"Mergers and Acquisitions in Banking and Finance - PDF"
The Effect of IT Investment on Mergers and Acquisitions in the U.S. Banking Industry: Integration Costs and Operational Efficiency (This is a student-authored paper) Ali Tafti (email@example.com) PhD Candidate Business Information Technology Ross School of Business University of Michigan 701, Tappan Street Ann Arbor, MI 48109-1234 Phone: 201-424-6276 M. S. Krishnan (firstname.lastname@example.org) Michael R. and Mary Kay Hallman e-Business Fellow Professor and Area Chair Business Information Technology Ross School of Business University of Michigan 701 Tappan Street Ann Arbor, MI 48109-1234 Phone: 734 763-6749 Fax: 734 936-0279 10/17/2007 2007 Conference on Information Systems and Technology 1 The Effect of IT Investment on Mergers and Acquisitions in the U.S. Banking Industry: Integration Costs and Operational Efficiency Abstract In this research-in-progress paper, we examine the influence of IT investment on merger integration costs and post-acquisition operating efficiency in the U.S. banking industry. Drawing from several data sources including annual 10K financial statements, our preliminary results indicate that IT investment reduces the impact of total acquisition value on integration costs. We also find that greater IT investment prior to acquisitions is associated with greater operating efficiency following the acquisitions through the following mechanisms: direct effects, mediated effects through integration costs, and moderating effects with acquisition volume. Our results suggest that banks with greater IT investments not only incur lower integration costs for each merger, but also appear to be better able to leverage synergies from their mergers. 10/17/2007 2007 Conference on Information Systems and Technology 2 1. Introduction A question of interest to researchers and managers is whether Information Technology (IT) has an enabling or a hindering effect on mergers and acquisitions (M&A). Among the reasons why firms engage in M&A are to gain market share and generate greater economies of scale. However, the task of integrating information systems between newly joined corporate entities can be complex, costly, and risky. On the other hand, IT infrastructure capabilities such as standardized processes and flexible architectures may reduce the costs of integration and facilitate business process transformations that enable firms to leverage the synergies of a merger. Developing such capabilities requires substantial investment in IT. In this research-in-progress paper, we study the impact of IT investment on merger-related integration costs and operating efficiencies in the U.S. banking industry. With IT expenditures that commonly range between eight to twelve percent of sales, according to data from InformationWeek (InformationWeek 2006), banks are among the largest consumers of IT. IT is central to the growth strategies of banks as they try to increase market share and leverage economies of scale. The banking industry was transformed by technological advances such as internet banking, electronic payments, and information exchanges enabling instant access to such information as customer credit scores (Berger 2003). Along with deregulations on geographical restrictions of bank expansions, technological change is considered to be a cause of the rapid rise in the number of bank M&As that began in the 1980s and that continues today (Berger et al. 1999). On the other hand, IT can also be a source of risk as banks engage in M&A. Since banking operations involve substantial automation and depend upon timely access to information, momentary lapses in service can lead to loss of customer value. Hence, the merging of information systems between banks presents a unique set of challenges. 10/17/2007 2007 Conference on Information Systems and Technology 3 In this study, we examine the effect of IT investments prior to bank acquisitions on the overall costs of integration following a merger. In addition, we examine the effect of IT investments on banks’ overall operating efficiency in the time period following a merger: First, through direct effects, second, through the mediating effect of decreased integration costs, and third, by moderating the effect of M&A activity on operating efficiency after controlling for integration costs. 2. Background Literature There is an active area of research on the performance of banks within the finance and accounting literatures (Berger et al. 1999), and within this body of literature there has been substantial research on the effects of M&A activity on the operating efficiency of banks. These studies frequently acknowledge and discuss the role of IT either as an enabler or as a potential hindrance to bank M&A (Berger 2003; Houston et al. 2001). However, we are not aware of any works within this stream of research that have explicitly incorporated quantitative measures of bank IT investment. Hence, we first turn to the literature on business value of IT as a starting point to examine the performance outcomes of interactions between IT investment and M&A activity in the banking sector. The business value of IT literature includes some empirical studies specifically on the banking sector. Banker and Kauffman (1988) study the impact of ATM network membership on market share in territorial competition among retail banks. Davamanirajan et al. (2002) examine the return on IT investments in the trade-services sector of global wholesale banking. In a process-level analysis in the global trade-services sector of international banking, Davamanirajan et al. (2006) examine the impact of electronic integration on labor productivity and cycle time, and show a link between these process-level measures of productivity and firm- 10/17/2007 2007 Conference on Information Systems and Technology 4 level profit margins. These studies demonstrate that IT investments have substantial and measurable economic impacts in the banking industry. We plan to contribute to this literature by considering the performance outcomes of the interaction between IT investment and merger activity. The banking sub-literature of finance and accounting has provided mixed evidence regarding the post-merger operating efficiency of banks (Berger et al. 1999). Akhavein, Berger, and Humphrey (1997) show that M&As led to greater profit-related efficiencies, and that neither cost-reduction nor consolidation-driven price increases resulting were driving these efficiencies. Berger and Mester (2003) find that profit productivity of banks engaging in mergers increased substantially while cost productivity of banks actually became worse during the 1991-1997 period. Based on these results, the authors argue that revenue gains from M&As are driven by banks’ increased capability to offer new IT-enabled products and services at a greater scale: “...banks involved in M&A’s spread the new or improved services afforded by technological advances to the acquired banks” (Berger et al. 2003 p. 166). In contrast to the above studies, there is evidence that cost-reduction has a critical role in driving post-merger performance of banks. For example, Houston (2001) finds that the stock market values projected cost-reduction of bank M&As more than projected revenue increases, suggesting either that managers are overstating the expected revenue gains, or that stock market is undervaluing them. Rhoades (1998) examines nine prior case studies of horizontal mega- mergers, those involving two banks with at least $1 billion in assets each, and finds, among other things, that cost-reductions result primarily from reduction in staff and elimination of redundant IT systems and processes. Based on case-study analysis, Rhoades points out: “It is not possible to isolate specific factors from these mergers that are most likely to yield efficiency gains, but 10/17/2007 2007 Conference on Information Systems and Technology 5 the most frequent and serious problem was unexpected difficulty in integrating data processing systems and operations” (Rhoades 1998 p. 273). Building on the business value of IT literature, and the banking sub-literature in finance and accounting, we consider that the relationship between IT investment and merger integration efforts may not be straightforward. Greater IT investment might increase the complexity of integration efforts, but may also contribute to competencies that can reduce integration costs and increase operating efficiencies derived from merger activity. Hence, the relationship between IT investment, integration costs, and post-merger performance needs to be examined empirically. 3. Hypotheses 3.1. Alternative Hypotheses: The Effect of Pre-Merger IT Investment on Post-Merger Integration Costs Merging banks often need to re-engineer business processes and restructure the organization. These entail substantial costs pertaining to personnel changes, physical restructuring, training, and transformation of business processes. The overall costs of integration may depend on whether the bank has decided to integrate a newly acquired firm or to keep it running as an independent entity, and also on its infrastructural capabilities to integrate the firm. The decision to integrate involves a trade-off between the potential benefits from leveraging synergies as a result of integration, against the potential costs and risks of integration. Banks often list merger-related integration or restructuring costs in their 10K financial statements, such as the following: IT systems conversion, computer hardware and equipment replacement, severance and personnel changes, the closing or opening of building space, branch sales, or operations restructuring. As banks invest in IT, they are perhaps more likely to have established flexible technology architectures that enable greater strategic agility for organizational restructuring 10/17/2007 2007 Conference on Information Systems and Technology 6 related to M&A (Broadbent et al. 1999; Byrd et al. 2000). For firms with flexible IT infrastructures, the cost of integration may decline as firms become increasingly adept in their use of IT to implement restructuring subsequent to mergers. In addition, banks may begin to accrue savings as a result of the downsizing of redundant personnel or systems. However, for banks to establish this level of flexibility in their technology infrastructures requires a substantial amount of IT investment. The total value of a bank’s acquisitions reflects the number of acquisitions and the size of the acquired banks. As the total value of a bank’s acquisitions increases in a given year, the organizational restructuring and integration costs as a result of merger activity will increase. However, we argue that this relationship will be moderated by IT investment prior to mergers, particularly for firms that have greater IT infrastructural flexibility. As business processes become more digitized and IT infrastructure capabilities increase, a bank’s flexibility for organizational transformation may also increase, reducing the impact of the total value of bank acquisitions on integration costs. H1: IT investment of the acquiring bank prior to mergers has a negative moderating influence on the effect of bank acquisition value on merger integration costs. On the other hand, there is also supporting evidence that IT investment may be associated with grater hindrances in the process of M&A between banks. Greater IT investment may be associated with greater complexity of systems and, accordingly, greater potential for errors and glitches in the integration process (Houston et al. 2001). For banks that are encumbered by legacy systems, greater IT investment would be associated with higher systems maintenance costs, greater systems complexity, and a larger number of technical processes that can falter in 10/17/2007 2007 Conference on Information Systems and Technology 7 the integration process. Hence, the relationship between IT investment and integration costs may increase as banks engage in more acquisitions. H1a: IT investment of the acquiring bank prior to mergers has a positive moderating influence on the effect of bank acquisition value on merger integration costs. 3.2. The Effect of Pre-Merger IT Investment on Post-Merger Operating Efficiency We define operating efficiency as the ratio of operating revenue over non-interest expenses, which is one among several versions of bank efficiency ratio that is used in industry and academic research as a measure of bank productivity (Rhoades 1998).1 Interest-related expenses are subject to exogenous shocks of the economic environment, and we want to focus on those operations-related costs that are independent of such shocks. While banks may engage in acquisitions for various strategic reasons such as increasing market share, one of the oft-cited reasons for bank consolidation is the aim of increasing overall operating efficiencies (Hancock et al. 1999; Linder et al. 1993). Banks capture economies of scale by reducing the redundancies that may exist in systems or personnel, so that a greater number of customers can be served with existing infrastructure capabilities. Greater IT investment can streamline processes and enhance business process flexibility in a way that increases a bank’s ability to leverage synergies from an acquisition. IT capabilities can enable banks to generate new sources of customer value and maintain greater customer retention during the merger. Hence, a firm’s IT capabilities can lead to enhanced customer value, fewer service disruptions, and the generation of new products. Firms with poor IT capabilities may be unable to support the creation of new systems functionality and product upgrades. Even 1 Efficiency ratio in the banking sector is usually given as some cost-related item divided by some income-related item, so that a lower efficiency ratio is better. However, we use the inverse in order to let readers unfamiliar with that convention associate a high efficiency ratio with higher performance—to ease interpretation of results. 10/17/2007 2007 Conference on Information Systems and Technology 8 when the infrastructural capabilities are not reflected in the costs of integration, as when banks attempt to keep their systems separate from those of newly acquired banks, infrastructural capabilities can influence a bank’s ability to generate new customer value, maintain employee morale, and fully leverage the synergies of a merger—all of which influence the bank’s operating efficiency (Aberg et al. 2005). H2a: IT investment of the acquiring bank prior to a merger is associated with greater operating efficiency after the merger. However, banks can also experience unexpected complications that make the process of integration costly, and that can result in reduced operating efficiency for a substantial period of time. If integration is not executed well, there may be additional costs of managing separate systems, and of maintaining makeshift patches that connect disparate systems. By reducing the costs associated with integration efforts, firms with greater IT investment prior to a merger can substantially increase overall operating efficiency. Integration costs represent a potential dissipation of firm value that may have impacts over time—representing fundamentally deeper persistent problems in the execution of a merger. Hence, the reduction integration costs through the use of IT may be a mediating mechanism between IT investment and post-merger operating efficiency. H2b: The effect of IT investment of the acquiring bank on operating efficiency is mediated by integration cost reduction. If IT is an enabler of mergers, we would expect the effect of IT investment on operating efficiency to increase with merger activity. Hence, we expect a complementary interaction between IT investment and merger integration volume, which is the total of value of firm acquisitions in a time period. IT capabilities can help banks generate economies of scale by 10/17/2007 2007 Conference on Information Systems and Technology 9 increasing the diversity of new IT-enabled products and services that reach a broader base of customers (Berger 2003; Sambamurthy et al. 2003). In addition, greater IT investment prior to mergers may indicate greater opportunity to reduce costs through elimination of redundant systems and processes (Houston et al. 2001). Therefore, the effect of IT investment on post- merger operating efficiency should increase with merger activity. H2c: IT investment of the acquiring bank prior to mergers has a positive moderating influence in the effect of bank acquisition value on post-merger operating efficiency. 4. Research Design and Methodology 4.1 Data Our study draws from several data sources. Our search for data begins with the fifty largest retail banks in the U.S. according to the Federal Deposit Insurance Corporation (FDIC), because we aimed to cover as much of the US banking industry as possible. However, we found that not all of these banks are listing their IT expenditures or their merger-related integration costs. Hence, we expanded our data set by looking in annual 10K statements for explicit keywords that are synonymous with merger-related integration costs and IT expenditures. The resulting sample includes a mixture of small and large banks. In an effort that is ongoing, we collect data regarding IT-investment, systems conversion, and merger-related restructuring costs from the annual 10K reports of these firms in the 10 year period between 1996 and 2006. We utilized a tool provided by 10-K Wizard, a provider of SEC EDGAR research services, to conduct the search and collect the data. Next, we collected data regarding mergers and acquisitions of these banks in the same ten year period, from the SDC Database (a product of the Thomson Corporation). Using this data, we determined the annual counts as well as total value of acquisitions for each bank in each year. 10/17/2007 2007 Conference on Information Systems and Technology 10 In order to maintain uniformity in the analysis, we consider only banks’ acquisitions of other banks or savings institutions. The challenges, costs, and opportunities pertaining to integration between banks may be very different from those pertaining to mergers between banks and other types of financial institutions such as insurance companies. By limiting our sample to mergers between banks, we reduce the potential heterogeneities associated with other types of product diversification that may complicate the analysis. Finally, we collected additional performance metrics for these banks from Compustat. Summary statistics and correlations are presented in Table 1. 4.2. Variable Definitions Acquisition Value (AV t-1): Rolling mean of the annual total transaction value of bank acquisitions; lagged by one year; aggregated at the one, two, or three-year levels. IT Expenditure (IT t-1): Rolling mean of annual IT expenditure, including all expenses related to data processing, software, hardware, and telecommunications; lagged by one year. Many banks report these as separate line items in annual 10K reports, while others group them together into a single line item. Seven of the banks in our sample lumped IT expenditure in with other equipment and premises costs. For these banks, we inferred IT investment by determining the average proportion of IT investment over total equipment and premises for all other firms in the sample—and found this proportion to be roughly one-half. Aggregated at the one, two, or three- year levels. Number of Acquisitions (NA t-1): Rolling count of bank acquisitions; lagged by one year; aggregated at the one, two, or three-year levels. 10/17/2007 2007 Conference on Information Systems and Technology 11 Efficiency ratio (R t/Et): Ratio of operating revenue (R t) over total non-interest expenses (Et), which is one of several ways of calculating bank efficiency ratio. Aggregated at the one, two, or three-year levels. Firm Size (St): Current-year firm size, measured as the total number of employees. Integration Costs (ICt): Rolling mean of merger integration costs, including personnel- re- organization, hardware-replacement, and technology systems-conversion projects related to merger activity. This does not include legal or investment banking fees. Aggregated at the one, two, or three-year levels. 4.3. Estimation Models We utilize variations of two models. The first model represents the effect of IT investment, total acquisition value, and their interaction on merger integration costs, controlling for firm size and number of acquisitions: log(ICt) = Constant + β1 log(AV t-1)+ β2 log(IT t-1) +β3 log(IT t-1) μ log(AV t-1) + β4 log(S t) + β5 NA t-1 + ε (1) The second model represents the effect of alliance value of IT investment, total alliance value, and their interaction, on operating efficiency: Efficiency Ratiot = R t / E t = Constant + β1 log(IC t)+ β2 log(AV t-1)+ β3 log(IT t-1) +β4 log(IT t-1) μ log(AV t-1) + β5 log(S t) + β6 NA t-1 + ε (2) We use one, two, and three-year rolling averages because merger-related integration costs are usually incurred during this period following the announcement of a merger, and the efficiency gains are also usually experienced within this period (Rhoades 1998). We use the log of all dollar amounts and number of employees (all variables except number of acquisitions and operation 10/17/2007 2007 Conference on Information Systems and Technology 12 efficiency) to linearize a distribution that is highly skewed from the smallest to largest banks. Note that we control for number of acquisitions in addition to total acquisition value because our data suggests that the two are not highly correlated—some banks may engage in a few mega- mergers while others engage in more frequent acquisitions of smaller size, which may have qualitatively different effects on integration costs and subsequent operating efficiency. 5. Results We present preliminary results of model estimates in Tables 2 through 5. Due to the small sample size, the statistical properties of the data discussed here may change as more data gets collected. However, we are beginning to see some interesting results. To ease interpretation, all variables representing dollar amounts or number of employees (all except for number of acquisitions and efficiency ratio) have been standardized. Tables 2 and 3 show fixed-effects regression results for models 1 and 2, respectively. Tables 4 and 5 show random-effects regression results for these models. We use White’s robust standard errors in each case to correct for possible heteroskedasticity. In Tables 4 and 5, we show the result of the Breusch-Pagan Lagrange Multiplier test (LM) and, wherever possible, the Hausman test comparing fixed and random effects models. The LM statistic, when significant, would indicate that OLS results are biased due to the panel structure of the data, while a significant Hausman test would indicate that random-effects panel results are biased due to unobserved fixed firm effects. Since random effects regression models have the advantage of capturing variation across firms and not just within firms, and since there are several cases in which the Hausman tests does not reject use of random effects estimation, we present the random-effects estimations in addition to fixed-effects estimations (Basant et al. 10/17/2007 2007 Conference on Information Systems and Technology 13 1996). All variables except for the firm size control are aggregated across one, two, or three years. We presented two alternative hypotheses H1 and H1a, which describe the moderating influence of IT investment in the effect of merger volume on integration costs. Our results show tentative support for H1 but not H1a, as evidenced by the negative and significant estimate of the coefficient β3 of the interaction term IT μ AV over the three year aggregation period (Tables 2 and 4). This effect appears to be greater in aggregation over three years as opposed to one or two years. Our results indicate that the complementary interaction of IT and AV has a negative association with merger integration costs. The results suggest several mechanisms by which IT investment influences post-merger operating efficiency, as seen in Tables 3 and 5. First, consistent with H2a, the positive and significant coefficient estimate for β3 in Tables 3 and 5 indicate a direct relationship between IT investment and operating efficiency. The magnitude of this effect appears to be largest at the two-year aggregation levels. Second, H2b predicts that this relationship may be influenced by the mediating factor of integration costs. Consistent with H2b, the estimates for β1 in Tables 3 and 5 indicate a negative and significant relationship between integration costs and operating efficiency. In combination with the result supporting hypothesis H1 that IT investment has a negative moderating influence in the effect of acquisition volume on integration costs (β3 of Tables 2 and 4), the estimates for β1 in Tables 3 and 5 suggest that integration cost reduction is a mediating mechanism between IT investment and operating efficiency. Third, it appears that IT investment positively moderates the effect of acquisition value on operating efficiency, as evidenced by the positive and significant coefficient estimate β4 of the interaction term IT μ AV. We see that this effect becomes stronger over time, and strongest over three years of aggregation. 10/17/2007 2007 Conference on Information Systems and Technology 14 In summary, the results indicate several types of association between IT investment and post- merger operating efficiency: a direct relationship, a relationship mediated by reduction of integration costs, and a moderating influence on the effect of acquisition value. 6. Discussion and Conclusion Our goal in this study was to examine whether IT enables banks to derive greater operating efficiencies through M&A. The complexities and risks of IT systems integration between newly merged banks can be substantial. While the synergies and potential benefits from improved operating efficiencies can outweigh the costs of integration, it appears that greater spending on merger-related restructuring is negatively related to operating efficiency in the period following a merger. However, our results indicate that IT investment prior to a merger has a positive influence on the impact of acquisition value on subsequent operating efficiency; and this is separate from the effect of integration costs. Among the limitations of this study are the small sample size and the lack of constructs which distinguish types of IT infrastructure investments. Also, we focused exclusively on the acquiring firm and did not consider characteristics of the acquired firms. We plan to expand the data set, and refine the analysis in subsequent work-- in particular, by addressing possible effects of endogeneity. There has been substantial interest in the finance and strategy literatures in the determinants of successful bank mergers. We contribute to this literature by introducing IT investment, which has often been a missing variable, and examining the quantitative relationship effect of IT investment and acquisition activity on integration costs and operating efficiency. Since IT is critical to almost every aspect of bank operations and bank growth strategies, future studies on the effects of mergers on bank operating efficiency should account for the role of IT investment in bank mergers. 10/17/2007 2007 Conference on Information Systems and Technology 15 Table 1. Summary Statistics and Correlations For Mean and Std. Dev., all except for Number of Acquisitions, Efficiency Ratio and Employees are expressed in thousands of dollars; at the one-year aggregation level. Significant at *5%. Mean Std. Dev. IC ER AV IT NA S Integration Costs (IC) 114,993 311,400 1.000 Efficiency Ratio (ER) 1.325 0.156 -0.220* 1.000 Acquisition Value (AV) 1,627,588 6,746,775 0.388* 0.030 1.000 IT Expenditure (IT) 266,279 612,870 0.661* -0.154 0.493* 1.000 Number of Acquisitions (NA) 0.734 1.131 -0.045 -0.052 0.121 -0.127 1.000 Firm Size (Employees) (S) 8,369,017 15,000,000 0.522* -0.034 0.481* 0.780* -0.103 1.000 Table 2. Fixed Effects Regression Results: Integration Costs Robust standard errors in parentheses. Dependent variable is Integration Costs (log(ICt)). All variables except number of acquisitions are logged, and all except for firm size (employees) are standardized. Significant at *10%, **5%, and ***1% level for 1-tailed t-tests. (1) (2) (3) Years One Two Three Acquisition 0.678* 0.200** 0.268** Value (AV) β1 (0.509) (0.104) (0.123) IT Expenditure 0.188 0.228 0.723* (IT) β2 (1.259) (0.67) (0.557) IT μ AV β3 -0.216 -0.181** -0.226** (0.391) (0.085) (0.1) Number of Acquisitions 0.038 0.053** 0.039*** (NA) β4 (0.157) (0.025) (0.016) Firm Size (S) β5 -0.389 0.096 0.026 (0.802) (0.125) (0.097) Constant 4.314 0.176 0.239 (7.05) (1.031) (0.783) Observations 77 107 118 Firms 20 31 31 F statistic 3.79 2.90 3.41 Prob > F 0.0053 0.0193 0.0075 R2 (overall) 0.0013 0.6440 0.7111 10/17/2007 2007 Conference on Information Systems and Technology 16 Table 3. Fixed Effects Regression Results: Operating efficiency Robust standard errors in parentheses. Dependent variable is operating efficiency ratio (ERt). All variables except number of acquisitions and efficiency ratio are logged, and all except for firm size (employees) and efficiency ratio are standardized. Significant at *10%, **5%, and ***1% level for 1- tailed t-tests. (1) (2) (3) Aggregation One Two Three (years) Integration Costs -0.020* -0.218*** -0.254*** (IC) β1 (0.01) (0.068) (0.08) Acquisition -0.063* -0.094 -0.21** Value (AV) β2 (0.038) (0.075) (0.122) IT Expenditure 0.184** 0.808** 0.535 (IT) β3 (0.093) (0.466) (0.46) IT μ AV β4 0.064** 0.108* 0.202** (0.029) (0.069) (0.106) Number of Acquisitions -0.019* -0.015* -0.014 (NA) β5 (0.012) (0.011) (0.012) Firm Size (S) β6 0.071 0.124 0.14** (0.059) (0.106) (0.082) Constant -0.480 -0.272 -0.098 (0.522) (0.761) (0.598) Observations 77 107 118 Firms 20 31 31 F statistic 5.03 15.26 10.87 Prob > F 0.0004 0.000 0.000 R2 (overall) 0.2196 0.0026 0.0056 10/17/2007 2007 Conference on Information Systems and Technology 17 Table 4. Random Effects Regression Results: Integration Costs Robust standard errors in parentheses. Dependent variable is Integration Costs (log(ICt)). All variables except number of acquisitions are logged, and all except for firm size (employees) are standardized. Significant at *10%, **5%, and ***1% level for 1-tailed t-tests. (1) (2) (3) Years One Two Three Acquisition 0.072 0.031 0.156** Value (AV) β1 (0.384) (0.084) (0.079) IT Expenditure 0.704 0.754* 0.929*** (IT) β2 (0.726) (0.464) (0.377) IT μ AV β3 0.128 -0.048 -0.144** (0.293) (0.071) (0.064) Number of Acquisitions 0.148 0.061*** 0.048*** (NA) β4 (0.144) (0.017) (0.012) Firm Size (S) β5 0.049 0.099** 0.081* (0.184) (0.058) (0.052) Constant -0.369 -0.333** -0.45*** (1.097) (0.151) (0.167) Observations 77 107 118 Firms 20 31 31 Wald statistic 21.08*** 185.46*** 198.12*** R2 (overall) 0.229 0.743 0.781 Hausman 11.78* 9.05 --- LM Test 0.05 44.72*** 52.40*** 10/17/2007 2007 Conference on Information Systems and Technology 18 Table 5. Random Effects Regression Results: Operating Efficiency Robust standard errors in parentheses. Dependent variable is operating efficiency ratio (ERt). All variables except number of acquisitions and efficiency ratio are logged, and all except for firm size (employees) and efficiency ratio are standardized. Significant at *10%, **5%, and ***1% level for 1- tailed t-tests. (1) (2) (3) Aggregation One Two Three (years) Integration Costs -0.022** -0.245*** -0.294*** (IC) β1 (0.01) (0.059) (0.069) Acquisition -0.053* -0.056 -0.137* Value (AV) β2 (0.036) (0.064) (0.085) IT Expenditure 0.139** 0.475* 0.408 (IT) β3 (0.083) (0.332) (0.341) IT μ AV β4 0.058** 0.093* 0.157** (0.027) (0.056) (0.072) Number of Acquisitions -0.022** -0.03*** -0.025*** (NA) β5 (0.011) (0.01) (0.011) Firm Size (S) β6 0.019 -0.009 0.006 (0.026) (0.046) (0.048) Constant 0.085 1.319*** 1.302*** (0.18) (0.155) (0.176) Observations 77 107 118 Firms 20 31 31 Wald statistic 32.85*** 59.74*** 61.08*** R2 (overall) 0.210 0.0877 0.0882 Hausman 8.81 9.03 16.11** LM Test 16.24*** 3.24* 3.51* 10/17/2007 2007 Conference on Information Systems and Technology 19 References Aberg, L., and Sias, D. "Taming Postmerger IT Integration," The McKinsey Quarterly (January 18, 2005) 2005. Akhavein, J.D., Berger, A.N., and Humphrey, D.B. 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