The Future of Work (FoW) has garnered significant attention among scholars and practitioners, with the advent of Artificial Intelligence (AI) playing an important role in shaping this discourse. ...Despite the common perception that intelligent machines pose a threat to workers in routine roles, AI technologies are increasingly being utilized for advanced tasks carried out by knowledge workers. Drawing on state-of-the-art research and real-life examples we develop an integrated framework to explore the future of academic work. Our focus is on academics, an essential yet under-researched group of knowledge workers, and we discuss their work in relation to AI across space, time, and task dimensions. Our analysis reveals that the usage of AI technologies can have implications for the research, teaching, and service activities of academics and thereby also for the creation, acquisition, dissemination, and application of knowledge. Based on our framework we develop scenarios and propose a future research roadmap.
•The increasing possibilities of Artificial Intelligence (AI) are likely to influence academics’ future of work.•A framework is presented that should be used to study if and how AI usage by academics can shape research, teaching, and academic services.•Potential changes in where, when, and what academic activities are performed and how AI shapes the acquisition, creation, dissemination, and application of knowledge are identified.•The impact of AI usage on academics is dependent on key drivers: AI technology-development, individual-level characteristics, and higher-level factors.•The state-of-the-art is presented in a future research roadmap as a basis for scenarios.
•COVID-19 has renewed the Intelligent Automation (IA) of work debate.•Strong arguments for COVID-19 increasing IA adoption levels.•Counter-arguments against COVID-19 increasing IA adoption also ...evident.•Implications for information management research and practice discussed.
As part of the urgent need to respond to the COVID-19 pandemic, governments, healthcare providers, and businesses have looked to applications of Artificial Intelligence (AI) to compensate for the unavailability of human workers. This interest has renewed the debate regarding the use of AI for the automation of work, which has been described as Intelligent Automation (IA). A new dimension to this debate is whether COVID-19 will be the catalyst for higher IA adoption levels. This article reviews arguments in favour of COVID-19 increasing the level of IA adoption and possible counter-arguments. Key arguments in favour of increased IA adoption include consumer preferences changing to favour IA, increasing familiarity of IA technologies, and increased business confidence in IA. Counter-arguments include big data availability and reliability limitations, many tasks still favouring human skills over IA, the narrow capabilities of IA technologies, and a high availability of human workers. The article also discusses the implications of this debate for information management research and practice.
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets.While AI and automation can augment the productivity of some ...workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists frommeasuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human–machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
Proponents of artificial intelligence (AI) have envisaged a scenario wherein intelligent machines would execute routine tasks performed by humans, thus, relieving them to engage in creative pursuits. ...While there is widespread fear of corresponding job losses, organizational think tanks vouch for the synergistic culmination of human-machine competencies. Using the dynamic skill, neo-human capital and AI job replacement theories, we contend that the introduction and adoption of AI calls for employees to upskill themselves. To determine the key skills deemed critical for the upskilling of employees, we interviewed 20 experienced professionals in multinational corporations (MNCs) in the information technology sector in India. Deploying Gioia's methodology for qualitative analysis, our investigation revealed five critical skills for employee upskilling: data analysis, digital, complex cognitive, decision making and continuous learning skills.
We review the literature on algorithmic management (AM) to bridge the gap between this emerging research area and the well-established theory and research on work design. First, we identify six ...management functions that algorithms are currently able to perform: monitoring, goal setting, performance management, scheduling, compensation, and job termination. Second, we show how each AM function affects key job resources (e.g., job autonomy, job complexity) and key job demands (e.g., workload, physical demands); with each of these resources and demands being important drivers of worker motivation and their well-being. Third, rejecting a deterministic perspective and drawing on sociotechnical systems theory, we outline key categories of variables that moderate the link between AM on work design, namely transparency, fairness and human influence (e.g., whether workers can control the system). We summarize our review in the form of a model to help guide research on AM, and to support practitioners and designers in the creation and maintenance of meaningful jobs in the era of algorithms.
•The article describes six management functions that AI-enabled algorithms are able to perform.•The influence of each AM function on work design characteristics is reviewed.•We review and discuss parameters likely to mitigate the effects of AM.
Research on automation and the future of work is a major focus for both academics and practitioners due to technological changes disrupting the labor market and educational pathways. Although recent ...articles have published projections about the types of tasks and jobs most likely to be automated in the coming years, little attention has been devoted to how different types of vocational interests are susceptible to automation, as well as resulting changes to the match between people's interests and their jobs. In the present article, we provide an integrative review of vocational interests and automation projections within and across jobs. By standardizing and mapping projections to Holland's RIASEC interest model, we found that Investigative (scientific) and Conventional (detail-oriented) interests, including STEM interests, are most susceptible to automation, whereas Social (people-oriented) and Realistic (hands-on) interests are least susceptible. For Artistic and Enterprising interests, some creative work, decision-making, and leadership skills may be affected by automation across a range of jobs. We build on these projections to propose a future research agenda integrating interests, technology, and careers. Specifically, we identify five areas for future research, including using intentional work design to enhance interests, the role of interests in career decisions related to project-based work, changes in people's interests following automation, increased use of basic interests, and the systematic impacts of automation on different groups of people. Overall, this review highlights how vocational interests will remain an important topic with high relevance for career guidance, education, and organizations as the future of work evolves.
•This paper integrated automation projections with vocational interests.•Investigative (scientific) and Conventional (detail-oriented) interests are most susceptible to automation.•Realistic (hands-on) and Social (people-oriented) interests are least susceptible to automation.•Future research should develop theory and implementation of intentional work design to enhance people's interests.•Researchers should study changes in people's interests following automation and the role of interests in a “jobless” future.
We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting ...of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings.
In this paper, we take stock of what The International Journal of Human Resource Management (IJHRM) has achieved in the past 30 years since its inception in 1990. By analyzing the publication trends ...indicated by keywords, we find that research published in IJHRM has shifted from a collective focus with institutional implications towards more individual concerns with organizational implications with relevance to HRM practices. We present research theme and approaches that distinguish IJHRM from other HRM journals, and offer suggestions of what we see as some of the important issues for future research that will take us beyond the current trajectory of HRM studies.
Navigating the shifting landscapes of HRM Harney, Brian; Collings, David G.
Human resource management review,
December 2021, 2021-12-00, Letnik:
31, Številka:
4
Journal Article
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A confluence of mega-trends mean that HR is experiencing disruption and change on an unprecedented scale. This special issue is designed to inform our understanding of these shifting landscapes of ...HRM. In this overview we detail the broad contextual backdrop of key changes, before providing an overview of the six articles that make up this special issue. The articles cover agile HR, HR disruption, strategic human capital, employee health and safety, HR co-creation and global flexible working arrangements. We conclude by threading key insights together with suggestions on how theory and research might seek to better embrace disruption and navigate the shifting landscapes of HRM. This includes striving for interdisciplinary insight, finding motivation in practice, looking back to go forward, using multiple pathways for understanding, challenging assumptions and accommodating HR agency. The understanding and insight offered in this special issue hold special relevance in the context of the COVID-19 crisis.
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