Considered Service-specific journals were Journal of Service Research, Journal of Service Management, Journal of Services Marketing, Journal of Service Theory and Practice, Service Industries Journal, Cornell Hospitality Quarterly, and Service Science.

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Kidwell, B., V. Lopez-Kidwell, C. Blocker and E. M. Mas (2020): Birds of a Feather Feel Together: Emotional Ability Similarity in Consumer Interactions, Journal of Consumer Research, 47(2), pp.215-236

The authors introduce emotional ability similarity to explain consumer satisfaction in interactions with frontline sales and service employees and other consumers beyond the effects of traditional relational variables in the similarity–attraction paradigm. Four studies examine how and why similar abilities for using emotional information between two people promote relational success in marketplace exchanges. We find that, when interacting with others, consumers who exchange nonverbal information with their partners experience (dis)similarity in their emotional ability (EA). Similar dyads who rely on expressive (high–high EA pairs) or inexpressive (low–low EA pairs) emotion norms experience significantly greater satisfaction in their interactions than consumers with dissimilar norms (high–low EA pairs). Together, these findings advance the understanding of consumer relationships and satisfaction by establishing EA similarity as a new avenue for consumer research.

Link: http://dx.doi.org/10.1093/jcr/ucaa011 [Google]

 

Borah, S. B., S. Prakhya and A. Sharma (2020): Leveraging service recovery strategies to reduce customer churn in an emerging market, Journal of the Academy of Marketing Science, 48(5), pp.848-868

Building on the properties of emerging markets, we investigate how a firm should align its service recovery strategies with different types of service failure to reduce customer churn in an emerging market. Using resource exchange theory and a multi-method approach, we show that the conventional wisdom related to service recovery needs to be reevaluated in emerging markets. Our results show that process failures lead to a higher likelihood of customer churn compared to outcome failures in emerging markets. Investigating service recovery mechanisms, we find that compensation is more effective in recovering from process failures than in recovering from outcome failures in emerging markets. Similarly, employee behavior has a stronger impact on mitigating the ill effects of process failures than those of outcome failures. The study contributes to the literature on service recovery and resource exchange theory and provides managerial insights for the effective management of customer churn due to service failures in emerging markets.

Link: http://dx.doi.org/10.1007/s11747-019-00634-0 [Google]

 

Hill Cummings, K. and A. E. Seitchik (2020): The differential treatment of women during service recovery: How perceived social power affects consumers’ postfailure compensation, Business Horizons, 63(5), pp.647-658

Female consumers often experience marketplace discrimination in service encounters. Researchers have examined women’s differential treatment in many settings, but they have yet to study how women are treated during service-recovery encounters. We found evidence that male providers discriminated against female consumers during the service-recovery process in three experiments. Specifically, male providers offered less compensation to female consumers who complained after a failure than to male consumers who experienced the same failure. Further, we found that perceptions of consumer social power may explain this effect. We offer suggestions for how firms can identify internal marketplace gender discrimination, as well as how they can prevent and treat this significant problem. We also suggest that managers create anticipatory protocols and scripts and engage employees in both bias and interpersonal accuracy training.

Link: http://dx.doi.org/10.1016/j.bushor.2020.06.002 [Google]

 

Babar, Y. and G. Burtch (2020): Examining the Heterogeneous Impact of Ride-Hailing Services on Public Transit Use, Information Systems Research, 31(3), pp.820-834

Over the past 10 years, app-enabled ride-hailing services such as Uber and Lyft have permeated several geographies, fundamentally changing the transit landscape. Ride-hailing services deliver an on-demand, door-to-door transport service that has the potential to interact with other preexisting modes of transport, potentially serving as a complement (e.g., expanding the geographic coverage of a transit mode) or as a replacement. In this study, the authors explore these effects on various modes of public transit across 200 cities in the United States, paying particular attention to the features of a transit mode and the local operating context. The study demonstrates that, on average, Uber has tended to displace city bus services while complementing commuter rail services. Further, the study demonstrates the importance of local context, in that the average effect on a particular transit mode is found to depend on a variety of surrounding factors, including weather patterns, rates of violent crime, gas prices, and the overall quality of the public transit services. This work offers actionable insights for policymakers, public-transit managers, and ride-hailing service operators. Estimates of the annual profit (cost) impacts that ride-hailing services have had on particular cities’ transit services are provided. We examine the impact that ride-hailing services have had on the demand for different modes of public transit in the United States, with a particular focus on understanding heterogeneity in the effects. We assess these effects using a panel data set that combines information on public transit utilization (from the Federal Transit Administration) with information on ride-hailing providers’ staggered arrival into different locations, based on public press releases and newspaper reports. Our analysis indicates that, on average, ride-hailing services have led to significant reductions in the utilization of city bus services while increasing utilization of commuter rail services. These average effects are also subject to a great deal of contextual heterogeneity, depending on the size of the local population, rates of violent crime, weather, gas prices, transit riders’ average trip distance, and the overall quality of public transit options. We demonstrate the robustness of our findings to alternative model specifications. Our findings contribute to the prior literature on technology substitution and complementarity and suggest explanations for contradictory findings that have been reported on ride-hailing’s influence on public transit demand. We also offer useful insights for policymakers, highlighting the nuanced implications of ride-hailing services for different transit operators, depending on the local context.

Link: http://dx.doi.org/10.1287/isre.2019.0917 [Google]

 

Hosseini, L., S. Tang, V. Mookerjee and C. Sriskandarajah (2020): A Switch in Time Saves the Dime: A Model to Reduce Rental Cost in Cloud Computing, Information Systems Research, 31(3), pp.753-775

With the rapid growth of cloud computing, firms face a dizzying array of choices and pricing structures for performing their computing tasks on the cloud. Unlike captive computing resources, cloud computing occurs as a pay-as-you-go contract, similar to the provision of electricity. We develop a method to reduce the rental cost of completing a given computing task with a certain deadline. The current practice is to use a single computing resource that can get the task done in the cheapest possible manner. Instead, costs can be significantly reduced if the task is switched between multiple resources, some more powerful and others less powerful. We apply our method to a real computing task at Cidewalk and show that costs can be significantly reduced. The goal to continually reduce operating costs while meeting computational needs is common to all modern organizations that use cloud computing. We study the problem of selecting computing resources with the goal of minimizing the total rental cost of completing a computing task in the presence of a time constraint. The problem is formulated as a scheduling problem that assigns computing resources to time periods of the planning horizon (time available to complete a single computing task). This (NP-hard) preemptive-resume type scheduling problem—new to the scheduling literature—has not been carefully addressed in practice to provide an implementable solution. Typically, the approach taken in practice is to use a single resource (a single virtual machine instance, or a cluster of identical virtual machine instances) to complete a computing task. The main insight of this study is that rather than completing a computing task using a single computing resource, rental costs can be significantly lowered by using a few resources (sometimes even just two) to complete the task. Thus, the computing task is switched from one resource to another to exploit the cloud provider’s price-performance schedule. Cloud computing has been recognized as an economically attractive computing environment whose adoption has been growing over time. However, providers (such as Amazon Web Services) offer a confusing and diverse set of computing resources with different configurations and unit rental costs. Our near-optimal solution is based on switching the computing task from one resource to another in way that leverages the relationship between the price and performance of the available computing resources. The performance of a given resource can vary randomly as well as be correlated with the performance of another (stronger or weaker) resource. We present a worst-case performance guarantee of the proposed solution. In addition, we study the performance using a detailed computational study and a real-world example of an actual company that can benefit from our proposed solution. In the computational study as well as the real-world example, the cost of our solution is usually about 15%–25% lower than the benchmark solution of using the best single computing resource to process the computing task. Practicing information technology managers can use our approach to migrate in-house solutions to the cloud in a cost-effective manner.

Link: http://dx.doi.org/10.1287/isre.2019.0912 [Google]

 

Danaher, P. J., L. Sajtos and T. S. Danaher (2020): Tactical use of rewards to enhance loyalty program effectiveness, International Journal of Research in Marketing, 37(3), pp.505-520

A key member benefit for participating in a loyalty program (LP) is the rewards earned for points accrued. One popular reward structure is a catalog of many diverse items. The rewards among this broad selection are likely to differ in their appeal due to their intrinsic differences and customer heterogeneity. Prior research has shown that after redeeming a reward, LP members are more motivated to increase their purchase volume/frequency and share-of-wallet within the program, thereby becoming more active. In this study, we fit a hidden Markov model to a 4½ year longitudinal data set of points accrual and reward redemption activity for about 4500 members of a large coalition LP. Our analysis reveals three latent states — active, hyperactive and inactive. We then investigate the likelihood of LP members transitioning between these states across successive time periods, and examine the reward categories and marketing effort associated with these transitions. Subsequently, we use our model to optimally promote particular reward categories to encourage LP member migration to managerially desirable states or prevent them sliding into a less desirable state. Our proposed optimal reward strategy potentially increases the estimated proportion of LP members in the hyperactive latent state from 35.7% to 40.1%, with a resultant increase in sales revenue for retailers and service providers in the LP of 7.7%. We find that rewards which are more fungible have the strongest influence on increasing points accrual activity.

Link: http://dx.doi.org/10.1016/j.ijresmar.2020.02.005 [Google]

 

Septianto, F., G. Northey, T. M. Chiew and L. V. Ngo (2020): Hubristic pride & prejudice: The effects of hubristic pride on negative word-of-mouth, International Journal of Research in Marketing, 37(3), pp.621-643

Prior research has demonstrated how negative emotions influence negative word-of-mouth (NWOM). However, what if there exist certain positive emotions that influence consumers to spread NWOM? This research develops and tests a novel prediction that shows how a discrete positive emotion – hubristic pride – can increase intention to engage in NWOM following a service failure. Results from six experiments support this prediction. Further, this research shows that psychological entitlement drives the effect of hubristic pride. Moreover, this effect is attenuated when consumers are nudged to focus on helping others. This research builds on current theory involving emotion and NWOM, presents a number of areas for future research, and discusses managerial implications stemming from the findings.

Link: http://dx.doi.org/10.1016/j.ijresmar.2019.11.003 [Google]

 

Song, H., A. L. Tucker, R. Graue, S. Moravick and J. J. Yang (2020): Capacity Pooling in Hospitals: The Hidden Consequences of Off-Service Placement, Management Science, 66(9), pp.3825-3842

Hospital managers struggle with the day-to-day variability in patient admissions to different clinical services, each of which typically has a fixed allocation of hospital beds. In response, many hospitals engage in capacity pooling by assigning patients from a service whose designated beds are fully occupied to an available bed in a unit designated for a different service. This “off-service placement” occurs frequently, yet its impact on patient and operational measures has not been rigorously quantified. This is, in part, because of the challenge of properly accounting for the endogenous selection of off-service patients. We use an instrumental variable approach to quantify the causal effects of off-service placement of hospitalized medical/surgical patients, having accounted for the endogeneity issues. Using data from a large academic medical center with 19.6% of medical/surgical patients placed off service on average, we find that off-service placement is associated with a 22.8% increase in remaining hospital length of stay (LOS) and a 13.1% increase in the likelihood of hospital readmission within 30 days. We find no significant effect on in-hospital mortality or clinical trigger (rapid response) activation. We identify longer distances to the service’s home unit as a key mechanism that drives the effect on LOS. In contrast, a mismatch in nursing specialization does not seem to explain this effect. By quantifying the effects of off-service placement on patient and operational outcomes, we enable clinicians and hospital managers to make better-informed short-term decisions about off-service placement and longer-term decisions about capacity allocation. This paper was accepted by Stefan Scholtes, healthcare management.

Link: http://dx.doi.org/10.1287/mnsc.2019.3395 [Google]

 

Ahuja, V., C. A. Alvarez and B. R. Staats (2020): Maintaining Continuity in Service: An Empirical Examination of Primary Care Physicians, Manufacturing & Service Operations Management, 22(5), pp.1088-1106

Problem definition: In many service operations, customers have repeated interactions with service providers. This creates two important questions for service design. First, how important is it to maintain the continuity of service for individuals? Second, because maintaining continuity is costly and, at times, operationally impractical for both the organization (because of potentially lower utilization) and providers (because of high effort required), should certain customer types, such as those with complex needs, be prioritized for continuity? These questions are particularly important in healthcare services where patients with chronic conditions visit primary care offices repeatedly. Therefore, we explore these questions in the context of diabetes, a chronic disease. Academic/practical relevance: Although the operations management (OM) and healthcare literatures suggest that higher continuity is better for health outcomes, the possibility that one could have too much continuity has not been explored. We draw on literature on continuity of care from the healthcare literature and learning effects from the OM literature to theorize and then show a curvilinear relationship. In addition, we further the literature on continuity by examining different categories for prioritization. Methodology: We use a detailed and comprehensive data set from the Veterans Health Administration, the largest integrated healthcare delivery system in the United States, which permits us to control for potential sources of heterogeneity. We analyze over 300,000 patients over an 11-year period who suffer from diabetes, a chronic disease whose successful management requires continuity of care, as well as kidney disease, a major complication of diabetes. We use an empirical approach to quantify the relationship between continuity of care and three important health outcomes: inpatient visits, length of stay, and readmission rate. We conduct extensive robustness checks and sensitivity analyses to validate our findings. Results: We find that continuity of care is related to improvements in all three health outcomes. Moreover, we find that the gains are not linearly improving in continuity, but rather the relationship is curvilinear, whereby outcomes improve and then decline in increasing continuity of care, suggesting that there may be value in having multiple providers. Additionally, we find that continuity of care is even more important for patients suffering from more complex conditions. Managerial implications: Identifying the amount of continuity of care to provide and determining which individuals to prioritize are both of interest to practitioners and policymakers because they can help in designing appropriate policies for staffing and work allocation.

Link: http://dx.doi.org/10.1287/msom.2019.0808 [Google]

 

Batt, R. J. and J. D. Tong (2020): Mean Service Metrics: Biased Quality Judgment and the Customer–Server Quality Gap, Manufacturing & Service Operations Management, 22(5), pp.975-995

Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily about the quality each customer experiences. When and how do server-level quality metrics differ from customer-experienced ones? Can people properly account for these differences, or do they drive human judgment and decision biases? Academic/practical relevance: Biased judgments about service quality can cause governments to fund programs suboptimally, organizations to promote the wrong employees, and customers to make disappointing purchases. We further our understanding of the role that cognitive biases play in services and how to manage quality information in light of them. Methodology: We use a mathematical model to define the gap between server-level and customer-experienced quality metrics. We use secondary data in the context of the higher-education industry to quantify the customer–server quality gap in practice. We construct a behavioral model to derive hypotheses about how environmental factors impact the direction and magnitude of judgment biases. Controlled laboratory experiments test the hypothesized biases and mitigation techniques. Results: Our empirical study reveals that the two measures differ enough to drive significant differences in the rank order of school majors, teachers, and airports. Our experiments support our main conjecture that judgments and decisions about customer-experienced metrics are biased toward server-level metrics. Consequently, (1) judgments about customer-experienced quality are biased high/low when quality and server load are negatively/positively correlated, (2) judgments about a server’s absolute impact on customer experience are biased high/low when a server has a smaller/larger load than average, and (3) providing customer-experienced quality metrics mitigate these biases. Managerial implications: Our results help identify when and why service-quality metrics are likely to mislead judgments and bias decisions as well as who is likely to benefit from such biases. The results also guide system designers on how to report metrics when seeking to help support effective decision making.

Link: http://dx.doi.org/10.1287/msom.2019.0783 [Google]

 

Baojiang, Y., M. Godinho de Matos and P. Ferreira (2020): THE EFFECT OF SHORTENING LOCK-IN PERIODS IN TELECOMMUNICATION SERVICES, MIS Quarterly, 44(3), pp.1391-1409

In this research note, we study the welfare implications of shortening the length of the lock-in period associated with triple play contracts using household level data, from a large telecommunications provider, for a period of 6 months. Using a multinomial logit model to explain consumer behavior we show that, in our setting, shortening the length of the lock-in period decreases the aggregated profit of the firms in the market more than it increases consumer surplus. This result arises because shortening the length of the lock-in period increases churn, and the costs to set up service for the consumers that churn and join a new carrier supersede the increase in the consumers’ willingness to pay for service when the length of the lock-in period shortens.

Link: http://dx.doi.org/10.25300/MISQ/2020/14839 [Google]

 

Dayarian, I. and M. Savelsbergh (2020): Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders, Production & Operations Management, 29(9), pp.2153-2174

Same‐day delivery of online orders is becoming an indispensable service for large retailers. We explore an environment in which in‐store customers supplement company drivers and deliver online orders on their way home. We consider a highly dynamic and stochastic same‐day delivery environment in which online orders as well as in‐store customers willing to make deliveries arrive throughout the day. Studying settings in which delivery capacity is uncertain is novel and practically relevant. Our proposed approaches are simple, yet produce high‐quality solutions in a short amount of time that can be employed in practice. We develop two rolling horizon dispatching approaches: a myopic one that considers only the state of the system when making decisions, and one that also incorporates probabilistic information about future online order and in‐store customer arrivals. We quantify the potential benefits of a novel form of crowdshipping for same‐day delivery and demonstrate the value of exploiting probabilistic information about the future. We explore the advantages and disadvantages of this form of crowdshipping and show the impact of changes in environment characteristics, for example, online order arrival pattern, company fleet size, and in‐store customer compensation on its performance, that is, service quality and operational cost.

Link: http://dx.doi.org/10.1111/poms.13219 [Google]

 

Rastpour, A., A. Ingolfsson and B. Kolfal (2020): Modeling Yellow and Red Alert Durations for Ambulance Systems, Production & Operations Management, 29(8), pp.1972-1991

Emergency systems are designed to almost always have enough capacity to respond to emergencies. However, capacity shortage periods do occur and these systems need to recover quickly. In this study, we apply queueing models and study whether it is better for an emergency system to add or to expedite servers, in order to quickly recover from a capacity shortage period. We focus on emergency medical service (EMS) systems and use Erlang loss models to study Red Alerts (when all ambulances are busy) and Yellow Alerts (when the number of available ambulances falls below a threshold). We analyze two loss models: one with Markovian state‐dependent service rates and one with generally and independently distributed service times. We validate the two models against EMS data sets from two cities. Despite the fact that the distribution of ambulance service times is a mixture of lognormal distributions, which is far from being exponential, we find that the loss model with Markovian state‐dependent service rates provides a better representation of empirical Yellow and Red alert statistics. We build on the model with state‐dependent rates and use the theory of absorbing Markov chains to quantify the impact of adding or expediting ambulances, with respect to two performance measures: (i) the duration of alert periods, and (ii) the number of lost calls. This quantification helps EMS staff (dispatchers and supervisors) to make better decisions to avoid, and to recover from, alert periods. For example, staff should not wait until a Red Alert before adding ambulances, which is a common practice, because the expected number of lost calls rapidly increases as the number of available ambulances at the action epoch decreases.

Link: http://dx.doi.org/10.1111/poms.13190 [Google]

 

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