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This research focuses on exploring a psychological mechanism between abusive supervision and job engagement via job insecurity and job alternative to understand how frontline construction workers' perceived abusive supervision impacts on their work engagement.

This study draws on the conservation of resources theory (COR) to postulate that workers' job insecurity acts as a mediation between abusive supervision and work engagement, and the perception of job alternative moderates the impact of abusive supervision on job insecurity. This study empirically tests survey data from 357 frontline construction workers and adopts partial least square structural equation modeling (PLS-SEM) to test the proposed model.

This paper proved the dark-side of abusive supervision, dual character of job insecurity and moderating role of job alternative. The game-changing mediation effects of job insecurity were examined, which suggested that work engagement would be higher when abusive supervision stimulates stronger job insecurity. Furthermore, the result indicated that the relationship between abusive supervision and job insecurity should be weaker when job alternative is high.

This research suggests construction project managers (CPMs) must take steps such as enhancing supervisors' emotional management skills to control frequency of abuse and thus lessen compromising engagement. Moreover, overall understanding frontline workers' job insecurity is meaningful for better construction performance management.

This research contributes to the disputed leadership style (abusive supervision) and positive psychology knowledge (work engagement) in construction projects by unveiling the psychosocial mechanism (game-changing effects of job insecurity) of construction frontline workers positive engaged feeling. This study highlights the function of personal and job resources in process of resource conservation.

This viewpoint paper aims to explore the adoption of artificial intelligence (AI) in personalized learning for employees and its potential, challenges and ethical consideration to transform the landscape of employee’s performance improvement.

A review of relevant research papers, articles and case studies is done to highlight developments in research and practice.

Personalised learning powered by AI will transform employee education and training as AI evolves, helping individuals and organisations succeed in an ever-changing, knowledge-driven world. Concerns like data privacy, algorithmic bias and human engagement must be addressed to achieve ethical and fair implementation.

This paper discusses the overall influence of AI adoption on personalised learning for employees. It examines how AI technologies have the ability to disrupt traditional learning methodologies, modify organisational learning cultures and encourage an environment that fosters ongoing learning and innovation.

This paper aims to identify such “entrepreneurial skills” that the scientific community can develop within themselves through training and practice to improve their academic research translation capabilities and performance.

From a qualitative perspective, this study currently presents a few findings from some of the best government-funded laboratories in India and Japan, where they are training their young researchers to be world-class scientists as well as entrepreneurs to help them commercialize their scientific research findings from lab to market for societal benefit.

This paper intends to focus on identifying some of the “best practices” in developing such capabilities that allow “entrepreneurial scientists” to take their science to the society through entrepreneurship. This paper presents two exemplary case studies about institutions that are trying to build innovation and entrepreneurship capabilities in their research groups through continuous training and practice in the fields of biotechnology (India) and autonomous transportation (Japan).

Policymakers and top management teams at academic institutions can learn from the “best practices” on envisioning, evaluation and execution skills for designing their innovation and entrepreneurship skill-building programs for their scientific community.

The purpose of this paper is to identify the negative aspects of blockchain technology and to shed the light on most productive years, countries, authors, sources and frequent keywords.

A Web of Science bibliographic data set containing 209 journal articles was evaluated using descriptive and network analytics. A two-step process is adopted in this study; descriptive analysis is initially carried out using RStudio to determine the most productive years, nations, sources and authors, and using co-occurrence of keyword analysis in VOSviewer, the most influential keywords are determined.

The findings reveal that 2022 is the most prolific year in terms of number of publications. It is discovered that China tops the list for having published the most articles. Similarly, the most productive authors are Kumar A and Abhishek K.

To the best of the authors’ knowledge, this bibliometric analysis is unique in that it takes a thorough approach to examine the negative aspects of blockchain technology and identify research trends and offer insights that might guide future research and practical solutions.

Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.

Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000.

1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation

We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure.

Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments.

To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial.

This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

US researchers reviewed previous studies of employee development programs (EDPs) to search for practical implications for organizations. They concluded that EDPs can be used to build a more cohesive workforce, achieving employee motivation, productivity, satisfaction and retention. Building mutual relationships between managers and employees using scheduled, formal EDPs will, therefore, benefit organizations.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

In the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries.

The authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies.

The research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE.

The novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.