This paper develops a theoretical framework to address how dynamic competitive interactions and customer preferences change the observed relationship between market price and quality, and it offers an empirical framework to study these phenomena. Our framework proposes that price–quality relationships in a market (the fair value line) evolve according to several processes. We define and discuss these processes, including: (1) line formation, (2) line evolution (comprised of line elevation, erosion, steepening, flattening, blurring, tightening, extension and contraction), and (3) line replacement, which involves redefining price or quality in the marketplace. Instead of assuming that prices are a stable function of observable product attributes only (the static equilibrium view), our framework generalizes to dynamic disequilibrium patterns observed in many industries. These patterns are empirically assessed and explained by customer, competitive, and technology forces analyzed in marketing and strategy literatures. For managers, we discuss how they should react to these processes or, even better, set them in motion. For marketing researchers, we specify several hypotheses on the processes’ antecedents and consequences, testable with readily available datasets.
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Throughout the manuscript, line refers to the fair value line, and not to the product line.
We agree with Dickson’s (1992) argument that “the theory is also general in that it proposes a set of premises and propositions that have yet to be expressed systematically in a set of mathematical equations. To rush such specification might produce a theory so constrained by its simplifying assumptions that it would have many of the limitations of the static equilibrium theory it is intended to supplement, if not supplant” (p.70)
Note that the hedonic regression does not explicitly incorporate the argument that consumers infer quality from price, which would require a regression of perceived quality on price. While some consumers may indeed use price to judge quality, the evidence in marketing literature is that such inference is (1) highly variable across individuals, (2) nonlinear, (3) highly variable by product category, (4) substantially reduced when other quality information is pres-ent (Zeithaml 1988) to the extent it matters least among quality attributes (Parasuraman et al 1985). Most important to our purpose, such price–quality inference would not explain the observed near-zero or even negative price–quality relationship (Tellis and Wernerfelt 1987) nor the fair value line dynamics we observe and seek to explain.
We acknowledge that some of these quality attributes may be highly correlated with each other, and refer to the technical appendix for dealing with this issue.
We abstract from consumer heterogeneity as our analysis is at the aggregate level, as is the hedonic pricing literature we are extending. However, our analysis may be performed for different segments, as we demonstrated in the empirical illustrations. Future research may extend the ideas in this paper to allow for consumer heterogeneity in industries and situations where individual-level data is available over time.
Often, novice customers start out with a cheap and functional product to satisfy a certain need (e.g., a cassette recorder for music entertainment, a small car for transportation) and then move up as their disposable income grows and/or the price of a more satisfying substitute comes down (e.g., an Apple iPod Shuffle for music entertainment, an SUV for transportation). As a result, the downscale markets are forever confronted with an “out-stream” of experienced customers, which take their primary quality perceptions with them, and an “in-stream” of new customers, which form their primary quality perception on the spot.
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We would like to thank Barbara Lawrence and Dave Reibstein, and the following participants from a Tuck School seminar on this paper for their very helpful comments: Kusum Alawadi, Dick Bower, Bob Hansen, Kevin Keller, Punam Keller, Jackie Luan, and Aviad Pe’er. Their help was invaluable for shaping this paper and contributing to its content.
Empirical issues with estimating dynamic regressions of price on quality components
In this appendix, we elaborate on the differences and similarities of our empirical approach to that of the hedonic pricing literature. The main empirical difference is that we estimate reduced-form equations, which are generalizable across situations as we do not impose a theoretical structure of competition. We believe that current theoretical structures do not capture many empirical phenomena of interest and fail to sufficiently address the dynamics of price–quality relationships. In contrast, the hedonic pricing literature builds a model with a specific type of competition in mind (e.g., Bertrand, Cournot, and monopolistic competition) and then solves the model using its specific competition and equilibrium assumptions to generate structural (closed-solution) equations, which are then tested with data. The main empirical similarity is that both our approach and hedonic pricing regress prices on product attributes. However, we look at this as an empirical test of relationships, not of whether the industry conditions exist as assumed in the hedonic pricing model. The remaining similarities are that we benefit from empirical issues have been extensively discussed in the literature: data gathering, combining highly correlated variables, estimation procedure, and assessing the significance of changes to the fair value line components. We discuss these in turn.
First, information on price and quality dimensions is often available through independent industry analysts. For our pickup truck and car market illustrations, we use biannual data reported in US Automotive News and Ward’s Automotive Directory. Consumer Reports provides reliability ratings, i.e., consumer complaints about defects.
Second, both price and quality variables may be highly correlated. For the car markets, fully loaded prices were highly correlated with base prices (ranging from 0.85 to 0.97), and we decided to use the latter for ease of interpretation. Next, a factor analysis indicated that several quality variables are highly correlated, and they are all related to vehicle size: larger vehicles have higher carrying capacity, engine power, and crashworthiness (vehicle damage claim reports from the Insurance Institute of America), but lower fuel efficiency (EPA mpg ratings) than smaller vehicles. We decided to combine these four variables into the new “platform” quality dimension (as suggested by, e.g., Thomas 2001, p. 649).
Likewise, acceleration performance is computed as engine power divided by chassis size (indicating more power for the size of the vehicle and better “pep”), and safety is a composite measure of the presence of driver-side, passenger-side, and side air bags, as well as driver and passenger injury rates from U.S. government crash tests. Third, much of the hedonic pricing literature in economics has focused on improving the estimation procedure, including one-step versus two-step estimation, the functional form (linear, log-log or log-linear), and heteroscedasticity issues. The current consensus is that one-step linear regressions perform best in a wide variety of industries, as long as each product is weighted by its sales units to correct for heteroscedasticity—in essence avoiding that marginally important products assert undue influence over the regression’s coefficients (Arguea and Hsiao 1993; Murray and Sarantis 1999).
As for the significance of dynamic changes to fair value line components, we assess this with the Chow (1960) test, adjusted for multicollinearity and outliers. This test is widely used in econometrics to assess structural change in regression coefficients.
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Pauwels, K., D’Aveni, R. The formation, evolution and replacement of price–quality relationships. J. of the Acad. Mark. Sci. 44, 46–65 (2016). https://doi.org/10.1007/s11747-014-0408-3