Abstract
Using a sample of U.S. firms from 1995 through 2015 and the customer satisfaction scores from the American Customer Satisfaction Index, we find strong evidence that firms with higher customer satisfaction scores enjoy lower cost of equity capital, even after controlling for other factors that determine the cost of equity. In addition, results from a propensity score matched sample analysis, a difference-in-differences analysis, and instrumental variable regressions suggest that our findings are robust to accounting for endogeneity. We also document that customer satisfaction is positively related to investor recognition and financial report quality. The effect of customer satisfaction on the cost of equity increases with the level of information asymmetry, consistent with customer satisfaction mitigating information asymmetry. Overall, our findings suggest that customer satisfaction lowers a firm’s risk and significantly attenuates its financing costs.
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Data availability
All data used in this study are publicly available.
Notes
A rising strand of research shows that intangible values of firms in the form of stakeholders’ satisfaction bring about significant benefits to shareholders. Edmans (2011) and Edmans et al. (2016) present findings that firms’ efforts and capital expended to improve employee welfare and satisfaction in the workplace eventually benefit shareholders as evidenced by higher market capital appreciation. Employee satisfaction in the workplace also leads to better corporate innovation (Chen et al. 2016) and lower cost of equity capital (Cao et al. 2015).
Higher customer satisfaction also leads to better operating margin (Rust and Zahorik 1993; Rust et al. 1995, 1996; Bolton 1998), higher future revenue (Chen 2009), higher investment returns (Anderson et al. 1994; Zeithaml 2000), improved accounting returns (Ittner and Larcker 1998), and lower systematic risk (Tuli and Bharadwaj 2009).
Similar to having traditional information intermediaries such as financial analysts or credit agencies covering firms (Healy and Palepu 2001; Yu 2008), customers reveal their aggregate opinion about firms through surveys and online platforms. This information could be value-relevant to financial markets. Consistent with this view, Larkin (2013) shows that favorable consumer opinions help enhance a firm’s information environment and lower its risk.
Research on the effect of firm performance on the cost of capital (Graham et al. 2005; Wittenberg-Moerman 2008; Brown et al. 2009; Ng et al. 2011) also predicts that customer satisfaction, when viewed as a dimension of firm performance, should reduce the level of adverse selection and the cost of equity capital.
Pástor et al. (2008) discuss the difference between expected and realized returns and advocate the use of the implied cost of equity capital as a superior proxy for expected returns. They offer theoretical underpinnings for the value of the implied cost of equity capital in uncovering the intertemporal risk-return relation.
“Customer satisfaction” in our study is based on independent surveys and captures the extent of how favorable customers’ views of firms are. Our tests therefore suggest that customer satisfaction places a directional impact on the cost of equity capital while such a relation is unlikely to be subject to the possibility that firm management exerts influences to manage equity financing cost.
A positive relation between customer satisfaction and realized stock returns (Fornell et al. 2006; Aksoy et al. 2008; Grewal et al. 2010, among others) can be taken as evidence of market inefficiency. However, it is unclear why some rational investors cannot exploit the inefficiency in the pricing of customer satisfaction, given that this information is released publicly on a periodic basis for large firms and there is no ambiguity from theoretical prediction for the value of this intangible dimension. Studies by Jacobson and Mizik (2009) and Ittner et al. (2009) argue that the “mispricing” of the ACSI in future realized stock returns may be limited and is largely due to inadequate risk adjustments in prior studies. Findings in our study also highlight why the implied cost of capital may be more appropriate than the noisy realized stock returns in studying customer satisfaction and the cost of equity financing.
The model that the ACSI employs to generate customer satisfaction scores was derived from the Swedish Customer Satisfaction Barometer (SCSB) and was first implemented in Sweden in 1989. Its consistency, reliability, and precision make ACSI scores comparable across firms and industries as well as periods.
We also cluster standard errors by industry, year, or both industry and year, and the results are qualitatively unchanged. In an unreported test, to examine whether the relation between industry-adjusted customer satisfaction and the cost of equity capital is sensitive to model specifications, we employ Fama-MacBeth (1973) regression and also find a significant negative relation between industry-adjusted customer satisfaction and the cost of equity capital.
In untabulated analyses, we standardize all independent variables to have zero mean and standard deviation of one. We find that book-to-market ratio has the strongest economic effect on the implied cost of equity capital and that the effect of industry-adjusted customer satisfaction is about one-third that of book-to-market ratio. In Table 13 of Appendix C, we adopt various industry classifications to adjust CS and report corresponding regression results. In Tables 14 and 15, the negative relation between industry-adjusted customer satisfaction (CS IND ADJ) and the implied cost of equity capital manifests consistently across a broad set of individual cost of equity capital estimates and in an annual change model.
Harvey (2017) suggests that researchers should take prior information into account by specifying a prior on all testable hypotheses and employ a Symmetric and Descending Minimum Bayes Factor (SD-MBF) approach. This is to assess the p value of an effect in a Bayesian framework instead of inferring research conclusions from the normal reported p value. Following Harvey (2017), on a prior probability of 20% that there might be an effect of CS IND ADJ on the average implied cost of equity capital and the reported p value of 0.001 for the coefficient of CS IND ADJ in model (4), we can compute the SD-MBF for the CS IND ADJ effect of 0.0188 with a Bayesianized p value of 0.0699. Thus there is only a 7% chance the null hypothesis (no effect from CS IND ADJ on the cost of equity, given a prior of 20% probability) is true.
We derive this implied volatility from the volatility surface in OptionMetrics in June each year to match the time of estimation of the implied cost of equity capital. Due to constraints on the availability of options data, the analysis using implied volatility is on a slightly reduced sample size of 2004 firm-year observations.
We could attribute this incremental relation to the measurement error in constructing risk variables. The measurement error is particularly eminent, because investors should price forward-looking risk while most of our risk measures are based on historical return data. Even when we control for implied volatility, a risk dimension that is forward-looking by construct, we still document a significant negative relation between industry-adjusted customer satisfaction and the implied cost of equity.
In an unreported analysis, we also examine the difference in control variables between the two samples. We find no evidence that the diagnostic statistics of the matching variables are statistically different between the two samples.
In a different approach, we calculate CS as the number of years to date since the start of the ACSI coverage (1994) during which the firm has appeared on the ACSI List, scaled by the total number of years to the end of the sample period that the firm appears in our sample. This is to address the concern that ACSI scores may be sticky and may not change much over time for a firm. Untabulated results also show a negative and significant relation between this alternative CS measure and the implied cost of equity capital.
The key behavioral assumption invoked by Merton’s model is that investors only use securities of firms that they know in constructing their optimal portfolios and not about the issue of information asymmetry between informed and uninformed investors. Thus it is chiefly the investors’ decision in owning or selling shares of a firm that really matters to expected returns, not necessarily their knowledge about the firm per se. Thus, while awareness may be similar between firms of high and low customer satisfaction scores, trading outcomes and capital market effects can differ starkly. In the context of our study on customer satisfaction, firms with high scores are more likely to be part of such portfolios while firms with low scores are not.
Huberman (2001) shows that investors are likely to invest in familiar firms. Frieder and Subrahmanyam (2005) show that individual investors disproportionately hold firms with greater brand visibility. Barber and Odean (2008) find that individual investors are net buyers of stocks in the news. Loughran and Schultz (2005) document that, because of lower investor recognition, firms in rural areas trade much less, are covered by fewer analysts, and are owned by fewer institutions than those in urban areas. Lehavy and Sloan (2008) show that future stock returns relate negatively to changes in investor recognition as proxied by breadth of institutional ownership.
From our conversations with buy-side analysts, customer satisfaction scores from the ACSI, if available for a firm, are usually incorporated in buy-side research that justifies long-term investment recommendations.
Several studies examine the usefulness of the Thomson Reuters News Analytics database. Hendershott et al. (2015), for example, employ it to examine whether financial institutions can predict the sentiment of firm-specific news. Heston and Sinha (2015) document strong return predictability of its news sentiment in the U.S. market. Li et al. (2015) show that the coverage of the database is comprehensive and covers over 92% of Compustat’s earnings announcements. The database has been also widely used by industry practitioners. The real-time news analytics are provided as additional services for users of Datastream’s Eikon – an established and popular platform among financial institutions.
Research documents that corporate reputation affects several corporate strategies, such as debt and equity financing (e.g., Gomes 2000; Siegel 2005), the extent of product advertising aggressiveness (Milgrom and Roberts 1986), and the choice of reputable auditors (Huang and Kang 2018). Cao et al. (2012) suggest that firms with high corporate reputation also produce financial reports of high quality, which results in a lower probability of financial reporting misstatement.
Verrecchia (1983) formalizes the concept of propriety costs as any information from disclosures that may expose a firm to certain competitive disadvantage. Several studies (Verrecchia and Weber 2006; Berger and Hann 2007; Ellis et al. 2012; Li et al. 2017) argue that firms may choose to deviate from high quality disclosures because firms bear high proprietary costs. These costs include competitive disadvantage from disclosing information to competitors and regulators, bargaining disadvantages with suppliers and consumers, and litigation that might follow informative disclosures.
The FOG Index suggests that more syllables per word or more words per sentence make the 10-K documents harder for investors to read (Li 2008). Specifically, the FOG Index is determined as (words_per_sentence + percent_of_complex_words) × 0.4, where complex words have three syllables or more. An alternative measure of readability is Length of 10-K report, which is the natural logarithm of the number of words from the 10-K reports.
Barth et al. (2013) deduce their measure of earnings transparency from the explanatory power of the returns-earnings relation. They suggest that the higher the explanatory power, the more earnings capture changes in firm value. To operationalize the earnings transparency measure, we measure the adjusted R2 from annual cross-sectional regressions based on the relation between contemporaneous stock returns and earnings and change in earnings deflated by stock price.
The main advantage of adopting the change specification is that it uses the firm’s variables as its own controls and helps mitigate any variable omission bias (Weber 2006; Naiker et al. 2013). Here, omitted variables are likely to be unchanged over time, and their annual changes therefore are approximately zero. However, because increase in customer satisfaction and investor recognition can be jointly caused by other possibilities, such as increases in marketing and advertising spending, we caution a causal interpretation of these findings.
The CS score in our study is a weighted average of three survey questions that measure different aspects of customer satisfaction: customer expectations, perceived quality, and perceived value. While the dimensions of “perceived quality” and “perceived value” are directly linked to products and services, the dimension of “customer expectations” can include satisfaction with the supplier, satisfaction with the overall distribution, or other types of nonproduct satisfaction.
We apply several filters for the data collection of product reviews. First, we seek incentivized reviews with key words, such as “discount” and “exchange for unbiased review” and remove those reviews. Second, we remove those reviews from the Amazon Vine Program, which allows manufacturers and publishers to pay Amazon a fee to receive reviews for their products on Amazon.com. Third, we remove all unverified product reviews. Finally, we filter out product reviews that could refer to nonproduct features. Specifically, product reviews with key words such as “arrived damaged,” “customer service,” “shipping,” “missing items,” “update,” and “delivery” are not included.
Many customer reviews and star ratings satisfy the conditions required for the “wisdom of crowds” phenomenon. That is, the average estimate of large crowds is likely to be closer to the true value than any individual customer’s estimate (Surowiecki 2004; Hong and Page 2004; Chen et al. 2014; Azar and Lo 2016) because individual customer errors should be cancelled out.
Controlling for risk factors, we do not find a negative relation between CS IND ADJ and future realized stock returns within the group of firms covered by the ACSI. This finding makes the inverse relation between CS IND ADJ and the implied cost of equity capital particularly interesting and important. We interpret this as suggesting investors discount future earnings of high CS IND ADJ firms at a significantly lower rate, leading to a negative CS IND ADJ-premium in the ex-ante cost of capital framework. At the same time, the pricing of customer satisfaction information in the current stock price is efficient, leading to no predictability of future realized stock returns.
Mr. Buffett shared this advice in his address to graduates from the Goldman Sachs 10,000 Small Businesses Development Program, LaGuardia Community College, Long Island City, New York. Buffett added: “Any business with delighted customers has a sales force they won’t have to pay. You don’t see them, but they are talking to people all the time” (“The one thing Warren Buffett says every business must do,” Quittner, Fortune Month 2016).
A broader genre of literature documents that nonfinancial qualities are important for firms and investors. For example, studies by Lev and Sougiannis (1996), Aboody and Lev (1998), Deng et al. (1999), and Chan et al. (2001) document that investors enjoy higher returns from firms with high R&D capital investments, advertising expenditure, patent citations, or software developments.
Easton and Monahan (2005), for example, show that analyst-based ICC poorly predicts returns, a result attributable to the lack of reliability of analysts’ earnings forecasts. More importantly, the resulting ICC estimate from the approach of Hou et al. (2012) powerfully predicts realized returns, a desirable property of an ICC.
Since the estimation requires 10 years of historical data, our data sampling starts in 1985.
Following Hribar and Collins (2002), the accruals measure is computed using the cash flow statement method as the difference between earnings and cash flows from operations.
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Acknowledgements
We thank participants at the 2017 International Conference of Accounting and Finance (Da Nang, Viet Nam), the 2017 American Accounting Association Annual Meeting (San Diego, USA), the University of Adelaide 2016 research seminar series (Adelaide, Australia), Stephen Brown, Nga Cao, Chen Chen, Charles Corrado, Paul Coram, Man Huu Dang, Lam Tung Dang, Viet Anh Dang, Sunyoung Kim, Shane Moriarity, Doan Nguyen, Nick Nguyen, Grant Richardson, Thomas Ruchti, Philip Shane, Avanidhar Subrahmanyam, Xiaoting Wei, Bohui Zhang, and Ralf-Yves Zurbrugg for helpful discussions and suggestions. We thank Elijah DePalma at Thomson Reuters for helping us understand the Thomson Reuters News Analytics database and Ningfei Zhu for excellent research assistance. Cameron Truong and Thu Ha Nguyen acknowledge financial support from the 2015 AFAANZ Research Grant, and Thanh Huynh acknowledges financial support from Monash Business School. This paper received the Best Paper Award at the 2017 ICOAF (Da Nang, Viet Nam).
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Appendices
APPENDIX
1.1 Customer satisfaction surveys and model
This section summarizes the customer satisfaction surveys and model used to quantify firm-level scores in the ACSI. The ACSI relies on customer assessments of the quality of goods and services produced by major companies and sold to U.S consumers. The ACSI aggregates individual customer surveys to generate customer satisfaction scores for over 300 companies with significant U.S. market shares, based on individual companies, 43 industries, 10 economic sectors, and the national ACSI. Industry customer satisfaction score is measured as the average of customer satisfaction scores of an industry’s firms weighted by their revenues. Likewise, customer satisfaction score of each sector is the average score of that sector’s industries weighted by its revenues. Similarly, the national ACSI score is the weighted average of all sector scores with the weight being the contribution of each sector to the U.S. gross domestic product.
The ACSI conducts email surveys at random, sent to company customers and government service users. In the first stage, the ACSI asks potential respondents about their purchase as well as their use of certain products and services within a specific timeframe. In the second stage, the ACSI sends questions to only those classified as respondents about which company or brand they have made purchases. Answers to the survey questions are then coded as an interview for each company in the ACSI list. Over 70,000 interviews are conducted annually to construct their firm-level customer satisfaction scores.
The ACSI employs a quantitative model that was developed at the University of Michigan’s Ross School of Business. This model originates from the Swedish Customer Satisfaction Barometer, which was first implemented in 1989. It is a cause and effect model that uses customer interviews as inputs with indices for satisfaction drivers (including customer expectations, perceived quality, and perceived value) being on the left side, satisfaction being in the center, and outcomes (including customer complaints and customer loyalty) being on the right side. The indices are measured by multiple survey questions that quantify customer assessments of each index determinant and the reported indices range between 0 and 100.
1.2 Merging ACSI data with CRSP/Compustat data
Linking ACSI data to CRSP/Compustat is not trivial, given the focus of our study. The ACSI only provides company names or brands without further identifiers. Since 1994, when the ACSI first published its data, many companies have merged or been acquired or delisted, which makes our data merging more challenging. Here, we describe steps taken to obtain identifiers of the firms covered in the ACSI sample, which are then used to link ACSI data to other accounting and finance datasets. We also outline our merging rules that are applied in different circumstances.
To obtain firm-unique identifiers, specifically the Compustat’s GVKEYs, for firms in the ACSI list, we first rely on the company name (full name or short name) or its brand or subsidiary as well as the company’s industry provided by the ACSI. These pieces of information allow us to obtain the full name of the company using various internet search engines.
In the first case, where the ACSI assigns customer satisfaction scores to a specific company and its correspondent industry (for example, Amazon and internet retail), it is straightforward to obtain the company full name and industry being used as a check tool. In the second case, where the ACSI assigns customer satisfaction scores to a company brand/subsidiary and its industry, the ACSI also provides the parent company name. For example, Jeep is a brand of Fiat Chrysler and has its correspondent industry of automobiles and light vehicles. This supplemental information allows us to obtain the full names of both the brand or subsidiary and the parent company.
Second, we carefully read a company profile to identify whether the (parent) company included in the ACSI list has been acquired or merged and note the full name of its current parent company (if it exists). Third, we obtain the GVKEYs of ACSI companies by searching for company names in the CRSP/Compustat merged database. When the GVKEY of a particular company is not available (due to M&A, for example) or the company is identified in the previous steps as a subsidiary, we obtain the GVKEY of the parent company instead. Finally, we audit our merge and matching results by tracing the company profile from the CRSP/Compustat merged database using the obtained GVKEY. This is to ensure that we have found the correct company (i.e. it has the same name and operates in the same industry as provided by the ACSI).
APPENDIX
ICC | Model | Source |
|---|---|---|
r GLS | \( {MV}_t={BV}_t+\sum \limits_{k=1}^{11}\frac{E_t\left[\left({ROE}_{t+k}-R\right)\times {BV}_{t+k-1}\right]}{{\left(1+R\right)}^k}+\frac{E_t\left[\left({ROE}_{t+12}-R\right)\times {BV}_{t+11}\right]}{R\times {\left(1+R\right)}^{11}} \), where MVt denotes the market value of equity in year t; R is the implied cost of equity (ICC); BVt is the book value of equity; Et[.] denotes market expectations based on information available in year t; and (ROEt + k − R) × BVt + k − 1 is the residual income in year t + k. Following Hou et al. (2012), we estimate the expected ROEt + k in years t + 1 to t + 3 using model-based earnings forecasts in year t + k. Book value of equity is computed using clean surplus accounting: BVt + k = BVt + k − 1 + Et + k − Dt + k, where Et + k is the earnings in year t + k, Dt + k is the dividend in year t + k, computed using the current dividend payout ratio for firms with positive earnings, or using current dividends divided by 0.06 × total assets as an estimate of the payout ratio for firms with negative earnings. After year t + 3, the expected ROE is assumed to mean-revert to the historical industry median value until year t + 11, after which point the residual income becomes a perpetuity. Loss firms are excluded from the calculation of industry median ROE (Gebhardt et al. 2001). | Gebhardt et al. (2001) |
r CT | \( {MV}_t={BV}_t+\sum \limits_{k=1}^5\frac{E_t\left[\left({ROE}_{t+k}-R\right)\times {BV}_{t+k-1}\right]}{{\left(1+R\right)}^k}+\frac{E_t\left[\left({ROE}_{t+5}-R\right)\times {BV}_{t+4}\right]\left(1+g\right)}{\left(R-g\right)\times {\left(1+R\right)}^5} \), where MVt denotes the market value of equity in year t; R is the implied cost of equity (ICC); BVt is the book value of equity; Et[.] denotes market expectations based on information available in year t; and (ROEt + k − R) × BVt + k − 1 is the residual income in year t + k. Following Hou et al. (2012), we estimate the expected ROEt + k in years t + 1 to t + 5 using model-based earnings forecasts in year t + k. Book value of equity is computed using clean surplus accounting: BVt + k = BVt + k − 1 + Et + k − Dt + k, where Et + k is the earnings in year t + k, Dt + k is the dividend in year t + k, computed using the current dividend payout ratio for firms with positive earnings, or using current dividends divided by 0.06 × total assets as an estimate of the payout ratio for firms with negative earnings. Following Claus and Thomas (2001), g is set to the current risk-free rate minus 3%. If the difference is less than zero, it is set to be the difference between average historical risk-free rate minus 3%. | Claus and Thomas (2001) |
r GM | \( R=A+\sqrt{A^2+\frac{E_t\left[{E}_{t+1}\right]}{MV_t}\times \left(g-\left(\gamma -1\right)\right)} \), where \( A=0.5\left(\left(\gamma -1\right)+\frac{E_t\left[{D}_{t+1}\right]}{M_t}\right),g=0.5\left(\frac{E_t\left[{E}_{t+3}\right]-{E}_t\left[{E}_{t+2}\right]}{E_t\left[{E}_{t+2}\right]}+\frac{E_t\left[{E}_{t+5}\right]-{E}_t\left[{E}_{t+4}\right]}{E_t\left[{E}_{t+4}\right]}\right) \), MVt denotes the market value of equity in year t; R is the implied cost of equity (ICC); BVt is the book value of equity; Et[.] denotes market expectations based on information available in year t; Et is the earnings in year t; Dt + 1 is the dividend in year t + 1, computed using the current dividend payout ratio for firms with positive earnings, or using current dividends divided by 0.06 × total assets as an estimate of the payout ratio for firms with negative earnings; g is the short-term growth rate. We follow Gode and Mohanram (2003) and use the average of forecasted near-term growth and five-year growth as an estimate of g. γ is the perpetual growth rate in abnormal earnings beyond the forecast horizon. It is set to the current risk-free rate minus 3%. If the difference is less than zero, it is set to be the difference between average historical risk-free rate minus 3%. | Gode and Mohanram (2003), Ohlson and Juettner-Nauroth (2005) |
r Easton | \( {MV}_t=\frac{E_t\left[{E}_{t+2}\right]+R\times {E}_t\left[{D}_{t+1}\right]-{E}_t\left[{E}_{t+1}\right]}{R^2} \), where MVt denotes the market value of equity in year t; R is the implied cost of equity (ICC); Et[.] denotes market expectations based on information available in year t; Et + 1 and Et + 2 are the earnings in year t + 1 and year t + 2, respectively; Dt + 1 is the dividend in year t + 1, computed using the current dividend payout ratio for firms with positive earnings, or using current dividends divided by 0.06 × total assets as an estimate of the payout ratio for firms with negative earnings. | Easton (2004) |
r AVE | \( {r}_{AVE}=\frac{r_{GLS}+{r}_{CT}+{r}_{GM}+{r}_{Easton}}{4} \) |
Hou et al. (2012) show that analysts’ forecasts exhibit critical biases, which can in turn, affect the final ICC estimate.Footnote 32 Moreover, relying on analysts’ forecasts constrains the cross-section to the sparse coverage of analysts (La Porta 1996; Hong et al. 2000; Diether et al. 2002). Therefore, we follow Hou et al., and, for each year between 1995 and 2015, we estimate the following pooled cross-sectional regression using the past ten years of data.Footnote 33
where Ej, t + τ (τ = 1, 2, 3, 4, or 5) represents the earnings of firm j in year t + τ. All explanatory variables are measured at the end of year t: TAj, t is the total assets; DIVj, t is the dividend payment; DDj, t is a dummy variable that takes a value of 1 for dividend payers or 0 otherwise; NegEj, t is a dummy variable that equals 1 for firms with negative earnings or 0 otherwise; and Accj, t is total accruals.Footnote 34 Similar to Hou et al., we focus on the dollar earnings to make the model’s forecasts comparable with analysts’ forecasts. To mitigate the potential impact of outliers, we also winsorize the explanatory variables at the first and 99th percentiles.
For each firm j and year t, earnings forecasts from year t + 1 to year t + 5 into the future are estimated by multiplying the explanatory variables as of year t with the estimated coefficients from (1). As the estimation is conducted in current year t, survivorship bias is avoided. Having estimated earnings forecasts, we derive each of the four individual ICC estimates for each firm at the end of June each year t by computing the internal rate of return such that the market equity at the end of June is equal to the present value of future earnings.
APPENDIX
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Truong, C., Nguyen, T.H. & Huynh, T. Customer satisfaction and the cost of capital. Rev Account Stud 26, 293–342 (2021). https://doi.org/10.1007/s11142-020-09555-8
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DOI: https://doi.org/10.1007/s11142-020-09555-8