Do institutional investors pay attention to customer satisfaction and why?

Abstract

Extant marketing, accounting, and finance research has neglected to examine the relevance of customer satisfaction information for institutional investors, despite their potential importance. This study develops and supports a framework suggesting that firms with positive changes in customer satisfaction are more attractive to transient institutional investors than to non-transient institutional investors. We also find that the impact of customer satisfaction on transient institutional investor holdings is contingent upon firm intangible asset intensity, product-market demand uncertainty, and financial market volatility. In addition, transient institutional investor holdings at least partially mediate the effects of changes in customer satisfaction on firm abnormal return and idiosyncratic risk. Thus, transient institutional investor investments represent a mechanism through which customer satisfaction affects firm value.

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Notes

  1. 1.

    We thank two anonymous reviewers for the suggestion to include this summary.

  2. 2.

    We thank one anonymous reviewer for this competing hypothesis logic.

  3. 3.

    A fund manager’s reputation is hurt less if everyone makes the same bad 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 limb with a contrarian strategy, even if the manager has information that the contrarian strategy has the higher probability of being correct (Scharfstein and Stein 1990).

  4. 4.

    Return is already a change of stock price, so the buy-hold return for quarter t is essentially a change in stock price or firm value triggered by the ACSI announcement in quarter t. We exclude quarter t when investigating the change in firm risk because the risk calculation in quarter t can be contaminated by the ACSI announcement. A comparison of risk between quarters t + 1 and t − 1 more cleanly shows the impact of changes in ACSI on firm risk.

  5. 5.

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

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Acknowledgments

We appreciate financial support from National Natural Science Foundation of China (Approval No. 71273013, 70802003, and 71132004), the support from China Ministry of Education Social Science and Humanities Research Planning Foundation (Approval No. 12YJA630186), and from Guanghua Leadership Institute (Approval #12-14). Aspara is grateful for research grants from Nasdaq OMX Nordic Foundation and Finnish Funding Agency for Technology and Innovation.

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Appendix A

Appendix A

Clustered standard error estimates

Petersen (2009) suggests that in most studies of corporate finance, the data are likely to have a fixed unobserved firm effect. Thus the residuals in the ordinary least squares (OLS) regression consist of a firm-specific component (γ i ) and an idiosyncratic component that is unique to each observation (η it ). Then the residue can be specified as

$$ {\epsilon}_{it}={\gamma}_{it}+{\eta}_{it} $$
(A1)

and the independent variable X also has a firm-specific component:

$$ {X}_{it}={\mu}_{it}+{\upsilon}_{it} $$
(A2)

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

$$ \begin{array}{ll}\begin{array}{l}\begin{array}{c}\hfill corr\left({X}_{it},{X}_{js}\right)=1\hfill \\ {}\hfill ={\rho}_X={\sigma}_{\mu}^2/{\sigma}_X^2\hfill \\ {}\hfill =0\hfill \end{array}\hfill \\ {}\begin{array}{c}\hfill corr\left({\epsilon}_{it},{\epsilon}_{js}\right)=1\hfill \\ {}\hfill ={\rho}_{\epsilon }={\sigma}_{\gamma}^2/{\sigma}_{\epsilon}^2\hfill \\ {}\hfill =0\hfill \end{array}\hfill \end{array}\hfill & \begin{array}{l}\begin{array}{l}\mathrm{for}\;i=j\;\mathrm{and}\;t=s\hfill \\ {}\mathrm{for}\;i=j\;\mathrm{and}\;\mathrm{all}\;t\ne s\hfill \\ {}\mathrm{for}\;\mathrm{all}\;i\ne j\hfill \end{array}\hfill \\ {}\begin{array}{l}\mathrm{for}\;i=j\;\mathrm{and}\;t=s\hfill \\ {}\mathrm{for}\;i=j\;\mathrm{and}\;\mathrm{all}\;t\ne s\hfill \\ {}\mathrm{for}\;\mathrm{all}\;i\ne j\hfill \end{array}\hfill \end{array}\hfill \end{array} $$
(A3)

Given equations (A1), (A2), and (A3), the true standard error of the OLS coefficient can be determined. The asymptotic variance of the OLS coefficient estimate is

$$ AVar\left[{\widehat{\beta}}_{OLS}-\beta \right]=\frac{\sigma_{\epsilon}^2}{\sigma_X^2 NT}\left(1+\left(T-1\right){\rho}_X{\rho}_{\epsilon}\right) $$
(A4)

Given the assumptions, the within-cluster correlations of both X and ε are positive and equal to the fraction of the variance that is attributed to the firm effect. When the data have a fixed firm effect, the OLS standard errors will understate the true standard error if and only if both ρ X and ρ ε are nonzero.

Therefore, the correlation of the residuals within a cluster is the problem the clustered standard errors are designed to correct. The covariance between residuals within the cluster is estimated by squaring the sum of X it ε it within each cluster and the squared sum of X it ε it is assumed to have the same distribution across the clusters.

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Luo, X., Zhang, R., Zhang, W. et al. Do institutional investors pay attention to customer satisfaction and why?. J. of the Acad. Mark. Sci. 42, 119–136 (2014). https://doi.org/10.1007/s11747-013-0342-9

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Keywords

  • Customer satisfaction
  • Investor community
  • Institutional investor holding
  • Intangibles
  • Marketing-finance interface