A Strategic Analysis of Multi-Channel Expert Services

Abstract

Using stylized models, we investigate when and how expert service providers should offer their services online, and whether they should charge separate prices for face-to-face and online services or provide the online service as a free supplement. Interestingly, consumer surplus can rise when a monopolist charges different prices for face-to-face and online services, and it may drop when the monopolist starts offering the online service as a free add-on to its face-to-face service. We find that a market-wide adoption of the online channel by competing experts in a duopoly setting intensifies price competition and thereby reduces overall profits. Furthermore, the rate of adoption is highest when the online service is moderately effective, whereas one of the experts refuses to offer the service when it is highly effective. These results provide theoretical support for the viability of online expert services as well as practical guidance on pricing strategies.

Key words and phrases:

• channel management
• expert services
• expertise pricing
• game theory
• online expertise
• pricing

Acknowledgment

The authors thank Sanjay Jain for his comments and help in earlier versions of this manuscript.

Funding

Zafer D. Ozdemir gratefully acknowledges generous support from the Farmer School of Business, Miami University for this research.

Supplemental File

Supplemental data for this article can be found on the publisher’s website at 10.1080/07421222.2017.1297637

Notes

1. See, for example, www.publicnewsservice.org/2014-06-26/health-issues/online-dr-visits-the-future-of-the-house-call-is-here/a40142-1.

2. While in some cases the pecuniary and nonpecuniary costs of returning a product may be prohibitive relative to the value of the refund, the decision ultimately belongs to the customer. In contrast, all fees paid to experts are effectively sunk costs.

3. What is critical here is that the online service is less likely to generate a satisfactory solution than the face-to-face service. We could incorporate the possibility of a failure for the face-to-face service by assuming a success probability of $\eta$, where $\eta >\alpha$, and redefining the expected value from the face-to-face service as $\eta v$, and our results would still be qualitatively the same. The current configuration, however, is cleaner and more tractable analytically.If the consumer anticipates that her problem is too complex to be handled online, the level of $\alpha$ will be so small that she will directly seek a face-to-face service. We discuss this possibility in the next section and show that our results would hold even if we allow$\alpha$to assume such low values.As technology improves, so would $\bar{\alpha }.$ See Online Appendix A for further information on this parameter.

4. In small towns and cities and especially in rural areas, one can find an individual who is the only expert in the respective specialty (e.g., the only physician in a rural area, the only tax adviser in a small town, and the only intellectual property lawyer in a small city). For there to be only one expert in a locality, one would expect the specialty of interest to be more sophisticated as the locality gets larger in size, since less sophisticated experts are more numerous.The primary reason for analyzing the monopoly case is to establish a benchmark to compare with the case of duopolistic competition. Doing so provides insights about how equilibrium strategies of expert service providers change when they face no competition at all versus when they experience some competition, so that we can begin to understand how and why the behavior of market participants might change in response to competitive pressures.

5. The number of customers with a satisfactory outcome equals ${\theta }_0+\alpha (1-{\theta }_0)=\frac{v_f-c}{2}+$ $\frac{\alpha }{2}(1+c-v_f)$, which is larger than that in the benchmark case when $c>v_f-1$.

6. The proofs of all propositions are available in Online Appendix A.

7. When the market is uncovered, the cost efficiency and market reach become an important motivation vis-à-vis customer valuation, and the monopolist retailer always finds it beneficial to adopt the online channel.

8. We assume that the experts already serve face-to-face and that they are concerned only with the question of whether they should also offer their services online. An alternative formulation would be to allow the experts to offer their services face-to-face only, online only, or both. Such a formulation is appropriate when considering new entry into the market and could lead to new interesting insights. However, since market entry is not the focus of this study, we leave it as a potential direction for future research.

9. If this condition is violated, all consumers first obtain an online service, and the face-to-face service is used only for second visits. This condition thus restricts our attention to the more realistic case. Nevertheless, the qualitative nature of our results would not change even if we were to relax this condition.

10. This is because the resulting price competition would drag the profits down substantially, causing at least one expert to withdraw from the online market. However, as we will see later, simultaneously offering online services can be an equilibrium outcome if the experts were to bundle their face-to-face and online services by charging a single price for both.

11. From the consumer’s perspective, this arrangement may also be viewed as a “free” face-to-face service being rendered subsequent to an online service that fails to satisfy.

12. This is similar to the idea that advance selling is often more profitable than spot selling (see [42, 47]). Note, however, that none of the consumers ex post regrets her decision, since no one ends up with a negative surplus. On the other hand, with separate prices, some consumers with a high $\theta$ value may end up with a net negative utility if they receive an unsatisfactory service online. Therefore, if we were to consider risk aversion, bundling the two services could potentially help consumers who are sufficiently risk averse.

13. For $\alpha \in \left({\alpha }_2^{\ast },\mathrm{m}\mathrm{i}\mathrm{n}({\alpha }_1^{\ast },{\alpha }_3^{\ast })\right)$, there exists an additional equilibrium in which both experts adopt the online channel. However, the “nonadoption” equilibrium pareto dominates this additional equilibrium. As is common in the literature (see, e.g., [14, 23, 27]), we use the pareto-dominance criterion in selecting from multiple equilibria.

14. Despite the drop in profits, neither expert has an incentive not to offer the online service because such a move implies an even worse outcome for the deviating expert.

15. We thank an anonymous reviewer for suggesting this connection.

16. See, for example, https://techcrunch.com/2016/03/22/y-combinator-demo-day-winter-2016/.

Additional information

Funding

Zafer D. Ozdemir gratefully acknowledges generous support from the Farmer School of Business, Miami University for this research.

Notes on contributors

M. Tolga Akçura

M. Tolga Akçura ([email protected]) is a professor of marketing at Ozyegin University in Istanbul, Turkey, and founder of a high-tech startup, eBrandValue.com. Previously, he taught at Purdue University and Carnegie Mellon University, from where he also holds a Ph.D. His research has appeared in Management Science, Marketing Science, Decision Sciences, Decision Support Systems, International Journal of Electronic Commerce, and others.

Zafer D. Ozdemir

Zafer D. Ozdemir ([email protected]; corresponding author) is a professor of information systems at the Farmer School of Business, Miami University. He holds a Ph.D. from Purdue University. His research focuses on economics of e-commerce and e-health and has appeared in Information Systems Research, Journal of Management Information Systems, International Journal of Electronic Commerce, Decision Sciences, Decision Support Systems, and others.