Purpose: This paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close interaction between artificial intelligence (AI) (primarily robots) and human workers especially at the team level. It further discusses important potential strategies, which can be useful to overcome these challenges based on a conceptual review of extant research.Design/methodology/approach: The current paper undertakes a conceptual work where multiple streams of literature are integrated to present a rather holistic yet critical overview of the relationship between AI (particularly robots) and HRM in contemporary organisations.Findings: We highlight that interaction and collaboration between human workers and robots is visible in a range of industries and organisational functions, where both are working as team members. This gives rise to unique challenges for HRM function in contemporary organisations where they need to address workers' fear of working with AI, especially in relation to future job loss and difficult dynamics associated with building trust between human workers and AI-enabled robots as team members. Along with these, human workers' task fulfilment expectations with their AI-enabled robot colleagues need to be carefully communicated and managed by HRM staff to maintain the collaborative spirit, as well as future performance evaluations of employees. The authors found that organisational support mechanisms such as facilitating environment, training opportunities and ensuring a viable technological competence level before organising human workers in teams with robots are important. Finally, we found that one of the toughest challenges for HRM relates to performance evaluation in teams where both humans and AI (including robots) work side by side. We referred to the lack of existing frameworks to guide HRM managers in this concern and stressed the possibility of taking insights from the computer gaming literature, where performance evaluation models have been developed to analyse humans and AI interactions while keeping the context and limitations of both in view.Originality/value: Our paper is one of the few studies that go beyond a rather general or functional analysis of AI in the HRM context. It specifically focusses on the teamwork dimension, where human workers and AI-powered machines (robots) work together and offer insights and suggestions for such teams' smooth functioning.

Purpose: The purpose of this paper is to algorithmically and objectively investigate the previous literature on supply chain resilience (SCR) and advance theory by synthesizing new research domains.Design/methodology/approach: A two-staged analysis approach, integrating systematic literature review (SLR) with VOSviewer co-occurrence analysis, was applied to the articles published between 2003 and 2018.Findings: The authors find exponential growth in the literature on SCR over the last decade; however, there is still a gap for empirical research on numerous drivers, barriers, theories, moderators, mediators and research methods intertwined in building SCR.Research limitations/implications: The review identifies major clusters in which SCR research is conducted and devises a future research agenda based on the findings of co-occurrence analysis.Practical implications: The findings provide managers with a broad spectrum of factors that are indispensable to build resilience and inform business policy.Originality/value: While some SLRs exist in the current literature of SCR, the authors undertake a unique analytical perspective, resulting in an idiosyncratic set of research domains for further investigation in the area.

In recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners’ awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place.

Contemporary business models need to re-imagine the production and distribution of goods and services embedding social, economic, and environmental goals concurrently. To this end, reverse logistics in streamlining a circular economy (take, make, use, reuse, repair, and recycle) catapulted to the top of the discussion for collecting and distributing products. Yet, little is empirically known about the role of reverse logistics in the circular economy. This study attempts to fill this knowledge gap in theory and practice through a multiple case study approach including 40 semi-structured interviews with reverse logistics specialists from the four largest retailing firms in the United Arab Emirates (UAE). Findings reveal multiple ways by which reverse logistics contributes to a circular economy: for instance, reverse logistics enables firms to develop a circular product design; the combination of reverse flow with the forward flow consolidates the high volume of products, thus mitigating waste; use of innovative tools (robots, autonomous bikes) in reverse logistics increases the used products’ return rate and thereby enhancing recycling; technological advances (e.g. big data and IoT) in reverse logistics help trace the product thus reducing waste. The paper offers several valuable insights for practitioners to build a circular economy via reverse logistics (waste reduction, responsible consumption, and sustainable logistics). Our study also contributes to multiple United Nations Sustainable Development Goals (SDG 11, SDG 12, and SDG 13).

Purpose: Most of the extant literature on resilience builds on normative, conceptual or silo approaches, thereby lacking an integrative approach to cold chain logistics risks (CCLRs) and resilience. The purpose of this paper is to bridge the current research gap by developing a model, based on broad empirical evidence, of the interplay between CCLRs, resilience and firm performance (FP) in perishable product supply chains (PPSCs).Design/methodology/approach: A mixed method approach is used with qualitative data from interviews and quantitative data from a survey across the supply chain. The analysis is framed by contingency theory and resource-based theory.Findings: Four significant sources of CCLRs and six resources used to build resilience are identified. Then, supply chain resilience (SCR) as a moderator of the negative relationship between CCLRs and FP is corroborated.Practical implications: The findings will help improve managerial understandings of critical sources of risks in cold chain logistics and resources indispensable to build resilience. The scope of the research is cold chain logistics for PPSCs, which has relevance to other cold supply chains as well.Originality/value: While some theoretical frameworks suggest resilience being a moderator in the negative relationship between cold chain risks and a firm’s performance, this study empirically tests this relationship using the survey across the entire supply chain. A new empirically and theoretically driven definition of SCR is also developed.