We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Submit a Manuscript to the Journal
European Journal of Work and Organizational Psychology

For a Special Issue on
Adapting to Emerging Technologies at Work: Effects on the Nature of Work and Employee Outcomes

Manuscript deadline
03 January 2022

Cover image - European Journal of Work and Organizational Psychology

Special Issue Editor(s)

Anna-Sophie Ulfert, Goethe University, Frankfurt, Germany
[email protected]

Sonja Rispens, Eindhoven University of Technology, the Netherlands
[email protected]

Pascale Le Blanc, Eindhoven University of Technology, the Netherlands
[email protected]

Sonja Scherer, Goethe University, Frankfurt, Germany
[email protected]

Maria Peeters, Eindhoven University of Technology and Utrecht University, the Netherlands
[email protected]

Submit an ArticleVisit JournalArticles

Adapting to Emerging Technologies at Work: Effects on the Nature of Work and Employee Outcomes

Technological innovations are being developed rapidly and are revolutionizing a wide array of industries (Parker & Grote, 2020). To give a few examples, take the use of robots to disarm and remove explosives in military operations (Peake, 2017), in medical settings where doctors collaborate with robots to make surgery less invasive and more precise (Pratt, 2018), or in logistic warehouses where robots like Amazon’s Kiva retrieve products for order pickers. Other examples are the algorithms which manage Uber drivers (Zwick, 2018) and chatbots that can take care of customer service tasks or certain HR services (Meyer von Wolff et al., 2019). Whether it is to comply with increasing customer demands (in e-commerce for instance) or to mitigate the (future) shortage of labor (in the health care or logistics sector for instance), business motives spur organizations to buy and implement continuously advancing technologies, such as robots or artificial intelligence (AI) systems (Berkers et al., 2019; Parker & Grote, 2020). At the same time, employees seem to have a more ambivalent attitude towards these technologies, some fear to fall behind and while others embrace the opportunities of technology-related change (Berkers et al., 2019; Ulfert & Scherer, 2020).

Currently, many companies seem to invest mainly in the technological innovation itself, instead of focusing on the human side of working with these technologies. These emerging technologies, including for example Smart Technologies, AI, Robotics, and Algorithms (Brougham & Haar, 2018), have been argued to fundamentally change how employees work today and in the future (see e.g. Parker & Grote, 2020). This particularly includes a change in how we interact with technology, involving a shift of agency from the employee to the technology, as systems become increasingly competent in self-learning (Parker & Grote, 2020; Schwab, 2017). Although, the study of technology interaction at work has a longstanding research tradition in psychology (e.g. studying automation in manufacturing settings), the described shift in agency that comes with increasing system capabilities, has not yet been adequately addressed concerning its consequences for organizations and employees. As a consequence, researchers have emphasized the importance of studying both how humans adapt to these technologies as well as how work and technology can be designed to better fit employee needs (Parker & Grote, 2020; Wang et al., 2020). For effective implementation and adoption of emerging technologies at work, it is of pivotal importance to pay attention to the people who work with these technologies. We argue that Work and Organizational psychologists have much to contribute to understanding these technology-related changes in the workplace and to the development of emerging technologies. However, these developments are currently predominantly driven from the technology sectors. Building on accumulated evidence of 100 years of research on how to design jobs that facilitate employee well-being, motivation, and performance (Parker et al., 2017), Work and Organizational psychology can help to make the tech revolution more ‘human-centered’.

Our goal with this Special Issue is to stimulate a scientific discussion on (1) the effects of introducing emerging technologies, and especially AI systems, in the workplace as well as (2) the role work and organizational psychologists can play in the development and introduction of these technologies. This will help to build new theories and sound practices regarding a human-centered development and implementation of emerging technologies at work. We argue that we need to move beyond merely discussing whether technologies threaten jobs and job security (Frey & Osborne, 2013; Parker & Grote, 2020). Rather, we need to examine how technologies will shape work in the future and how we can design high-quality work (Wang et al. 2020). Already today, we experience a technology-related change of work characteristics, such as job demands, autonomy, relational aspects, and job significance (Wang et al., 2020), which has been described to intensify with increasing technology capabilities (e.g. AI; Ulfert & Scherer, 2020). Although theoretical models give a first indication of how the use of emerging technologies, and particularly AI, may change the work environment (e.g. Parker & Grote, 2020; Wang et al. 2020), there is still a lack of empirical studies on the way these technologies shape work today as well as the factors that impact employee outcomes. Therefore, in order to gain a deeper understanding of consequences and influencing factors, we need to further investigate questions such as, how employees appraise these technologies, how jobs and job quality change, and how responsibilities shift from employees to the technology.
We further argue that existing theories and models are too limited to guide both researchers and practitioners. For example, technology acceptance theories and models (Venkatesh & Bala, 2008; Venkatesh et al., 2016) aim to predict acceptance and adoption of technology by individual users. These models are distanced from the reality of the work context, only partially considering the complex and dynamic structures of the organizations in which these technologies are implemented. The organizational change literature on the other hand, points at the key role of human and social (context) factors for successful transitions, such as adequate top-down and bottom-up communication, (opportunities for) participation, and support (see Hayes, 2018, for an overview). However, this tradition hardly considers technology-related change and is less clear about the operational changes to which employees need to adapt. Furthermore, research in the human computer interaction domain, although very informative about how individuals react to and work with (or against) technology, often miss to address the larger social context (for an exception see Díaz-Boladeras et al., 2015) and do not explicitly focus on the work context.

To develop new theories and sound practices for implementing emerging technologies in organizations, we need to start discussing the topic of emerging technologies, and particularly AI, on a multi-disciplinary level, with a strong perspective from Work and Organizational psychology perspective. That is to say, more in-depth research on human/social context factors affecting the (optimal) implementation of technologies in the workplace, and effects on for example job quality and employee well-being is needed to formulate recommendations for a human-centered implementation of technology in the workplace.

Proposed contributions (field and experimental studies as well as high quality theoretical papers) for the Special Issue could include:

1. Overview and current developments in the use of emerging technologies /AI at work: Multidisciplinary views (combining theories from computer science, Work and Organizational Psychology, and practice) on current trends, applications, and misconceptions. Submissions should particularly focus on how these emerging technologies and AI differ from other technologies used in the work context (e.g. research on automation in factories).

2. Transformation & implementation of emerging technologies and AI at work: How does work change (e.g. how do new types of teams, such as human-agent teaming, interact)? How can these technologies be implemented while taking work context and human factors into account?

3. Consequences of emerging technologies and AI at work: What are positive and negative effects of implementing these systems at work? How does the role of the employee change (e.g. how does a change of agency in human-AI collaboration affect employee outcomes)?

4. AI methods in Work and Organizational Psychology research: How can we use methods of AI in research within Work and Organizational Psychology to understand how we interact with emerging technologies and AI at work? How can Work and Organizational Psychology researchers contribute to building better AI systems?

Submission Instructions

We seek innovative contributions and encourage high-quality theoretical or empirical papers across a range of methodologies and analytical techniques. Please note that the regular author guidelines of EJWOP apply (e.g. no studies with only student samples), for further details, please visit the journal's Instructions for Authors page.

Papers should be submitted through the journal’s online Submission Portal, as a submission for this Special Issue.

Timeline
(0) Submission deadline: January 3rd 2021
(1) Reviews + decision round 1: December 1st, 2021
(2) Resubmission deadline: February 1st, 2022
(3) Reviews + decision round 2: May 1st, 2022
(4) Final submission round 3: September 1st 2022
(5) Publication of the Special Issue by December 2022

Reviewing Process
Each submission will initially be screened by teams of two editors to ensure fit with the proposed special issue and quality of the work. After this initial process, the guest editors will inquire for reviewers for the selected submissions from their personal research network. Each article will be assigned one editor and a minimum of two reviewers.

For more information or to discuss ideas for the Special Issue, please contact any of the Guest Editors.

Instructions for AuthorsSubmit an Article