Does Advertising Reduce Price Elasticity? Evidence from Retail Markets
DOI:
https://doi.org/10.61132/iceat.v3i1.242Keywords:
Advertising Intensity, Brand Differentiation, Price Elasticity, Retail Demand, Structural Equation ModelingAbstract
Advertising and price sensitivity are pivotal in retail competitiveness, as companies communicate to increase demand while protecting margins in markets characterised by high price transparency. This study investigates the impact of advertising on price elasticity in retail markets and examines the underlying mechanism. It addresses conflicting findings in previous research regarding whether advertising differentiates companies and reduces price sensitivity or enhances market information and price comparison. The study evaluates the effect of advertising intensity on retail price elasticity and examines whether brand attitude mediates its influence on consumer purchasing behaviour. Seven hypotheses were tested concerning the direct effects of advertising on brand attitude, price sensitivity, and purchase intention, as well as indirect effects through mediation. A quantitative approach was employed using retail panel data on prices, advertising exposure, promotions, and sales, complemented by consumer survey data on brand attitude, price sensitivity, and purchase intention. Analysis integrated panel regression, difference-in-differences, propensity score matching, hierarchical modelling, factor analysis, confirmatory factor analysis, and covariance-based structural equation modelling. Results indicate that advertising reduces price elasticity primarily through improved brand perception. Advertising strengthens brand preference, reduces price sensitivity, improves brand attitude, and increases purchase intention through direct and indirect effects. The findings contribute to advertising theory and provide practical implications for managers and policymakers regarding pricing authority and brand resilience.
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References
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. doi:10.1177/002224299405800304
Ataman, B., Pauwels, K., Srinivasan, S., & Vanhuele, M. (2024). Advertising’s Impact on Brand Price Elasticity. HEC Paris Research Paper No. 1500. doi:10.2139/ssrn.4694207
Comanor, W. S., & Wilson, T. A. (1974). Advertising and market power. Harvard Business Review, 52(6), 39–45.
Farris, P. W., & Reibstein, D. J. (2010). Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Pearson.
Gijsenberg, M. J., Kaptein, M., & Tulder, F. (2025). The immediate and long-run effects of retailer advertising: An event study. Journal of Retailing (forthcoming).
Kot, M. (2022). Agent-based model of consumer choice: An evaluation of pricing and advertising strategy. Operations Research and Decisions, 32(1), 74–94. doi:10.37190/ord220104.
Marsden, A., Gaeth, G., & Jain, R. (2000). Consumer responses to price and advertising: Mediating effect of brand knowledge. Journal of Business Research, 48(3), 239–253.
McFadden, D. (1981). Econometric models of probabilistic choice. In Structural Analysis of Discrete Data with Econometric Applications (C. Manski & D. McFadden, Eds.). MIT Press.
Mela, C. F., Gupta, S., & Lehmann, D. R. (1997). The long-term impact of promotion and advertising on consumer brand choice. Journal of Marketing Research, 34(2), 248–261.
Neslin, S. A., & Shankar, V. (2011). Key issues in multi-channel customer management: Current knowledge and future directions. Journal of Interactive Marketing, 25(2), 91–104.
Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4), 729–754.
Sethuraman, R., & Tellis, G. J. (2002). Does manufacturer advertising suppress or stimulate retail price promotions? Marketing Science, 21(1), 17–37.
Shankar, V., & Bayus, B. (2003). Network effects and competition: An empirical analysis of the home video game industry. Strategic Management Journal, 24(4), 375–384.
Shankar, V., & Bolton, R. (2004). An empirical analysis of determinants of retailer pricing strategy. Marketing Letters, 15(3), 221–236.
Shankar, V., Inman, J. J., Mantrala, M., Kelley, E., & Rizley, R. (2011). Innovations in shopper marketing: Current insights and future research issues. Journal of Retailing, 87(Suppl 1), S29–S42.
Zaheer, A., & Shervani, T. A. (1995). Turnaround: Causes, processes and their relationship. Journal of Business Research, 34(1), 3–19.
Appendix:
We conducted comprehensive robustness tests to verify the stability of our findings. Initially, we modified the model requirements. Employing a nonlinear (log-log) demand model in contrast to the primary linear specification produced an identical qualitative outcome: the advertisement coefficient consistently exhibited a considerably negative relationship with price elasticity. The inclusion of quadratic and interaction variables (e.g., Price², Price×Ad) did not alter the primary effect of advertising. Secondly, we evaluated different advertising strategies. We evaluated advertising share (the proportion of TV commercials) and monthly advertising expenditure instead of a binary ad indicator. Both proxies yielded comparable decreases in estimated elasticity, suggesting that the results are not affected by the coding of advertising intensity.
Third, we divided the sample into subgroups. Conducting distinct regressions for various chains or product categories revealed consistent trends: an increase in advertisements consistently correlated with reduced elasticity, without any sign reversal. We bootstrapped the complete dataset (1,000 resamples) and re-evaluated all models; the advertising-elasticity effect remained significant in 94% of bootstrap samples.
Fourth, we assessed for omitted variable bias. We implemented further controls (e.g., store fixed effects, week-of-year dummies, competitive price indices) and observed that the core coefficient altered by less than 5%. Variance inflation factors remained below 3 for all variables, signifying that collinearity is minimal. We performed Hausman tests to compare fixed and random effects models; the advertising effect remained consistent across both methodologies.
Fifth, we tackled endogeneity. The application of lagged instruments inside a system GMM dynamic panel architecture yielded results consistent with the baseline, where lagged Ad and Price served as instruments for the current values. The Wu–Hausman test indicates that advertising is appropriately addressed. Furthermore, our difference-in-differences model assessed whether an external shock (the launch of a new advertising campaign) significantly impacted elasticity: treated products exhibited an immediate 10% decrease in elasticity compared to controls (p<0.01). This aids in validating causality.
Sixth, we addressed absent data and anomalies. We employed linear interpolation to address sporadic missing pricing or sales information and determined that the imputation did not influence the primary outcomes. Excluding extreme outliers (the highest 0.5% of prices or Q) also preserved the results. We computed the Variance Inflation Factors (VIFs), all of which were below 2.5, indicating that multicollinearity is not influencing the coefficients.
A sensitivity analysis was conducted by introducing Gaussian noise to the data or by randomly permuting the advertising indicator (placebo test). The placebo test demonstrated that advertising had no effect, as anticipated, so affirming the relevance of the findings in the actual data. Throughout all these evaluations, the finding remained consistent: increased advertisement intensity reliably forecasts diminished anticipated price elasticity. Therefore, our results are reliable, valid, and not a product of particular modelling decisions.
Supporting Tables and Figures: The essential metrics are included in organised tables (e.g., factor loadings, reliability, path coefficients, R²), and the integrated SEM diagram (Figure 1) depicts the conceptual model.
The primary conclusion is that advertising indirectly diminishes price elasticity by fostering brand preference. The substantial relationship between Ad and BrandAtt, along with the inverse correlation from BrandAtt to PriceSens, suggests that advertising campaigns enable businesses to increase prices with minimal reduction in sales. This offers new empirical evidence for the differentiation theory of advertising within a retail framework, indicating that the direct impact of advertising on elasticity is less pronounced.
Figure 1. Illustration of Advertising’s Role in Retail Demand. Advertising exposure (left) boosts brand consideration and preference (center), which in turn makes consumers less sensitive to price (i.e. lowers price elasticity), while also directly contributing to demand. (Adapted conceptually from Gijsenberg et al., 2025).
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