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We investigate the relation between corporate culture and the likelihood of discontinuing business operations.

We rely on regression analysis in our study.

We find a significant negative relation, suggesting that firms with strong corporate culture are less likely to discontinue business operations. To enhance the incremental contributions of our study, we delve into the moderating role of corporate culture on the aforementioned relation and identify several factors that, when coupled with corporate culture, could indirectly impact the decision-making process regarding discontinuing operations. We also find that the negative relation between corporate culture and discontinued operations is mainly driven by firms with lower earnings performance, and this relation becomes stronger for high-tech firms. Lastly, we find that stronger culture is associated with a larger magnitude of discontinued operations for firms reporting discontinued operations, and this positive association is largely driven by firms reporting income-decreasing discontinued operations.

Our analysis adds to two independent streams of research: corporate culture in management literature and discontinued operations in accounting literature. Prior research, in particular, focuses on examining if and how managers exploit discontinued operations to manipulate earnings. By showing a significant negative impact of corporate culture on the likelihood of discontinuing business operations, our research undoubtedly adds to the body of understanding regarding the factors that lead managers to discontinue certain operations.

In recent years, organizations have progressively adopted electronic performance monitoring (EPM) to obtain accurate employee performance data and improve management efficiency in response to the growing complexity of the work environment. However, existing research has primarily focused on examining the effect of EPM on employee behaviors within established job designs, neglecting the consequential role of EPM in shaping employees’ bottom-up job redesign (i.e. job crafting). This study aims to explore whether and how EPM affects employee job crafting.

To test proposed hypotheses, we conducted two time-lagged surveys across different cultural contexts and a scenario experiment on an online platform in China.

The results revealed the negative indirect relationship between EPM and employee job crafting via role breadth self-efficacy. This indirect relationship was moderated by constructive supervisor feedback and job complexity, with the above relationships being weak (versus strong) when constructive supervisor feedback was high (versus low) or job complexity was low (versus high).

The results have crucial implications for organizational practices, suggesting that managers should provide constructive feedback to break the trade-off between EPM and job crafting. Additionally, managers may need to give employees with high job complexity more autonomy rather than intense monitoring.

This study is the first to clarify the effect of EPM on employee job crafting. As job crafting captures the important value of employees in organizational job design, our effort helps to enrich the understanding of EPM effectiveness.

The purpose of this study is to explore how to select a suitable supply chain collaboration paradigm (SCCP) based on the intelligent manufacturing model (IMM) of enterprises.

Given the fit between internal collaboration and external collaboration, we propose a model to select a suitable SCCP based on two-sided matching between SCCPs and IMMs. In this decision problem, we invited five university scholars and seven related consultants to evaluate SCCPs and IMMs based on the regret theory, which is used to obtain the perceived utility and matching results. The evaluation values are comfortably expressed through probabilistic linguistic term sets (PLTSs). Also, we set the lowest acceptance threshold to improve the accuracy of matching results.

The findings indicate that the characteristics of IMMs can significantly influence the selection of SCCPs, and an SCCP is not suitable for all IMMs. Interestingly, the study findings suggest that the selection of SCCP is diverse and multi-optional under the constraints of IMMs.

Existing studies have explored supply chain collaboration (SCC) in Industry 4.0 to improve supply chain performance, but less attention has been paid to the impact of the match between SCCPs and IMMs on supply chain performance. And even fewer studies have addressed how to select a suitable SCCP in different IMMs. This study provides a unique contribution to the practice of SCC and expands the understanding of supply chain management in Industry 4.0.

This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech businesses.

About 67 high-tech businesses in China focusing on technical innovation from the Guotai’an database are selected to carry out empirical analysis.

It is observed that the age, educational and professional backgrounds of college entrepreneurs profoundly influence their ventures geared toward high-tech innovation. Moreover, the transformation abilities, managerial proficiency and growth capabilities, which characterize these ventures, notably affect business performance. They further serve as a moderator in the relationship between the entrepreneurial backgrounds of college students and the overall business performance of their enterprises.

It insinuates novel strategic avenues for collegiate entrepreneurs’ entrepreneurial mindset and industrial positioning. Moreover, our findings will not only augment the practical research in the realm of collegiate entrepreneurship but also enhance the study of technological innovation theories, thereby offering further insight and guidance for collegiate entrepreneurs’ innovative endeavors and entrepreneurial pursuits.

The construction industry and its activities harmfully affect the environment. Hence, adopting green building (GRB) practices can be helpful in achieving sustainable development goals. Therefore, this study aims to identify the factors affecting the intention to adopt GRB practices by extending theory of planned behavior (TPB).

Using non-probability purposive sampling technique, data was gathered from consultant and contractor engineers in the construction industry through a questionnaire. The analysis was done using partial least square-structural equation modeling technique on a useful sample of 290.

Findings revealed that the core constructs of TPB [i.e. attitude (AT), subjective norms (SUBN) and perceived behavioral control (PBC)] significantly affect the intention to adopt GRB practices. Moreover, government support and knowledge of green practices (KNGP) were found to be critical influencing factors on AT, SUBNs and PBC. Lastly, the findings confirmed that environmental concerns (ENC) play as a moderating between SUBN and intention to adopt GRB practices, as well as AT and intention to adopt GRB practices.

This study contributes to existing knowledge on GRB, offering evidence base for policy choices regarding climate change adaptation and mitigation in the construction industry.

This study provides insights from the perspective of a developing economy and confirms the applicability of TPB in the adoption of GRB practices. Moreover, this study confirms the moderation role of ENC in between TPB constructs and intention to GRB that is not tested earlier in the context of GRB. This study also confirms that government sustainable support positively affects PBC, and KNGP significantly affects SUBNs.

The purpose of this research is to address the challenges of selecting optimal drones for disaster response operations under uncertainties. Traditional static (deterministic) models often fail to capture the complexities and uncertainties of disaster scenarios. This study aims to develop a more resilient and adaptable decision-making framework by integrating the best-worst method (BWM) with stratified multi-criteria decision-making (SMCDM), focusing on various uncertainty scenarios such as weather conditions, communication challenges and navigation and control issues.

The methodology involves identifying seven essential criteria for drone evaluation, guided by contingency theory. The BWM derives optimal weights for each criterion by comparing the best and worst alternatives. The SMCDM incorporates different uncertainty scenarios into the decision-making process. Sensitivity analysis assesses the robustness of decisions under various criterion weightings and operational scenarios. This integrated approach is demonstrated through a practical application to the Kerala flood scenario.

The integrated stratified BWM method proves to be highly effective in adapting to different uncertainty scenarios, enabling decision-makers to consistently identify the optimal drone for disaster response. The method’s ability to account for uncertain conditions such as weather, communication challenges and navigation issues ensures that the optimal drone is selected based on the situation at hand.

The methodology fills critical gaps in the literature by offering a comprehensive model that incorporates various scenarios and criteria for optimal drone selection. However, there are certain limitations. The reliance on expert opinions for criterion weightings introduces subjectivity, potentially affecting the generalizability of the results. In addition, the study’s focus on a single case, the Kerala floods, limits its applicability to other geographic contexts. Integrating real-time data analytics into the decision-making process could also enhance the model’s adaptability to evolving conditions and improve its practical relevance.

This research offers a practical, adaptable framework for selecting optimal drones in disaster scenarios. By integrating BWM with SMCDM, the methodology ensures decision-makers can account for real-time uncertainties, such as weather or communication disruptions, to make more informed choices. This leads to better resource allocation and more efficient disaster response operations, ultimately enhancing the speed and effectiveness of relief efforts in various contexts. The method’s ability to adjust based on scenario-specific factors ensures that drones are optimally deployed according to the unique demands of each disaster.

By incorporating SMCDM, the proposed methodology assists decision-makers in appropriately choosing drones based on their characteristics crucial for specific scenarios, thereby enhancing the efficiency and effectiveness of relief operations.

This study presents a unique integration of the BWM with SMCDM, creating a dynamic framework for drone selection that addresses the challenges posed by uncertain disaster environments. Unlike traditional methods, this approach allows decision-makers to adjust criteria based on evolving disaster conditions, resulting in more reliable and responsive drone deployment. The method bridges the gap in existing literature by offering a comprehensive tool for disaster response, providing new insights and practical applications for optimizing drone operations in complex, real-world scenarios.

Metaverse technology has attracted much attention in many contexts, including industry, education, marketing and business. Some recent studies have focused on qualitative studies based on the actual definition of the metaverse. However, practical research related to metaverse platforms remains in its infancy. This study provides actionable insights into the determinants of metaverse adoption by using perceived fluidity.

A two-stage structural equation modeling (SEM) approach and Hayes’ Macro approach are used to examine the proposed hypotheses.

Results show that technology features (e.g. real-time rendering, interactivity and immersion) increase users’ perceived fluidity, which in turn leads to positive intentions to use the metaverse. A high level of perceived realism is not an advantage for metaverse technology and plays a negative moderating role in this mechanism. The interaction of awe with technological features can enhance the negative moderating effects of realism.

This study pioneers the examination of perceived fluidity as a key determinant of metaverse adoption, offering a novel perspective beyond traditional factors. It uniquely identifies the paradoxical role of perceived realism, demonstrating its potential negative impact on user experience. In addition, the research highlights the reinforcing effect of awe on this relationship.