Journal of Enterprise Information Management (JEIM) contributes to the normative literature, providing conceptual and practical insights underpinned by innovative findings that add to the body of knowledge.

JEIM publishes research findings from internationally distinguished experts. Scholarly examinations of the latest theory and practice from the foremost research institutions are augmented by contributions from senior business managers and consultants, who report on specific enterprise case experiences that promote the learning of others by comparing and contrasting environmental settings.

This blended contribution of theoretical and practical outcomes results in an extensive communication of commercial findings and audience understanding of current, applied and rigorous information management that spans an enterprise and its rich supply chains.

Special Issue on Generative AI and Society: Organisational and Social Impact, Ethics and Policies
提出日: 2026-03-31

When ChatGPT was launched in November 2022, it amassed 100 million active users in two months (Hu, 2023) and, as of April 2025, is now estimated to have around 1 billion monthly active users (Paris, 2025). A strong driver of this growth was that it can provide human-like answers with minimal overlap from existing works (Farina & Lavazza, 2023). This is because ChatGPT is a large-language model (LLM), which is a form of generative artificial intelligence (GenAI), that utilises machine learning algorithms to ‘generate’ new outputs based on prompts (Peres et al., 2023). As GenAI can create something ‘new’, it has been able to find applications in almost all industries, such as education (Ellis & Slade, 2023; Michel-Villarreal et al., 2023), medicine and healthcare (Ooi et al., 2023), cybersecurity (Gupta et al., 2023), marketing (Peres et al., 2023), manufacturing (Bonomi Savignon et al., 2023), among many others. As such GenAI can sustain and revolutionise enterprises, organisations, society, and the economy. Yet, whilst GenAI has expanded rapidly, it’s novelty has given way to questions of its societal impact – be it positive (Freire et al., 2023; Nguyen et al., 2025a, b) or negative (e.g., mental health; De Freitas & Cohen, 2025).

Positively, by augmenting human creativity, it is posited that GenAI will eliminate the cost of creation and knowledge work, massively contributing to productivity within enterprises and organisations (Eapen et al., 2023). Besides this, GenAI will promote rapid urbanisation, provide better medical care and assistance for an ageing population, and improve global healthcare and mobility (Freire et al., 2023). Scholarly works also indicate that GenAI can help better manage societal issues like poverty, climate change and inequality (Arsenyan & Piepenbrink, 2023). Further, the United Nations is including GenAI within their initiatives (Tomasev et al., 2020).

Negatively, GenAI inherently gives rise to numerous ethical issues and concerns. For example, privacy concerns regarding the sharing and misuse of sensitive information (Ferm et al., 2022; Niederman & Baker, 2023). GenAI also has the potential to impersonate voice, text, and facial features, which, if misused, can pose a considerable threat to society (Wach et al., 2023). Due to the increasing implementation of GenAI in the workplace (e.g., Nguyen et al., 2025b), it risks elevating the digital divide and marginalisation (Ray, 2023) and the subsequent possibility of manipulating human behaviour. Within organisations, this raises issues of fairness, transparency, and inclusivity, particularly when deploying GenAI for recruitment, training, and performance management (Díaz-Rodríguez et al., 2023). Further, GenAI’s training data can promote underrepresentation and stereotyping (Cuartielles et al., 2023). It also presents copyright and ownership issues (e.g., Allyn, 2025), and the lack of clear guidelines for its safe and ethical usage (Vinsel, 2023). Extant literature also highlights GenAI’s high resource cost (Alsharhan et al., 2023; Voß, 2023) and thus questioning its environmental sustainability (Govindan, 2022; Meena et al., 2025). 

List of Topic Areas

To address the positive and negative impact of GenAI, we suggest to include the following topics for research:

    How does GenAI contribute to societal upgradation? For example, it promotes social welfare, benefits the disabled and ageing population, and drives global healthcare and mobility, among others.
    Why is GenAI said to augment human skills? How can it serve as an effective collaborator, advance engineering techniques such as offering automation of knowledge work, enhancing productivity, etc.? What skills are necessary to leverage this technology (Cetindamar et al., 2022)?
    Why is there a need to address the sustainability aspect of GenAI? What characteristics of this technology impede sustainability? What can be probable measures to mitigate them? How does it raise serious environmental concerns?
    What human issues need to be addressed to ensure that GenAI does not pose a safety concern for the users? For example, how to assess the boundary between authentic and artificial human content? How does technology have a probability of interfering with and manipulating human behaviour or affect human rights such as freedom of speech?

To address the orgnisational and societal impact of GenAI, we suggest including the following topics for research:

    Does GenAI present a paradigm in the transformation of both society and organisations, critically examining the change it brings about at the micro level (e.g., individuals), meso level (e.g., organisations and institutions), and macro level (e.g., governments and global systems)? What would be the extent of this transformation and investigating the possibility of the sustainability of this?
    How does GenAI affect the norms and values and operational of organisations and society, driving cultural transformation? What can be done to manage this transformation effectively? What can be the possible consequences?
    How can seminal (e.g., Innovation Resistance Theory, Disruptive Innovation Theory, Technological Determinism, Digital Divide Theory, among others) and novel theoretical frameworks be used to conceptualize and uncover the foundations of Generative AI driving organizational societal changes?
    What is the perspective of different stakeholders in organisations and society towards the impact GenAI is set to make? Which stakeholders will be impacted the most? Should regulations and policies vary by different stakeholders? What needs to be done to equip these stakeholders to manage the organizational and societal changes manifested by the advent of GenAI?

To address the ethical impact of GenAI, we suggest including the following topics for research:

    Is GenAI responsible for mirroring the existing societal issues, such as promoting bias and stereotyping? What possible solutions can address these issues?
    What are the different ethical concerns the technology poses to society, for example, accounting for threats to fundamental human rights and autonomy, non-discrimination, explainability, transparency, etc.?
    In a bid to develop Responsible AI, what different aspects need to be included to manage ethical concerns effectively? For example, it needs to inform the Explainability of GenAI, promote fair and ethical use, etc.
    What ethical issues pose a threat to human existence? For example, the rise of semi-humans which can imitate their features to the extent of not realising the boundary between artificial and authentic humans. 

To address the policy and regulatory impact of GenAI, we suggest including the following topics for research:

    Why do you think there is a need to draft policies or regulatory guidelines/frameworks that can aid in the effective management of GenAI and mitigate the negative effects of this technology on society?
    What role does geographical variation play in the development, penetration and impact of GenAI on society? Should policies and regulatory guidelines vary by geographical region? Will a global law/policy/regulation suffice for GenAI?
    How should the policies or regulatory guidelines be developed in order to account for stakeholders’ perceptions and needs, fair and ethical use, and strike the optimal balance between negative and positive aspects of GenAI?
    How do we conceptualise the policies and regulatory guidelines to ensure fair use of GenAI in society? For example, what different aspects should it include, such as impact assessment, responsible AI, legitimate use of the technology, etc.?