This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.
Category: 4/ Fellow”s Projects
Aula Fellow Project
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‘Mind the gap’: artificial intelligence and journalism training in Southern African journalism schools
This article examines journalism schools (J-schools) responses to the Artificial Intelligence (AI) ‘disruption’. It critically provides an exploratory examination of how J-Schools in Southern Africa are responding to the AI wave in their journalism curriculums. We answer the question: How are Southern African J-Schools responding to AI in their curriculums? Using a disruptive innovation theoretical lens and through documentary review of university teaching initiatives and accredited journalism curriculums, augmented by in-depth interviews, we demonstrate that AI has opened up new horizons for journalism training in multi-dimensional ways. However, this has brought challenges, including covert forms of resistance to AI integration by some Journalism educators. Furthermore, resource constraints and the obduracy of J-schools’ curriculums also contribute to the slow introduction of AI in J-schools.
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A labeled clinical-MRI dataset of Nigerian brains
There is currently a paucity of neuroimaging data from the African continent, limiting the diversity of data from a significant proportion of the global population. This in turn diminishes global health research and innovation. To address this issue, we present and describe the first Magnetic Resonance Imaging (MRI) dataset from individuals in the African nation of Nigeria. This dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality, with 35 images from healthy control subjects, 31 images from individuals diagnosed with age-related dementia, and 22 from individuals with Parkinson’s Disease. Given the potential for Africa to contribute to the global neuroscience community, this unique MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent.
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Community energy justice: A review of origins, convergence, and a research agenda
The transition to zero‑carbon sustainable energy systems is critical and must take an equity-oriented approach to avoid exacerbating societal injustices. We explore the concept of “community” and its potential as a viable and effective tool for studying, understanding, and fostering justice and equity in energy transitions. This paper outlines community energy justice as an area of scholarship emerging through convergence around three key concepts: community, energy transition, and justice. Using a narrative literature review approach, we unpack the origins of community energy justice research, rooted in two scholarship pillars of energy justice and community energy. We outline four driving forces and two key approaches leading to convergence between both areas of scholarship. Encompassing energy transition initiatives that incorporate both justice and community themes, we find that the overarching objective …
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Africa’s Energy Poverty in An Artificial Intelligence (AI) World: Struggle for Sustainable Development Goal 7
Energy poverty remains a significant challenge in Sub-Saharan Africa (SSA), where approximately 600 million people lack proper access to electricity. This paper examines the region’s current state of energy poverty, highlighting its socio-economic impacts and the barriers to achieving Sustainable Development Goal 7 (SDG7), which aims for affordable, reliable, sustainable, and modern energy for all by 2030. Despite the region’s rich renewable energy potential, inadequate infrastructure, economic constraints, and governance issues continue to impede progress. This work employs a doctrinal research methodology, focusing on the critical analysis of existing legal and policy frameworks relevant to energy poverty and the integration of AI in energy management. This paper presents an overview of energy poverty in SSA, underpinned by current statistics and trends. It then examines the dual role of artificial intelligence (AI) and how it impacts this area: while AI technologies, through its data centre s, for example, significantly increase energy consumption, AI also offers innovative solutions for energy management, efficiency, and the integration of renewable energy sources. This paper critically analyzes these dynamics using Marxist and Third World Approaches to International Law (TWAIL) frameworks to understand the broader socio-economic inequalities and global power dynamics at play. Major findings indicate that current policy frameworks are inadequate in addressing the unique challenges of energy poverty and the growing role of AI in the energy sector. The paper reviews existing policy and regulatory frameworks, identifying gaps and proposing actionable recommendations for integrating AI into policies to address energy poverty. It concludes with actionable policy recommendations to achieve a just and inclusive energy transition, contributing to the broader discourse on sustainable development and technological equity.








