Search results

This paper aims to explore John Mbiti’s concept of African time in line with the studies on the crisis of the post-independence African state. Then, how this concept offers new analytical spaces for understanding the modern nation-states inconveniences in African contexts.

The paper analyses the Mbitian African concept of time in light of the works on post-independence African state and communalism.

By using the Mbitian concept of time politically after explaining African communalism and African concepts of personhood and destiny, the paper reached a conclusion. This conclusion claims that due to the highly existential nature of the African concept of destiny and the past-oriented feature of the African concept of time, Africans cannot be restrained by any supernatural claim or any futuristic promises that are irrelevant to their context and cut from the communal values of ancestral past. Africans do not think supernaturally or bet for the future. However, those futuristic and supernatural claims that cannot restrain the African subjectivity – ironically – characterize the modern nation state with its “progress orientation” and “social-contract” metaphysics. Unfortunately, this radical difference in perceptions between the past-oriented African temporality and the future-oriented modern state temporality rendered the post-independence. African state dysfunctional and unable to operate as a medium for authority. This, consequently, opened the door for informal conflict and strife.

The paper is novel with regard to offering a new theory on the conceptual problems of the nation state in African contexts.

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.

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.

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.

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.