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The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team psychological empowerment and information systems development (ISD) team performance.

A survey approach was employed to collect time-lagged, multi-source data for testing the proposed model of this study (N = 514 responses from 88 teams). PROCESS macro was used to analyze the data to generate empirical results.

The results suggest that instrumental AI use indirectly influences ISD team performance by enhancing team psychological empowerment. Additionally, it moderates the effects of team-level LMX on team psychological empowerment and ISD team performance. Furthermore, the results demonstrate that the interaction effect of LMX and instrumental AI use on ISD team performance is mediated by team psychological empowerment.

While research on ISD consistently demonstrates that teams, data, and technology collectively contribute to the success of these projects. What is less known, however, is how the exchange relationship between ISD teams and their leader, as well as technological factors, contribute to ISD projects. This study draws on LMX theory to propose how team-level LMX and the instrumental use of AI by team members influence team psychological empowerment and ISD team performance. The study puts forth a mediated moderation model to develop a set of hypotheses. It offers valuable contributions to AI and LMX, along with implications for ISD team management.

The development of an effective corporate vision is a necessary issue for corporate performance, and it is a key issue for corporate sustainable development as well. The recognition of questions like “what is the role of corporate vision in corporate performance” is directly related to the attitude and practice of entrepreneurs and managers toward the development of corporate vision as well as the effectiveness of the corporate vision itself. To better answer the questions concerning the role of corporate vision development and effectively guide the practice of corporations, the authors study the pathways and mechanisms by which corporate visions operate to assist businesses in achieving high performance.

The article completes the construction of indicators to measure each dimension of the corporate vision in line with social cognitive theory and analyzes the relationship between corporate vision and corporate performance by combining qualitative comparative analysis (QCA) and necessary condition analysis (NCA) research methods. The article provides insights into the logic of constructing and adjusting corporate visions from a process perspective.

The mechanisms by which corporate visions can be articulated, accepted and transformed within the organization are also the means by which corporate visions can improve corporate performance. In a dynamic environment, the corporate vision setting and acceptance process integrates the requirements of various stakeholders, leading to the adjustment and acceptance of the corporate vision. As a result, the vision has continuous validity in a changing environment. Both start-ups and non-start-ups can benefit from the guidance provided by a strong corporate vision in overcoming a variety of issues and obstacles to produce strong business performance.

This is the first study that shows the relationship between corporate vision and corporate performance from a process perspective. The authors are interested in understanding which characteristics for building a corporate vision are more accepted by organizational members and, in turn, create high corporate performance. The authors also explore the conditions for corporate vision acceptance. This research has positive implications for shedding some light on the mechanisms by which corporate visions improve corporate performance.

This study aims to analyse the relationship between a patent’s network position in a knowledge search network and the likelihood and speed of patent transactions. Additionally, it explores whether patent scope moderates these relationships.

In this empirical study, the authors collected a sample of patents in the artificial intelligence industry over the period of 1985–2018. Then, the authors examined the direct roles of degree centrality, betweenness centrality and closeness centrality on the likelihood and speed of patent transactions and the moderating role of patent scope in the knowledge search network using the logit and accelerated failure time models.

The findings reveal that degree centrality positively affects both the likelihood and speed of patent transactions, while betweenness centrality enhances the likelihood, and closeness centrality significantly boosts both. However, regarding the speed of patent transactions, closeness centrality is the most impactful, followed by degree centrality, with no significant influence of betweenness centrality. Additionally, the patent scope moderates how betweenness centrality affects the likelihood of transactions.

This study has limitations owing to its exclusive use of data from the Chinese Intellectual Property Office, lack of visibility of the confidential terms of most patent transactions, omission of transaction directionality and focus on a single industry, potentially restricting the breadth and applicability of the findings. In the future, expanding the data set and industries and combining qualitative research methods may be considered to further explore the content of this study.

This study has practical implications for developing a better understanding of how network structure in the knowledge search network affects the likelihood and speed of patent transactions as well as the identification of high-value patents. These findings suggest future directions for patent holders and policymakers to manage and optimise patent portfolios.

This study expands the application boundaries of social network theory and the knowledge-based view by conducting an in-depth analysis of how the position characteristics of patents within the knowledge search network influence their potential and speed of transactions in the technology market. Moreover, it provides a theoretical reference for evaluating patent value and identifying high-quality patents by quantifying network positions. Furthermore, the authors construct three centrality measures and explore the development of patent transactions, particularly within the context of the developing country.

This paper aims to examine the strength characteristics of a stressful event (i.e. novelty, disruption, and criticality) as factors that drive people’s social media use for seeking different types of supportive resources (i.e. emotional, appraisal, informational, and instrumental support) to facilitate emotion-focused and problem-focused coping. We further assess the impact of different types of social support obtained via social media use on people’s coping effectiveness.

Our study uses an online survey collecting data at two points in time from 291 social media users during the COVID-19 pandemic. Structural equation modeling was used for data analysis.

Empirical results reveal the usefulness and limitations of social media use as a coping mechanism. All three event strength characteristics influence people’s social media use for both emotion-focused and problem-focused coping. Event novelty motivates people’s pursuit of informational support on social media, event disruption drives social media use for seeking all four types of support, and event criticality motivates social media use for seeking emotional and informational support. However, only emotion-focused resources – emotional support and appraisal support – are found to significantly affect people’s coping effectiveness.

Our study contributes to a better understanding of the role played by social media when people cope with a stressful event. Applying the three characteristics of event strength allows us to identify people’s need for different supportive resources depending on how they perceive the event. Our analysis of the main and mediating effects of the four types of social support shows that not all types of social support can significantly enhance users’ coping effectiveness.

With growing concern about sustainable development and increased awareness of environmental issues, digital technologies (DTs) are gaining prominence and becoming a promising trend to improve productivity, sustainability and project performance in the construction industry. Nonetheless, the uptake of DTs in the construction industry has been limited and plagued with roadblocks. This study aims to identify critical barriers for construction organisations to adopt DTs and to demonstrate relationships between organisational characteristics and the perceived DTs adoption barriers.

This study adopted an explanatory sequential design by combining the advantages of quantitative and qualitative data. Data collection methods include literature review, a pilot study, questionnaire survey, and semi-structured interviews. Questionnaire data were analysed by using SPSS and multivariate regression technique. The interview data were processed by using content analysis to validate and supplement findings from the questionnaire.

Based on the survey and interview results, eight critical barriers were identified: the three top critical barriers are (1) “status quo industry standards”, (2) “lack of client interest” and (3) “lack of financial need/drive for using DTs”. The eight critical barriers were further classified into technical, environmental, and social dimensions to determine the major constructs that hinder DTs adoption. A theoretical framework articulating critical barriers with underlying components and root causes was also proposed. Furthermore, by using multivariate regression analysis, a model was developed to link the organisational characteristics with barriers to DTs adoption.

By referring to the framework and the model developed, academics, industry practitioners, and decision makers can identify pivotal areas for improvement, make informed decisions and implement remedial measures to remove the barriers to digitalisation transformation.

This study contributes to the literature on construction innovations by investigating barriers to DTs adoption holistically as well as perceptions of the impact of organisational attributes on these barriers. It establishes the groundwork for future empirical research into the strategic consolidation of movement of DTs adoption and diffusion.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

Turnover intention is a critical predictor of an employee’s turnover behaviour. A high level of turnover rate significantly affects the productivity and morale of an enterprise. Previous research has indicated that job satisfaction plays a critical role in influencing an employee's turnover intention, but the underlying factors related to job satisfaction remain under-explored, which impedes the development of effective strategies for reducing turnover intention. In addition, little research examined job satisfaction and turnover intention in the context of the COVID-19 pandemic, specifically in the Chinese construction industry. This study aims to investigate the impact of job satisfaction on turnover intention among professionals in the construction industry.

A questionnaire survey was employed to collect viewpoints from 449 professionals in the Chinese construction industry, followed by descriptive analysis, correlation analysis and structural equation modelling analysis to derive results.

The findings indicate that professionals in the industry generally have a slightly high level of job satisfaction while a slightly low level of turnover intention in the special period of the pandemic outbreak. Leadership and management, training and career development and interpersonal relationships are critical underlying factors leading to their turnover intention. Although demographic factors have no moderating effect between job satisfaction and turnover intention, among them, age, marital status and years of working experience have strongly positive relationships with job satisfaction while significantly negative relationships with turnover intention.

The findings provide valuable insights to fully understand the critical factors leading to turnover intention from the perspective of job satisfaction, which is helpful in developing effective measures to address the turnover problems for enterprises in the Chinese construction industry and those industries with similar characteristics in other regions.

To meet environmental performance (EP) goals, this study aims to identify the complex interaction between green Industrial Internet of Things (GIIoT), circular economic practices (CEPs) and dynamic capabilities (DC). This study analyzes how technological, operational and cultural compatibilities enhance GIIoT adoption.

Data were collected from diverse Chinese manufacturing firms (n = 339) through a quantitative survey. The research model was proposed, and hypotheses were tested using structural equation modeling. Moreover, the robustness of the structural model is further tested using Fuzzy Set Qualitative Comparative Analysis and importance performance map analysis.

The empirical results indicate that higher organizational compatibilities boost GIIoT adoption and EP. DC was assessed as a higher-order construct to examine its mediation of GIIoT adoption and EP. DC positively mediates GIIoT adoption-EP. Similarly, CEP’s positive impact on EP, partially mediating the relationship between GIIoT adoption and EP, has also been proved.

This research bridges current understanding and contributes useful insights for fostering environmental sustainability inside manufacturing firms and advances the theoretical understanding of technology adoption, sustainable development and dynamic capacity theory. It illuminates the way forward to harmonize and successfully integrate technology, CEP and EP. This research advances the area and gives decision-makers practical advice for creating sustainable and technologically sophisticated organizations.

Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.