The medical service is a special credit commodity, and trust plays a very important role in patients’ online medical choice behavior. By collecting information about the doctors on China’s leading online medical platform (Platform A), a regression analysis model was constructed, based on the credibility theory model, which has the following three dimensions: ability trust, benevolence trust, and integrity trust. The results showed that the medical title of the doctors, their department’s reputation, the number of gifts given to them, and the number of patients who registered with them after diagnosis, among other factors, had a significant, positive impact on the behavior of choosing doctors. Among these considerations, the number of patients registered after diagnosis had the greatest impact on the behavior of choosing doctors. This factor is the result of each doctor’s personal brand management, which reflects their comprehensive ability, reputation and integrity. Compared with previous studies, this paper creatively analyzed the important influence of departmental reputation and the number of patients registered after diagnosis on medical choice behavior and puts forward that a doctor can use the number of patients registered after diagnosis to manage their personal brand. Based on the results of this study, we will also put forward suggestions from the perspectives of patients, doctors and the online medical community.

As an indispensable part of contemporary medical services, Internet-based medical platforms can provide patients with a full range of multi-disciplinary and multi-modal treatment services. Along with the emergence of many healthcare influencers and the increasing connection between online and offline consultations, the operation of individual physicians and their teams on Internet-based medical platforms has started to attract a lot of attention. The purpose of this paper is to, based on an Internet platform, study how the information on physicians’ homepages influences patients’ consultation behavior, so as to provide suggestions for the construction of physicians’ personal websites. We distinguish variables into strong- and weak-ties types, dependent on whether deep social interactions between physicians and patients have happened. If there exist further social interactions, we define the variable as the “strong ties” type, otherwise, “weak ties”. The patients’ consultation behavior will be expressed as the volume of online consultation, i.e., the number of patients. We obtained the strong and weak ties information of each physician based on EWM (entropy weight method), so as to establish a regression model with explained variable, i.e., the number of patients, and three explanatory variables, i.e., the strong and weak ties information, and their interaction term. The estimation results verified our hypotheses and proved to be robust. It showed that both strong and weak ties information can positively influence patients’ consultation behavior, and the influence of weak ties information is greater. Regarding the positive influence of strong and weak ties, we found a trade off effect between them. Based on the results, we finalize with some suggestions on how to improve a physician’s online medical consultation volume.

As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers.

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot’s initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field.

This study investigates the spillover effects of physicians’ prosocial behavior, specifically knowledge sharing, on the paid consultations of other physicians within the same specialty and offline hospital. Using data from an online healthcare platform, we apply propensity score matching to explore how the sharing of medical knowledge by physicians influences the consultation outcomes of their colleagues. The results reveal significant positive spillover effects, indicating that prosocial behavior benefits other physicians within the same specialty and healthcare institution, thereby enhancing collaboration within the healthcare ecosystem. The spillover effect is stronger within the same offline hospital’s physicians on the online healthcare platform, suggesting that knowledge sharing has a more localized impact within the same healthcare institution. Furthermore, the study examines heterogeneity across both physician-level characteristics (e.g., popularity, title, price, gender) and contextual factors (e.g., specialty type, hospital level, wait time, regional GDP). The findings show that the magnitude and direction of spillover effects differ by subgroup, shaped by professional visibility, authority, and organizational structure. These insights contribute to the understanding of how prosocial behavior can foster collaboration and benefit healthcare networks beyond individual physicians, offering practical implications for healthcare platforms, administrators, and policymakers.