Veränderungen der Arbeitswelt durch Künstliche Intelligenz
Anwendungsmöglichkeiten und Auswirkungen des Einsatzes künstlicher Intelligenz auf den Arbeitsmarkt werden breit diskutiert. Welche Folgen für Beschäftigung, Löhne und Qualifikationsanforderungen sind zu erwarten? Birgt die Nutzung automatisierter Entscheidungssysteme (z.B. für die Personalauswahl) ein Diskriminierungsrisiko? Wie wirkt sich der Einsatz von künstlicher Intelligenz auf die Arbeitsqualität aus?
Dieses Themendossier stellt Literatur zum Stand der Forschung zusammen.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
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Literaturhinweis
Who uses Generative AI? Patterns and inequalities across the EU: Employment and labour markets (2026)
Zitatform
Adăscăliței, Dragoș (2026): Who uses Generative AI? Patterns and inequalities across the EU. Employment and labour markets. (Eurofound working paper), Dublin, 19 S.
Abstract
"This paper describes generative AI use patterns across EU27 Member States in 2025, analysing crossnational variation and socio-demographic inequalities based on Eurostat aggregate data. Overall use of generative AI reaches 32.7% at the EU27 level, ranging from 17.8% in Romania to 48.4% in Denmark. Country patterns do not follow clear geographic clustering, with high and low adopters distributed across all European regions. Private use systematically exceeds professional use, whilst educational use remains concentrated among young populations. Educational attainment emerges as a strong predictor of AI use, with high-educated individuals using generative AI at more than double the rate of low-educated individuals. Age is also a strong predictor, with 63.8% of those aged 16-24 having used generative AI in the past three months compared to just 6.5% of those aged 65 and above. The gender gaps in AI use are moderate but widen with education. In terms of broad occupational groups, the analysis demonstrates that uptake is heavily skewed towards ICT professions. Labour force status also matters decisively, with students (72.0%) far exceeding employed (36.4%), unemployed (28.3%), and retired/inactive populations (12.9%) in technology usage. These patterns reveal stratified diffusion of generative AI use with implications for labour market inequalities across the EU." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation, Learning, and Career Dynamics (2026)
Zitatform
Afrouzi, Hassan, Andres Blanco, Andrés Drenik & Erik Hurst (2026): Automation, Learning, and Career Dynamics. (NBER working paper / National Bureau of Economic Research 35157), Cambridge, Mass, 58 S.
Abstract
"We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model (2026)
Zitatform
Agrawal, Ajay K., John McHale & Alexander Oettl (2026): Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model. (NBER working paper / National Bureau of Economic Research 34781), Cambridge, Mass, 44 S.
Abstract
"The task-based approach has become the dominant framework for studying the labor-market effects of artificial intelligence (AI), typically emphasizing the replacement of human workers by machines. Motivated by growing empirical evidence that contemporary AI is more often used as a tool that augments workers, this paper develops two related task-based models in which AI enhances worker productivity without automating tasks. Abstracting from capital, we develop a pair of related task-based models that examine how technological progress in AI that provides new tools to augment workers affects aggregate productivity and wage inequality. Both models emphasize the role of human capital in intermediating the effects of AI-related technological shocks. In the first model, AI use requires specialized expertise, and technological progress expands the set of tasks for which such expertise is effective. We show that a larger supply of AI expertise amplifies the productivity gains from improvements in AI technology while attenuating its adverse effects on wage inequality. The second model focuses on non-AI skills, allowing AI tools to alter the set of tasks that workers can perform given their skills. In equilibrium, workers allocate across tasks in response to wages, generating an endogenous distribution of skills across the task space. A central result is that aggregate productivity and wage inequality depend on different global properties of this equilibrium distribution: productivity is particularly sensitive to thinly staffed tasks that create bottlenecks, while wage inequality is driven by the concentration of workers in a narrow set of tasks. As a result, improvements in AI tools can induce non-monotonic co-movement between productivity and inequality. By linking these mechanisms to multidimensional human capital---including AI expertise and higher-order non-AI skills---the paper highlights the role of education and training policies in shaping the economic consequences of AI-driven technological change." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index (2026)
Akyildirim, Erdinc; Gozgor, Giray; Bekci, Suzan;Zitatform
Akyildirim, Erdinc, Suzan Bekci & Giray Gozgor (2026): Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index. (CESifo working paper 12653), München, 13 S.
Abstract
"This paper constructs an AI Attention Index from LexisNexis news coverage and embeds it within an augmented Phillips curve framework. It then examines the relationship between AI attention and unemployment in the United States using monthly data from January 2000 to December 2025. We find that greater AI attention is associated with lower unemployment. Nonlinear estimates reveal a U-shaped relationship, indicating diminishing marginal effects within the observed data range. The relationship weakens after the onset of COVID-19, with both linear and nonlinear effects reduced. These findings indicate that labour market effects of AI-related expectations are sensitive to macroeconomic regime shifts." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
AI adoption, productivity and employment: Evidence from European firms (2026)
Aldasoro, Iñaki; Gambacorta, Leonardo; Pal, Rozalia; Wolski, Marcin; Weiß, Christoph; Revoltella, Debora;Zitatform
Aldasoro, Iñaki, Leonardo Gambacorta, Rozalia Pal, Debora Revoltella, Christoph Weiß & Marcin Wolski (2026): AI adoption, productivity and employment: Evidence from European firms. (Economics - working papers / European Investment Bank 2026/02), Luxembourg, 32 S. DOI:10.2867/1772538
Abstract
"This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12,000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
auch erschienen als: BIS Working Papers, 1325 -
Literaturhinweis
The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson (2026)
Aldred, Jonathan;Zitatform
Aldred, Jonathan (2026): The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson. In: Structural Change and Economic Dynamics, Jg. 78, S. 188-196. DOI:10.1016/j.strueco.2026.03.008
Abstract
"Task-based models of production have led to a theoretical reappraisal of the effect of new technology on labour demand. This paper critically assesses the policy implications of this research agenda, with particular reference to Acemoglu and Johnson’s recent book, Power and Progress. While Acemoglu and Johnson take welcome steps away from previous orthodoxy, their analysis has several flaws which affect the policy lessons to be drawn, including: (i) the explanation for anti-labour bias is unclear; (ii) worker-friendly technologies are not clearly characterised in theory, and hard to identify in practice; (iii) macroeconomic policy orthodoxy is largely unquestioned. More generally, much of Power and Progress remains unhelpfully constrained by theoretical commitments to mainstream economics." (Author's abstract, IAB-Doku, © 2026 The Author. Published by Elsevier B.V.) ((en))
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Literaturhinweis
The end of work feels near. How do people perceive the impact of digital technologies and automation? (2026)
Zitatform
Arntz, Melanie, Sebastian Blesse & Philipp Doerrenberg (2026): The end of work feels near. How do people perceive the impact of digital technologies and automation? In: Labour Economics, Jg. 102, 2026-05-11. DOI:10.1016/j.labeco.2026.102897
Abstract
"Anxieties about technological change in the context of the labor market are a recurring historical phenomenon. Using customized survey data collected in 2019 in the US and Germany, prior to the recent wave of generative AI applications, we study how respondents perceive the impact of the digital (automation) technologies available at the time of the survey on the labor market. We document that a majority views digital technologies and automation as a major threat to overall employment and as a cause of rising inequality, while a quarter is concerned about their own labor market prospects. Providing scientific information on the likely labor market implications of digital technologies in a randomized experiment reduces these concerns. Yet, treatment responses depend on prior beliefs about the future of work, resulting in heterogeneous and opposing treatment effects on policy demand." (Author's abstract, IAB-Doku, © 2026 Elsevier) ((en))
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Literaturhinweis
Systematic literature review on the digital transformation of the personnel selection process (2026)
Zitatform
Baranyi, Virág (2026): Systematic literature review on the digital transformation of the personnel selection process. In: German Journal of Human Resource Management, Jg. 40, H. 2, S. 223-254. DOI:10.1177/23970022251363012
Abstract
"Digital Transformation technologies (DT technologies) are reshaping work processes, including personnel selection, an area traditionally viewed as inherently human-centric. While prior studies have examined various digital technologies in personnel selection, they have not provided sufficient evidence on the different levels of digitalization in selection processes and the factors influencing organizations’ adoption decisions. To address these gaps, this study systematically reviews 94 Scopus-indexed studies to analyze how DT technologies are applied across selection stages, categorizing practices into Manual, Digitalized, and Digitally Transformed approaches. By further distinguishing between Digital Technologies and AI Enhancements, this study offers a structured framework for understanding how organizations integrate digital technologies into selection and what drives or hinders their adoption. The findings highlight both the benefits (efficiency gains, potential bias reduction, improved candidate experience) and challenges (ethical concerns, algorithmic bias, technical and cultural barriers, and candidate perceptions) associated with these technologies, providing insights for both academic research and HR practice." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Automation Experiments and Inequality (2026)
Zitatform
Benzell, Seth Gordon & Kyle R. Myers (2026): Automation Experiments and Inequality. (NBER working paper / National Bureau of Economic Research 34668), Cambridge, Mass, 26 S., App. DOI:10.3386/w34668
Abstract
"Many experiments study the productivity effects of automation technologies such as generative algorithms. A key test in these experiments relates to inequality: does the technology increase output more for high- or low-skill workers? However, the theoretical content of this empirical test has been unclear. Here, we formalize a theory that describes the experimental effect of automation technologies on worker-level output and, therefore, inequality. Worker-level output depends on a task-level production function, and workers are heterogeneous in their task-level skills. Workers perform a task themselves or delegate it to the automation technology. The inequality effect of improved automation depends on the interaction of two factors: (i) the correlation in task-level skills across workers, and (ii) workers' skills relative to the technology's effective skill. In many cases we study, the inequality effect is non-monotonic --- as technologies improve, inequality decreases then increases. The model and descriptive statistics of skill correlations generally suggest that the diversity of automation technologies will play an important role in the evolution of inequality." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Mind the Gap: AI Adoption in Europe and the US (2026)
Zitatform
Bick, Alexander, Adam Blandin, David Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: AI Adoption in Europe and the US. (RF Berlin - CReAM Discussion Paper Series 102/26), Berlin, 71 S.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- spätere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US : BPEA Conference Draft, March 26-27, 2026" in: Brookings Papers on Economic Activity, Conference Draft (2026), 1-70
- auch erschienen u.d.T. "Mind the Gap: KI-Einführung in Europa und den USA" als: CEPR discussion paper / Centre for Economic Policy Research, 21337
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Literaturhinweis
Mind the Gap: KI-Einführung in Europa und den USA (2026)
Zitatform
Bick, Alexander, Adam Blandin, David Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: KI-Einführung in Europa und den USA. (CEPR discussion paper / Centre for Economic Policy Research 21337), London, 80 S.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We find no clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- spätere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US : BPEA Conference Draft, March 26-27, 2026" in: Brookings Papers on Economic Activity, Conference Draft (2026), 1-70
- auch erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US" als: RF Berlin - CReAM Discussion Paper Series, 102/26
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Literaturhinweis
Mind the Gap: AI Adoption in Europe and the US: BPEA Conference Draft, March 26-27, 2026 (2026)
Zitatform
Bick, Alexander, Adam Blandin, David J. Deming, Nicola Fuchs-Schündeln & Jonas Jessen (2026): Mind the Gap: AI Adoption in Europe and the US. BPEA Conference Draft, March 26-27, 2026. In: Brookings Papers on Economic Activity, Jg. Conference Draft, H. Spring, S. 1-70.
Abstract
"This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
- frühere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: KI-Einführung in Europa und den USA" als: CEPR discussion paper / Centre for Economic Policy Research, 21337
- frühere (möglicherweise abweichende) Version erschienen u.d.T. "Mind the Gap: AI Adoption in Europe and the US" als: RF Berlin - CReAM Discussion Paper Series, 102/26
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Literaturhinweis
Der KI-Irrtum: Warum Deutschland auf Zuwanderung angewiesen ist: Leitartikel (2026)
Zitatform
Brücker, Herbert, Yuliya Kosyakova & Enzo Weber (2026): Der KI-Irrtum: Warum Deutschland auf Zuwanderung angewiesen ist. Leitartikel. In: Wirtschaftsdienst, Jg. 106, H. 5, S. 304-305. DOI:10.2478/wd-2026-0074
Abstract
"Sieben Millionen - so viele Arbeitskräfte wird Deutschland in den nächsten 15 Jahren allein aufgrund des demografischen Wandels verlieren. Bereits seit vielen Jahren ist der demografische Effekt negativ, mit mehr als 400.000 Arbeitskräften pro Jahr. Tatsächlich beginnt der deutsche Arbeitsmarkt jedoch erst jetzt zu schrumpfen. Denn bislang konnte dieser Rückgang überkompensiert werden - durch eine steigende Erwerbsbeteiligung von Älteren und Frauen; und vor allem durch Zuwanderung. Doch diese Ausgleichsmechanismen stoßen zunehmend an Grenzen. Europa altert insgesamt, und die Dynamik der Zuwanderung innerhalb Europas nimmt ab. Zugleich sind viele der besonders mobilen, jüngeren Kohorten bereits gewandert. Vor diesem Hintergrund wird Migration schwieriger - und genau hier setzt ein verbreitetes Argument an: Wenn Künstliche Intelligenz (KI) zunehmend Aufgaben übernimmt, braucht man doch keine zusätzlichen Arbeitskräfte mehr. Diese Folgerung ist ein Trugschluss. Arbeitskräfteknappheit lässt sich gesamtwirtschaftlich nicht einfach wegdigitalisieren." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Das knappe Gut Arbeit: Automatisierung, Arbeitskräftemangel und sozialer Konflikt (2026)
Zitatform
Butollo, Florian (2026): Das knappe Gut Arbeit. Automatisierung, Arbeitskräftemangel und sozialer Konflikt. (Edition Suhrkamp 2815), Berlin: Suhrkamp Verlag, 254 Seiten.
Abstract
"Angesichts von Digitalisierung und Künstlicher Intelligenz wird allerorten vor massiven Arbeitsplatzverlusten gewarnt. Gleichzeitig reißen die Klagen über Fachkräftemangel nicht ab, zahllose Stellen bleiben unbesetzt, und dem Pflegesektor droht der Kollaps. Florian Butollo geht diesem Paradoxon auf den Grund und analysiert, warum gerade Automatisierung immer mehr Arbeit schafft – und damit zur Keimzelle eines neuen sozialen Konflikts wird: Die anbrechende Ära der Arbeitskräfteknappheit ist geprägt vom Leiden an Überlastung und den Kämpfen dagegen. Zugleich stellt sich die Frage nach der Sinnhaftigkeit von Arbeit neu: Wofür wollen wir angesichts sozialer und ökologischer Krisen künftig unsere Arbeitskraft einsetzen – und welche Tätigkeiten können verschwinden?" (Verlagsangaben, IAB-Doku)
Weiterführende Informationen
Inhaltsverzeichnis -
Literaturhinweis
Revisiting the occupational impact of AI in the generative AI era (2026)
Casas, P.; González-Vázquez, I.; Salotti, S.; Martínez-Plumed, F.; Gómez, E.; Fernández-Macías, E.;Zitatform
Casas, P., E. Fernández-Macías, F. Martínez-Plumed, E. Gómez, I. González-Vázquez & S. Salotti (2026): Revisiting the occupational impact of AI in the generative AI era. (JRC working papers series on labour, education and technology 2026,02), Sevilla, 71 S.
Abstract
"Generative AI is reshaping what artificial intelligence can do in the workplace, calling into question pre-GenAI assessments of which workers and tasks are most exposed. In this paper we trace the evolution of AI exposure in the European labour market from 2008 to 2024 by linking 352 AI benchmarks to 14 cognitive abilities, 108 work tasks and 127 ISCO-3 occupations, weighting benchmarks by their research intensity in the AI literature and thus deriving AI exposure by cognitive ability. Bundling work tasks into occupations based on intensity indicators, we explore occupational exposure to AI. We find that the cognitive abilities most exposed to the recent surge of AI research are ideas-related, such as attention and search, comprehension and expression and logical reasoning. Because the associated information processing and problem-solving tasks are the most transversal across occupations, we find an exponential increase in AI exposure across all occupational categories of workers, even though comparatively high-skilled occupations are more exposed than elementary occupations. This points at a substantial and transversal labour market impact of AI." (Author's abstract, IAB-Doku) ((en))
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Literaturhinweis
Measuring Task-Based Advanced Automation Technologies & Digital Assistance Systems in the National Educational Panel Study (2026)
Zitatform
Dicks, Alexander, Martin Ehlert, Insa Grüttgen, Benjamin Schulz & Basha Vicari (2026): Measuring Task-Based Advanced Automation Technologies & Digital Assistance Systems in the National Educational Panel Study. (NEPS Survey Papers 128), Bamberg, 31 S. DOI:10.5157/NEPS:SP128:1.0
Abstract
"Wir stellen ein neues taskbasiertes Instrument zur Messung der Automatisierung am Arbeitsplatz vor. Dabei bezeichnen wir die Automatisierungstechnologien als „digitale Assistenzsysteme“, die wir als fortschrittliche Automatisierungssysteme definieren, wie beispielsweise Software und Geräte, die Aufgaben automatisch ausführen, um die Arbeitnehmer zu unterstützen. Dazu gehören Chatbots mit künstlicher Intelligenz, Algorithmen zur Bild- und Videoerstellung, Tools für vorausschauende Wartung und Analyse, digitale Anleitungssysteme sowie Wearables und kollaborative Roboter. Aufbauend auf dem taskbasierten Ansatz und unter Berücksichtigung der jüngsten technologischen Fortschritte haben wir für das Nationale Bildungspanel (NEPS) sieben Items entwickelt, die verschiedene Arten von digitalen Assistenzsystemen erfassen. Ergebnisse auf Grundlage von Daten der NEPS-Startkohorten 4 und 6 zeigen, dass die Nutzung der digitalen Assistenzsysteme je nach Alter und Bildungsniveau variiert und mit komplementären beruflichen Aufgaben sowie Indikatoren für die Digitalisierung am Arbeitsplatz korreliert. Das neue Instrument ergänzt bestehende NEPS-Items zur Digitalisierung am Arbeitsplatz und bietet Potenzial für eine Längsschnittanalyse des technologischen Wandels am Arbeitsplatz und dessen Auswirkungen auf Qualifikationsanforderungen, Ungleichheit und lebenslanges Lernen." (Autorenreferat, IAB-Doku)
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Literaturhinweis
Biased by Design? Case Managers' Multidimensional Preferences Toward the Design of Algorithmic Decision Support Systems (2026)
Zitatform
Dietz, Martin, Christopher Osiander, Mareike Sirman-Winkler & Markus Tepe (2026): Biased by Design? Case Managers' Multidimensional Preferences Toward the Design of Algorithmic Decision Support Systems. In: Public Administration Review, S. 1-14. DOI:10.1111/puar.70111
Abstract
"This study examines whether street-level bureaucrats' preferences toward algorithmic decision support (ADS) induce a unilateral shift of technology-related risks onto clients of the public employment service. Expanding on public value theory and research on moral agency in public service work, we argue that case managers' choices of ADS designs are shaped by a plurality of professional, service, and efficiency values. To test this argument, we conducted a conjoint experiment on a representative sample of German Federal Employment Agency case managers. Respondents compared pairs of hypothetical ADS systems that differed in their design features, reflecting varying degrees of the realization of public values. The empirical results indicate that case managers' choices do not result in biased design. Instead, case managers balance design features reflecting professional and service values while maintaining administrative efficiency. Case managers appreciate ADS support but firmly reject the mandatory use of such advice." (Author's abstract, IAB-Doku, © Wiley) ((en))
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Literaturhinweis
Impact of robotics on unemployment: Moderating effect of the national culture (2026)
Zitatform
Du, Guangjie, Suman Lodh, Monomita Nandy, Marina Dabić, Vikas Kumar & Jyoti Choudrie (2026): Impact of robotics on unemployment: Moderating effect of the national culture. In: Technological forecasting & social change, Jg. 227. DOI:10.1016/j.techfore.2026.124643
Abstract
"The integration of robotics has created significant opportunities while simultaneously introduced labour market challenges. The aim of this exploratory research is to investigate the impact of robotic adoption on unemployment in a cross-country context. By presenting a novel theoretical framework that integrates the Innovation Diffusion Theory (IDT) and the Absorptive Capacity Theory (ACT), and analysing a panel data from 33 countries, we show that countries with stronger absorptive capacity are better positioned to convert robotics adoption into employment gains. We further test whether national culture, proxied by Hofstede's cultural dimensions, moderates this relationship, uncovering a substantial heterogeneity across six cultural factors. This provides a practical blueprint for policymakers with clear evidence that uniform approaches to robotics adoption are unlikely to be effective and robotics policies must be tailored to local cultural norms, institutional capabilities, and national readiness. The policymaker should prioritise capability building alongside adjustment measures that support inclusive labour-market transitions." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier Inc.) ((en))
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Literaturhinweis
Algorithmic Profiling: Effective Tool for Targeting Active Labour Market Policies? (2026)
Zitatform
Eppel, Rainer, Ulrike Huemer, Helmut Mahringer & Lukas Schmoigl (2026): Algorithmic Profiling: Effective Tool for Targeting Active Labour Market Policies? In: Labour, S. 1-20. DOI:10.1111/labr.70013
Abstract
"Digitisation has sparked interest in automated decision making in Public Employment Services (PES). We evaluate an algorithmic profiling model in Austria that predicts the reemployment prospects of unemployed individuals to classify and assign them to active labour market policies. Our analysis shows that reallocating resources from jobseekers with low to medium prospects, as proposed by the PES, does not yield the expected efficiency gains. We find no systematic evidence that programmes are less effective for individuals with low predicted employment prospects than for those with medium prospects. These findings caution against crude algorithmic profiling and highlight the need for nuanced targeting strategies that prioritise the most disadvantaged jobseekers." (Author's abstract, IAB-Doku, Published by arrangement with John Wiley & Sons) ((en))
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Literaturhinweis
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design (2026)
Zitatform
Fahn, Matthias, Jin Li & Chang Sun (2026): Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design. (CESifo working paper 12612), München, 23 S., App.
Abstract
"We study how AI affects compensation and job design when performance depends on workers' non-contractible effort. In a principal–agent model with limited liability, AI reduces effort costs but disproportionately lowers the cost of achieving satisfactory performance. This raises the incentive cost of sustaining high effort and can induce firms to replace high-wage, high-effort good jobs with low-wage, low-effort bad jobs, even when good jobs create more total surplus. As a result, AI can lower wages, reduce worker welfare, and even depress profits. If workers can adopt AI unilaterally, adoption occurs even when the resulting equilibrium harms both parties; when adoption requires worker cooperation, resistance is strongest where AI erodes rents embodied in good jobs. In a search-and-matching extension, endogenous outside options amplify these forces, reinforcing a bad-job economy and potentially reducing employment." (Author's abstract, IAB-Doku) ((en))