Large language model, as new application of artificial intelligence, has transformed the way in which employees work, and it is practically meaningful to explore its impact on the content and the form of job crafting. Based on the job demand-resource model, a theoretical framework for the impact of large language model application in the workplace on job crafting is constructed. The application of large language model can bring about structural job resources and social job resources, increase challenging job requirements and reduce hindering job requirements, and further, this will prompt employees to engage in task crafting, relationship crafting, and cognitive crafting. Therefore, the research value of the theoretical framework is discussed and based on current limitations, future research directions are pointed out.

Based on the social information processing theory, an analytical framework of “the social information processing process of leaders-team interaction process” is established to elucidate the mechanism of leaders’ tenacity empowering team innovation. Through a survey conducted on leaders from 643 micro, small and medium-sized enterprises (MSMEs), it is found that leaders’ tenacity has a direct positive impact on the team’s  innovation performance. Compared to outcome innovation (i.e. product innovation and market innovation) performance, leaders’ tenacity is more likely to affect potential innovation (i.e. strategic innovation, behavioral innovation, process innovation) performance. Mechanism testing reveals that team behavioral intention plays a mediating role between leaders’ tenacity and innovation performance, while vision communication serves as a mediator between leaders’ tenacity and team behavioral intention. In terms of organizational environment, both trust adversity and objective consistency adversity strengthen the link between leaders’ tenacity and vision communication. The findings reveal the social information processing and emotional stimulation processes that leaders’ tenacity can empower team innovation, and this inherent motivation mechanism is of significant importance in promoting the innovative development of MSMEs.

 Data business is a new way of creating economic value that is centered on data. The firm should select the strategies and set the governance objectives on data business based on its resources and governance ability. Firstly, four data business strategies, that is, supplier strategy, transformer strategy, integrator strategy, and facilitator strategy, are identified based on the data business process model. Secondly, a firm’s selection of data business strategy depends on two factors, data resource control ability and its knowledge about customers’ demands. Finally, the governance objectives of these four data business strategies differ in data quality and data compliance are analyzed. The findings contribute to the literature about data business strategies, digital governance and data factor market, and meanwhile the findings are guides for adjustment of data business strategies and construction of data industrial ecosystem by firms, and for categorized regulation of data business by regulators.

 Focusing on the issue of high cost and low efficiency associated with manual processing of customer complaints by operators, a quantitative research method based on TF-IDF (term frequency-inverse document frequency) algorithm is proposed, aiming to efficiently and accurately identify the reasons for customer complaints. Jieba, combined with the custom dictionary and the list of stopword is used to extract key words from complaint worksheets. The top six key words with the highest TF-IDF values in each issue are obtained, and a set of key words is output, thereby enhancing the accuracy and efficiency of keyword extraction. Furthermore, by comparing this method with the sole use of TF and the application of the TextRank algorithm, the importance of IDF and the differences in algorithmic principles are highlighted. Results indicate that issues related to optical modems, routers, and set-top boxes widely exist in complaints. In terms of these issues, this study provides operators with relevant suggestions for improving products and services, which have certain value to operators’ managing and solving problems.

To explore ways of creating a maternity-friendly environment from the perspective of enterprises, 103 leader-employee paired data is used to analyze the relationship between family motivation and workplace behavior of employees with “only child”. Results show that family motivation positively affects employees’ proactive behavior and negatively affects their withdrawal behavior. Employees’ occupational role commitment not only moderates the relationship between child’s age and employees’ family motivation, but also moderates the indirect impact of child’s age on employees’ proactive behavior and withdrawal behavior through family motivation, and the mediating effect of employees’ family motivation is significant when occupational role commitment is low. This study not only expands the research on the reasons of family motivation and on family-work promotion, but also proves that child and career can be balanced, which has important implications for creating a maternity-friendly business environment.

 The income effect of Chinese households’ social donation behavior is re-examined. Using data from Chinese Family Panel Studies in 2014, 2016 and 2018, truncated regression and Heckman two-stage model are employed to test and solve possible truncation and sample selection bias problems in OLS (ordinary least square) regression. It is found that the proportion of households’ participation in social donations in China is 19.1%, and the average annual donation in these households is 449.61 yuan. The income elasticity of social donations is 0.29, and the difference from the result of the OLS (0.21) is mainly caused by left truncation. Due to the lower participation rate of middle-income households, social donations show a clear “U”-shaped income relationship. The three modes of households’ participation in tertiary distribution, i.e., gifts, financial support and social donations are compared and the reasons for the differences in the income effects of the three modes are analyzed. Based on the above discussion, it is proposed that channels for households’ participation in tertiary distribution should be further broadened.

 Crises occur more often in modern society. The speed of governmental emergency response is constrained, but the potential for emergency response by the public is great. It is vital for the government to extend emergency management work downward to the public, that is, to strengthen the public’s emergency participation and emergency capacity through publicity, training, emergency team building in primary level and so on, so as to respond to public emergencies in a timely and effective way. The impact of extending governmental emergency management work downward on the public’s emergency capacity is empirically tested, and based on the crisis learning theory, its underlying mechanism is further explored. Results show that extending governmental emergency management work downward can significantly improve the public’s emergency capacity and the crisis learning ability plays a mediating role. The education and income level of the public has a significant impact on their emergency capacity. The government should increase the frequency of the public’s participation in emergency management practice, cultivate their crisis learning ability, and improve their emergency response capacity, especially in the aspect of mutual rescue.

 With the rapid development of artificial intelligence technology, artificial intelligence systems have been gradually introduced into the field of judicial adjudication, and play the roles of “connecting bridge” “reference book” and “warning device” in the process of assisting judicial adjudication. However, due to the gradual development of science and technology, artificial intelligence assisting judicial adjudication still faces many difficulties, which includes the limitations of artificial intelligence technology, the judges’ insufficient ability to apply artificial intelligence, and the lack of uniqueness of the products provided by technology companies. To solve the practical problems of digital justice, first of all, the limitations of traditional technology should be broken through and the macro design of advanced trial system should be perfected; secondly, systematic technical training programs should be set up to improve users’ operational ability; finally, the technology access standards should be strictly implemented to regulate product provision, so as to provide a strong guarantee for the application of artificial intelligence-assisted judicial adjudication.

 Platform complementors have become an increasingly important object of platform research. By combining the VOSviewer bibliometric analysis method with the traditional literature review method, the ecological relationships of complementors are systematically sorted out and the strategic choices that complementors make when dealing with various ecological relationships are identified. The study shows that: (1) research achievements on platform complementors are increasingly fruitful, and the value creation process is a current research hotspot; (2) platform complementors have governing and governed relationship, cooperative and competitive relationship, independent value satisfaction and network value delivery relationship with platform owners, other complementors, users, respectively; (3) on the basis of clarification of the ecological relationships, platform complementors can select the corresponding ecological strategy according to different members and different strategic purposes. The potential directions for future research on complementors are also pointed out.

To investigate the mental health status of university students and provide empirical evidence for mental health interventions, using a cluster sampling method, 17180 science and engineering students from universities were selected as the research subjects, and a survey was conducted by using the self-rating depression scale, self-rating anxiety scale, and Connor-Davidson resilience scale. The results show that: (1) The average depression score of college students is 42.80, and the average anxiety score is 39.16, which are much higher than the average level of adult norms; The detection rates of depression and anxiety are 23.11% and 10.05%, respectively; (2) The detection rate and standard score of depression and anxiety in boys are higher than those in girls; Undergraduate students have the highest detection rate and standard score for depression and anxiety; (3) Resilience negatively predicts depression and anxiety, explaining 57% and 43% of the variance, respectively. The research conclusion provides insights for mental health interventions from the perspective of resilience.