼ a0 þ a1 Pre-Ratio þ a2 Non-ERP Adoption þ e ð1Þwhere, Financial Ratio=post-adoption performance, as measured by average performanceover the time period t+1 to t+3 for the ratios ROA, ROS, ATO, and ROI, Pre-Ratio=pre-adoption performance, as measured by average performance over the time period tÀ3 to tÀ1for the ratios ROA, ROS, ATO, and ROI, Non-ERP Adoption=1 if the firm was a non-ERPadopter, and 0 if an ERP adopter, e=error term. To be included in the sample, a firm must have at least one value in the time period tÀ3 totÀ1. For firms with values in more than 1 year (tÀ3 to tÀ1), we averaged the values and used theaverage as the measure of the pre-ratio. The regression results helped to ensure that differencesbetween ERP and non-ERP-adopting firms were not due to an omitted variables problem.4 To test hypothesis 2 (H2a and H2b), we regressed the financial ratios for ERP firms on thefirms’ size, financial health, and the interaction of firm size and financial health, along withthe control variable reflecting pre-adoption performance: Financial Ratio ¼ b0 þ b1 Pre-Ratio þ b2 Size þ b3 Health þ b4 ðSize Â HealthÞ þ u ð2Þwhere, Financial Ratio=post-adoption performance, Pre-Ratio=pre-adoption performance, asmeasured by performance in the time period tÀ3 to tÀ1, Size=measured as the log of total 4 Barber and Lyon (1996) indicate that in addition to controlling for industry and size, it is important that pre-event performance be controlled in models testing for abnormal performance. The Pre-Ratio variable used in thestudy represents such a lagged performance measure.
174 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184assets at time t0, Health=financial health, measured using Altman’s z-score at time t0, andu=error term. Since we matched firms on size during the implementation year (t0), it was selected as themost appropriate year for measuring the size variable in H2a and H2b.5 For consistency, wealso measured financial health at time t0. However, if financial health was measured at timetÀ1, the results presented in the next section remain qualitatively the same.64. Results4.1. Descriptive statistics The ERP sample was distributed over 23 two-digit SIC classifications (see Table 2, panel A),indicating little clustering by industry. We matched 62 of the firms using at least a two-digit SIC classification, with one firm matched on a one-digit SIC classification.7 Asindicated by Table 2, panel B, over 58% of ERP implementation announcements occurredin 1996. This result is not surprising given the relatively recent development of ERPsystems.8 Table 3, panel A, indicates that, on average, ERP firms were slightly larger than non-ERPfirms when measured by assets or sales. However, we did not find a statistically significantdifference when we measured assets or sales in either the year of the announcement (t0) or theyear preceding the announcement (tÀ1), as a was greater than .10 for both years. Table 3 alsoprovides raw descriptive statistics for years tÀ3 through tÀ1 with respect to the performancemeasures (ROA, ROS, ATO, and ROI). While the raw measures provided on Table 3 are notused in the study tests, they do provide an indication that there was no trend in performancemeasures prior to ERP adoption and that the measures were skewed due to extremeobservations. To reduce the influence of extreme performance measure observations, werestated outliers to the 5th and 95th percentiles for the pre- and post-performance 3-yeardistributions used in the subsequent tests.9 5 An analysis was also conducted using the log of sales as the size variable. The levels of significance for theERP firms remained unchanged using sales as the size variable. For the non-ERP firms, none of the inferencesreported using total assets were changed. 6 When using the z-score at tÀ1, the interaction term for ROI was not significant at a<.10. 7 Baber and Lyon (1996) indicate that the power of the tests is not significantly affected by the use of a four-digit match versus a two-digit match. 8 All tests reported in this study were rerun controlling for event clustering in 1996. While results were slightlystronger after controlling for the clustering, the reported inferences remain unchanged. 9 Restating to the 95th and 5th percentiles is a procedure followed by Balakrishnan et al. (1996). Restatementis recommended by Barber and Lyon (1996) as a method of reducing conservatism of the parametric t statistic.That is, resetting the outliers reduces the differences between the parametric and nonparametric statistics. Testswere rerun using the 1st and 99th percentiles. The highly nonnormal distributions at the 1st and 99th percentilesgreatly increased the conservatism of the parametric statistics.
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 175Table 2Sample characteristicsPanel A: SIC classification of ERP sample firmsTwo-digit SIC Full sample Reduced sample firms firms (H1) (H2a and H2b)a13 1 115 1 120 4 422 1 125 2 127 2 228 5 529 2 230 1 132 1 133 4 434 2 235 6 636 10 937 1 038 5 548 4 449 3 351 1 159 1 163 1 173 3 380 2 2Total 63 60Panel B: Year in which ERP sample firm announcements occurredAnnouncement year Number of firms Number of firms for the for the full sample reduced sample1992 5 51993 2 21994 11 111995 8 71996 37 35Total 63 60 a For analysis of hypothesis 2(H2a and H2b), three observations are deleted due to missing z-scores.4.2. Hypothesis 1 The results of the tests of H1 are reported on Table 4. The last column of Table 4 presentsthe difference in pre- and post-adoption performance. As expected, no significant differenceoccurred between pre- and post-performance for ERP firms. However, over the same period,non-ERP firms experienced a significant decline in ROA (t=2.239; one-sided P=.014), ROI
176 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184Table 3Descriptive statistics for ERP firms and non-ERP firmsPanel A: Matching ERP firms and non-ERP firms on firm size (n=63)Item Sample Mean Median Standard t Statistic P valuea deviationTime=tÀ1Total assets ERP adopter 10,861 3654 17,448 nonadopter 9195 3003 13,161 0.61 .55Net sales ERP adopter 9877 4492 15,967 nonadopter 8308 2969 12,209 0.62 .54Time=t0Total assets ERP adopter 11,051 4394 16,888 nonadopter 9483 3396 13,008 0.58 .56Net sales ERP adopter 10,111 4814 15,521 nonadopter 8540 3105 12,004 0.64 .53Panel B: Descriptive information concerning firm performanceTime Item Sample Mean Median Standard n deviationtÀ3 ROA ERP adopter 5.557 4.626 7.871 55 nonadopter 5.467 4.739 6.853 49 ROI ERP adopter 9.020 7.431 11.764 57 nonadopter 9.255 7.975 11.442 51 ROS ERP adopter 0.057 0.045 0.077 57 nonadopter 0.048 0.056 0.053 52 ATO ERP adopter 1.193 1.001 0.718 51 nonadopter 1.244 1.082 0.738 48tÀ2 ROA ERP adopter 5.603 4.923 8.037 60 nonadopter 5.639 4.945 6.392 56 ROI ERP adopter 11.151 8.913 18.367 62 nonadopter 9.039 7.024 11.397 61 ROS ERP adopter 0.057 0.056 0.080 63 nonadopter 0.058 0.052 0.053 61 ATO ERP adopter 1.202 1.008 0.688 57 nonadopter 1.268 1.030 0.754 51tÀ1 ROA ERP adopter 4.831 5.346 10.170 63 nonadopter 5.788 4.937 5.318 63 ROI ERP adopter À2.578 9.042 92.685 63 nonadopter 8.369 7.695 7.081 63 ROS ERP adopter 0.035 0.054 0.145 63 nonadopter 0.057 0.057 0.053 63 ATO ERP adopter 1.202 1.078 0.667 63 nonadopter 1.195 1.016 0.690 63 a A nonparametric Wilcoxon test provides P values which are larger than those reported below, indicating nosignificant difference at (a<.10) between the ERP firms and non-ERP firms.
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 177Table 4Performance comparison between and within ERP firms and non-ERP firmsaPanel A: Pre- and post-adoption means [medians] for ERP firms (n=63)Financial Pre Post1 Post2 Post3 Pre vs. Post3cratiobROA 5.341 [4.637] 4.448 [5.031] 4.916d [4.715]d 4.899d [4.692]d t=0.731; P=.468ROS 0.052 [0.050] 0.048 [0.045] 0.050 [0.053] 0.054 [0.054] t=0.351; P=.727ATO 1.158 [1.041] 1.136 [1.105] 1.128 [1.091] 1.135 [1.051] t=1.016; P=.314ROI 8.576 [8.614] 7.691 [8.957]d 7.533d [8.411]e 8.002e [8.395]e t=0.518; P=.606Panel B: Pre- and post-adoption means [medians] for non-ERP firms (n=63)Financial Pre Post1 Post2 Post3 Pre vs. Post3fratioROA 5.455 [5.280] 3.946 [3.725] 3.571 [3.151] 3.720 [3.501] t=2.239; P=.014ROS 0.053 [0.047] 0.045 [0.043] 0.042 [0.045] 0.047 [0.045] t=1.018; P=.157ATO 1.156 [1.018] 1.057 [0.959] 1.061 [0.936] 1.066 [0.989] t=2.976; P=.002ROI 8.498 [7.861] 5.804 [5.462] 4.649 [5.153] 5.457 [5.731] t=2.397; P=.010 a The Pre information represents the average for the time periods À3 through À1. The Post informationreflects performance in year +1 for Post1, the average of performance in years +1 and +2 for Post2, and theaverage of performance in years +1 through +3 for Post3. Due to non-normality, means for the Pre sample and thePost samples were winsorized at the 5th and 95th percentile. b For a description of the financial ratios, see Table 1. c P values are for two-sided tests of the mean difference between Pre performance and Post3 performance. Atwo-sided test is used since we made no prediction concerning direction of performance for ERP firms. d Indicates that the ERP firm ratio is significantly larger than the non-ERP firm ratio using a one-sided P<.10based on t tests of means, or Wilcoxon tests of medians. e Indicates that the ERP firm ratio is significantly larger than the non-ERP firm ratio using a one-sided P <.05based on t tests of means, or Wilcoxon tests of medians. f P values are for one-sided tests of the mean difference between Pre performance and Post3 performance. Aone-sided test is used since we anticipated a significant decline in performance for non-ERP firms.(t=2.397; one-sided P=.010), and ATO (t=2.976; one-sided P=.002). While non-ERP firmROS also experienced a noticeable decline, it was not significant at conventional levels.10 Atest (not shown) comparing pre-adoption performance to base year (t0) performance indicateda moderately significant (t=1.680; P=.098) increase in ROS for ERP firms and a significant(t=2.473; one-sided P=.008) decrease in ATO for non-ERP firms. No other performancedifferences were significant at conventional levels. The results indicate that subsequent toERP adoption, ERP firms’ performance was relatively unchanged, while non-ERP firms’performance significantly declined. Table 4 also shows that performance of ERP firms differed little from non-ERP firms untiltwo years after adoption, when non-ERP firms performed significantly ( P<.10) worse thanERP firms on ROA and ROI. 3 years after adoption, non-ERP firms also performed 10 A Wilcoxon matched-pairs test provided qualitatively similar results; however, the results were not as strongas those provided by the t test.
178 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184Table 5Regression results for performance variables, controlling for pre-adoption results (n=126)Financial ratio for third year (t+3)=Intercept+Pre-Ratio+Non-ERP adopteraFinancial Intercept Pre-Ratio Non-ERP adopter Adjustedratio R2ROA 2.772 (3.82) [<.001] 0.398 (4.90) [<.001] À1.225 (À1.49) [.069] .161ROS 0.024 (3.37) [.001] 0.573 (6.58) [<.001] À0.008 (À0.99) [.162] .252ATO 0.170 (3.70) [<.001] 0.833 (24.98) [<.001] À0.067 (À1.92) [.029] .834ROI 5.284 (4.27) [<.001] 0.317 (3.60) [<.001] À2.521 (À1.82) [.036] .103 a For each independent variable, the coefficient value, (t statistic) and [ P values] are provided. The P values forthe Non-ERP Adopter variable are one-sided, all others are two-sided. The Pre-ratio reflects the average of thefinancial ratio from time period tÀ3 through tÀ1. Non-ERP Adopter is 1 if the firm is not an ERP adopter and 0 if thefirm is an ERP adopter. The dependent variable and the pre-financial variable are winsorized. Cook’s D indicates noinfluential observations that could be unduly affecting results. Variable descriptions are provided in Table 1.significantly worse than ERP firms on ROA ( P<.10) and ROI ( P<.05). Although ROSappeared to remain stable for ERP firms while decreasing for non-ERP firms, the differencewas not significant. Additionally, there was no significant difference in ATO between theERP and non-ERP firms, although ATO did decline between the pre- and post-adoptionperiods for non-ERP firms relative to ERP firms. The results reported on Table 4 suggest thatperformance benefits accruing from ERP adoption may take several years to realize.Therefore, the remainder of the tests focused on firm performance 3 years after adoption. The results on Table 4 lend partial support to H1, as non-ERP firms experienced asignificant decline in performance; however, when compared to ERP firms, the declines wereonly significantly lower for ROA and ROI. As an additional test of H1, we regressedperformance in the third year after adoption on ERP firms versus non-ERP firms, controllingfor pre-adoption performance.11 For ROA and ROI, the results were essentially unchangedafter controlling for pre-adoption performance (see Table 5), as ROA (t=À1.49; P=.069) andROI (t=À1.82; P=.036) remained significantly lower for non-ERP-adopting firms. In addition,after controlling for pre-adoption performance, ATO was significantly lower (t=À1.92;P=.029) for non-ERP firms than ERP firms, indicating that there may be efficiency benefitsassociated with ERP adoption.12 Overall, test results partially supported the first hypothesis.134.3. Hypothesis 2 We also hypothesized that ERP firm performance may be influenced by the interaction offirm size and financial health. Test results (Table 6) indicated a generally positive association 11 Regression allows for control of pre-adoption performance, which Barber and Lyon (1996) indicate isnecessary to minimize misspecification of test statistics. 12 The large adjusted R2 for the ATO model was related to the high correlation (.90) between the ATO variableand the Pre-Ratio. An analysis indicated that the Pre-Ratio explained .79 of the variance associated with ATO. 13 Regression results for performance in year +1 and year +2 (not shown) substantially support the resultsfound on Table 4.
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 179Table 6Within ERP adopters regressions related to the effect of firm size and health on financial performance (n=60)aFinancial Intercept Pre-Ratio Size Health Size*health Adjustedratio R2ROA À10.962 (À3.18) 0.315 (2.86) 1.476 (3.68) 1.485 (3.16) À0.130 (À2.24) .497 [.002] [.006] [<.001] [.003] [.029]ROS À0.130 (À3.67) 0.378 (3.38) 0.017 (4.19) 0.018 (3.77) À0.002 (À2.74) .522 [<.001] [.001] [<.001] [<.001] [.008]ATO 0.117 (0.63) 0.837 (19.06) 0.010 (0.50) 0.004 (0.18) À0.002 (À0.57) .877 [.528] [<.001] [.619] [.860] [.573]ROI À19.447 (À3.20) 0.243 (2.04) 2.700 (3.83) 2.577 (3.14) À0.240 (À2.31) .384 [.002] [.046] [<.001] [.003] [.025] a For each independent variable the coefficient value, (t statistic) and [two-sided P values] are provided. Pre-Ratio is the average of the financial ratio from the time periods À3 through À1. The Financial Ratio is the averageof the ratio from time periods +1 through +3. Variable definitions are provided in Table 1. The dependent variablesand Pre-Ratio variables have been winsorized.between ERP firm performance and pre-ratio (control variable), firm size, and financialhealth. Firm size significantly and positively affected performance as measured by ROA(t=3.68; P<.001), ROI (t=3.83; P<.001), and ROS (t=4.19; P<.001). Additionally, firm healthsignificantly and positively influenced ROA (t=3.16; P=.003), ROI (t=3.14; P=.003), andROS (t=3.77; P<.001). Neither size nor health significantly affected the ATO of ERPadopters. As expected, there was a significant interaction between firm size and financial healthrelative to ROA (t=À2.24; P=.029), ROI (t=À2.31; P=.025), and ROS (t=À2.74; P=.008) forERP adopters. Moderated regression analysis (Sharma et al., 1981) indicated that the presenceof the interaction term significantly (a=.05) increased the explanatory power of the ROA,ROI, and ROS regressions. Interpretation of the interaction term indicates that as firm sizeand financial health increased, there was a decrease in firm performance. However, to betterillustrate the precise nature of the interaction, we performed a subgroup analysis on thosemodels from Table 6 with significant interactions (Sharma et al., 1981). To conduct the analysis, we first divided the sample at the mean into large and smallfirms.14 Then, we regressed performance measures on the control variable and financialhealth (see Table 7). Panel A of Table 7 indicates that for larger firms, financial healthsignificantly and negatively affected ROI (t=À1.67; one-sided P=.059). While financialhealth also negatively affected ROA and ROS, the results were not significant at conventionallevels. The subgroup analysis for large firms lends partial support to the overall interactionfound on Table 6 and H2a. Panel B of Table 7 indicates that for small firms, financial health 14 Since we had no basis for determining how to define relative firm size, selection of the mean was arbitrary.However, analyses were also conducted using the median of total assets and by dividing the sample into one-thirdlarge and two-thirds small firms. As the analysis moved from the mean (about 1/4 of the sample reflects largefirms) to the median, the association between health and performance for large firms became less negative andincreasingly insignificant. Small firm analysis was unchanged.
180 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184Table 7An analysis of firm sizea and financial healthb for ERP adoptersPanel A: large ERP adopters (n=16)cFinancial Intercept Pre-Ratio (À)d Health Adjustedratio R2ROA 2.204 (3.63) [.003] 0.933 (4.15) [.001] À0.377 (À0.99) [.171] .801ROS 0.010 (1.22) [.243] 1.114 (9.25) [<.001] À0.003 (À0.96) [.177] .901ROI 4.124 (4.26) [.001] 0.946 (5.12) [.000] À0.824 (À1.67) [.059] .786Panel B: Small ERP adopters (n=44)cFinancial Intercept Pre-Ratio (+)d Health Adjustedratio R2ROA 0.978 (1.01) [.320] 0.279 (1.98) [.055] 0.425 (2.72) [.005] .374ROS À0.003 (À0.29) [.777] 0.428 (2.86) [.007] 0.005 (2.89) [.003] .419ROI 2.523 (1.45) [.154] 0.176 (1.17) [.247] 0.646 (2.50) [.008] .227 a Firms are divided into small and large based on the mean of total assets. b Variable descriptions are provided in Table 1. c Mean (standard deviation) [ P value]. d Hypothesized direction of results.significantly and positively impacted ROA (t=2.72; one-sided P=.005), ROI (t=2.50; one-sided P=.008) and ROS (t=2.89; one-sided P=.003). The result supports H2b, indicating thatfor smaller ERP firms, as financial health improved, so did financial performance.4.4. Post hoc analyses While Hypotheses 2a and 2b focus on how firm size and financial health affect theperformance of ERP firms, no hypotheses concerning size and health were provided for non-ERP firms. However, we did conduct non-ERP adopter tests (not reported) to ensure that theresults obtained for ERP adopters were associated with ERP adoption and not a spuriouscorrelation. We found no significant positive association between performance and firm size,firm health, or the interaction of firm health and firm size. The lack of significant associationsin the non-ERP firms lends some support to the arguments made in hypotheses H2a and H2b. While Altman’s z-score has been used in prior studies (e.g., Barron et al., 1999; Hayes etal., 2001; Miller and Skinner, 1998) as a proxy for financial health, there is no theoreticalbasis for defining financial health. Therefore, to test the robustness of our results using z-scores as a proxy of financial health, a second financial health variable was tested. In thesecond set of tests, we defined financial health as the difference between ‘income beforeextraordinary items at year zero (the year of implementation)’ minus ‘the average of incomebefore extraordinary items in the 3 years prior to year zero (tÀ3 through tÀ1).’ This secondvariable yielded levels of significance comparable to those using z-scores for the health andinteraction variables. However, using this alternative metric for financial health, there was nolonger a significant (a<.10) main effect for size in the models (ROA, ROI, ROS) reported on
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 181Table 6. Since we did not hypothesize a main effect for size, the results of our analyses werenot affected. Because small samples can be sensitive to departures from regression assumptions, weconducted several tests to ensure the robustness of the reported results. To test forheteroscedasticity, we used White’s (1980) test. Results indicated that heteroscedasticitywas a problem for two of the regression equations examined on Table 6. White’s correction 15yielded essentially the same levels of significance as those reported earlier; therefore, theuncorrected statistics were reported for all regression equations. We checked to determine if the ERP vendor was significantly associated with firmfinancial performance. Vendors were divided into two groups—SAP and Peoplesoft versus allother vendors (in accordance with Hayes et al., 2001). Including a vendor control variableslightly increased the test statistics reported for the interaction terms in Table 6; however, theoverall inferences remained unchanged. Cook’s D test revealed two potentially influential observations related to the ERP firmregressions reported on Table 6, panel A (Neter et al., 1990). Eliminating the two influentialobservations did not influence the explanatory power of the regressions, and the results wereessentially the same. Finally, neither variance inflation factors (VIF) (Neter et al., 1990) normulticollinearity diagnostics (Belsley et al., 1980) revealed that multicollinearity wassignificant among the pre-ratio, size, and health variables.5. Discussion In this study, we investigated the longitudinal impact of ERP adoption on firm perform-ance. To accomplish this objective, we compared the financial performance of 63 ERPadopters to 63 nonadopters in a matched-pair design. We examined the following perform-ance metrics: ROA, ROS, ATO, and ROI. We predicted that firm performance would be greater for adopters than nonadopters,primarily because the financial performance of nonadopters would decline by comparison.Overall, research findings suggest that three (ROA, ATO, and ROI) of the four performancemetrics support this hypothesis. More specifically, study results indicate that ROA, ROI, andATO were significantly lower for nonadopters than adopters, the third year after ERPimplementation. Additionally, the average 3-year ROA and ROI were significantly greaterfor adopters when compared to nonadopters. Subanalysis revealed that the performancemetrics for adopters did not change significantly from pre- to post-adoption, but the metricsdeclined for nonadopters over the same time period. In addition, we investigated the interactive effect of firm size and financial health on theperformance of ERP adopters. We found a significant interaction between size and health forthree of the financial measures (ROA, ROI, and ROS). Our results suggest that large/unhealthy adopters experience better ROI than large/healthy adopters. We also found that 15 White’s correction adjusts the t statistic to account for misspecification due to heteroscedasticity. Foradditional information on the correction, see White (1980).
182 J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184small/healthy firms that adopt ERP systems demonstrate better performance (ROA, ROI, andROS) than small/unhealthy firms. Accordingly, the second hypothesis (H2a and H2b) waspartially supported. Our significant findings are consistently related to two measures of firm performance—ROA and ROI. We also examined two components of ROA (ROS and ATO) that are oftendescribed as measures of profitability (ROS) and efficiency (ATO). We found that ATOdecreased significantly for non-ERP adopters, and there was some indication that ROSdecreased for nonadopters; however, the difference was not significant. Since ROA iscomputed by multiplying these two measures, study results indicate that improvements infirm performance are the result of the combined efficiency and profitability gains. With respect to pre- to post-ERP adoption gains, our results are similar to Poston andGrabski (2001), in that both studies found limited evidence of efficiency gains, but littleevidence of gains elsewhere. However, a comparison of ERP adopters and nonadopters shedslight on the productivity paradox, suggesting that financial gains arising from ERP adoptionmay be passed on to customers in the form of lower prices; hence, the performance ofnonadopters declines by comparison. We recognize that we do not directly test whether ERPgains are being transferred to customers, as micro-level archival data of this nature is notavailable. Thus, future researchers might test this theory via survey or case data, as well asexamine competing reasons that might explain why the relative performance of nonadoptersdeclines when compared to ERP adopters. Our findings are also consistent with Hayes et al. (2001) and Hunton et al. (submitted forpublication) in that potential ERP adopters should be aware of performance improvementlimitations that may result from a combination of size and health. That is, large/unhealthyfirms can expect greater performance gains than their large/healthy counterparts, as large/unhealthy firms have more room for potential efficiency and effectiveness gains. Addition-ally, small/healthy firms can anticipate greater future benefit from ERP adoption than small/unhealthy firms, primarily because small/healthy firms can likely acquire needed resources tocomplete full integration and thereby become larger players in the marketplace. Unfortu-nately, small/unhealthy firms might suboptimize the potential impact of ERP systems, as theymay be forced into partial implementations due to resource constraints. As a result, businessprocess innovation may not occur to the extent desired which can hamper further improve-ment in financial health. However, the disadvantage of small/unhealthy firms may dissipate in the near future, asERP vendors are now making it more affordable for medium- and small-size firms toimplement ERP systems by scaling back the complexity of their systems and assisting theirclients in implementation activities. One relatively recent method of increasing affordabilityis the use of application service providers (ASPs), where an ASP firm offers to host andmaintain the ERP system, and the service recipient pays a fee to use the system. This strategyminimizes the technical and financial burden on medium- and small-size firms, while offeringto them the full range of ERP functionality. The spread of ERP systems across business organizations has implications for accountingresearchers and professionals, since the systems automate a wide array of business processesand decrease the need for many accounting functions currently being performed by
J.E. Hunton et al. / Int. J. Account. Inf. Syst. 4 (2003) 165–184 183accountants. As a result, new internal control and auditing questions arise regarding theunique risks inherent with ERP systems, as compared to legacy accounting systems.Accountants and auditors must also deal with ways to report financial information in a moretimely manner than in the past (e.g., near-continuous financial reporting) and attest to thereliability of such information. Additionally, eliminating some of the currently performedaccounting functions will eliminate many lower level accounting positions, while raising thesophistication of tasks performed by upper level accountants. For instance, degreedaccountants can be expected to perform deeper financial analyses, provide more value-addedadvice regarding business process improvements, develop internal controls commensuratewith ERP risks, and make tactical and strategic decisions aimed at improving firmperformance. These issues, and more, provide the backdrop for investigating a host ofinteresting and relevant issues related to ERP adoption.ReferencesBalakrishnan R, Linsmeier TJ, Venkatachalam M. Financial benefits from JIT adoption: effects of customer concentration and cost structure. Account Rev 1996;1(2):183 – 205.Barber BM, Lyon JD. Detecting abnormal operating performance: the empirical power and specification of test statistics. J Financ Econ 1996;41(3):359 – 99.Barron OE, Kile CO, O’Keefe TB. MD&A quality as measured by the SEC and analysts earnings forecasts. Contemp Account Res 1999;16(1):75 – 109.Barua A, Kriebel CH, Mukhopadhyay T. Information technologies and business value: an analytic and empirical investigation. Inf Syst Res 1995;6(1):3 – 23.Belsley DA, Kuh E, Welsch RE. Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley; 1980.Bharadwaj AS. A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quart 2000;24(1):169 – 96.Brakely HH. What makes ERP effective? Manuf Syst 1999;17(3):120+.Cooke DP, Peterson WJ. SAP implementation: strategies and results. New York (NY): The Conference Board; 1998.Davenport T. Putting the enterprise into the enterprise system. Harv Bus Rev 2000;76(4):121 – 31.Dos Santos B, Peffers K, Mauer DC. The impact of information technology investment announcements on the market. Inf Syst Res 1993;4:1 – 23.Drucker PF. The coming of the new organization. Harv Bus Rev 1988;66(1):45 – 53.Eliashberg J, Chatterjee R. Analytical models of competition with implications for marketing issues, findings, and outlook. J Mark Res 1985;22(August):283 – 96.Eliashberg J, Chatterjee R. Stochastic issues in modeling the innovation diffusion process. In: Mahajan V, Wind Y, editors. Innovation diffusion models of new product acceptance. Cambridge (MA): Ballinger Press; 1986. p. 151 – 99.Eliashberg J, Jeuland AP. The impact of competitive entry in a developing market upon dynamic pricing strategies. Mark Sci 1986;5(1):20 – 37.Grover V, Teng J, Segars AH, Fielder K. The influence of information technology diffusion and business process change on perceived productivity: the IS executive’s perspective. Inf Manage 1998;34(3):141 – 59.Harris DH. Organizational linkages: understanding the productivity paradox. Washington (DC): National Acad- emy Press; 1994.Hayes DC, Hunton JE, Reck JL. Market reaction to ERP implementation announcements. J Inf Syst 2001; 15(1):3 – 18.
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