Co-Opetition in Service Clusters with Waiting-Area Entertainment

Abstract

Problem definition: Unoccupied waiting feels longer than it actually is. Service providers operationalize this psychological principle by offering entertainment options in waiting areas. A service cluster with a common space provides firms with an opportunity to cooperate in the investment for providing entertainment options while competing on other service dimensions. Academic/practical relevance: Our paper contributes to the literature by being the first to examine co-opetition in a service setting, in addition to developing a novel model of waiting-area entertainment. It also sheds new light on the emerging practice of service clusters and small-footprint retailing. Methodology: Using a queueing theoretic approach, we develop a parsimonious model of co-opetition in a service cluster with a common space. Results: By comparing the case of co-opetition with two benchmarks (monopoly and duopoly competition), we demonstrate that a service provider that would otherwise be a local monopolist can achieve higher profitability by joining a service cluster and engaging in co-opetition. Achieving such benefits, however, requires a cost-allocation scheme that properly addresses an efficiency-fairness tradeoff—the pursuit of fairness may backfire and lead to even lower profitability than under pure competition. Managerial implications: We show that as much as co-opetition facilitates resource sharing in a service cluster, it heightens price competition. Furthermore, as the intensity of price competition increases, surprisingly, service providers may opt to charge higher service fees, albeit while providing a higher entertainment level.

Sustainable supply chain clusters: an integrated framework

“…The developed framework can help governments develop clusters continuously (Xin and Li-Ying, 2013;Dana et al, 2013;Yuan et al, 2021) as DCs can help organisations overcome the trade-off between connectedness and adaptability (Geng et al, 2013b;Simmie and Martin, 2010), which will eventually help in achieving SDGs (UN, 2018b) and enhance economic growth (Monteiro, 2016;Dong, 2011;Panyathanakun et al, 2013). The framework shows the importance of developing DCs for SCC members to enhance sustainability; in addition, it incentives them through linking it to their performance (Esfahbodi et al, 2016).…”

“…The developed framework can help governments develop clusters continuously (Xin and Li-Ying, 2013; Dana et al ., 2013; Yuan et al ., 2021) as DCs can help organisations overcome the trade-off between connectedness and adaptability (Geng et al. , 2013b; Simmie and Martin, 2010), which will eventually help in achieving SDGs (UN, 2018b) and enhance economic growth (Monteiro, 2016; Dong, 2011; Panyathanakun et al ., 2013).…”

“…However, there is a debate of how information sharing through networked collaboration can affect organisational competitive advantage, as some organisations tend not to share valuable information in order to protect their comparative advantage (Dyer andHatch, 2006, Lei andHuang, 2014). In addition, the cost of sharing information among competitors might outweigh its benefits, as illustrated through the empirical study of Yuan et al (2021). Still, there is empirical evidence that supports the positive impact of networked collaboration on organisational performance in a supply SCC context (Ye et al, 2020).…”

Impacts of the COVID‐19 pandemic on grocery retail operations: An analytical model

“…We use the systems' volume‐based capacity concept—introduced in Allon and Federgruen (2007) and used in Yuan et al. (2021)—to determine the total capacity $$\mu _c$$ required for fulfilling the curbside pickup orders as follows: $$\begin{align} \mu _c=\theta _c \lambda _c + \dfrac{1}{w_c}, \end{align}$$where the shopping rate $$\lambda _c$$ for curbside pickup customers follows Equation (); therefore, $$\mu _c$$ depends on the curbside pickup service premium $$p_c$$, minimum shopping amount $$\underline{R}_c$$, and expected pickup service waiting time $$w_c$$. Equation () ensures that the capacity $$\mu _c$$ meets the desired expected pickup service waiting time $$w_c$$ set by the retailer—for example, to match it with the service waiting time of the third‐party delivery platforms.…”

“…However, they may need to hire additional staff for fulfilling curbside pickup orders. We use the systems' volume-based capacity concept-introduced in Allon and Federgruen (2007) and used in Yuan et al (2021)-to determine the total capacity 𝜇 c required for fulfilling the curbside pickup orders as follows:…”