Online medical consultation has become a crucial channel for patients seeking health support. Based on data from a psychiatric department in a leading online medical consultation platform in China, this study examines two possible types of online self-presentation strategies (positive impression management strategy and blending-in impression management strategy) employed by doctors in three dimensions: informational management, affective management, and image management, and explores their impact on patient selection. Meanwhile, an information integration perspective was incorporated and the interaction effects between impression management strategies taken by doctors and patient reviews expressed by online patients are also explored. Results indicate that the information quantity (representing the informational management dimension) in doctors’ profiles has a negative impact on patient selection (β = −0.142, p < 0.01), while the positive emotion expression (representing affective management) (β = 0.423, p < 0.01) and profile photo (representing image management) (β = 1.098, p < 0.01) positively influence patient selection. Patient reviews related to expertise positively moderate the effect of information quantity in doctors’ introduction on patient selection (β = −0.632, p < 0.05). In contrast, patient reviews concerning attitude (β = −0.882, p < 0.01) and credibility (β = −0.488, p < 0.01) negatively moderate the effect of emotion expression and profile photos on patient selection, respectively. The findings extend the applicability of impression management theory, providing a novel perspective for comprehending the impact of doctors’ self-presentation on patient selection and its interaction effect with patient impressions.

Cloud operators face massive unused excess computing capacity with a stochastic non-stationary nature due to time-varying resource utilization with peaks and troughs. Low-priority spot (pre-emptive) cloud services with real-time pricing have been launched by many cloud operators, which allow them to maximize excess capacity revenue while keeping the right to reclaim capacities when resource scarcity occurs. However, real-time spot pricing with the non-stationarity of excess capacity has two challenges: (1) it faces incomplete peak–trough and pattern shifts in excess capacity, and (2) it suffers time and space inefficiency in optimal spot pricing policy, which needs to search over the large space of history-dependent policies in a non-stationary state. Our objective was to develop a real-time pricing method with a spot pricing scheme to maximize expected cumulative revenue under a non-stationary state. We first formulated the real-time spot pricing problem as a non-stationary Markov decision process. We then developed an improved reinforcement learning algorithm to obtain the optimal solution for real-time pricing problems. Our simulation experiments demonstrate that the profitability of the proposed reinforcement learning algorithm outperforms that of existing solutions. Our study provides both efficient optimization algorithms and valuable insights into cloud operators’ excess capacity management practices.

Purpose: This paper examines factors of payment decision as well as the role each factor plays in casual configurations leading to high payment intention under systematic and heuristic information processing routes. Design/methodology/approach: Based on heuristic-systematic model (HSM), we propose a configurational analytic framework to investigate complex casual relationships between influencing factors and payment decision. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze data crawled from Zhihu.com. Findings: The number of previous consultations is a necessary element in all five equivalent configurations which lead to high intention in payment decision. The heuristic processing route plays a core role while the systematic processing route plays a peripheral role in payment decision-making process. Research limitations: Research is limited in that moderating effect of professional fields has not been considered in the framework. Practical implications: Configurations in results can assist managers of knowledge communities and paid Q&A service providers in the management of information elements to motivate more payment decision. Originality/value: This paper is one of the few studies to apply HSM theory and fsQCA method with respect to the payment decision in paid Q&A.

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level.

In the new normal of the supply chain, shortage risk is increasingly becoming a primary concern for operations management. While increasing order quantities can help alleviate shortage costs, surplus order quantity concurrently raises the risk of overstocking. Shortage and holding risk are affected by both demand and supply uncertainty, and the distributions of the random variables remain unknown. Consequently, solving the stochastic optimization problem analytically proves to be challenging. This study aims to exploit the data-driven stochastic optimization method and to analyze the optimal ordering decisions under different demand and supply scenarios. Numerical results indicate that the linear machine learning (LML) method incorporating data features outperforms the sample average approximation (SAA) method in terms of optimization performance. Incorporating $ l_2 $ regularization into the LML method effectively mitigates overfitting and produces similar results under different scenarios. In addition, the optimal order quantity may decrease as the supply variance increases when the unit shortage cost is relatively low, while the optimal order quantity may increase as the supply variance increases when the unit shortage cost is high. The opposite is true in the context of demand research. Finally, smaller supply and demand variances, together with larger supply means, lead to lower firm costs. The effect of the demand mean on the firm's costs is negligible. At the same time, the effect of the supply mean on the firm's costs is more pronounced than that of the supply variance.