Call for Papers

Call for short papers (pdf)

Artificial intelligence (AI) is rapidly transforming organizational ecosystems, including HR ecosystems (Budhwar et al., 2023; Einola & Khoreva, 2023; Kelan, 2024). Organizational ecosystems refer to networks of interdependent human actors, technologies, institutions, and other entities that shape organizational practices and outcomes (Snell et al., 2023), while HR ecosystems focus specifically on the interconnections underpinning human resource management processes, e.g., talent acquisition, workforce development, and employee engagement (Budhwar et al., 2023). Extending beyond traditional organizational boundaries, they include interactions with external environments, including socio-economic systems, regulatory frameworks, cultural norms, and ecological processes (Burke & Morley, 2023). They are characterized by constant evolution as power relations, technological advancements and institutional structures coalesce to influence organizational behaviour and impact (Malik et al., 2023).
 
As AI becomes increasingly central to these ecosystems (Einola & Khoreva, 2023), its transformative potential brings both opportunities and challenges. While AI promises to enhance productivity, innovation, and decision-making in organizations, its integration raises profound concerns about perpetuating structural inequalities (Arora et al., 2023; Tambe et al., 2019). The International Monetary Fund (IMF) warns that nearly 40% of jobs worldwide could be disrupted by the rise of AI, with its effects expected to deepen inequalities. Developed economies may see up to 60% of roles impacted, disproportionately affecting white-collar workers, whose tasks are more susceptible to automation than manual labour roles (IMF, 2024a).
 
Thus, organizational ecosystems are not neutral; they are shaped by socio-historical contexts and entrenched power dynamics that often reproduce inequities across intersecting identities such as race/ethnicity, gender, class, and migration status (Amis et al., 2023; Donnelly & Hughes, 2023). These inequities manifest in both visible and invisible forms of discrimination (Bankins, 2021; Walkowiak, 2023). To address these complexities, we need not only to apply existing theories but also to critically examine their limitations, which demands a rethinking of how organizational ecosystems are understood, how power operates within them, and how inclusivity can be reconceptualized in light of such entanglements.
 
Algorithmic biases embedded within organizational AI systems highlight the institutionalized encoding and perpetuation of historical inequities in decision-making processes in organizational/HR ecosystems (Snell et al., 2023). Automated hiring tools, for instance, frequently disadvantage candidates with non-linear career trajectories – such as caregivers or individuals recovering from illness – by prioritizing traditional career patterns (Bankins, 2021; Walkowiak, 2023). These tools are often designed to prioritize efficiency, yet they inadvertently perpetuate biases in historical hiring data, thereby limiting workforce diversity and inclusion (Noble, 2018). Joan Acker’s (1990) theory of gendered organizations highlights this structural embedding of inequality, but does not fully account for the non-human agents – algorithms and data systems – that now mediate inclusion and exclusion. Participatory approaches and perspectives (e.g., critical, feminist, actor-network theories), seeking to include marginalized voices in AI system development, offer a promising challenge to these entrenched inequities, though implementation remains inconsistent across organizations and business sectors (Tambe et al., 2019).
 
The global distribution of AI adds another layer of complexity, exposing neo-colonial dynamics. Nations in the Global North dominate AI research and reap significant economic rewards, while the Global South faces systemic barriers – including insufficient digital infrastructure and limited educational opportunities – that hinder their ability to participate equitably in AI-driven economies (IMF, 2024b). For example, the extraction of rare earth minerals in Africa, critical for AI hardware, perpetuates labour exploitation and environmental harm, while sustaining innovation cycles in wealthier nations (Arora et al., 2023; Zulfiqar, 2023). These dynamics exacerbate global inequalities, as data and labour extracted from resource-constrained regions sustain innovation and benefit organizations in wealthier countries, without proportionate returns (Zulfiqar, 2023).
 
Furthermore, the energy demands of training AI models disproportionately affect nations in the Global South, already vulnerable to climate change (Budhwar et al., 2023). Socio-political contexts amplify AI’s organizational challenges, with populist narratives framing it as a threat to “ordinary people” and fuelling fears of displacement (Radu, 2019; Vesa & Tienari, 2020). These fears are not unfounded: automation could displace 60% of Bangladesh’s garment jobs by 2030, jeopardizing livelihoods, reinforcing populist rhetoric, intensifying anxieties, and undermining trust in equitable workplace transformations (Radu, 2019). Institutional gaps, such as inadequate reskilling initiatives and social protections, exacerbate these disparities, further marginalizing vulnerable communities.
 
Theorists tackle AI’s challenges in organizational ecosystems through distinct lenses. Neo-institutionalists emphasize aligning technology with ethical goals like sustainable sourcing and renewable energy (Voronov & Weber, 2020), while critical, feminist, and queer theorists amplify marginalized voices (Bankins, 2021). These perspectives reveal how norms and identities shape AI’s impact but fail to address distributed agency – the interplay between human and non-human actors and its critical role in shaping organizational outcomes.
 
Despite these challenges, studies have shown that AI offers significant transformative potential when deployed ethically. Examples such as AI-powered learning platforms improving education in rural India or diagnostic tools enhancing healthcare in South Africa demonstrate its capacity for societal change (Fan & Qiang, 2024). Globally, AI-driven productivity gains could boost GDP by 7% annually over the next decade, underscoring its potential for economic growth if managed equitably (Goldman Sachs, 2023). Achieving such positive outcomes requires reimagining organizational ecosystems through pluralistic theoretical lenses that integrate human and non-human actors. It also requires organizations to reimagine their business purpose as doing good for society (e.g., McPhail et al., 2024).
 
This sub-theme invites contributions that explore how organizations can navigate the complex entanglements of AI, inequality, and sustainability. Scholars are encouraged to interrogate how algorithmic biases and ecological impacts are co-produced, how organizational strategies can balance innovation with ethical responsibility, and how the participatory design of AI can foster inclusion. Papers should aim to foster theoretical, methodological and practical insights. While the list is not exhaustive, key questions papers may address include:

• How can organizations mitigate algorithmic biases to ensure equitable outcomes for diverse employee demographics?

• What strategies can bridge the AI readiness gap in low-income countries to enable equitable participation in global economies?

• How do ecological and technological actors shape organizational inequalities, and how can these dynamics be addressed?

• What theoretical frameworks best illuminate the entanglements of human and non-human actors in AI-powered ecosystems?

• How can organizations align their AI strategies with ethical and environmental sustainability?

• How do populist socio-political contexts influence organizational trust and AI integration, particularly in polarised societies?

• What are the long-term socio-economic impacts of AI-driven job displacement, and how can organizations address these through equitable workforce strategies?

• How can participatory design processes be implemented effectively to include marginalized voices in AI system development?

• In what ways can organizations balance the ecological costs of AI innovation with global commitments to climate action and equity?

• What role can interdisciplinary approaches play in advancing theoretical and practical insights into the intersection of AI, inequality, and organizational practices?

References

• Acker, J. (1990): “Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations.” Gender & Society, 4 (2), 139–158.
• Bankins, S. (2021): “The Ethical Use of Artificial Intelligence in Human Resource Management: A Decision-Making Framework.” Ethics and Information Technology, 23 (4), 841–854.
• Budhwar, P., et al. (2023): “Human Resource Management in the Age of Generative Artificial Intelligence.” Human Resource Management Journal, 33 (3), 606–659.
• Burke, C.M., & Morley, M.J. (2023): “Toward a Non-Organizational Theory of Human Resource Management? A Complex Adaptive Systems Perspective on the Human Resource Management Ecosystem in (Con)temporary Organizing.” Human Resource Management, 62 (1), 31–53.
• Donnelly, R., & Hughes, E. (2023): “The HR Ecosystem Framework: Examining Strategic HRM Tensions in Knowledge-Intensive Organizations with Boundary-Crossing Professionals.” Human Resource Management, 62 (1), 79–95.
• Einola, K., & Khoreva, V. (2023): “Best Friend or Broken Tool? Exploring the Co-Existence of Humans and Artificial Intelligence in the Workplace Ecosystem.” Human Resource Management, 62 (1), 117–135.
• Fan, Q., & Qiang, C.Z. (2024): “Tipping the scales: AI’s dual impact on developing nations.” World Bank Blogs, https://blogs.worldbank.org/en/digital-development/tipping-the-scales--ai-s-dual-impact-on-developing-nationsG.
• Goldman Sachs (2023): Generative AI: The Macroeconomic Implications. Goldman Sachs Research Report, New York.
• IMF (2024a): AI and Wage Inequality: A Cross-Country Perspective. International Monetary Fund Working Paper, Washington, D.C.
• IMF (2024b): Navigating AI and Inequality: Implications for Global Economies. International Monetary Fund Policy Review, Washington, D.C.
• Kelan, E.K. (2024): “Algorithmic Inclusion: Shaping the Predictive Algorithms of Artificial Intelligence in Hiring.” Human Resource Management Journal, 34 (3), 694–707.
• Malik, A., Budhwar, P., Mohan, H., & Srikanth, N.R. (2023): “Employee Experience – The Missing Link for Engaging Employees: Insights from an MNE’s AI-Based HR Ecosystem.” Human Resource Management, 62 (1), 97–115.
• McPhail, K., Kafouros, M., McKiernan, P., & Cornelius, N. (2024): “Reimagining Business and Management as a Force for Good.” British Journal of Management, 35 (3), 1099–1112.
• Noble, S.U. (2018): Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.
• Radu, M. (2019): “Artificial Intelligence and Populism: How to Avoid a Catastrophe?” euronews, https://www.euronews.com/2018/11/27/artificial-intelligence-and-populism-how-to-avoid-a-catastrophy-view.
• Snell, S.A., & Morris, S.M. (2021): “Time for Realignment: The HR Ecosystem.” Academy of Management Perspectives, 35 (2), 219–236.
• Snell, S.A., Swart, J., Morris, S.S., & Boon, C. (2023): “The HR Ecosystem: Emerging Trends and a Future Research Agenda.” Human Resource Management, 62 (1), 5–14.
• Tambe, P., Cappelli, P., & Yakubovich, V. (2019): “Artificial Intelligence in Human Resources Management: Challenges and a Path Forward.” California Management Review, 61 (4), 15–42.
• Vesa, M., & Tienari, J. (2020): “Artificial Intelligence and Rationalized Unaccountability: Ideology of the Elites?” Organization, 29 (6), 1133–1145.
• Voronov, M., & Weber, K. (2020): “People, Actors, and the Humanizing of Institutional Theory.” Journal of Management Studies, 57 (4), 873–884.
• Walkowiak, E. (2023): “Digitalization and Inclusiveness of HRM Practices: The Example of Neurodiversity Initiatives.” Human Resource Management Journal, 34 (3), 578–598.
• Zulfiqar, G.M. (2023): “Digital Financialization and Surveillance Capitalism in the Global South: The New Technologies of Empire.” Organization, 30 (6), 1246–1251.