Digitale Arbeitswelt – Chancen und Herausforderungen für Beschäftigte und Arbeitsmarkt
Der digitale Wandel der Arbeitswelt gilt als eine der großen Herausforderungen für Wirtschaft und Gesellschaft. Wie arbeiten wir in Zukunft? Welche Auswirkungen hat die Digitalisierung und die Nutzung Künstlicher Intelligenz auf Beschäftigung und Arbeitsmarkt? Welche Qualifikationen werden künftig benötigt? Wie verändern sich Tätigkeiten und Berufe? Welche arbeits- und sozialrechtlichen Konsequenzen ergeben sich daraus?
Dieses Themendossier dokumentiert Forschungsergebnisse zum Thema in den verschiedenen Wirtschaftsbereichen und Regionen.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
- Gesamtbetrachtungen/Positionen
- Arbeitsformen, Arbeitszeit und Gesundheit
- Qualifikationsanforderungen und Berufe
- Arbeitsplatz- und Beschäftigungseffekte
- Wirtschaftsbereiche
- Arbeits- und sozialrechtliche Aspekte / digitale soziale Sicherung
- Deutschland
- Andere Länder/ internationaler Vergleich
- Besondere Personengruppen
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Literaturhinweis
Auswirkungen von KI auf die Nutzer: Erhalten und Fördern der menschlichen Intelligenz bei zunehmendem Einsatz künstlicher Intelligenz - Wozu? Wie? (2025)
Hacker, Winfried;Zitatform
Hacker, Winfried (2025): Auswirkungen von KI auf die Nutzer: Erhalten und Fördern der menschlichen Intelligenz bei zunehmendem Einsatz künstlicher Intelligenz - Wozu? Wie? (baua: Fokus), Dortmund, 6 S. DOI:10.21934/baua:fokus20251218
Abstract
"Die Entwicklung der KI verändert die Anforderungen an die menschliche Intelligenz: Denkleistungen können überflüssig werden. Dadurch kann eine arbeitsbedingte Dequalifizierung der Arbeitenden entstehen, denen jedoch die Kontrolle und Korrektur der KI-Ergebnisse obliegt, wofür diese Denkleistungen benötigt werden. Auswege sind die "Zusammenarbeit" von KI und Mensch sowie insbesondere einfache Maßnahmen zum Erhalten der Denkfähigkeit im Arbeitsprozess, die dargestellt werden." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Arbeiten mit Künstlicher Intelligenz, aber auch mit Köpfchen. Anforderungen an Future Skills in der Erwerbsarbeit (2025)
Zitatform
Hall, Anja & Ana Santiago Vela (2025): Arbeiten mit Künstlicher Intelligenz, aber auch mit Köpfchen. Anforderungen an Future Skills in der Erwerbsarbeit. In: Berufsbildung in Wissenschaft und Praxis H. 4, S. 21-25.
Abstract
"Künstliche Intelligenz (KI) verändert nicht nur, was wir arbeiten, sondern auch wie. Auf Basis der BIBB/BAuA-Erwerbstätigenbefragung 2024 zeigt der Beitrag die aktuelle Verbreitung von KI auf dem Arbeitsmarkt. KI wird vor allem in kognitiv-analytischen und interaktiven Nichtroutinetätigkeiten genutzt und geht mit Anforderungen an Future Skills wie Probleme lösen, Wissenslücken schließen, kreativ sein oder überzeugen einher. Damit rücken im Kontext von KI neben fachlichen Anforderungen auch überfachliche Kompetenzen stärker in den Fokus. Berufliche Handlungskompetenz ist daher weiterhin gezielt zu fördern." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Generative KI: Schritt halten durch gezielte Kompetenzentwicklung (2025)
Hammermann, Andrea; Kürten, Louisa;Zitatform
Hammermann, Andrea & Louisa Kürten (2025): Generative KI: Schritt halten durch gezielte Kompetenzentwicklung. (IW-Kurzberichte / Institut der Deutschen Wirtschaft Köln 2025,24), Köln, 3 S.
Abstract
"Der Einsatz von generativer Künstlicher Intelligenz (KI) transformiert die Arbeitswelt in einem rasanten Tempo. Eine wichtige Säule zur Ausschöpfung der möglichen KI-Potenziale sind das Wissen und die Anwendungskompetenz von Beschäftigten. Weiterbildung und das Lernen am Arbeitsplatz gewinnen vor diesem Hintergrund an Bedeutung." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Artificial Intelligence and the Labor Market (2025)
Zitatform
Hampole, Menaka, Dimitris Papanikolaou, Lawrence D. W. Schmidt & Bryan Seegmiller (2025): Artificial Intelligence and the Labor Market. (NBER working paper / National Bureau of Economic Research 33509), Cambridge, Mass, 58 S.
Abstract
"We leverage recent advances in NLP to construct measures of workers' task exposure to AI and machine learning technologies over the 2010 to 2023 period that vary across firms and time. Using a theoretical framework that allows for a labor-saving technology to affect worker productivity both directly and indirectly, we show that the impact on wage earnings and employment can be summarized by two statistics. First, labor demand decreases in the average exposure of workers' tasks to AI technologies; second, holding the average exposure constant, labor demand increases in the dispersion of task exposures to AI, as workers shift effort to tasks that are not displaced by AI. Exploiting exogenous variation in our measures based on pre-existing hiring practices across firms, we find empirical support for these predictions, together with a lower demand for skills affected by AI. Overall, we find muted effects of AI on employment due to offsetting effects: highly-exposed occupations experience relatively lower demand compared to less exposed occupations, but the resulting increase in firm productivity increases overall employment across all occupations." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Generative AI's Impact on Student Achievement and Implications for Worker Productivity (2025)
Zitatform
Hausman, Naomi, Oren Rigbi & Sarit Weisburd (2025): Generative AI's Impact on Student Achievement and Implications for Worker Productivity. (CESifo working paper 11843), München, 39 S.
Abstract
"Student use of Artificial Intelligence (AI) in higher education is reshaping learning and redefining the skills of future workers. Using student-course data from a top Israeli university, we examine the impact of generative AI tools on academic performance. Comparisons across more and less AI-compatible courses before and after ChatGPT's introduction show that AI availability raises grades, especially for lower-performing students, and compresses the grade distribution, eroding the signal value of grades for employers. Evidence suggests gains in AI-specific human capital but possible losses in traditional human capital, highlighting benefits and costs AI may impose on future workforce productivity." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Alter(n) im Betrieb: Stereotype Altersbilder, Fachkräftemangel und digitale Transformation (2025)
Zitatform
Heyer, Philipp, Kathrin Weis, Sabine Mohr & Wiebke Schmitz (2025): Alter(n) im Betrieb: Stereotype Altersbilder, Fachkräftemangel und digitale Transformation. (BIBB-Report 2025,05), Leverkusen: Verlag Barbara Budrich, 16 S.
Abstract
"Against the backdrop of demographic change, age-appropriate human resources policies are becoming increasingly important. Nevertheless, negative age stereotypes continue to prevail in many firms, hindering the recruitment and further training of older employees – and thus leaving existing skilled labor potential untapped. Based on current data from the establishment survey “BIBB Establishment Panel on Training andCompetence Development,” this BIBB Report analyzes stereotypical images of age in firms as well as company characteristics that promote the employment of older people. Particular attention is given to the role of digital technologies. The results show that the perceptions of older employees vary depending on the industry, firm size, and use of technology. A positive perception is associated with higher employment rates of older people. However, older employees are less strongly represented in firms with above-average use of digital technologies. Based on these findings, it is recommended to counteract age stereotypes, provide targeted further training for older employees, and actively involve them in digital work processes. An age-appropriate human resources policy not only strengthens the supply of skilled workers, but also diversity and, ultimately, the productivity of firms." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Large Language Models, Small Labor Market Effects (2025)
Zitatform
Humlum, Anders & Emilie Vestergaard (2025): Large Language Models, Small Labor Market Effects. (BFI Working Papers / University of Chicago, Becker Friedman Institute for Research in Economics 2025,56), Chicago, 64 S. DOI:10.2139/ssrn.5219933
Abstract
"We examine the labor market effects of AI chatbots using two large-scale adoption surveys (late 2023 and 2024) covering 11 exposed occupations (25,000 workers, 7,000 workplaces), linked to matched employer-employee data in Denmark. AI chatbots are now widespread —most employers encourage their use, many deploy in-house models, andtraining initiatives are common. These firm-led investments boost adoption, narrow demographic gaps in take-up, enhance workplace utility, and create new job tasks. Yet, despite substantial investments, economic impacts remain minimal. Using difference-in-differences and employer policies as quasi-experimental variation, we estimate precise zeros: AI chatbots have had no significant impact on earnings or recorded hours in any occupation, with confidence intervals ruling out effects larger than 1%. Modest productivity gains (average time savings of 3%), combined with weak wage pass-through, help explain these limited labor market effects. Our findings challenge narratives of imminent labor market transformation due to Generative AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
auch erschienen als: NBER working paper, 33777 -
Literaturhinweis
Technostress and generative AI in the workplace: a qualitative analysis of young professionals (2025)
Zitatform
Högemann, Malte, Laura Hein, Jan-Oliver Britsche & Oliver Thomas (2025): Technostress and generative AI in the workplace: a qualitative analysis of young professionals. In: Frontiers in artificial intelligence, Jg. 8. DOI:10.3389/frai.2025.1728881
Abstract
"Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
The Impact of AI on Global Knowledge Work (2025)
Ide, Enrique; Talamas, Eduard;Zitatform
Ide, Enrique & Eduard Talamas (2025): The Impact of AI on Global Knowledge Work. (CEPR discussion paper / Centre for Economic Policy Research 20801), London, 34 S.
Abstract
"Artificial Intelligence (AI) is reshaping offshoring and globalization by automating knowledge work and altering trade patterns. We analyze this transformation in a two-region world where firms structure work hierarchically to use knowledge efficiently: the most knowledgeable individuals specialize in problem-solving, while others perform routine work. Before AI, the Advanced Economy specializes in problem-solving services, while the Emerging Economy focuses on routine knowledge work. We model AI as a technology that converts compute into autonomous “AI agents,” which serve as perfect substitutes for humans with a given level of knowledge. Reflecting the concentration of AI infrastructure in advanced economies, we assume that all compute is located in the Advanced Economy. We show that basic AI reduces the Advanced Economy’s net exports of problem-solving services, potentially reversing pre-AI trade patterns. In contrast, sophisticated AI increases the Advanced Economy’s net exports of problem-solving services, reinforcing existing trade patterns. We also examine the effects of restricting AI autonomy, finding that a global restriction redistributes AI’s benefits toward lower-skilled workers, while a regional restriction - such as banning autonomous AI in the Emerging Economy - does little to benefit lower-skilled workers and harms the most knowledgeable individuals in that region. Our results underscore the need for a coordinated global approach to AI regulation." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Robots & AI exposure and wage inequality: a within occupation approach (2025)
Zitatform
Jaccoud, Florencia (2025): Robots & AI exposure and wage inequality: a within occupation approach. In: Eurasian business review, Jg. 15, H. 4, S. 1035-1090. DOI:10.1007/s40821-025-00306-w
Abstract
"This paper examines the linkages between occupational exposure to recent automation technologies and inequality across 19 European countries. Using data from the European Union Structure of Earnings Survey (EU-SES), a fixed-effects model is employed to assess the association between occupational exposure to artificial intelligence (AI) and to industrial robots–two distinct forms of automation–and within-occupation wage inequality. The analysis reveals that occupations with higher exposure to robots tend to have lower wage inequality, particularly among workers in the lower half of the wage distribution. In contrast, occupations more exposed to AI exhibit greater wage dispersion, especially at the top of the wage distribution. We argue that this disparity arises from differences in how each technology complements individual worker abilities: robot-related tasks often complement routine physical activities, while AI-related tasks tend to amplify the productivity of high-skilled, cognitively intensive work." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Demographic change, secular stagnation, and inequality: automation as a blessing? (2025)
Zitatform
Jacobs, Arthur & Freddy Heylen (2025): Demographic change, secular stagnation, and inequality: automation as a blessing? In: Journal of demographic economics, Jg. 91, H. 4, S. 508-548. DOI:10.1017/dem.2024.10
Abstract
"We study whether the increased adoption of available automation technologies allows economies to avoid the negative effect of aging on per capita output. We develop a quantitative theory in which firms choose to which extent they automate in response to a declining workforce and rising old-age dependency. An important element in our model is the integration of two capital types: automation capital that acts as a substitute to human labor, and traditional capital that is a complement to labor. Empirically, our model's predictions largely match data regarding automation (robotization) density across OECD countries. Simulating the model, we find that aging-induced automation only partially compensates the negative growth effect of aging in the absence of technical progress in automation technology. One reason is that automated tasks are no perfect substitutes for non-automated tasks. A second reason is that automation raises the interest rate and thus inhibits positive behavioral reactions to aging (later retirement and investment in human capital). Moreover, increased automation generates a falling net labor share of income and rising welfare inequality. We evaluate alternative policy responses to cope with this inequality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Wie lässt sich die Nachfrage nach KI- und anderen Kompetenzen auf dem Arbeitsmarkt besser messen? (2025)
Janssen, Simon ; Wiederhold, Simon ; Langer, Christina; Stops, Michael ; Rounding, Nicholas; Nagler, Markus ;Zitatform
Janssen, Simon, Christina Langer, Markus Nagler, Nicholas Rounding, Michael Stops & Simon Wiederhold (2025): Wie lässt sich die Nachfrage nach KI- und anderen Kompetenzen auf dem Arbeitsmarkt besser messen? (ROA external reports / Researchcentrum voor Onderwijs en Arbeidsmarkt (Maastricht) 2025,10 ai:conomics policybrief), Maastricht, 6 S.
Abstract
"Eine umfangreiche Forschungsliteratur zeigt, dass der technologische Wandel erhebliche Auswirkungen auf die Arbeitsmärkte hat, da moderne digitale Technologien die Nachfrage nach bestimmten Kompetenzen verändern. Zum einen können neue Technologien einige menschliche Tätigkeiten ersetzen. Zum anderen Seite können sie neue Tätigkeiten schaffen oder ergänzen (Acemoglu et al., 2015; Acemoglu & Restrepo, 2018, 2019, 2020). Mit der starken Verbreitung Künstlicher Intelligenz in den letzten Jahren gewinnen bestimmte Fragen in der öffentlichen Diskussion und der Forschung zunehmend an Bedeutung: Wächst die Arbeitsnachfrage nach KI-Kompetenzen auch auf dem deutschen Arbeitsmarkt? Führt die steigende Nachfrage nach KI-Kompetenzen dazu, dass andere Kompetenzen – bei niedrig-, mittel- und hochqualifizierten Arbeitskräften – weniger gefragt sind? Ziel dieses Forschungsprojekts ist es, eine belastbare Datengrundlage zu schaffen, um solche Fragen in Zukunft fundierter beantworten zu können. Die Entwicklungen bei generativer Künstlicher Intelligenz, insbesondere von Tools wie ChatGPT, hat die Diskussion über die Auswirkungen von KI auf den Arbeitsmarkt sowohl in der Wissenschaft als auch in der öffentlichen Debatte und in der Politik deutlich verstärkt. Während Computer und Software die Arbeitswelt durch die präzisere und effizientere Ausführung routinemäßiger Aufgaben verändert haben, können moderne KI-Systeme nun komplexe, nichtroutinemäßige Aufgaben übernehmen, ohne auf detaillierte Anweisungen oder wiederholende Regeln angewiesen zu sein (Brynjolfsson et al., 2025). Infolgedessen sehen viele das produktive Potenzial dieser neuen Technologie optimistisch. Andere hingegen befürchten, dass KI die Arbeitsmärkte disruptiv verändern könnte." (Autorenreferat, IAB-Doku)
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Literaturhinweis
How can we better measure the demand for AI and other skills on the labour market? (2025)
Janssen, Simon ; Wiederhold, Simon ; Nagler, Markus ; Langer, Christina; Stops, Michael ; Rounding, Nicholas;Zitatform
Janssen, Simon, Christina Langer, Markus Nagler, Nicholas Rounding, Michael Stops & Simon Wiederhold (2025): How can we better measure the demand for AI and other skills on the labour market? (ROA external reports / Researchcentrum voor Onderwijs en Arbeidsmarkt (Maastricht) 2025,10 ai:conomics policybrief), Maastricht, 5 S.
Abstract
"A large body of research literature shows that technological change has a significant impact on labour markets, as modern digital technologies are changing the demand for certain skills. On the one hand, new technologies can replace some human activities. On the other hand, they can create or complement new activities (Acemoglu et al., 2015; Acemoglu & Restrepo, 2018, 2019, 2020). With the proliferation of artificial intelligence (AI) in recent years, certain questions are becoming increasingly important in public debate and research: Is the demand for AI skills also growing on the German labour market? Does the increasing demand for AI skills mean that other skills - among low, medium and highly qualified workers - are less in demand? The aim of this research project is to create a reliable data basis in order to be able to answer such questions in a more informed way in the future. Developments in generative AI, particularly tools such as ChatGPT, have significantly intensified the discussion about the impact of AI on the labour market, both in academia and in public debate and policy. While computers and software have transformed the world of work by performing routine tasks more precisely and efficiently, modern AI systems can now take on complex, non-routine tasks without relying on detailed instructions or repetitive rules (Brynjolfsson et al., 2025). As a result, many are optimistic about the productive potential of this new technology. Others, however, fear that AI could disrupt labour markets. In the course of the intensive scientific and public debate on AI, there is a growing body of literature that deals with the effects of AI on labour markets. These initially focus on specific occupations such as call centre workers (Brynjolfsson et al., 2025, Dijksman et al., 2024), consultants (Dell’ et al., 2023), writers or developers (Peng et al., 2023). However, a major challenge is to measure how the demand for and supply of skills has changed in the wake of the emergence of AI." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Artificial intelligence in the workplace: insights into the transformation of customer services (2025)
Janssen, Simon ; Stops, Michael ; Dijksman, Sander; Montizaan, Raymond ; Steens, Sanne; Levels, Mark ; Rounding, Nicholas; Fourage, Didier; Özgül, Pelin; Fregin, Marie-Christine ; Eijkenboom, Danique; Graus, Evie;Zitatform
Janssen, Simon, Michael Stops, Sanne Steens, Pelin Özgül, Nicholas Rounding, Sander Dijksman, Raymond Montizaan, Mark Levels, Didier Fourage, Danique Eijkenboom, Evie Graus & Marie-Christine Fregin (2025): Artificial intelligence in the workplace: insights into the transformation of customer services. In: IAB-Forum H. 22.04.2025, 2025-04-22. DOI:10.48720/IAB.FOO.20250422.01
Abstract
"How does the use of artificial intelligence in training affect employee productivity? These and other questions were investigated as part of the long-term research project “ai:conomics” using company data from various large European companies. Initial results suggest that AI can have a positive impact on employee productivity, especially for new employees." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Overlapping crises (re)shaping the future of regional labour markets [OVERLAP]: Main Report (2025)
Khabirpour, Neysan; Pagnini, Costanza; Bronka, Patryk; Hoch, Markus; Limbers, Jan; Kreuzer, Philipp; Pelizzari, Lorenzo; Richiardi, Matteo ;Zitatform
Khabirpour, Neysan, Lorenzo Pelizzari, Jan Limbers, Markus Hoch, Philipp Kreuzer, Matteo Richiardi, Patryk Bronka & Costanza Pagnini (2025): Overlapping crises (re)shaping the future of regional labour markets [OVERLAP]. Main Report. Luxembourg: ESPON 2030, 101 S.
Abstract
"Europe’s labour market is entering a decade in which structural forces converge and pull unevenly on every region. First, the demographic change is steadily thinning the labour supply: by 2050, the EU’s labour force is set to decrease by 35 million people. (Secondly) This demographic transition comes at a time when Member States are increasing their efforts to achieve the decarbonization targets, and (thirdly) when they are ramping up investments to digitalise the economy. While the digital transition is accelerating demand for specialised skills faster than workers can acquire them, the green transition suggests both disruption and expansion. To deliver the REPowerEU targets, the Commission estimates that more than 3.5 million additional jobs will be needed by 2030. Explained very shortly, these interacting factors may amplify long-standing territorial disparities in age structure, industrial fabric and human-capital endowment. Understanding where labour will transform and where new demand will arise is therefore indispensable. It is precisely this spatial intelligence that the OVERLAP project supplies—by charting the possible employment trajectories of every NUTS-3 labour market – for the 2035 perspective - under a varied set of assumptions, driven by policy or shock. This is done within a scenario-driven exercise, covering ageing, green ambition and digital diffusion. In doing so, as a forward-looking exercise, the study equips policymakers with the granular evidence needed to anticipate potential shortages, target up- and reskilling investments, and steer and match transition funding to address local needs and the regions that need it most. The study starts from two overarching objectives: Compile a granular portrait of Europe’s regional labour markets by tracing demographic dynamics and their (possible) implications for employment trends, at NUTS-3 level, out to 2035. Gauge how major drivers—including population change and the twin digital-green transition—may reshape labour demand under a range of forward-looking (possible) scenarios, out to 2035. From these aims, flow the main guiding research questions: which territories and sectors are set to gain or lose employment as ageing, automation and decarbonisation unfold simultaneously? And what policy mixes can cushion vulnerable regions while helping them capture new growth niches? Addressing these questions across the ESPON space—i.e. all EU Member States plus Iceland, Liechtenstein, Norway and Switzerland—requires a geography-sensitive lens; hence results are mapped down to individual NUTS-3 regions. To deliver evidence at that resolution, the project combined a dual analytical architecture. Quantify and regionalise macro-trends: the top-down stream employs the DINOS dynamic input-output model (developed by PROGNOS) to translate demographic, technological and climate-policy shocks into sectoral employment and wage shifts, then regionalises these outputs to the full NUTS-3 grid. The macro-level modelling strategy begins with national economic aggregates, traces broad structural trends across industries, and subsequently disaggregates the resulting labour-market effects to individual regions. By working from the “whole economy” downward, this framework captures systemic interactions—such as supply-chain spill-overs—beyond the reach of purely regional models. Provide a micro-analytical perspective: in parallel, the bottom-up stream extends the SimPaths dynamic microsimulation platform—already validated for the United Kingdom as a baseline —to Greece, Hungary, Italy and, enriching the macro picture with individual life-course trajectories. This novel, regional, micro-analytical framework sheds additional light on the distributional impact of the ongoing economic and social transformations, going beyond the broad picture and simplified assumptions that had to be made in the top-down approach (macro-analyses). The dynamic framework integrates a defining feature in every simulated period: inputs from a static tax-benefit calculator (EURO-MOD), hence allowing to study to what extent tax and benefit systems can smooth out transitional dynamics. However, it is important to highlight from the onset, that this study should not be perceived as a crystal globe, as it does not cover all possible shocks or situations, but acts upon the accumulated knowledge, in order to provide some modelled scenarios that are aimed at informing and opening the forward-looking strategies, with a pre-emptive component." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Does AI at Work Increase Stress? Text Mining Social Media About Human–AI Team Processes and AI Control (2025)
Zitatform
Klonek, Florian & Sharon Parker (2025): Does AI at Work Increase Stress? Text Mining Social Media About Human–AI Team Processes and AI Control. In: Journal of organizational behavior, S. 1-15. DOI:10.1002/job.70000
Abstract
"With rising use of artificial intelligence (AI) in organizations, alongside increasing mental health issues, we seek to understand how AI use affects human stress. Drawing on the automation–augmentation perspective, we propose that AI control over decision-making thwarts human autonomy and thus contributes to stress. Drawing on models of teamwork and augmentation, we expect that human–AI team processes (i.e., transition, action, and interpersonal processes) help people meet their goals and reduce stress. Finally, we argue that human–AI team processes provide an important social resource, which buffers the stress-enhancing role of AI control. To test our hypotheses, we analyzed over 2700 tweets. Using a trained large language model, validated against human ratings, we indexed key measures. Results confirm that high AI control was associated with increased stress, whereas human–AI team processes were associated with decreased stress. In support of the moderation hypothesis, two human–AI team processes (action and interpersonal) helped further reduce the stress-enhancing effect of AI control. We discuss implications for work design theory and the importance of regulating levels of AI control to protect workers' mental health." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
KI Navigator #10: Wie KI dem Arbeitsmarkt hilft (2025)
Zitatform
Koch, Christian & Michael Stops (2025): KI Navigator #10: Wie KI dem Arbeitsmarkt hilft. In: Heise online, 2025-03-14.
Abstract
"Stellenanzeigen können viel über den Wandel des Arbeitsmarkts verraten. Künstliche Intelligenz hilft dabei, diese Daten zu interpretieren."
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Literaturhinweis
Automation in shared service centres: Implications for skills and autonomy (2025)
Zitatform
Kowalik, Zuzanna, Piotr Lewandowski, Tomasz Geodecki & Maciej Grodzicki (2025): Automation in shared service centres: Implications for skills and autonomy. In: The Economic and Labour Relations Review, Jg. 36, H. 2, S. 563-581. DOI:10.1017/elr.2025.10026
Abstract
"The offshoring-fueled growth of the Central and Eastern European business services sector gave rise to shared service centers (SSCs) – quasi-autonomous entities providing routine-intensive tasks for the central organization. The advent of technologies such as intelligent process automation, robotic process automation, and artificial intelligence jeopardises SSCs’ employment model, necessitating workers’ skills adaptation. The study challenges the deskilling hypothesis and reveals that automation in the Polish SSCs is conducive to upskilling and worker autonomy. Drawing on 31 in-depth interviews, we highlight the negotiated nature of automation processes shaped by interactions between headquarters, SSCs, and their workers. Workers actively participated in automation processes, eliminating the most mundane tasks. This resulted in upskilling, higher job satisfaction, and empowerment. Yet, this phenomenon heavily depends upon the fact that automation is triggered by labor shortages, which limit the expansion of SSCs. This situation encourages companies to leverage the specific expertise entrenched in their existing workforce. The study underscores the importance of fostering employee-driven automation and upskilling initiatives for overall job satisfaction and quality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Between control and participation: The politics of algorithmic management (2025)
Zitatform
Krzywdzinski, Martin, Daniel Schneiß & Andrea Sperling (2025): Between control and participation: The politics of algorithmic management. In: New Technology, Work and Employment, Jg. 40, H. 1, S. 60-80. DOI:10.1111/ntwe.12293
Abstract
"Understanding the role of human management is crucial for the debate over algorithmic management—to date limited to studies on the platform economy. This qualitative case study in logistics reconstructs the actor constellations (managers, engineers, data scientists and workers) and negotiation processes in different phases of algorithmic management. It offers two major contributions to the literature: (1) a process model distinguishing three phases: goal formation, data production and data analysis, which is used to analyse (2) the politics of algorithmic management in conventional workplaces, which differ significantly from platform companies. The article goes beyond surveillance to elucidate the role of the regulatory framework, various actors' knowledge contributions to the algorithmic management system, and the power relations resulting therefrom. While the managerial goals in the examined case were not oriented towards a surveillance regime, the outcome was nevertheless a centralisation of knowledge and disempowerment of workers." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Das Produktionsmodell der deutschen Automobilindustrie auf dem Prüfstand: Arbeitsstrukturen und Arbeitsanforderungen in Montagewerken im Wandel? (2025)
Zitatform
Kuhlmann, Martin, Britta Matthes & Stefan Theuer (2025): Das Produktionsmodell der deutschen Automobilindustrie auf dem Prüfstand. Arbeitsstrukturen und Arbeitsanforderungen in Montagewerken im Wandel? (SOFI-Impulspapier), Göttingen, 6 S.
Abstract
"Das in den 1980er-Jahren etablierte Produktionsmodell der deutschen Automobilhersteller lässt sich beschreiben als innovations- und exportorientierte Produktion qualitativ hochwertiger Produkte auf Basis qualifizierter Arbeit, guter Bezahlung und hoher Beschäftigungssicherheit sowie starken gewerkschaftlichen Interessenvertretungen. Politische Vorgaben, wie die Umstellung auf die Produktion von Elektroautos, veränderte Wettbewerbsbedingungen sowie die weiter voranschreitende Digitalisierung haben dazu geführt, dass dieses Produktionsmodell derzeit auf dem Prüfstand steht. Getrieben durch aufkommende Zweifel an der technologischen Überlegenheit deutscher Automobilhersteller und Nachfrageschwächen beim Übergang auf Elektromobilität ist die Unsicherheit in der Branche gegenwärtig groß. In einem laufenden Forschungsprojekt untersuchen wir, inwiefern sich durch die Produktion von Elektroautos und die fortschreitende Digitalisierung Arbeitsstrukturen und Arbeitsanforderungen in den Endmontagewerken der deutschen Automobilhersteller verändert haben und ob sich arbeitsbezogen ein Wandel des deutschen Produktionsmodells abzeichnet." (Autorenreferat, IAB-Doku)
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Aspekt zurücksetzen
- Gesamtbetrachtungen/Positionen
- Arbeitsformen, Arbeitszeit und Gesundheit
- Qualifikationsanforderungen und Berufe
- Arbeitsplatz- und Beschäftigungseffekte
- Wirtschaftsbereiche
- Arbeits- und sozialrechtliche Aspekte / digitale soziale Sicherung
- Deutschland
- Andere Länder/ internationaler Vergleich
- Besondere Personengruppen