How Achieving the Dual Goal of Customer Satisfaction and Efficiency in Mergers Affects a Firm’s Long-Term Financial Performance

Abstract

This article investigates how engaging in a merger moderates the joint impact of a firm’s achievement of dual goals of customer satisfaction and firm efficiency on a firm’s long-term financial performance. Many prominent firms grow through mergers. Recent examples in the services context include the merger between Toronto-Dominion Bank and Canada Trust, and the merger between Continental and United Airlines. Our results show that joint achievement of customer satisfaction and efficiency is beneficial in merger contexts, but not in nonmerger contexts. We investigate the moderating role of mergers using a longitudinal panel of 429 observations across multiple firms and industries. These results suggest that merging firms should not take a myopic perspective of only wresting efficiencies (as the finance literature suggests). Rather, merging firms should focus on simultaneously improving customer satisfaction and improving efficiency to maximize long-term firm value.

Keywords

merger dual emphasis customer satisfaction efficiency financial performance firm performance

Introduction

The number of mergers and acquisitions in the United States has varied from 7,802 in 2002 to 9,995 in 2005; 7,789 in 2010; 9,615 in 2011; and to 9,590 in 2012 (The Thomson Corporation 2006, 2011). In 2006, the value of these mergers was US$1.3 trillion, which translated into roughly 10% of the U.S. gross domestic product. Subsequent years saw a sharp downturn, until 2010 when merger activity rose again to US$822 billion. Despite the frequency of mergers, only 20–40% of all mergers are successful at creating monetary value (Christofferson, McNish, and Sias 2004; Dyer, Kale, and Singh 2004; Marks and Mirvis 2001). How does a merger contribute to the success of a firm? We argue that mergers can enable a firm to achieve a simultaneous enhancement of customer satisfaction and efficiency or a “dual emphasis” which can impact long-term financial performance (Mittal et al. 2005; Rust, Moorman, and Dickson 2002).

Arguing that singular focus on efficiency or on customer satisfaction may be misplaced, Mittal et al. (2005, p. 553, italics in original) state there is a “widespread belief that successful firms must focus either on customer satisfaction or low costs (Porter 1980) and that attempts to achieve a dual emphasis will leave firms ‘stuck in the middle.’” They articulate a virtuous cycle for firms achieving a dual emphasis that improves firm value. Notably, past research distinguishes management’s desire to achieve a dual emphasis versus management actually achieving a dual emphasis. In other words, a desire to achieve a dual emphasis is different than an actual achievement of dual emphasis. According to Rust, Moorman, and Dickson (2002), a dual emphasis, while desirable, can be very difficult to achieve. This article focuses on a particular context—merger—where a dual emphasis has been achieved and examines its impact on long-term firm value.

A qualitative look inside recently merged firms shows that both efficiency and customer satisfaction improvements in a merger context can be important. For instance, the 2000 Toronto-Dominion (TD) Bank and Canada Trust merger (Campbell and Kazan 2009, p. 4) highlights the role of improving efficiency while simultaneously improving customer satisfaction: “The success of the merger hinged on building scale and generating higher returns through revenue growth and cost savings.” The newly formed TD Canada Trust addressed both these goals through buy-in and empowerment of frontline branch employees who embraced the cost-cutting efforts while also delivering superior banking experiences to enhance customer satisfaction. Similarly, the recent United-Continental merger highlighted the importance of obtaining both merger-related efficiencies and customer satisfaction. Citing the dual goals, Greg Taylor, United’s Chief Integration Officer explicitly, stated that in addition to becoming more efficient, the merged entity would focus on “customer experience to ensure everything is well coordinated and one hand knows what the other is doing” (The New York Times 2010, p. TR3). Understanding the extent to which simultaneously achieving customer satisfaction and efficiency during a merger affects firm value is the focus of this article.

We test the hypothesis that there is a differential impact of achieving a dual emphasis on firm value in a merger context, relative to a nonmerger context. Testing this hypothesis answers the call of Rust, Moorman, and Dickson (2002, p. 20) that researchers need to examine a “wider set of contingencies that could influence the financial implications of various quality profitability emphases.” Our article becomes particularly important because organizational contexts that could promote an effective (performance-enhancing) dual emphasis strategy have not been studied in the literature. For instance, Morgan and Piercy (1996) interview a number of British managers and cast doubt on the feasibility of a dual emphasis, in part because it is hard to argue that a single approach is equally beneficial for all types of firms. Rust, Moorman, and Dickson (2002) posit that the reason they found negative returns from a dual emphasis might be due to nonreinforcing dynamics or a fixed budget. Mittal et al. (2005) address the feasibility issue, while this article addresses the fixed resources issue and hypothesizes that a merger context presents a less restrictive environment, in terms of budget and otherwise.

The next section outlines the conceptual framework. The second section, “Data and Modeling,” uses a time-series cross-sectional sample of firms across various industries. The sample involves 429 observations including 233 mergers and acquisitions from 1996 to 2003. We focus on firm value (i.e., Tobin’s q) as the measure of financial performance. The results of the estimation are presented next. We conclude by summarizing the results and discussing the implications of the findings.

Theoretical Development

The finance and strategy literature examining success factors in mergers is extensive (Capron 1999; Chatterjee 1986; Eckbo 1983; Farrell and Shapiro 2001) and generally concludes that efficiency gains resulting from synergies between merging entities contribute to financial success in a merger context (Anand and Singh 1997; Datta, Pinches, and Narayanan 1992; Larsson and Finkelstein 1999; Montgomery and Singh 1984; Sirower 1997). For instance, Anand and Singh (1997) find that Tobin’s q values increase for firms in declining industries if they merge with similar firms to create economies of scale efficiencies. Within marketing, there is a recent stream of research arguing that a firm’s ability to acquire the appropriate marketing resources during a merger can strengthen firm performance after a merger (Homburg and Bucerius 2005; Swaminathan, Murshed, and Hulland 2008; Wiles, Morgan, and Rego 2012). Thus, the marketing literature adopts a resource-based perspective which argues that resources which are valuable, rare, hard to imitate, and difficult to substitute enable a firm to achieve a distinctive competitive advantage (Barney 1991; Teece 1982). Such resources include identification, tracking, and redeployment of profitable customers, as well as a greater focus on more profitable product offerings. We argue that these available resources have a positive impact on the merged firm from improving the value-impact of customer satisfaction improvements.

The Joint Impact of Customer Satisfaction and Efficiency

Prior research has argued theoretically that if a firm can achieve a dual emphasis—both customer satisfaction and efficiency improvements—it should financially outperform a company that emphasizes only one of these factors (Mittal et al. 2005). Although challenging to achieve (Rust, Moorman, and Dickson 2002), customer satisfaction and efficiency improvements complement each other because additional revenues generated from higher customer satisfaction can be invested in additional efficiency-enhancing initiatives (e.g., improvements in information technology). Thus, a virtuous feedback cycle ensues (Mittal et al. 2005), making it feasible for firms that simultaneously achieve customer satisfaction and efficiency to get superior financial returns. How might a merger context moderate the effect of achieving a dual emphasis on firm value?

Dual Emphasis and Financial Performance in a Merger Context

We argue that a merger provides a unique context that not only facilitates the achievement of a dual emphasis but where achieving a dual emphasis may also become particularly rewarding for firms. Organizational restructuring following a merger, particularly in a service industry context, helps reallocate human and other resources to their most productive use, which should increase the financial benefits. Previous research demonstrates that resource redeployment following mergers enables acquiring firms to earn abnormal returns (Capron 1999; Capron and Pistre 2002). For example, transfer of employees from the acquiring firms to target firms could result in greater performance enhancements, because resources can be utilized to their full potential. Thus, mergers enable employee redeployments which, in turn, convert into better performance outcomes.

A large stream of empirical research shows that customer satisfaction is a critical contributor to a firm’s shareholder value (Mittal and Frennea 2010) because it positively influences key customer behaviors such as repurchase and positive word of mouth. A merger presents an opportunity for firms to identify and focus on their most profitable customer segments in order to strengthen their overall profitability. A merger setting also facilitates access to new markets and may be able to attract new target segments whose needs are more aligned with the firm’s offerings. These new segments may have more profitable customers resulting in greater revenues derived for every unit of change in customer satisfaction levels. Further, the ability to restructure product offerings allows for greater performance improvements. For instance, Karim and Mitchell (2000) find that hospitals that underwent mergers or acquisitions had more new product lines, fewer old product lines, newer industry relevant resources, and resource extensions compared to hospitals that did not acquire other firms. In addition, postmerger integration efforts may result in better identification of profitable customers and better customer tracking. For instance, when Harrah’s Entertainment acquired Caesars Entertainment Inc., it was expected that extending Harrah’s customer loyalty systems and processes to Caesars’ clients could help drive more than 90% of the new revenue expected for the combined firm (Computerworld 2005). For these reasons, a unit change in customer satisfaction may have a greater impact on firm value in a merger context than in a nonmerger context.

Efficiency improvements associated with a merger potentially also contribute to long-term performance. Firms can improve their value through various initiatives such as improvements in information technology (Rust, Moorman, and Dickson 2002), economies of scale and scope (Dranove and Shanley 1995), and elimination of overlaps and redundancies through downsizing and pruning excess resources ( The New York Times 2001, 2005; The Wall Street Journal 2005). For instance, Robbins and Stylianou (1999) find that although mergers may disrupt the organizations involved in the short term, carefully planned postmerger integration ultimately can strengthen the organizational systems and capabilities of the firms involved. Thus, achieving the dual goal of customer satisfaction and efficiency is likely to have a stronger, positive impact on firm performance in a merger context than in a nonmerger context. This impact may also occur if the merger context enables firms to enhance their market power resulting in lower input prices (Focarelli and Panetta 2003).

To be sure, it is also possible that firms that ignore customer satisfaction in pursuit of efficiency in a merger context may have lower value. As an example, take the case of the airline industry. A well-publicized merger of Delta and Northwest Airlines in 2008 was largely heralded as a successful merger from the cost savings perspective. Following the merger, Delta adopted a disciplined approach including cutting costs and enacting capacity utilization strategies which ensured that costs were kept under control. On the revenue side, Delta increased its fares, which translated into revenue growth. Together, the successful implementation of this strategy resulted in greater short-term profitability. Despite this outcome, Delta’s customer satisfaction level had declined significantly in the postmerger period, and its American Customer Satisfaction Index (ACSI 2004) score was among the worst in the industry (The New York Times 2011). The airline also had the worst record for on-time arrivals, and accounted for a third of all customer complaints (The New York Times 2011). If efficiency is the only driver of long-term performance, one would expect Delta to have an increased stock market valuation. Contrary to this belief, Delta Airlines has fallen short of earnings targets, and its stock market value has lagged the broader Standard & Poor’s (S&P) 500 Index since 2007, with the S&P change in the 5-year period from 2007 to 2012 at −13% and the comparable change in Delta’s stock market value at −45%. Thus, anecdotal evidence suggests that a focus on efficiency alone may not suffice to enhance firm value; rather an improvement in customer satisfaction from potentially achieving a dual emphasis through both customer satisfaction and efficiency is essential to a firm’s success following a merger.

Finally, the rationale for why a simultaneous achievement of customer satisfaction and efficiency improvement enhances long-term financial performance during mergers also can be viewed from the perspective of the fundamental economic model that contributes to profitability. Mergers enable firms to focus their efforts on more profitable customers resulting in increased revenues, even for a given level of customer satisfaction. Higher efficiency leads to lower operations and marketing costs per sales dollar, which lowers variable costs and increases the gross margin per unit sales. Mergers also can create economies of scale and scope that can further stimulate experience-curve effects and lower fixed costs (Chatterjee 1986; Lubatkin 1983). Thus, an emphasis on a higher quality customer base can increase sales and an emphasis on efficiency can increase the return from each sale. The fundamental economic model is profit equals sales times gross margin less fixed costs. Since customer satisfaction drives sales and efficiency drives gross margin, customer satisfaction and efficiency improvements must have a multiplicative effect on financial performance in the long term. However, achievement of a dual emphasis may not have a multiplicative effect on short-term financial performance, because efforts to increase customer satisfaction produce costs that reduce efficiency improvements in the short term.

However, in the absence of a merger context, a firm may lack many of the resources to efficiently achieve performance increases resulting from a dual emphasis. For instance, they will not benefit from identification of more profitable segments, or a focus on more profitable product lines. These firms also will not have ready access to new markets and new market resources, or realize newfound synergies that a merged firm has created.

Therefore, the opportunities created by a merger (i.e., the ability to successfully redeploy employees, the identification of profitable customer segments that are less costly to serve) allow for the increased performance impact of customer satisfaction and efficiency improvements compared to nonmerger scenarios. In summary, we hypothesize that the joint effect of simultaneous improvements in both customer satisfaction and efficiency will have a stronger multiplicative impact on firm value in a merger context relative to a nonmerger context.

Study: Data Set and Model Development

Our arguments predict a joint effect of customer satisfaction and efficiency in a merger context on firm value. In developing the empirical model, it is important to remove the rents that are attributable to monopoly factors and firm-specific factors. We control for change in firm size, unanticipated return on investment (ROI), change in industry concentration, merger type (e.g., horizontal or vertical), number of mergers in a given year, change in cash flow, industry growth rate, industry type (e.g., goods vs. services), and age of firm. The inclusion of these covariates mitigates the impact of alternative sources of value creation other than customer satisfaction and efficiency.

Data Description

Data for this research were gathered from multiple sources. Customer satisfaction data were obtained from the ACSI (www.theacsi.org). Firm financial data were obtained from Compustat. Merger data were collected from The Wall Street Journal and SDC Platinum. Our sample is constrained because we could only focus on firms that have customer satisfaction data in ACSI. Further, we used a model which is based on first differencing of customer satisfaction data as well as financial (efficiency and firm performance) data. As a result of this approach, firms have to have data present both for each year and also the preceding year in order to effectively difference the customer satisfaction and financial data. Due to the fact that a number of firms enter and exit the ACSI data set, we have an unbalanced panel. In a given year, between 9% and 40% of firms enter or leave the data set. For all these reasons, our sample of firms is rather constrained; we acknowledge that this is a limitation since our methodology of first differencing may result in eliminating firms that may have not survived for longer periods of time. Our final sample consisted of 429 observations from 1995 to 2003, 233 of which (54%) involved a merger, and the remaining firm-year observations (N = 196) had no merger. Each firm appears only once per year and the merger variable is an indicator which takes on the value 1 if a merger was undertaken in a given year and remained 0 otherwise. When a firm entered into multiple mergers in a given year the number of mergers that the firm underwent is controlled for.¹ We collected yearly firm data for each of the independent and dependent variables, and the unit of analysis is a firm in a given year.

Measures

Long-Term Financial Performance

We used change in Tobin’s q to measure firm value. Tobin’s q is a measure of a firm’s long-term financial performance (Anderson, Fornell, and Mazvancheryl 2004; Luo and Bhattacharya 2006). Tobin’s q measures the ratio of market value of a firm’s securities to the replacement costs of its total assets. It is based on stock prices, which means it adjusts to market reaction, helps ameliorate questions of risk and manager manipulation, and is supported by underlying existing asset measures, such as profits and cash flow (Villalonga 2004). According to Montgomery and Wernerfelt (1988, p. 627), “Tobin’s q implicitly uses the correct risk-adjusted discount rate, imputes equilibrium returns, and minimizes distortion.” It is a forward-looking measure that provides a market-based view of investor expectations of the firm’s future potential and success in the long run (Anderson, Fornell, and Mazvancheryl 2004). Consistent with prior research in marketing (Lee and Grewal 2004), we use Chung and Pruitt’s (1994) method for calculating Tobin’s q:

T o b i n' s q = (M V E + P S + D E B T) / T A,

where MVE is the product of a firm’s share price and the number of common stock shares outstanding (calculated on the last day of the year); PS is the liquidating value of the firm’s outstanding preferred stock; DEBT is the value of the firm’s short-term liabilities net of assets, plus the book value of the firm’s long-term debt; and TA is the book value of the total assets of the firm. Change in Tobin’s q is measured as the value of Tobin’s q in a given year less the value of Tobin’s q from the previous year: $Δ T o b i n' s q_{t} = T o b i n' s q_{t} - T o b i n' s q_{t - 1}$ . Note that all subsequent change variables are calculated in the same manner.

Change in Customer Satisfaction

Following Mittal et al. (2005), a firm’s achievement of revenue emphasis is measured by the extent to which it achieves improvements in customer satisfaction. Customer satisfaction measures were obtained from the ACSI. More than 65,000 customers are interviewed annually to obtain data on customer satisfaction for various brands (Fornell et al. 1996). ACSI satisfaction scores range from 0 to 100. A key benefit of the ACSI is that scores for brands can be compared over time and across industries. One potential concern is combining brand-level data provided by ACSI with company-level performance metrics. However, past research has utilized ACSI data extensively in explaining firm performance (Mittal and Frennea 2010). Additionally, our dependent measure is a stock market–based measure of firm performance (i.e., Tobin’s q) and brand names constitute a large portion of the intangible value of a firm (Villalonga 2004). For these reasons, combining ACSI data (which is at a brand level) with firm-level performance metrics is a reasonable approach. When there are multiple brands per firm, the ACSI scores are averaged to obtain a firm-level measure. This approach is consistent with past research practice (Anderson, Fornell, and Mazvancheryl 2004; Mittal et al. 2005). In the final data set, 45.5% of the firms had a decrease in ACSI scores, 16.5% had no change, and 38% of firms had an increase in ACSI scores.

Change in Efficiency

Following Anderson, Fornell, and Rust (1997), sales per employee was used as a measure of firm efficiency. Similar to customer satisfaction, change in firm efficiency was measured as the difference between the current year’s value and the previous year’s value.

Merger

To collect merger information, The Wall Street Journal and SDC Platinum were used. SDC Platinum was used as the primary source of the mergers while The Wall Street Journal was used to verify the SDC Platinum data and to provide new data. A dummy variable took the value 1 only in the year in which a merger was completed and remained 0 otherwise. The firm-year observations representing mergers represented 54% of the sample and nonmergers 46%. Since some firms undertake multiple mergers within a given year, in addition to the merger dummy, we control for number of mergers undertaken by a firm in a given year. In the final data set, 35.4% of the firms had a decrease in efficiency and 64.6% of the firms had an increase in efficiency.

Type of Merger (Horizontal vs. Vertical)

In addition to coding the presence of a merger, it is important to control for the type of merger. A horizontal merger is one in which firms within the same industry merge, and such mergers have shown to have significant implications for financial performance due to gains in market power (Chatterjee 1986; Montgomery 1985; Swaminathan, Murshed, and Hulland 2008). We coded merger type as 1 if any of the mergers announced in a given year by a firm was a horizontal merger or a merger of two firms within the same industry; the horizontal merger variable remained 0 otherwise. A match of four-digit Standard Industry Classification (SIC) codes determined whether a merger was horizontal, determining that 37% of the observations involved a horizontal merger.

Change in Industry Concentration

Change in industry concentration is a control variable. Industry concentration was measured using the Herfindahl-Hirschman Index (HHI), which was defined as a sum of the squares of the market shares of each firm in the industry (see Hirschman 1964). An industry was defined based on a four-digit SIC code. This is a commonly used measure of the industry concentration (Aiginger, Brandner, and Wüger 1995; Cotterill 1986; Golan, Judge, and Perloff 1996; Milne 1992). An HHI closer to 1 implies a higher industry concentration (few firms) and an HHI closer to 0 represents a less concentrated industry (many firms).

Change in Firm Size

Change in firm size is introduced as a control variable because larger firms (with a positive change in firm size) may be able to devote more resources to increasing efficiency or improving customer satisfaction. We use the log of number of employees from Compustat as a proxy for firm size (Chandy and Tellis 2000). This metric correlates highly with alternative measures such as sales data (r = .80).

Industry Type

Anderson, Fornell, and Rust (1997) find differences in customer satisfaction between services and goods industries, so we introduced a dummy variable to control for this characteristic of firms; 61% of the observations in our sample were composed of services industries.

Unanticipated ROI

We also controlled for the effect of unanticipated accounting performance during the 1-year time period. To calculate unexpected accounting performance, we first measure ROI by dividing net income by the book value of assets. Consistent with past research (Mizik and Jacobson 2007), we use a fourth-order autoregressive model to approximate annual accounting performance. The model was of the following form:

{R O I}_{i t} - {\overline{R O I}}_{t} = b_{0} + \sum_{k = 1}^{4} b_{k} ({R O I}_{i, t - k} - \overline{{R O I}_{t - k}}) + ϵ_{i, t} .

In the above equation, we constructed an instrument for ROI for firm i in a given year t. In order to do this, we used lagged values of ROI in four previous time periods (years) subtracted from the concurrent economy-wide mean in the respective time periods. We then regressed the current period ROI (subtracted from the economy-wide mean ROI) against these lagged values. We then obtained an instrumental variable estimate of ROI. This instrument for ROI will be uncorrelated with the error of the Tobin’s q equation if there is no serial correlation in the errors. The residual in this equation (∊ _it ) is the unanticipated ROI.

As a robustness check, we also reanalyzed the data using an alternative measure of unanticipated ROI based on the approach used previously (Aaker and Jacobson 1994; Mizik and Jacobson 2003). Specifically, we controlled for the effect of unanticipated accounting performance by estimating a first-order autoregressive model of the following form: ${R O I}_{i, t} = b_{0} + b_{1} {R O I}_{i, t - 1} + ϵ_{i, t}$ . The residual from this equation was used as a measure of unanticipated ROI. The correlation between the two measures is high (r = .57, p < .01). Reassuringly, both approaches yield identical results. Since the first approach draws on more recent approaches and more precisely accounts for potential endogeneity, we used the first measure of unanticipated ROI.

Industry Growth Rate

We controlled for industry growth rate by taking the difference in industry sales during the 1-year period and dividing it by the original sales level.

Change in Cash Flow

The availability of cash flow could determine the extent to which a firm can embark on projects that could contribute to overall financial value. Consistent with the other measures in the model, the cash flow variable also was differenced.

Firm Age

We controlled for firm age by using the earliest year the firm appeared in Compustat.

Time

We controlled for specific year effects by including 8-year dummies (Years 1–8).

Model Development

In addition to controlling for various firm factors through the inclusion of covariates, there is a need to control for estimation biases arising from omitted fixed, random, and time-varying effects as well as possible measurement errors (Jacobson 1990). These unobservable firm-specific effects can include a company’s specific environment, managerial expertise, access to scarce resources, specific ability to provide the customer with superior value, and even plain luck (Anderson, Fornell, and Rust 1997). A variety of methods typically are used to control for these effects. Our methodology employs first differencing, which accounts for unmeasured firm-specific effects in the data though it can produce smaller R ² values. Based on these considerations, the final model is based on first-differencing of all the relevant time-varying variables.² All continuous variables are mean centered to decrease potential multicollinearity.

It could be argued that changes in efficiency and changes in customer satisfaction are not exogenous, but are endogenously determined as a consequence of the merger itself. Hausman’s specification test can be used to determine if it is necessary to use an instrumental variables method rather than a more efficient ordinary least squares (OLS) estimation. The standard approach is to compare an instrumental variable regression to a standard OLS approach using a Hausman test. However, our subsequent modeling approach required that we utilize an approach that addresses the unbalanced panel structure of the data—we utilize a random effects generalized least squares (GLS) model to account for this. Therefore, we also contrast a random effects GLS with a 2SLS (stage least squares) approach. Based on the Hausman test, with change in customer satisfaction, change in efficiency, and change in Tobin’s q as well as their interactions (including the two-way interactions of these variables with merger) were endogenous, we found no evidence of endogeneity. Thus, a random effects GLS was preferred to 2SLS (χ² = 23.23, df = 22, ns). Further, 2SLS was compared to a 3SLS specification and 2SLS was no worse than a 3SLS (χ² = 23.56, df = 22, ns).

We also compared the more traditional OLS to 2SLS and OLS was found to be no worse (χ² = 16.04, df = 22, ns). Although OLS is a simpler specification, OLS does not account for the unbalanced panel nature of the data. Random effects GLS accounts for the heterogeneity due to firm effect and was shown to be more appropriate than a fixed effects specification based on a Hausman test (χ² = 19.72; df = 21; p > .10). Therefore, a final model utilizing a random effects GLS is estimated using STATA and is described below. The time period of differencing is a single year, that is, year t minus year t − 1. Firm effects are present in our equation because first differences may vary across firms. Therefore, the random effects specification accounts for the firm and time effects.

\begin{array}{l} Change in Tobin's q_{i t} {= β}_{0} {+ β}_{1} (Δ {Customer Satisfaction}_{i t} {) + β}_{2} (Δ {Efficiency}_{i t} {) + β}_{3} {(Merger}_{i t}) + \\ β_{4} (Δ {Customer Satisfaction}_{i t} \times Δ {Efficiency}_{i t} {) + β}_{5} (Δ {Customer Satisfaction}_{i t} \times {Merger}_{i t}) + \\ β_{6} (Δ {Efficiency}_{i t} \times {Merger}_{i t} {) + β}_{7} (Δ {Customer Satisfaction}_{i t} \times Δ {Efficiency}_{i t} \times {Merger}_{i t} {) + β}_{8} (Δ {HHI}_{i t}) \\ {+ β}_{9} {(Services}_{i t} {) +  β}_{10} (Δ {Firm Size}_{i t} {) + β}_{11} {(Unanticipated Return on Investment}_{i t} {) + β}_{12} (Δ {Cash Flow}_{i t}) \\ {+ β}_{13} {(Firm Age}_{i t} {) + β}_{14} {(Industry Growth}_{i j t} {) + β}_{15} {(Horizontal Merger}_{i t} {) + β}_{16-23} (Year Dummies) + v_{i t} . \end{array}

All variables pertain to a particular firm i in year t. The error structure of the equation below consists of two components and can be represented as: $v (i, t) = e (i, t) + u (i)$ , where u(i) represents the firm effect, and e(i,t) represents the remaining error.

Results

Table 1 summarizes the means, standard deviations (SDs), and correlations of key variables. The results for the first-differencing equations are shown in Table 2. This table presents the results with change in Tobin’s q as the dependent variable, with the models analyzed with the full sample of mergers. Variance inflation factors were all less than 4, indicating minimal multicollinearity. All continuous variables were mean centered to avoid multicollinearity.

Table 1.

Descriptive Statistics.

	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17
1. Change in Tobin’s q	0.03	0.85	1.00
2. Change in customer satisfaction	0.00	2.56	0.00	1.00
3. Change in efficiency	0.00	156.03	0.22	−0.01	1.00
4. Merger			−0.04	0.00	0.07	1.00
5. Customer Satisfaction × Efficiency	−3.65	434.79	0.10	−0.11	−0.23	0.04	1.00
6. Customer Satisfaction × Merger	0.00	1.91	−0.01	0.75	0.02	0.00	−0.05	1.00
7. Efficiency × Merger	5.56	116.10	0.24	0.02	0.75	0.04	−0.38	0.03	1.00
8. Customer Satisfaction × Efficiency × Merger	6.05	305.80	0.16	−0.06	−0.40	0.02	0.70	−0.08	−0.54	1.00
9. Change in industry concentration	0.00	0.07	0.04	0.04	0.11	0.03	0.03	−0.01	0.00	0.01	1.00
10. Services			−0.06	0.00	0.02	−0.06	0.00	0.00	0.09	0.03	0.01	1.00
11. Change in firm size (sales)	1192.00	4981.00	0.01	−0.04	0.41	0.02	−0.20	−0.02	0.47	−0.34	0.02	0.09	1.00
12. Unanticipated ROI	0.01	0.10	−0.01	0.03	−0.01	−0.03	0.03	0.05	0.00	−0.01	−0.01	0.00	0.01	1.00
13. Change in cash flow	−103.53	2850.00	0.01	−0.04	0.03	0.01	0.00	0.06	0.03	−0.02	0.06	0.03	0.21	0.12	1.00
14. Age of firm	36.80	15.75	−0.08	0.05	0.06	0.13	−0.10	0.02	0.03	−0.09	0.01	0.00	0.00	−0.04	−0.02	1.00
15. Industry growth	0.07	0.11	−0.01	−0.01	0.22	0.01	0.01	0.05	0.21	−0.03	−0.03	0.20	0.22	−0.01	0.01	−0.01	1.00
16. Horizontal mergers			0.01	0.09	0.08	0.17	0.00	0.06	0.03	−0.03	0.07	0.03	0.03	−0.02	0.03	0.04	−0.04	1.00
17. Number of mergers	1.59	1.43	−0.04	−0.06	−0.02	0.57	0.10	−0.10	−0.07	0.11	0.04	0.02	−0.04	−0.07	0.01	0.15	−0.02	0.14	1.00

Note. ROI = return on investment.

N = 429. Variables in italics are significant at p < .05. It should be noted that means and standard deviations are not meaningful for the categorical variables (i.e., mergers, horizontal and services variables) and therefore have not been reported here.

Table 2.

Customer Satisfaction, Efficiency, and Tobin’s q in a Merger Context.

Change in Tobin’s q Model	Parameter	SE
Intercept	0.233	0.213
Δ Customer satisfaction	0.017	0.022
Δ Efficiency	0.001*	0.000
Merger	−0.078	0.097
Δ Customer Satisfaction × Δ Efficiency	0.000	0.000
Δ Customer Satisfaction × Merger	−0.007	0.029
Δ Efficiency × Merger	0.004***	0.001
Δ Customer Satisfaction × Δ Efficiency × Merger	0.001***	0.000
Δ Industry concentration	0.562	0.517
Services	−0.228**	0.110
Δ Firm size (ln(emp))	0.000	0.000
Unanticipated ROI	−0.171	0.368
Δ Cash flow	0.000	0.000
Firm age	−0.003	0.003
Industry growth	−0.678*	0.363
Horizontal merger	0.147	0.137
Mergers per firm per year	0.010	0.035
Year 1 (1996)	0.317	0.286
Year 2 (1997)	0.177	0.213
Year 3 (1998)	0.240	0.149
Year 4 (1999)	0.364**	0.144
Year 5 (2000)	−0.126	0.143
Year 6 (2001)	−0.135	0.147
Year 7 (2002)	−0.092	0.134
Year 8 (2003)	−0.179	0.131
R ²	.25

Note. ROI = return on investment.

N = 429.

*p < .10. **p < .05. ***p < .01.

Differenced variables were mean centered following the differencing. The models also were run with nonmean centered variables, and similar results were obtained.

Model Estimates: Tobin’s q

The overall model is significant (model χ² = 150.34, p < .001; R ² = 25%). The results also indicate that 20% of the variance is due to differences across panels (i.e., due to the firm-level heterogeneity). The full-sample model (Table 2) with change in Tobin’s q as the dependent variable shows the coefficients for change in efficiency are significant at the 10% level of significance (b = .001, p < .10). However, both mergers (b = −.078, ns) and change in customer satisfaction (b = .017, ns) are statistically nonsignificant. Among the control variables, service industry (b = −.228, p < .05), industry growth (b = −.678, p < .10), and year 4 (b = .364, p < .05) are significant. The rest of the control variables, including age of the firm (b = −.003, ns), firm size (b = .000, ns), unanticipated ROI (b = −.171, ns), and horizontal merger (b = .147, ns), were statistically nonsignificant. Thus, in general, with a few exceptions, it appears that our results are not influenced by these control variables. Most likely, the differencing model eliminates the impact of several firm and industry differences.

Next, we examine the interaction terms. Among the three possible two-way interactions, the interaction of change in customer satisfaction and change in efficiency (b = .000, ns) and the interaction of change in customer satisfaction and merger (b = −.007, ns) are statistically nonsignificant; in contrast, the change in efficiency and merger interaction is significant (b = .004, p < .01). However, these two-way interactions are qualified by the three-way interaction of change in customer satisfaction, change in efficiency, and merger; this three-way interaction is positive and significant (b = .001, p < .01), which is consistent with our theorizing.

To facilitate a visual interpretation of the three-way interaction, we followed the methodology described in Aiken and West (1991) to produce the graphs in Figure 1A and B. These figures examine the mean performance gains in Tobin’s q under all eight combinations of increase and decrease in customer satisfaction, increase and decrease in efficiency, and merger and nonmerger contexts. In order to create high and low levels of satisfaction and efficiency changes, we added and subtracted 1 SD from the mean levels of satisfaction and efficiency changes. Figure 1A shows the results for a merger context and Figure 1B shows the results for a nonmerger context.

Figure 1.

The moderating role of merger on the effects of changes in efficiency and customer satisfaction on the change in Tobin’s q (N = 429). (A) Merger context. (B) Nonmerger context.

Merger Context

As seen in Figure 1A, the simple slope for efficiency increase is significant (b = .225, p < .01) and the slope for efficiency decrease is also significant (b = −.155, p < .01). In a merger context, a customer satisfaction increase along with a simultaneous efficiency increase yields the highest change in Tobin’s q (1.43). Interestingly, a satisfaction increase accompanied by an efficiency decrease leads to a decline in long-term financial performance (−0.78). In contrast, a customer satisfaction decrease is associated with only a small increase in Tobin’s q when there is an efficiency decrease (0.01). However, an increase in efficiency combined with a satisfaction decrease leads to a larger change in Tobin’s q (0.27). Note, this is smaller than the increase in Tobin’s q when both customer satisfaction and efficiency simultaneously increase (1.43). Taken together, firm value created by a combination of simultaneous satisfaction and efficiency increase is higher than all other combinations in a merger context; therefore, these findings provide strong support for the beneficial impact of dual-goal achievement in a merger context on long-term performance.

Nonmerger Context

As can be seen in Figure 1B, the simple slope for efficiency increase (b = −.001, ns) and decrease (b = .030, ns) are statistically nonsignificant. These results indicate that satisfaction increases combined with efficiency increases do not enhance firm value in a nonmerger context compared to the three other scenarios (e.g., satisfaction decrease and efficiency decrease). We discuss these results in greater detail in the Discussion section.

Additional Robustness Checks

To establish robustness of the results presented above, we examine several alternative specifications of the measures as well as the models.

Alternative Measure of Tobin’s q

A potential issue with the measurement of the dependent measure is that Tobin’s q is calculated using data from the last day of the year (i.e., after the ACSI score release). In some industries, based on the ACSI release date, there could be a difference in when Tobin’s q data are calculated relative to the ACSI release date. To address this potential concern, we reanalyzed the data after measuring Tobin’s q as the average of the four quarters’ share price and number of shares outstanding. We estimated the model and plotted the graphs, using the revised results. The pattern of results remains the same. However, the approach of averaging across four quarters produces an inconsistency across this measure of Tobin’s q and the other variables in the model (e.g., change in efficiency, change in cash flow, change in firm size), which are measured at a single point in time, typically reported annually by Compustat. In order to adopt a standardized method of calculating all of the variables, we retained our earlier approach of measuring Tobin’s q at the end of the year.

Results Excluding Utilities and Telecommunications Firms

Utilities and telecommunications are subject to regulation and the effect of customer satisfaction has been shown to change when utilities industries are excluded (Jacobson and Mizik 2009). Therefore, we reestimated the model after excluding utilities and telecommunications industries, both of which were subject to considerable regulation. The resultant sample size was 325. The results of this estimation are summarized in Table 3. As before, the three-way interaction of change in customer satisfaction, change in efficiency, and merger is positive and significant (b = .003, p < .01). We also analyzed the data after excluding only utilities industries from the sample (rather than both utilities and telecom). The results do not change.

Table 3.

Customer Satisfaction, Efficiency, and Tobin’s q in a Merger Context (Excluding Utilities and Telecom).

Change in Tobin’s q Model	Parameter	SE
Intercept	−0.079	0.212
Δ Customer satisfaction	0.020	0.028
Δ Efficiency	0.001*	0.001
Merger	0.017	0.108
Δ Customer Satisfaction × Δ Efficiency	0.000	0.000
Δ Customer Satisfaction × Merger	0.029	0.037
Δ Efficiency × Merger	0.007**	0.001
Δ Customer Satisfaction × Δ Efficiency × Merger	0.003**	0.000
Δ Industry concentration	0.275	0.518
Services	−0.128	0.086
Δ Firm size (ln(emp))	0.000	0.000
Unanticipated ROI	−0.487	0.408
Δ Cash flow	0.000	0.000
Firm age	0.001	0.003
Industry growth	−0.116	0.551
Horizontal merger	0.231	0.144
Mergers per firm per year	0.030	0.040
Year 1 (1996)	0.396	0.294
Year 2 (1997)	0.261	0.231
Year 3 (1998)	0.168	0.168
Year 4 (1999)	0.225	0.165
Year 5 (2000)	−0.186	0.161
Year 6 (2001)	−0.240	0.170
Year 7 (2002)	−0.043	0.156
Year 8 (2003)	−0.183	0.149
R ²	.50

Note. ROI = return on investment.

N = 325.

*p < .10. **p < .01.

We plotted the three-way interaction of change in customer satisfaction, change in efficiency, and mergers as described earlier (see Figure 2A and B). Reassuringly, the graphs in the merger context are similar to those in the full sample. As in the full sample, but more pronounced, the simple slopes for efficiency increase are positive and statistically significant in a merger context, whereas efficiency decrease is negative and statistically significant (Efficiency increase: b = .345, p < .01; Efficiency decrease: b = −.253, p < .01.).

Figure 2.

The moderating role of merger on the effects of changes in efficiency and customer satisfaction on the change in Tobin’s q (excluding utilities and telecom industries; N = 325). (A) Merger context. (B) Nonmerger context.

The results of the simple slopes in a merger context can be compared to the full sample. The combination of efficiency increase with satisfaction increase offers the most positive change in long-term financial performance (1.51) consistent with the finding for the full sample. The impact of efficiency increase with satisfaction decrease results in a negative change in Tobin’s q (−0.06), and the impact of efficiency decrease with satisfaction increase also results in a negative change in Tobin’s q (−1.46). The simultaneous decrease in efficiency and decrease in satisfaction is associated with a negative change in Tobin’s q (−0.30). This pattern of results in which the dual emphasis on customer satisfaction and efficiency dominates the other combinations is similar to the result in the full sample. The graphs in the nonmerger context (Figure 2B) show that a dual emphasis on customer satisfaction increase and efficiency increase yields a small positive increase in Tobin’s q (0.04). Interestingly, different from the full sample result, none of the other combinations of customer satisfaction and efficiency changes yield a positive change in Tobin’s q. This suggests that dual emphasis yields stronger returns in a merger context relative to a nonmerger context. Further, an increase in customer satisfaction alone and an increase in efficiency alone is not necessarily value creating, particularly when the sample consists of nonutility and nontelecom industries. Perhaps, our approach of first differencing provides a more conservative test of the role of customer satisfaction and efficiency in influencing firm performance, particularly in well-established industries and for firms that have been in existence for a longer period of time. This could potentially explain why our findings regarding the performance implications of customer satisfaction changes appear to be restricted to certain industry contexts and to those settings when satisfaction changes are accompanied by merger activity and efficiency increases

Horizontal and Vertical Merger Interaction

One interesting remaining question is whether the moderating impact of mergers on the relationship between dual emphasis and performance varies based on the type of merger. Mergers can be differentiated based on whether they are within-industry horizontal mergers or across-industry vertical mergers. We estimated a new model where we included two dummy variables, that is, horizontal and vertical mergers, in place of the merger variable. The base case was the nonmerger context. We included a three-way interaction of change in customer satisfaction, change in efficiency, and horizontal merger, and also a three-way interaction of change in customer satisfaction, change in efficiency, and vertical merger as another variable, as well as associated two-way interactions in the same model. The three-way interaction involving horizontal mergers was significant (p < .01), and the three-way interaction involving vertical mergers was marginally significant (p < .10). The implication is that changes in customer satisfaction and efficiency in a merger context may have a greater lasting impact on long-term performance when the merger is horizontal compared to when the merger is vertical.

Discussion

We have argued that during mergers, firms that achieve a dual emphasis—simultaneously increasing customer satisfaction and efficiency—are more likely to accrue significant financial value than firms in a nonmerger context. Consistent with our theorizing, we find the largest long-term value accrues to firms that have undergone a merger and are able to achieve a dual-goal emphasis by simultaneously increasing efficiency and customer satisfaction.

This is an intriguing finding, with strategic implications for firms contemplating a merger-based strategy. When firms merge, they may or may not have a goal to simultaneously enhance efficiency and customer satisfaction. Our study shows that firms that are actually able to achieve both show the highest increase in long-term financial performance. Thus, a simultaneous enhancement of customer satisfaction and efficiency is an important outcome worth achieving during mergers to enhance long-term firm value. Of course, the secondary data we use preclude us from providing process evidence as to why a merger context facilitates value creation from a dual emphasis strategy. In future research, a survey of managers could be used to clearly identify specific actions and initiatives that facilitate the achievement of a dual emphasis during a merger.

What about firms that do not undergo a merger? We find that these firms may not realize high firm value increases from a dual emphasis, and efficiency does not seem to play a major role. More interestingly, we find that in a sample of firms in nontelecommunications and nonutility industries, there is a small decline in Tobin’s q associated with satisfaction increase alone and efficiency increase alone, while a dual emphasis on customer satisfaction and efficiency still yields a small positive change in Tobin’s q. Although these are not statistically significantly different from each other, they do appear to present new evidence that does not necessarily confirm previous research on this topic. We posit that there are three potential explanations for this. First, there are other contingency variables which moderate the impact of customer satisfaction increases on long-term financial performance within a nonmerger context. Second, we focus only on a subset of firms that have data in ACSI as well as financial performance measures available, and our differencing approach requires that firms have data for both the present and the previous year. This approach could result in a sample consisting of larger firms and the presence of survivorship bias could have influenced the results. Because of the presence of larger, more well-established firms in the data, increases in customer satisfaction or efficiency may not have a strong impact on changing the Tobin’s q. Third, our model uses a differencing approach; this is different than the levels approach that have been used in various articles that have examined customer satisfaction in the past. With these in mind, the present findings are a first step in understanding when and how customer satisfaction can influence financial performance and highlight the important role of both mergers and efficiency increases in moderating this relationship.

Implications for Marketing Strategy

As discussed earlier, contexts that facilitate or hinder the achievement of a dual emphasis are not well understood. This was identified as a key research area by both Rust, Moorman, and Dickson (2002) and Mittal et al. (2005). Our article addresses this research gap showing that a merger context is a key moderator that can enhance the financial impact of implementing a dual-goal strategy.

The current research focuses on firms that have already achieved a dual emphasis. Thus, it does not provide insights to firms considering a dual emphasis approach. In other words, what are the antecedents of successful dual emphasis implementation in a merger context? Rust, Moorman, and Dickson (2002) point to numerous institutional, resource-based, and organizational culture constraints that prevent firms from achieving a dual emphasis even if top management is desirous of such a goal. Future research should identify factors that can impose or relax institutional, resource-based, and organizational culture constraints in a firm’s ability to achieve a dual emphasis in a merger context. Such knowledge would also benefit managers who seek to implement a dual emphasis following a merger.

Our results also inform the recent debate regarding the association between customer satisfaction and financial performance (Fornell, Mithas, and Morgeson 2009; Jacobson and Mizik 2009). Some researchers argue that the association between customer satisfaction and financial performance is either not present or is restricted to certain industries (Jacobson and Mizik 2009). Our research shows that rather than examining the customer satisfaction-financial performance link as present or absent, researchers need to examine it as an association that is context-dependent and contingent in nature. In other words, the issue is not if customer satisfaction is associated with financial performance, but when is the association between customer satisfaction and financial performance stronger or weaker. As our research shows, financial return from customer satisfaction is contingent upon changes in efficiency in the context of a merger. Further, as noted previously, it is likely that the merger context facilitates customer and brand redeployments, which contributes to value increases following implementation of a dual emphasis strategy.

Earlier, we cited some anecdotes when top management of merging firms stated their intent for achieving efficiency and/or customer service. Our results show that such statements need to be considered very carefully in light of the strategic context facing the firm. For instance, when contemplating a merger, firms could put together an explicit strategy for achieving a dual emphasis and then implementing it during the merger context. This can help them realize that all customer satisfaction and efficiency improvements may not be equally value creating. For example, are there specific customer segments that should be emphasized during the merger? Though our research has uncovered mergers as a specific context facilitating the financial impact of a dual emphasis achievement, additional research is needed to uncover how and why firms can implement such a strategy for their specific firm and industry context. For instance, one may argue that customer satisfaction is more important for service firms than for manufacturing firms (Anderson, Fornell, and Rust 1997). Future research is needed to explore these and other contingency factors.

Our results also add to the list of factors that can help or hinder merger success including resources and capabilities (Sorescu, Chandy, and Prabhu 2007), synergy or fit (Swaminathan, Murshed, and Hulland 2008), extent and speed of integration (Homburg and Bucerius 2005), and brand factors (Bahadir, Bharadwaj, and Srivastava 2008; Jaju, Joiner, and Reddy 2006; Wiles, Morgan, and Rego 2012). By shedding light on the dual role of simultaneous increases in customer satisfaction and efficiency, the present research opens the door for additional investigations. For instance, organizational factors that result in successful achievement of a dual-goal strategy could be examined. Survey data from managers could be collected to examine factors related to organizational culture, structure, and processes that contribute to financial performance improvements and the moderating impact of customer satisfaction-efficiency improvements. It will help to examine these issues over a longer period (say 2 or 3 years following the completion of the merger) as well as examine performance metrics in the long term. Researchers could also examine the implementation of customer satisfaction-efficiency improvements in different divisions and business units of firms. In a services industry context, employee satisfaction has been shown to have an important impact on customer outcomes (Evanschitzky et al. 2011). Therefore, examining the role of employee satisfaction following a merger will help to provide insights into the role of a dual-goal implementation and the influence on merger performance.

Our research is not without limitations. Guided by the dual emphasis framework, our focus is on customer satisfaction and efficiency enhancements as keys to long-term financial performance in a merger. However, other variables may also influence the strength of these results. For instance, additional postmerger integration variables also may moderate the effects shown in this study. In addition, our data set is unbalanced because the ACSI adds and deletes firms on a yearly basis. As noted previously, the use of ACSI data and first differencing focuses our sample on firms that have survived for longer periods of time, and potentially excludes firms that have exited the industry. Another possible limitation is that ACSI data are based on brand-level information. In the case of firms with only a single brand (e.g., Bank of America), the analysis at the brand level can be compared with firm-level performance metrics. However, this may not be true for firms in other industries where the firm consists of multiple brands. Though this approach has been used in a number of articles in the literature (Anderson, Fornell, and Mazvancheryl 2004; Anderson, Fornell, and Rust 1997), it does merit further attention in future research. Finally, we do not investigate these effects across various industries, and differences may emerge across industries, even within the services sector. These are promising areas for future research.

Footnotes

Authors’ Note

Much of Christopher Groening's work was done while at the University of Missouri.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Aaker

David A.

Jacobson

Robert

(1994), “The Financial Information Content of Perceived Quality,” Journal of Marketing Research, 31 (2), 191–201.

Aiginger

Karl

Brandner

Peter

Wüger

Michael

(1995), “Measuring Market Power for Some Industrial Sectors in Austria,” Applied Economics, 27 (4), 369–376.

Aiken

Leona S.

West

Stephen G.

(1991), Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage.

American Customer Satisfaction Index (2004), (accessed November 12, 2004), [available at http://www.theacsi.org].

Anand

Jaideep

Singh

Harbir

(1997), “Asset Redeployment, Acquisitions and Corporate Strategy in Declining Industries,” Strategic Management Journal, 18 (1), 99–118.

Anderson

Eugene

Fornell

Claes

Mazvancheryl

Sanal K.

(2004), “Customer Satisfaction and Shareholder Value,” Journal of Marketing, 68 (4), 172–185.

Anderson

Eugene

Fornell

Claes

Rust

Roland T.

(1997), “Customer Satisfaction, Efficiency, and Profitability: Differences between Goods and Services,” Marketing Science, 16 (2), 129–145.

Bahadir

S. Cem

Bharadwaj

Sundar G.

Srivastava

Rajendra K.

(2008), “Financial Value of Brands in Mergers and Acquisitions: Is Value in the Eye of the Beholder?” Journal of Marketing, 72 (6), 49–64.

Barney

Jay B.

(1991), “Firm Resources and Sustained Competitive Advantage,” Journal of Management, 17 (1), 99–120.

10.

Campbell

Dennis

Kazan

Brent

(2009), TD Canada Trust (Abridged), HBS No. 9-110-049. Boston, MA: Harvard Business School Publishing.

11.

Capron

Laurence

(1999), “The Long-term Performance of Horizontal Acquisitions,” Strategic Management Journal, 20 (11), 987–1018.

12.

Capron

Laurence

Pistre

Nathalie

(2002), “When Do Acquirers Earn Abnormal Returns,” Strategic Management Journal, 23 (9), 781–793.

13.

Chandy

Rajesh K.

Tellis

Gerard T.

(2000), “The Incumbent's Curse? Incumbency, Size, and Radical Product Innovation,” Journal of Marketing, 64 (3), 1–17.

14.

Chatterjee

Sayan

(1986), “Types of Synergy and Economic Value: The Impact of Acquisitions on Merging and Rival Firms,” Strategic Management Journal, 7 (2), 119–139.

15.

Christofferson

Scott A.

McNish

Robert S.

Sias

Diane L.

(2004), “Where Mergers Go Wrong,” The McKinsey Quarterly, 2004 (2), 92–99.

16.

Chung

Kee H.

Pruitt

Stephen W.

(1994), “A Simple Approximation of Tobin’s q,” Financial Management, 23 (3), 70–74.

17.

Cotterill

Ronald W.

(1986), “Market Power in the Retail Food Industry: Evidence from Vermont,” Review of Economics & Statistics, 68 (3), 379–386.

18.

Datta

Deepak K.

Pinches

George E.

Narayanan

V. K.

(1992), “Factors Influencing Wealth Creation from Mergers and Acquisition,” Strategic Management Journal, 13 (1), 67–84.

19.

Dranove

David

Shanley

Mark

(1995), “Cost Reductions or Reputation Enhancement as Motives for Mergers: The Logic of Multihospital Systems,” Strategic Management Journal, 16 (1), 55–74.

20.

Dyer

Jeffrey H.

Kale

Prashant

Singh

Harbir

(2004), “Do You Know When to Ally or Acquire?”, Harvard Business Review, 82 (7/8), 109–115.

21.

Eckbo

B. E.

(1983), “Horizontal Mergers, Collusion, and Stockholder Wealth,” Journal of Financial Economics, 11 (1-4), 241–273.

22.

Evanschitzky

Heiner

Groening

Christopher

Mittal

Vikas

Wunderlich

Maren

(2011), “How Employer and Employee Satisfaction Affect Customer Satisfaction: An Application to Franchise Services,” Journal of Service Research, 14 (2), 136–148.

23.

Farrell

Joseph

Shapiro

Carl

(2001), “Scale Economies and Synergies in Horizontal Merger Analysis,” Antitrust Law Journal, 68 (3), 685–710.

24.

Focarelli

Dario

Panetta

Fabio

(2003), “Are Mergers Beneficial to Consumers? Evidence from the Market for Bank Deposits,” American Economic Review, 93 (4), 1152–1172.

25.

Fornell

Claes

Mithas

Sunil

Morgeson

Forrest V.

III (2009), “The Economic and Statistical Significance of Stock Returns on Customer Satisfaction,” Marketing Science, 28 (5), 820–825.

26.

Fornell

Claes

Johnson

Michael D.

Anderson

Eugene W.

Cha

Jaesung

Bryant

Barbara Everitt

(1996), “The American Customer Satisfaction Index: Nature, Purpose, and Findings,” Journal of Marketing, 60 (4), 7–18.

27.

Golan

Amos

Judge

George

Perloff

Jeffrey M.

(1996), “Estimating the Size Distribution of Firms Using Government Summary Statistics,” Journal of Industrial Economics, 44 (1), 69–80.

28.

Hirschman

Albert O.

(1964), “The Paternity of an Index,” American Economic Review, 54 (5), 761–762.

29.

Hoffman

Thomas

(2005), “Harrah’s Bets on Loyalty Program in Caesars Deal,” Computerworld, 39, 10.

30.

Homburg

Christian

Bucerius

Matthias

(2005), “A Marketing Perspective on Mergers and Acquisitions: How Marketing Integration Affects Postmerger Performance,” Journal of Marketing, 69 (1), 95–113.

31.

Jacobson

Robert

(1990), “Unobservable Effects and Business Performance,” Marketing Science, 9 (1), 74–85.

32.

Jacobson

Robert

Mizik

Natalie

(2009), “The Financial Markets and Customer Satisfaction: Reexamining Possible Financial Market Mispricing of Customer Satisfaction,” Marketing Science, 28 (5), 810–819.

33.

Jaju

Anupam

Joiner

Christopher

Reddy

Srinivas K.

(2006), “Consumer Evaluations of Corporate Brand Redeployments,” Journal of the Academy of Marketing Science, 34 (2), 206–215.

34.

Karim

Samina

Mitchell

Will

(2000), “Path-dependent and Path-breaking Change: Reconfiguring Business Resources Following Acquisitions in the US Medical Sector, 1978-1995,” Strategic Management Journal, 21 (10-11), 1061–1081.

35.

Larsson

Rikard

Finkelstein

Sydney

(1999), “Integrating Strategic, Organizational, and Human Resource Perspectives on Mergers and Acquisitions: A Case Survey of Synergy Realization,” Organization Science, 10 (1), 1–26.

36.

Lee

Ruby P.

Grewal

Rajdeep

(2004), “Strategic Responses to New Technologies and Their Impact on Firm Performance,” Journal of Marketing, 68 (4), 157–171.

37.

Lubatkin

Michael

(1983), “Mergers and the Performance of the Acquiring Firm,” The Academy of Management Review, 8 (2), 218–225.

38.

Luo

Xueming

Bhattacharya

C. B.

(2006), “Corporate Social Responsibility, Consumer Satisfaction, and Market Value,” Journal of Marketing, 70 (4), 1–18.

39.

Marks

Mitchell Lee

Mirvis

Philip H.

(2001), “Making Mergers and Acquisitions Work: Strategic and Psychological Preparation,” Academy of Management Executive, 15 (2), 80–92.

40.

Milne

George R.

(1992), “A Marketing Approach for Measuring Product Market Differentiation and Concentration in Antitrust Cases,” Journal of Public Policy and Marketing, 11 (1), 90–100.

41.

Mittal

Vikas

Frennea

Carly

(2010), Customer Satisfaction: A Strategic Review and Guidelines for Managers. Cambridge, MA: MSI Fast Forward Series, Marketing Science Institute.

42.

Mittal

Vikas

Anderson

Eugene W.

Sayrak

Akin

Tadikamalla

Pandu

(2005), “Dual Emphasis and the Long-term Financial Impact of Customer Satisfaction,” Marketing Science, 24 (4), 544–555.

43.

Mizik

Natalie

Jacobson

Robert

(2007), “Myopic Marketing Management: Evidence of the Phenomenon and Its Long-term Performance Consequences in the SEO Context,” Marketing Science, 26 (3), 361–379.

44.

Mizik

Natalie

Jacobson

Robert

(2003), “Trading Off between Value Creation and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis,” Journal of Marketing, 67 (1), 63–76.

45.

Montgomery

Cynthia

Wernerfelt

Birger

(1988), “Diversification, Ricardian Rents, and Tobin’s q,” RAND Journal of Economics, 19 (4), 623–632.

46.

Montgomery

Cynthia

(1985), “Product-Market Diversification and Market Power,” Academy of Management Journal, 28 (4), 789–798.

47.

Montgomery

Cynthia

Singh

Harbir

(1984), “Diversification Strategy and Systematic Risk,” Strategic Management Journal, 5 (2), 181–191.

48.

Morgan

Neil A.

Piercy

Nigel F.

(1996), “Competitive Advantage, Quality Strategy and the Role of Marketing,” British Journal of Management, 7 (3), 231–245.

49.

Mouawad

Jad

(2011), “Details, Details,” The New York Times, March 19, B1.

50.

Porter

Michael E.

(1980), Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York, NY: Free Press.

51.

Richtel

Matt

(2001), “Compaq and Hewlett Face More Skepticism,” The New York Times, September 7, C8.

52.

Robbins

Stephanie S.

Stylianou

Antonis C.

(1999), “Post-merger Systems Integration: The Impact on IS Capabilities,” Information and Management, 36 (4), 205–212.

53.

Rust

Roland T.

Moorman

Christine

Dickson

Peter R.

(2002), “Getting Return on Quality: Revenue Expansion, Cost Reduction, or Both?,” Journal of Marketing, 66 (4), 7–24.

54.

Sirower

M. L.

(1997), The Synergy Trap: How Companies Lose the Acquisition Game. New York, NY: The Free Press.

55.

Sorescu

Alina B

Chandy

Rajesh K.

Prabhu

Jaideep C.

(2007), “Why Some Acquisitions Do Better Than Others: Product Capital as a Driver of Long-term Stock Returns,” Journal of Marketing Research, 44 (1), 57–72.

56.

Stellin

Susan

(2010), “When Two Airlines Tie the Knot,” The New York Times, May 23, TR3.

57.

Swaminathan

Vanitha

Murshed

Feisal

Hulland

John

(2008), “Value Creation Following Merger and Acquisition Announcements: The Role of Strategic Emphasis Alignment,” Journal of Marketing Research, 45 (1), 33–47.

58.

Teece

David J.

(1982), “Towards an Economic Theory of the Multiproduct Firm,” Journal of Economic Behavior and Organization, 3 (1), 39–63.

59.

The New York Times (2005), “Qwest Cites Savings in an MCI Merger,” The New York Times, March 1, C3.

60.

The Thomson Corporation (2011), “website” (accessed February 6, 2006), [available at http://www.thomson.com/].

61.

The Wall Street Journal (2005), “US Air-America West Talks Intensify; Airbus Loan of $250 Million Moves Merger Still Closer, But Future Challenges Await,” The Wall Street Journal, A3.

62.

Villalonga

Belén

(2004), “Intangible Resources, Tobin’s q, and Sustainability of Performance Differences,” Journal of Economic Behavior & Organization, 54 (2), 205–230.

63.

Wiles

Michael A.

Morgan

Neil A.

Rego

Lopo L.

(2012), “The Effect of Brand Acquisition and Disposal on Abnormal Stock Returns,” Journal of Marketing, 71 (1), 38–58.

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