Innovative Applications of O.R.
Evolutionary location and pricing strategies for service merchants in competitive O2O markets

https://doi.org/10.1016/j.ejor.2016.03.030Get rights and content

Highlights

  • Online-to-offline (O2O) business is believed to be the biggest pie in e-commerce.

  • We propose an agent-based competitive O2O model as a complex adaptive system.

  • Profit-maximizing service firms optimize competitive pricing and location strategies.

  • Customer’s behaviour and words-of-mouth are considered and explicitly modelled.

  • Our findings are obtained from the micro interactions among the agents.

Abstract

Attracting customers in the online-to-offline (O2O) business is increasingly difficult as more competitors are entering the O2O market. To create and maintain sustainable competitive advantage in crowded O2O markets requires optimizing the joint pricing-location decision and understanding customers’ behaviours. To investigate the evolutionary location and pricing behaviors of service merchants, this paper proposes an agent-based competitive O2O model in which the service merchants are modeled as profit-maximizing agents and customers as utility-maximizing agents that are connected by social networks through which they can share their service experiences by word of mouth (WOM). It is observed that the service merchant should standardize its service management to offer a stable expectation to customers if their WOM can be ignored. On the other hand, when facing more socialized customers, firms with variable service quality should adopt aggressive pricing and location strategies. Although customers’ social learning facilitates the diversity of services in O2O markets, their online herd behaviors would lead to unpredictable offline demand variations, which consequently pose performance risk to the service merchants.

Introduction

Modern information technologies (IT) and their offspring, such as the Internet, smart phones, mobile APPs, have thoroughly changed the way people find and share information. Thanks to business globalization and the existence of international supply networks, consumers can virtually purchase any product from any corner of the world as long as consumers have its information. With the evolution and proliferation of online shopping, new physical items for online sale have become less standardized. A good example is Amazon.com, which started as an online book store, later adding diversified products (e.g., DVDs, toys, consumer electronics etc.), and now selling many non-standardized commodities (e.g., clothing, shoes, jewellery etc).

What is the next to sell online? The most possible answer is service, which generally has the following characteristics: intangibility, heterogeneity, inseparability, and perishability (Lovelock, Gummesson, 2004, Moeller, 2010, Pride, Ferrell, 2014). A case in point is that many firms in China, both big and small, are endeavouring to enter the online-to-offline (O2O) business, which is believed to be the biggest pie in e-commerce (Reuters, 2014). According to Rampell (2010), who first coined the term, O2O commerce aims to “find customers online and bring them into real-world stores”. From the perspective of service providers, however, attracting customers in the O2O market is increasingly difficult as more rivals are rushing in. Groupon.com, a world-wide company providing group-buying information and coupons for local service deals, reported that it retained about 950,000 featured merchants by the end of 2014, a remarkable increase of 46% over 2013 (Groupon, 2015). Such a phenomenal growth rate illustrates the rapid development of O2O commerce, but also raises a significant question to both researchers and service merchants: How to optimize service management to create and maintain sustainable competitive advantage in the crowded O2O market?

It is very difficult to answer this question directly, since service management consists of a multitude of operations in practice. The existing service marketing literature could help us to identify the most important decision variables in the context of the O2O business. The service marketing mix has been extended from the traditional “four P’s” (McCarthy, 1960) to the “seven P’s” (Booms & Bitner, 1981) whereby people, physical evidence, and process are added to the original product, place, promotion, and price. These elements in the marketing mix, however, vary significantly in importance for different types of products/services (Kurtz & Boone, 1987). Therefore, the research scope of this paper is narrowed by observing some real O2O cases. As found from Groupon’s website, it generally provides the following information on a local deal to customers: service description, price, merchant location(s), and reviews from experienced customers, revealing that these features are the basic and key aspects for capturing online business opportunities in today’s O2O market. In terms of service management, the four features can be generally categorized into two competitive factors.

The first competitive factor comprises competitive pricing and location strategies. In response to competition, profit-maximizing service firms may change their pricing strategies in the short term to attract more consumers, or change their locations in the long term to reduce transport cost and offer more efficient services to customers (He, Cheng, Dong, & Wang, 2014). It is worth noting that, this paper only focuses on medium-sized merchants with multiple physical stores. The reasons for this choice are as follows: (1) Most services cannot be delivered to customers who are too far away. In other words, the service coverage of a physical store is bounded by the farthest distance that customers can accept. To vie for the demands distributed throughout a city-wide region, opening more stores may be the most effective way to gain market share. (2) Unlike large companies that have gained market dominance, medium-sized firms are more pressed by peer competition. (3) The findings observed in this paper are applicable to the case with small-sized merchants by reducing the number of stores. In view of the above considerations, this work is able to provide timely and meaningful insights concerning the joint pricing-location decision for numerous small- and medium-sized service merchants that have to or tend to participate in the highly competitive O2O market.

The second competitive factor embraces customer’s behavior and words-of-mouth (WOM). Customers play an increasingly important role in service management in the contemporary IT era due to the following reasons: (1) Customers are not only the ones who purchase services and provide reviews, but also the service co-producers or co-creators of value (Vargo & Lusch, 2008). Most modern products are manufactured by assembly line robots, which are powerful to control product quality precisely. In contrast, service quality, which contains many features that cannot be objectively measured (Fitzsimmons & Fitzsimmons, 2011), is variable due to the heterogeneities in employees’ skills, customers’ needs, and employee-customer interactions (Edvardsson, Gustafsson, & Roos, 2005). (2) The variability of service quality could lead to a difference between customer perception and expectation. According to the classical service quality gap model (Parasuraman, Zeithaml, & Berry, 1985), the various forms of difference between customer perception and expectation determine customer satisfaction and consequently WOM of customers (Zeithaml, Berry, & Parasuraman, 1996). (3) Customers are increasingly encouraged to share their WOM on services via social networks. For example, LivingSocial.com, another popular O2O platform, offers a customer a free deal if three of his/her friends purchase the same deal by clicking his/her referral link (Livingsocial, 2015). From Facebook.com and Twitter.com, it can be observed that people share their WOM of their service experiences with one another in a spontaneous manner. (4) Many studies (see, e.g., Glynn Mangold, Miller, Brockway, 1999, Berger, 2014) have shown that WOM has a powerful impact on customers’ purchasing behavior as it reduces their perceived risk of service quality before purchase (Ennew, Banerjee, Li, 2000, File, Cermak, Prince, 1994). (5) The cost for customers to gather service information has dramatically decreased, which allows customers to more conveniently find and evaluate substitute services on online storefronts. For instance, when browsing some interested deals on Groupon, it will automatically recommend deals based on your location and personal preferences. Facing these challenges from competitors and clients, managers are keen to understand how to adapt to and co-evolve with the changing behaviors of online customers.

This paper aims to study the optimal decisions of multi-store service firms in response to increasingly fierce O2O competition and more socialized customer behavior. The authors attempt to shed light on the following challenging research and practical issues for service management:

  • 1.

    What are the optimal pricing and location strategies for profit-maximizing service firms in competitive O2O markets?

  • 2.

    What are the impacts of more socialized customer behavior on the above strategies?

This paper employs the technique of agent-based modeling (ABM) to create an agent-based competitive O2O model (ACOM). Section 2 introduces ABM and provides three reasons for choosing ABM to simulate competitive O2O markets. In the ACOM, the service merchants are modeled as profit-maximizing agents and customers as utility-maximizing agents that are connected by social networks through which they can share their service experiences by WOM. All the agents’ decision-making processes are carefully modeled from the perspective of optimization in Section 3. In Section 4 the authors design two scenarios and conduct many computational experiments. Section 5 presents the experimental results and discuss the findings. Finally, Section 6 concludes the paper and suggest potential topics for future research.

This study advances previous works in several aspects. First, existing studies are extended by developing a promising framework for modeling individual agents’ optimal behaviors in competitive O2O markets. In addition, the authors consider not only firms’ evolutionary pricing and location strategies, but also consumers’ behaviors that are often neglected in traditional Operations Research (OR) modeling research. Moreover, the findings are obtained from the micro interactions among the agents throughout the evolution of the ACOM, which provide service merchants with valuable and practical managerial insights to gain a competitive edge in competitive O2O markets.

Section snippets

Literature review

The literature is reviewed based on three related research streams, namely (1) competitive location and pricing decisions, (2) word of mouth, and (3) agent-based modeling. Since each research stream contains a large body of literature, the authors only survey the studies that are most relevant to this research for the sake of conciseness.

Overall structure

The ACOM explicitly models micro-scale interactions among the agents and macro-scale feedback of market transactions. Fig. 1 shows the overall structure of the ACOM, which consists of one market, several competing firms that own stores and provide services with uncertain quality, and a specified number of customers connected by their social relationships, which are illustrated as imaginary lines.

In order to make the ACOM more realistic, the authors borrow and extend many traditional and widely

Experimental design

Eight experiments are conducted using the ACOM under two different scenarios, namely Scenario A and Scenario B. Table 2 presents the parameters that remained unchanged in all the experiments. Most parameters’ values, such as the number of agents and customers’ budgets, locations and their social links, come from the “55-node network” in the original PMAXCAP (see Fig. 1 and Table 3 by Serra and ReVelle (1999) for the full data) as a benchmark for validating the ACOM. Besides, we assigned random

Scenario A

Under Scenario A, customers’ expected quality will be updated only if they eventually purchase the service. The authors illustrate all the data output from hundreds of simulations and draw a box plot to show their distributions in Fig. 3. Table 4 presents the means and standard deviations of the above indices in Exp. A1–A3 under this scenario. Besides, we take snapshots for all the experiments in the final time step in Fig. 4.

In the first experiment Exp. A1, the ACOM reduces to a PMAXCAP-like

Conclusions

This paper proposes an agent-based competitive O2O model (ACOM) to investigate the evolutionary location and pricing behaviors of service merchants. The ACOM consists of four types of agents in a two-dimensional plane: (1) Profit-maximizing firm agents provide services with variable quality and pursue suitable pricing strategies. (2) Store agents owned by firms search for optimal location decisions to minimize the total transportation cost, so attracting more clients. (3) Heterogeneous customer

Acknowledgment

We thank the anonymous referees for their helpful comments on earlier versions of our paper. This research was supported in part by The Hong Kong Polytechnic University under Grant Number G-UA39.

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