Service Science最新文献

The COVID-19 pandemic has caused unprecedented damage to restaurant businesses, especially indoor dining services, because of the widespread fear of coronavirus exposure. In contrast, the online food ordering and delivery services, led by DoorDash, Grubhub, and Uber Eats, filled in the vacancy and achieved explosive growth. As a result, the restaurant industry is experiencing dramatic transformations under the crossfire of these two driving forces. However, these changes are not fully exposed because of the lack of firsthand data, let alone their potential consequences and implications. This study, thus, leverages foot traffic data to reveal and understand the trends of restaurant service demand through the pandemic. We devise a mixture model to decompose the aggregate foot traffic by dwelling time patterns into dine-in and takeout volumes. The transitions of demand structures are then identified for various restaurant sectors by service types, price levels, and locations. We observe that limited-service and budget restaurants saw a significantly faster recovery than full-service counterparts given their comparative advantages in adapting toward takeout channels. But, in the long run, our results suggest more robust demands for dine-in services at full-service restaurants, particularly those that provide more premium dining experiences. Comparatively, the off-line channels at limited-service restaurants appeared vulnerable to the cannibalization from online ordering and delivery channels, which strengthened even after society moved out of lockdown. Regionally, exurban restaurants seem to trend toward the takeout mode, whereas urban areas did not see a notable modal migration between dine-in and takeout from restaurants.

Digital platforms share their customers’ data with social planners, who may utilize it to improve socioeconomic infrastructure. This may benefit customers because of the experience of improved infrastructure. On the contrary, it may lead to privacy concerns among them (as these data sets may include sensitive information). In this paper, we analyze the game-theoretic model to characterize the granularity of data sharing between firms and the social planner and the investments by the social planner to improve public infrastructure. In order to analyze the impact of regulation on data sharing strategy, we consider the cases when data sharing is regulated (decided by the social planner) and unregulated (strategically decided by firms). Our analysis reveals that the firms as well as the social planner decrease the granularity of data with an increase in privacy concerns among customers. To analyze the impact of regulation, we compare the granularity of data shared under unregulated and regulated scenarios. We find that when the firm is monopolist, it shares data with a higher level of granularity in the unregulated scenario. Interestingly, we find that under market competition, the data granularity may be higher or lower compared with the regulated scenario. Specifically, we find that if firms jointly determine the granularity of data to be shared, they share data with higher granularity under the unregulated scenario; however, if they do not collaborate and individually decide on data sharing, we find that regulation leads to higher granularity of data to be shared. Finally, we find that firms’ payoffs and customer surplus are higher under the unregulated data-sharing setup if they jointly determine the granularity of data; however, if they do not collaborate on data sharing, their payoffs, as well as customer surplus, are higher under regulation.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0052.

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a CCS, the service provider should implement observed and unobserved customer segmentation to optimize revenue and social welfare, respectively. In an NCCS, the impact of customer segmentation on revenue maximization depends on the value of regular customers, their loss reference-dependent preferences, and the system’s offered load. Alternatively, if the service provider seeks to maximize social welfare, the provider’s use of customer segmentation relies solely on the system’s offered load and customers’ reference-dependent preferences. Our findings also indicate that reference dependence can have varying impacts under different conditions, suggesting the effectiveness of tailored service and pricing strategies. Notably, a CCS generates more revenue than does an NCCS because of its captive nature, and, surprisingly, increasing the service rate can decrease revenue while improving social welfare. These insights have significant implications for service management strategies for a CCS and an NCCS.

Funding: J. Liu was supported by the National Natural Science Foundation of China (General Program) [Grant 72071112]. J. Chen was supported by the National Natural Science Foundation of China (Major Program) [Grant 71490723].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0033.