Skip to main content
Springer Nature Link
Log in

Customer satisfaction and firm performance: insights from over a quarter century of empirical research

  • Review Paper
  • Published:
Journal of the Academy of Marketing Science Aims and scope Submit manuscript

Abstract

Emphasizing customer satisfaction as a strategic lever for enhancing business performance is a widespread business practice. However, just over 25 years of empirical studies by academic researchers has produced evidence that is sometimes contradictory. Hence, greater academic clarity and improved managerial understanding could result from a meta-analysis of the customer satisfaction-business performance relationship. To that end, the authors analyzed 251 correlations from 96 studies published between 1991 and 2017. While the satisfaction-performance relationship is positive and statistically significant on average (r = .101), more meaningful insights emerge from the explication of moderating and mediating relationships. Illustrative of these insights is the finding that satisfaction is more appropriately depicted as mediating the effects of selected marketing strategy variables on firm performance outcomes. Moreover, when satisfaction is viewed in the right setting using the right satisfaction and performance measures, a most favorable contingencies (MFC) perspective, the estimated correlation is reasonably strong (r = .349).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Another market measure of financial performance that has been the focus of investigation in the satisfaction-performance literature is Tobin’s q. However, concerns have been raised recently by Bendle and Butt (2018) regarding the use and misuse of Tobin’s q in marketing (see Bendle and Butt 2018). Because of these concerns (brought to our attention by a reviewer for which we are grateful, and a reviewer’s related suggestion), the analysis of Tobin’s q is presented in the Supplemental Material.

  2. A breakout of the mean satisfaction-performance correlation by time period (i.e., year of study publication) similarly fails to illuminate a meaningful pattern of ever increasing or decreasing effect sizes. Specifically, for studies published (i) before 1997, M = −.110 (95% CI of −.224 to .007, n = 15), (ii) during the period 1998–2002, M = .092 (95% CI of .046 to .138, n = 42), (iii) during the period 2003–2007, M = .097 (95% CI of .064 to .131, n = 66), (iv) during the period 2008–2012, M = .140 (95% CI of .089 to .191, n = 90), and (v) during the period 2013–2017, M = .112 (95% CI of .044 to .180, n = 38). Thus, while the respective means for the last four time periods are each significantly stronger than the mean estimate for the time period “before 1997” (note, the mean for “before 1997” is not statistically significant), it should be noted that the respective means for each of the four, more recent time periods are not significantly different from one another.

  3. Prior to estimating the final PM models, a full mediating (FM) model (i.e., marketing strategy, firm, and industry factors only directly impacting satisfaction and satisfaction then directly impacting performance) was estimated to determine whether fit would be superior. The MASEM findings indicate that the FM model is a poor fitting model across the performance measure while the respective partial-mediating versions of those models can be considered to fit the data well. Regarding the FM pooled performance model, the χ2 (6) of 67.34 is significant (p < .05), the CFI of .57 and TFI of .04 are below the critical threshold of .95 suggestive of a good fit, the SRMR of .05 is reasonable, but values of zero represent perfect fit, and the RMSEA of .11 and its 90% CI having an upper bound that exceeds .10 (CI = .087 to .133) are further indicants of poor fit. Similarly, the FM model is a poor fit for market share. The Δχ2 (5) of 921.56 is significant (p < .05), the CFI is .06 and TFI is −1.06 (which is a non-normed value), the SRMR is .16, and the RMSEA of .48 with a 90% CI of .455 to .508 are also indicants of poor fit. Likewise, the FM model is a poor fit to the data when profit is the performance measure. The χ2 (6) of 36.83 is significant (p < .05), the CFI of .72 and TFI of .40 are below the threshold of .95, the SRMR of .04 is reasonable, but the RMSEA of .08 and its 90% CI having an upper bound that exceeds .10 (CI = .055 to .104) are indicative of poor model fit. The resulting modification indices (in the case of each of these three FM models) pointed to estimating a partial mediating model instead, with one or more of the exogenous factors further exerting a direct effect on the respective performance metric.

References

  • Adjei, M. T., Griffith, D. A., & Noble, S. M. (2009). When do relationships pay off for small retailers? Exploring targets and contexts to understand the value of relationship marketing. Journal of Retailing, 85(4), 493–501.

    Article  Google Scholar 

  • Ailawadi, K. L., Dant, R. P., & Grewal, D. (2004). The difference between perceptual and objective performance measures: an empirical analysis. Cambridge: Marketing Science Institute.

    Google Scholar 

  • Aksoy, L., Cooil, B., Groening, C., Keiningham, T., & Yalcin, A. (2008). The long-term stock market valuation of customer satisfaction. Journal of Marketing, 72(4), 105–122.

    Article  Google Scholar 

  • Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share and profitability: findings from Sweden. Journal of Marketing, 58(3), 53–66.

    Article  Google Scholar 

  • Anderson, E. W., Fornell, C., & Mazvancheryl, S. (2004). Customer satisfaction and shareholder value. Journal of Marketing, 68(4), 172–185.

    Article  Google Scholar 

  • Barger, P. B., & Grandey, A. A. (2006). Service with a smile and encounter satisfaction: emotional contagion and appraisal mechanisms. Academy of Management Journal, 49(6), 1229–1238.

    Article  Google Scholar 

  • Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. Hedges, & J. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 377–395). New York: Russell Sage Foundation.

    Google Scholar 

  • Beckers, S. F., van Doorn, J., & Verhoef, P. C. (2017). Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value. Journal of the Academy of Marketing Science, 45(3), 1–18.

    Google Scholar 

  • Bendle, N. T., & Butt, M. N. (2018). The misuse of accounting-based approximations of Tobin’s q in a world of market-based assets. Marketing Science, 37(3), 484–504.

    Article  Google Scholar 

  • Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184.

    Article  Google Scholar 

  • Bharadwaj, S. G., & Mitra, D. (2016). Satisfaction (Mis)pricing revisited: real? Really big? Journal of Marketing, 80(5), 116–121.

    Article  Google Scholar 

  • Bharadwaj, S. G., & Varadarajan, R. (2004). Toward an integrated model of business performance. In N. K. Malhotra (Ed.), Review of marketing research (pp. 207–243). Armonk: M. E. Sharpe.

    Google Scholar 

  • Blundell, R., Griffith, R., & Van Reenen, J. (1999). Market share, market value and innovation in a panel of British manufacturing firms. The Review of Economic Studies, 66(3), 529–554.

    Article  Google Scholar 

  • Bolton, R. N. (1998). A dynamic model of the duration of the customer’s relationship with a continuous service provider: the role of satisfaction. Marketing Science, 17(1), 45–65.

    Article  Google Scholar 

  • Borenstein, S. (1991). Selling costs and switching costs: explaining retail gasoline margins. RAND Journal of Economics, 22(3), 354–369.

    Article  Google Scholar 

  • Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: Wiley.

    Book  Google Scholar 

  • Bradburd, R. M., & Caves, R. E. (1982). A closer look at the effect of market growth on industries' profits. The Review of Economics and Statistics, 64(4), 635–645.

    Article  Google Scholar 

  • Brown, S., & Lam, S. (2008). A meta-analysis of relationships linking employee satisfaction to customer responses. Journal of Retailing, 84(3), 243–255.

    Article  Google Scholar 

  • Buzzell, R. D., & Wiersema, F. D. (1981). Modelling changes in market share: a cross-sectional analysis. Strategic Management Journal, 2(1), 27–42.

    Article  Google Scholar 

  • Cai, G. (2010). Channel selection and coordination in dual-channel supply chains. Journal of Retailing, 86(1), 22–36.

    Article  Google Scholar 

  • Camisón, C., & Forés, B. (2010). Knowledge absorptive capacity: new insights for its conceptualization and measurement. Journal of Business Research, 63(7), 707–715.

    Article  Google Scholar 

  • Churchill, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491–504.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Erlbaum.

    Google Scholar 

  • Cotterill, R. W., & Putsis, W. P. (2000). Market share and price setting behavior for private labels and national brands. Review of Industrial Organization, 17(1), 17–39.

    Article  Google Scholar 

  • De Haan, E., Verhoef, P. C., & Wiesel, T. (2015). The predictive ability of different customer feedback metrics for retention. International Journal of Research in Marketing, 32(2), 195–206.

    Article  Google Scholar 

  • Doorley, T. L., & Donovan, J. M. (1999). Value-creating growth: how to lift your company to the next level of performance. San Francisco: Jossey-Bass.

    Google Scholar 

  • Dunlap, W. P. (1999). A program to compute McGraw and Wong’s common language effect size indicator. Behavior Research Methods, Instruments, & Computers, 31(4), 706–709.

    Article  Google Scholar 

  • Edvardsson, B., Jognson, M. D., Gustafsson, A., & Strandvik, T. (2000). The effects of satisfaction and loyalty on profits and growth: products versus services. Total Quality Management, 11(7), 917–927.

    Article  Google Scholar 

  • Eisend, M. (2015). Have we progressed marketing knowledge? A meta-meta-analysis of effect sizes in marketing research. Journal of Marketing, 79(3), 23–40.

    Article  Google Scholar 

  • Falk, T., Hammerschmidt, M., & Schepers, J. J. L. (2010). The service quality-satisfaction link revisited: exploring asymmetries and dynamics. Journal of the Academy of Marketing Science, 38(3), 288–302.

    Article  Google Scholar 

  • Fornell, C. (1992). A national customer satisfaction barometer: the Swedish experience. Journal of Marketing, 55(1), 1–21.

    Google Scholar 

  • Fornell, C., Mithas, S., Morgenson, F. V., III, & Krishan, M. S. (2006). Customer satisfaction and stock prices: high returns, low risk. Journal of Marketing, 70(1), 1–14.

    Article  Google Scholar 

  • Fornell, C., Morgeson, F. V., III, & Hult, G. T. M. (2016a). An abnormally abnormal intangible: stock returns on customer satisfaction. Journal of Marketing, 80(5), 122–125.

    Article  Google Scholar 

  • Fornell, C., Morgeson, F. V., III, & Hult, G. T. M. (2016b). Stock returns on customer satisfaction do beat the market: gauging the effect of a marketing intangible. Journal of Marketing, 80(5), 92–107.

    Article  Google Scholar 

  • Gatignon, H., & Hanssens, D. M. (1987). Modeling marketing interactions with application to salesforce effectiveness. Journal of Marketing Research, 24(3), 247–257.

    Article  Google Scholar 

  • Geyskens, I., Stenkamp, J., & Kumar, N. (1999). A meta-analysis of satisfaction in marketing channel relationships. Journal of Marketing Research, 36(2), 223–238.

    Article  Google Scholar 

  • Grewal, R., Chandrashekaran, M., & Citrin, A. (2010). Customer satisfaction heterogeneity and shareholder value. Journal of Marketing, 47(4), 612–626.

    Article  Google Scholar 

  • Grewal, D., Puccinelli, N., & Monroe, K. B. (2018). Meta-analysis: integrating accumulated knowledge. Journal of the Academy of Marketing Science, 46(1), 1–22.

    Article  Google Scholar 

  • Gruca, T., & Rego, L. (2005). Customer satisfaction, cash flow, and shareholder value. Journal of Marketing, 69(3), 115–130.

    Article  Google Scholar 

  • Harbord, R. M., & Higgins, J. P. (2008). Meta-regression in Stata. Meta, 8(4), 493–519.

    Google Scholar 

  • Hartung, J., Knapp, G., & Sinha, B. K. (2011). Statistical meta-analysis with applications (Vol. 738). Hoboken: Wiley.

    Google Scholar 

  • Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ [British Medical Journal], 327(7414), 557–560.

    Article  Google Scholar 

  • Homburg, C., Koschate, N., & Hoyer, W. D. (2005). Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. Journal of Marketing, 69(2), 84–96.

    Article  Google Scholar 

  • Homburg, C., Allmann, J., & Klarmann, M. (2014). Internal and external price search in industrial buying: the moderating role of customer satisfaction. Journal of Business Research, 67(8), 1581–1588.

    Article  Google Scholar 

  • Hurley, R. F., & Estelami, H. (1998). Alternative indexes for monitoring customer perceptions for services quality: a comparative evaluation in retailing. Journal of the Academy of Marketing Science, 26(3), 209–221.

    Article  Google Scholar 

  • Jacobson, R., & Mizik, N. (2009). Rejoinder—customer satisfaction-based mispricing: issues and misconceptions. Marketing Science, 28(5), 836–845.

    Article  Google Scholar 

  • Jak, S. (2015). Meta-analytic structural equation modelling. New York City: Springer.

    Book  Google Scholar 

  • Johnson, M. S., Garbarino, E., & Sivadas, E. (2006). Influences of customer differences of loyalty, perceived risk and category experience on customer satisfaction ratings. International Journal of Market Research, 48(5), 601–622.

    Article  Google Scholar 

  • Jones, M. A., & Suh, J. (2000). Transaction-specific satisfaction and overall satisfaction: an empirical analysis. Journal of Services Marketing, 14(2), 147–159.

    Article  Google Scholar 

  • Keiningham, T. L., Aksoy, L., Cooil, B., Andreassen, T. W., & Williams, L. (2008). A holistic examination of net promoter. Journal of Database Marketing & Customer Strategy Management, 15(2), 79–90.

    Article  Google Scholar 

  • Koschat, M. A. (2008). Store inventory can affect demand: empirical evidence from magazine retailing. Journal of Retailing, 84(2), 165–179.

    Article  Google Scholar 

  • Kumar, V. (2016). Introduction: is customer satisfaction (ir) relevant as a metric? Journal of Marketing, 80(5), 108–109.

    Article  Google Scholar 

  • Lehmann, D. R., & Reibstein, D. J. (2006). Marketing metrics and financial performance. Cambridge: Marketing Science Institute.

    Google Scholar 

  • Loo, R. (2002). A caveat on using single-item versus multiple-item scales. Journal of Managerial Psychology, 17(1), 68–75.

    Article  Google Scholar 

  • Luo, X., & Homburg, C. (2007). Neglected outcomes of customer satisfaction. Journal of Marketing, 71(2), 133–149.

    Article  Google Scholar 

  • Luo, X., & Homburg, C. (2008). Satisfaction, complaint, and the stock market gap. Journal of Marketing, 72(4), 29–43.

    Article  Google Scholar 

  • Luo, X., Homburg, C., & Wieseke, J. (2010). Customer satisfaction, analyst stock recommendations, and firm value. Journal of Marketing Research, 47(6), 1041–1058.

    Article  Google Scholar 

  • Luo, X., Wieseke, J., & Homburg, C. (2012). Incentivizing CEOs to build customer-and employee-firm relations for higher customer satisfaction and firm value. Journal of the Academy of Marketing Science, 40(6), 745–758.

    Article  Google Scholar 

  • Maiga, A. S., Nilsson, A., & Jacobs, F. A. (2013). Extent of managerial IT use, learning routines, and firm performance: a structural equation modeling of their relationship. International Journal of Accounting Information Systems, 14(4), 297–320.

    Article  Google Scholar 

  • McGraw, K. O., & Wong, S. P. (1992). A common language effect size statistic. Psychological Bulletin, 111(2), 361–365.

    Article  Google Scholar 

  • Mittal, V., & Kamakura, W. A. (2001). Satisfaction, repurchase intent, and repurchase behavior: investigating the moderating effect of customer characteristics. Journal of Marketing Research, 38(1), 131–142.

    Article  Google Scholar 

  • Mittal, V., Kamakura, W. A., & Govind, R. (2004). Geographic patterns in customer service and satisfaction: an empirical investigation. Journal of Marketing, 68(3), 48–62.

    Article  Google Scholar 

  • Morgan, N. A., & Rego, L. L. (2006). The value of different customer and loyalty metrics in predicting business performance. Marketing Science, 25(5), 426–439.

    Article  Google Scholar 

  • Oliver, R. L. (2010). Satisfaction: A behavioral perspective on the consumer (2nd ed.). London: ME Sharp Incorporated.

    Google Scholar 

  • Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness of relationship marketing: a meta-analysis. Journal of Marketing, 70(4), 136–153.

    Article  Google Scholar 

  • Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50.

    Article  Google Scholar 

  • Rego, L. L., Morgan, N. A., & Fornell, C. (2013). Reexamining the market share–customer satisfaction relationship. Journal of Marketing, 77(5), 1–20.

    Article  Google Scholar 

  • Reichheld, F. F., & Sasser, W. E., Jr. (1990). Zero defections: quality comes to services. Harvard Business Review, 68(5), 105–111.

    Google Scholar 

  • Robinson, W. T. (1988). Sources of market pioneer advantages: the case of industrial goods industries. Journal of Marketing Research, 25(1), 87–94.

    Article  Google Scholar 

  • Romano, J. L., & Kromrey, J. D. (2009). What are the consequences if the assumption of independent observations is violated in reliability generalization meta-analysis studies? Educational and Psychological Measurement, 69(3), 404–428.

    Article  Google Scholar 

  • Rothstein, H. R., & Hopewell, S. (2009). Grey literature. In H. Cooper, L. Hedges, & J. Valentine (Eds.), The handbook of research synthesis and meta-analysis (pp. 103–125). New York: Russell Sage Foundation.

    Google Scholar 

  • Röver, C., Knapp, G., & Friede, T. (2015). Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Medical Research Methodology, 15(1), 99–105.

    Article  Google Scholar 

  • Rubera, G., & Kirca, A. H. (2017). You gotta serve somebody: the effects of firm innovation on customer satisfaction and firm value. Journal of the Academy of Marketing Science, 45(5), 741–761.

    Article  Google Scholar 

  • Ruef, M., & Scott, W. R. (1998). A multidimensional model of organizational legitimacy: hospital survival in changing institutional environments. Administrative Science Quarterly, 43(4), 877–904.

    Article  Google Scholar 

  • Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention and market share. Journal of Retailing, 69(2), 193–215.

    Article  Google Scholar 

  • Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: correcting error and bias in research findings (3rd ed.). Los Angeles: Sage Publications.

    Book  Google Scholar 

  • Shackman, G. (2001). Sample size and design effect. Albany Chapter of American Statistical Association, http://faculty.smu.edu/slstokes/stat6380/deff%20doc.pdf. Accessed August 2017.

  • Singh, J., & Sirdeshmukh, D. (2000). Agency and trust mechanisms in consumer satisfaction and loyalty judgments. Journal of the Academy of Marketing Science, 28(1), 150–167.

    Article  Google Scholar 

  • Slater, S. F., & Narver, J. C. (1994). Does competitive environment moderate the market orientation-performance relationship? The Journal of Marketing, 58(1), 46–55.

    Article  Google Scholar 

  • Song, R., Jang, S., & Cai, G. G. (2016). Does advertising indicate product quality? Evidence from prelaunch and postlaunch advertising in the movie industry. Marketing Letters, 27(4), 791–804.

    Article  Google Scholar 

  • Sorescu, A., & Sorescu, S. M. (2016). Customer satisfaction and long-term stock returns. Journal of Marketing, 80(5), 110–115.

    Article  Google Scholar 

  • Sridhar, S., Narayanan, S., & Srinivasan, R. (2014). Dynamic relationships among R&D, advertising, inventory and firm performance. Journal of the Academy of Marketing Science, 42(3), 277–290.

    Article  Google Scholar 

  • Swaminathan, V., Groening, C., Mittal, V., & Thomaz, F. (2014). How achieving the dual goal of customer satisfaction and efficiency in mergers affects a firm’s long-term financial performance. Journal of Service Research, 17(2), 182–194.

    Article  Google Scholar 

  • Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: a meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35.

    Article  Google Scholar 

  • Szymanski, D. M., Bharadwaj, S. G., & Varadarajan, R. (1993). An analysis of the market share-profitability relationship. Journal of Marketing, 57(3), 1–18.

    Article  Google Scholar 

  • Szymanski, D. M., Kroff, M., & Troy, L. C. (2007). Innovativeness and new product success: insights from the cumulative evidence. Journal of the Academy of Marketing Science, 35(1), 35–52.

    Article  Google Scholar 

  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Article  Google Scholar 

  • Tellis, G. J. (1988). The price elasticity of selective demand: a meta-analysis of econometric models of sales. Journal of Marketing Research, 25(4), 331–341.

    Article  Google Scholar 

  • Terpstra, M., & Verbeeten, F. H. (2014). Customer satisfaction: cost driver or value driver? Empirical evidence from the financial services industry. European Management Journal, 32(3), 499–508.

    Article  Google Scholar 

  • Thursyanthy, V., & Tharanikaran, V. (2017). Antecedents and outcomes of customer satisfaction: a comprehensive review. International Journal of Business and Management, 12(4), 144–156.

    Article  Google Scholar 

  • Van Doorn, J., & Verhoef, P. (2008). Critical incidents and the impact of satisfaction on customer share. Journal of Marketing, 72(4), 123–142.

    Article  Google Scholar 

  • Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181.

    Article  Google Scholar 

  • Vorhies, D. W., Morgan, R. E., & Autry, C. W. (2009). Product-market strategy and the marketing capabilities of the firm: impact on market effectiveness and cash flow performance. Strategic Management Journal, 30(12), 1310–1334.

    Article  Google Scholar 

  • Voss, G. B., Godfrey, A., & Seiders, K. (2010). How complimentary and substitution alter the customer-repurchase link. Journal of Marketing, 74(4), 111–127.

    Article  Google Scholar 

  • Wall, T. D., Michie, J., Patterson, M., Wood, S. J., Sheehan, M., Clegg, C. W., & West, M. (2004). On the validity of subjective measures of company performance. Personnel Psychology, 57(1), 95–118.

    Article  Google Scholar 

  • Wiley, J. W. (1991). Customer satisfaction: a supportive work environment and its financial cost. People and Strategy, 14(2), 117–127.

    Google Scholar 

  • Williams, P., & Naumann, E. (2011). Customer satisfaction and business performance: a firm-level analysis. Journal of Services Marketing, 25(1), 20–32.

    Article  Google Scholar 

  • Yang, J. (2010). The knowledge management strategy and its effect on firm performance: a contingency analysis. International Journal of Production Economics, 125(2), 215–223.

    Article  Google Scholar 

  • Yu, W., Jacobs, M. A., Salisbury, W. D., & Enns, H. (2013). The effects of supply chain integration on customer satisfaction and financial performance: an organizational perspective. International Journal of Production Economics, 146(1), 346–358.

    Article  Google Scholar 

  • Zhang, J., Wedel, M., & Pieters, R. (2009). Sales effects of attention to feature advertisements: a Bayesian mediation analysis. Journal of Marketing Research, 46(5), 669–681.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hankamer School of Business, Baylor University, One Bear Place #98007, Waco, TX, 76798, USA

    Ashley S. Otto

  2. University of North Florida, 1 UNF Drive, Jacksonville, FL, 32224, USA

    David M. Szymanski

  3. Mays Business School, Texas A&M University, 4112 TAMU, College Station, TX, 77843, USA

    Rajan Varadarajan

Authors
  1. Ashley S. Otto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David M. Szymanski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajan Varadarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashley S. Otto.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mark Houston and John Hulland served as Special Issue Editors for this article.

Electronic supplementary material

ESM 1

(DOCX 31.7 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Otto, A.S., Szymanski, D.M. & Varadarajan, R. Customer satisfaction and firm performance: insights from over a quarter century of empirical research. J. of the Acad. Mark. Sci. 48, 543–564 (2020). https://doi.org/10.1007/s11747-019-00657-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11747-019-00657-7

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy