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This study examines how customer loyalty among DiDi users in China is affected by two types of online social interaction (transactional and interpersonal) and how the online interaction–customer loyalty relationship is mediated by three kinds of perceived benefits (functional, social-hedonic and safety).

This study empirically examines research hypotheses based on a questionnaire survey of 428 DiDi consumers.

The results reveal that transactional interactions significantly enhance customer loyalty among DiDi users via the partial mediating effects of customers' perceived functional, social-hedonic and safety benefits. By contrast, interpersonal interactions do not directly influence customer loyalty, and only social-hedonic benefits fully mediate the positive influence of interpersonal interactions on loyalty.

This study contributes to the theoretical development of interactive marketing management by examining how two types of online social interactions contribute to customer loyalty on sharing economy platforms by influencing the perception of benefits. It also provides useful managerial insights to help ride-sharing platforms design online social interaction functions that improve customer perceptions and loyalty.

This research explores the key factors that contribute to the success of Black students in a predominantly White institution (PWI). Two measures of success are examined: cumulative grade point average (GPA) and graduation status.

Using student-level data from a southeastern university, this research estimates education production functions using ordinary least squares regression.

While the negative effect of being Black is significant for both cumulative GPA and graduation status, the effect becomes overshadowed when peer effects are added. The authors also found the critical effect of institutional support on student success.

The student-level data are restricted to a single institution over a relatively short period of time, which limits the authors' ability to analyze institution-level factors.

This research provides a broad view of many significant factors for student success with particular highlights on the importance of encouraging Black students to utilize institutional support.

This study is an extension of the education production function model in the field of student success. The study identified peer effects and institutional support as more powerful determinants of student success than race.

Lead users are essential participants in crowdsourcing innovation events; their continuance intention significantly affects the success of the crowdsourcing innovation community (CIC). Although researchers have acknowledged the influences of network externalities on users' sustained participation in general information systems, limited work has been conducted to probe these relationships in the CIC context; particularly, the predictors of lead users' continued usage intention in such context are still unclear. Hence, this paper aims to explore the precursors of lead users' continuance intention from a network externalities perspective in CIC.

This work ranked users' leading-edge status to recognize lead users in the CIC. And then, the authors proposed a research model based on the network externalities theory, which was examined utilizing the partial least squares (PLS) technique. The research data were collected from an online survey of lead users (n = 229) of a CIC hosted by a China handset manufacturer.

Results revealed that the number of peers, perceived complementarity and perceived compatibility significantly influence lead users' continuance intention through identification and perceived usefulness.

This work contributes to the crowdsourcing innovation research and provides views regarding how lead users' sustained participation can be developed in the CICs. This work also offers an alternative theoretical framework for further research on users' continued intention in open innovation activities.

The purpose of this study is to investigate the effect of nano ZrC particles on the mechanical and electrochemical corrosion properties of FeCrAl alloys, providing a beneficial reference basis for the development of high-performance carbide reinforced FeCrAl alloys with good mechanical and corrosion properties in the future.

Nano ZrC reinforced FeCrAl alloys were prepared by mechanical alloying and spark plasma sintering. Phases composition, tensile fractography, corrosion morphology and chemical composition of nano ZrC reinforced FeCrAl alloys were analyzed by X-ray diffraction, scanning electron microscopy and energy dispersive X-ray spectroscopy, respectively. Microhardness and tensile properties of nano ZrC reinforced FeCrAl alloys were investigated by mechanical testing machine and Vickers hardness tester. Electrochemical corrosion properties of nano ZrC reinforced FeCrAl alloys were investigated by electrochemical workstation in 3.5 wt.% NaCl solution.

The results showed that addition of nano ZrC can effectively improve the mechanical and corrosion properties. However, excessive nano ZrC could decrease the mechanical properties and reduce the corrosion resistance. In all the FeCrAl alloys, FeCrAl–0.6 wt.% ZrC alloy exhibits the optimum mechanical properties with an ultimate tensile strength, elongation and hardness of 990.7 MPa, 24.1% and 335.8 HV1, respectively, and FeCrAl–0.2 wt.% ZrC alloy has a lower corrosion potential (−0.179 V) and corrosion current density (2.099 µA/cm2) and larger pitting potential (0.497 V) than other FeCrAl–ZrC alloys, showing a better corrosion resistance.

Adding proper nano ZrC particles can effectively improve the mechanical and corrosion properties, while the excessive nano ZrC is harmful to the mechanical and corrosion properties of FeCrAl alloys, which provides an instruction to develop high-performance FeCrAl cladding materials.

Recent high-profile ethical scandals in start-up organizations have made people wonder whether entrepreneurship may cultivate a work environment with less emphasis on ethics. This study examined a psychological process about how an organization’s entrepreneurial orientation (EO) can affect its treatment of probationary employees, a vulnerable yet understudied group of workers.

The authors recruited 241 participants through Amazon Mechanical Turk. They answered an online survey about their experiences as probationary employees.

This study found that job feedback and meaning moderated the relationship between EO and ethical climate, such that this relationship was statistically significant and positive only among participants who reported high levels of feedback and job meaning. Ethical climate, in turn, was found to be related to a reduction in workplace incivility experienced by probationary employees. The indirect effect of EO on incivility via ethical climate was contingent on job feedback and meaning.

This study extends the discussion on the entrepreneurial context, adds to EO literature with findings on its indirect effect on nonfinancial performance and reinforces institutional theory through job characteristics’ moderating roles. However, a methodological limitation is conducting a cross-sectional single-source survey due to limited access to firms and probationary employees, considering the hidden population involved.

This study found no evidence of probationary employee exploitation in high EO organizations. Job seekers should embrace probationary work at start-ups. Entrepreneurial leaders should balance being proactive, innovative and caring toward employees.

It is debatable whether entrepreneurship leads to unethical organizational conduct. By studying a vulnerable group of employees, the authors discovered that EO, when paired with favorable job design factors, can create a more ethical workplace where temporary talents are treated with dignity and respect.

One of the most cited literature in SHRM is Schuler and Jackson’s (1987) behavioural model. This model proposes that organisational performance is dependent on the extent to which HRM practices can be effectively connected to competitive strategy and desired employee behaviours. Importantly, this model recognises the salient role of employee behaviour in performance outcomes and, moreover that different competitive strategies imply both promulgation and reinforcement of different sets of employee skills and behaviours. Surprisingly, despite its significant influence on SHRM, studies rarely examine this model in its entirety. Motivated by the need to better understand this model’s arguments in contemporary settings, our study uses a multi-actor design to explore the connections between competitive strategies (cost reduction and differentiation), employee behaviours, and HRM practices in service environments.

Adopting a multi-level, multi-actor survey design, our exploratory deductive study assesses the utility of strategic HRM’s (SHRM) behavioural model. Drawing on data from a sample of service organisations and using univariate analyses, we compare operationalised HRM practices and employee behaviours across different strategy types.

Results lend provisional support for the behavioural model, particularly in the case of a differentiation strategy where notable differences in HRM practices and employee behaviours were observed. Findings suggest growing levels of memetic and competitive isomorphism may be occurring, with this likely attributable to the increased incidence of idea generation and information sharing about best practices occurring amongst practitioners, as well as a growing nuance in operating markets, managerial preferences, employee expectations, stakeholder objectives, and the like.

Our study suggests refinements to the behavioural model are needed. Some support for the model’s key tenets is found, but these appear context specific. Thus, the merit in developing a priori typologies linking strategy type to HRM practices and employee behaviours where organisations operate in environments which are particularised and tumultuous appears debatable.

This study highlights the behavioural model’s nuance to modern service organisations and, by doing so, practitioners are provided with a behavioural pathway for achieving competitive advantage through their HRM practices. Findings also suggest that increasingly competitive environments might be encouraging practitioners to engage in isomorphic behaviours.

The use of a comparative research design allowed our study to contribute much needed empiricism to the largely conceptually informed stylised typologies depicting the linkages between different competitive strategies, implied employee role behaviours and HRM practices, thereby supporting the need for model refinement.

Learning from past construction accident reports is critical to reducing their occurrence. Digital technology provides feasibility for extracting risk factors from unstructured reports, but there are few related studies, and there is a limitation that textual contextual information cannot be considered during extraction, which tends to miss some important factors. Meanwhile, further analysis, assessment and control for the extracted factors are lacking. This paper aims to explore an integrated model that combines the advantages of multiple digital technologies to effectively solve the above problems.

A total of 1000 construction accident reports from Chinese government websites were used as the dataset of this paper. After text pre-processing, the risk factors related to accident causes were extracted using KeyBERT, and the accident texts were encoded into structured data. Tree-augmented naive (TAN) Bayes was used to learn the data and construct a visualized risk analysis network for construction accidents.

The use of KeyBERT successfully considered the textual contextual information, prompting the extracted risk factors to be more complete. The integrated TAN successfully further explored construction risk factors from multiple perspectives, including the identification of key risk factors, the coupling analysis of risk factors and the troubleshooting method of accident risk source. The area under curve (AUC) value of the model reaches up to 0.938 after 10-fold cross-validation, indicating good performance.

This paper presents a new machine-assisted integrated model for accident report mining and risk factor analysis, and the research findings can provide theoretical and practical support for accident safety management.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

The purpose of this paper is to analyze the relationship between affect space and bra design factors based on the observer's vision judgment.

First, using two dimensions of attractiveness and satisfaction to define the bra emotion space based on literature. Then, the mapping relation between bra design factors and emotional space is analyzed in visual perception. Finally, the model of bra emotion recognition based on design factors is established using the neural network BRP.

Users' emotions stimulated by bras can be automatically recognized based on their visual design factors. (1) attractiveness and satisfaction which are used to define bra emotions space show a linear correlation between each other in the human visual domain. (2) The design factors that cause attractiveness can also cause satisfaction. However, the factors that stimulate satisfaction do not necessarily attract users' interest, such as the ratio, location of embellishments, hollowed, core decoration and contour lines.

The analysis of bra emotion space may help designers to understand the relationship between bra visual appearance and emotion, and ask them to pay attention to empathic design factors. With the provided results, designers can also carry out and evaluate emotional bra design with high attractiveness and satisfaction.

This paper discusses the emotional attributes of bra visual design factors based on bra emotion space and explores the methods of bra emotion design.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”