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This paper aims to explore the effects of parents’ support factors on Chinese university students’ digital entrepreneurship motivation on live streaming platforms. Based on the Social support theory, this study divides influencing factors into emotional, instrumental, informational and appraisal aspects. Meanwhile, considering the impact of China’s regional differences, the paper refers to the Regional difference theory and performs a multi-group analysis to assess the differences based on Chinese university students’ regional backgrounds.

By testing 556 samples based on the partial least squares path modelling and variance-based structural equation modelling, all support factors parents provide can stimulate Chinese university students’ digital entrepreneurship motivation.

Based on the multi-group comparison, parents’ informational support exerts a more substantial influence on the digital entrepreneurship motivation for university students from central and east regions rather than those from the western region, and parents’ instrumental support exerts a lower influence on digital entrepreneurship motivation for east university students than for west university students.

This paper applies the Social support theory as a theoretical framework to divide the impact factors, and it uses the Regional difference theory as a guide for the multi-group analysis of correlations, which is significant for online entrepreneurial motivation research and a better understanding of student groups. In addition to testing the hypotheses, the study also measures the importance–performance map analysis to explore additional findings of influencing factors and discuss managerial implications.

Artificial Intelligence (AI) is revolutionizing the world. Despite the numerous advantages of AI in terms of faster processing and higher efficiency, AI hasn’t been widely accepted by humans yet. This study aims to shed light on this phenomenon by exploring the Dunning–Kruger Effect in AI knowledge and examining how AI knowledge affects AI acceptance through AI-related self-efficacy.

By collecting data from 179 managers, we examined the Dunning–Kruger Effect in AI knowledge and used mediation analysis to explore the mechanisms by which AI knowledge leads to AI acceptance.

Our findings indicated the presence of the Dunning–Kruger Effect in AI knowledge. Furthermore, our results revealed that AI knowledge has a nonlinear effect on AI acceptance through AI-related self-efficacy.

In contrast to previous research that posited a linear link between knowledge and acceptance of technology, this study offers a new framework for the nonlinear relationships between AI knowledge, AI-related self-efficacy and AI acceptance by extending the Dunning–Kruger effect to the AI field.

A live-stream failure occurs when the product which is highly recommended by the influencers and exhibited quality problems. This study investigated how brand trust and influencers trust affect live-streaming purchase intentions (PIs) of consumers when live-streaming e-commerce failures occur and the role of the Stealing Thunder (ST).

Totally 584 adults with live-streaming shopping experience were invited to investigate the impact and mechanisms of live-streaming failure incidents, as well as the moderating mechanisms of ST.

Firstly, live-streaming failure accidents decreased the PI of consumers by reducing both brand trust and influencer trust, with trust in the influencers having a stronger impact on PI than brand trust. Secondly, if the influencers used the ST after a live-stream failure, the PI was the same as that in a non-failure scenario. Thirdly, the ST acts as a moderating variable neutralized the negative impact of live-streaming failure on trust in the influencers. Lastly, the negative impact of brand trust on PI was less significant using the ST.

This research deepens the understanding of service failures in live-streaming e-commerce. It provides insights into the consumer behavior and practical guidance on how influencers can actively respond to live-streaming failure to sustain reciprocal relationships in live-streaming e-commerce. The study addresses the effects of the coping strategies of influencers on the interests of product brands and discusses the potential solutions.

Innovation quality is a critical component of enterprise innovation. Prior research primarily focuses on company-level and external policy-level factors that affect innovation quality, while ignoring social-level factors. Based on institutional isomorphism theory, this study examines how the innovation quality of three-dimensional institutional equivalence, which is an important and unique reference group for firms to follow the “law of imitation of close preference”, affects the likelihood of firms' innovation quality.

This study conducts firm random effects and industry/year fixed effects models using China's listed companies from 2002 to 2021.

This study finds that compared with the innovation quality of its other industry, community, or network peers, the innovation quality of three-dimensional institutional equivalence has a greater impact on firm innovation quality. Furthermore, technological intensity significantly increases the effect of three-dimensional institutional equivalence on focal company innovation quality, while financing constraints significantly attenuate this effect. Additionally, when there is no institutional equivalent, the innovation quality of network, industry, and community peers has significant positive effects on enterprise innovation quality. Heterogeneity analysis also indicates that, under the conditions of non-state-owned enterprises, a low regional legal environment, or low regional factor market development, three-dimensional institutional equivalence contributes significantly to firm innovation quality.

This study focuses on the effect of three-dimensional institutional equivalence on Chinese enterprises' innovation quality. Nonetheless, research samples from other countries are not considered in this study.

This study explores the impact of three-dimensional institutional equivalence on firm innovation quality within a systematic theoretical framework and incorporates firm attributes into this framework.

Online grocery stores are facing challenges. The intense competition in the online grocery market has driven companies to seek technological innovation. Moreover, the operations of online grocery stores on both the supply and demand sides are not sufficiently meeting the requirements of consumers and managers. The powerful capabilities of the Generative Pre-Trained Transformer (GPT) technology align with the needs of online grocery stores for innovation and upgrading. This study uniquely leverages GPT’s advanced natural language processing, adaptive learning and generative capabilities to analyze and optimize the online grocery supply chain competition in ways not possible with traditional analytical tools.

This paper constructs a Stackelberg game model, comprising a secondary supply chain consisting of a supplier who provides products and a retailer who sells them. This study explores the impact of GPT technology on online grocery store operations from the demand side and supply side, specifically including the value of service information, demand information and information-sharing behavior.

The findings reveal several vital conclusions: (1) On the demand side, the service information plays a crucial role in enhancing service levels and increasing consumer demand; (2) On the supply side, demand information provides positive incentives for retailers and suppliers and (3) Information-sharing behaviors can lead to cooperative relationships between upstream and downstream supply chain members, significantly increasing their respective service levels. This study not only explores the impact of GPT on the online grocery supply chain but also presents a rigorous framework for validating GPT-generated insights, ensuring the reliability of our findings.

This study provides valuable insights into a promising field. It employs game theory to analyze the impact of GPT technology on the overall operation of the online grocery supply chain and the market strategy of online grocery stores.

The rapid development of information technology, epitomized by AIGC and the metaverse, presents unprecedented challenges to techno-ethics, exposing society to significant risks and uncertainties. A systematic investigation and discussion of the construction of techno-ethical order become crucial under the paradigm of risk society theory. The selection of conditions and pathways for constructing a techno-ethical order under the risk society theory paradigm becomes an unavoidable and vital issue.

Drawing on risk society theory, this study employs scientometrics and qualitative comparative analysis (QCA) to empirically analyze the key factors and conditional pathways in the construction of techno-ethical order. Initially, a quantitative analysis is conducted on 1,490 thematic literature retrieved from CNKI and WoS to identify the hot topics and core concepts in techno-ethical research. Subsequently, QCA configuration analysis is applied to calculate eight evaluation indicators and their weights from the perspectives of government, society and individuals. Finally, the study explores the mechanisms of the impact of these indicators’ weights on the construction of techno-ethical order.

The analysis of factor weights and pathways indicates that the selection of pathways for constructing techno-ethical order is influenced both by the inherent development patterns of technology and societal systems and cultural values. Literature metrics analysis reveals an overall trend of sustained growth in techno-ethical research, indicating an unprecedented prosperity in this field. Alongside technological advancements, keywords related to “artificial intelligence” play a crucial role in current techno-ethical research. Configuration analysis demonstrates that conditional variables from the dimensions of government, society and individuals form a configuration pathway, influencing and synergistically impacting the overall level of techno-ethical order construction. Attention should be given to the mutual constraints and synergistic effects of factors related to technological development, societal systems and cultural values.

This study, grounded in the risk society theory paradigm, quantitatively explores the key factors and pathways of techno-ethical order construction in academic texts, expanding new perspectives, providing novel insights, establishing innovative methodologies and extending new boundaries in the field. Further enrichment of the dataset and in-depth discussions are warranted for continued advancement.

This study aims to examine the impact of coworkers’ sexist views on individual citizenship behaviors and performance outcomes and explores how this relationship is affected by the presence of female managers.

Drawing on diversity management literature and gendered organizational theory, the study analyzes data from 2,166 employees and 141 store managers in an international fast-food chain as well as archival work-unit-level information on performance.

The results support the hypothesis that peers’ sexist views are negatively associated with employees’ citizenship behavior and performance outcomes. Work units with members holding such views are less likely to achieve performance goals, and individuals who work with peers who hold gender inequality views are less likely to engage in citizenship behaviors. This relationship is particularly pronounced in units managed by male managers.

Our study recognizes the complex and multifaceted nature of gender dynamics in organizations and provides insights into the ways in which these dynamics impact organizational outcomes.

The study’s findings have significant implications for organizational policies and practices, specifically in terms of promoting gender equality and fostering more inclusive work environments through well-designed interventions.

This study is grounded in the gendered organizational theory, which highlights the interplay between gender, gender views and the relational dynamics of psychological, cultural and social dimensions operated within organizations. By examining how gender inequality views among coworkers affect perceived support, citizenship behaviors and performance outcomes, we contribute to the growing body of research in this area.

In the vibrant world of social commerce (SC), where information flows freely, interactions thrive and online purchases abound, there is an escalating challenge. Users are uninstalling and disengaging due to approach and avoidance stimuli, a trend mirroring the approach-avoidance motivation model (AAMM). Our study, anchored in AAMM and the stimulus-organism-response (SOR) model, aims to dive into the complex dynamics of these factors that shape users' SC continuance intentions.

Our findings, drawn from 472 SC users in Malaysia, paint an intriguing research framework via PLS-SEM analysis by testing the proposed hypotheses. A purposive sampling technique was utilized, deliberately selecting respondents based on specific criteria. Subsequently, data were gathered through the distribution of face-to-face questionnaires at selected shopping malls, facilitating a focused and comprehensive exploration of consumer perspectives.

The empirical results demonstrate the following: (1) Users' determination to stay engaged on SC platforms hinges on approach factors, like emotional support, surveillance gratification and multisensory gratification. (2) Simultaneously, avoidance factors such as technostress and perceived deception exert their negative influence. (3) Flow experience, rooted in flow theory, emerges as the underlying mechanism connecting these duality stimuli, influencing the continuance intention.

In a departure from conventional research, our study pioneers a comprehensive approach and boldly confronts the research gap by introducing a rich tapestry of antecedents, embracing both the appeal of approach factors and the deterrence of avoidance ones, using the AAMM that sheds light on how individuals navigate between embracing opportunities and avoiding pitfalls based on perceived gains and losses. This holistic approach enables us to redefine our understanding of digital engagement dynamics, offering a captivating journey into the realm of user experience and intention that transcends the ordinary.

Public service announcements (PSAs) have been shown to be effective instruments that raise awareness, educate society, and change behaviors and attitudes. Many governments and organizations have utilized PSAs on social media to promote online safety among children and youth. However, we have limited understanding of the range of topics that these PSAs address and how they present their content to audiences. This study provides an inventory of the types of online safety topics that current PSAs address and a catalogue of the types of persuasive features employed by PSAs.

A content analysis of 220 YouTube PSA videos on online safety was conducted. Various topics under the umbrella of online safety were identified. Guided by the prospect theory and exemplification theory, different persuasive features employed in the PSAs were sought.

The findings highlight that the primary focus of these PSAs is on online safety behaviors and general instructions on online hygiene. Interestingly, nearly half of the videos employ a neutral frame, while a significant portion provides no evidential support. Additionally, video length was associated with the number of views and likes it gathered but not with the number of comments.

The inventory of PSAs can help researchers, practitioners, and policymakers better understand the type of content being produced and disseminated online as well as identify topics that are either over or under-represented. Further, the catalogue of the types of persuasive features employed by PSAs would be helpful in guiding research, practice, and policymaking in the context of creating effective online safety videos.

The purpose of this study is to develop a novel approach that addresses time-cost tradeoffs in repetitive construction projects while considering the uncertainty in activity durations and the risk preferences of planners.

Our study involves work in three aspects. Firstly, it employs triangular fuzzy numbers to represent activity durations in different units, which facilitates the management of scenarios characterized by limited historical data or the presence of ambiguous information. Secondly, it introduces a fuzzy chance-constrained programming model, which is aimed at minimizing the project budget while ensuring that the risks associated with cost overruns and schedule delays are confined to specified limits. Thirdly, it advances an enhanced genetic algorithm, integrating an electromagnetism-like mechanism and a scheduling repair process, to improve the efficiency and effectiveness of the optimization process.

A real-life street renovation project was analyzed to demonstrate the applicability of the proposed algorithm. The analysis explored three common types of risk preferences: risk-averse, risk-neutral and risk-loving. The results indicate that the proposed algorithm surpasses existing fuzzy repetitive scheduling methods in terms of risk management. It effectively generates schedules that align with the risk preferences of planners and provides worst-case estimates of project performance.

This research makes a significant contribution to the field by developing a fuzzy chance-constrained programming model and an associated optimization algorithm that is specifically designed for time-cost tradeoffs in repetitive construction projects. A key distinction is that this study considers the risk preferences of planners, which sets it apart from previously developed models. As a result, it provides a practical approach for effective risk management.