Review writing style in digital platforms plays a key role in shaping brand image and exerting influence over shopping decisions. However, how merchants can influence the review writing style of users by developing response language strategies remains a pressing issue in merchant interaction management on digital platforms. To address this problem, this paper, employing the communication accommodation theory and taking user reviews and managerial responses data from the digital travel platform as research samples, explores the effect of linguistic style matching in managerial responses on user review writing style, through the construction of a two-way fixed effect model. The results show that the linguistic style matching between managerial responses and user reviews positively affects the authentic writing style of subsequent reviews, and negatively affects the analytical writing style of subsequent reviews, and merchant reputation exerts a significant positive moderating effect on this relationship. This paper reveals the impact of linguistic style matching in managerial responses on subsequent review writing style, and expands the research on managerial responses and review writing style in digital platforms.

Based on the signaling theory, this paper seeks to thoroughly investigate the direct impact of demonstration signal and description signal released by team behaviors of physicians on individual service performance in the online healthcare context. Concurrently, it explores the potential interaction between these two types of signals. The panel data of 34324 physicians over four months were collected from the Good Doctor online platform, and hypotheses were tested using a panel negative binomial model. The results indicate that both team demonstration signal and team description signal positively influence the individual service performance of physicians. Moreover, these two signals exhibit a substitutive relationship rather than a complementary one in affecting the service performance of physicians. Additionally, compared to physicians with higher clinical titles, less experienced physicians do not have effective improvement in individual performance by providing team service channels. The research findings offer practical guidance for improving the functionality of online medical platforms and enhancing the performance of physicians.

On online health platforms, physicians provide guidance for patients using written language, and it is unlikely that all guidance will be fully accepted by patients with diverse diseases. Based on the language expectancy theory, this paper identifies the medical professional language and interpersonal language of physicians under the online physician-patient interaction context and explores their effects on patients’ satisfaction perception and reward behavior, as well as the moderating role of patients’ disease severity. Approximately 52000 physician-patient interactions are collected from a leading health platform in China and processed by the text-mining, machine learning, and other methods. The empirical results indicate that the professional language of physicians has a positive effect on both the satisfaction perception and reward behavior of patients, while interpersonal language has a negative impact. Patients’ disease severity positively moderates the effects of the medical professional language, but it negatively moderates the effects of the interpersonal language. These findings provide guidance for physicians on how to effectively use written language in interactions to meet the expectations of patients with varying disease severities.

Fintech platforms represent digital platforms and are the core driving force for the development of the digital economy. Grasping the non-linear impact, spatial spillover effect, and the heterogeneity of impact of digital technology innovation on the regional development of fintech platforms is the key to promoting the coordinated regional development of fintech platforms. First, the regional development index of fintech platforms is constructed based on the enterprise development dimension, platform application dimension, and public demand dimension. Then, the spatial Durbin econometric model and the threshold model are employed to analyze the threshold effect of the impact of digital technology innovation on the regional development of fintech platforms, and to explore the spatial spillover effect of digital technology innovation on the regional development of fintech platforms. Finally, whether there is spatial heterogeneity in the impact of digital technology innovations on the regional development of fintech platforms is investigated. The result shows that there is a threshold effect of informatization level when digital technology innovation promotes the regional development of fintech platforms. The catalytic effect is pronounced when the informatization level is low. The catalytic effect weakens when the informatization level is high. Digital technology innovation has a negative spatial spillover effect on the regional development of fintech platforms. The impact of digital technology innovation on the regional development of financial technology platform has spatial heterogeneity, and the effect is greater in underdeveloped regions than in developed regions.

A new power system consisting of a power generation enterprise and an electricity sales enterprise was constructed to address the issue of who will invest in renewable energy storage devices in the new power system. Three scenarios were considered: a power generation enterprise (integrating renewable energy and traditional energy), an electricity sales enterprise, and a renewable energy enterprise, investing in the construction of energy storage equipment. Corresponding Stackelberg models were constructed and solved using the reverse induction method. Based on equilibrium solution, it is found that when the renewable energy generation enterprise invests in energy storage devices, consumers pay a lower electricity price, which helps to increase electricity demand and achieve the highest social welfare. However, when the electricity sales enterprise invests in energy storage devices, consumers pay a higher electricity price, thereby suppressing electricity demand and thus suppressing social welfare. The increase in the cost coefficient of traditional energy generation is beneficial for the access of renewable energy power generation. Therefore, this paper analyzes the impact of energy storage device investment mode on energy storage quality, which, to some extent, would enrich the research in the field of energy storage. Finally, the situation of consumers participating in energy storage investment was studied, further demonstrating the above conclusions.

Considering “carbon tax and cap-and-trade” regulations, this paper analyzed the joint emission reduction decision-making in the supply chain. Consistent with reality, a two-echelon supply chain model consisting of a leading manufacturer and a retailer was proposed, where the upstream manufacturer depended on remanufacturing and the green technology to reduce carbon emissions, and the downstream retailer conducted relevant low-carbon promotions. By taking the low-carbon goodwill as a state variable, three differential game decision-making models were constructed to analyze the impacts of hybrid carbon regulations and different decision-making models on decisions, low-carbon goodwill, profits, and total carbon emissions. The results show that compared with the existing cap-and-trade regulation, the implementation of hybrid carbon regulations can further encourage low-carbon behaviors of the manufacturer and enhance the low-carbon goodwill from a long-term perspective, but will not affect the retailer’s low-carbon promotion decision-making. In addition, the supply chain decision-making mode will affect the effectiveness of hybrid carbon regulations. If the government sets a reasonable carbon tax price, compared with the decentralized decision-making mode, the centralized decision-making mode can further expand the demand for low-carbon products and enhance the positive environmental externality. Finally, the improved two-way cost-sharing contract can achieve perfect coordination of the supply chain. The results provide valuable reference for different members and the government. 

Hazardous materials transportation accidents have the characteristics of low probability and high consequences, and reducing the risk of accidents is the focus of research on hazardous materials transportation. The risk of hazardous materials transportation is accompanied by the uneven risk distribution in space. Previous studies have focused on the trade-off between the minimum total risk and the fairness of risk, but less consideration has been given to the transportation network in uncertain demand. Fluctuations in transportation demand may lead to increased risk. In order to solve the problem of hazardous materials network design considering risk fairness under uncertain demand, a double-layer mixed integer programming model for the design of transportation network with multiple origin-destination (OD) for hazardous materials is constructed. The problems are that the supervisor chooses the closed road section and the carrier chooses the optimal route, which are solved by the adaptive genetic algorithm and the local search algorithm respectively. An example analysis indicates that different demand scenarios, especially when some OD pairs are interrupted, produce different optimal network design schemes, and the genetic algorithm used is better than the local search algorithm when the running time is slightly longer.

In order to provide a rational explanation for the special evaluation phenomena such as “the weak overcame the strong in the context of power disparity” from a theoretical perspective, a new random-aggregation evaluation method is proposed, which is demonstrated in detail from the solution, test, and other aspects. Specifically, the concept of “relative winner” is proposed, based on which a distributed random transformation method of precise information is put forward by considering the data distribution. A fusion framework for random data is constructed, which has the characteristics of strong inclusiveness, classification calculation, and independent solution. The solution of the fusion framework is discussed from both holistic and local perspectives, and the mathematic proofs of boundary conditions and relationship with influencing factors, concerning the “superiority probability”, are given. Two test criteria for solution results are introduced, which shows that the evaluation method proposed is effective and practical by a simulating arithmetic example and a practical application.

The prize-collecting Steiner tree problem is derived from the Steiner minimum tree problem of graphs, which is also a NP-hard problem in combinatorial optimization. In this paper, the mathematical properties of the problem are proposed and proved that the scale of the problem can be reduced by using the mathematical properties. In addition, based on the mathematical properties of the problem, the upper and lower bound sub algorithm, the reduced order sub algorithm, and the backtracking sub algorithm are designed. By using the upper and lower bound sub algorithm and the lower order sub algorithm, the size of the solution space of the problem can be reduced, to shorten the search time of backtracking sub algorithm, and then reduce the time to solve the optimal solution of the problem. Moreover, the application case, the numerical example analysis, the algorithm analysis, and the comparison show that the algorithm designed in this paper can not only find the optimal solution of the problem, but also has a lower time complexity than the general backtracking algorithm that does not consider the mathematical properties of the problem.

Aiming at the problems of low transaction efficiency, poor operation economy, and weak enforceability in the traditional single chain blockchain, a multi-type producer consumer power transaction optimization model based on multi-chain blockchain is proposed. By storing the transaction information, power information and contract information separately, the power transaction architecture based on the multi-chain blockchain is established to effectively improve the transaction efficiency and enforceability. Combined with the behavior diagnosis of various types of consumers, the power flow constraints, and the slice verification mechanism, the transaction decision-making model of multiple types of consumers is constructed to optimize the allocation of market point-to-point trading resources. Using the fuzzy evaluation theory and the node reputation value incentive mechanism, the independent verification of each block information in multi-chain coupling operation is achieved. On this basis, the multi-objective evolutionary algorithm of the Chebyshev decomposition method is used to solve the optimal decision-making solution based on the conflict of multiple sub objectives. The example results show that the model proposed in this paper can effectively improve the transaction processing efficiency and overall income, verify the transaction scheme of multiple types of consumers in real time, and provide theoretical support and decision-making support for optimizing the transaction decision-making problem among multiple types of consumers.

Large-scale water resources allocation projects have the characteristics of linear distribution. The project legal person generally organizes multiple contractors to construction project in parallel, i.e., trade with multiple contractors at the same time. From the perspective the principal-agent theory, this is a “one-to-many” principal-agent relationship. In this situation, the project legal person may face moral hazards originated from multiple contractors at the same time. This paper constructs a tournament incentive model that considers psychological preferences in the characteristics of large-scale water resources allocation projects, and analyzes the effect of wage gaps and psychological preferences on multi-contractor effort levels. The research results show that the contractor’s optimal effort levels in terms of quality, construction period, and safety will increase with the increase in the tournament incentive gap, and decrease with the increase of the contractor’s effort cost coefficients. In addition, the contractor’s optimal effort levels will decrease with the increase of the sympathy preference coefficient, and increase with the increase of the jealous preference coefficient. Therefore, identifying the psychological preferences of multi-contractor and formulating an appropriate tournament incentive mechanism are of great significance to improve the construction performance in large-scale water resources allocation projects.

The online healthcare community has abundant doctors and multiple sources of doctor online evaluation information, where patients will face choice overload problems and are not sure how to choose the appropriate doctor based on various information. In this paper, based on the configuration perspective of holism, the second-hand data in the haodf. com was collected, and the fsQCA method was utilized to investigate how the coordination of information from multiple sources affects patients’ choices for online consultations. The research results show that the doctors’ online evaluation information cannot separately constitute a necessary condition for the online consultation choice of high or non-high patients. there are four driving modes that generate high patient online consultation choices and four driving modes that generate non-high patient online consultation choices, with an asymmetric relationship. This paper, for the first time, analyzes the driving factors of patients’ online consultation choice from the perspective of holistic configuration, expanding the research scope of patients’ online consultation choice, and providing important practical value for online matching of doctors and patients and improving doctors’ online influence.

This paper explores the influence mechanism of social interaction in the knowledge sharing community on the disclosure of user privacy information from the aspects of user privacy disclosure willingness and disclosure level. Based on the stimulus-organism-response(S-O-R) theoretical framework, it investigates how external environmental stimuli in users’ social interaction affect the internal state of privacy information disclosers, thereby affecting their privacy information disclosure intentions. Using a questionnaire survey, it collects data to validate the model proposed and hypotheses, and utilizes user behavior data from knowledge sharing community to explore the impact of social interaction among users on their level of information disclosure. The results show that social interaction significantly contributes to users’ self-efficacy, trust between users, and social identity, which, in turn, affect users’ willingness to disclose private information. Social interaction affects users’ disclosure levels to a certain extent, and account authentication can play a certain positive moderating role. The results provide suggestions for promoting appropriate disclosure behavior in knowledge sharing community.

Digital talent resources has become a key resource for firms to achieve digital transformation and digital innovation, and is a key digital resource for the acquirer firm to absorb and retain in digital mergers and acquisitions (M&A). In the context of digital M&A, this paper, taking new generation information technology firms as the research object, considering the digital background executives of the target absorbed by the acquirer’s firm as a digital resource based on the resource orchestration theory, and dividing digital transformation into two dimensions, digital technology integration and digital technology application, examined the interaction mechanism of retained target’s digital executives, digital transformation, and digital innovation performance after M&A. It also conducted an empirical test using regression analysis, taking the digital M&A events as the research sample. It is found that retained target’s digital executives have a positive impact of on the acquirer’s digital innovation performance in both the short and long term. Both digital technology integration and digital technology application have a mediating effect. In the relationship between the retained targets digital executives and the acquirer’s short-term digital innovation performance, digital technology integration plays a stronger mediating role than digital technology application. However, in the relationship between the retained target’s digital executives and the acquirer’s long-term digital innovation performance, the mediating effect of digital technology application is stronger than that of digital technology integration. This paper helps to clarify the mechanisms for achieving digital innovation performance in the context of digital M&A and helps firms to achieve their digital transformation and digital innovation goals.

From the perspective of multiple networks, this paper analyzes the complex effects of partnership and patent citation relationships on firm innovation as important ways of knowledge flow. Taking the ICT industry as an example, based on the industry-university-research institute collaboration patents and patent citations data, it  conducts empirical analysis on 1924 firms by negative binomial regression. The results show that the ego-network dynamics of collaboration networks promote firm innovation performance. The centrality of the patent citation network plays a mediating role between the stability of ego-network in the collaboration network and innovation performance. Firms can improve the centrality in the patent citation network by maintaining valuable stable cooperative relationships to promote knowledge flow, thus improving their innovation performance.  However, the centrality of the patent citation network does not mediate the influence of expansion on firm innovation performance. The structure hole of the collaboration network has a negative moderating effect on the relationship between the centrality of patent citation network and innovation performance, i.e., the higher the level of the cooperation network structural hole, the less the promoting effect of patent citation network centrality on innovation performance. However, the centrality of the collaboration network has no positive moderating effect on the relationship between them. This paper expands the research perspective of knowledge flow and collaborative innovation network and enriches the research on antecedent variables affecting firm innovation. It also has a guiding significance for firm innovation practice.

The crude oil futures market is highly vulnerable to extreme event shocks such as geopolitical conflicts and financial crises. In this paper, four indicators are respectively constructed based on the multifractal detrended fluctuation analysis and recurrence plot method to quantitatively analyze the effectiveness of Shanghai crude oil futures market with Brent and WTI futures markets as comparisons, and the dynamic evolution characteristics of market effectiveness under the shock of extreme events are systematically studied. In addition, the long history data of WTI are analyzed in detail, and the time-varying evolution behavior of the effectiveness  under the impact of profound extreme events over the past 30 years is reviewed. It is found that in the same sample period, due to the superiority of China’s crude oil futures system, prudent and timely risk-control policies, and economic fundamentals with a strong resilience, the impact of extreme event shock on the effectiveness of the international crude oil futures market is more negative than that on the domestic market, which ultimately leads to the effectiveness of the Shanghai crude oil futures market being higher than those of Brent and WTI crude oil futures markets at different time scales. The effectiveness of the crude oil futures market is not fixed and has a significant mean reversion feature. Extreme emergencies will have serious negative effects on the effectiveness of the crude oil futures market. However, the crude oil futures market system has the ability to self-repair, and the temporary turbulence caused by exogenous shocks will be gradually absorbed and resolved to maintain the relative stability of its own market effectiveness. The market efficiency indicators established in this paper can be used for early warning of the risk of crude oil futures.

Based on the pricing behavior of micro-enterprises, this paper theoretically analyzes the nonlinear relationship between different markup enterprises in terms of price transmission under cost shocks. The theoretical research finds that high markup enterprises show a stronger price-raising behavior under positive cost shocks, i.e., price transmission shows spillover effects. However, under negative cost shocks, product pricing has not been significantly adjusted with cost reductions, but the impact of cost shocks has been absorbed by markup, which shows the absorption effect. In contrast, for low markup enterprises, price transmission shows the absorption effect under positive cost shocks and the spillover effect under negative cost shocks. This paper further empirically tests the above research conclusions at the industry level by using the double/debiased machine learning (DML) model based on nonparametric inference with continuous treatments. The research in this paper provides a new perspective for understanding price transmission behavior, which in turn has important reference significance for the prediction and prevention of inflation in China.

Effect is an important criterion to measure the success or failure of product design. This paper innovatively analyzes and quantifies the risk compensation effect inherent in perpetual bonds, and optimizes the conversion clause based on the control of risk compensation effect. It especially considers the scenario of “partial conversion”, which means perpetual bonds held by each investor would be converted into equity in a certain proportion in times of mild financial distress, which compensates for the drawbacks of the existing “one-size-fits-all” mode. Furthermore, it proposes a structured pricing method for the equity value of each stakeholder that is suitable for the designed conversion clause, and constructs a constraint optimization model to determine the conversion ratio that reflects the incentive compatibility of each stakeholder, fully reflecting the “co construction and sharing” characteristics between the issuer and investors. The numerical analysis shows that the risk compensation effect can be well controlled by appropriate design. The conversion clause provided not only takes into account the interests of investors but also protects the interests of shareholders. Meanwhile, it increases the value of the issuing bank. In addition, the conversion clause provided is operable under various scenarios. It is expected to provide useful references for the benign development of perpetual bonds.

Based on 279 questionnaires from gig workers, with fairness, privacy and autonomy legitimacy as intermediary variables and AI risk perception as adjusting variable, this paper discusses the double-edged sword effect of perceived algorithmic control on gig workers’ emotional exhaustion. The results show that legitimacy judgment negatively affects gig workers’ emotional exhaustion and completely mediates the impact of perceived algorithmic control on emotional exhaustion. Perceptual algorithm specification guidance negatively affects gig workers’ emotional exhaustion through fairness legitimacy judgment, perceptual algorithm tracking evaluation positively affects gig workers’ emotional exhaustion through privacy legitimacy judgment, and perceptual algorithm behavior constraints positively affect gig workers’ emotional exhaustion through autonomous legitimacy  judgment. AI risk perception strengthens the negative impact of perception algorithm tracking and evaluation on privacy legitimacy judgment.

With the increase of enterprise innovation input, the frequency of product upgrading is accelerated. Since new products on the market will lead to the depreciation of the market value of similar products in the market, the deviation between the expected depreciation and the actual depreciation of the purchased products will lead to the emotional change of disappointment or joy, and the emotional utility will affect their purchasing decisions. In this paper, considering that the market value of general durable goods has the characteristics of depreciation and the consumer emotional utility caused by depreciation, by constructing the continuous pricing model of the new and old products, the optimal pricing of the new and old products and the optimal time to market of the new products were studied. The analysis shows that the impact of new products on the market value of old products affects the pricing of new products. After the new product is launched, the pricing of the old product shall take into account the market value of the old product and the market discount rate of the new product. After the new product is launched, the optimal pricing of the new and old products is affected by the deviation of consumers’ expectation of the depreciation of the product market value. The ratio between the listing price of the new product and the pricing of the old product affects the impact of the new product on the market value of the old product. The launch time of the new product should be determined by the market value of the old product, the derogation rate of the natural value of the market, and the gap between the positive and negative emotions of consumers. In addition, the optimal launch strategy of the new product is determined by the value of the new product.