We study a seller managing a finite inventory of two substitutable products over a finite time horizon. To better align demand with available supply, the seller implements a novel selling mechanism: a dynamic opaque product. An opaque product is an additional option within the existing assortment, the exact identity of which is revealed to the customer immediately after purchase. The seller can dynamically adjust both the realization and the price of the opaque product based on the remaining time in the horizon and the current inventory levels. We demonstrate that the optimal opaque policy can increase revenue and reduce waste in finite-horizon inventory settings. We further observe that the opaque selling strategy can lower stockout probabilities under a wide range of parameter settings. Finally, we show that the demand distribution as a function of the opaque product’s price can be learned with an acceptable regret rate.
This study explores how digital leadership shapes psychological well-being among service employees in China, focusing on the mediating role of digital communication quality and the moderating influence of knowledge sharing willingness. Drawing on Social Exchange Theory, we develop and test a moderated mediation model using data collected from 308 service-sector employees across multiple Chinese cities. Confirmatory factor analysis confirmed construct validity, and structural equation modeling demonstrated that digital leadership positively affects communication quality, which in turn enhances psychological well-being. Moderation analysis revealed that the relationship between digital leadership and communication quality is stronger when employees are more willing to share knowledge. Further, PROCESS analysis supported a significant conditional indirect effect, suggesting that the mediating path is amplified under high knowledge sharing willingness. These findings underscore the importance of employee disposition in shaping the outcomes of digital leadership. The study contributes to leadership and organizational behavior literature by integrating communication and knowledge behaviors into a relational leadership framework, with implications for improving well-being in digitally intensive service settings.
Clients seeking paramedical and rehabilitation services require recurring treatment sessions over an extended period. Unlike single-visit problems, these services must assign each accepted client to a fixed, recurring day-time slot that remains occupied throughout the treatment program. This structure creates long-term capacity commitments that limit future scheduling flexibility and complicate acceptance decisions, particularly when clients differ in availability and required program durations. Motivated by an early intervention program for infants and toddlers with developmental delays, we study scheduling policies designed to address this combination of heterogeneity, uncertainty, and recurrence constraints. We model the multisession appointment scheduling problem as a Markov decision process in which requests arrive sequentially and decisions must consider both immediate feasibility and the long-term implications of blocking a slot across many periods. Our analysis identifies key structural elements of the scheduling decision, including a slot-selection guideline that assigns accepted clients to the least popular feasible slot and a duration-based threshold that characterizes acceptance behavior. These insights highlight the value of preserving flexibility and anticipating demand when scheduling recurring appointments under uncertainty. Building on these results, we develop a heuristic that groups schedule states into occupancy categories and applies simplified acceptance thresholds. Computational experiments show that this anticipatory approach outperforms first come, first served benchmarks, particularly when slot popularity is uneven or program durations vary widely. Whereas we do not model health outcomes directly, prior research links improved access, timely initiation, and continuity of care with better therapeutic results, underscoring the broader potential impact of more efficient scheduling.
Strategic and effective use of online review systems is a critical challenge for firms in the service industry, which can only participate in these systems while abiding by each system’s unique rules and designs. Through an experimental study, we extended the theoretical understanding and explored the practical benefits that firms can receive by writing managerial responses. We showed that topic-congruent responses have a strong positive influence on users’ purchase intention through increased trust and major trust antecedents, such as perceived diagnosticity and social presence. In positive reviews, both perceived diagnosticity and perceived social presence were significant mediators. On the other hand, for negative reviews, the only significant mediation was through perceived social presence. Our work complements previous research on personalized managerial responses, which mostly depends on archival data and does not provide causal insight into the individual mechanism behind the positive effects of writing managerial responses for the firm. The results of our study are useful to any firm that wants to optimize its managerial response strategy in online platforms by providing a detailed explanation of how potential customers can be influenced by the different strategies. We also expand the existing knowledge of how consumers develop trust and purchasing intentions in an electronic commerce environment when exposed to different managerial response strategies.
We consider the problem of optimally maintaining an offshore wind farm wherein wind turbines progressively degrade over time because of normal usage and exposure to a randomly varying environment. The turbines exhibit both economic and stochastic dependence due to shared maintenance setup costs and their common environment. However, even modestly sized farms can be difficult to analyze because of dimensionality considerations. Our aim is to identify near-optimal replacement policies that minimize the total expected discounted setup, replacement, and lost production costs over an infinite time horizon. The problem is formulated using a Markov decision process model that becomes intractable as the number of turbines increases. Therefore, we introduce a novel value function decomposition within the approximate linear programming (ALP) framework that exploits the unique features of the problem. Using a column generation algorithm to solve the dual of ALP (DALP) formulation, we establish lower and upper bounds for the value function, enabling us to quantify the optimality gap, which is under 1% for homogeneous farms and between 4% and 6% for heterogeneous farms. Furthermore, we bound the optimality gap between the exact value function and the approximate one using the optimal solutions of the dual of the linear programming formulation and the DALP formulation. Finally, we provide conditions under which an optimal policy can be retrieved from the approximate value function. Our results offer invaluable guidance to wind farm operators on managing the high costs of offshore wind farm maintenance. Because a significant portion of wind energy costs are linked to the replacement of major components, near-optimal replacement policies may help reduce these costs and make wind energy more cost-effective. Through sensitivity analyses, we explain the impact of three key factors-the environment, shared setup costs, and the number of turbines-on the characteristics of these policies. Additionally, we provide insights into the structure of the near-optimal policies and explain nonintuitive behaviors within the context of offshore wind turbine farms.
