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Reverse mentoring is gaining attention as a means to engage and retain young employees by facilitating the exchange of knowledge, ideas and perspectives with their senior counterparts (leaders). Despite its widespread recognition, there remains a significant scarcity of empirical evidence regarding its enablers and effectiveness. Building on this research gap, this study aims to investigate the association between leader humility, reverse mentoring and subordinate turnover intentions using the theoretical frameworks of job demand resource theory and social exchange theory. In addition, the study assesses how leader competence moderates the impact of leader humility on promoting reverse mentoring.

The study used time-lagged multi-wave data with a two-week interval between each wave, collected from 386 information technology professionals working in different organisations in India. The hypotheses developed were tested using partial least square structural equation modelling.

The finding from the analysis reveals that leader humility had a significant impact in promoting reverse mentoring, which consequently led to reduced subordinate turnover intentions. In addition, leader competence positively moderates the association between leader humility and reverse mentoring.

To the best of the authors’ knowledge, this study is the first to empirically examine the impact of reverse mentoring on subordinates’ turnover intentions, as well as the indirect effect of leader humility on turnover intention through reverse mentoring. Furthermore, the study sheds light on the previously under-researched boundary conditions of leader humility.

The success of SMEs' financial and market performance (MAP) depends on the firms' level of blockchain technology adoption (BCA) and identifying the crucial antecedents that influence SMEs' adoption. Therefore, this research attempts to develop an integrated model to understand and predict the determinants of BCA and its effect on SMEs' performance. The purpose of this paper is to address this issue.

The theoretical foundations are the technology–organization –environment (TOE) framework and the resource-based view (RBV) perspective. The authors distributed a survey to SMEs in South Africa and received 311 responses. The covariance-based structural equation modeling (CB-SEM) followed by the artificial neural network (ANN) technique was used for the data analysis.

The SEM results showed that SMEs' relative advantage, compatibility, top management support (TMS), organizational readiness (ORD), competitive pressures (COP), external support, regulations and legislation significantly influence SMEs' BCA. However, complexity negatively impacts SMEs' BCA. The analysis results also revealed that SMEs' BCA significantly influences the financial performance of the firms, followed by MAP. Furthermore, model determinants were input to an ANN modeling. The ANN results showed that TMS is the most critical predictor of SMEs' BCA, followed by ORD, COP, external support, and regulations and legislation.

The results provide valuable information for SMEs when maneuvering their adoption strategies in the scope of blockchain technology. Additionally, from the perspective of an emerging market, the study has successfully contributed the TOE framework and the RBV.

This study is the first work to explore the determinants of BCA in the context of SMEs from a developing country. This paper is also one pioneer in attempts to develop a causal and predictive statistical model for predicting the determinants of BCA in SMEs' performance.

This study attempts to develop a practical understanding of the positive and negative employee experiences due to artificial intelligence (AI) adoption and the creation of technostress. It unravels the human resource development-related challenges with the onset of Industry 4.0.

Semi-structured interviews were conducted with 32 professionals with average work experience of 7.6 years and working across nine industries, and the transcripts were analyzed using NVivo.

The findings establish prominent adverse impacts of the adoption of AI, namely, information security, data privacy, drastic changes resulting from digital transformations and job risk and insecurity brewing in the employee psyche. This is followed by a hierarchy of factors comprising the positive impacts, namely, work-related flexibility and autonomy, creativity and innovation and overall enhancement in job performance. Further factors contributing to technostress (among employees): work overload, job insecurity and complexity were identified.

The emerging knowledge economy and technological interventions are changing the existing job profiles, hence the need for different skillsets and technological competencies. The organizations thus need to deploy strategic manpower development measures involving up-gradation of skills and knowledge management. Inculcating requisite skills requires well-designed training programs using specialized tools and virtual reality (VR). In addition, employees need to be supported in their evolving socio-technical relationships, for managing both positive and negative outcomes.

This research makes the unique contribution of establishing a qualitative hierarchy of prominent factors constituting unintended consequences, positive impacts and technostress creators (among employees) of AI deployment in organizational processes.

The high degree of likely disruption challenges organizations at all levels to develop and implement innovative strategies. Ensuring supply chain continuity even during emergency and complex situations is critical for organizations. Therefore, this study explores some strategies adopted by firms based on innovation and blockchain-enabled digital transformation to reduce risk in their supply chain.

This study follows the qualitative form of enquiry. The authors interviewed 26 professionals from the supply chain domain. After three-layered coding and mapping multiple layers to the data of interviews, the authors identified emerging themes and sub-themes through a thematic analysis.

The authors identified type of risks that can affect global supply chains along with both the role of blockchain and innovation culture in minimizing the degree of such risks and the challenges in adopting blockchain technologies. This led us to develop a framework to address supply chain risk through digital transformation through innovation and blockchain.

This research offers exciting implications for practice by drawing on the insights gathered to facilitate supply chain risk management through innovation and blockchain applications for organizations that are strongly impacted by digital transformation practices around the world. The study also offers the utilization of a framework followed by propositions to reduce supply chain risks in the digital transformation era.

This study focuses on presenting a mechanism of supply chain risk management through the application of innovation and blockchain technology for the digital transformation of a value chain. Blockchain can offer an innovative platform to ready the supply chain for future dynamic situations.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.