ORIGINAL EMPIRICAL RESEARCH

Do institutional investors pay attention to customersatisfaction and why?

Xueming Luo & Ran Zhang & Weining Zhang &

Jaakko Aspara

Received: 7 August 2012 /Accepted: 11 June 2013# Academy of Marketing Science 2013

Abstract Extant marketing, accounting, and finance re-search has neglected to examine the relevance of customersatisfaction information for institutional investors, despitetheir potential importance. This study develops and supportsa framework suggesting that firms with positive changes incustomer satisfaction are more attractive to transient institu-tional investors than to non-transient institutional investors.We also find that the impact of customer satisfaction ontransient institutional investor holdings is contingent uponfirm intangible asset intensity, product-market demand un-certainty, and financial market volatility. In addition, tran-sient institutional investor holdings at least partially mediatethe effects of changes in customer satisfaction on firm ab-normal return and idiosyncratic risk. Thus, transient institu-tional investor investments represent a mechanism throughwhich customer satisfaction affects firm value.

Keywords Customer satisfaction . Investor community .

Institutional investor holding . Intangibles .

Marketing-finance interface

Introduction

Institutional investors are the major players in the capitalmarket. According to the U.S. Securities and ExchangeCommission (SEC) 13F filings, U.S. common stocks aremainly owned by institutional investors, such as retirementand pension funds, investment banks, and mutual funds (Yanand Zhang 2009). Total assets held by institutional investorshave surpassed $81.90 trillion (Jiang 2010a). Compared toindividual investors, institutional institutions typically tradeand hold larger amounts of firm stocks, and can thereforemore powerfully influence firm stock prices (Bushee andMiller 2012; Helwege et al. 2012).

Figure 1 illustrates prior research on the importance of insti-tutional investors for firm value and the related determinants.1

Specifically, studies find that institutional investor holdings canaffect firm value in terms of abnormal returns and financial risks(Ali et al. 2004; Nofsinger and Sias 1999). Also, institutionalinvestor holdings are determined by management relations(Wahal and McConnell 2000), analyst forecasts (Chen andCheng 2006), information disclosure (Ke and Ramalingegowda2005), and intangibles such as corporate social responsibility andR&D spending (Dhaliwal et al. 2011).

However, no published studies in marketing, accounting,finance, or strategy have connected the key marketing metricof customer satisfaction to institutional investors’ trading.Although the direct link of customer satisfaction to stockvaluation has been studied, the reactions that institutionalinvestors, in particular, exhibit vis-à-vis customer satisfac-tion have not been examined, nor the question whether suchreactions may play an important role in channeling customersatisfaction’s impact on firm valuation in the stock market.

Therefore, our study addresses this knowledge gap by in-vestigating the following questions: (1) Are positive changes in

1 We thank two anonymous reviewers for the suggestion to include thissummary.

X. Luo (*)University of Texas at Arlington, Arlington, TX, USAe-mail: luoxm@uta.edu

R. ZhangGuanghua School of Management, Peking University, Beijing,Chinae-mail: rzhang@gsm.pku.edu.cn

W. ZhangCheung Kong Graduate School of Business, Beijing, Chinae-mail: wnzhang@ckgsb.edu.cn

J. AsparaAalto University School of Business, Helsinki, Finlande-mail: jaakko.aspara@aalto.fi

J. of the Acad. Mark. Sci.DOI 10.1007/s11747-013-0342-9

customer satisfaction of a firm related to positive changes ininstitutional investor holdings for the firm? (2) Do positivechanges in customer satisfaction result in different kinds ofinstitutional investor holdings? (3) Can these effects on institu-tional holdings vary across different situations of intangibleasset intensity, product-market demand uncertainty, and finan-cial market volatility? And (4) to what extent do institutionalinvestor holdings channel customer satisfaction’s possible im-pact on firm value?

The key contributions of this research are as follows: First,to our knowledge, we are the first to theorize and test institu-tional investors’ reactions to the core marketing metric ofcustomer satisfaction. Thus, our results provide new under-standing of the financial, stock market impact of customersatisfaction to the marketing-finance interface research in gen-eral (Hanssens et al. 2009) and to the research on customersatisfaction’s financial market impact in particular. This under-standing is important also because there is a heated, ongoingdebate (Fornell et al. 2009; Jacobson and Mizik 2009a;O’Sullivan et al. 2009) about whether and how customersatisfaction information may influence stock pricing over andabove accounting information. We uncover new mechanisms

that explicate the financial impact of customer satisfaction,showing how institutional investor trading plays a mediatingrole between customer satisfaction and firm value in the stockmarket. Our results also show that “transient” institutional in-vestors are at the forefront in reacting to changes in firms’customer satisfaction, as well as in impounding this informa-tion on to firm stock prices. Furthermore, we demonstrate theconditions (e.g., firm intangible asset intensity, product-marketdemand uncertainty, financial market volatility) wherein tran-sient institutional investors are more likely to react to customersatisfaction changes.

Our work has important practical contributions, especiallyfor marketing executives, but also for investment community.For Chief Marketing Officers (CMOs), our research providesnew insights into why investments in customer satisfactionmay enhance firm valuation, as well as what kind of commu-nication about the firm’s customer satisfaction (and towardswhich investors) may lead to optimal investor and stockmarket responses. Thus, with our results, CMOs are able tobetter communicate firm competitive advantages in terms ofcustomer satisfaction to the Wall Street community. In addi-tion, our study of the role played by the non-financial metric of

Simultaneous accounting profitability changes

Research gap

Fig. 1 An overview of institutional investor holding literature

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customer satisfaction speaks directly to the investment com-munity, considering the guidelines of the FinancialAccounting Standards Board (FASB). Because FASB requiresfirms to disclose such non-financial information to investorsthat helps them assess the growth and/or volatility of futurecash receipts (Gupta 2009; Kimbrough 2007), our work mayencourage firms to proactively announce changes in customersatisfaction to certain investors and report the quality of thefirm’s customer base in reports and Securities and ExchangeCommission 10-K/10-Q filings to the investment community.

Literature review: institutional investor behavior

Typically, institutional investors are specialized organizationsthat invest large pools of money in securities in the capitalmarkets. Examples of institutional investors include retirementand pension funds, investment banks, mutual funds, insurancecompanies, and hedge funds (Brancato and Gaughan 1991;Kochhar and David 1996). In the following, we provide a briefreview of extant literatures on the (1) antecedents of institu-tional investor investing (i.e., institutional stock ownership orholdings) as well as (2) the consequences of institutional inves-tor investing on stock prices. These literatures, as well as therelated research gaps, are illustrated in Fig. 1.

Antecedents of institutional investor investments

Regarding the antecedents of institutional investors’ invest-ments in firm stocks, one stream of extant research concen-trates on the question of how factors related to firms’ man-agement, in particular, influence institutional investors. Forinstance, Parrino et al. (2003) show that institutional investorsmay disfavor firms expected to change CEOs, and Bushee andMiller (2012) show that companies initiating investor relations(IR) management programs exhibit increases in institutionalinvestor ownership.

Another research stream studies how actors external to thefirm, such as investment analysts, may influence institutionalinvestors’ attraction to firm stocks. Chen and Cheng (2006),for instance, find that investment analysts’ recommendationsfor stocks have a significant positive effect on institutionalinvestors’ investments, while O’Brien and Bhushan (1990)showed that institutional investor ownership in a firm corre-lates with the extent to which analysts follow the firm.

The rest of the extant research on the antecedents of institu-tional investor investing concentrates mostly on how differentpieces or sources of information affect institutionalinvestors―which is the closest research stream to presentresearch, focusing on customer satisfaction information.Bushee and Noe (2000) find that the level of informationclosure by firms themselves is associated with greater institu-tional investor ownership. Several studies, such as Ke and

Ramalingegowda (2005), also find that institutional investorsattend to firms’ earnings information and are, naturally enough,attracted to invest in firms with positive earnings surprises.

More importantly, extant research also provides prelimi-nary evidence that institutional investors may even react tonon-financial information―typically called “intangible” in-formation (e.g., Daniel and Titman 2006; Jiang 2010b). Piecesof intangible information, which have been shown to affectinstitutional investors, include information about firms’ R&D,corporate social responsibility (Cox et al. 2004; Dhaliwal et al.2011; Graves and Waddock 1994; Johnson and Greening1999), product quality (Mavrinac et al. 1995), and advertising(Grullon et al. 2004; Oak and Dalbor 2010). However, none ofthese studies have so far investigated the responses of institu-tional investors to firms’ customer satisfaction information.Customer satisfaction information warrants an investigation inits own right, since it is a key marketing performance oroutcome metric―unlike the previously studied intangiblepieces of information which mostly represent input metrics(e.g., R&D or advertising investments, or CSR programs).

Stock valuation consequences of institutional investorinvestments

With regard to stock valuation consequences of institutionalinvestor investing, prior literature finds that institutional inves-tors’ investments in stocks generally anticipate favorable valu-ations and returns for those stocks. That is, on average institu-tional investors buy stocks that subsequently enjoy good returns(Chakravarty 2001; Nofsinger and Sias 1999). This is explainedby the fact that institutional investors are typically well “in-formed” investors, meaning that they have superior resourcesand abilities to gather and process firm-specific information as abasis of their investment and trading decisions (e.g., Walther1997; Ke and Ramalingegowda 2005). With the advantage oftheir information gathering and processing, institutional inves-tors can hence engage in informed trading in response to future-oriented information they gather “privately” (e.g., Ali et al.2004; El-Gazzar 1998)―that is, prior to or even without thepublic disclosure of such information. Thus, institutional in-vestors generally have an information advantage regarding thefirms they follow, allowing them to make investments in stocksthat are likely to perform well subsequently (Arbel et al. 1983;Kochhar and David 1996; Schnatterly et al. 2008).

Prior literature also provides preliminary evidence thatinstitutional investor trading may act as a mediator thatchannels new firm-specific information about certain tangi-ble (financial accounting-based) or intangible (non-finan-cial-accounting-based) factors into stock valuations. For in-stance, Ali et al. (2004) and Piotroski and Roulstone (2004)show that institutional investors’ buying of stocks plays rolein impounding firm-specific earnings information on to sub-sequent stock prices. Jiang (2010b), in turn, shows that the

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tendency of institutional investors to buy stocks based onpositive intangible information can also contribute to subse-quent value premium for those stocks. However, it remainsyet to be studied, whether the intangible marketing metric ofcustomer satisfaction is also information that institutionalinvestors react to and channel to firm valuations. In market-ing research, either, it has not been studied whether institu-tional investors may act as a mediator to the potential effectof customer satisfaction information on firm value, as mostextant studies explore the direct effect of customer satisfac-tion on firm value (see details below, and Fig. 1).

Three types of institutional investors

Based on prior literature, it is also possible that not all institu-tional investors―but only certain kinds of institutionalinvestors―are attracted to invest in firm stocks based on in-tangible information such as customer satisfaction. Indeed,institutional investors are not a homogeneous group. In themost widely-used classification by Bushee (1998, 2001), insti-tutional investors are classified as “transient”, “dedicated”, or“quasi-indexer” institutions, based on their investment behav-iors, strategies, and horizons.

Specifically, transient institutional investors are character-ized by a short investment horizon and high turnover tomaximize short-term profits. Typically, they seek near-termappreciation of their stocks, holding a stock for an average of1.9 years or less (Yan and Zhang 2009). These investorsengage in varied information search on a wide selection offirms, to extensively gauge potential investment prospects.In fact, they are often “speculative” traders in the sense thatthey tend to utilize varied pieces of (a) information that mighthave implications to firms’ earnings forecasts as well as (b)information that might affect the short-term investor senti-ment related to a stock (Bushee 2001; Glushkov 2006; Keand Ramalingegowda 2005). Thus, transient institutions areoften willing to trade on any tangible or intangible piece ofinformation, whether related to actual earnings or not, aslong as it might affect the firm’s short-term returns or senti-ment. Moreover, research shows that transient institutionsare, on average, effective in doing this: their investing pre-dicts subsequent stock returns (Yan and Zhang 2009).

Compared to transient institutions, dedicated institutions areconcentrated on rather few firms at a time, and commit them-selves to providing long-term capital and support for theirholding firms. In other words, they are virtually permanentshareholders who seek long-term shareholder value, and theirgoals are more relationship than transaction driven (Porter1992). Because dedicated institutions hold significant stakesfor long periods of time, they may be less sensitive (thantransient institutions) to individual pieces of current informa-tion, such as current earnings or intangible information in theform of changes in firms’ intangible assets (Ali et al. 2004).

The third group of institutional investors, quasi-indexers,use investment strategies characterized by high diversifica-tion and low portfolio turnover. They generally follow apassive buy-and-hold strategy with diversified, small hold-ings. Their diversified strategy leads them to gather relative-ly little information on firms but, rather, follow the portfoliocomposition of broader stock indices such as the S&P 500, ortermed as quasi-indexers (Bushee 1998; Porter 1992).

Hypotheses on customer satisfaction and institutionalinvestors

Figure 2 provides an overview of the relationships in ourtheoretical framework. This framework suggests that (1)certain institutional investors are attracted to invest in firmsthat exhibit improved customer satisfaction (i.e., changes in afirm’s customer satisfaction have a positive impact onchanges in its institutional investor stock holdings), (2) thelink between customer satisfaction and institutional investorholdings is moderated by the firm’s intangible asset intensity,product-market demand uncertainty, and financial marketvolatility, and (3) institutional holdings at least partiallymediate the relationship between customer satisfaction andfirm value (as reflected in stock returns and risk).

As mentioned above, extant research has generally foundthat satisfaction changes may have direct effects on stockprices (e.g., Anderson et al. 2004; Fornell et al. 2009; Luoet al 2010, 2013; Riley et al. 2003)―but so far concluded thatsuch direct effects are mostly mediated by simultaneous ac-counting profitability changes (which the satisfaction changesgenerate) (e.g., Jacobson andMizik 2009a, b; O’Sullivan et al.2009). There is also controversy about whether investors ingeneral attend to customer satisfaction information or not (seee.g. Jacobson and Mizik 2009a, b).

Our framework proposes an alternative, indirect channelof influence of customer satisfaction on firm valuation,through institutional investors’ trading. This channel is pos-ited as complementary to (i.e., goes over and above) thepreviously-identified channel through accounting profitabil-ity. Thus, we aim to reconcile some of the previous contro-versy by proposing that (1) certain institutional investors, inparticular, attend to and get attracted by customer satisfac-tion information, especially in some conditions―even if notall investors would necessarily do so in all conditions.Moreover, we propose that (2) certain institutional inves-tors’ trading of stocks based on customer satisfaction infor-mation further leads to that information getting impoundedon to firm value in terms of stock returns and risk. In otherwords, certain institutional investors are attracted by cus-tomer satisfaction information and act as a previously-unidentified mediating channel that impounds the customersatisfaction information on firm value.

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The impact of customer satisfaction on institutional investors

Because different types of institutional investors have variousabilities to process information and perform informed trading(Yan and Zhang 2009), we expect different responses to satis-faction between the three types of institutional investors (tran-sient, dedicated, and quasi-indexers). According to Bushee andNoe (2000), the importance of public disclosure to institutionalinvestors depends on their information-gathering capabilities,investment horizon, and governance activities. Prior literaturehas reached a consensus that when a firm’s information isreleased publicly, transient institutions are more likely to tradeon such information (Yan and Zhang 2009). Because transientinstitutional investors possess higher abilities to monitor firmassets and actively seek near-term share appreciation by trad-ing on firm-specific information, they are expected to beattracted to firms with informative disclosure such as customersatisfaction improvement.

On the other hand, dedicated and quasi-indexer institutionsare not frequent traders and the liquidity benefits of disclosureare likely to be less important to them because of their longerinvestment horizons and lower sensitivity to firm-specific in-formation (Ali et al. 2004; Ke and Petroni 2004; Ke et al.2008). Thus, compared to transient institutions, dedicated andquasi-indexer institutional investors are less likely to trade

based on customer satisfaction information, even if they under-stand its implications. Thus, we have the following hypothesis.

H1: Positive changes in customer satisfaction of a firmpositively influence transient institutional investors’investments in the firm’s stock―and more so thandedicated and quasi-indexer institutions’ investments.

Moderating conditions

Our theoretical framework also suggests that the influence ofcustomer satisfaction on institutional investors is contingent oncertain firm and market conditions. Prior research in marketingsuggests that customer satisfaction has different performanceeffects depending on the degree to which the firm’s assets areintangible (i.e., intangible asset intensity = [TotalAssets—Property, Plant, and Equipment] /Total Assets; Tuliet al. 2010) and on the volatility or uncertainty of the firm’sproduct-market demand (i.e., product-market demand uncertain-ty) (Anderson et al. 2004; Luo et al. 2010). This gives reason toexpect that institutional investors’ attention to customer satisfac-tion may also differ according to intangible asset intensity andproduct-market demand uncertainty. In turn, studies in financeand accounting imply that institutional investors’ responses to

Fig. 2 Theoretical framework. Notes: The bolded boxes and arrowsindicate the present the channel of influence examined in the present study,positing that changes in customer satisfaction attract investments fromcertain institutional investors, which impounds the customer satisfactionchanges further into firm value. The dashed arrows indicate the standard

influence channel established in previous marketing-finance studies, as-suming that customer satisfaction may only influence firm value by simul-taneous changes in accounting profitability (e.g., Jacobson and Mizik2009a, b; i.e., that customer satisfaction does not have firm value effects,when accounting profitability changes are controlled for)

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intangible, non-financial information may be contingent uponfinancial market volatility as well (Bailey et al. 2003; Dennisand Strickland 2002; Mavrinac and Siesfeld 1997).

Extending these streams of research, hence, we propose thatthe influence of customer satisfaction on institutional investorscan be amplified or buffered, contingent on firm intangible assetintensity, product-market demand uncertainty, and financial mar-ket volatility. In effect, we treat the directions of thesemoderatingeffects as empirical questions, without posing unilateral hypoth-eses of their signs. However, we develop, below, alternativehypotheses for the potentially different signs of the effects.

First, it can be expected that the firm’s intangible assetintensity, measured as the percentage of intangible assets tototal assets, may buffer the influence of customer satisfactionchanges on institutional investors. Namely, when a particularfirm has a great deal of other intangible assets that could serveas substitutes for customer satisfaction, the changes in theintangible asset of customer satisfaction may have less strongrelative influence on institutional investors, than in the casewhen the firm’s other intangible asset base is limited (cf.Mavrinac and Siesfeld 1997). In other words, for firms withhigh (low) intangible asset intensity, investors may find cus-tomer satisfaction information to be less (more) useful ininferring the firm’s future cash flows and value.

However, on the other hand, if a firm is low on intangibleassets in general, it is necessarily high on tangible assets (Tuliet al. 2010), potentially making firm value simpler to assess,without depending on customer satisfaction.2 If so, institutionalinvestors would pay less attention to customer satisfactionchanges, likely reducing the impact of customer satisfaction ontransient institutional investors’ investments when the firm islow on intangible assets.

In sum, hence, we provide the following alternative hypoth-eses concerning the moderating effect that the firm’s intangibleasset intensity has on the influence of customer satisfactionchanges on (transient) institutional investors’ investments infirm stocks.

H2: The impact of changes in customer satisfaction ontransient institutional investors’ investments is weak-er for firms with high intangible asset intensity thanfor firms with low intangible asset intensity.

H2alt: The impact of changes in customer satisfaction ontransient institutional investors’ investments is stron-ger for firms with high intangible asset intensity thanfor firms with low intangible asset intensity.

In addition, previous research has found that when product-market demand is uncertain, critical marketing variables suchas market orientation and customer satisfaction play a more

important role in determining customer loyalty and, ultimately,firm shareholder value (e.g., Grewal et al. 2010). This is be-cause when demand uncertainty is high, customer satisfactionis a more crucial metric for gauging the quality of the customerbase and, hence, firm future performance (Fornell et al. 2006;Grewal et al. 2010; Tuli et al. 2009). In turn, when demanduncertainty is low, market participants’ composition and be-havior are more predictable and relatively stable (Jaworski andKohli 1993), and positive changes in customer satisfaction maythen play a less important role in forecasting sales performanceand a firm’s future value. Therefore, it could be expected thatcustomer satisfaction triggers more transient institutional inves-tor trading for firms that operate in product markets with ahigher rather than a lower demand uncertainty.

However, an opposite moderating effect is also possible,again. Namely, it is possible that in an uncertain market de-mand, investment managers may feel it riskier to rely on firm-specific intangible information such as customer satisfaction inmaking trading decisions. In other words, in such conditions,they may discard intangible information and simply tend to“herd” or mimic other investors’ decisions (cf. Nofsinger andSias 1999) instead of attending to customer satisfaction informa-tion. In sum, we propose the following alternative hypotheses:

H3: The impact of changes in customer satisfaction ontransient institutional investors’ investments is stron-ger when product-market demand uncertainty is highthan when it is low.

H3alt: The impact of changes in customer satisfaction ontransient institutional investors’ investments is weakerwhen product-market demand uncertainty is high thanwhen it is low.

Furthermore, studies in the finance and accounting literaturesuggest that the investment decisions of institutional investorsmay depend on financial-market wide volatility as well (Busheeand Noe 2000; Yan and Zhang 2009). More importantly, priorstudies suggest that in financial markets with high (versus low)volatility, greater fluctuations may increase the difficulty forinstitutional investors in gathering information for informedtrading (Scharfstein and Stein 1990). Under high market vola-tility, investment managers also become concerned about theirreputations and tend towards herding with others instead oftrading based on firm-specific information such as customersatisfaction changes (Dennis and Strickland 2002).3 If this isthe case, then institutional investors are likely to spend less effort

2 We thank one anonymous reviewer for this competing hypothesis logic.

3 A fund manager’s reputation is hurt less if everyone makes the samebad decision than if only the manager makes the bad decision. A risk-averse manager will run with the pack instead of going out on a limbwith a contrarian strategy, even if the manager has information that thecontrarian strategy has the higher probability of being correct(Scharfstein and Stein 1990).

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on collecting, digesting, and trading on customer satisfactioninformation under high financial market volatility compared tolow financial market volatility.

Yet, the contrary may also hold. Similarly as with highproduct-market demand uncertainty, the financial communitymay during volatile financial markets recognize and forecastthat “a greater portion of the firm value lies in intangibles,rather than tangible assets… Investors thus may bank on com-panies rich in intangible assets such as brands” (Farrell 2009, p.64). Consequently, transient institutional investors may becomemore motivated to attend to intangible customer equity assetssuch as customer satisfaction, and therefore reflect more oncustomer satisfaction information in their trading in high thanin low financial market uncertainty. In summary:

H4: The impact of changes in customer satisfaction on tran-sient institutional investors’ investments is weaker whenfinancial market uncertainty is high than when it is low.

H4alt: The impact of changes in customer satisfaction ontransient institutional investors’ investments is stron-ger when financial market uncertainty is high thanwhen it is low.

The mediating role of institutional investors for the effectsof customer satisfaction on firm value

Thus far, we have offered hypotheses on the impact of customersatisfaction on institutional investor holdings. As we discussed,institutional investor holdings are directly linked to abnormalreturns (Yan and Zhang 2009) and risk (Bushee and Noe 2000).That is, more institutional investor holdings are associated withhigher subsequent cash flows and stock returns, as well as lesservulnerability of those cash flows, indicating lower idiosyncraticstock risk. Given that customer satisfaction affects institutionalinvestor holdings, which in turn affect firm value, it is reasonableto expect a “chained” relationship: from satisfaction to theintermediate outcome of institutional investor holdings and thento firm return and risk. This chain implies that because institu-tional investor holdings, especially transient institution holdings,likely act as a channel throughwhich news of satisfaction impactstock prices.We believe that insofar as institutional investors caneffectively account for firm-specific information, such as cus-tomer base quality and satisfaction, their trading more reliablyreflects the true value of the firm (McAlister et al. 2007), and theinformation content of customer satisfaction is more likely to becaptured by stock return and risk. The more the firm enjoyspositive institutional investor holding with higher levels of sat-isfaction, the more likely the information content of satisfactionis to pass through their trading and thus contribute to firm value.

In contrast, if institutional investors ignore vital market-based assets such as customer satisfaction, such disregardwould contribute to mis-assessment of true firm value and

thus generate insignificant associations between customersatisfaction and firm return or risk (Tuli et al. 2009). Thus,institutional investor holding may represent an intermediatemechanism accounting for the impact of customer satisfactionon firm return and risk. We suggest that institutional investorholding may channel the effects of customer satisfaction in-formation on firm value. Nevertheless, as mentioned, there arealso alternative channels; the primary alternative channel isconstituted by the simultaneous accounting profitabilitychanges, which accompany the customer satisfaction changes,as illustrated in Figs. 1 and 2. Also, satisfaction can affect firmvalue through other channels. For example, prior research hassuggested that satisfaction also affects investment analysts(Luo et al. 2010), who in turn significantly influence firmreturn and risk (Godes and Mayzlin 2004; Luo 2009). Assuch, the mediation of the effect of customer satisfaction onfirm value through institutional investors is likely to be partial:

H5: Institutional investor holdings at least partially mediatethe associations between changes in customer satisfac-tion and firm return and risk.

Research design

Data

In testing the hypotheses, we collect data on customer satisfac-tion, institutional investor investments, firm value, and a set ofcontrol variables. Multiple sources are involved, including theAmerican Customer Satisfaction Index (ACSI), the ThompsonFinancial CDA/Spectrum database of SEC 13F filings(Thomson Reuters Institutional [13F] Holdings), the Centerfor Research in Security Prices (CRSP), and Compustat.Table 1 summarizes the data sources and measures.

Customer satisfaction data

Following the marketing literature (Aksoy et al. 2008; Luo et al.2010), we measure customer satisfaction using the ACSI, whichwas developed by the National Quality Research Center of theStephen M. Ross Business School at the University ofMichigan. The index measures the quality of goods and servicespurchased in the United States produced by both domestic andforeign firms with substantial U.S. market shares, and is anational barometer of customer satisfaction (Fornell et al.1996). The ACSI reports satisfaction scores on a scale of zeroto 100 and produces indexes for 10 national-level economicsectors, 43 industries, and more than 200 companies and federaland local government agencies. The measured companies, in-dustries, and sectors in the index are broadly representative ofthe U.S. economy serving U.S. households.

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The ACSI was first published in October 1994. Since then,it has been updated quarterly, on a rolling basis, with new datafor one or more of the measured sectors replacing data col-lected the prior year. Typically, the ACSI results are madepublicly available on the third Tuesday of February (fourth-quarter results from the previous year), May (first-quarterresults), August (second-quarter results), and November(third-quarter results). We obtain ACSI data for this studyover a 15-year period (1995–2009).

Because the ACSI offers satisfaction data quarterlyfor each company once a year (Fornell et al. 2010; Tuliet al. 2009), we needed a mechanism to carefully mergeACSI data with Thomson Reuters Institutional (13F)Holdings, CRSP, and Compustat data quarter by quarter.As shown in the timeline in Fig. 3, for firms with ACSIscores reported in quarter t of year 2, we calculate thechange of institutional investor ownership changes fromthe beginning of the quarter t to the end of quarter t.We calculate the change in ACSI by taking the differ-ence between the ACSI score in quarter t of year 2 andthat in the same quarter of the previous year. Whentesting the association between the change of ACSIand firm value, we use the return in quarter t and thedifference in idiosyncratic risk between quarters t+1 and

t−1.4 As a result of merging ACSI with ThomsonReuters Institutional (13F) Holdings, CRSP, and Compustatdata sources, we obtain 1,032 pooled firm–year observations.Because of changes in the variables, we lose 1 year of observa-tion, which leaves us with 916 usable observations for the finaldataset. Table 2 presents the yearly summary statistics for thedata on customer satisfaction and institutional investor holdings.

Measuring institutional investor investments

The institutional investor investment data in this study comesfrom the Thompson Financial CDA/Spectrum database ofSEC 13F filings. All institutional investors with greater than$100 million of securities under management are required toreport their holdings to the SEC on Form 13F. Holdings arereported quarterly; all common stock positions greater than10,000 shares or $200,000 must be disclosed. A drawback of

4 Return is already a change of stock price, so the buy-hold return forquarter t is essentially a change in stock price or firm value triggered bythe ACSI announcement in quarter t. We exclude quarter t wheninvestigating the change in firm risk because the risk calculation inquarter t can be contaminated by the ACSI announcement. A compar-ison of risk between quarters t+1 and t−1 more cleanly shows theimpact of changes in ACSI on firm risk.

Table 1 Data and measures

Variables Measures Data source

Institutional Investor Holdings (INST) Percentage ownership of institutional investors relative tototal shares outstanding

Thomas Ruters F13 File

Customer Satisfaction (ACSI) The overall consumption experience of customers surveyed in the ACSI;more than 200 customers per firm for nearly 200 companiesare surveyed each year

ACSI

Leverage (LEV) Ratio of total debt to total asset Compustat

Dividend (DIV) Ratio of cash dividends to market value of equity Compustat

EP Ratio (EP) Ratio of earnings before extraordinary items to market value of equity Compustat

Book-to-Market Ratio (BM) Ratio of book value to market value of equity Compustat

Log of Market Capitalization(MV) Natural log of market value of equity Compustat

Log of Shares (SHRS) Natural log of outstanding shares Compustat

Sales Growth (SGR) Percentage change of annual sales Compustat

Std of Return (SRET) Daily stock return volatility CRSP

Adjusted Return (MRET) Annual market-adjusted returns CRSP

Beta (BETA) Beta, calculated from a market model using daily stock returnsover an annual period

CRSP

Idiosyncratic Return Volatility (IRISK) Standard deviation of market model residuals over an annual period CRSP

Trading Volume (TVOL) (Average monthly trading volume over an annual period relative tototal shares outstanding)*1,000

CRSP

Analyst Coverage (NANA) 12-month average of number of analysts who issued annualearnings forecasts in IBES

IBES

Rating (RATE) Stock quality rating Compustat

Current Ratio (CR) The current ratio of a firm (current asset/current liability) Compustat

Intangible Asset Intensity (IA) (Total Assets—Property, Plant, and Equipment PP&E)/Total Assets Compustat

Demand Uncertainty (DU) Volatility of aggregated industry sales in the previous three years Compustat

Financial Market Volatility (FMV) Degree of fluctuation of the general stock market returns CRSP Compustat

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this measure is that our sample is restricted to larger institutionalinvestors who are required to file Form 13F with the SEC, sonot all institutional investments are necessarily counted.Nevertheless, this way our measure of institutional investorinvestments emphasizes larger institutional investors, who arealso the ones which our theory and prior literatures focus on.

Similarly to prior studies (e.g., Ke and Ramalingegowda2005; Ramalingegowda and Yu 2008), we obtain institutionalinvestors’ trading classifications (transient, dedicated, and quasi-indexer) from Brian Bushee’s work. According to Bushee(1998), the classification of institutional investors is based on acollection of nine variables that capture the past investmentbehavior of each institutional investor in terms of both portfoliodiversification and turnover. Bushee (1998, 2001) then usesprincipal component analysis to produce a factor that capturesthe average size of an institution’s stake in its portfolio firms andanother factor that captures the degree of portfolio turnover.Cluster analysis is then performed to group similar institutionsinto three clusters: transient, dedicated, and quasi-indexers.

With the combination of principal component analysis andcluster analysis, Bushee’s investor classification approach is verysimilar to simple customer segmentation approaches. Besides

simplicity, the advantage of adopting Bushee’s approach is that itis widely used in prior literature and research, making the presentresults on the transient, dedicated, and quasi-indexer institutionsdirectly comparable to prior work. In effect, the approach clas-sifies most institutions as transient institutions or quasi-indexers,and aminority (appr. 6%) of institutions as dedicated institutions.This is consistent with the observation that transient as well asquasi-indexer investment strategies have become most popularin recent years (Bird et al. 2005; Porter 1992).

The measures used in the analyses for the different institu-tional investor classes’ (transient, dedicated, quasi-indexer)investments, is the percentage of shares owned by each inves-tor class to total shares outstanding. Changes in these percent-ages are entered into the analyses, as described above.

Model specifications

We consider three different types of institutional investors:dedicated (DED), transient (TRA), and quasi-indexer (QIX)institutional investors. To determine whether customer satis-faction improvements attract institutional investors, we fol-low Bushee and Noe (2000) and estimate the change model

ΔINST t ¼ β0 þ β1ΔACSIt þ β2ΔLEV t þ β3ΔDIV t þ β4ΔEPt þ β5ΔBMt þ β6ΔMVt þ β7ΔSHRSt þ β8ΔSGRt

þβ9ΔSRETt þ β10ΔMRETt þ β11ΔBETAt þ β12ΔIRISKt þ β13ΔTVOLt þ β14ΔRATEt þ β15ΔNANAt

þβ16ΔCRt þ εit

ð1Þ

where INST represents institutional investors’ investments asstock holdings by dedicated (DED), transient (TRA), orquasi-indexer (QIX) institutional investors. ACSI representscustomer satisfaction.

We include a large number of control variables to capturepreviously documented determinants of institutional ownershipand stock return volatility. MRET is the market-adjusted buy-and-hold stock return measured over the past year, whichproxies for firm performance and has been shown to be posi-tively associated with institutional ownership and stock returnvolatility (Lang and McNichols 1997; Bushee and Noe 2000).SRET is the log of the standard deviation of return, which hasalso been shown to be an important determinant of institutional

ownership (Bushee and Noe 2000). TVOL, a liquidity proxy, isthe average monthly trading volume relative to total sharesoutstanding, and controls for institutional investor preferencesfor more liquid stocks (Gompers and Metrick 2001); for illus-tration purpose, we multiply this variable with 1,000.

Following Bushee and Noe (2000), we also include pastBeta (BETA), calculated from a market model using daily stockreturns over an annual period, idiosyncratic risk (IRISK), mea-sured as the lagged standard deviation of market model re-siduals calculated using daily stock returns, and leverage(LEV), measured as debt-to-assets, to capture firm risk alongdifferent dimensions. In turn, to control for tangible accountingmeasures, EP is the net earnings before extraordinary items,

Year 1 Year 2

t-5 t-4 t-3 t-2 t-1 t t+1 t+2

ACSI (t-4) announced ACSI (t) announced

Change of ACSI (t) Change of INST (t) [Table 3, 4]

Fig. 3 Timeline for the effects

J. of the Acad. Mark. Sci.

Tab

le2

Dataforcustom

ersatisfactionandinstitu

tionalho

ldings

PanelA:Descriptiv

estatistics

Customer

satisfaction

Percentageof

stockho

ldings

bytransient

institu

tionalinvestors

Percentageof

stockho

ldings

bydedicatedinstitu

tionalinvestors

Percentageof

stockho

ldings

byqu

asi-

indexinstitu

tionalinvestors

Mean

STD

Min.

Max.

Mean

STD

Min.

Max.

Mean

STD

Min.

Max.

Mean

STD

Min.

Max.

1995

78.483

6.35

363

.000

88.000

6.75

74.94

80.00

020

.231

11.638

8.79

50.00

039

.640

25.461

13.773

0.116

55.440

1996

78.600

5.97

760

.000

88.000

6.68

15.95

90.00

036

.286

12.219

9.22

20.00

040

.123

23.606

13.063

0.19

453

.267

1997

76.426

6.30

560

.000

85.000

7.23

26.49

50.00

033

.029

13.566

9.42

80.00

041

.223

25.578

13.719

0.19

163

.420

1998

76.963

6.28

961

.000

85.000

10.402

8.54

90.00

050

.218

0.24

11.19

10.00

08.117

36.747

19.880

0.02

484

.659

1999

75.667

6.65

961

.000

86.000

11.344

9.27

90.00

149

.360

7.50

56.73

20.00

035

.956

30.264

16.690

0.01

366

.654

2000

77.259

6.56

259

.000

87.000

9.28

06.46

20.00

026

.682

8.53

78.32

20.00

045

.067

31.084

15.323

0.15

168

.753

2001

77.264

5.57

962

.000

86.000

18.168

10.244

0.03

648

.088

9.61

48.00

50.00

032

.294

22.225

11.721

0.07

155

.388

2002

77.136

5.76

461

.000

87.000

19.353

10.100

0.10

443

.267

11.278

8.82

30.00

043

.179

22.454

10.489

0.06

739

.387

2003

77.217

5.77

963

.000

86.000

13.716

8.41

50.14

943

.911

9.30

37.16

50.00

027

.696

29.438

14.159

0.07

064

.665

2004

77.694

5.08

566

.000

88.000

6.38

55.112

0.00

924

.547

3.50

94.08

10.00

015

.399

46.886

20.086

0.82

482

.684

2005

76.910

5.87

763

.000

87.000

6.40

55.59

40.00

027

.745

3.12

93.83

70.00

013

.585

47.126

20.955

0.06

682

.604

2006

77.536

5.96

563

.000

87.000

7.67

85.44

30.16

228

.946

3.54

84.08

20.00

016

.339

49.822

19.923

1.90

184

.216

2007

76.972

5.98

361

.000

88.000

10.587

7.23

70.34

741

.984

3.15

84.77

00.00

021

.711

50.467

19.400

2.18

588

.086

2008

77.377

6.29

356

.000

87.000

9.64

66.84

00.23

353

.972

1.20

13.27

00.00

017

.728

51.718

18.192

1.39

285

.296

2009

77.542

5.82

663

.000

88.000

16.733

8.74

90.01

761

.552

2.69

93.07

80.00

012

.805

43.175

15.595

0.61

275

.729

PanelB:Correlatio

nmatrix(top

-right:Pearson

;bo

ttom-left:Spearman),p-valueisbelowthecorrelationcoefficient

12

34

56

78

91

ΔTransient

0.16

8−0.25

40.05

40.011

0.011

−0.04

40.19

6−0.118

(0.001

)(0.001

)(0.100

)(0.733

)(0.744

)(0.183

)(0.001

)(0.000

)

2ΔDedicated

0.07

4−0.22

9−0.05

40.03

6−0.01

3−0.06

3−0.03

00.00

0

(0.026

)(0.001

)(0.102

)(0.270

)(0.705

)(0.057

)(0.371

)(0.998

)

3ΔQuasi-Ind

exer

−0.12

4−0.09

6−0.05

10.04

8−0.04

80.05

80.01

30.03

1

(0.000

)(0.004

)(0.125

)(0.149

)(0.148

)(0.081

)(0.696

)(0.341

)

4Δlog(Customer

Satisfaction)

0.02

2−0.05

0−0.05

00.00

4−0.01

20.05

50.07

4−0.05

2

(0.501

)(0.131

)(0.127

)(0.915

)(0.710

)(0.095

)(0.024

)(0.115

)

5ΔIntang

ibleAsset(IA)

0.02

2−0.04

5−0.011

−0.02

10.06

4−0.12

20.04

8−0.00

7(0.497

)(0.178

)(0.742

)(0.530

)(0.054

)(0.000

)(0.149

)(0.828

)

6ΔDem

andUncertainty

(DU)

0.00

7−0.01

5−0.112

0.00

80.116

0.06

70.07

50.06

5

(0.831

)(0.657

)(0.001

)(0.818

)(0.000

)(0.041

)(0.023

)(0.048

)

7ΔFinancialMarketVolatility

(FMV)

0.02

5−0.09

10.05

50.04

6−0.13

00.01

80.03

30.20

1

(0.456

)(0.006

)(0.095

)(0.165

)(0.001

)(0.585

)(0.318

)(0.001

)

8Quarterly

Abn

ormalReturn

0.16

5−0.02

2−0.00

50.06

40.03

80.08

00.00

4−0.211

(0.001

)(0.513

)(0.885

)(0.051

)(0.252

)(0.016

)(0.894

)(0.001

)

9ΔQuarterly

Idiosyncratic

Risk

−0.09

8−0.06

20.01

5−0.02

2−0.00

20.08

20.15

3−0.13

5(0.003

)(0.062

)(0.643

)(0.502

)(0.950

)(0.013

)(0.001

)(0.001

)

J. of the Acad. Mark. Sci.

DIV is dividends, and BM is the book value―all these aredivided with the market value of equity to get values relativeto market capitalization. SGR is the percentage change inannual sales. We include DIV, EP, BM, and SGR to controlfor changes in firm accounting fundamentals that can affect theinvestment decisions of institutional investors (Bushee 2001).Firm size, measured as the log of market value (MV), capturesdifferences in institutional ownership and stock return volatilitybetween small and large firms. The variable RATE, in turn, isthe S&P stock rating that captures the preference of institutionalinvestors for well-reputed firms (Del Guercio 1996). The termSHRS is the logarithmic transformation of shares outstandingand its change proxies for equity issuance or repurchases thatmay affect institutional investors following. We also includeNANA, the number of analysts following (Luo et al. 2010;Ngobo et al. 2012), and CR, the current ratio, to control forfactors that may also affect institutional investor holdings.

Following Luo et al. (2010) and Tuli et al. (2009), we accountfor observable heterogeneity with many control variables, aslisted above. In addition, we accommodate unobservable hetero-geneity by employing the changes-in-changes models.Furthermore, the panel data may have time-serial correlated re-siduals within a firm, which can violate the assumption thatstandard errors are independent and identically distributed andthus lead to biased standard errors of coefficients. Following theestablished procedures recommended by Petersen (2009) in thefinance literature, we account for this problem by estimatingregressionswith clustered standard errors by firm.5 This clusterederror approach is more efficient than other approaches, such asthose of Fama-MacBeth, generalized least squares, White, andNewey-West on the basis of simulations (Petersen 2009, p. 435).See Appendix A for more details on this approach.

Considering the high number of independent variables in ourmodel, a check for multicollinearity is warranted. Therefore, forTables 3, 4 and 5, we report variance inflation factor for eachindependent variable and none of them is larger than 10. Thiseliminates the concern of multicollinearity.

Results

Effect of customer satisfaction on institutional investorinvestments

Hypothesis 1 posits that positive changes in customer satis-faction have a positive effect on institutional investorinvesting, especially by transient institutional investors. AsTable 3 illustrates, the coefficient of customer satisfaction

(ACSI) is positive and significant (c=0.105, p<.01). Also,we find that changes in satisfaction do not affect ownershipincreases by dedicated or quasi-indexer institutional investors(p>.10). Thus, we find evidence supporting H1. In a robust-ness test, following Tuli et al. (2009) and Luo et al. (2010), wealso included the lagged change of ACSI as an independentvariable and found that the results remained qualitatively thesame with the inclusion of this lagged variable: a positivechange in lagged customer satisfaction is associated withtransient investors’ institutional holdings (lagged term=0.938,p<.01) but not those of non-transient investors (p>.10).

Moderating role of intangible asset intensity, demanduncertainty, and financial volatility

Hypotheses 2 and 2alt predict that the impact of changes insatisfaction on transient institutional investments will be dif-ferent for firms with low intangible asset intensity and forfirms with high intangible asset intensity. As Table 4 shows,the coefficient of Δcustomer satisfaction × Δintangible assetintensity is negative and significant (δ=−2.364, p<.05). Thus,concerning the direction of the effect, the results support H2and disconfirm the alternative hypothesis H2alt : i.e., thepositive impact of satisfaction on transient institutional inves-tor investments is weaker for firms with higher intangibleasset intensity. This result is illustrated in Panel A (negativeslope) of Fig. 4.

Further, H3 and H3alt predict that the influence of changesin customer satisfaction on transient institutional investorinvestments is different under higher vs. lower product-market demand uncertainty. As Table 4 shows, the coefficientof Δcustomer satisfaction × Δdemand uncertainty is positiveand significant (δ=2.602, p<.01). This result supports H3 anddisconfirms H3alt: i.e., when demand uncertainty is high,there is a stronger positive association between customersatisfaction and transient institutional investor investments.This result is illustrated in Panel B (positive slope) of Fig. 4.

Hypotheses 4 and 4alt , in turn, predict that the impact ofchanges in customer satisfaction on transient institutional inves-tor investments is different in high vs. low financial marketvolatility. As Table 4 shows, the coefficient of Δcustomer satis-faction × financial market volatility is negative and marginallysignificant (δ=−19.755, p<.10). Thus H4 is marginally support-ed, while H4alt is disconfirmed: i.e., when financial marketvolatility is high, there is a weaker positive association betweencustomer satisfaction and transient institutional investor invest-ments. Panel C (negative slope) of Fig. 4 illustrates this.

Mediating influence of institutional investor investmentson firm value

H5 predicts that institutional investor investments at leastpartially mediate the associations between customer

5 Petersen (2009) generously provides STATA and SAS codes on howto realize the cluster methods in empirical research on his own website(http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/standarderror.html).

J. of the Acad. Mark. Sci.

satisfaction and firm value. According to Baron and Kenny(1986), to establish mediation, customer satisfaction mustaffect institutional investor investments, and institutional in-vestor investments must affect firm value. As we alreadyreported above, customer satisfaction affects transient institu-tional investor investments. In addition, the results in Table 5suggest that transient institutional investor further affects firmvalue. Namely, because the inclusion of transient institutionalinvestors’ investments in the model reduces the strength of theeffect of customer satisfaction on abnormal return (fromW=.578, p<.05, to W=.473, p<.10, only marginally signifi-cant), the data support a partial mediating role of institutionalinvestor investments, in the link between customer satisfac-tion changes and firm value in terms of stock returns. Thisresult is as expected in H5. Notably, we observe this mediationeven in the presence of the control variables related to simul-taneous accounting profitability changes (e.g., earnings, divi-dends), which suggests that transient institutional investors’buying of the firm’s stock following customer satisfactionchanges is indeed an alternative channel through which cus-tomer satisfaction improvements are channeled to firm value,over and beyond accounting measures.

Furthermore, the inclusion of institutional investor invest-ment makes the influence of customer satisfaction on idiosyn-cratic risk insignificant (from p<.05 to p>.10), in support of full

mediation. Therefore, the data support H5 for firm value interms of idiosyncratic risk as well. In addition, the inclusion ofthe mediating effects of institutional investor investments im-proves the fit of the full models, as Table 5 shows. Specifically,adding transient institutional investments into the model leads toan incremental increase in R-square of .023 (p<.05) for abnor-mal return, and .01 for idiosyncratic risk, thus explaining signif-icantly more variance of the firm value metrics.

These mediation results are noteworthy because they revealfiner-grained evidence for the presence or absence of the impactof customer satisfaction on firm value (i.e., depending on themediating role of institutional investors’ investments ignored inthe previous customer satisfaction literature). Our calculationsshow that satisfaction’s direct effects on abnormal return are0.473, while its indirect effects through institutional investorholding are 0.108=0.115 * 0.939 (for the corresponding co-efficients, see Tables 4 and 5). While customer satisfaction’sdirect effects on idiosyncratic risk are insignificant, its indirecteffects through transient institutional investors’ investments are−0.164=0.115 * (−1.426). In addition, we conducted Sobel’s(1982) test for mediation to assess whether the indirect media-tion effects are statistically significant. The standard Sobel test

model isZvalue ¼ ab=ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia2s2b þ b2s2a þ s2a þ s2b

q, where a and sa

are coefficient and standard error, respectively, for the impact ofthe independent variable on the mediator, and b and sb are

Table 3 Results for the impact of customer satisfaction changes on institutional investors

ΔStock holdingsby transient institutions

ΔStock holdingsby dedicated institutions

ΔStock holdingsby quasi-indexer institutions

Coefficient t-value VIF Coefficient t-value VIF Coefficient t-value VIF

Constant 0.004*** 3.02 −0.001 −0.42 0.002 0.45

Δlog (Customer Satisfaction) 0.105** 2.22 1.03 −0.052 −1.34 1.03 −0.099 −1.41 1.03

ΔLeverage −0.041 −1.49 1.08 0.011 0.49 1.08 −0.009 −0.12 1.08

ΔDividend −0.186 −0.67 1.18 0.132 0.73 1.18 −0.110 −0.36 1.18

ΔEarnings 0.002 0.32 2.34 −0.002 −0.60 2.34 0.008 0.91 2.34

ΔBook 0.006 1.57 2.98 0.004 1.21 2.98 −0.001 −0.33 2.98

ΔLog of Market Capitalization 0.024*** 2.82 3.01 0.000 0.06 3.01 −0.013 −1.14 3.01

ΔLog of Shares −0.011 −1.20 1.28 0.004 0.44 1.28 0.016 1.03 1.28

ΔSales Growth 0.004 0.49 1.08 0.013* 1.67 1.08 −0.004 −0.58 1.08

ΔStd of Return −0.582 −0.61 2.15 −0.878 −1.49 2.15 1.931* 1.74 2.15

ΔAdjusted Return −0.005 −1.34 1.58 −0.003 −1.02 1.58 0.010* 1.93 1.58

ΔBeta 0.008** 2.09 1.39 0.011*** 2.97 1.39 −0.011 −1.47 1.39

ΔIdiosyncratic Return Volatility 0.348 0.32 9.18 0.782 1.03 9.18 −2.289* −1.67 9.18

ΔTrading Volume 0.073*** 2.45 1.24 0.007 0.31 1.24 0.014 0.06 1.24

ΔRating 0.000 −0.58 1.01 0.000 −0.21 1.01 0.000 −0.17 1.01

ΔAnalyst Coverage 0.000 0.66 1.07 0.001 1.36 1.07 0.001* 1.95 1.07

ΔCurrent Ratio 0.000 0.01 1.02 −0.003 −0.47 1.02 0.006 0.56 1.02

Adjusted R2 0.065 0.012 0.022

N 916 916 916

***, **, and * denote significance at the 1, 5, and 10% levels, respectively

J. of the Acad. Mark. Sci.

coefficient and standard error, respectively, for the impact of themediator on the dependent variable. We find that the Sobel testresults are consistently significant (smallest z value=3.03,p<.05) for all indirect mediations. Thus, by and large, customersatisfaction’s indirect effects on firm value through institutionalinvestor holdings are significant. We conclude that institutionalinvestor investments serve as a mechanism that partially chan-nels the effects of customer satisfaction on firm return and risk.

Implications and conclusion

Despite the importance of institutional investors suggested inthe accounting and finance literature (Bushee and Noe 2000;Jiang 2010b), extant marketing research has neglected toexamine the impact of key marketing instruments on institu-tional investors. Analogously, prior finance and accountingresearch on institutional investors overlooks customer satis-faction and other marketing constructs. To bridge this gap in

the marketing-finance interface (M-F), our study theorizes andsupports a framework suggesting that customer satisfactioninformation is relevant for institutional investors. However,institutional investors are not homogeneous; firms with posi-tive changes in customer satisfaction attract transient institu-tional investors to a greater extent than non-transient institu-tional investors. In addition, the impact of customer satisfac-tion on transient institutional investors’ investments is contin-gent upon firm intangible asset intensity, product-market de-mand uncertainty, and financial market volatility. Also, tran-sient institutional investor holdings represent a mechanismthrough which customer satisfaction affects firm value.

Implications for research

This study makes several contributions to marketing research.First, prior literature documents that firms’ customer satisfactioninformation may affect their stock prices and investor relations(Aksoy et al. 2008; Fornell et al. 2006; Luo et al. 2010; Tuli et al.

Table 4 Results for the moderated impact of customer satisfaction changes on institutional investors

ΔStock holdingsby transient institutions

ΔStock holdingsby dedicated institutions

ΔStock holdingsby quasi-indexer institution

Coefficient t-value VIF Coefficient t-value VIF Coefficient t-value VIF

Constant 0.002 1.22 −0.001 −0.39 0.004* 1.79

Δlog (Customer Satisfaction) (ACSI) 0.115** 2.37 1.17 −0.055 −1.20 1.17 −0.083 −1.23 1.17

ΔIntangible Asset (IA) −0.018 −0.39 1.12 0.038 1.13 1.12 0.080 1.20 1.12

ΔDemand Uncertainty (DU) 0.014 0.39 1.07 −0.031 −1.09 1.07 −0.157*** −2.68 1.07

ΔFinancial Market Volatility (FMV) 2.872*** 3.66 3.47 −0.095 −0.21 3.47 −1.089 −1.30 3.47

ΔACSI * ΔIA −2.364** −2.18 1.07 −0.377 −0.39 1.07 1.537 1.04 1.07

ΔACSI * ΔDU 2.602*** 2.46 1.17 −0.227 −0.26 1.17 0.952 0.61 1.17

ΔACSI * ΔFMV −19.755* −1.74 1.32 4.251 0.48 1.32 −1.111 −0.08 1.32

ΔLeverage −0.050* −1.91 1.10 0.007 0.32 1.10 −0.006 −0.09 1.10

ΔDividend −0.179 −0.59 1.19 0.134 0.74 1.19 −0.400 −1.34 1.19

ΔEarnings 0.001 0.15 2.45 −0.003 −0.87 2.45 0.007 0.62 2.45

ΔBook 0.006 1.20 3.12 0.003 1.00 3.12 −0.004 −0.45 3.12

ΔLog of market capitalization 0.024*** 2.82 3.09 0.000 0.02 3.09 −0.028*** −2.77 3.09

ΔLog of shares −0.007 −0.73 1.31 0.003 0.28 1.31 0.013 0.86 1.31

ΔSales growth 0.004 0.59 1.09 0.013* 1.67 1.09 −0.011 −1.49 1.09

ΔStd of return −3.904*** −2.62 8.71 −0.756 −1.18 8.71 3.166** 2.24 8.71

ΔAdjusted return −0.006 −1.58 1.60 −0.003 −0.99 1.60 0.013*** 2.49 1.60

ΔBeta 0.022*** 3.67 2.12 0.010*** 2.81 2.12 −0.021*** −3.47 2.12

ΔIdiosyncratic return volatility 3.040** 2.08 6.10 0.704 0.88 6.10 −3.722*** −2.47 6.10

ΔTrading volume 0.076*** 2.46 1.25 0.009 0.37 1.25 0.009 0.19 1.25

ΔRating 0.000 −0.88 1.02 0.000 −0.15 1.02 0.000 −0.21 1.02

ΔAnalyst coverage 0.000 0.74 1.07 0.001 1.26 1.07 0.002*** 2.78 1.07

ΔCurrent ratio 0.000 −0.05 1.02 −0.003 −0.45 1.02 0.004 0.47 1.02

Adjusted R2 0.095 0.007 0.025

N 916 916 916

***, **, and * denote significance at the 1, 5, and 10% levels, respectively

J. of the Acad. Mark. Sci.

2009). However, there has been controversy (e.g., Jacobson andMizik 2009a, b) over whether customer satisfaction has anyinfluence on firm value over and above simultaneous accountingprofitability changes, as well as whether investors in general, orany investor segments (such as institutions) in particular, attendto or respond to customer satisfaction information. Thus, thepresent study is important because it shows, for the first time,that especially the transient institutional investors react to cus-tomer satisfaction and that a previously neglected channel forhow customer satisfaction influences firm value goes throughtransient institutional investors. These findings partly reconcilethe previous controversy about whether or not customersatisfaction predicts stock returns, i.e., by showing thatwhen transient institutional investors react to customersatisfaction changes, those changes are predictive ofstock returns and risk.

Second, at a broader level, this study advances the M-Fresearch stream. The rise of institutional investors is a newtrend in the financial world (Connelly et al. 2010; Jiang2010b), and institutional investors are major and powerfulplayers in asset and firm equity pricing (Edmans 2009). Weusher in, to M-F research stream, an important set of institu-tional investor-based metrics directly from the finance andaccounting literature. These metrics (institutional investorholdings, and transient, dedicated, quasi-indexer institution-al holdings in particular) may enlarge the scope of marketingresearch because they add a new perspective of marketing’simpact on investment community, beyond that of financialanalysts (Gupta et al. 2004; Luo et al. 2010; Ngobo et al.2012).

Third, our work makes a contribution by bringing togethertwo streams of research to examine how customer satisfaction

Table 5 Results for the role of transient institutional holding in the impact of customer satisfaction changes on firm value

Dependent variable = Quarterly abnormal return (%) ΔQuarterly idiosyncratic risk (%)

Coefficient t-value VIF Coefficient t-value VIF Coefficient t-value VIF Coefficient t-value VIF

Constant 0.024** 2.30 0.022** 2.13 −0.074** −2.41 −0.060* −1.95

ΔStock holdings bytransient institution

0.939*** 4.72 1.13 −1.426** −2.19 1.13

Δlog (customersatisfaction) (ACSI)

0.578** 2.22 1.17 0.473* 1.92 1.18 −1.462** −1.98 1.17 −0.312 −0.44 1.18

ΔIntangible Asset (IA) −0.163 −0.80 1.12 −0.145 −0.71 1.12 0.715 1.18 1.12 0.831 1.26 1.12

ΔDemandUncertainty (DU)

0.243 0.99 1.07 0.231 0.93 1.07 0.630 0.87 1.07 0.882 1.19 1.07

ΔFinancial MarketVolatility (FMV)

7.842** 2.19 3.47 5.114 1.45 3.58 −24.729 −1.95 3.47 −20.884 −1.60 3.58

ΔACSI * ΔIA 1.702 0.28 1.07 3.910 0.64 1.08 −12.838 −0.73 1.07 −15.533 −0.86 1.08

ΔACSI * ΔDU −6.506 −0.82 1.17 −8.97 −1.11 1.18 10.690 0.41 1.17 5.468 0.23 1.18

ΔACSI * ΔFMV −101.985 −1.21 1.32 −83.913 −1.03 1.32 365.543* 1.71 1.32 277.722 1.14 1.32

ΔLeverage −0.136 −0.98 1.10 −0.089 −0.68 1.11 −0.421 −1.20 1.10 −0.131 −0.28 1.11

ΔDividend −4.064*** −2.46 1.18 −3.816** −2.24 1.18 13.679*** 2.77 1.19 12.788*** 2.56 1.19

ΔEarnings 0.116** 2.20 2.45 0.114** 2.13 2.45 −0.435*** −2.72 2.45 −0.358* −1.90 2.45

ΔBook 0.057 1.44 3.12 0.052 1.32 3.12 −0.042 −0.41 3.12 −0.083 −0.66 3.12

ΔLog of marketcapitalization

0.221*** 5.18 3.08 0.200*** 4.60 3.16 −0.047 −0.36 3.09 −0.080 −0.68 3.17

ΔLog of shares −0.074* −1.68 1.31 −0.067 −1.60 1.31 0.233** 2.16 1.31 0.249** 2.09 1.31

ΔSales growth 0.033 1.01 1.09 0.029 0.99 1.09 −0.200* −1.86 1.09 −0.218** −1.98 1.09

ΔStd of return 4.331 0.64 8.86 8.085 1.28 9.52 31.299 1.34 8.71 32.541 1.30 9.35

ΔAdjusted return 0.037** 1.96 1.59 0.043** 2.34 1.60 0.105 1.55 1.60 0.067 0.88 1.61

ΔBeta 0.011 0.38 2.12 −0.010 −0.35 2.18 −0.125* −1.81 2.12 −0.124 −1.63 2.18

ΔIdiosyncratic returnvolatility

−3.806 −0.49 6.25 −6.760 −0.92 6.52 −10.389 −0.43 6.10 −8.563 −0.33 6.36

ΔTrading volume 0.099 0.62 1.25 0.026 0.16 1.26 0.562 0.89 1.25 −0.074 −0.14 1.26

ΔRating 0.001 0.17 1.02 0.001 0.26 1.02 0.024* 1.91 1.02 0.022 1.74 1.02

ΔAnalyst coverage −0.002 −0.85 1.07 −0.003 −1.06 1.08 −0.006 −0.88 1.07 −0.004 −0.63 1.08

ΔCurrent ratio 0.013 0.44 1.02 0.013 0.45 1.02 −0.111 −1.31 1.02 −0.136 −1.49 1.02

Adjusted R2 0.189 0.207 0.210 0.217

N 916 916 916 916

***, **, and * denote significance at the 1, 5, and 10% levels, respectively

J. of the Acad. Mark. Sci.

in the marketing domain can influence institutional investorholdings in the finance domain. For marketing research, weagree with prominent marketing scholars who acknowledge“the importance of the investor community in the design andexecution of marketing plans.… Investors do react to changesin important marketing assets and actions that are believed tochange the outlook on the firms’ cash flows” (Hanssens et al.2009, p. 118). To this notion, we add the insight that it iscrucial to understand the reactions of different types of playersin the investor community (transient versus non-transient) tocustomer satisfaction information.

Moreover, our study makes some specific contribu-tions to accounting and finance research as well. First,although institutional investor behavior has been a ma-jor interest of accounting and finance, prior studies havelargely focused on financial information such as breaksin consecutive earnings increases (Ke and Petroni 2004),future dividend increases (Amihud and Li 2006), andpost-earnings announcement drift (Bartov et al. 2000;Ke and Ramalingegowda 2005). In contrast, our re-search extends the much more limited and nascent re-search stream examining how institutional investorstrade shares in response to non-financial, intangibleinformation in general (Jiang 2010b), and certain piecesof intangible information (e.g., advertising; Grullonet al. 2004; Oak and Dalbor 2010), in particular. Ourpaper extends this literature by showing that the intan-gible marketing outcome metric of customer satisfactionalso attracts transient institutional investors. We addi-tionally show how the institutional investor behaviorfurther channels the influence of the customer satisfac-tion on stock prices and firm value. Finally, our resultsshow that transient institutional investors may have aninformation advantage not only in exploring the valueof customer satisfaction, but also in trading on thisinformation according to different conditions, such asfirm intangible asset intensity, product-market demanduncertainty, and financial market volatility, to obtainhigher investment performance.

Implications for practice

This study offers several implications for marketingmanagers and executives, as well as portfolio invest-ment managers or investors. First, resonating withCoyne and Witter (2002, p. 30), our results are

a

b

c

�Fig. 4 a Slope of change of ACSI on change of Transient InstitutionalOwnership (TRA) over the range of change of Intangible Asset Intensity(IA) b Slope of change of ACSI on change of transient institutionalownership (TRA) over the range of change of product-market DemandUncertainty (DU) c Slope of change of ACSI on change of TransientInstitutional Ownership (TRA) over the range of change of FinancialMarket Volatility (FMV)

J. of the Acad. Mark. Sci.

consistent with the notion that “what makes firm stockprice go up or down” may be largely due to a handfulof major institutional investors—especially in the caseof a marketing metric such as customer satisfaction. Yet,while the findings show that customer satisfactionchanges have a positive effect on transient institutionalinvestors’ attraction to the firm’s stock, which in turnimproves stock returns, the findings do not state that alltransient institutional investors would automatically beaware of or attend to customer satisfaction improve-ments. This creates a window of opportunity for mar-keting executives regarding investor relations: If moretransient institutional investors are made aware of thefirm’s customer satisfaction improvements, the firm canexpect greater improvements in its firm value (stockreturns, risk) to be realized as well.

Thus, CMOs should ramp up communication efforts toensure that a wide population of transient institutional in-vestors are made aware, whenever the firm is able to achievecustomer satisfaction improvements (on its own measures, orthird-party measures such as ACSI). Likewise, CMOsshould also consider making transient institutional investorsaware of steps taken to improve the firm’s customer satis-faction, such as customer service investments and consumerbrand preferences (Srinivasan et al. 2010) or product design(Aspara 2010).

In terms of how the communication with investors can bedone in practice, we agree with Tuli et al. (2009, p. 184) that“it would be useful for firms to disclose their customersatisfaction scores in their annual report to shareholders”.We also add that more interactive and instantaneous com-munication methods (e.g., direct investor relations contacts,mailing list, press releases) should be used to communicatecustomer satisfaction improvements to transient institutions.This is because transient institutional investors are, by defi-nition, quick in their moves and high in portfolio turnover,and the greatest effect on them is likely to be achieved if theyare provided with “early news” of customer satisfactionchanges to support their investment decision-making (i.e.,more than once per year).

Correspondingly, to investors on Wall Street and invest-ment managers interested in “beating the market,” our resultsprovide one key finding. Namely, as to investment strategy,investment managers who pursue abnormal returns shouldbe well-off by “tracking” transient institutional investors’trades on customer satisfaction. That is, following how andin which cases transient institutions react to customer satis-faction improvements, and making similar investments, islikely to be a good investment strategy―for other investorsthan transient institutions, as well (including other institution-al investors and individual investors). Moreover, the resultsimply that investment managers can not only pursue abnormalreturns but also seek reduced investment risk by following

transient institutional investors’ responses to customer satis-faction information.

Conclusion

In conclusion, this research examines the links between cus-tomer satisfaction, institutional investors, and firm value. Theresults offer important implications for academics and mar-keting executives as well as investors. We hope that ourfindings motivate more research to study institutionalinvestor-based mechanisms in the value creation role ofmarket-based assets of customer satisfaction.

Acknowledgments We appreciate financial support from NationalNatural Science Foundation of China (Approval No. 71273013,70802003, and 71132004), the support fromChinaMinistry of EducationSocial Science and Humanities Research Planning Foundation (ApprovalNo. 12YJA630186), and from Guanghua Leadership Institute (Approval#12-14). Aspara is grateful for research grants fromNasdaq OMXNordicFoundation and Finnish Funding Agency for Technology and Innovation.

Appendix A

Clustered standard error estimates

Petersen (2009) suggests that in most studies of corporatefinance, the data are likely to have a fixed unobserved firmeffect. Thus the residuals in the ordinary least squares (OLS)regression consist of a firm-specific component (γi) and anidiosyncratic component that is unique to each observation(ηit). Then the residue can be specified as

εit ¼ γit þ ηit ðA1Þ

and the independent variable X also has a firm-specificcomponent:

X it ¼ μit þ υit ðA2Þ

The components of X (μ and υ) and ε (γ and η) have zeromean, finite variance, and are independent of each other.Both the independent variable and the residual are correlatedacross observations of the same firm, but are independentacross firms. So,

J. of the Acad. Mark. Sci.

corr X it;X js

� � ¼ 1¼ ρX ¼ σ2

μ

.σ2X

¼ 0corr εit; εjs

� � ¼ 1¼ ρε ¼ σ2

γ

.σ2ε

¼ 0

for i ¼ j and t ¼ sfor i ¼ jandall t≠sfor all i ≠ jfor i ¼ j and t ¼ sfor i ¼ jandall t≠sfor all i ≠ j

ðA3ÞGiven equations (A1), (A2), and (A3), the true standard

error of the OLS coefficient can be determined. The asymp-totic variance of the OLS coefficient estimate is

AVar bβOLS−βh i

¼ σ2ε

σ2XNT

1þ T−1ð ÞρXρεð Þ ðA4Þ

Given the assumptions, the within-cluster correlations ofboth X and ε are positive and equal to the fraction of thevariance that is attributed to the firm effect. When the data havea fixed firm effect, the OLS standard errors will understate thetrue standard error if and only if both ρX and ρε are nonzero.

Therefore, the correlation of the residuals within a clusteris the problem the clustered standard errors are designed tocorrect. The covariance between residuals within the clusteris estimated by squaring the sum of Xitεit within each clusterand the squared sum of Xitεit is assumed to have the samedistribution across the clusters.

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