Abstract
We consider the spatial competition between two traditional physical (or offline) retailers and an Internet (or online) retailer where the efficiency of the latter differs from that of the former. We assume that consumers are heterogeneous across two dimensions: (1) the costs of traveling to either of the offline retailers; and (2) the costs of purchasing from the online retailer. Both dimensions depend on the spatial location of consumers and are independent of each other. We show that the online retailer maximizes its profit at an intermediate level of the consumer disutility of online purchase when its efficiency is low.
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Notes
When a customer purchases at an offline retailer, of course, she/he may have to incur a waiting time (e.g., in the case of ordering specific size/model/color items) although some consumers immediately pick up commodities at offline retailers (with little waiting time) if she/he is ready to buy them and the commodities are available.
For example, Amazon.com allows customers to read parts of books; Yoox.com, an online clothing store, creates customer-specific e-shops that are based on personal purchase histories; and Poppin.com, an online office supplies and furniture store, has a section on its website that allows customers to personalize desks fully by assembling different items.
As is common in the online versus offline competition literature (see, for example, Balasubramanian 1998; Bouckaert 2000; Nakayama 2009), we consider only a single online retailer. In the case of many online retailers, their location irrelevancy would then determine price undercutting and zero profits unless we introduced an additional source of differentiation.
Later, we also refer to a case in which the marginal costs of each offline retailer and the online retailer are different, although the results are qualitatively the same.
Several other analyses also consider two-dimensional Hotelling models (Economides 1986; Tabuchi 1994; Veendorp and Majeed 1995; Ansari et al. 1998; Irmen and Thisse 1998). However, to our knowledge, none of these assumes the existence of a third (different) retailer and consumers who hold heterogeneous preferences with respect to both of the two otherwise identical retailers (the first dimension of heterogeneity) or the different retailer (the second dimension of heterogeneity).
If we allow Firm E to choose \( y = [0, \, 1] \) on the vertical axis, we expect that we could have qualitatively similar results if it chooses \( y = 0 \) (equivalently \( y = 1 \)) or \( y = {1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-0pt} 2} \). Suppose that Firm E chooses \( y = {1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-0pt} 2} \). In this case, compared with the case of \( y = 0 \) (or \( y = 1 \)), the distance between Firm E and the farthest consumers becomes half, which reduces Firm E’s costs in obtaining consumer demands; on the other hand, the demand for Firm E becomes more price elastic, which increases competition between the firms. Therefore, Firm E needs to consider this tradeoff.
For example, the offline retailers and Firm E may provide goods with different qualities: When the good of each offline store is better (worse) than that of the online store, s is positive (negative) because all consumers obtain higher (lower) utility from purchasing from the offline retailers. Jiang and Balasubramanian (2014) empirically show that for some experiential products, consumers are likely to rely on a physical examination, which is possible only in offline retailers. In this case, s takes a positive value. Of course, the opposite can also hold. Consider a consumer who purchases a car with a certain paint job and other options. In an online store, consumers can visualize all possible color combinations, wheel types, and door appearances on their computer screens, which is impossible to do in offline retailers. In such a case, s is negative. Of course, an offline store could also have access to software that would show all colors on a device.
Note that we can modify the consumer distribution assumption by changing the range of x or y from \( [0, \, 1] \) to \( [0, \, k] \) without modifying the results qualitatively.
By focusing only on situations where all firms sell a positive quantity, eight cases are in principle possible. However, as the physical retailers are symmetric, we focus only on the symmetric situations; thus we reduce the relevant cases to four. See the derivation of the demand functions in the Technical Appendix in Colombo and Matsushima (2019) for details.
“Not available” indicates the case in which Firm E is inactive.
The basic properties of the profits of the offline retailers do not change even when s is negative.
There is another positive effect on the profits of Firm E: A price change for Firm E becomes less influential on the competition between Firm E and each offline store because direct competition between the offline retailers becomes more important.
In other words, a greater value of \( \tau \) could serve, to some extent, as a device to soften the competition between Firm E and Firms 1 and 2, even if a larger \( \tau \) implies worse service and thereby lower demand for the online retailer.
Considering the optimal amount of advertising, Balasubramanian (1998) also shows that in some cases fully informed advertising by the direct marketer is not optimal, which implies that some consumers do not know of the existence of the direct marketer. For this consumer segment, side-by-side physical retailers directly compete. However, in Balasubramanian (1998) the emergence of direct competition between side-by-side physical retailers depends only on the first-stage choice by the direct marketer. Therefore, direct competition emerges if, and only if, the direct marketer does not advertise its product to all consumers. In addition, partial advertising seems difficult for online retailers, whereas it is possible for a direct marketer that uses catalog marketing.
We note that if s is very low and \( \tau \) is very high, direct competition emerges; but because \( \tau \) is high, a further increase of \( \tau \) is detrimental for profits because the shrinkage of demand effect dominates. Therefore, the profits of Firm E are strictly decreasing with \( \tau \).
Related to the vertical dimension: If there are only two types of consumers with (1) zero disutility and (2) disutility level \( \tau ( > 0) \), an increase in \( \tau \) diminishes the profits of Firm E [see the Technical Appendix in Colombo and Matsushima (2019)].
We omit the explicit expressions.
See Larralde et al. (2009) for the use of numerical simulations when dealing with multidimensional models.
That is, the term \( \int_{{\frac{{p_{E}^{3*} - p_{1}^{3*} + s}}{t}}}^{{\hat{x}*}} {\int_{{\hat{y}_{1} *}}^{1} {(v - p_{1}^{3*} - tx)dydx} } + \int_{{\frac{{p_{E}^{3*} - p_{1}^{3*} + s}}{t}}}^{{\hat{x}*}} {\int_{0}^{{\hat{y}_{1} *}} {(v - p_{E}^{3*} - s)dydx} } \) in (9) increases with \( \tau \).
That is, the term \( \int_{0}^{{\frac{{p_{E}^{3*} - p_{1}^{3*} + s}}{t}}} {\int_{0}^{1} {(v - p_{1}^{3*} - tx)dydx} } \) in (9) decreases with \( \tau \).
One might recall the scenario of damaged goods proposed by Deneckere and McAfee (1996), although they consider a model with a monopolist that produces two types of goods: one is a low-quality good with a relatively higher production cost.
See the Technical Appendix in Colombo and Matsushima (2019) for further details.
See the Technical Appendix in Colombo and Matsushima (2019) for the proof.
References
Ansari, A., Economides, N., & Steckel, J. (1998). The max–min–min principle of product differentiation. Journal of Regional Science, 38(2), 207–230.
Balasubramanian, S. (1998). Mail versus mall: A strategic analysis of competition between direct marketers and conventional retailers. Marketing Science, 17(3), 181–195.
Bernstein, F., Song, J.-S., & Zheng, X. (2008). “Bricks-and-mortar” vs. “clicks-and-mortar”: An equilibrium analysis. European Journal of Operational Research, 187(3), 671–690.
Bouckaert, J. (2000). Monopolistic competition with a mail order business. Economics Letters, 66(3), 303–310.
Cattani, K., Gilland, W., Heese, H. S., & Swaminathan, J. (2006). Boiling frogs: Pricing strategies for a manufacturer adding a direct channel that competes with the traditional channel. Production and Operations Management, 15(1), 40–56.
Chen, B., & Chen, J. (2017). When to introduce and online channel and offer money back guarantees and personalized pricing? European Journal of Operational Research, 257(2), 614–624.
Chen, Y. C., Fang, S.-C., & Wen, U.-P. (2013). Pricing policies for substitutable products in a supply chain with Internet and traditional channels. European Journal of Operational Research, 224(3), 542–551.
Chiang, W.-Y. K., Chhajed, D., & Hess, J. D. (2003). Direct marketing, indirect profits: A strategic analysis of dual-channel supply chain design. Management Science, 49(1), 1–20.
Choi, J., Bell, D. R., & Lodish, L. M. (2012). Traditional and IS-enabled customer acquisition on the Internet. Management Science, 58(4), 754–769.
Colombo, S., & Matsushima, N. (2019). Competition between offline and online retailers with heterogeneous customers. ISER discussion paper no. 1056, Osaka University. The latest version is available on http://ssrn.com/abstract=3396820.
Deneckere, R. J., & McAfee, R. P. (1996). Damaged goods. Journal of Economics and Management Strategy, 5(2), 149–174.
Economides, N. (1986). Nash equilibrium in duopoly with products defined by two characteristics. RAND Journal of Economics, 17(3), 431–439.
Guo, W.-C., & Lai, F.-C. (2014). Spatial competition and quadratic transport costs and one online firm. Annals of Regional Science, 52(1), 309–324.
Guo, W.-C., & Lai, F.-C. (2017). Prices, locations and welfare when an online retailer competes with heterogeneous brick-and-mortar retailers. Journal of Industrial Economics, 65(2), 439–468.
Hendel, I., & Neiva de Figueiredo, J. (1997). Product differentiation and endogenous disutility. International Journal of Industrial Organization, 16(1), 63–79.
Huang, W., & Swaminathan, J. M. (2009). Introduction of a second channel: Implications for pricing and profits. European Journal of Operational Research, 194(1), 258–279.
Irmen, A., & Thisse, J.-F. (1998). Competition in multi-characteristics spaces: Hotelling was almost right. Journal of Economic Theory, 78(1), 76–102.
Jiang, P., & Balasubramanian, S. K. (2014). An empirical comparison of market efficiency: Electronic marketplaces vs. traditional retail formats. Electronic Commerce Research and Applications, 13(2), 98–109.
Larralde, H., Stehlé, J., & Jensen, P. (2009). Analytical solution of a multi-dimensional Hotelling model with quadratic transportation costs. Regional Science and Urban Economics, 39(3), 343–349.
Matsumura, T., & Matsushima, N. (2007). Congestion-reducing investments and economic welfare in a Hotelling model. Economics Letters, 96(2), 161–167.
Nakayama, Y. (2009). The impact of e-commerce: It always benefits consumers, but may reduce social welfare. Japan and the World Economy, 21(3), 239–247.
Peng, S.-K., & Tabuchi, T. (2007). Spatial competition in variety and number of stores. Journal of Economics and Management Strategy, 16(1), 227–250.
Smith, M. D., Bailey, J. P., & Brynjolfsson, E. (2000). Understanding digital markets: Review and assessment. In E. Brynjolfsson & B. Kahin (Eds.), Understanding the digital economy: Data, tools, and research (pp. 99–136). Cambridge, MA: MIT Press.
Tabuchi, T. (1994). Two-stage two-dimensional spatial competition between two firms. Regional Science and Urban Economics, 24(2), 207–227.
Veendorp, E. C. H., & Majeed, A. (1995). Differentiation in a two-dimensional market. Regional Science and Urban Economics, 25(1), 75–83.
Yoo, W. S., & Lee, E. (2011). Internet channel entry: A strategic analysis of mixed channel structures. Marketing Science, 30(1), 29–41.
Acknowledgements
The authors would like thank participants at the 2014 Industrial Organization Conference held at the University of Salento in Alberobello, Italy, for their useful suggestions—particularly Mark Armstrong, Sandro Shelegia, Marcella Scrimitore, Antonella Nocco, and Michele Giuranno. They would also like to thank the editor and two anonymous reviewers for their useful comments on a previous version of this paper. The second author gratefully acknowledges the warm hospitality of MOVE (Markets, Organizations and Votes in Economics) at Universitat Autònoma de Barcelona while writing part of this paper, and the financial support of the JSPS through the “Strategic Young Researcher Overseas Visits Program for Accelerating Brain Circulation.” The authors acknowledge the financial support of the Japan Society for the Promotion of Science (JSPS) via Grants-in-Aid for Scientific Research [Nos. (S) JP15H05728, (A) JP17H00984, (B) JP15H03349, JP18H00847, JP19H01483, (C) JP24530248, JP18K01593], and the International Joint Research Promotion Program at Osaka University.
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Colombo, S., Matsushima, N. Competition Between Offline and Online Retailers with Heterogeneous Customers. Rev Ind Organ 57, 647–664 (2020). https://doi.org/10.1007/s11151-019-09734-1
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DOI: https://doi.org/10.1007/s11151-019-09734-1