Considering consumer fairness concerns, this paper investigates an e-commerce platform’s selling scheme choice when it adopts a wholesale selling scheme or an agency selling scheme to create a contract with a manufacturer. We find that the intensity of the fairness concerns and the platform fee are key factors affecting the platform’s optimal selling scheme choice. Specifically, when these two factors are relatively high or low, the wholesale selling scheme outperforms the agency selling scheme in terms of the e-commerce platform’s profit. Otherwise, the e-commerce platform should adopt the agency selling scheme. Moreover, when these two factors are sufficiently large or small, the wholesale selling scheme will yield a win-win result for the players of the e-commerce supply chain. Interestingly, we find that, considering fairness-minded consumers, a larger platform fee may be harmful to the platform. We also extend the baseline model to consider the consumer heterogeneity of fairness concerns, proportional platform fee, fairness concern about the manufacturer’s profit, and endogenous platform fee. We find that the main insights remain qualitatively unchanged under these model extensions.

User behavior intention is an important evaluation criterion for the construction of mobile library application. To help libraries and mobile application, developers better understand factors influencing user behavior intention and jointly improve the mobile service quality of library. Based on grounded theory, this study experimentally manipulates user behavior intention to use mobile library application related to the survey questionnaire that was designed to obtain data from college teachers and students. The results showed that the user behavior intention to use mobile library application is mainly influenced by system feature (i.e., accessibility, relevance, and system help), interface feature (i.e., screen design, navigation, and term), and individual difference (i.e., performance expectancy, domain knowledge, and social influence). Furthermore, system feature and interface feature are the external driver of user's usage behavior intention, and individual difference is the internal driver of user's usage behavior intention.

Open Access (OA) has emerged as a pivotal driver shaping the dissemination scope and academic impact of research findings. To clarify the impact of publishing models such as open access on the citation performance of biochemical papers, this study selects 177,745 biochemistry professional papers included in the core collection of the Web of Science (WoS CC) as the research data; we conduct an analysis of citation and impact advantages in biochemistry research. Employing correlation analysis, baseline regression modeling, and two-way ANOVA, our analysis indicates that: OA publications in biochemistry exhibit notable citation and impact advantages, which are positively correlated with the degree of openness, and the key determinants of the OA advantage encompass funding sources, reference count, and publication region. At present, China accounts for a disproportionately small proportion of OA papers in this field. In the context of the open-science paradigm, Chinese academic journals must systematically address their developmental bottlenecks and formulate publication innovation strategies to enhance the quality of academic publishing.

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.