How Do Investments in Enterprise Application Integration ... - CiteSeerX

Proceedings of the 40th Hawaii International Conference on System Sciences - 2007

How Do Investments in Enter pr ise Application Integr ation Dr ive Stock Pr ices? Narcyz Roztocki School of Business State University of New York at New Paltz [email protected]

Abstract Enterprise application integration (EAI) technologies are critical to making diverse corporate computer systems work together properly, and as such should be expected to have a positive effect on business value. The event study presented and discussed in this paper seeks to ascertain the relevance of EAI to achieving tangible benefits from the investors’ perspective by examining 81 corporate announcements of EAI implementations. The analysis of our data however suggests that EAI investments are not always greeted as good news by investors and do not generally result in positive stock price reactions. Furthermore, the data suggests negative market reaction to financially distressed companies investing in EAI. Particularly in a bear market, investors seem to view an investment in EAI more as a burden than as an asset.

1. Introduction Financial markets are important funding sources for large firms. Essentially, rising stock prices are helpful for securing additional capital while dropping stock prices make the acquisition of additional funds more costly and difficult. Poor stock performance often leads to turnover in top management [1], and furthermore, managerial compensation is often linked to the stock price. Therefore, it is not surprising that top management of most publicly traded companies pays high attention to the development of their firms’ stocks. Linking corporate decisions with stock prices represents a common research approach of event studies. Several previous event studies have examined the link between investments in information technology (IT) and stock prices. (For a more recent and

Heinz Roland Weistroffer School of Business Virginia Commonwealth University [email protected]

comprehensive review of event studies in IT literature see [2].) Most prior event studies in the field of IT looked at multiple types of technology, or IT in general. In contrast, this study focuses on investments in one kind of technology only, namely enterprise application integration (EAI). Investigating a single technology has the advantage of more precise comparison. Following a confirmatory and exploratory research approach, the objective of our paper is two-fold. First, our intention is to confirm the results of a prior event study on EAI [3] with an expanded and more current sample of investment announcements. Second, we explore the potential effect of market conditions on stock price reaction. The remainder of our paper is structured as follows. First, we introduce the EAI technology and its benefits. Next, we briefly review event studies in IT research. This is followed by hypotheses development and methodology description. After presenting and discussing the results, we conclude by pointing out the contributions of our study as well as its limitations, and discuss some of the managerial implications of our results.

2. Theoretical background 2.1 Enter pr ise application integr ation technology For many companies, the integration of their different information systems (IS) represents a great challenge. In the past, decisions about new investments in IS often were made at department levels. Consequently, each department tended to choose applications that best suited their specific, departmental needs. These systems were often bought from different vendors, using different technologies and interfaces,

Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS'07) 0-7695-2755-8/07 $20.00 © 2007 1530-1605/07 $20.00 © 2007 IEEE

1


and usually could not communicate well with each other. This approach resulted in the company as a whole to be a collection of many isolated systems. Furthermore, as companies continued to grow thru acquisition, additional, often non-compatible, systems were added to the existing structure. Only a modest level of integration was achieved by creating customized, often home-made middleware. Nevertheless, the total integration problem was far from being solved. In the last 15 or so years, EAI technology has been developed to overcome this integration issue [4,5]. EAI is a sophisticated type of middleware which allows a high level of integration. In addition, EAI generally increases flexibility of an organization’s information systems and thus prolongs the lifecycle of many corporate applications [6]. Furthermore, EAI technology enables information sharing, which results in more efficient operations and flexible delivery of business services [7]. Thus, investments in EAI can reasonably be expected to have a positive impact on a company’s efficiency and, therefore, on business value. EAI is now considered to be a mature technology and has been implemented by a large number of corporations. However, in spite of the obvious acceptance of EAI, there is only limited anecdotal evidence, supported by a few isolated case studies, that it increases business value [8]. Not much is known how EAI implementations are received by investors and what their possible effect on stock performance may be.

2.2 Event study methodology Event study methodology is an approach for gauging corporate decisions from an investor’s perspective. Event studies examine the reactions in stock prices to explore the relevance and implications of different corporate decisions [9]. This methodology is grounded on the efficient market theory, according to which stock prices fully reflect all available information at a given time [10,11]. Thus, when a company makes an unexpected announcement, the stock market reacts without delay to the released information. Stock prices increase if the news is perceived as positive and stock prices decrease, if the news is perceived as having negative implication [12]. Thus, in essence, event studies observe movements in stock markets to gauge and understand the actions and events revealed by organizations in the announcements. The methodology has been successfully applied in the past to gain a better understanding of new governmental regulations, changes in corporate strategies, corporate mergers, corporate performance,

among other proceedings. The effectiveness of the event study approach has more recently started to attract researchers to investigate various aspects related to IT investments and payoffs from IT investments. One of the first event studies in the field of IT looked at industry type and innovation [13]. IT investments by financial firms were expected to have a greater effect on stock prices than IT investments by manufacturing firms, because of the information intensity of the financial industry. This premise was not supported, however. With respect to innovation, the study presupposed that unleashing new technology in an organization would give the organization a competitive advantage, at least until the technology becomes routine within the industry. The study did find positive stock price reactions for innovative IT investments, supporting this claim. A more recent study investigated stock price reaction to IT investments within the context of industry, firm size, and time [14]. The study found positive market reactions for smaller firms and for newer announcements. Another event study [15] found favorable stock price reactions to announcements of ecommerce initiatives, with stock price reactions to business-to-business (B2B) initiatives being only marginally different from price reactions to business-toconsumer (B2C) initiatives. Offering tangible, traditional products as opposed to digital goods also were not found to result in substantially different market reactions. Investments that target IT infrastructure as opposed to those investments that primarily target particular applications have also been examined using event study methodology [16,17], with some evidence indicating positive market reactions to transformative IT investments. On the whole, event studies in the IS/IT field have made notable contributions to the understanding of IT productivity, while pointing to many further, promising research avenues in this area.

2.3 Business value of enter pr ise application integr ation fr om investor s’ per spective The evident technical advantages of EAI should reasonably be expected to lead to better business performance and thus to significant additional business value. Therefore, according to the efficient market theory, announcements of EAI investments should be well received by the investors and trigger positive stock market reactions. However, EAI implementations

Proceedings of the 40th Annual Hawaii International Conference on System Sciences (HICSS'07) 0-7695-2755-8/07 $20.00 © 2007

2


are very complex issues and even the highest level of integration does not necessarily guarantee higher future cash flows. This possible ambiguity from the investors’ perspective provides motivation for our study presented in the following sections.

3. Hypotheses development Previous event studies [13,14] did not find evidence of any significant reaction in stock prices for investments in IT in general. In contrast, the study presented in this paper looks exclusively at IT investments related to EAI technology. We believe that this narrow focus allows higher validity of results. Notwithstanding the undeniable technical advantages provided by EAI, we argue that for most companies, investments in EAI will not convince potential investors that these initiatives result in competitive advantages leading to higher future cash flows. Furthermore, achieving and maintaining a competitive advantage is a complex issue, and technical advantage often presents only a marginal component. For this reason, we expect that the expanded sample will merely confirm the results of a prior study on EAI investments [3], which despite a narrow focus on only one technology, failed to detect any significant stock reaction. Accordingly, looking at the full sample of companies, we hypothesize: H1: Announcements of investments in EAI do not, on average, significantly affect stock prices. Early implementation of a new technology may at times present an opportunity to achieve competitive advantage over those companies that lag behind. For example, companies which embraced EAI technology as it became commercially available in the late 1990s and early 2000s were able to derive tangible benefits while their rivals were struggling with various, incompatible systems. According to the literature, these organizations fall into the early adopters and early majority category [18]. However, now EAI has become a standard tool and the use of this technology is more or less expected. Companies which only now invest in EAI are considered as falling into the late majority category. These companies are investing in EAI under pressure, just to keep up with their competitors that already have EAI. On the other hand, investments in technology pose less of a risk as the technology matures, and corporate decision makers and IS consultants may benefit from past experiences and be guided by earlier EAI implementation frameworks and methods [5,19]. In contrast to the prior event study on EAI [3] which offered only inconclusive results, we

expect that by using a more recent sample of announcements, our analysis will be better able to capture the live cycle of this technology. Therefore we hypothesize: H2: The magnitude of stock price reactions to announcements of investments in EAI diminishes over time. Building on the attention-based view of the firm [20], one may argue that most investors follow a strategy of avoiding poor investments while not missing good opportunities [21]. More specifically, this means that investors try to avoid being overly engaged in stocks of inefficient and poorly managed companies. One measure of relative market risk is provided by the security’s beta [22] from the Capital Asset Pricing Model (CAPM) [23,24]. Beta is calculated as the ratio of the covariance of the stock’s returns and the market’s index returns, with the variance of the market’s returns. Under-performing firms tend to have a higher beta than their successful competitors. Sometimes companies tend to invest in technology in an effort to compensate for serious organizational problems. Badly managed companies may see new technology as a cure for their problems. Frequently, these investments are conducted under immense pressure from external forces, such boards of directors, seeking short-term improvements. Investments in EAI, however, are no panacea for critical organizational problems. To the contrary, such investments may actually cause additional managerial troubles, while also adding extra costs. Therefore, it may be reasonable to expect that after an announcement of an investment in EAI by a company perceived as risky to start with, some investors will decide to sell their stocks and move the released funds to more promising prospects. Following this argument, we hypothesize: H3: Stocks of firms with high beta will respond less favorably to announcements of investments in EAI than stocks of firms with low beta. A recent study in the field of IT, which examined the effect of outsourcing, introduced a control variable for market conditions [2], differentiating between generally favorable (bull market) and unfavorable markets (bear market). We argue that market conditions are an important aspect for investment decisions and deserve more attention in IT research. As observed by researchers from other disciplines, a bull market encourages investments [25], whereas a bear market discourages large investments. In addition, in a bear market, investors are more cautious about retaining stocks of


3


risky companies and are more likely to sell. Therefore we hypothesize: H4: During unfavorable market conditions, stocks will respond less positively to announcements of investments in EAI than under favorable market conditions.

4. Methodology 4.1 Data collection The Lexis-Nexis database was used to obtain announcements about EAI investments. To find appropriate announcements, somewhat complex queries had to be used. which included combinations of words related to EAI technology, such as enterprise, application, integration, platform, and EAI. Often, these queries resulted in a large number of announcements, the vast majority of which, after careful examination, turned out to be not relevant to our study and had to be excluded. For example, some of the announcements were related to private companies for which stock data was not available. The earliest announcements about EAI investments which we were able to identify were from the year 1998. Furthermore, to avoid possible seasonal effects we used December 31, 2005 as a cut-off date. The final sample, after excluding those that were not applicable, consisted of 81 announcements for eight full years. Table 1 shows the distribution of the announcements by year. Table 1. Distribution of announcements by year Year 1998 1999 2000 2001 2002 2003 2004 2005 Total

Number of Announcements 5 6 16 20 8 6 10 10 81

As shown in Table 1, investments in EAI appear to have reached a peak in the years 2000-2001.

4.2 Data analysis 4.2.1 Assessing the stock pr ice r eaction. To better compare our results to prior published research, we used similar methodology applied in earlier event studies [26] to assess the stock price reaction to the EAI announcements. Consequently, for a given stock, we calculated the expected returns based on the CAPM. Unexpected, or abnormal returns (AR), possibly brought about by the EAI investment announcements, are the differences between the actual returns and the calculated, expected returns. The day of announcement is defined as day 0. To determine the alpha and beta parameters required for the CAPM calculation, a 200 day estimation period was used, starting 201 days and ending 2 days before the announcement. The SP 500 index served as proxy for stock market returns. To measure the stock price reaction to the EAI investments, two different event windows were used, both starting one day before the announcement (day -1), but the one ending on the day of the announcement (day 0), and the other ending one day after the announcement (day 1). Thus, the first event window is denoted as (-1,0) and the second as (-1,1). Cumulative abnormal returns (CAR) were calculated for each of the event windows by summing the AR for the days of the event window. The stock price reaction was further tested for significance using cumulative standardized abnormal returns (CSAR) [26]. 4.2.2 Par titioning the sample. The complete sample was used to test our first hypothesis, while various sub-samples were used to test the remaining three hypotheses. Accordingly, to test the second hypothesis, our sample was partitioned into two subsamples: “older announcements” including the four years 1998 to 2001 and “newer announcements” including the four years 2002 to 2005. To test the third hypothesis , the announcements were divided between “low risk” and “high risk” sub-samples. To this effect, companies with beta below 1.2 were designated as “low risk” firms while companies with beta of 1.2 and higher were classified as “high risk” firms. The prior event study on EAI [3] used beta of 1.1 to classify companies as high risk investments, however, in this study we decided to use a higher beta as make the investment risk more evident. Finally, in order to test our fourth hypothesis, the sample was divided into two subsamples based on market conditions at the time before the announcements. When during the 200 day estimation period the total market returns, estimated by


4


SP 500, were positive, the particular announcement was classified as falling into a “bull market.” In contrast, when the returns for the stock market were negative during the estimation period, the announcement was classified as falling into a “bear market.“

5. Results As shown in Table 2, the CSAR values for the full sample, though slightly negative, are not significantly different from zero, supporting Hypothesis 1 that, on average, investments in EAI do not lead to significant abnormal returns. With respect to Hypothesis 2, the CSAR values are insignificantly negative for “older announcements” and insignificantly positive for “newer announcements.” No significant differences in abnormal stock returns between the ”older announcements” and the “newer announcements” are evident. Furthermore, as depicted in Table 3, a non-parametric regarding the frequency of abnormal positive returns is failing to point out statistically significant differences between the two investment periods. However, the standard deviations of the CAR values, as shown in Table 4, are greater for the earlier EAI adoptions. The F-test for equality of variances for the event window (-1,0) suggests that the variability of CAR was indeed greater for the “older announcements” than the “newer announcements, at a significance level of 0.01, thus supporting Hypothesis

2. The difference in variability of CAR values using the event window (-1,1) was at a 0.05 significance level. Thus overall, Hypothesis 2 seems to be at least partially supported. The CSAR values for “low risk” firms are insignificantly positive. However, as the significantly negative CSAR values for the “high risk” companies (beta > 1.2) indicate, Hypothesis 3 seems to be supported. Hypothesis 4 is supported as indicated by the CSAR values. In a bear market, investors’ reaction to the investment announcements appears to be negative. These results are summarized in Table 5. In summary, our results suggest that not all announcements of investments in EAI technology automatically move stocks up. Furthermore, the magnitude of stock price reaction to EAI announcements appears to lessen over time, as the technology matures. Furthermore, stocks of financially distressed companies that are perceived as investment risks trigger negative reactions to announcements of investments in EAI. Stock market conditions also seem to be an important factor. While under favorable market conditions the abnormal returns tend to be positive, (however statistically insignificantly), they tend to be significantly negative in a bear market.

Table 2. Cumulative standardized abnormal returns (CSAR) Sample Full sample Breakdown by time 1998-2001 2002-2005 Breakdown by beta factor below 1.2 1.2 or higher Breakdown by stock market conditions Bull market Bear market * Significant at a=0.1

Number of Announcements 81

CSAR [-1,0] -0.086

Z-Value

Z-Value

-0.78

CSAR [-1,1] -0.084

47 34

-0.181 0.045

-1.25 0.26

-0.225 0.111

-1.54 0.65

64 17

0.024 -0.501

0.19 -2.07**

0.017 -0.464

0.13 -1.91*

46 35

0.192 -0.453

1.31 -2.68***

0.117 -0.348

0.79 -2.06**

-0.45

** Significant at a=0.05 *** Significant at a=0.01


5


Table 3. Distribution of positive cumulative standardized abnormal returns (CSAR) Sample Full Sample Breakdown by time 1998-2001 2002-2005

Total Number of Announcements 81

CSAR [-1,0] 37 (45.7%)

Z-Value

Z-Value

-0.78

CSAR [-1,1] 39(48.1%)

47 34

20(42.6%) 17 (50%)

-1.02 0.00

19(40.4%) 20 (58.9%)

-1.31 1.03

48.1

Table 4. Cumulative abnormal returns (CAR) Sample

Number of Announcements

Full Sample Breakdown by time 1998-2001 2002-2005

Std. D. in %

81

CAR [-1,0] in % -0.68

47 34

-1.16 -0.03

Std. D. in %

3.73

CAR [-1,1] in % -0.69

4.50 2.19

-1.34 0.21

4.36 3.10

3.93

Table 5. Summary of results Hypothesis

Results

1

Supported

2

Partially supported

3

Supported

4

Supported

Comments Overall, investments in EAI do not result in abnormal returns The magnitude of stock price reactions may diminish as technology becomes more established Announcements of investments in EAI in companies perceived as risky investments result in negative stock price reactions Under unfavorable stock market conditions, announcements of investments in EAI are less positively received that in good markets

6. Discussion In this event study, we examined investors’ expectations regarding the business value derived from investments in EAI. Overall, our results do not confirm common expectation, but rather suggest that investors do not believe that EAI implementation in general have a positive impact on future cash flows. One possible explanation for this may be that top management in many companies fails to properly explain the necessity of EAI investments and communicate the anticipated advantages to the stockholders. The communication between management and shareholders appears to be particularly important in a bear market, when new investments are received with greater skepticism. Furthermore, our results indicate that early implementation of EAI does not necessarily gain investors’ support. This lack of positive stock price

reaction may suggest that first-movers, who embrace a particular type of new technology such as EAI, are not always perceived as being able to achieve competitive advantage that way. Also, the first-movers’ advantages need to be contrasted to the first-movers’ disadvantages [27]. Late-movers may benefit from reduced implementation costs and more mature, and thus stable, technology.

7. Contributions, limitations, and managerial implications Notwithstanding the enormous research efforts in the field of IT productivity, there is still lack of general agreement on the factors that determine the success of IT investments. Consequently, the kind of research that takes the investors’ perspective clearly deserves academic attention, as strong investors’ support is


6


often a key factor in the commercial success of a new technology. Anticipating and correcting investors’ perception about the business value of a particular technology, such as EAI, should be of great advantage as supportive investors are more likely to provide the necessary funding. We believe that our study offers a worthwhile contribution to the existing body of knowledge, while pointing to several possible avenues for future research. First, in contrast to many previous studies, we have investigated investments limited to only one particular technology, namely EAI. This narrow focus allows for a better comparison of results and for more accurate testing of hypotheses related to technology live cycles, as exemplified in our second hypothesis. Secondly, again using the advantage of comparability in technology, we investigated the effect of the beta factor. This provides further evidence that companies’ characteristics, in our case perceived financial risk, plays an important role. Third, this is perhaps the first event study which systematically tested for possible effects of stock market conditions on investments in IT. A limitation of our study, as in other event studies, is the lack of accounting for possible confounding events. Though we made every attempt to exclude from the final sample the announcements with perceived, potential confounding events around the release time, the existence of such confounding events can never completely be excluded. For example, changes in regulatory environment may have an impact on a particular industry, but is hardly traceable to a single company. A further limitation is related to our announcement collection. It is quite likely that other researchers using different queries will not identify the exact same set of announcements. In addition, our results are limited to EAI technology. It is quite possible that focusing on a different technology will yield fully different results. For example, it is possible that some technologies, in striking contrast to EAI, are received positively in under-performing companies and in a bear market. Regarding the practical implication for management, our study provides further evidence that not all investments in EAI receive positive reaction in stock markets. Managers should not view EAI implementation as a way to compensate for organizational problems. Our results indicate that financial markets mostly respond negatively to EAI announcements when the announcing company is perceived as an investment risk or the announcement is released during bear market conditions. Therefore, decision-makers in under-performing or financially

distressed companies may be advised to prioritize their attention and allocate their financial resources toward solving higher-priority problems, rather than investing in EAI. Making tangible improvements and fixing organizational problems may do more to restore investors’ confidence. Regarding investments in EAI in bear markets, managers should dedicate more attention to better communicate the advantages of the technology. Though the relatively small sample size and other limitations described above limit the generalizability of our findings, other scholars may build on our results and perhaps validate our findings through further event studies and other approaches.

8. Refer ences [1] J.B. Warner, Watts. Ross L., & K.H. Wruck, "Stock Prices and Top Management Changes", Journal of Financial Economics, Vol. 20, 1988, pp. 461-92. [2] W. Oh, M.J. Gallivan, & J.W. Kim, "The Market's Perception on the Transactional Risks of Information Technology Outsourcing Announcements", Journal of Management Information Systems, Vol. 22, No. 4, 2006, pp. 271-303. [3] N. Roztocki & H.R. Weistroffer, "Enterprise Application Integration and its Effect on Business Value: A Stock Market View"., Proceedings of the Twelfth Americas Conference on Information Systems (AMCIS 2006), Acapulco, Mexico. [4] P. Johannesson & E. Perjons, "Design principles for process modelling in enterprise application integration", Information Systems, Vol. 26, No. 3, 2001, pp. 165-84. [5] M. Themistocleous, "Justifying the decisions for EAI implementation: a validated proposition of influential factors", The Journal of Enterprise Information Management, Vol. 17, No. 2, 2004, pp. 85-104. [6] Z. Irani, M. Themistocleous, & P.E.D. Love, "The impact of enterprise application integration on information system lifecycles", Information & Management, Vol. 41, No. 2, 2003, pp. 177-87. [7] N. Erasala, D.C. Yen, & T.M. Rajkumar, "Enterprise Application in the Electronic Commerce World", Computer Standards and Interfaces, Vol. 25, No. 2, 2003, pp. 69-82.


7


[8] T. Puschmann & R. Alt, "Enterprise application integration systems and architecture - the case of the Robert Bosch Group", Journal of Enterprise Information Management, Vol. 17, No. 2, 2004, pp. 105-16. [9] A. McWilliams & D. Siegel, "Event Studies in Management Research: Theoretical and Empirical Issues", Academy of Management Journal, Vol. 40, No. 3, 1997, pp. 626-57. [10] E.F. Fama, "Efficient Capital Markets: A Review of Theory and Empirical Work", The Journal of Finance, Vol. 25, No. 2, 1970, pp. 383-417.

[19] Lam W. & V. Shankararaman, "An Enterprise Integration Methodology", IT Professional, Vol. 6, No. 2, 2004, pp. 4048. [20] W. Ocasio, "Towards an attention-based view of the firm", Strategic Management Journal, Vol. 18, No. Summer Special Issue, 1997, pp. 187-206. [21] R. Gulati & M.C. Higgins, "Which ties matter when? The contingent effects of interorganizational partnerships on IPO success", Strategic Management Journal, Vol. 24, No. 2, 2003, pp. 127-44.

[11] E.F. Fama, "Efficient Capital Markets: II", The Journal of Finance, Vol. 46, No. 5, 1991, pp. 1575-617.

[22] R.J. Fuller & G.W. Wong, "Traditional versus Theoretical Risk Measures", Financial Analysts Journal, Vol. 44, No. 2, 1988, pp. 52-57.

[12] E.F. Fama, L. Fisher, M.C. Jensen, & R. Roll, "The Adjustments of Stock Prices to New Information", Inter national Economic Review, Vol. 10, No. 1, 1969, pp. 121.

[23] J. Lintner, "The Evaluation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets", The Review of Economics and Statistics, Vol. 47, No. 1, 1965, pp. 13-37.

[13] B.L. Dos Santos, K. Peffers, & D. Mauer, "The Impact of Information Technology Investment Announcements on the Market Value of the Firm", Information Systems Research, Vol. 4, No. 1, 1993, pp. 1-23.

[24] W.F. Sharpe, "Capital Asset Prices: A Theory of M arket Equilibrium under Conditions of Risk", The Journal of Finance, Vol. 19, No. 3, 1964, pp. 425-42.

[14] K.S. Im, K.E. Dow, & V. Grover, "Research Report: A Reexamination of IT Investment and the Market Value of the Firm - An Event Study Methodology", Information Systems Research, Vol. 12, No. 1, 2001, pp. 103-17. [15] M. Subramani & E. Walden, "The Impact of ECommerce Announcements on the Market Value of Firms", Information Systems Research, Vol. 12, No. 2, 2001, pp. 13554.

[25] R.B. Barsky & J.B. De Long, "Bull and Bear Markets in the Twentieth Century", The Journal of Economic History, Vol. 50, No. 2, 1990, pp. 265-81. [26] C.F. Loderer & D.C. Mauer, "Corporate Dividends and Seasoned Equity Issues: An Empirical Investigation", The Journal of Finance, Vol. 47, No. 1, 1992, pp. 201-25. [27] M.B. Lieberman & D.B. Montgomery, "First-Mover Advantages", Strategic Management Journal, Vol. 9, 1988, pp. 41-58.

[16] B. Dehning, V.J. Richardson, & R.W. Zmud, "The Value Relevance of Announcements of Transformational Information Technology Investments", MIS Quarterly, Vol. 27, No. 4, 2003, pp. 637-56. [17] D. Chatterjee, C. Pacini, & V. Sambamurthy, "The Shareholder-Wealth and Trading-Volume Effects of Information-Technology Infrastructure Investments", Journal of Management Information Systems, Vol. 19, No. 2, 2002, pp. 7-42. [18] F.M. Bass, "A New Product Growth for Model Consumer Durables", Management Science, Vol. 15, No. 5, 1969, pp. 215-27.


8