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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Huang, M.-H. and R. T. Rust (2021): A strategic framework for artificial intelligence in marketing, Journal of the Academy of Marketing Science, 49(1), pp.30-50
The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.
Rosenbaum, M. S., K. Edwards and G. C. Ramirez (2021): The benefits and pitfalls of contemporary pop-up shops, Business Horizons, 64(1), pp.93-106
As e-commerce growth continues to surpass that of brick-and-mortar retail, temporary retail spaces, also known as pop-up shops, are becoming an important promotional strategy, especially for online retailers and service providers. Success in today’s retail environment means being able to create and maintain brand communities, to generate instant and measurable hype, and to deliver personalized consumer experiences—all of which can be readily achieved through a strategically placed physical business presence. In this study, a survey of retailing organizations worldwide reveals that among those that had implemented at least one pop-up shop, more than 80% considered it a success. The results also show that the primary reasons for activating pop-up shops were to create connections with current and potential customers, to increase brand awareness, to introduce a new product or brand to the marketplace, and to stage a new product or brand. While the respondents deemed revenue generated at pop-up shops important, they considered improving market visibility (e.g., through social media, website traffic, or media coverage) a more significant objective. Given the economic potential of pop-up shops, this research provides retailing organizations with guiding principles for developing and operating successful pop-up shops in the current marketplace.
Babic, B., I. G. Cohen, T. Evgeniou and S. Gerke (2021): When Machine Learning Goes Off the Rails, Harvard Business Review, 99(1), pp.76-84
Products and services that rely on machine learning—computer programs that constantly absorb new data and adapt their decisions in response—don’t always make ethical or accurate choices. Sometimes they cause investment losses, for instance, or biased hiring or car accidents. And as such offerings proliferate across markets, the companies creating them face major new risks. Executives need to understand and mitigate the technology’s potential downside. Machine learning can go wrong in a number of ways. Because the systems make decisions based on probabilities, some errors are always possible. Their environments may evolve in unanticipated ways, creating disconnects between the data they were trained with and the data they’re currently fed. And their complexity can make it hard to determine whether or why they made a mistake. A key question executives must answer is whether it’s better to allow smart offerings to continuously evolve or to “lock” their algorithms and periodically update them. In addition, every offering will need to be appropriately tested before and after rollout and regularly monitored to make sure it’s performing as intended.
Luangrath, A. W., J. Peck and A. Gustafsson (2020): Should I Touch the Customer? Rethinking Interpersonal Touch Effects from the Perspective of the Touch Initiator, Journal of Consumer Research, 47(4), pp.588-607
Previous research has highlighted the effects of receiving interpersonal touch on persuasion. In contrast, we examine initiating touch. Individuals instructed to touch engage in egocentric projection in which they project their own affective reaction onto their expectations for how the recipient will feel (i.e. empathic forecast), how they appear to the recipient (i.e. metaperception), and the evaluation of the interaction itself (i.e. interaction awkwardness). Touch initiators expect that recipients will feel worse with touch, express concern for how they, themselves, will be perceived, and think that interactions are more awkward. Interestingly, touch recipients do not evaluate these interactions more negatively and leave higher tips after having been touched; touch initiators do not expect this to be the case. As a result, instructed touch initiators (vs. volitional touch initiators) are less (more) likely to engage in subsequent interactions with customers, potentially undermining future service provided to customers. Across five studies, four of which involve actual dyadic interactions, we test the consequences of initiating touch with an inquiry into the effects of interpersonal touch on the initiator. We discuss theoretical and managerial implications.
C. Köbis, N., I. Soraperra and S. Shalvi (2021): The Consequences of Participating in the Sharing Economy: A Transparency-Based Sharing Framework, Journal of Management, 47(1), pp.317-343
The sharing economy is estimated to add hundreds of billions of dollars to the global economy and is rapidly growing. However, trust-based commercial sharing—the participation in for-profit peer-to-peer sharing-economy activity—has negative as well as positive consequences for both the interacting parties and uninvolved third parties. To share responsibly, one needs to be aware of the various consequences of sharing. We provide a comprehensive, preregistered, systematic literature review of the consequences of trust-based commercial sharing, identifying 93 empirical papers spanning regions, sectors, and scientific disciplines. Via in-depth coding of the empirical work, we provide an authoritative overview of the economic, social, and psychological consequences of trust-based commercial sharing for involved parties, including service providers, users, and third parties. Based on the aggregate insights, we identify the common denominators for the positive and negative consequences. Whereas a well-functioning infrastructure of payment, insurance, and communication enables the positive consequences, ambiguity about rules, roles, and regulations causes non-negligible negative consequences. To overcome these negative consequences and promote more responsible forms of sharing, we propose the transparency-based sharing framework. Based on the framework, we outline an agenda for future research and discuss emerging managerial implications that arise when trying to increase transparency without jeopardizing the potential of trust-based commercial sharing.
Ingene, C. A., M. E. Parry and Z. Xu (2020): Resale Price Maintenance: Customer Service Without Free Riding, Journal of Retailing, 96(4), pp.563-577
RPM is not always manufacturer-optimal.The optimal RPM strategy can entail a price ceiling or a price floor.The optimal RPM floor may only constrain one retailer.The optimal RPM strategy may increase the profits of the larger retailer. Recent judicial legalizations of resale price maintenance (RPM) allow a manufacturer to establish limits to the prices that its retailers charge: a maximum price (a ceiling) or a minimum price (a floor). A manufacturer can also choose not to apply an RPM constraint. To determine how RPM affects service levels and the distribution of profit between channel members, we derive the optimal RPM strategy for a manufacturer that sells through competing retailers who provide their own optimal level of demand-enhancing customer service. Our analysis yields four key insights into a manufacturer’s use of RPM in the absence of free riding. First, there are conditions for which a manufacturer benefits by applying either an RPM Ceiling or an RPM Floor, but there are also conditions that cause the manufacturer not to intervene in its retailers’ pricing decisions. Second, a manufacturer’s use of RPM depends on how effectively its retailers provide service, and their service-effectiveness disparity. Third, intensity of inter-retailer competition affects the manufacturer’s optimal application of RPM. Fourth, RPM Floors often enhance a large retailer’s profit relative to the absence of RPM – but this does not hold for a small retailer. Our findings generate significant managerial implications, insights into the consequences of laws that allow manufacturers to limit their retailers’ pricing decisions. We also offer suggestions for future research.
Kim, T. T. I. (2021): When Franchisee Service Affects Demand: An Application to the Car Radiator Market and Resale Price Maintenance, Marketing Science, 40(1), pp.101-121
This study provides a novel empirical framework to quantify the effect of a firm’s unobserved endogenous service on demand, in conjunction with endogenous price. It is well understood that a downstream firm’s service can impact the performance of vertical channels. Although many academic works address the service provision of the downstream firm, empirically quantifying the impact has been challenging because the downstream firm’s service is often unobservable to the researcher. I propose a new empirical framework that incorporates the downstream firm’s unobserved endogenous service provision by modifying the standard demand model. I apply this empirical framework to proprietary data from a franchise network in the car radiator market to quantify the downstream firms’ (e.g., franchisees’) endogenous service. Counterfactuals under maximum resale price maintenance (RPM) policies show that the standard demand model ignoring the franchisees’ endogenous service reduction (i.e., service externality) results in more optimistic counterfactual predictions than the developed framework does. Such service externality can be mitigated if the service provision cost is lower for franchisees. Last, I examine boundary conditions: under the extreme regime of maximum RPM aiming to fully extract franchisees’ profit, I find that information asymmetry is a greater concern for the upstream firm within the focal industry. Additionally, when service externality is combined with channel information asymmetry, maximum RPM at such extremes may no longer increase the franchisor’s profit.
Tian, L. and F. M. Feinberg (2020): Optimizing Price Menus for Duration Discounts: A Subscription Selectivity Field Experiment, Marketing Science, 39(6), pp.1181-1198
To optimize price menus for duration discounts, both randomized field experiments and careful modeling of consumer self-selection are critical. Subscription services typically offer duration discounts, rewarding longer commitments with lower per-period costs. The “menu” of contract plan prices must be balanced to encourage potential customers to select into subscription overall and to nudge those that do to more profitable contracts. Because subscription menu prices change infrequently, they are difficult to optimize using historical pricing data alone. We propose that firms solve this problem via an experiment and a model that jointly accounts for whether to opt in and, conditionally, which plan to choose. To that end, we conduct a randomized online pricing experiment that orthogonalizes the “elevation” and “steepness” of price menus for a major dating pay site. Users’ opt-in and plan choice decisions are analyzed using a novel model for correlated binary selection and multinomial conditional choice, estimated via Hamiltonian Monte Carlo. Benchmark comparisons suggest that common models of consumer choice may systematically misestimate price substitution patterns, and that a key consideration is the distinctiveness of the opt-out (e.g., nonsubscription) option relative to others available. Our model confirms several anticipated pricing effects (e.g., on the margin, raising prices discourages both opt-in overall and choice of any higher-priced plans), but also some that alternative models fail to capture, most notably that across-the-board pricing increases have a far lower negative impact than standard random-utility models would imply. Joint optimization of the menu’s component prices suggests the firm has set them too low overall, particularly so for its longest-duration plan.
Minerbo, C., M. Kleinaltenkamp and L. A. L. Brito (2021): Unpacking value creation and capture in B2B relationships, Industrial Marketing Management, 92(), pp.163-177
Although the relationships among different dimensions of value creation, characteristics of dyadic relationships and value concepts are well studied, they have been conceptualized independently and without much linked theorizing. Hence, little is known about how these concepts and their effects interplay with each other. This article takes a configurational approach and investigates how different dimensions of value creation and relationship factors affect value capture. The study draws on an embedded case study encompassing relationships of a focal customer in the financial payments industry with six specialized service suppliers, followed by a Qualitative Comparative Analysis (QCA) of 29 relationship conditions. For both buyers and suppliers, value creation is based on “core” value dimensions, and relationship characteristics, such as power and change in supply strategy. Five different configurations of these constructs represent sufficient conditions to increase value capture, by either negotiating better prices or shifting volume among the parties involved. Focusing on both buyer and supplier perspectives of the same phenomenon, the study increases knowledge on how contextual variables interact in influencing value capture. From a practical perspective, the proposed configurations help managers to choose adequate supply strategies, or better allocate resources according to specific conditions of a business relationship. • Various streams of research on value creation and capture have remained largely independent of each other • A “configurational perspective” was used to understand the combinatory effects of different drivers of value capture • For both buyers and suppliers, value creation is based on “core” value dimensions, power, and change in supply strategy • Five different configurations of these constructs represent sufficient conditions to increase value capture • The study helps managers to better allocate resources in relationships more appropriate to their specific situation
Gebauer, H., A. Arzt, M. Kohtamäki, C. Lamprecht, V. Parida, L. Witell and F. Wortmann (2020): How to convert digital offerings into revenue enhancement – Conceptualizing business model dynamics through explorative case studies, Industrial Marketing Management, 91(), pp.429-441
Equipment manufacturers are currently utilizing new digital technologies such as the Internet of Things (IoT), Artificial Intelligence, or Big Data, for new digital offerings. However, these offerings seldom enhance revenue, because companies struggle with business model (BM) dynamics. By analyzing 27 companies through an explorative case-study approach, the authors consider how companies can successfully achieve revenue enhancement through digital offerings. The result is a threefold framework for revenue enhancement through digital offerings. First, this framework distinguishes between three phases of BM dynamics: 1) augmenting products through a “hardware plus” logic, 2) developing a portfolio of multiple logics for creating customer value, 3) integrating this portfolio through platform logic. Second, the framework emphasizes that three barriers, which we refer to as confidence, mixing, and collaboration barrier, limit the progress from Phases 1 to 3. Third, the framework reveals that each phase contains certain modifications of BM components. In the first phase, companies adapt their BM components slightly, so as to advance toward a “hardware plus” logic. In the second phase, companies embrace more radical BM innovations in order to convert services into an outcome-based BM and develop a new software subscription BM. In the third phase, companies modify BM components in order to integrate the BMs internally and to open them up for external collaboration partners. • We explore how equipment-manufacturing companies can successfully achieve revenue enhancement through digital offerings and we develop a threefold framework. • Our framework distinguishes between three phases of BM dynamics: 1) augmenting products through a “hardware plus” logic, 2) developing a portfolio of multiple logics for creating customer value, 3) integrating this portfolio through platform logic. • We emphasize three barriers limiting the progress from phases 1 to 3, which we refer to as confidence, mixing, and collaboration barrier. • The framework reveals that each phase contains certain modifications of BM components. • Our findings enhance the multifaceted nature of BM dynamics and the interplay between holistic business logic, management cognition and BM components.
Vafeas, M. and T. Hughes (2020): Resource integration: Adopting a paradox perspective to inform the management of tensions in customer resource allocation, Industrial Marketing Management, 91(), pp.596-609
Service-dominant logic maintains that value is created collaboratively through a process of resource integration. Knowledge-intensive business services, the context for this study, are heavily dependent on customer resources for the fulfilment of the value proposition. Value co-creation is compromised when resources are not allocated in appropriate quality or quantity. While there is a growing body of research identifying antecedents to customer resource input, few studies investigate how customers might overcome barriers to resource allocation, particularly when faced with competing demands. This article uses a paradox perspective to explore the management of tensions affecting resource allocation. Empirically, we draw on interviews with service providers to identify perceived resource deficiencies, and with customers to explore resource allocation management. We show that it is possible to manage resource allocation tensions by devising novel solutions that integrate the two opposing demands. In addition, these solutions can result in an ‘augmented’ resource, particularly if the service provider is permitted to influence customer resource deployment. Finally, these novel solutions can activate an unintended by-product or secondary resource, enhancing the relationship between provider and customer. • Paradox theory can help to overcome customer resource allocation dilemmas • Novel integrative solutions can also augment the original resource allocation • The service provider can influence the deployment of the customer resource • Novel solutions can activate an unintended by-product or secondary resource • The secondary resource enhances the relationship between provider and customer
Shiue, W., A. Tuncdogan, F. Wang and J. Bredican (2021): Strategic enablers of service-sales ambidexterity: A preliminary framework and research agenda, Industrial Marketing Management, 92(), pp.78-86
In recent years, service-sales ambidexterity was proposed as a new type of ambidexterity. In particular, the emerging literature on service-sales ambidexterity builds on the contextual ambidexterity literature to argue that the two key activities of a salesperson – that is, service activities and sales activities – can be simultaneously maximized through finding and exploiting synergies between these two activities. While research in this area has so far focused on the drivers of service-sales ambidexterity, our knowledge regarding the strategic enablers of this construct is impoverished. In this paper, drawing upon the dynamic capabilities framework, we devise a preliminary framework of the strategic enablers of service-sales ambidexterity. Then, we further extend that framework by identifying key classes of strategic variables that can potentially enable service-sales ambidexterity and by providing illustrative examples. This paper has contributions to and implications for the literature on service-sales ambidexterity and dynamic capabilities. • Recently, service-sales ambidexterity was proposed as a new type of ambidexterity. • So far research has focused on the drivers of service-sales ambidexterity. • Our knowledge regarding the strategic enablers of this construct is impoverished. • We draw upon the dynamic capabilities literature to devise a preliminary framework. • Then, we extend that framework by identifying key classes of strategic variables.
Saifee, D. H., Z. Zheng, I. R. Bardhan and A. Lahiri (2020): Are Online Reviews of Physicians Reliable Indicators of Clinical Outcomes? A Focus on Chronic Disease Management, Information Systems Research, 31(4), pp.1282-1300
Are Online Reviews of Physicians Reliable Indicators of Clinical Outcomes? A Focus on Chronic Disease Management Policy/practice abstract Because online reviews of physicians have grown in popularity, it is important to ask if such reviews actually convey useful information about the quality of care delivered by physicians. We address this issue by examining whether online reviews of physicians treating chronic conditions are indicative of clinical outcomes, such as inpatient readmission rates, based on a large longitudinal data set of physician reviews. The main finding is that online reviews are not informative in the context of chronic diseases and are not useful from the viewpoint of prospective patients. This is because the outcomes of chronic disease treatment are often not easy to comprehend by patients who have not undergone extensive medical training. Further, unlike outcomes of many episodic treatments, such as surgeries, the outcomes of chronic disease treatments may not be easily discernible to patients, and possible relapses can cloud patients’ assessment of changes in their health conditions. The broader lesson is that consumer feedback is unlikely to be useful in contexts involving credence goods whose quality is difficult to ascertain even after consumption. Our research suggests that prospective patients should use online reviews with caution, and policymakers should make alternative information sources available to facilitate better decision making by patients. Current trends on patient empowerment indicate that patients who play an active role in managing their health also seek and use information obtained from online reviews of physicians. However, it is far from certain whether patient-generated online reviews accurately reflect the quality of care provided by physicians, especially in the context of chronic disease care. Because chronic diseases require continuous care, monitoring, and multiple treatments over extended time periods, it can be quite hard for patients to assess the effectiveness of a particular physician accurately. Given this credence nature of chronic disease care, the research question is the following: what is the information value associated with online reviews of physicians who treat chronic disease patients? We address this issue by examining the link between online reviews of physicians and their patients’ actual clinical outcomes based on a granular admission–discharge data set. Contrary to popular belief, our study finds that there is no clear relationship between online reviews of physicians and their patients’ clinical outcomes, such as readmission risk or emergency room visits. Our findings have two major implications: (a) online reviews may not be helpful in the context of healthcare services with credence aspects; (b) because treatments of chronic diseases have more credence good characteristics when compared with surgeries or other acute care services, one should not extrapolate research on surgeries and acute care services to chronic disease care. Rather, one should acquire a better understanding of the information conveyed in online reviews regarding a physician’s ability to deliver certain clinical outcomes before drawing inferences. Our findings have important ramifications for all stakeholders including hospitals, physicians, patients, payers, and policymakers.
Ke, Z., D. Liu and D. J. Brass (2020): Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision, Information Systems Research, 31(4), pp.1322-1336
Online reviews are a crucial source of information for consumer decision making. Many businesses, companies, and platforms are interested in encouraging more consumers to review their products but are dubious about using financial incentives to buy online reviews. Our research describes a social way of growing online reviews. We show that cultivating an online community for reviewing by showing members reviews written by their online friends cannot only increasing their willingness to contribute but also the quality of the resulting reviews. The takeaways of this study include (1) unlike using financial rewards to incentivize review contribution, the studied approach can motivate review contributions without compromising the quality of reviews contributed; (2) the effect of exposing consumers to their friends’ reviews is comparable to that of Yelp’s weekly newsletters (thus this can be a powerful way of motivating consumer reviews); and (3) to effectively leverage friend reviews, online platforms should facilitate social networking among users and build an online community that recognizes and rewards members who make frequent, high-quality contributions to online reviews. User-generated online reviews are crucial for consumer decision making but suffer from underprovision, quality degradation, and imbalances across products. This research investigates whether friend contributions cues, in the form of highlighted reviews written by online friends, can motivate users to write more and higher-quality reviews. Noting the public-good nature of online reviews, we draw on theories of pure altruism and competitive altruism to understand the effects of friend-contribution cues on review provision. We test our hypotheses using data from Yelp and find positive effects of friend-contribution cues. Users are three times more likely to provide a review after a recent friend review than after a recent stranger review, and this effect cannot be solely explained by homophily. Furthermore, reviews written after a friend’s review tend to be of higher quality, longer, and more novel. In addition, friend reviews tend to have a stronger effect on less-experienced users and less-reviewed products/services, suggesting friend-contribution cues can help mitigate the scarcity of contributions on long-tail products and from infrequent contributors. Our findings hold important implications for research and practice in the private provision of online reviews.
Ho, Y.-J., S. Dewan and Y.-C. Ho (2020): Distance and Local Competition in Mobile Geofencing, Information Systems Research, 31(4), pp.1421-1442
The growing ubiquity of GPS-enabled smartphones has ushered in a new era of location-based services and online-to-offline commerce. Geofencing is one instance of this broader phenomenon, and it is being widely adopted in the context of retail, restaurant, entertainment, and other local services. By targeting users on mobile apps while they are in the vicinity of physical establishments, there is potential for higher levels of engagement and consumption of the products or services on offer. However, the level of consumer interest is likely to depend on distance from the establishment and local competition in the surrounding areas. This study examines the impact of these two factors on consumer response to geofence advertising at different points in the purchase funnel, namely the click stage and conversion phase. Analyzing a rich data set from one of the leading location-based marketing agencies and using a sophisticated Bayesian empirical methodology, we find that having one more competitor in the consumer’s vicinity reduces click-through rate by about 1%, and a 1-mile increase in distance is associated with a 17.6% reduction in conversion rate. These and other results suggest that accounting for distance and local competition in data-analytic mobile targeting would increase both the return on advertising spend and consumer welfare. This research studies the performance of geofencing, a practice where mobile users are targeted within a predefined virtual geographic boundary around an advertiser’s establishment. We argue the significance of distance (i.e., the mileage from a consumer to a focal establishment) and local competition (i.e., the number of alternatives in consumer vicinity) in ad responses. Drawing on the notion of the purchase funnel, we develop a two-stage hierarchical Bayesian model to examine consumer click and conversion choices. A unique data set of geofencing ad impressions is collected from one of the largest location-based marketing agencies in the United States. The results suggest that local competition matters in the click stage, whereas distance influences the propensity of conversion. Quantitatively, one additional competitor in the consumer vicinity zone lowers the click-through rate by 1.03%, whereas a 1-mile increase in distance results in a 17.64% decrease in the conversion rate. We also find a significant interactive effect, whereby a higher degree of local competition amplifies the negative impact of distance on the likelihood of conversions. Additionally, product differentiation ameliorates the effects of distance and local competition, whereas these effects are found to be more prominent during office working hours. This study discovers the stage-varying roles of distance and local competition along the customer journey and offers new directions for more effective location-based targeting.
Alexander, D., C. Boone and M. Lynn (2021): The Effects of Tip Recommendations on Customer Tipping, Satisfaction, Repatronage, and Spending, Management Science, 67(1), pp.146-165
A field experiment involving 94,571 orders from 24,637 customers of an app-based laundry pick-up, cleaning, and delivery service examined the effects of various randomly assigned tip recommendations on consumers’ tip amounts, satisfaction ratings, frequency of return, and bill size. We find that tip recommendations affect tip amounts, but not customer satisfaction, patronage frequency, or bill size, which implies that neither the processes underlying the tip-recommendation effects on tipping nor consumer tipping itself affect these other consumer outcomes. From a practical perspective, these results and conclusions inform efforts to increase or decrease tipping. Recommending larger tip amounts, at least within the $2–$10 or 5%–25% ranges studied here, appears to be a safe means of increasing the amounts customers leave. More generally, altering customers’ tipping behavior will not itself adversely affect those customers’ subsequent satisfaction, repatronage, or spending, as long as the means used to alter tipping do not directly affect these other outcomes. This paper was accepted by John List, behavioral economics.
Mejia, J. and C. Parker (2021): When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms, Management Science, 67(1), pp.166-184
Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders’ gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider’s picture is displayed. Our primary research question is to what extent a rider’s gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers’ behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-Caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community. This paper was accepted by Vishal Gaur, operations management.
Huang, G. and K. Sudhir (2021): The Causal Effect of Service Satisfaction on Customer Loyalty, Management Science, 67(1), pp.317-341
We propose an instrumental-variable (IV) approach to estimate the causal effect of service satisfaction on customer loyalty by exploiting a common source of randomness in the assignment of service employees to customers in service queues. Our approach can be applied at no incremental cost by using routine repeated cross-sectional customer survey data collected by firms. The IV approach addresses multiple sources of biases that pose challenges in estimating the causal effect using cross-sectional data: (1) the upward bias from common-methods variance resulting from the joint measurement of service satisfaction and loyalty intent in surveys, (2) the attenuation bias caused by measurement errors in service satisfaction, and (3) the omitted variable bias that may be in either direction. In contrast to the common concern about the upward common-methods bias in estimates using cross-sectional survey data, we find that ordinary-least-squares substantially underestimates the causal effect, suggesting that the downward bias resulting from measurement errors and/or omitted variables is dominant. The underestimation is even more significant with a behavioral measure of loyalty, where there is no common-methods bias. This downward bias leads to significant underestimation of the positive profit impact from improving service satisfaction and can lead to underinvestment by firms in service satisfaction. Finally, we find that the causal effect of service satisfaction on loyalty is greater for more difficult types of services. This paper was accepted by Juanjuan Zhang, marketing.
Berry, R., M. Honig, T. Nguyen, V. Subramanian and R. Vohra (2020): The Value of Sharing Intermittent Spectrum, Management Science, 66(11), pp.5242-5264
We examine a model of Cournot competition with congestion motivated by recent policy to allow commercial sharing of wireless spectrum that is assigned to other users such as government agencies. A key feature of such spectrum is that it is intermittently available because of the incumbent user’s activity. In our model, wireless service providers (SPs) compete for a common pool of customers using their own proprietary (exclusively licensed) bands of spectrum along with access to an additional intermittent band. When the intermittent band is unavailable, any traffic carried on that band must be shifted to the proprietary bands. Customers are sensitive to both the price they pay and the average congestion they experience across the bands of spectrum from which they receive service. We compare two different access policies for this intermittent band: one in which it is open to all SPs and one in which it is licensed to a single SP. We also allow the band to be divided into multiple subbands where each subband is either open or licensed. We characterize trade-offs between social welfare and consumer welfare that depend on the choice of different access policies and assignments of subbands to SPs. These can involve subtle issues related to the ability of a larger SP to make more efficient use of intermittent spectrum and the increase in competition by assigning more spectrum to smaller SPs. This paper was accepted by David Simchi-Levi, operations management.
Baucells, M. and L. Zhao (2020): Everything in Moderation: Foundations and Applications of the Satiation Model, Management Science, 66(12), pp.5701-5719
Models in which current utility depends solely on current consumption (a.k.a. time-separable preferences) are widely acknowledged to be unrealistic, especially when attempting to describe preferences over consumption rates. Alternatively, one may stipulate that instant utility also depends on a state, for example, some stock of past consumption. Escaping the gravitational pull of time separability, however, is difficult because (1) the behavioral axioms that characterize the state and the instant utility are not known, (2) how to elicit the preference parameters—most notably the initial level of the state and the decay rate—is not known, and (3) managerial applications where state-dependent preferences produce interesting insights and solutions are scarce. This paper makes advances on these three fronts by proposing a novel set of axioms that characterize the satiation model, a proof of concept on how to elicit all preference parameters using consumption rates, and a mixed-integer linear formulation to solve the optimal design of experiential services under satiation. Our preferences introduce a de-satiation motive, absent in separable preferences, and we explore how to optimally manage this motive. This paper was accepted by David Simchi-Levi, decision analysis.
Belavina, E. (2021): Grocery Store Density and Food Waste, Manufacturing & Service Operations Management, 23(1), pp.1-18
We study the impact of grocery-store density on the food waste generated at stores and by households. Food waste is a major contributor to carbon emissions (as big as road transport). Identifying and influencing market conditions that can decrease food waste is thus important to combat global warming. We build and calibrate a stylized two-echelon perishable-inventory model to capture grocery purchases and expiration at competing stores and households in a market. We examine how the equilibrium waste in this model changes with store density. An increase in store density decreases consumer waste due to improved access to groceries, whereas increasing retail waste due to decentralization of inventory increased variability propagation in the supply chain (cycle truncation) and diminished demand by customers. Higher density also induces more competition which further increases (decreases) waste when stores compete on prices (service levels). Overall, consumer waste reductions compete with store waste increases and the effects of increased competition. Our analysis shows that higher density reduces food waste up to a threshold density; it leads to higher food waste beyond this threshold. Put differently, in so far as food waste is concerned, there exists an optimal store density. Calibration using grocery industry, economic, and demographic data reveals that actual store density in most American cities is well below this threshold/optimal level, and modest increases in store density substantially reduce waste; for example, in Chicago, just 3–4 more stores (per 10 sq km) can lead to a 6%–9% waste reduction, and a 1%–4% decrease in grocery expenses. These results arise from the principal role of consumer waste, suggesting that activists and policy makers’ focus on retail waste may be misguided. Store operators, urban planners, and decision makers should aim to increase store densities to make grocery shopping more affordable and sustainable.
Yuan, X., T. Dai, L. G. Chen and S. Gavirneni (2021): Co-Opetition in Service Clusters with Waiting-Area Entertainment, Manufacturing & Service Operations Management, 23(1), pp.106-122
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.
Keyvanshokooh, E., C. Shi and M. P. Van Oyen (2021): Online Advance Scheduling with Overtime: A Primal-Dual Approach, Manufacturing & Service Operations Management, 23(1), pp.246-266
Problem definition: We study a fundamental online resource allocation problem in service operations in which a heterogeneous stream of arrivals that varies in service times and rewards makes service requests from a finite number of servers/providers. This is an online adversarial setting in which nothing more is known about the arrival process of customers. Each server has a finite regular capacity but can be expanded at the expense of overtime cost. Upon arrival of each customer, the system chooses both a server and a time for service over a scheduling horizon subject to capacity constraints. The system seeks easy-to-implement online policies that admit a competitive ratio (CR), guaranteeing the worst-case relative performance. Academic/practical relevance: On the academic side, we propose online algorithms with theoretical CRs for the problem described above. On the practical side, we investigate the real-world applicability of our methods and models on appointment-scheduling data from a partner health system. Methodology: We develop new online primal-dual approaches for making not only a server-date allocation decision for each arriving customer, but also an overtime decision for each server on each day within a horizon. We also derive a competitive analysis to prove a theoretical performance guarantee. Results: Our online policies are (i) robust to future information, (ii) easy-to-implement and extremely efficient to compute, and (iii) admitting a theoretical CR. Comparing our online policy with the optimal offline policy, we obtain a CR that guarantees the worst-case performance of our online policy. Managerial implications: We evaluate the performance of our online algorithms by using real appointment scheduling data from a partner health system. Our results show that the proposed online policies perform much better than their theoretical CR, and outperform the pervasive First-Come-First-Served (FCFS) and nested threshold policies (NTPO) by a large margin.
Delana, K., N. Savva and T. Tezcan (2021): Proactive Customer Service: Operational Benefits and Economic Frictions, Manufacturing & Service Operations Management, 23(1), pp.70-87
Problem definition: We study a service setting where the provider has information about some customers’ future service needs and may initiate service for such customers proactively, if they agree to be flexible with respect to the timing of service delivery. Academic/practical relevance: Information about future customer-service needs is becoming increasingly available through remote monitoring systems and data analytics. However, the literature has not systematically examined proactive service as a tool that can be used to better match demand to service supply when customers are strategic. Methodology: We combine (i) queueing theory, and in particular a diffusion approximation developed specifically for this problem that allows us to derive analytic approximations for customer waiting times, with (ii) game theory, which captures customer incentives to adopt proactive service. Results: We show that proactive service can reduce customer waiting times, even if only a relatively small proportion of customers agree to be flexible, the information lead time is limited, and the system makes occasional errors in providing proactive service—in fact, we show that the system’s ability to tolerate errors increases with (nominal) utilization. Nevertheless, we show that these benefits may fail to materialize in equilibrium because of economic frictions: Customers will underadopt proactive service (due to free-riding) and overjoin the system (due to negative congestion-based externalities). We also show that the service provider can incentivize optimal customer behavior through appropriate pricing. Managerial implications: Our results suggest that proactive service may offer substantial operational benefits, but caution that it may fail to fulfill its potential due to customer self-interested behavior.
Lei, W., K. Gunasti, R. Shankar, J. Pancras and R. Gopal (2020): IMPACT OF GAMIFICATION ON PERCEPTIONS OF WORD-OF-MOUTH CONTRIBUTORS AND ACTIONS OF WORD-OF-MOUTH CONSUMERS, MIS Quarterly, 44(4), pp.1987-2011
Gamification has been shown to encourage contributions of user-generated reviews (word-of-mouth: WOM) in various domains, including travel and leisure related platforms (Foursquare, TripAdvisor), e-commerce (Amazon), and auctions (eBay). WOM contributors write reviews about products/services provided by business venues and WOM consumers read reviews and use them to form attitudes and make purchase decisions. Gamification elements such as points and badges, awarded to WOM contributors for various reasons, and displayed to WOM consumers, have a dual role in WOM context. First, points awarded for user contributions help motivate WOM contributors to increase their participation. Second, badges awarded to users for visiting business venues signal prior experience or competence, and they help determine how WOM consumers perceive WOM contributors and form their judgments based on the reviews. While the first role of gamification (i.e., motivating users) has been widely studied, the impact of WOM presented along with gamification elements on the perceptions and behavior of the target audience, WOM consumers, has not been examined. This is important to businesses that are looking to attract customers. Drawing on social psychology literature, we show that gamification symbols signaling experience that accompany WOM leads to perceptions of positive WOM contributors as more competent. This leads to important changes in behavioral outcomes such as willingness to visit/buy and willingness to recommend the reviewed outlets.
Wani, D., M. Malhotra and J. Clark (2021): Strategic Service Design Attributes, Customer Experience, and Co‐Created Service Choice: Evidence from Florida Hospitals, Production & Operations Management, 30(1), pp.210-234
Services are typically characterized by the direct participation of customers in the production process. The service‐dominant (S‐D) logic argues that value to consumers is always co‐created by service providers and consumers jointly and reciprocally. While prior research has addressed the indirect impact of structural, infrastructural, and technology service design attributes on a customer’s perceived value via the realized service delivery system, an important question remains unanswered: Do these strategic service design attributes directly affect co‐created service choice? In this study, we explicitly consider the extent to which service delivery design characteristics—structural, infrastructural, and technology attributes—directly affect this co‐created choice. Using detailed patient‐level data from Florida hospitals on elective hip and knee surgeries, we find evidence of structural, infrastructural, and technological influences on patient–surgeon co‐created choice of hospitals. We also find that the characteristics of the service encounter—in this case, the interpersonal care experience—interact in essential ways with these design attributes to influence co‐created service choice. Specifically, structural and technological attributes may serve as a substitute for interpersonal care experience in determining co‐created choice of a hospital. These results inform our understanding of how patients choose hospitals for elective surgeries under conditions of information asymmetry. An extended analysis of data for elective heart surgery patients generally supports our framework. Theoretical and practical implications of our results are discussed.
Akçay, Y., Y. Li and H. P. Natarajan (2020): Category Inventory Planning With Service Level Requirements and Dynamic Substitutions, Production & Operations Management, 29(11), pp.2553-2578
We study a single‐period inventory planning problem for a category of substitutable products. This is an important practical problem facing category managers who have to maintain high service levels for constantly expanding product catalogs. We formulate the problem as a stochastic optimization model that minimizes the total stocking cost subject to service level requirements, which consist of product‐specific and category‐wide targets for inventory availability (ready rates) through the selling season. Our model accounts for stochastic customer arrivals, captures stockout‐based substitutions, and determines initial stocking quantities jointly for all products. Recognizing the challenges that these aspects pose in solving the problem, we propose an optimization‐based method that estimates the ready rates using a deterministic approximation and discretizes the selling season into a finite number of time intervals. This novel modeling approach permits us to recast the stochastic optimization model as a deterministic mixed integer linear program that can accommodate several common stockout‐based substitution schemes. We characterize the worst‐case behavior of this approach to develop performance guarantees. We also implemented and applied this model to randomly generated numerical instances featuring different types of product differentiation and varying in parameter values. We observe that the approach is robust to changes in problem parameter values and yields solutions very quickly, outperforming an enumeration‐based alternative, a practical heuristic, and an approach based on extant literature. Finally, we applied our approach to data from a re‐seller of Information Technology products. Results illustrate that our approach scales well and has the potential to generate savings in inventory costs.
Ye, C., C. F. Hofacker, J. Peloza and A. Allen (2020): How online trust evolves over time: The role of social perception, Psychology & Marketing, 37(11), pp.1539-1553
Using customer service scenarios in an online retail context, the current study examines how cognitive and affective trust develop over time and how service failure negatively impacts trust, along with the trust restoration opportunities provided by recovery. Study 1 findings reveal that the relationship between Web site social perception and affective trust is stronger for repeat visitors than for first‐time visitors. Study 2 findings indicate that failure timing and recovery duration play important roles in service failure situations. Overall, the results demonstrate that consumer cognitive and affective trust develop through the number of interactions with a retail Web site over time and that increased Web site social perceptions facilitate the trust‐building process.
Wu, R., X. Han and F. R. Kardes (2021): Special fonts: The competing roles of difficulty and uniqueness in consumer inference, Psychology & Marketing, 38(1), pp.86-100
The use of special fonts in marketing communications may have more complex effects than expected. This study examines multiple effects of special fonts and proposes boundary conditions for the effects. Special fonts are perceived as more unique and difficult to read than regular fonts. Five experimental studies show that whereas the perception of uniqueness decreases the awareness of missing information, leading to more favorable initial judgments but a higher likelihood of regret later, the perception of difficulty has the opposite effects. These competing effects are moderated by contextual cues that vary the salience of uniqueness versus difficulty associated with special fonts. Specifically, consumers are more influenced by the uniqueness of special fonts when they rate the degree of uniqueness before the degree of difficulty or when they evaluate a product category (e.g., a handmade item or a décor) that is generally expected to be unique. On the contrary, they are more influenced by difficulty when they rate difficulty first or when they evaluate a product category (e.g., “a tax preparation service”) that is unexpected to be unique. Implications of the results for understanding the effects of fonts on information processing and consumer inference are discussed.