Today, we identify service articles published in Marketing, Management, Operations, Productions, Information Systems, and Practitioner-Oriented Journals in the last months.
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Berry, L. L., T. S. Danaher, T. Keiningham, L. Aksoy and T. W. Andreassen (2025): Social Profit Orientation: Lessons from Organizations Committed to Building a Better World, Journal of Marketing, 89(4299), pp.1-19
Services marketing originated as a discipline to guide managers in marketing intangible products; in today’s world, it must also guide managers in serving society. This research develops the concept of a social profit orientation, whereby organizations invest resources for the express purpose of enhancing the common good, especially the well-being of people and the health of the planet. Implementing social initiatives that serve this broader mission is no small challenge, but exemplary organizations are nevertheless charting a practical course. The authors conducted 62 in-depth executive interviews across 21 organizations in multiple countries, spanning both for-profit and nonprofit sectors, yielding valuable insights into how they create social profit for individuals, communities, and society at large through their initiatives. This research, grounded in published theory and directed toward practical implementation, defines the parameters of a social profit orientation and introduces an innovative framework that distills its antecedents, moderators, and outcomes. The experiences shared by the sample of forward-looking, future-oriented organizations can inform and inspire other companies, organizations, and institutions as they operate in environments where far more is expected of them than ever before.
Link: http://dx.doi.org/10.1177/00222429241258495 [Google]
Steinhoff, L., J. J. Kim, V. K. Kanuri and R. W. Palmatier (2025): Unintended consequences of selling B2B digital subscription add-ons for customer onboarding, Journal of the Academy of Marketing Science, (4300), pp.1-35
Business-to-business (B2B) software-as-a-service (SaaS) providers increasingly bundle add-on services with their core service. The implications of such bundles for onboarding customers to the relationship remain unclear; in particular, the common practice of trying to maximize add-on bundling during the customer acquisition phase arguably might conflict with goals to achieve long-term retention of customers. The current study therefore focuses explicitly on the impact of add-on bundling on customer retention during the onboarding stage, using multiple methods. A theories-in-use exploration suggests that the positive effects of add-on bundling may not manifest in the initial relational stage of customer onboarding. A field study involving a B2B SaaS provider further reveals that bundling more add-on services can significantly decrease customer retention during the onboarding stage. Moving to leaner communication channels can aggravate such attrition. Finally, a cross-industry survey of B2B managers identifies complexity perceptions as the likely source of these detrimental effects of add-on bundling during the customer onboarding stage.
Link: http://dx.doi.org/10.1007/s11747-025-01088-3 [Google]
Van Crombrugge, M. and S. Stremersch (2025): Engagement in platform markets: A (video) game changer?, Journal of the Academy of Marketing Science, (4301), pp.1-25
Empirical studies of two-sided platform markets, like the video game console industry, typically rely on software and platform sales data, thereby overlooking today’s managerial focus on engagement. This present research leverages a unique dataset tracking the daily engagement of over 14,000 users of Microsoft’s Xbox One and Xbox Series video game platforms to remedy this gap. We investigate how software development and release characteristics affect consumers’ engagement with software titles and the platforms on which they release. Our analysis finds that releasing software on subscription services is the strongest determinant of engagement, overshadowing established determinants like software quality or exclusivity. While superstar software and exclusive titles generate engagement, their relative importance is smaller compared to sales-based findings, reported in prior literature. Instead, franchises, non-superstars, and multihomed software perform much better on engagement than on sales, especially when included in a subscription service. These findings have important industry implications.
Link: http://dx.doi.org/10.1007/s11747-025-01089-2 [Google]
Makkar, M., S. Appau and R. W. Belk (2025): Value outcomes in Airbnb as a chronotopic service, International Journal of Research in Marketing, 42(4302), pp.55-73
• Chronotopic services commercialize time-in-space as the unit of transaction. • Ethnographic Airbnb data shows how chronotopic services shape consumption value. • Chronotopic service is afforded via temporal, spatial, spatiotemporal dimensions. • Consumption value is orchestrated via heterotemporality, heterotopia, hybridity. • Contributions to understanding the role of time-in-space in consumption. Many types of consumption—including events, traveling, and accommodation—primarily focus on selling time in space to consumers. That is, their business model is based on charging consumers for spending time in provided spaces (or places), with prices varying depending on the type of space and how much time consumers spend in that space. Using Bakhtin’s notion of chronotopes, we develop the concept of chronotopic services to describe these types of services that primarily sell time-in-space to consumers. Airbnb is an example of a chronotopic service. In a six-year multimethod qualitative study of consumers’ experiences with Airbnb we examine how such chronotopic services occur and how they shape consumption value outcomes in Airbnb. We conceptualize chronotopic services by noting three affordances that characterize Airbnb—temporal, spatial, and spatiotemporal affordances. We show how these three chronotopic affordances enable the orchestration of consumption value outcomes through heterotemporality, heterotopia, and hybridity. We contribute to services and consumer research on the role of space and time in the consumption of services by demonstrating how chronotopic affordances shape consumption outcomes and consumer experiences. This research also highlights the implications of the time-in-space aspects of chronotopic services and how value is created and destroyed because of its chronotopic nature. We discuss the implications of our findings for understanding the consumption of other chronotopic services and how chronotopic affordances shape other forms of consumption.
Link: http://dx.doi.org/10.1016/j.ijresmar.2024.05.008 [Google]
Wenli Zou, L. and C. K. Yim (2025): Customer traffic and customer experience: Creating a contrived similarity to address the crowding dilemma, International Journal of Research in Marketing, 42(4303), pp.133-152
• Contrived similarity is a similarity that is assigned, trivial, and observable. • Contrived similarity alleviates perceived crowding when density is high. • In-group identification mediates the mitigating effect of contrived similarity. • Contrived similarity works only when customers are high in self-uncertainty. • Seven studies, including two field experiment, affirm the effects. Improving customers’ experiences by reducing their negative reactions to a crowded environment continues to be a challenge for brick-and-mortar stores. Drawing from the social identity theory, this research proposes that stores could mitigate customers’ crowding perceptions in a high customer density environment by creating a contrived similarity shared among customers that is assigned, observable, and trivial. A total of seven studies (N = 3,343), including two field experiments, one simulated study, and four online experiments, affirm the contrived similarity effect on alleviating customers’ perceptions of crowding when customer density is high, and this effect is mediated by eliciting a situational in-group identification among customers and moderated by customers’ perceived self-uncertainty. This research enriches the literatures on crowding and similarity, as well as social identity theory. Its results also provide implications for service managers facing the crowding dilemma, who must find ways to manage customer traffic and customer experience effectively.
Link: http://dx.doi.org/10.1016/j.ijresmar.2024.07.006 [Google]
Xie, Y., Z. Tong and Z. Wu (2025): Artificial Intelligence or Human Service, Which Customer Service Failure Is More Unforgivable? A Counterfactual Thinking Perspective, Psychology & Marketing, (4304), pp.1
ABSTRACT With the continuous development and progress of Artificial Intelligence (AI) technology, intelligent customer service stands on the tip of the wind and waves of AI. This rapid development of the customer service industry has made the comparison and collision between AI customer service and artificial customer service a hot topic. This paper proposes a model exploring how the customer service failure of AI and human personnel influences customer satisfaction differently, with the mediation variable of counterfactual thinking and the moderation variables of psychological distance and empathy. Four studies using experimental design were conducted. Study 1 (
Link: http://dx.doi.org/10.1002/mar.22215 [Google]
Benjaafar, S., Z. Wang and X. Yang (2025): Human in the Loop Automation: Ride-Hailing with Remote (Tele-)Drivers, Management Science, 71(4305), pp.2527-2543
Tele-driving refers to a novel concept by which drivers can remotely operate vehicles (without being physically in the vehicle). By putting the human back in the loop, tele-driving has emerged recently as a more viable alternative to fully automated vehicles with ride-hailing (and other on-demand transportation-enabled services) being an important application. Because remote drivers can be operated as a shared resource (any driver can be assigned to any customer regardless of trip origin or destination), it may be possible for such services to deploy fewer drivers than vehicles without significantly reducing service quality. In this paper, we examine the extent to which this is possible. Using a spatial queueing model that captures the dynamics of both pickup and trip times, we show that the impact of reducing the number of drivers depends crucially on system workload relative to the number of vehicles. In particular, when workload is sufficiently high relative to the number of vehicles, we show that, perhaps surprisingly, reducing the number of drivers relative to the number of vehicles can actually improve service level (e.g., as measured by the amount of demand fulfilled in the case of impatient customers). Having fewer drivers than vehicles ensures that there are always idle vehicles; the fewer the drivers, the likelier it is for there to be more idle vehicles. Consequently, the fewer the drivers, the likelier it is for the pickup times to be shorter (making overall shorter service times likelier). The impact of shorter service time is particularly significant when the workload is high, and in this case, it is enough to overcome the loss in driver capacity. When workload is sufficiently low relative to the number of vehicles, we show that it is possible to significantly reduce the number of drivers without significantly reducing service level. In systems in which customers are patient and willing to queue up for the service, we show that reducing the number of drivers can also reduce delay, including stabilizing a system that may otherwise be unstable. In general, relative to a system in which the number of vehicles equals the number of drivers (as in a system with in-vehicle drivers), a system with remote drivers can result in savings in the number of drivers either without significantly degrading performance or actually improving performance. We discuss how these results can, in part, be explained by the interplay of two counteracting forces: (1) having fewer drivers increasing service rate and (2) having fewer drivers reducing the number of servers with the relative strength of these forces depending on system workload. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: This work was supported by the US National Science Foundation [Grant SCC-1831140], and the Guangdong (China) Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [2023B1212010001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01687.
Link: http://dx.doi.org/10.1287/mnsc.2022.01687 [Google]
Daw, A., A. Castellanos, G. B. Yom-Tov, J. Pender and L. Gruendlinger (2025): The Co-Production of Service: Modeling Services in Contact Centers Using Hawkes Processes, Management Science, 71(4306), pp.2635-2656
In customer support contact centers, every service interaction involves a messaging dialogue between a customer and an agent; together, they exchange information, solve problems, and collectively co-produce the service. Because the service progression is shaped by the history of conversation thus far, we propose a bivariate marked Hawkes process cluster model of the customer-agent interaction. To evaluate our stochastic model of service, we apply it to an industry contact center data set containing nearly 5 million messages. Through both a novel residual analysis comparison and several Monte Carlo goodness-of-fit tests, we show that the Hawkes cluster model indeed captures dynamics at the heart of the service and surpasses classic models that do not incorporate the service history. Furthermore, in an entirely data-driven simulation, we demonstrate how this history-dependent model can be leveraged operationally to inform a prediction-based routing policy. We show that widely used and well-studied customer routing policies can be outperformed with simple modifications according to the Hawkes model. Through analysis of a stylized model proposed in the contact center literature, we prove that service heterogeneity can cause this underperformance and, moreover, that such heterogeneity will occur if service closures are not carefully managed. This paper was accepted by Elena Katok, operations management. Funding: The authors are grateful for the generous support of this work by the National Science Foundation Division of Graduate Education [Grant DGE-1650441] (A. Daw), the Israel Science Foundation [Grant 336/19] (G. B. Yom-Tov), and the United States-Israel Binational Science Foundation [Grant 2022095] (A. Daw, G. B. Yom-Tov). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04060.
Link: http://dx.doi.org/10.1287/mnsc.2021.04060 [Google]
Feng, Q., Z. Jiang, J. Liu, J. G. Shanthikumar and Y. Yang (2025): The Operational Data Analytics (ODA) for Service Speed Design, Management Science, 71(4307), pp.2467-2486
We develop the operational data analytics (ODA) framework for the classical service design problem of G/G/c/k systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data-integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time-scaling property of the stochastic service process. Validating the data-integration model against the long-run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data-integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation-and-then-optimization solutions in the small sample regime. This paper was accepted by David Simchi-Levi, operations management. Funding: Z. Jiang’s research is supported by the National Natural Science Foundation of China [Grant 71931007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00655.
Link: http://dx.doi.org/10.1287/mnsc.2023.00655 [Google]
Li, W. (2025): Investor-Paid Credit Ratings and Managerial Information Disclosure, Management Science, 71(4308), pp.2142-2169
Unlike issuer-paid credit rating agencies (CRAs), investor-paid CRAs are compensated by investors for providing rating services. Exploiting the staggered timing of rating initiation by an investor-paid rating agency (the Egan Jones Ratings (EJR)), I document that the coverage by EJR increases rated firm managers’ voluntary disclosure of negative news. Consistent with EJR’s rating coverage deterring managerial bad news hoarding by informing investors of downside risks, I find that the effect of EJR coverage is more pronounced when issuer-paid CRAs tend to assign inflated ratings and when rated firms’ managers have a stronger incentive to conceal bad news. I also document that firms unwind upward earnings management after being covered by EJR. In contrast, coverage by an issuer-paid CRA (Standard & Poor’s) is not associated with changes in managerial information disclosure. I conclude that investor-paid CRAs function as a type of effective information intermediary to discipline firm managers and improve corporate transparency. This paper was accepted by Brian Bushee, accounting. Funding: The author acknowledge financial support from the MOE Project of the Key Research Institute of Humanities and Social Science in University [Grants 22JJD790093 and 22JJD790094], the Program for Innovative Research Team of Shanghai University of Finance and Economics, and the 111 Project [Grant B18033]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.01914.
Link: http://dx.doi.org/10.1287/mnsc.2021.01914 [Google]
Hu, K., L. Kong and Z. Jia (2025): Supplier Selection Criteria Under Heterogeneous Sourcing Needs: Evidence From an Online Marketplace for Selling Production Capacity, Production & Operations Management, 34(4309), pp.168-186
Supplier selection is critical in the sourcing process: retailers evaluate candidate suppliers across multiple attributes, including price, quality, speed, and service, to locate an ideal supply chain partner. While related theoretical research requires prior knowledge of retailers’ preferences across attributes, little empirical evidence about the criteria has been drawn from actual sourcing decisions. Capitalizing on an innovative online marketplace where manufacturers sell production capacity, our research reveals the relative importance assigned by retailers to over twenty supplier attributes including price, product quality, speed in multiple phases from ordering to delivery, and various ancillary services. Our research enables a direct connection between theoretical models and business practices and solidifies existing survey studies by removing reporting bias and increasing representative samples. Using various machine learning approaches, we discover that speed and price attributes are considered the most important, followed by quality, and finally, service offerings. We further investigate how supplier selection criteria are adjusted based on order sizes and product features. We reveal that, as the order size increases, price and quality attributes become more important while speed and service attributes plummet in importance. Furthermore, we find that retailers attach a higher value to speed and service attributes with trendy innovative products, but care more about the price dimension with long-life-cycle functional products. Based on these findings regarding supplier selection criteria, we provide investment guidelines for suppliers by quantifying the economic value associated with each non-price attribute. Also, to enable more efficient information disclosure, we recommend that online platforms consolidate their service menus by removing services with low enrollment rates and low impact on deal formation.
Link: http://dx.doi.org/10.1177/10591478241279384 [Google]
Thornton, L. M., A. L. Jones, A. E. Ekpo, P. Kent and W. K. Story (2025): A Tale of Two Frontlines: Critically Assessing the Dynamics of Interracial Service Encounters, Production & Operations Management, 34(4310), pp.401-411
Minority frontline workers are often required to engage in interracial service encounters, which often involve additional complexities due to the subtle biases and stereotypes held by customers. Improving service delivery in interracial service encounters requires understanding the experiences of minority frontline workers. We begin by explaining the challenges faced by minority frontline workers and then offer an analytical framework that will allow operations management research to better account for these experiences. Next, we discuss opportunities for operations management researchers to explore how interracial service encounters impact employees, service operations, and service supply chains.
Link: http://dx.doi.org/10.1177/10591478241276136 [Google]
Hu, Y. J., J. Rombouts and I. Wilms (2025): Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms, Information Systems Research, 36(4311), pp.552-571
Practice- and policy-oriented abstract: The success of on-demand service platforms crucially hinges upon their ability to make fast and accurate demand forecasts so that its workers are always at the right time and location to serve customers promptly. Yet demand forecasting is challenging for several reasons. First, demand data are typically released as high-frequency streaming time series, which requires an algorithm that has a fast processing time. Second, a digital platform often operates in many different geographic regions, thereby giving rise to a large heterogeneous geographical collection of high-frequency demand streams that need to be forecast and requiring a scalable algorithm. Third, a platform business usually operates in an unstable, rapidly changing environment and faces irregular growth patterns, which requires agility when forecasting demand because slow reactions to such instabilities causes forecast performance to break down. We offer a novel forecast framework called fast forecasting of unstable data streams that is fast and scalable and automatically assesses changing environments without human intervention. We test our framework on a unique data set from a leading European on-demand delivery platform and a U.S. bicycle sharing system and find strong (i) forecast performance gains, (ii) financial gains, and (ii) computing time reduction from using our framework against several industry benchmarks. On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe and find strong performance gains from using our framework against several industry benchmarks across all geographical regions, loss functions, and both pre- and post-COVID periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs. History: Olivia Liu Sheng, Senior Editor; Huimin Zhao, Associate Editor. Funding: I. Wilms was financially supported by the Dutch Research Council (NWO) [Grant VI.Vidi.211.032]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.0130.
Link: http://dx.doi.org/10.1287/isre.2023.0130 [Google]
Jiang, L., J. Hou, X. Ma and P. A. Pavlou (2025): Punished for Success? A Natural Experiment of Displaying Clinical Hospital Quality on Review Platforms, Information Systems Research, 36(4312), pp.285-306
The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management. The healthcare market faces severe information asymmetry; patients struggle to evaluate the quality of hospitals and make informed decisions about their healthcare. Review platforms (e.g., Yelp) have begun to display the clinical quality of hospitals (alongside consumer reviews) to inform patients about hospital selection. In 2017 and 2019, Yelp introduced a feature with clinical measures of maternity care for hospitals that deliver babies in select markets. We study how clinical quality measures displayed on Yelp—especially for those (clinically) high-quality hospitals—influence subsequent patients’ ratings of hospitals. Our difference-in-differences estimation shows that when clinical quality measures are displayed, high-quality hospitals are surprisingly punished with lower subsequent ratings on Yelp, especially hospitals with low staffing capacity. This novel finding is consequential for hospitals as patient dissatisfaction can jeopardize the federal funding that hospitals receive (CMS.gov). To tease out the underlying mechanism, we queried SafeGraph’s precise foot traffic data, and we observed a significant patient surge for hospitals that have high maternity care clinical scores displayed on Yelp. We used transfer deep learning to show that because of the patient surge, (only) hospitals with high maternity scores that were short staffed received significantly more negative patient reviews and more complaints about key hospital service areas, thus driving patient dissatisfaction and lower ratings. We contribute to theory and practice by elucidating the role of publicly displaying clinical quality measures in patient (dis-)satisfaction with hospitals. History: Eric Zheng, Senior Editor; Atanu Lahiri, Associate Editor. Funding: This research was supported by the Behavioral Research Assistance Grant, generously provided by the C. T. Bauer College of Business, University of Houston. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0630.
Link: http://dx.doi.org/10.1287/isre.2021.0630 [Google]
Liu, H., W. Wen, A. Barua and A. B. Whinston (2025): Making Lemonade from Lemons: A Transaction Cost Economics Perspective on Third-Party Disruptions in a Multivendor Information Technology Service, Information Systems Research, 36(4313), pp.41-60
In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for enterprise customers. In this study, we seek to understand how uncertainties introduced by third-party services shape enterprise customers’ use of various IT services in these multivendor service settings. Specifically, we analyze a case of disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. We find a temporary increase in the use of first-party services that serve as similar-goal substitutes during the disruption; however, there is a net decline in the total use of services in the long run. To assess what actions the first party can take during such disruptions to turn the challenge into an opportunity, we analyze the first party’s technical support log using deep learning techniques. We find that if the first party offers high-quality technical support that addresses product-related issues, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers’ use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect. In modern enterprise computing environments, multiple information technology (IT) services from first and third parties are often integrated to form coherent solutions for business customers. Using transaction cost economics (TCE) as a theoretical foundation, we seek to understand how uncertainties introduced by third-party services shape enterprise customers’ use of various IT services in these multivendor service settings. Specifically, we analyze a case of service disruption caused by a third party that affects the multivendor service but does not directly affect the first-party services. In line with the tenets of TCE, we find a temporary increase in the use of first-party services that can serve as a similar-goal substitute to fulfill the organization’s needs during the disruption; however, on average, we observe a net decline in the total use of services in the long run. We empirically analyze the role of first-party technical support during the disruption. Based on textual data from the first party’s technical support log, we use deep learning to assess what actions the first party can take during such disruptions to turn the challenge into an opportunity. We find that if the first party offers high-quality technical support that specifically addresses issues related to its product, it may be able to make lemonade out of lemons. Such technical support effectively boosts customers’ use of first-party services in the long run. Curiously, however, similar efforts by the first party in the predisruption period are ineffective in achieving the same effect. History: Bin Gu, Senior Editor; Yili (Kevin) Hong, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.0033.
Link: http://dx.doi.org/10.1287/isre.2022.0033 [Google]
