An urgent call for I-O psychologists to produce timelier technology research

White, Jerod C.; Ravid, Daniel M.; Siderits, Ian; Behrend, Tara S.

doi:10.1017/iop.2022.26

Cited by 20 publications

(5 citation statements)

References 39 publications

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“…However, both impact how employees experience their jobs and behave accordingly and can thus have psychological implications in the present (Makarius et al 2020;Schwabe and Castellacci 2020). This requires research methods that not only examine the status quo or past experiences, but also capture expectations and beliefs about the future (see, e.g., White et al 2022, for timelier technology research in psychology). A common problem with this, however, is their susceptibility to error (e.g., forecasts are often subject to positivity bias; Baumeister et al 2016;Monroe et al 2017).…”

Section: Ai Ai-driven Automation and Challenges Of Empirical Assessmentmentioning

confidence: 99%

Psychological Implications of AI-driven Automation for Employees – Insights from Healthcare and Financial Services in Germany

Sureth,

Nachtwei

2024

Preprint

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Advances in artificial intelligence (AI) are shaking up the world of work. The capabilities of AI are becoming more and more sophisticated and AI systems are increasingly capable of performing not only routine but also non-routine and cognitive tasks autonomously. This triggered the widespread adoption of AI to automate an increasing number of job tasks. Subsequently, jobs are changing, both in quantity and quality. While the debate on the impact of AI and automation on employment has intensified, little attention has been paid to the subjective experience of employees and the psychological implications of AI-induced changes in the workplace. Thus, we examined the psychological implications of AI-driven automation with an explicit focus on employees’ experience and behaviour. To do so, we investigated the application fields of AI, the resulting changes in job tasks, and the associated psychological implications in two exemplary sectors, i.e., healthcare and financial services. Using an exploratory approach, we conducted five interview studies with a total of 91 German experts and employees, examining three occupational groups, including physicians, psychotherapists, and bank clerks. Using qualitative content analyses, we compiled comprehensive and detailed data that provide insights into the psychological implications of AI-induced changes of job tasks in the two investigated sectors. Our results serve as a starting point for further research in this area and highlight the importance of taking a human-centric approach.

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Section: Ai Ai-driven Automation and Challenges Of Empirical Assessmentmentioning

confidence: 99%

Psychological Implications of AI-driven Automation for Employees – Insights from Healthcare and Financial Services in Germany

Sureth,

Nachtwei

2024

Preprint

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“…Journals might also require shortened introductions, but more detailed methods and results sections, to enhance reporting rigor (not only for AI/ML, but for other methods as well). Further, I-O and OBHR journals will need to increase the speed of decisions, as typical AI/ML publishing outlets in computer science provide final decisions in a matter of weeks rather than months for a first revision (see White et al, 2022).…”

Section: New Ways To Publish Ai/ml-related Researchmentioning

confidence: 99%

Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice

Woo,

Tay,

Oswald

2024

Personnel Psychology

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Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern‐day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways.

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“…This is especially relevant in research on technology, as technology as a research subject is constantly and rapidly evolving. As White et al (2022) point out, psychological research on workplace technologies lags an average of six years behind the respective technologies' market launch and is thus often unable to be actively involved in shaping future workplace technologies.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Digitisation and automation in training and development: a meta-review of new opportunities and challenges

Wesche,

Handke

2023

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PurposeTo remain competitive, efficient and productive, organisations need to ensure that their employees continuously learn and develop. This is even more challenging and critical in times characterised by volatility, uncertainty, complexity and ambiguity (VUCA). Hence, several technological applications have been introduced with the promise to make organisational training and development (T&D) more efficient and targeted through digitisation and automation. However, digitising and automating processes in the sensitive field of T&D also poses challenges and perils for employees and organisations as a whole.Design/methodology/approachStructured by the T&D process of (1) assessment/planning, (2) design/implementation and (3) evaluation, the authors present different digitisation and automation possibilities and discuss the specific opportunities and challenges they pose. Subsequently, the authors identify and discuss overarching themes of opportunities and challenges of technology use in T&D via a meta-review.FindingsThis synthesis revealed three central topics that decision-makers in T&D should carefully consider when it comes to the implementation of technological applications: opportunities and challenges of (1) data collection, (2) decision-making and (3) the value of human contact.Originality/valueThis review integrates previously fragmented research on specific technologies applied to specific T&D functions and provides researchers and practitioners with a fuller picture of the opportunities and challenges of technology applied in T&D.

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An urgent call for I-O psychologists to produce timelier technology research

Cited by 20 publications

References 39 publications

Psychological Implications of AI-driven Automation for Employees – Insights from Healthcare and Financial Services in Germany

Psychological Implications of AI-driven Automation for Employees – Insights from Healthcare and Financial Services in Germany

Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice

Digitisation and automation in training and development: a meta-review of new opportunities and challenges

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