Abstract
Endogeneity in empirical marketing research is an increasingly discussed topic in academic research. Mentions of endogeneity and related procedures to correct for it have risen 5x across the field’s top journals in the past 20 years, but represent an overall small portion of extant research. Yet there is often substantial difficulty in reconciling issues of endogeneity with many of the substantive questions of interest to marketing strategy for both theoretical and/or practical reasons. This paper provides an overview of main causes of endogeneity, approaches to addressing it, and guidance to marketing strategy researchers to balance these issues as the field continues to move towards more methodological sophistication, potentially at the expense of managerial tractability.
Similar content being viewed by others
Notes
Our discussion on endogeneity focuses on empirical research with firm data. For researchers interested in endogeneity issues in survey data, Sande and Ghosh (2018) provide an overview.
A strong caveat is the assumptions needed for a structural model to be identified. Often, these result in models that will not fit a marketing strategy application.
For ease of exposition we do not include any exogenous variables in the model formulations.
The key idea is to leverage variation independent of the variable of concern. For example, online spend (ad costs) for Portland in our case when the focal market is Seattle.
For 2 Stage Least Squares (SLS) please see: https://www.rdocumentation.org/packages/AER/versions/1.2-5/topics/ivreg. For simultaneous estimation please see https://cran.r-project.org/web/packages/ivmodel/ or https://cran.r-project.org/web/packages/ivpack/.
The R package for 2SLS estimation called “ivreg” (see Footnote 2) also provides a Hausman test.
The R package “ivreg” provides the Sargan test for 2SLS estimation. For Generalized Methods of Moments (GMM) estimation the R function “sargan” calculated the Hansen-Sargan test, please see: https://www.rdocumentation.org/packages/plm/versions/1.6-5/topics/sargan.
The key idea is to leverage variation independent of the variable of concern. For example, online spend (ad costs) for Portland in our case when the focal market is Seattle.
Please note that Kenneth Train as software available on his website, albeit only for the Mixed Logit Model: https://eml.berkeley.edu/~train/software.html.
Park and Gupta (2012) give more details how to non-parametrically estimate the density of the marginal distribution of the endogenous variable, p.570–572.
In more detail, \( \tau ={\sigma}_{\varepsilon}\sqrt{1-{\rho}^2}{\varpi}_2 \) where ρ is the correlation coefficient, ϖ2~N(0, 1) and σε is the standard deviation of ε.
The goal of a field experiment is to be able to compare outcomes directly between treated and non-treated groups to determine the effect of the treatment, e.g. a given marketing strategy. In non-experimental data, the researcher cannot distinguish between a treatment and control group as the use of a particular strategy is generally non-random, but rather self-selected by the firm to implement, making the choice to implement a strategy, and thus the “effect” of the strategy, endogenous. To remedy potential self-selection bias in observational data, it may be possible to “match” firms exhibiting a given strategy with firms that do not, but otherwise appear very similar on multiple criteria through propensity score matching. This idea is to determine the probability of treatment assignment, for example, the propensity to start a loyalty program, conditional on observed baseline characteristics, e.g. firm size, customer demographics, and so forth. This allows the researcher to design and analyze an observational study to mimic the characteristics of a randomized controlled trial between the observed treated group and a matched non-treated group (Rosenbaum and Rubin 1983). In practice however, this may not always be feasible due to data constraints on which to produce a propensity score for each observation (see Austin 2011, p.414–415). For further reading on methodology, see Austin (2011), Heckman (1979), Rosenbaum and Rubin (1983), Guo and Fraser (2010), and the PSMATCH2 STATA module from Leuven and Sianesi (2003). For recent substantive applications and extensions in marketing research see Kumar et al. (2016), Ballings et al. (2018), and de Haan et al. (2018).
We thank an anonymous reviewer for raising this point.
References
Abhishek, V., Hosanagar, K., & Fader, P. S. (2015). Aggregation bias in sponsored search data: the curse and the cure. Marketing Science, 34(1), 59–77.
Ailawadi, K. L., Pauwels, K., & Steenkamp, J.-B. E. M. (2008). Private-label use and store loyalty. Journal of Marketing, 72(6), 19–30.
Anderson, E. T., & Simester, D. I. (2004). Long-run effects of promotion depth on new versus established customers: three field studies. Marketing Science, 23(1), 4–20.
Andrews, R. L., & Ebbes, P. (2014). Properties of instrumental variables estimation in logit-based demand models: finite sample results. Journal of Modelling in Management, 9(3), 261–289.
Andrews, R. L., Ainslie, A., & Currim, I. S. (2002). An empirical comparison of logit choice models with discrete vs. continuous representations of heterogeneity. Journal of Marketing Research, 39(4), 479–487.
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of casual effects using instrumental variables. Journal of the American Statistical Association, 91(434), 444–455.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.
Ataman, M. B., Van Heerde, H. J., & Mela, C. F. (2010). The long-term effect of marketing strategy on brand sales. Journal of Marketing Research, 47(5), 866–882.
Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.
Bagozzi, R. P. (1980). Performance and satisfaction in an industrial sales force: an examination of their antecedents and simultaneity. Journal of Marketing, 44(2), 65–77.
Balakrishna, N., & Lai, C. D. (2009), Concepts of stochastic dependence. In Continuous bivariate distributions (pp. 105–140) Springer, New York.
Ballings, M., McCullough, H., & Bharadwaj, N. (2018). Cause marketing and customer Profitabilitys. Journal of the Academy of Marketing Science, 46(2), 234–251.
Berry, S. T. (1994). Estimating discrete-choice models of product differentiation. RAND Journal of Economics, 25(2), 242–262.
Bronnenberg, B. J., & Sismeiro, C. (2002). Using multimarket data to predict performance in markets for which no or poor data exist. Journal of Marketing Research, 39(1), 1–17.
Calantone, R. J., Schmidt, J. B., & Michael Song, X. (1996). Controllable factors of new product success: a cross-national comparison. Marketing Science, 15(4), 341–358.
Chintagunta, P., Dubé, J.-P., & Singh, V. (2003). Balancing profitability and customer welfare in a supermarket chain. Quantitative Marketing and Economics, 1(1), 111–147.
Chintagunta, P., Kadiyali, V., & Vilcassim, N. J. (2004). Structural models of competition: A marketing strategy perspective. In C. Moorman & D. Lehmann (Eds.), Assessing marketing strategy performance. Cambridge: Marketing Science Institute.
Chintagunta, P., Erdem, T., Rossi, P. E., & Wedel, M. (2006). Structural modeling in marketing: review and assessment. Marketing Science, 25(6), 604–616.
Clougherty, J. A., Duso, T., & Muck, J. (2016). Correcting for self-selection based endogeneity in management research: review, recommendations and simulations. Organizational Research Methods, 19(2), 286–347.
Danaher, P. J., & Smith, M. S. (2011). Modeling multivariate distributions using copulas: applications in marketing. Marketing Science, 30(1), 4–21.
de Haan, E., Kannan, P. K., Verhoef, P. C., & Wiesel, T. (2018). Device switching in online purchasing: examining the strategic contingencies. Journal of Marketing, 82(5), 1–19.
Dekimpe, M. G., & Hanssens, D. M. (1995). The persistence of marketing effects on sales. Marketing Science, 14(1), 1–21.
Dinner, I. M., van Heerde, H. J., & Neslin, S. A. (2014). Driving online and offline sales: the cross-channel effects of traditional, online display, and paid search advertising. Journal of Marketing Research, 51(5), 527–545.
Dubé, J.-P., Chintagunta, P., Petrin, A., Bronnenberg, B., Ron, G., Seetharaman, P. B., Sudhir, K., Thomadsen, R., & Zhao, Y. (2002). Structural applications of the discrete choice model. Marketing Letters, 13(3), 207–220.
Ebbes, P., Wedel, M., Steerneman, T. G. M., & Bockenholt, U. (2005). New evidence for the effect of education on income: solving endogeneity with latent instrumental variables. Quantitative Marketing and Economics, 3(4), 365–392.
Ebbes, P., Papies, D., & Van Heerde, H. J. (2011). The sense and non-sense of holdout sample validation in the presence of endogeneity. Marketing Science, 30(6), 1115–1122.
Ebbes, P., Papies, D., & van Heerde, H. J. (2016). Dealing with Endogeneity: A Nontechnical Guide for Marketing Researchers. C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research, Springer International Publishing AG.
Elberse, A., & Eliashberg, J. (2003). Demand and supply dynamics for sequentially released products in international markets: the case of motion pictures. Marketing Science, 22(3), 329–354.
Erdem, T., & Keane, M. P. (1996). Decision-making under uncertainty: capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Science, 15(1), 1–20.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gatignon, H., & Xuereb, J.-M. (1997). Strategic orientation of the firm and new product performance. Journal of Marketing Research, 34(1), 77–90.
George, M., Kumar, V., & Grewal, D. (2013). Maximizing profits for a multi-category catalog retailer. Journal of Retailing, 89(4), 374–396.
Germann, F., Ebbes, P., & Grewal, R. (2015). The chief marketing officer matters! Journal of Marketing, 79(3), 1–22.
Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: evidence from a field test. Marketing Science, 28(4), 721–739.
Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: implications for theory testing. Marketing Science, 23(4), 519–529.
Grewal, R., Kumar, A., Mallapragada, G., & Saini, A. (2013). Marketing channels in foreign markets: control mechanisms and the moderating role of multinational Corporation Headquarters–subsidiary relationship. Journal of Marketing Research, 50(3), 378–398.
Grewal, D., Puccinelli, N. M., & Monroe, K. B. (2018). Meta-analysis: error cancels and truth accrues. Journal of the Academy of Marketing Science, 46(1), 9–30.
Guo, S., & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and applications. Thousand Oaks: Sage Publications.
Hahn, J., & Hausman, J. (2002). A new specification test for the validity of instrumental variables. Econometrica, 70, 163–189.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054.
Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.
Hausman, J. (1978). Specification tests for econometrics. Econometrica, 46, 1251–1271.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: conceptual foundations. Journal of Marketing, 60(3), 50–68.
Houston, M. B. (2016). Is ‘strategy’ a dirty word? Journal of the Academy of Marketing Science, 44(5), 557–561.
Jaworski, B. J. (2011). On managerial relevance. Journal of Marketing, 75(4), 211–224.
Johnson, G. A., Lewis, R. A., & Nubbemeyer, E. I. (2017). Ghost ads: improving the economics of measuring online ad effectiveness. Journal of Marketing Research, 54(6), 867–884.
Kadiyali, V., Sudhir, K., & Rao, V. R. (2000). Structural analysis of competitive behavior: new empirical industrial organization methods in marketing. International Journal of Research in Marketing, 18(1–2), 161–186.
Kennedy, P. (2008). A guide to modern econometrics, vol (2nd ed.). Oxford, UK: Blackwell.
Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29–72.
Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental design with marketing research applications. Journal of Marketing Research, 31(4), 545–557.
Kuksov, D., & Villas-Boas, J. M. (2008). Endogeneity and individual consumer choice. Journal of Marketing Research, 45(6), 702–714.
Kumar, V., Zhang, X., & Luo, A. (2014). Modeling customer opt-in and opt-out in a permission-based marketing context. Journal of Marketing Research, 51(4), 403–419.
Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. K. (2016). From social to sale: the effects of firm generated content in social media on customer behavior. Journal of Marketing, 80(1), 7–25.
Lee, J.-Y., Sridhar, S., Henderson, C. M., & Palmatier, R. W. (2014). Effect of customer-centric structure on long-term financial performance. Marketing Science, 34(2), 250–268.
Leenheer, J., van Heerde, H. J., Bijmolt, T., & Smidt, A. (2007). Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self selecting members. International Journal of Research in Marketing, 24(1), 31–47.
Lehmann, D. R., McAlister, L., & Staelin, R. (2011). Sophistication in research in marketing. Journal of Marketing, 75(4), 155–165.
Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Statistical Software Components S432001, Boston College Department of Economics, revised 01 Feb 2018.
Luan, Y. J., & Sudhir, K. (2010). Forecasting marketing-mix responsiveness for new products. Journal of Marketing Research, 47(3), 444–457.
Lucas, R. (1976). Econometric policy evaluation: A critique. The Phillips curve and labor markets. In Brunner, K.; Meltzer, A, Carnegie-Rochester Conference Series on Public Policy, New York: American Elsevier, pp. 19–46.
Mallapragada, G., Grewal, R., Mehta, R., & Dharwadkar, R. (2015). Virtual Interorganizational relationships in business-to-business electronic markets: heterogeneity in the effects of organizational interdependence on relational outcomes. Journal of the Academy of Marketing Science, 43(5), 610–628.
McAlister, L. (2016). Rigor versus method imperialism. Journal of the Academy of Marketing Science, 44(5), 565–567.
Naik, P. A., & Tsai, C.-L. (2000). Controlling measurement errors in models of advertising competition. Journal of Marketing Research, 37(1), 113–124.
Nelsen, R. B. (2006). An introduction to copulas. Springer series in statistics. New York: Springer.
Newey, W. K., & McFadden, D. (1994). Large sample estimation and hypothesis testing. Handbook of Econometrics, 4(1), 2111–2245.
Novak, S., & Stern, S. (2009). Complementarity among vertical integration decisions: evidence from automobile product development. Management Science, 55(2), 311–332.
Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: purpose, process, and structure. Journal of the Academy of Marketing Science, 46(1), 1–5.
Papies, D., Ebbes, P., & Van Heerde, H. J. (2017). Addressing endogeneity in marketing models. In P. Leeflang, J. Wieringa, T. Bijmolt, & K. Pauwels (Eds.), Advanced methods for modeling markets. International series in quantitative marketing. Cham: Springer.
Park, S., & Gupta, S. (2012). Handling endogenous regressors by joint estimation using copulas. Marketing Science, 31(4), 567–586.
Petrin, A., & Train, K. (2010). A control function approach to endogeneity in consumer choice models. Journal of Marketing Research, 47(1), 3–13.
Reibstein, D. J., Day, G., & Wind, J. (2009). Guest editorial: is marketing academia losing its way? Journal of Marketing, 73(4), 1–3.
Reiss, P. C., & Wolak, F. A. (2007). Structural econometric modeling: rationales and examples from industrial organization. Handbook of Econometrics, 6(1), 4277–4415.
Rinallo, D., & Basuroy, S. (2009). Does advertising spending influence media coverage of the advertiser? Journal of Marketing, 73(6), 33–46.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Rossi, P. E. (2014). Even the rich can make themselves poor: a critical examination of IV methods in marketing applications. Marketing Science, 33(5), 655–672.
Rutz, O. J., & Trusov, M. (2011). Zooming in on paid search ads—a consumer-level model calibrated on aggregated data. Marketing Science, 30(5), 789–800.
Rutz, O. J., Bucklin, R. E., & Sonnier, G. P. (2012). A latent instrumental variables approach to modeling keyword conversion in paid search advertising. Journal of Marketing Research, 49(3), 306–319.
Sande, J. B., & Ghosh, M. (2018). Endogeneity in survey research. International Journal of Research in Marketing, 35(2), 185–204.
Sanderson, E., & Windmeijer, F. (2016). A weak instrument F-test in linear IV models with multiple endogenous variables. Journal of Econometrics, 190(2), 212–221.
Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, 26(3), 393–415.
Sen, S., Bhattacharya, C. B., & Korschun, D. (2006). The role of corporate social responsibility in strengthening multiple stakeholder relationships: a field experiment. Journal of the Academy of Marketing Science, 34(2), 158–177.
Sonnier, G. P., McAlister, L., & Rutz, O. J. (2011). A dynamic model of the effect of online communications on firm sales. Marketing Science, 30(4), 702–716.
Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: an overview. Journal of the Academy of Marketing Science, 45(2), 186–207.
Srinivasan, R., Sridhar, S., Narayanan, S., & Sihi, D. (2013). Effects of opening and closing stores on chain retailer performance. Journal of Retailing, 89(2), 126–139.
Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518–529.
Sun, B. (2005). Promotion effect on endogenous Consumption0. Marketing Science, 24(3), 430–443.
Van Heerde, H. J., Gijsenberg, M. J., Dekimpe, M. G., & Steenkamp, J.-B. E. M. (2013). Price and advertising effectiveness over the business cycle. Journal of Marketing Research, 50(2), 177–193.
Varadarajan, R. (2010). Strategic marketing and marketing strategy: conceptual domain, definition, fundamental issues and foundational premises. Journal of the Academy of Marketing Science, 38(2), 119–140.
Villas-Boas, J. M., & Winer, R. S. (1999). Endogeneity in brand choice models. Management Science, 45(10), 1324–1338.
Wedel, M., Kamakura, W., Arora, N., Essec, A. B., Chiang, J., Elrod, T., Johnson, R., Lenk, P., Nesling, S., & Poulsen, C. S. (1999). Discrete and continuous representations of unobserved heterogeneity in choice modeling. Marketing Letters, 10(3), 219–232.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. Cambridge: MIT Press.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Nelson Education.
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.
Zhang, X., Kumar, V., & Cosguner, K. (2017). Dynamically managing a profitable email marketing program. Journal of Marketing Research, 54(6), 851–866.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
John Hulland served as Editor for this article.
Rights and permissions
About this article
Cite this article
Rutz, O.J., Watson, G.F. Endogeneity and marketing strategy research: an overview. J. of the Acad. Mark. Sci. 47, 479–498 (2019). https://doi.org/10.1007/s11747-019-00630-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11747-019-00630-4