Abstract
In “Marketing Information: A Competitive Analysis,” Sarvary, M., and P. M. Parker. 1997. “Marketing Information: A Competitive Analysis.” Marketing Science 16 (1): 24–38 (S&P) argue that in part of the parameter space that they considered, a reduction in the price of one information product can lead to an increase in demand for another information product, i.e. information products can be gross complements. This result is surprising and has potentially important marketing implications. We show that S&P obtain this complementarity result by implicitly making the following internally inconsistent assumptions: (i) after purchasing information products, consumers update their beliefs using a Bayesian updating rule that assumes they have a diffuse initial prior (i.e. their initial prior variance is ∞ before receiving any information); (ii) if consumers choose not to purchase any information product, it is assumed that their initial prior variance is 1 (implied by the utility function specification). This internal inconsistency leads to the possibility that when information products are uncorrelated and their variances are close to 1, marginal utility is increasing in the number of products purchased, and hence information products can be complements in their model. We show that if we remove this internal inconsistency, in the parameter space considered by S&P, information products cannot be complements because the marginal utility of information products will be diminishing. We also show that, in parts of the parameter space not considered by S&P, it is possible that information products are complements; this space of parameters requires consumer’s initial prior to be relatively precise and information products to be highly correlated (either positively or negatively).
We explain how we derive the intervals in the example discussed in Subsection 2.2.
Claim 1:
A consumer buys no report if and only if her type,
Proof. Note that a consumer buys no report if and only if her consumer surplus of buying no report is (i) greater than buying one report, and (ii) greater than buying both reports. We can formulate the necessary and sufficient condition for buying no report as CS (no report) > CS (both reports) & CS (no report) > CS (one report), and derive the interval for the corresponding consumer type as follows:
Therefore, a consumer buys no report if and only if her type (θ) is on the interval
Claim 2:
A consumer buys both reports if and only if her type,
Proof. Note that a consumer buys both reports if and only if her consumer surplus of buying both reports is (i) greater than buying one report, and (ii) greater than buying no report. We can formulate the necessary and sufficient condition for buying both reports as CS (both reports) ≥ CS (one report) & CS (both report) ≥ CS (no report), and derive the interval for the corresponding consumer type as follows:
Therefore, a consumer buys both reports if and only if her type (θ) is on the interval
Here, we formally show how to derive pure strategy equilibrium prices in our model. The logic of the proof is similar to S&P and Tirole (1988, p. 96–97). In the model, each consumer faces four different choices: (i) buying both products, (ii) product 1 only, (iii) product 2 only, and (iv) no product, and chooses one of them to maximize her consumer surplus. Note that we can simply regard buying both products as buying a high quality composite product. Let’s define product i’s quality,
Scenario 1:
Suppose
Given prices p
1 and p
2, a consumer type, θ′, is indifferent between buying both products and product 2 only iff
Because the demand for the composite good is
The demand functions for products 1 and 2 can be re-written as:
Firm 1 maximizes the following profit:
Firm 2 maximizes the following profit:
The candidate equilibrium prices are:
Now we want to check whether the candidate equilibrium prices satisfy the supposition. Let’s consider the first inequality of the supposition,
Let’s consider the second inequality of the supposition,
The supposition also states
The above two inequalities lead to
Scenario 2:
Suppose
Given prices p
1 and p
2, a consumer type, θ′, is indifferent between buying both products and product 2 only iff
Because
Because the demand for the composite good is
The demand for product 1 is:
Therefore, the demand for product 1 and product 2 under this scenario can be re-written as:
Firm 2 maximizes the following profit:
Firm 1 maximizes the following profit:
The best response of firm 2 to p 1:
The best response of firm 1 to p 2:
The candidate equilibrium prices are:
Now we want to check whether the candidate prices satisfy the supposition:
Let’s consider the second inequality of the supposition,
It is clear that condition (B3) is stronger than condition (B2). We show that the candidate equilibrium derived above is indeed an equilibrium iff
We can also confirm that information products are substitutes in this scenario from
Scenario 3:
Suppose
Given prices p
1 and p
2, a consumer type, θ′, is indifferent between buying both products and product 1 only iff
Because
The demand functions for products 1 and 2 can be re-written as:
Firm 1 maximizes the following profit:
Firm 2 maximizes the following profit:
The candidate equilibrium prices are:
Now we want to check whether the candidate prices satisfy the supposition. Let’s consider the first inequality of the supposition,
Scenario 4:
Suppose
Scenario 5:
Suppose
Given prices p
1 and p
2, a consumer type, θ′, is indifferent between buying both products and no product iff
Consumer types (θ) on the interval
Because the demand for the composite good is
The demand functions for products 1 and 2 can be re-written as:
Firm 1 maximizes the following profit:
Firm 2 maximizes the following profit:
The best response of firm 1 to p 2:
The best response of firm 2 to p 1:
The candidate equilibrium prices are:
Now we want to check whether the candidate prices satisfy the supposition. Let’s consider the first inequality of the supposition,
By substituting
Hence, we show that this scenario will lead to an equilibrium iff
References
Admati, A. R., and P. Pfleiderer. 1987. “Viable Allocations of Information in Financial Markets.” Journal of Economic Theory 43 (1): 76–115, https://doi.org/10.1016/0022-0531(87)90116-5.Search in Google Scholar
Arora, A., and A. Fosfuri. 2005. “Pricing Diagnostic Information.” Management Science 51 (7): 1092–100, doi:https://doi.org/10.1287/mnsc.1050.0362.Search in Google Scholar
Banerjee, S., J. Davis, and N. Gondhi. 2018. “When Transparency Improves, Must Prices Reflect Fundamentals Better?” The Review of Financial Studies 31 (6): 2377–414, https://doi.org/10.1093/rfs/hhy034.Search in Google Scholar
Bergemann, D., and A. Bonatti. 2015. “Selling Cookies.” American Economic Journal: Microeconomics 7 (3): 259–94, https://doi.org/10.1257/mic.20140155.Search in Google Scholar
Chen, Y., C. Narasimhan, and Z. J. Zhang. 2001. “Individual Marketing with Imperfect Targetability.” Marketing Science 20 (1): 23–41, doi:https://doi.org/10.1287/mksc.20.1.23.10201.Search in Google Scholar
Ching, A. T., and H. Lim. 2020. “A Structural Model of Correlated Learning and Late-Mover Advantages: The Case of Statins.” Management Science 66 (3): 1095–123, https://doi.org/10.1287/mnsc.2018.3221.Search in Google Scholar
Ching, A. T., R. Clark, I. Horstmann, and H. Lim. 2016. “The Effects of Publicity on Demand: The Case of Anti-Cholesterol Drugs.” Marketing Science 35 (1): 158–81.10.1287/mksc.2015.0925Search in Google Scholar
Christen, M. 2005. “Research Note-Cost Uncertainty is Bliss: The Effect of Competition on the Acquisition of Cost Information for Pricing New Products.” Management Science 51 (4): 668–76, doi:https://doi.org/10.1287/mnsc.1040.0320.Search in Google Scholar
Christen, M., and M. Sarvary. 2007. “Competitive Pricing of Information: A Longitudinal Experiment.” Journal of Marketing Research 44 (1): 42–56, https://doi.org/10.1509/jmkr.44.1.42.Search in Google Scholar
Gal-Or, E., T. Geylani, and T. P. Yildirim. 2012. “The Impact of Advertising on Media Bias.” Journal of Marketing Research 49 (1): 92–9, doi:https://doi.org/10.1509/jmr.10.0196.Search in Google Scholar
Goldstein, I., and L. Yang. 2015. “Information Diversity and Complementarities in Trading and Information Acquisition.” The Journal of Finance 70 (4): 1723–65, https://doi.org/10.1111/jofi.12226.Search in Google Scholar
Guo, L. 2006. “Consumption Flexibility, Product Configuration, and Market Competition.” Marketing Science 25 (2): 116–30, https://doi.org/10.1287/mksc.1050.0169.Search in Google Scholar
Guo, L., and Y. Zhao. 2009. “Voluntary Quality Disclosure and Market Interaction.” Marketing Science 28 (3): 488–501, https://doi.org/10.1287/mksc.1080.0418.Search in Google Scholar
Iyer, G., and D. Soberman. 2000. “Markets for Product Modification Information.” Marketing Science 19 (3): 203–25, https://doi.org/10.1287/mksc.19.3.203.11801.Search in Google Scholar
Jensen, F. O. 1991. “Information Services.” In The AMA Handbook of Marketing for the Service Industries, chap. 22, edited by M. L. Congram, and C. A. Friedman, 423–43. New York: American Management Association.Search in Google Scholar
Ke, T. T., and S. Lin. 2020. “Informational Complementarity.” Management Science 66 (8): 3699–716, https://doi.org/10.1287/mnsc.2019.3377.Search in Google Scholar
Lambrecht, A., A. Goldfarb, A. Bonatti, A. Ghose, D. G. Goldstein, R. Lewis, A. Rao, N. Sahni, and S. Yao. 2014. “How Do Firms Make Money Selling Digital Goods Online?” Marketing Letters 25 (3): 331–41, https://doi.org/10.1007/s11002-014-9310-5.Search in Google Scholar
Murphy, K. P. 2007. Conjugate Bayesian Analysis of the Gaussian Distribution. Tech. Rep. Also available at http://www.cs.ubc.ca/∼murphyk/Papers/bayesGauss.pdf.Search in Google Scholar
Raju, J. S., and A. Roy. 2000. “Market Information and Firm Performance.” Management Science 46 (8): 1075–84, https://doi.org/10.1287/mnsc.46.8.1075.12024.Search in Google Scholar
Sarvary, M. 2002. “Temporal Differentiation and the Market for Second Opinions.” Journal of Marketing Research 39 (1): 129–36, https://doi.org/10.1509/jmkr.39.1.129.18933.Search in Google Scholar
Sarvary, M., and P. M. Parker. 1997. “Marketing Information: A Competitive Analysis.” Marketing Science 16 (1): 24–38, https://doi.org/10.1287/mksc.16.1.24.Search in Google Scholar
Tirole, J. 1988. The Theory of Industrial Organization. Cambridge: The MIT Press.Search in Google Scholar
Winkler, R. L. 1981. “Combining Probability Distributions from Dependent Information Sources.” Management Science 27 (4): 479–88, https://doi.org/10.1287/mnsc.27.4.479.Search in Google Scholar
Xiang, Y., and M. Sarvary. 2013. “Buying and Selling Information Under Competition.” Quantitative Marketing and Economics 11 (3): 321–51, https://doi.org/10.1007/s11129-013-9135-1.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Complementarity of Information Products
- Interdependences of Products in Market Baskets: Comparing the Conditional Restricted Boltzmann Machine to the Multivariate Logit Model
- Taking It a Step Further: When do Followers Adopt Influencers’ Own Brands?
- Choosing Among Alternative Brands: Revisiting the Way Involvement Drives Consumer Selectivity
- The Examination of Cultural Values and Social Media Usages in China
- The Effects of Country-Image and Animosity on Asian Consumers’ Responses to Foreign Brands
- Sales – Response Model in Marketing Revisited in the Times of Uncertainty and Global Turmoil
Articles in the same Issue
- Frontmatter
- Complementarity of Information Products
- Interdependences of Products in Market Baskets: Comparing the Conditional Restricted Boltzmann Machine to the Multivariate Logit Model
- Taking It a Step Further: When do Followers Adopt Influencers’ Own Brands?
- Choosing Among Alternative Brands: Revisiting the Way Involvement Drives Consumer Selectivity
- The Examination of Cultural Values and Social Media Usages in China
- The Effects of Country-Image and Animosity on Asian Consumers’ Responses to Foreign Brands
- Sales – Response Model in Marketing Revisited in the Times of Uncertainty and Global Turmoil