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The purpose of this study is to identify the business practices and performance of multi-purpose cooperatives in San Jose, Occidental Mindoro and its relationship. Based on the findings of the study, the following conclusions were drawn: The number of multi-purpose cooperatives in San Jose, Occidental Mindoro engage in credit and service operations are 9, however in credit, production and service are 11. The extent of business practices of multi-purpose cooperative is very high in terms of marketing, human resource management, facilities management and total quality management. The level of business performance of multi-purpose cooperative is very high in terms of credit, production and service operation. From the findings and conclusion, the following were recommended: The multi-purpose cooperatives may have a conducive operation by maintaining the lay-out and overall facilities. And in order to have a better performance, the multi-purpose cooperatives may engage also in the production operations, more particularly in producing and providing the basic needs of the clients.

Thanks to the abundant user-generated content on the review websites, customers can easily make a casual inference on the service speed through observing and learning from others' service experiences. We study a service system where customers make their join-or-balk decisions based on a sample set of service speed observations and examine how such a sampling process shapes customer behavior. We show how the amount of information utilized by customers to make informed decisions affects the system performance, including throughput, social welfare, and revenue. In particular, we provide exact conditions under which random sampling of service rates by customers with a finite sample size, or full disclosure of the service rate, is efficient from the perspective of maximizing the throughput or social welfare. First, when the potential system load is high, the throughput is maximized if the sample size is very small; when the potential system load is not high, there is a threshold on the fractional part of the normalized service reward below which revealing the service capacity maximizes throughout and above which a finite sample size maximizes throughput. Moreover, there exists a threshold (that could be one) on the fractional part of the normalized service reward, below which a finite sample size maximizes social welfare and above which revealing the service capacity maximizes social welfare. These analyses yield insights on whether small businesses or a social planner may want to rely on customers' random sampling or simply self-disclose the service capacity information.

We study a gig economy platform's problem of finding optimal compensation schemes when faced with workers who myopically base their participation decisions on limited information with respect to their earnings. The stylized model we consider captures two key, related features absent from prior work on the operations of on-demand service platforms: (i) workers' lack of information regarding the distribution from which their earnings are drawn and (ii) worker decisions that are sensitive to variability in earnings. Despite its stylized nature, our model induces a complex stochastic optimization problem whose natural fluid relaxation is also a priori intractable. Nevertheless, we uncover a surprising structural property of the relaxation that allows us to design a tractable, fast-converging heuristic policy that is asymptotically optimal amongst the space of all policies that fulfill a fairness property. In doing so, via both theory and extensive simulations, we uncover phenomena that may arise when earnings are volatile and hard to predict, as both the empirical literature and our own data-driven observations suggest may be prevalent on gig economy platforms.

E-healthcare platforms start to integrate medical services across online and offline channels, where providers can perform online consultations, schedule patients’ offline visits, synchronize relevant medical records, and finally, answer online inquiries regarding follow-up and recovery anytime and anywhere within one operations management function (i.e., online-offline service integration). In this study, we seek to quantify the effects of online-offline service integration on the e-healthcare providers’ demand and reputational outcomes, noting that it is not altogether clear how the service integration function will affect the providers who adopt such a function in e-healthcare platforms. Leveraging a quasi-natural experiment on an e-healthcare platform, we conducted difference-in-differences analyses in tandem with a variety of matching strategies, including propensity score matching and look-ahead propensity score matching. Further, we explored the moderating roles of provider-specific characteristics. Our results reveal a set of robust and interesting findings: i) e-healthcare providers, on average, experience increases in online demand and decreases in offline demand post online-offline service integration; ii) the service integration function also improves the professional reputation of participating providers; iii) the impact of channel integration on the outcomes are weaker for providers with lower (vs. higher) professional titles; and iv) the providers who specialize in treating chronic (vs. acute) diseases experience greater increases in online demand and reputational outcomes, yet insignificant changes in offline demand. This work contributes to related prior literatures on healthcare operations management, e-healthcare, and online-offline channel integration, offering design implications for the service operations of e-healthcare platforms.

Nonprofit organizations (NPOs) exist to reduce inequalities in public services created by failures in the public and private sectors. Yet, whether NPOs do so is not always clear. In the education sector, researchers have found that NPOs are an avenue through which existing inequalities are maintained, and perhaps perpetuated: Well-resourced schools tend to have the most NPO partners. Schools serving high proportions of low-income students, which have greater need, have fewer NPO partners. An open question is how this happens. In our study, we adapt a process modeling framework (Little's Law), often used to calculate the inventory in production systems, to represent the school's problem of allocating limited time and resources to build and maintain a high inventory of NPO partners. To contextualize the process flow framework in this new setting, we draw on 36 hour-long, one-on-one interviews with principals and teachers from six public and charter schools representing different levels of socio-economic hardship. Our model, taken together with these rich first-hand accounts, contributes an operational understanding of how under-resourced schools come to maintain fewer NPO partnerships than well-resourced schools. Finally, we use our process model as a tool to analyze policy interventions that could re-balance school-NPO partnerships to better promote equity in school services and educational outcomes.

The constitutionality of an all-male military draft is currently being litigated in National Coalition for Men v. Selective Service System. Unfortunately, the effect this issue will have on transgender and non-binary people is largely being ignored. This essay considers how current Selective Service standards are not only psychologically harmful to transgender and non-binary people but also how they negatively affect their ability to receive college financial aid, federal jobs, and job training.

In many service encounters front-line workers (often referred to as gatekeepers) have the discretion to attempt to resolve a customer request, or to transfer the customer to an expert service provider. We study the gatekeeper’s transfer decision using an integrated approach that consists of a field study, an analytical model, and a behavioral experiment. Drawing on a proprietary dataset of interactions between the customers and call center agents of a mid-size US-based bank, we formulate and solve an analytical model of the gatekeeper’s transfer response to different incentive schemes and to different congestion loads and then test several model predictions experimentally. Our experiments show that human behavior matches the predictions qualitatively, but not always in magnitude. Specifically, transfer rates are disproportionately low in the presence of monetary penalties for transferring, even after controlling for the economic (dis)incentive to transfer, suggesting an overreaction to transfer cost. In contrast, the transfer response to varying congestion shows no systematic bias. Taken together, these results advance our understanding of cognitive capabilities and rationality limits on human server behavior in queueing systems.

The COVID-19 pandemic creates an unseen challenge to the world's business operations. In order to survive, companies have taken all kinds of measures. However, for small and medium enterprises, owing to relatively limited resources, surviving COVID-19 requires innovative measures. In this article, we examine two interesting “innovative solutions” which are practiced in Hong Kong as operational strategies to deal with COVID-19. Managerial implications are also discussed.

Wage transparency has become increasingly prevalent across industries as a result of government mandates, companies' voluntary disclosure, and digitization. A commonly cited benefit of wage transparency is that it can prompt underpaid workers to negotiate and thus reduce wage inequality. To empirically address how peer wage information influences wage negotiation and the subsequent implications for wage inequality, we conducted two field experiments on online labor markets where we manipulated information about historical peer wages and assessed workers' responses to job offers. Contrary to predictions of the prior literature, workers become more likely to negotiate not only when they learn that they are paid lower than others (by 34%-132%), but also when they learn that they are paid the same wage as others (by 38%-88%). We conjecture that the latter finding occurs because at least some workers believe they should be paid more than the average due to (warranted or inflated) positive assessments about themselves. This conjecture is supported by a laboratory experiment. Based on our empirical findings, we build a reference-dependent model to describe worker behavior and use it to derive the long-term implications of wage transparency for wage inequality. Our model and the subsequent numerical study reveal that wage transparency may amplify wage inequality by prompting workers who are already highly paid to ask for more. Our work highlights that wage transparency is not necessarily a panacea for wage inequality, and discusses additional measures that governments and organizations may consider along with wage transparency to combat wage inequality.

Service system design is often informed by queueing theory. Traditional queueing theory assumes that servers work at constant speeds. That is reasonable in computer science and manufacturing contexts. However, servers in service systems are people, and, in contrast to machines, systemic incentives created by design decisions influence their work speeds. We study how server work speed is affected by managerial decisions concerning (i) how many servers to staff and (ii) whether and when to turn away customers, in the context of a finite-buffer many-server queue (an M/M/N/k queue) in which the work speeds emerge as the solution to a noncooperative game. We show that a symmetric equilibrium always exists in a loss system (N=k) and provide conditions for equilibrium existence in a single-server system (N=1). For the general M/M/N/k system, we provide a sufficient condition for the existence of a solution to the relevant first-order condition and bounds on such a solution; however, showing that it is an equilibrium is challenging due to the existence of multiple local maxima in the utility function. Nevertheless, in an asymptotic regime in which demand becomes large, the utility function becomes concave and the first-order condition simplifies to that for an infinite buffer (k=∞) system, allowing us to characterize "stable" prelimit equilibria.

Problem definition: We investigate how the degree of standardization (DoS) across service chain-belonging units impacts units’ performance outcomes. We study this question in the nursing home industry, focusing on the DoS in three operational dimensions—customer mix, service offering, and service delivery—and its impact on three nursing home outcomes: financial performance, clinical outcome, and resident welfare. Academic/practical relevance: All service chains must decide how unique or standardized each of their units should be, but it is unclear how this decision affects units’ performance outcomes. We assess standardization strategies in multiple operational dimensions in chain-belonging nursing homes and their impact on important operational outcomes, and generalize our findings to other service industries. Methodology: We use a panel data set covering 11 years (2005–2015) of comprehensive measurements of all nursing homes in the United States. We estimate the relationship between the DoS and important nursing home outcomes using fixed-effect models with instrumental variables and clustered standard errors. Results: We find that chain-belonging nursing homes that (1) customize service delivery and standardize customer mix relative to the chain norm tend to experience improved financial performance; (2) standardize customer mix tend to experience improved clinical outcome; and (3) customize service delivery tend to experience enhanced resident welfare. Managerial implications: To our knowledge, this study is the first systematic multidimensional assessment of multiunit service firms’ standardization strategies from a chain perspective. Our results guide nursing home chains in their (1) strategies of within-chain standardization, and (2) investment decisions in acquiring new properties. Our results also provide broader managerial insights for service chains’ standardization strategies and merging and acquisition activities. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1111 .

Problem Definition: Customers and providers alike often consider one-on-one service delivery to be ideal, assuming – perhaps unquestioningly – that devoting individualized attention maximizes customer engagement and improves outcomes. Alternatively, shared service delivery, in which customers are served in batch, may – through the dynamics of group interaction – lead to increased customer engagement. However, the loss of privacy and personal connection in shared delivery models may undermine engagement, and consequently, outcomes. Academic / Practical Relevance: The engagement dynamics in one-on-one and shared delivery models have not been rigorously studied. To the extent that shared delivery may result in comparable or better engagement than one-on-one delivery, service providers in a broad array of contexts may be able to create more value for customers by delivering service in batches. Methodology: We conducted a randomized con- trolled trial with 1,000 patients who were undergoing glaucoma treatment over a three-year period at a large eye hospital. Using verbatim and behavioral transcripts from over 20,000 minutes of video recorded during our trial, we examine how shared medical appointments (SMAs) – in which groups of patients with similar conditions meet with a doctor simultaneously, and each receives one-on-one care in turn – impact patients’ engagement during their appointments. Results: Patients who experienced SMAs asked 37.2% more questions per minute, made 8.2% more comments per minute, answered 3.6% more questions per minute, and exhibited higher levels of non-verbal engagement across a wide array of measures (attentiveness, positivity, head wobbling or ‘talai taḷḷāṭṭam in Tamil – a South Indian gesture to signal agreement or understanding – eye contact and end-of-appointment happiness), relative to patients who attended one-on-one appointments. Managerial Implications: These results shed light on the potential for shared service delivery models to increase customer engagement and enhance service performance.

Electric vehicle (EV) sharing not only changes how people travel but also provides economic and environmental benefits. It also creates a series of new issues affecting service operations considering the characteristics of EVs. From the EV-sharing supply side, the issues are unreasonable station design, inconvenient charging, and high vehicle relocation costs, while from the demand side, they include insufficient understanding of the factors affecting EV sharing and inaccurate demand forecasts. These issues have hindered the rapid spread of EV sharing, and if development of this industry is to accelerate, they must be resolved urgently. To this end, the most recent literature on EV-sharing systems based on different methodologies are reviewed from the perspectives of supply and demand. According to research theme, first the literature on the supply side of EV-sharing systems is classified into strategic planning (station design—location, number and charging capacity—and fleet size) and daily operations (vehicle relocation and charging management) and integrated modelling for strategic planning and daily operations. Next, the literature on the demand side of EV-sharing systems is categorized into the analysis of factors affecting EV-sharing demand and the methods for forecasting EV-sharing demand. Optimization models and algorithms are surveyed in each theme. Finally, this study not only considers future research directions for EV-sharing systems from the perspectives of supply and demand but also highlights future research opportunities for EV-sharing systems given autonomous driving technology.

COVID-19 virus is a virus that is very dangerous because it can attack the respiratory system, acute pneumonia can even cause death. COVID-19 can also spread quickly both through direct interaction and through media such as inanimate objects. And the government has appealed to stay at home and social distancing. Meanwhile, supermarkets are a very risky place for the transmission of the COVID-19 virus. Therefore, the Supermarket must change its service system to minimize the spread of the COVID-19 virus by implementing digital innovation and dynamic organizations in its service and organizational structure.

This paper studies a pricing problem for a single-server queue where customers arrive according to a Poisson process. For each arriving customer, the service provider announces a price rate and a system wait time, and the customer decides whether to join the queue, and, if so, the duration of the service time. The objective is to maximize either the long-run average revenue or social welfare. We formulate this problem as a continuous-time control model whose optimality conditions involve solving a set of delay differential equations. We develop an innovative method to obtain the optimal control policy, whose structure reveals interesting insights. The optimal dynamic price rate policy is not monotonic in wait time. In particular, in addition to the congestion effect often reported in the literature, i.e., the optimal price rate increases in the queue length (measured by the wait time in our setting), we find a compensation effect, meaning that the service provider should lower the price rate when the wait time is longer than a threshold. Compared with the prevalent flat pricing policy, our optimal dynamic pricing policy improves the objective value through admission control, which, in turn, increases the utilization of the server. We use a real data set obtained from a public charging station to calibrate our model with an objective of maximizing the average revenue. We find that our optimal pricing policy outperforms the best flat pricing policy, especially when the arrival rate is low and drivers are impatient. Interestingly, our revenue-maximizing pricing policy also improves social welfare over the flat pricing policy in most of the tested cases.

Motivated by the popularity of mobile-order-and-pay applications that are especially prevalent in coffee shops, we explore how to design efficient omni-channel service systems — where customers have the option to employ mobile applications for self-ordering — with respect to sojourn times, throughput, and/or social welfare. Our models are two-stage queues with two customer classes: walk-ins require service at both stages, while mobile customers skip the first stage by self-ordering. We identify Pareto efficient prioritization policies, highlighting trade-offs between each class's mean sojourn times. We allow for customers to make strategic joining decisions based on their anticipated delays under an information structure where walk-ins observe the queue length of the first stage, and are left to infer that of the second. Our performance evaluations draw from a wide array of techniques, including steady-state, transient, busy period, and hitting-time analyses, together with matrix analytic methods. We showcase the significance of the choice of prioritization policy on the system throughput and social welfare, and we demonstrate the existence of settings where the (typically beneficial) transformation of a traditional service system to an omni-channel one can reduce throughput.

We consider the rostering decisions of a long-term care facility that assigns a set of interchangeable caregivers working a particular shift to units, and evaluate whether part-time or full-time workers should have higher priority to work in their “home” units. The facility’s objective is to minimize the monthly inconsistency level – i.e., a widely promoted quality metric representing the number of different caregivers working in each unit. We introduce simple rostering heuristics that prioritize part-time or full-time workers, and present a stochastic model of the repeated rostering problem to analytically compare their performance. This analysis supports prioritizing the assignment of part-time workers to their home units, while flexibly assigning full-time workers to minimize the inconsistency level. Using data from over 15,000 shifts worked by certified nursing assistants at three nursing homes, we compare the actual schedules to the consistency-maximizing schedules using hindsight optimization and test the performance of our rostering heuristics via trace-based simulation. This reinforces the superiority of prioritizing part-time workers, observing reductions in the inconsistency level between 20% and 30% in trace-based simulations of historical schedules. We also establish an upper bound for a threshold on the time horizon above which a policy giving assignment priority to part-time workers outperforms one giving priority to full-time workers.

Based on the data of four agricultural census of the United States from 1997 to 2012 and relevant data, this paper has summarized the development and the status quo of the American farms. According to the reality of china’s agricultural development, some suggestions are put forward. First, giving priority to cultivate and develop large-scale business entities. Second, perfecting agricultural socialized service system actively, and fostering subsequent forces agricultural field. On September 26, 2018, the CPC Central Committee and the State Council issued the Strategic Plan for Rural Revitalization (2018-2022), which set out the relevant development requirements of “establishing a modern agricultural operation system, strengthening new types of agricultural operation entities, and facilitating the organic connection between the production of small farmers and the development of modern agriculture”. As a new type of main business entity in China, family farm has been developing rapidly. By the end of 2012, China had 877,000 family farms with 176 million mu of cultivated land managed, accounting for 13.4 % of the country’s contracted arable land. On the whole, however, family farms are still in the initial stage of exploration and development, and many aspects still need to be further improved. This paper summarizes the development of American family farms by using the data of the US agricultural Census from 1997 to 2012, the annual statistics of the US agriculture in 2017 and other relevant data, and analyzes the enlightenment of the US experience to China based on the actual situation of China, so as to provide references for the development of Chinese family farms.

As digitization enables service platforms to access users' information, important questions arise about how digital service platforms should disseminate information to improve platform operations. We contribute to this understanding by examining how providing customer-related information at the beginning of a service encounter affects service capacity and enjoyment. We evaluate this strategy via a field experiment on a live-streaming platform that connects hundreds of millions of individual broadcasters and viewers. When viewers entered shows, we provided viewer-related information to broadcasters who were randomly assigned to the treatment condition (but not to control broadcasters). Our analysis, involving a subsample of 49,998 broadcasters, demonstrates that relative to control broadcasters, treatment broadcasters expanded service capacity by 12.62% by increasing both show frequency (3.31%) and show length (7.10%), thus earning 10.44% more based on our conservative estimate. Moreover, our intervention increased service enjoyment (measured by viewer watch time) by 4.51%. Two surveys and additional analyses provide evidence for two mechanisms: (1) information about viewers presented shortly after their entry enables personalized greetings and facilitates social interaction, which addresses both viewers' and broadcasters’ needs for affiliation and satisfies viewers' preference for personalized treatment; (2) broadcasters receiving viewer-related information have a more vivid depiction of viewers and thus feel that their service is more appreciated and meaningful, which addresses broadcasters’ needs for competence. We also rule out several alternative explanations. Our low-cost, information-based intervention has important implications for digital service platforms that have little control over service providers’ work schedules and service quality.

This research explores how customer needs dispersion influences a firm's intent to outsource, and, if the firm does decide to outsource, how it can retain the potential cost advantages offered by a third-party supplier. Customer service plays a crucial role for firms to retain customers and spur revenue growth. It is, however, often outsourced to a third-party supplier, driven by cost savings and ability to focus on core businesses. While there is a large body of literature studying service outsourcing, the dynamic nature of dispersion in customer needs has received sparse attention in the past but is becoming a major concern due to increased market turbulence. This research develops a normative model to explore key drivers that impact service outcomes with heterogeneous and evolving customer needs over time. We find that customer needs dispersion can accelerate a firm's propensity to outsource, and a firm typically benefits from outsourcing, at least initially. Such benefits, however, can dissipate rapidly over time due to knowledge decay with outsourcing; that is, the firm becomes less certain about the underlying customer needs because it is no longer directly involved in service provision. Monitoring efforts can potentially help the firm to arrest the dissipation of the service benefit over time; there exists, however, a threshold of monitoring effort where the benefit of monitoring emerges only if this threshold is surpassed. Hence, a firm is more likely to benefit from outsourcing when an efficient, consistent monitoring effort is implemented over time.

With evolving mobile technologies, increasing number of firms are running multiple channels to serve customers. Due to the novelty of these systems, questions related to the design of such omnichannel systems and their implications for the firm and customers remain open. In particular, the question of whether or not a firm should disclose queue information to its customers in an omnichannel setting, has not been extensively addressed in prior literature. In this paper, we address some of these open questions of design of omnichannel service system, especially focusing on the issue of congestion information disclosure and its impact on customer channel choice behavior. We benchmark the omnichannel model against a conventional single channel model, and compare these settings in terms of the firm’s throughput and average consumer surplus.

To address financial shortage for public service provision, a government may delegate service provision to a private firm subject to regulation in service price or wait time, or jointly finance, own, and run a service system with the firm. However, the conflict between the government's and firm's objective may lead such public-private partnership to low efficiency or even unappealing consequences. Exploring the interaction between the government and firm under different scenarios thus help identify and understand which scheme can be the best for service provision. We model the service system as an unobservable M/M/1 queue. The interaction between the government and firm under regulation is modeled as a game. We consider two practically motivated regulation instruments, i.e., price and wait time, and two types of interaction, i.e., sequential move (when the government has commitment power) and simultaneous move (when the government's commitment power is absent). The scenario under joint venture is modeled as an optimization problem whose objective is a mix of profit maximization and social welfare creation. While wait time regulation is more efficient than price regulation when the government lacks commitment power, the relationship is reversed when having commitment power. Somewhat surprisingly, price regulation without commitment power may backfire. Furthermore, joint venture outperforms complete privatization (in terms of maximizing welfare), thereby avoiding the possible backfire of price regulation. In some instances, joint venture outperforms all regulation schemes even when the government takes only a small share in the project. Our work uncovers that the government's commitment power plays a critical role in choosing the regulation instrument. Joint venture can be an efficient method to deliver public service, especially when the private system is very congested.

The approaching decline in the U.S. college-age population, sometimes referred to as a “demographic storm,” has been identified as an existential threat to the future of American colleges and universities. This article conducts a model-driven analysis of three plausible college-level responses to declining applications. It draws on systems theory to conceptualize a tuition-dependent college as a complex service system and to develop a system dynamics model that captures key causal interrelationships and multiple feedback effects between faculty, facilities, tuition revenue, financials, reputation, and outcomes. Simulations with the college model suggest that common solutions such as reducing faculty or adding campus facilities may improve the college’s short-term financial position, but they are insufficient to ensure the long-term viability without the continuous tuition hikes. This model contributes to the research literature on the economics of higher education, and model-driven academic management and strategy. It also provides useful implications and insights that can inform policy-makers and college leaders.

We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

The U.S. K–12 public education system has been notoriously hard to improve. Some argue new education technologies (EdTech) can help transform schools for the better. Yet, as large-scale policy reforms have had only limited success, it seems unlikely that small changes from EdTech could have any measurable impact. In this paper, we study DonorsChoose, a nonprofit that operates a teacher crowdfunding platform. We ask whether DonorsChoose improves educational outcomes, specifically at low-income schools. Combining DonorsChoose data with data on student test scores in Pennsylvania from 2012–2013 to 2017–2018, we find that an increase in the number of DonorsChoose projects funded at a school leads to higher student performance, after controlling for selection biases. For a school with zero funded projects, one funded project—of about $400 in value—translates to between two to nine more students scoring basic and above in all subjects in high school and science and language arts in primary and middle school. We find this effect is driven mostly by low-income schools, indicating funded projects help close the gap in educational outcomes between students at low- versus high-income schools. Based on a textual analysis of 20,000 statements from all funded teachers describing how project resources are used, we find two channels of improvement most effective in the lowest income schools. We demonstrate that, although DonorsChoose projects are small, they improve outcomes and reduce inequality because they come directly from frontline workers—teachers—who know most intimately the obstacles their students face and how to help. This paper was accepted by Charles Corbett, operations management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4331 .

Smart learning services offer customized learning solutions by systematically considering the context of the learner. However, it is still unclear how the value co-creation within smart learnings is affected by context considerations. Therefore, it is important to understand how the context can influence the value co-creation potential in smart learning services. To investigate how context shapes value co-creation smart learning services, we first conduct a systematic literature review to investigate context factors within learning processes. Afterwards, we develop a conceptual model to explain how context in smart learning systems can be used to enhance the co-creation potential in learning processes. Overall, we provide a better understanding of context considerations in service systems as a theoretical contribution of our conceptual investigation. Finally, we provide practical implications for educational service providers for designing smart learning services under the specific consideration of different contexts

In many service encounters, front-line workers (often referred to as gatekeepers) have the discretion to attempt to resolve a customer request or to transfer the customer to an expert service provider. Motivated by an incentive redesign at a call center of a mid-size US-based bank, we formulate and solve an analytical model of the gatekeeper's transfer response to different incentive schemes and to different congestion levels. We then test several model predictions experimentally. Our experiments show that human behavior matches the predictions qualitatively, but not always in magnitude. Specifically, transfer rates are disproportionately low in the presence of monetary penalties for transferring, even after controlling for the economic (dis)incentive to transfer, suggesting an overreaction to transfer cost. In contrast, the transfer response to congestion information shows no systematic bias. Taken together, these results advance our understanding of cognitive capabilities and rationality limits on human server behavior in queueing systems.

In today’s VUCA world, that is characterized by high volatility, uncertainty, complexity and ambiguity, service provisioning is required to realize flexible and adaptable reconfiguration of service delivery systems and its stakeholders’ resources. However, services are often embedded in greater service systems and the context information of both customer and service provider form both its boundary conditions the suitable solution service. To capture the complexity and leverage the dynamic of service systems, we propose the formal service system model (SSM) method. Following general systems theory, we define boundaries for service delivery and show SSM’s applicability for ad-hoc service operations. We show its usefulness for structuring a service system for service operations, specifically scheduling, planning, and pricing of service provisioning. We contribute to service systems engineering by applying one generalizable mathematical model for both structuring and operationalizing service systems and provide insights in-to capturing the complex relationships of its components

The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that, for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance. This paper was accepted by Charles Corbett, operations management.

Voice-based artificial intelligence (AI) systems have been deployed gradually to replace traditional interactive voice response (IVR) systems in call center customer service, but little evidence exists on how the implementation of AI systems impacts customer behavior, as well as AI systems’ effects on call center customer service performance. Leveraging the proprietary data from a natural field experiment, we examine how the introduction of voice-based AI affects call length, customers’ demand for human service, and customer complaints in the call center customer service of a large telecommunication service firm. We find that the implementation of the AI system significantly increases call length and decreases customer complaints. Although the AI-based service system presumably reduces users’ efforts to transfer to human agents, we do not find any significant increase in customers’ demand for human service. Furthermore, our results show interesting heterogeneity in the effectiveness of the AI-based service system. For simple service requests, the AI-based service system reduces customer complaints for both experienced and inexperienced customers. For relatively complex quests, customers learn from prior experience of interacting with the AI system, and this learning effect leads to fewer complaints. Moreover, the AI-based system exerts a significantly larger effect on reducing customer complaints for older and female customers, as well as for customers who are experienced in using the IVR system. Finally, in examining details in customer-AI conversations, we find that speech-recognition failures in customer-AI interactions result in an increase in customers’ demand for human service and customer complaints.

This fictional, international case explores online delivery models through the voices of two female protagonists: Melis Aydin, COO, who is from Turkey; and Athena Galini, CEO, who is from Greece. They have recently begun an online grocery-delivery (OGD) service called GidaExpress (i.e., Express Food) by closing on a TRY300,000 low-interest loan and entering a start-up incubator in Istanbul, Turkey. Aydin and Galani are evaluating three models for hiring drivers—all full-time employees who can be trained (and perhaps provide more consistent and higher-quality customer service); all independent workers who are paid a wage rate that escalates in order to meet demand; or a hybrid of the two. This story allows for a discussion of the strategic trade-offs between investing in a full-time workforce and sourcing independent workers of unknown quality using a fluctuating wage rate, and how these decisions impact the ability of the business to deliver on the value proposition. Excerpt UVA-OM-1635 Dec. 3, 2019 GidaExpress: Grocery Delivery in Turkey You Can't Spend It Twice Melis Aydin, COO, and Athena Galini, CEO, sat together in their new conference room in a start-up incubator in Istanbul, Turkey. The two women had just finalized a TRY300,000 low-interest loan through a program offered in a partnership between the Turkish government and the European Bank for Reconstruction and Development (EBRD) to start their new business, GidaExpress. GidaExpress, roughly translated as "express food," would be an online grocery-delivery (OGD) service. The business model for GidaExpress was similar to that of US-based InstaCart, in which users placed orders online, available drivers who were also professional shoppers accepted the orders, and then those same drivers performed the shopping and delivered groceries to the customers. Several years prior, Yemeksepeti, a Turkish food-delivery service, had been acquired by Delivery Hero for USD589million, and GidaExpress's founders were enthusiastically hoping to duplicate this success in the OGD market. Aydin, who was from Turkey, and Galani, who was from Greece, had met while studying accounting at the London School of Economics. Aydin had then gone on to earn her MBA at the Darden School of Business at the University of Virginia, while Galani had earned hers at the University College Dublin School of Business. They planned to purchase a basic but functional software platform from another former classmate to meet their needs, but also would require some additional investment to modify for the Turkish market. The friends were now building their first venture together on a sweat-equity basis, and had recently experienced the first serious disagreement of their partnership. They had only TRY150,000 budgeted for payments to drivers, and, while both of them wanted to stretch these funds as far as possible, they had very different ideas on how to accomplish this. (See Table 1 for the initial company budget.) Once that money was spent, if their fledgling venture wasn't pulling in enough money to support their other business functions, they would run out of money. . . .

A quality-speed trade-off occurs in many service systems in which employees have discretion over their service times. An increase in service time often increases the quality of the service but also increases congestion. We investigate this trade-off between quality and speed when the service rate can change over time based on exogenous future demand changes. This work is the first analysis of the quality-speed trade-off with exogenous time-dependent demand. It shows how service providers can benefit from optimizing service rates based on future demand changes. We derive structural optimality results from an analysis of stationary stochastic systems. For time-dependent demand, a time-dependent performance approximation based on a stationary backlog-carryover approach is integrated into an optimization model. In addition, we present a smoothing heuristic that employs the stationary results. The impacts of different service value functions and congestion measures are investigated. For waiting costs, which are based on the expected number of customers in the system, the optimal stationary service rates increase in the demand. Counterintuitively, for waiting costs, which are based on the expected sojourn time, the optimal stationary service rates first show a decrease and then an increase in the demand. For time-dependent service rates, the integrated model delivers reliable results. Even though such service systems cannot produce to stock, future demand changes influence time-dependent service rates several periods beforehand. Substantial benefits compared to stationary approaches can be achieved by considering time-dependent demand in the decision on service rates, especially for cases with a low waiting cost and a moderate to high sensitivity of quality to the service rate.

Consumers tend to perceive a service of higher value when more consumers, under the influence of others, make the same choice. In this paper, we investigate the price and capacity decisions in a service system under the influence of social interactions. We model such a service process by an M/M/1 queue system with observable queues. Consumers perceive a positive (negative) value of the service if the observed queue length is longer (shorter) than the base queue length --- an anchor point in their value perception. We examine the effects of the intrinsic value of the service and the consumer's value perception (in terms of the queue length anchor point) on the price and capacity decisions under the influence of social interactions. We also quantify the potential gain or loss in profit when the influence of social interactions is ignored. Theoretical results are derived to guide the price and capacity decisions to exploit the influence of social interactions. It is found that there exist delicate dynamics among key parameters and variables, when ignored, that could lead to undesirable outcomes. The study offers new insights to balance the demand-inducing effect and the congestion-aggravating effect in a service system under the influence of social interactions.

Small businesses usually have sustainability difficulty. It is, therefore, the main aim of this study to assess the sustainability indicators of a small restaurant business, in particular, is a coffee shop in Thailand named Montreux Cafe and Farm. It specifically dealt with four indicators, such as the marketing promotional strategy, the service/product mix, the service environment, the service physical environment, and the service delivery system. The study utilized the qualitative way of research through interviews and observations. Reviews and comments were also gathered. It was found that the four indicators are the ones given attention in order for the coffee shop to stay within five years or beyond. It is then recommended that the small restaurant business to become sustainable should use social media for its marketing promotional strategy and pay more attention to what the customers would expect to experience in terms of the service/product mix, service physical environment, and service delivery system.

We study an integrated data center network design problem for Internet-related services and cloud computing, of which the objective is to minimize total operating cost and service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features such as latency, power, multiple resources, configuration limits, and interdependent footprints. We formulate the optimal design problem as a nonlinear mathematical program. We analyze a queueing model to approximate the service latency and provide tractable reformulations. We further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem to enhance computational efficiency for large-scale problems. Our numerical studies demonstrate that the proposed integrated model, which jointly optimizes location, allocation, and resource provisioning, can significantly reduce cost and improve quality-of-service when compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state-of-the-art commercial software in computational performance. Based on real-world data sets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.

The Philippine Disaster and Risk Reduction Management Act of 2010 mandates for a multi‐stakeholder participation in the development, updating and sharing of DRRM information system, while the Climate Change Act of 2009 directs government agencies and other institutions for the use of database technology for risk assessment. In line with these legal frameworks, the National DRRM Plan was formulated to serve as a guide on how sustainable development can be achieved through inclusive growth while building the adaptive capacities of communities; increasing the resilience of vulnerable sectors; and optimizing disaster mitigation opportunities with the end in view of promoting people’s welfare and security towards gender-responsive and rights-based sustainable development. This study came up with an Electronic Learning Resource Information and Communication (eLRIC) having a Decision Support System (DSS) capability for the Eastern Visayas State University. It is a simplified and resilient electronic warehouse of vital institutional documents, most importantly signature-bearing and/or official-seal-bearing ones. It is best to be implemented in offices with mountains of physical records that are difficult to reproduce when original copies are either damaged or lost. A safety mechanism of the system offers redundant off-site and on-site backup procedure for compact document safekeeping. Retrieval of shared documents via a web browser is possible anywhere. As a descriptive research, this technology introduced an eLRIC that offers portability, accessibility, convenience thru a simple, interactive and user-friendly interface that is expected to solve the problems that could be encountered during disaster, especially the loss of documents due to physical and electronic damages. Its outcome includes the deployment of a virtual library system, a virtual office transactional management service system, a research data reservoir and configure an electronic Decision Support Systems (DSS) for its administrators/supervisors and staff. The development of this application could help much the University in monitoring programs, projects and activities related to disaster risk reduction management and climate change adaptation and mitigation, as well the management of vital institutional information and data.

Despite their pervasive use in practice, the operational implications of offering free trials are not well understood. Motivated by this, we consider a service provider that has the option of offering free trials to a new market while catering to an existing market of price and delay sensitive customers. Then, we pose the following question: Under what conditions is offering free trials beneficial and what is the extent of this benefit? This question is relevant because with free trials the firm essentially gives away part of its capacity to potentially generate more revenue in the future. Hence, it is not apriori clear if offering free trials is beneficial. To answer these questions, we pursue an asymptotic analysis that allows us to glean novel insights as to when and how a large-scale service firm should offer free trials: When the firm offers free trials with no control, we find, somewhat surprisingly, that offering free trials is beneficial only for small free trial market sizes with correspondingly small relative benefits. When capacity can be adjusted, we derive tractable expressions that can accurately determine when offering free trials is beneficial and the extent of this benefit. Consequently, we establish that offering free trials is always beneficial when capacity can be adjusted below a cost threshold. Thus, we conclude that service firms should opt to simultaneously increase capacity as free trials are offered to unlock the expected benefits, and otherwise not offer free trials at all.

Purpose: The purpose of this paper is to examine the relationships of service marketing mix (SMM) as service input and service output in terms of students’ performance, satisfaction and referral act in context to higher and technical education (HTE) through the application of structural equation modeling. Design/methodology/approach: A quantitative research, conducted through a self-administered survey composed by a closed-ended structured questionnaire, was incorporated for the students who were enrolled in the technical educational institutions situated in the Khandesh region of India. Findings: The findings of this study revealed that traditional SMM is statistically linked with the performance of students in terms of skill and knowledge enhancement, satisfaction and referral act of students, which are perceptible new emerging SMM; performance, pleasure and pointing out in terms of service output. Practical implications: Integrating SMM as service input and service output are productive for HTE in enhancing growth (quantitatively) by the inclusivity of diversified students and development (qualitatively) by enhancing their performance for global standing, making them satisfied and motivating them for recommending their institution to others. This integration can be utilized as a yardstick by the institutions for staying ahead in students’ market with a distinctive competitive advantage. Social implications: Growth and development of HTE will raise a society’s quality of life and thereby increase a country’s socio-economic status. Originality/value: The study has exhibited SMM as input and output of a service system that is useful for the growth and development of HTE. The measurement tool presented is effective in (re)framing policies on SMM as service input based on desired service output.

We model and evaluate an emergency service system in which cross-trained fire-medics respond to both fire calls and medical emergencies. Compared to the traditional system, adding the server flexibility may be beneficial in terms of both cost savings and response time performance. A cross-trained fire-medic may be in one of three states: available, busy at an emergency medical incident, or busy at a fire call. We develop an exact spatial queuing model and an iterative approximation method that decouples the problem to two simpler systems. We prove that our method has a unique fixed point and provide the convergence condition. We demonstrate numerically that our algorithm will always converge in a few iterations. In constructed examples, performance errors are very small compared to the exact model. We apply our model to the fire-medic system in St. Paul, MN. and find close agreement between predicted and actual average response times. We compare St. Paul’s system to a traditional system having separate engine and medical units with the same number of personnel, and show the very large response time reductions achievable under the fire-medic system. The fire-medic approach and our modeling have widespread applicability to other cities.

In manufacturing and service operations, flexibility is beneficial for matching supply with demand, but it comes at a cost. In modern digital workplaces, agents possess a spectrum of different skills associated with corresponding and variable task preferences: Some are inclined to give up part of their payment to avoid unfavorable matches. The platform manager, in turn, gains extra freedom in allocating tasks by possibly charging servers for a favorable assignment. Innovative marketplaces facilitate task discretion and seek for novel and beneficial implementations in the platform design. This naturally leads to the problem of exploring and optimizing the task allocation process. We introduce an innovative mechanism for task assignment in the workplace, and compare it against the traditional one where task routing is solely the platform's decision. To improve all users' welfare, agents are allowed some task discretion in exchange for a fee. We model different working environments and servers' preferences via different distributions, and we study how the agents' preferences, the task cost, and the flexibility fee, affect the equilibrium assignment. In a multi-server system, a server may request autonomy and choose the costly-to-the-platform option, considering the other agents’ behavior. Thereupon, it may or may not be beneficial for the platform when the agents' and the platform's preferences are misaligned. In every case, by pricing and offering flexibility the platform can do at least as well as the no-flexibility scheme. An important conclusion is that pricing task discretion can often improve the agents' welfare and the labor platform's profit.

The notion of omnichannel, an integration of brick-and-mortar stores with online channels, has been thriving in recent years and is reforming the traditional service industry. Many service chains, such as Starbucks and McDonald's, established omnichannel capability by allowing customers to order online in advance before visiting stores for pick-up. The premise of omnichannel services is that when customers take advantage of the low-cost-of-waiting online channel, both their utility and the provider's revenue will increase. Although simply adding an online ordering option to the conventional walk-in model stimulates revenue, it also inflicts interference on the walk-in channel. We show that online ordering inadvertently reduces customers' individual utility and social welfare when both channels are used in equilibrium. Moreover, the less it costs to order and wait online, the more the social welfare is reduced. We then evaluate two industry state-of-the-art operational remedies: regulating the use of the online channel and establishing channel-dedicated capacities. While both remedies may improve the throughput over the walk-in-only service or even the first-come-first-served omnichannel service, they are unlikely to achieve this without jeopardizing the social welfare. We thus propose prioritizing walk-in customers and show that such prioritization can deliver this premise, i.e., simultaneously benefiting the service provider and customers in comparison with the conventional walk-in-only service when both channels are used in equilibrium.

Problem Definition: A growing number of businesses are being built around a model that provides consumers access to a service up to a specified amount (an allowance). Examples include cloud-based data storage, mobile phone plans, fractional ownership of vacation homes, jets, and boats, among others. In most cases, the firm providing the access offers consumers a menu of prices and usage allowances to chose from. The realized usage of consumers is often uncertain with some consumers experiencing a need for usage that is below their allowance while others experiencing a need for usage that exceeds their allowance. This offers an opportunity for a marketplace to emerge, in which consumers trade unused capacity among each other. We study the effect of such a marketplace on service providers, consumers, and social welfare. Academic/Practical Relevance: As more firms adopt an allowance-based business model, there are more opportunities for peer-to-peer trading marketplaces to emerge. However, there is a debate on whether or not peer-to-peer trading of excess capacity is beneficial to service providers and consumers. Our paper sheds light on this debate and identifies conditions under which the existence of such marketplaces can be a win-win situation for all parties. Methodology: We develop a game-theoretic model in which consumers participate in a simultaneous coordination game. Consumers are strategic and take into account the opportunity of purchasing or selling extra capacity on the trading market. Our model captures the heterogeneity of consumers’ demand and the service provider’s ability to modify service plans in view of this trading among consumers. Results: We compare equilibrium outcomes with and without trading and show that outcomes with regard to service provider profit, consumer surplus, and social welfare are crucially dependent on service cost and trading price. A service provider would benefit from trading as long as the trading price is not too low (a low trading price encourages more consumers to opt for the low plan) and the service cost is not too high (a high service cost makes increased consumption due to trading too costly). A trading price that is too low can decrease consumer surplus and social welfare. Hence, a social planner would be interested in inducing a moderate or high trading price. In settings where the service provider is able to modify prices, consumers are no longer guaranteed to benefit from trading. In this case, trading can hurt consumers if the trading price is either sufficiently high (resulting in consumers paying a higher price for the higher plan) or sufficiently low (resulting in less consumption because more consumers opt for the low plan). Managerial Implications: Our results provide guidance to service providers, consumers, and policymakers as to when peer-to-peer trading may or may not be beneficial. The results highlight the important interplay between trading price and cost of service in determining various outcomes. For policymakers, the results can be useful in pointing out when such trading improves outcomes for consumers or social welfare and to potential policy levers that could be deployed to affect outcomes.

Problem Definition: Omnichannel retailers can fulfill online orders by allowing consumers to have their orders delivered from the retailers’ stores to the consumers’ homes (home fulfillment) or collect the orders at the stores (in-store fulfillment). Because the latter option does not involve costly last-mile deliveries to consumers’ homes, retailers have sought to make it more attractive by waiving the associated in-store fulfillment fees indiscriminately to consumers. We analyze the economic value of this strategy and compare it with that of a strategy we develop based on the use of targeted incentives to induce individual consumers to switch to in-store fulfillment after initially choosing home fulfillment for their orders. Academic/Practical Relevance: We contribute an analysis of the economic value of price incentives involving home and in-store fulfillment options in omnichannel retailing. In so doing, we isolate determinants of consumer preferences between these options and show how retailers can use such preferences to develop pricing strategies to improve the economic value of their fulfillment services. Methodology: We use field data to evaluate the pricing strategies based on econometric models as well as machine-learning and routing algorithms. Results: While the removal of in-store fulfillment fees increases revenues from additional demand, these gains are much lower than the losses caused by (1) the revenue lost from the fees no longer collected and (2) the additional operating costs at the stores necessary to fulfill a higher volume of orders. Conversely, the costs from using targeted incentives are lower (almost 3 times) than the savings in last-mile delivery costs. Managerial Implications: To promote in-store fulfillment as an alternative to home fulfillment, retailers must go beyond incentive strategies that reward consumers indiscriminately, regardless of the value they attach to these fulfillment services. We show how retailers can develop dynamic pricing solutions to generate value in these services.

Problem Definition: Customers arriving at a service provider are faced with a queue. On joining the queue, they get to observe the service speed while waiting in the queue. They renege if the updated wait times are too long. When and how should a revenue-maximizing service provider share service information? How does information improve or destroy consumer welfare when customers may renege? Academic/Practical Relevance: Although reneging strategies are intuitive, a rational reneging model is highly non-trivial (Hassin 2016, pp. 299). This paper models customers’ rational reneging decisions in observable queues which is void in the extant literature. Broadly, the research contributes to informationstructural issues in service operations. Methodology: Our modeling framework employs a Markovian queue with two classes of customers. Results: We fully characterize queueing dynamics in the presence of reneging customers and population characteristics. We provide analytical characterization of when a service provider should reveal the service speed information to the customers, based on reneging actions. We show that failing to account for reneging can lead to a wrong decision, and hence a significant revenue loss for the server. Interestingly, providing more information does not always benefit the customers. A calibrated provision of service speed information can realign opposing incentives and help both the firm and customers. Managerial Implications: The server’s revenue decreases and consumer welfare increases with respect to how fast customers are able to observe the service speed in-queue, but the service provider may find it beneficial to reveal the service speed information depending on the patience threshold distribution of the customers who join the queue in the first place. When the patience threshold distribution is either low or highly dispersed, it creates a stronger incentive for the service provider to reveal the service speed information. This result holds for any patience threshold distribution.

Problem Definition. Putting customer experience at the heart of service design has become a governing principle of today’s “experience economy.” Echoing this principle, our paper addresses a service designer’s problem of how to select and sequence activities in designing a service package. Academic/Practical Relevance. Empirical literature shows an ideal sequence often entails an interior peak; that is, the peak (i.e., highest-utility) activity is placed neither at the beginning nor the end of the package. Theoretic literature, by contrast, advocates placing the peak activity either at the beginning or the end. Our paper bridges this gap by developing a theory accounting for interior peaks. It also provides managerial implications for activity sequencing and selection. Methodology. We model the activity sequencing and selection problem as a nonlinear optimization problem and reformulate its objective as an additive function to generate structural insights. Results. We show heterogeneity in memory decay explains the phenomenon of interior peaks. The optimal sequence is in either an “IU” or “UI” shape. An interior peak is optimal when the memory-decay rate of the peak activity is neither too high nor too low. Managerial Implications. Our research sheds light on service sequencing by weighing the phenomenon of interior peaks. In the presence of an interior peak, we show it is optimal to schedule a low point immediately before or after the peak activity, creating a contrast in customer experience. In addition, interior peaks arise partly because the peak activity is more memorable than others. Guided by this logic, as the peak activity becomes even more memorable, one might be tempted to move it to an earlier slot; we show, counterintuitively, moving it to a later slot can be optimal. Our research also provides implications for activity selection by showing the optimal portfolio may consist of activities with the highest and lowest utility values, but not those with medium values.

Problem definition: Firms providing products and services to low income Base of the Pyramid (BOP) customers are increasingly utilizing independent contractor agents rather than employees in their distribution models. We empirically investigate the best way to help agents perform better. Academic/Practical Relevance: BOP customers represent one-third of the world's economy, but make 5 USD or less daily. Providing goods and services to these customers is difficult for traditional firms because most retail activity occurs at small-scale independent outlets. Improving agent performance can help firms reach customers in this environment. We enhance the literature on agent-based models in BOP settings, decision making, technology in developing economies, and field experiments. Methodology: In partnership with a Tanzanian mobile money operator, we perform a randomized controlled trial with 4,771 agents to examine how differing types of guidance, and whether in-person training is offered, impact agents' inventory management. Mobile money is a platform whereby firms in developing economies provide financial services to customers via cell phones. Mobile money agents service customer withdrawals and deposits as branchless banking outlets. Every day, they decide how much money to stock to service customers' transactions, from which they earn commissions. Results: We find that those agents given only explicit recommendations (as opposed to summary statistics or both) who were invited to in-person training (as opposed to simply received an automated notification) improve their performance. Agents in other treatments showed no statistically significant change. The effect is concentrated in agents who never replenished their money at a bank, and whose money inventory levels were low in the pre-treatment period. Managerial Implications: We show empirically how firms can better manage agents, thereby improving the value proposition of serving BOP customers. We show the utility of segmentation based on agent heterogeneity. This can improve firm performance, agent profits, and customer service.

Recent years have witnessed the rise of queue-scalping in congestion-prone service systems. A queue-scalper has no material interest in the primary service but proactively enters the queue in hopes of selling his spot later. This paper develops a queueing-game-theoretic model of queue-scalping and generates the following insights. First, we find that queues with either a very small or very large demand volume may be immune to scalping, whereas queues with a non-extreme demand volume may attract the most scalpers. Second, in the short run, when capacity is fixed, the presence of queue-scalping often increases social welfare; can increase or reduce system throughput, but tends to reduce consumer surplus. Third, in the long run, the presence of queue-scalping motivates a welfare-maximizing service provider to adjust capacity using a "pull-to-center" rule, increasing (reducing) capacity if the original capacity level is low (high). When the service provider responds by expanding capacity, the presence of queue-scalping can increase social welfare, system throughput, and even consumer surplus in the long run, reversing its short-run detrimental effect on customers. Despite these potential benefits, such capacity expansion does little to mitigate scalping and may only generate more scalpers in the queue. Finally, we compare and contrast queue-scalping with other common mechanisms in practice, namely, (centralized) pay-for-priority, line-sitting, and callbacks.

Problem definition: We consider a service system in which customers must travel to the queue to be served. In our base model, customers observe the queue length and then decide whether to travel. We also consider alternative information models and investigate how the availability of queue-length information affects customer equilibrium strategies, throughput, and social welfare. Academic/Practical Relevance: A common assumption in queueing models is that once a customer decides to join the queue, joining is instantaneous. This assumption does not fit real-life settings, where customers possess online information about the current wait time at the service, but while traveling to the service, its queue length may change. Motivated by this realistic setting, we study how queue-length information prior to traveling affects customers' decision to travel. Methodology: We prove a symmetric equilibrium exists in our base model. We perform the calculation numerically as a result of the model complexity, which is due to the fact that the arrival rate to the traveling queue depends on the current state of the service queue, and vice versa. The alternative models are tractable, and we present their analytical solution. Results: When customers can observe the service-queue length prior to traveling, their probability of traveling is monotonically non-increasing with the observed queue length. We find customers may adopt a generalized mixed-threshold equilibrium strategy: travel when observing short queue lengths, avoid traveling when observing long queue lengths, and mix between traveling and not traveling when observing intermediate queue lengths, with a decreasing probability of traveling. Managerial Implications: Our results imply that when system congestion is high, the provider can increase throughput by disclosing the queue-length information, whereas at low congestion, the provider benefits from concealing the information. With respect to social welfare, queue-length information prior to departure is beneficial when congestion is at intermediate to high levels, and yields the same social welfare otherwise.

Problem Definition: We explore how to design professional services in markets that cater to customers with differential usage capabilities. Professional customers avail the service and tailor it to their use, whereas amateur customers, lacking the relevant expertise, obtain the service through a third-party intermediary. Such differential service is germane to the burgeoning industry of Infrastructure as a Service (IaaS), such as the cloud computing service, where professional customers build their own computing infrastructure to meet their needs. The configuration of such infrastructure, however, is technically challenging to amateur customers, who therefore procure the service through an intermediary. Academic/Practical Relevance: Much of the existing literature on service operations focuses on services that can be readily used upon a purchase. Our paper examines a novel perspective, as little is known about how to optimally position professional services to markets mixed with professional and amateur customers. Methodology: We develop a queuing-game-theoretic model to study such services. Results: We identify the proportion of amateur customers as a critical driver of equilibrium outcomes, prices, and revenue. If a single price is enforced, a sufficient base of amateur customers allows professional customers to "free-ride". Price discrimination allays free-riding but may drive prices downward compared to the optimal single price. Typically, firms prefer the high profit margin conferred by professional customers. Despite this preference, reallocating capacity to prioritize amateur customers can bring additional revenues and improve social welfare relative to the First-Come-First-Serve policy or prioritizing professional customers. Managerial Implications: Our results offer normative guidelines for managing professional services, clarifying regimes for exclusive servicing, price discrimination, and prioritization. For instance, we advocate that prioritizing amateur customers can lead to improved market coverage and a higher utilization of server capacity.