Wei Zhang

With the rapid development of IoT technology, the e-commerce sector has encountered unprecedented opportunities and challenges. This study focuses on Xiaomi smart bands, collecting health and behavior data from 50 users over a three-month period. By integrating decision tree classification, K-means clustering, and association rule mining, we constructed a systematic brand-building analysis model. The results show that the decision tree classification model achieved an accuracy of 87.5%, effectively identifying user groups with high brand interaction frequency. K-means clustering further segmented these high-interaction users into three distinct groups, revealing significant differences in user behavior characteristics. Additionally, association rule mining uncovered key behavioral patterns, such as users with more than 8,000 steps per day being more likely to purchase sports shoes (support: 0.3, confidence: 0.85). These findings indicate that the proposed model can provide strong data support for brands in developing personalized marketing strategies and enhancing user experience. Although there are some limitations in data sources and model generalization, this study demonstrates the significant potential of combining IoT and data mining technologies in advancing e-commerce. Future research will aim to expand the dataset and optimize the model to enhance its applicability and practicality.

The digital transformation(DT) of manufacturing industry is influenced by multiple factors, including internal and external factors. Many scholars have conducted relevant research from the aspects of technology, organization, environment, etc., and have achieved rich results. However, there is not much research on the role of government in the digital transformation process of manufacturing industry and the factors affecting government service efficiency. This article explores the main influencing factors of government service efficiency improvement in promoting the DT of manufacturing industry from the perspective of enterprises, providing targeted strategies for local governments to improve service efficiency and better promote the DT in manufacturing industry.

This paper uses the China Labor Dynamics Survey (CLDS) database to screen out the last migration information of migrant labor in Guangdong, and empirically tests the impact of industrial intelligence on migrant labor migration decisions in Guangdong by using conditional logit model. It is found that (1) Industrial intelligence has a significant positive absorption effect on the migrant labor force in Guangdong, indicating that the labor force production effect of industrial intelligence in Guangdong is dominant. (2) From the perspective of flow direction, industrial intelligence improves the probability of labor flow from non-Pearl River Delta region to the Pearl River Delta region, promotes labor flow in the Pearl River Delta region, and reduces labor flow in the non-Pearl River Delta region. This paper empirically confirms the influence of industrial intelligence on the migration direction of migrant labor in Guangdong, and provides useful inspiration for Guangdong to realize the optimal allocation of labor factor space.

In view of the characteristics of small- and medium-sized manufacturing enterprises and the status quo of digitalization, it is necessary to develop a more applicable digital transformation maturity model. The decision testing and evaluation laboratory method (DEMATEL) is used to provide the visual impact relationship between digital transformation criteria, and combined with the network analytic hierarchy process (ANP) to determine the mixed weight of indicators, and then fuzzy comprehensive evaluation is used to evaluate the digital maturity of small- and medium-sized manufacturing enterprises. The empirical analysis of small- and medium-sized manufacturing enterprises in Guangdong Province shows that digital strategy and information technology play a key role in the digital transformation of enterprises, and digital process and digital innovation are the main problems faced by small- and medium-sized enterprises. In addition, the digital maturity of enterprises is related to the industrial base, regional policies, industry types, etc. This study provides some guidance for the implementation path selection of small- and medium-sized enterprises’ digital transformation and accelerates the digital transformation and sustainable development of small- and medium-sized manufacturing enterprises.

This study used the Predisposition Proposition Theory of implicit followership to determine the effect of a proactive personality and core self-evaluation on the implicit followership of different schemas. Intertemporal survey data for one month from 452 university graduates were collected to evaluate that core self-evaluation significantly and positively affects positive implicit followership and significantly and negatively affects negative followership. However, the effect of proactive personality on implicit followership is not significant. The results of data analysis support the interpretation of propensity propositions in the study of personality traits. This study also determines the theoretical significance and practical application value.

Amid the growth of COVID-19 pandemic, SMEs are facing greater uncertainties and pressures to survive because even though they are efficiently managed, their human resource organizations lack a large number of resources and a well-developed training system to foster the sustainable development of employees. Employees are important assets of the company, and their continuous growth and development are keys to the survival of the company. In this context, the individual worker’s assessment of his or her job role and how the assessments drive the employee to exhibit an appropriate proactive work behavior are particularly important. Previous research has typically focused on how organizations and leaders perceive employees but has rarely explored employees’ own implicit followership cognitive states. This study integrates the traits of positive implicit followership of employees, namely, industry trait, enthusiasm trait, and good citizen trait, with perceived supervisor support (PSS) and feedback-seeking behavior (FSB) into one research framework. In this study, 207 valid questionnaires were collected by using offline convenience sampling, and structural equation modeling (SEM) analysis was conducted. The results show that employees’ industry traits directly and positively influence FSB, while enthusiasm traits and good citizen traits have no direct effect on promoting FSB. In addition, industry trait, enthusiasm trait, and good citizen trait significantly and positively influence PSS, with good citizen trait having the greatest positive effect on PSS. Furthermore, PSS has a significant positive effect on FSB. Finally, PSS was found to mediate between industry traits and FSB. Corresponding to the results of the study, the actions shaping employees’ positive implicit followership cognition and forming a good supportive atmosphere to promote employees’ performance of more feedback-seeking behaviors are recommended.

Under turbulent, boundaryless, and Internet age, the characteristics of career sustainability development have shifted from the perspective of development within the organization to the career development track of self-efficacy. New employees usually face the difficult stage of adapting to the new environment and establishing interpersonal relationships with new colleagues. When new employees enter an organization, they usually have different implicit followership cognitions. Previous studies have focused on the treatment of new employees by the organization and the leader, however, the implicit followership cognitive state of new employees has not been studied specifically. This research integrates employees' positive and negative implicit followership, perceived supervisor support, workplace friendship, and perceived self-efficacy into a research framework. This study used a questionnaire survey by an online professional survey website. A total of 394 valid questionnaires were collected. Structural equation model (SEM) analysis was carried out and according to the results, new employees' positive and negative implicit followership significantly affects perceived supervisor support. Furthermore, perceived supervisor support had a significant impact on perceived self-efficacy. Moreover, perceived supervisor support was found in a mediating role between the relationship of implicit followership theories and perceived self-efficacy. Finally, workplace friendship was found to be a significant moderator in the relationship between perceived supervisor support and perceived self-efficacy. Based on the research results, business managers are suggested to pay more attention to new employees' self-cognition of their job roles and enhance the self-efficacy of new employees in the entry stage.