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Effective management of risk and knowledge is critical to ensure the success of industry–university collaboration (IUC) projects. However, the intricate dynamics through which these factors influence the performance of IUC projects have yet to be fully investigated. The purpose of this study is to explore the interplay between risk management and knowledge management capabilities and their impact on IUC project performance.

A model was constructed and evaluated through the examination of a sample of 188 collaborative innovation projects located in the United Arab Emirates (UAE), utilizing structural equation models (SEM) and hierarchical regression analysis.

The findings indicate that social system risk, technical system risk and project management risk have a negative impact on the performance of university–industry collaboration (UIC) projects, while cultural, technical and structural knowledge management capabilities can mitigate the negative impact of these risks on the performance of IUC projects.

The study concludes with three recommendations aimed at improving the management of UIC projects, including the establishment of a distinct and precise management strategy, the deployment of a comprehensive and systematized management methodology and the adoption of a balanced management framework.

The originality and value of this study lie in its exploration of the interplay between risk management and knowledge management capabilities in IUC projects. While previous studies have examined either risk management or knowledge management in IUC projects separately, this study provides a comprehensive analysis of both factors and their combined impact on project performance. The study also contributes to the literature by highlighting the specific risks and knowledge management capabilities that are most relevant to the context of IUC projects in the UAE. The practical recommendations offered by the study can help project managers and stakeholders to improve the success of collaborative innovation projects.

Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.

This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.

Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.

This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.

This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.

This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

This study aims to investigate how a firm's management team's capacity to efficiently use its resources affects the firm's exposure to climate change. Specifically, the authors investigate the intriguing question – does managerial ability affect a firm's climate change exposure?

The authors use an unbalanced panel dataset of 4,230 US based firms listed on Compustat from 2002–2019 and test the hypothesis by panel regression analysis. To mitigate endogeneity concerns, difference-in-differences and instrumental variable approaches are used.

The baseline analysis shows a negative, statistically significant impact of managerial ability on climate change exposure. The findings hold after controlling for endogeneity using two-stage least squares regression and difference-in-differences tests. The authors find the negative effect is stronger for managers engaged in socially responsible activities, and after climate change issues receiving greater public awareness following the 2006 release of the Stern Review and the 2016 signing of the Paris Accord.

Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the Sautner, Van Lent, Vilkov and Zhang's machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.

Motivated by the resource-based theory and the natural resource-based view of the firm model, the empirical results support the view that greater managerial ability protects the firm against environmental challenges through efficient use of firm resources. Compared with traditional climate change measures that are plagued by disclosure issues, the use of the machine learning based dataset utilizing earning conference calls provides stronger, robust findings that will be useful to management and investors in environmental performance assessments.

Abusive supervision has been a prevalent issue in the workplace. This study aims to explore the consequences of abusive supervision on employee affective organizational commitment and general health in the hospitality industry and further explores the boundary conditions of employee assistance programs (EAPs).

The participants of this study were 231 frontline employees from the hospitality industry in Taiwan. Quantitative data was collected using questionnaires from two time periods separated by a two-week interval. The data was analyzed using PROCESS macro for SPSS.

The findings from this study suggested that abusive supervision have negative impacts on both subordinates’ affective organizational commitment and general health. As expected, perceived effectiveness of EAPs moderated the relationship between perceived abusive supervision and affective organizational commitment, whereas the moderating effect of perceived effectiveness of EAPs on the relationship between abusive supervision and employee general health was not significant.

The results of this study showed that EAP practices can mitigate the negative effects of abusive supervision. It is expected to encourage managers in the hospitality industry to minimize or even prevent abusive supervision. Further, the authors suggest organizations implement specific strategies in their EAPs to assist employees in coping with the negative emotions accompanying abusive supervision.

This study offers empirical evidence that illustrates the importance of EAPs and how they may reduce the negative impacts of abusive supervision.

This study investigates the crucial link between sustainable practices and organizational development, focusing on sustainable knowledge management (SKM), green innovation (GI) and corporate sustainable development (CSD) in diverse Pakistani organizations.

This study employs a comprehensive research methodology involving advanced statistical techniques, such as confirmatory factor analysis, structural equation modeling and hierarchical linear modeling. These methods are instrumental in exploring the complex interrelationships between SKM, GI, moderating factors and CSD.

This research generates significant findings and actively contributes to sustainable development. The following sections (Sections 4 and 5) delve into the specific findings and in-depth discussions, shedding light on how industry regulation, organizational sustainability priorities, workplace culture collaboration and alignment between green culture and knowledge management practices influence the relationships between SKM, GI and CSD. These findings provide valuable insights for the research community and organizations striving for sustainability.

The study’s findings have practical implications for organizations seeking to enhance their sustainability efforts and embrace a socially and environmentally conscious approach to organizational growth.

This study contributes to the literature on sustainable practices and organizational development. Researchers and business people can learn a lot from it because it uses advanced econometric models in new ways and focuses on the link between knowledge management, GI and sustainable corporate development.

A crucial question still remains unanswered as to whether data analytics-oriented business intelligence (hereafter, BI) technologies can bring organizational value and benefits. Thereby, several researchers called for further empirical research to extend the limited knowledge in this critical area. In an attempt to deal with this issue, we presented and tested a theoretical model to assess BI effectiveness at the organizational benefits level in this research article.

The suggested research model expands the application of the DeLone and McLean model in BI technology success or effectiveness research from individual level to organizational level. A cross-sectional survey is developed to obtain primary quantitative data from business and technology managers who are depending on BI technologies to make operational, technical and strategic decisions in Jordanian-listed firms.

Empirical findings show that system quality, information quality and training quality are significant predictors of user satisfaction, but not of perceived benefit. Data quality was found to be a strong predictor of both perceived benefit and user satisfaction. The influence of perceived benefit on user satisfaction was significant in turn both factors positively affect organizational benefits.

This research paper is a pioneering effort to assess BI technology effectiveness at an organizational level outside the context of developed countries. To the best of the authors’ knowledge, no prior research has combined all dimensions used in this research in one single model.