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Friend or foe? How innovative digital technologies affect motivational work characteristics and employees' perceived job insecurity
Abstract
The rising distribution of smart technology, artificial intelligence, robotics, algorithms, and automation (STARA) in today’s world of work has the potential to revolutionize what, how, where, and when humans work by supplementing and substituting work processes. However, it is largely unclear how these technological changes affect employees. While employees may benefit from the substitution of tedious and dangerous, they may suffer from STARA creating micro-jobs, polarizing required skill levels, and evoking job insecurity. Although these rapid technological changes have crucial implications for employee outcomes, empirical evidence of their effects on employees is surprisingly scarce. Neglecting to investigate these effects could result in poorly designed, demotivating workplaces of the future.
Building on the integration of numerous theoretical models and frameworks, this dissertation examines how the introduction of intelligent assistance systems (IASs) modifies motivational work characteristics in modern assembly. It considers various scenarios, such as the assembly of simple products, the assembly of more complex products, and the assembly with intensified product changes. Applying online experiments with vignette methodology, we experimentally manipulated hypothetical assembly workstations and instructed participants to rate them regarding motivational work characteristics. In the first online experiment (N1 = 203 German and British blue-collar workers) participants were randomly assigned to one of three conditions (work without vs. work with vs. work with voluntary use of IASs). Results indicated enhanced feedback from job and information processing when working with IASs in the assembly of simple products. Thus, they highlight the purely positive effects of IASs on motivational work characteristics.
Transferring these findings to the assembly of more complex products in the second online experiment (work without vs. work with IASs, N2 = 169 German workers) was limited. Findings illuminated that IASs restrict work scheduling, decision-making, and work methods autonomy besides increasing feedback from job and information processing. Therefore, they indicate the contradictory effects of IASs on motivational work characteristics. In a third online experiment (N3 = 176 German workers) we also manipulated the extent of task rotation (no task rotation vs. task rotation after one hour). We fully replicated the results highlighting the contradictory role of IASs, regardless of the extent of task rotation.
This dissertation further illuminates how employees appraise STARA to threaten their employment by contributing a thorough construct validation of affective automation-related job insecurity, a refinement of the STARA Awareness construct (Brougham & Haar, 2018). Findings from two cross-sectional studies (N4 = 215, N5 = 224 German employees) and one longitudinal study with a total time lag of one year (N6 = 233 German employees) demonstrated a fluctuating fit of the measurement model of STARA Awareness between independent samples. With the exclusion of cognitive elements and the inclusion of the substitution of core tasks within jobs, we reconceptualized the construct, renamed it to affective automation-related job insecurity, and adapted its measurement. While affective automation-related job insecurity is weakly associated with cognitive and affective job insecurity, and negatively with core self-evaluations, it exhibits unique associations with indicators of technological change (positive relations with objective substitution potential and use of STARA). Moreover, we identified rising levels of affective automation-related job insecurity over time for employees with moderate use of STARA, and decreasing levels for employees with low and high use of STARA.
Overall, this dissertation provides vital empirical evidence on the beneficial and detrimental effects of STARA on workplaces and employees in today’s world of work. Its findings represent a firm foundation for fruitful research in digital work design, human-machine interaction, and related fields. Finally, considering these findings contributes to the human-centered development, implementation, and use of STARA to maintain a healthy and motivated workforce.
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Intelligent assistance systems (IAS) are designed to counteract rising cognitive demands caused by increasingly individualized manufacturing processes in assembly. How IAS affect work characteristics which are crucial for promoting work motivation of employees is yet unclear. Based on the cyber-physical systems transformation framework, the model of routine-biased technological change, and a comprehensive model of work design, we expected in- and decreases in motivational work characteristics (MWC) when working with IAS. Furthermore, we posited a buffering effect of the option of voluntary use on decreasing knowledge characteristics. Applying an online case study with experimental vignette methodology (EVM) allowed us to identify effects of the IAS on MWC before it is widely implemented. 203 German and British blue-collar workers evaluated an assembly workplace according to three experimental conditions (work without IAS, work with IAS, work with voluntary use of IAS). We identified enhanced feedback from the job and information processing in work with IAS in contrast to a traditional assembly workplace but found no restrictions (or elevations) in terms of other task (i.e., autonomy) or knowledge characteristics (i.e., job complexity, problem solving, specialization, skill variety). Thus, our results indicate that the IAS improves some motivational work characteristics of the assembly workplace, although it misses the primary goal of cognitive relief. Our study highlights the need for work design theories that specify the effect of IAS on motivational work characteristics and the potential benefit of IAS in assembly of the future.
Increased use and implementation of automation, accelerated by the COVID-19 pandemic, gives rise to a new phenomenon: occupation insecurity. In this paper, we conceptualize and define occupation insecurity, as well as develop an Occupation Insecurity Scale (OCIS) to measure it. From focus groups, subject-matter expert interviews, and a quantitative pilot study, two dimensions emerged: global occupation insecurity, which refers to employees’ fear that their occupations might disappear, and content occupation insecurity, which addresses employees’ concern that (the tasks of) their occupations might significantly change due to automation. In a survey-study sampling 1373 UK employees, psychometric properties of OCIS were examined in terms of reliability, construct validity, measurement invariance (across gender, age, and occupational position), convergent and divergent validity (with job and career insecurity), external discriminant validity (with organizational future time perspective), external validity (by comparing theoretically secure vs. insecure groups), and external and incremental validity (by examining burnout and work engagement as potential outcomes of occupation insecurity). Overall, OCIS shows good results in terms of reliability and validity. Therefore, OCIS offers an avenue to measure and address occupation insecurity before it can impact employee wellbeing and organizational performance.
Crises like the COVID-19 pandemic can trigger concerns about loss of employment and changes in work conditions, and thereby increase job insecurity. However, little is known about how perceived job insecurity subsequently unfolds over time and how individual differences in habitual coping moderate such a trajectory. Using longitudinal data from 899 US-based participants across 5 waves (March to June 2020), we investigated the trajectory of job insecurity during the COVID-19 pandemic and how this trajectory depended on habitual coping strategies such as planning, reappraisal, and distraction. Results from latent growth curve analysis indicated that on average, job insecurity initially increased and then decreased after signing of the coronavirus stimulus bill, suggesting a pattern of shock followed by adjustment. During the shock phase, habitual use of distraction was related to less increases in job insecurity. Later during the adjustment phase, decreases in job insecurity were more pronounced for individuals with higher habitual use of planning, but were not affected by reappraisal or distraction. Hence, different coping strategies appear beneficial in different phases of adjustment, and the beneficial effect of planning may take time to manifest. Altogether, our study highlights how in the context of extraordinary and uncontrollable events, coping strategies can impact the trajectory of a stressor.
We respond to commentaries on our 2020 article ‘Automation, algorithms, and beyond: Why work design matters more than ever in a digital world’ and report on research on the topic since that publication. A top-down work design perspective on digital technologies appears even more important than ever yet still neglected, as suggested by recent studies. The opportunities and challenges of new technologies have been addressed somewhat more successfully through bottom-up work design in the form of job crafting. The specific topic of virtual working has also attracted significant research attention. Nevertheless, we continue to advocate more scholarly and practical attention to the following: how to proactively redesign work when introducing new technologies; how work design issues can be built into the design and procurement of work technologies; the need to identify and understand both the organizationally oriented strategies and macro-level change needed for successful sociotechnical application; and how to upskill employees, managers, unions and other stakeholders, in work design and related topics. There is also more scope for consideration of the role of individual differences. Finally, we call for interdisciplinary research that involves, for example, the designers of technology, and we advocate the importance of intervention studies. © 2022 The Authors. Applied Psychology published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.
This article examines the consequences of implementing an automation technology (i.e., Robotic Process Automation; RPA) for work characteristics and employee well-being. Based on the job demands-resources framework we examined to what extent the utilization of RPA was related to job resources (i.e., autonomy and task variety) and a job demand (i.e., information processing), and to what extent these work characteristics were related to work engagement and exhaustion. Data were collected among 420 employees working for a Dutch ministry where RPA was recently introduced. Structural equation modeling revealed that RPA use was negatively related to both autonomy and task variety, which formed a threat to employee work engagement. Contrary to our expectations, RPA use was unrelated to information processing and subsequent exhaustion. These findings put emphasis on the importance of designing new technologies with sufficient job resources to create and maintain a healthy and motivated workforce during and after implementing workplace automation.
The growing trend of introducing robots into employees’ work lives has become increasingly salient during the global COVID‐19 pandemic. In light of this pandemic, it is likely that organizational decision‐makers are seeing value in coupling employees with robots for both efficiency‐ and health‐related reasons. An unintended consequence of this coupling, however, may be an increased level of work routinization and standardization. We draw primarily from the model of passion decay from the relationship and clinical psychology literature to develop theory and test a model arguing that passion decays as employees increasingly interact with robots for their work activities. We demonstrate that this passion decay leads to an increase of withdrawal behavior from both the domains of work and family. Drawing further from the model of passion decay, we reveal that employees higher in openness to experience are less likely to suffer from passion decay upon more frequent interactions with robots in the course of work. Across a multi‐source, multi‐wave field study conducted in Hong Kong (Study 1) and a simulation‐based experiment conducted in the United States (Study 2), our hypotheses received support. Theoretical and practical implications are discussed.
The COVID 19 crisis has had a massive impact on the world of work. Based on a standardized survey of 540 company sites and 34 qualitative case studies in six industries (automotive, chemicals, mechanical engineering, logistics, healthcare and financial services), this study examines how companies' digitalization and automation strategies have changed in the context of the pandemic. The analysis shows that the companies surveyed have by and large coped well with the crisis. However, an increasing polarization is becoming apparent. Digital pioneers intensified digitalization measures to a greater extent than those that were already behind in this respect before the pandemic. The focus of digitalization during the pandemic was particularly on supporting processes in administration, human resources management and sales. Automation also primarily played a role in these fields and only a minority of respondents expected employment losses in the medium term. Another key finding of this study is the correlation between technical and organizational innovation. It can be observed that those companies that invested more heavily in technical digitalization processes were also more inclined to make organizational changes with regard to management styles, working hours and work organization.
A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how this problem affects different outputs of the factor analysis: multivariate normality (MVN), estimation process, goodness-of-fit, recovery of factor loadings, and reliability. In doing so, a Monte Carlo study was conducted. Data were generated following the linear selective sampling model, simulating tests varying their sample size (N = 200 and 500 cases), test size (J = 6, 12, 18, and 24 items), loading size (L = .50, .70, and .90), and restriction size (from R = 1, .90, .80, and so on till .10 selection ratio). Our results systematically suggest that an interaction between decreasing the loading size and increasing the restriction size affects the MVN assessment, obstructs the estimation process, and leads to an underestimation of the factor loadings and reliability. However, most of the MVN tests and most of the fit indices employed were nonsensitive to the RR problem. We provide some recommendations to applied researchers.
The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of study results point in the opposite direction. There is currently no comprehensive common ground on the circumstances under which anthropomorphism promotes interaction with robots. Our meta-Analysis aims to close this gap. A total of 4856 abstracts were scanned. After an extensive evaluation, 78 studies involving around 6000 participants and 187 effect sizes were included in this meta-Analysis. The majority of the studies addressed effects on perceptual aspects of robots. In addition, effects on attitudinal, affective, and behavioral aspects were also investigated. Overall, a medium positive effect size was found, indicating a beneficial effect of anthropomorphic design features on human-related outcomes. However, closer scrutiny of the lowest variable level revealed no positive effect for perceived safety, empathy, and task performance. Moreover, the analysis suggests that positive effects of anthropomorphism depend heavily on various moderators. For example, anthropomorphism was in contrast to other fields of application, constantly facilitating social HRI. The results of this analysis provide insights into how design features can be used to improve the quality of HRI. Moreover, they reveal areas in which more research is needed before any clear conclusions about the effects of anthropomorphic robot design can be drawn. © 2021 American Association for the Advancement of Science. All rights reserved.
Assembly timing planning, which aims to solve the assembly action sequence and assembly part sequence with the shortest assembly time as the goal, is a necessary and critical step in intelligent assembly process planning. However, the current focus of assembly process planning is assembly sequence planning, whereas little research has been performed on assembly timing planning. A novel assembly timing planning method based on knowledge and mixed integer linear programming (MILP) is proposed in this paper. First, a knowledge base of the assembly process for timing planning is constructed using ontology. Then, based on the proposed strategy of dividing assembly timing planning into within-group planning and between-group planning, a MILP model of assembly timing planning for automatic assembly system is constructed. In addition, a software that realizes timing planning through human–machine collaboration is developed to verify and visualize the proposed timing planning method. The implementation is as follows: assembly action sentences are formed by searching the ontology keyword library, then timing knowledge for the action sequence and assembly sequence is established, and finally optimal assembly timing results are obtained after the calculation. Compared with the traditional serial assembly process, this method significantly reduces the assembly time, thereby improving the assembly efficiency, and the assembly schedule can be obtained automatically and quickly to guide the assembly process design.
Objective
To investigate the effect of using a passive back-support exoskeleton (Laevo V2.56) on muscle activity, posture, heart rate, performance, usability, and wearer comfort during a course of three industrial tasks (COU; exoskeleton worn, turned-on), stair climbing test (SCT; exoskeleton worn, turned-off), timed-up-and-go test (TUG; exoskeleton worn, turned-off) compared to no exoskeleton.
Background
Back-support exoskeletons have the potential to reduce work-related physical demands.
Methods
Thirty-six men participated. Activity of erector spinae (ES), biceps femoris (BF), rectus abdominis (RA), vastus lateralis (VL), gastrocnemius medialis (GM), trapezius descendens (TD) was recorded by electromyography; posture by trunk, hip, knee flexion angles; heart rate by electrocardiography; performance by time-to-task accomplishment (s) and perceived task difficulty (100-mm visual analogue scale; VAS); usability by the System Usability Scale (SUS) and all items belonging to domains skepticism and user-friendliness of the Technology Usage Inventory; wearer comfort by the 100-mm VAS.
Results
During parts of COU, using the exoskeleton decreased ES and BF activity and trunk flexion, and increased RA, GM, and TD activity, knee and hip flexion. Wearing the exoskeleton increased time-to-task accomplishment of SCT, TUG, and COU and perceived difficulty of SCT and TUG. Average SUS was 75.4, skepticism 11.5/28.0, user-friendliness 18.0/21.0, wearer comfort 31.1 mm.
Conclusion
Using the exoskeleton modified muscle activity and posture depending on the task applied, slightly impaired performance, and was evaluated mildly uncomfortable.
Application
These outcomes require investigating the effects of this passive back-supporting exoskeleton in longitudinal studies with longer operating times, providing better insights for guiding their application in real work settings.
Although there exist numerous publications on job and task rotation from various disciplines, there is no consistent evidence of their effectiveness. Drawing on theories from industrial and organizational psychology, knowledge management, ergonomics, and management science, we meta-analytically investigated relationships between job/task rotation and employee attitudes, learning and development, psychological and physical health, and organizational performance. Due to a conceptual overlap and frequent confusion of terminology, we analyzed the design of the rotation (job rotation vs. task rotation) as a possible moderator. The three-level meta-analysis on 56 studies ( N = 284,086) showed that rotation was significantly associated with job satisfaction ( r = 0.27), organizational commitment ( r = 0.16), career success ( r = 0.31), labor flexibility ( r = 0.32), general psychological health ( r = 0.20), stress/burnout ( r = −0.13), individual performance ( r = 0.13), and productivity ( r = 0.13). Positive relationships between rotation and physical health could only be found when rotation was compared to high-intensity work. Task rotation yielded stronger relationships with attitudinal outcomes, job rotation with learning and development, psychological health, and organizational performance outcomes. Further moderator analyses showed that individualism decreased relationships between task rotation and attitudes, and correlations with organizational performance and physical health were stronger for subjective measures. The findings indicate that many expectations toward job and task rotation are not fully supported.
The rapid advancement of new digital technologies, such as smart technology, artificial intelligence (AI) and automation, robotics, cloud computing, and the Internet of Things (IoT), is fundamentally changing the nature of work and increasing concerns about the future of jobs and organizations. To keep pace with rapid disruption, companies need to update and transform business models to remain competitive. Meanwhile, the growth of advanced technologies is changing the types of skills and competencies needed in the workplace and demanded a shift in mindset among individuals, teams and organizations. The recent COVID-19 pandemic has accelerated digitalization trends, while heightening the importance of employee resilience and well-being in adapting to widespread job and technological disruption. Although digital transformation is a new and urgent imperative, there is a long trajectory of rigorous research that can readily be applied to grasp these emerging trends. Recent studies and reviews of digital transformation have primarily focused on the business and strategic levels, with only modest integration of employee-related factors. Our review article seeks to fill these critical gaps by identifying and consolidating key factors important for an organization’s overarching digital transformation. We reviewed studies across multiple disciplines and integrated the findings into a multi-level framework. At the individual level, we propose five overarching factors related to effective digital transformation among employees: technology adoption; perceptions and attitudes toward technological change; skills and training; workplace resilience and adaptability, and work-related wellbeing. At the group-level, we identified three factors necessary for digital transformation: team communication and collaboration; workplace relationships and team identification, and team adaptability and resilience. Finally, at the organizational-level, we proposed three factors for digital transformation: leadership; human resources, and organizational culture/climate. Our review of the literature confirms that multi-level factors are important when planning for and embarking on digital transformation, thereby providing a framework for future research and practice.
Featured Application
The research study proposed in this paper provides valuable knowledge to support companies and industrial practitioners in the shift from traditional to advanced assembly systems in the new Industry 4.0 era matching current industrial and market features.
Abstract
Industry 4.0 emerged in the last decade as the fourth industrial revolution aiming at reaching greater productivity, digitalization and operational efficiency standard. In this new era, if compared to automated assembly systems, manual assembly systems (MASs) are still characterized by wide flexibility but poor productivity levels. To reach acceptable performances in terms of both productivity and flexibility, higher automation levels are required to increase the skills and capabilities of the human operators with the aim to design next-generation assembly systems having higher levels of adaptivity and collaboration between people and automation/information technology. In the current literature, such systems are called adaptive automation assembly systems (A3Ss). For A3Ss, few design approaches and industrial prototypes are available. This paper, extending a previous contribution by the Authors, expands the lacking research in the field and proposes a general framework guiding toward A3S effective design and validation. The framework is applied to a full-scale prototype, highlighting its features together with the technical- and human-oriented improvements arising from its adoption. Specifically, evidence from this study show a set of benefits from adopting innovative A3Ss in terms of reduction of the assembly cycle time (about 30%) with a consequent increase of the system productivity (about 45%) as well as relevant improvements of ergonomic posture indicators (about 15%). The definition of a general framework for A3S design and validation and the integration of the productivity and ergonomic analysis of such systems are missing in the current literature, representing an element of innovation. Globally, this research paper provides advanced knowledge to guide research, industrial companies and practitioners in switching from traditional to advanced assembly systems in the emerging Industry 4.0 era matching current industrial and market features.
Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.
Today’s workers around the world are experiencing growing uncertainty about their future employment. Living in the chronic threat to the continuity of their employment (i.e., job insecurity) has adverse consequences. To understand where job insecurity comes from, we take a resources-demands perspective to synthesize and meta-analyze 57 theoretical sources of job insecurity. Using three-decade (1986-2018) data from 425 independent samples representing 219,190 individuals from 39 countries, we find that the vast majority of theoretical predictors explain meaningful variance in job insecurity. Interestingly, resources (facilitating goal attainment), compared to demands (hindering goal attainment), have stronger relationships with job insecurity. Moreover, individualism, gross domestic product, and egalitarianism at the country level strengthen the negative relationships between resources and job insecurity and attenuate the positive relationships between demands and job insecurity, while power distance, national unemployment rate, and income inequality at the country-level lessen the negative relationships between resources and job insecurity and aggravate the positive relationships between demands and job insecurity. Finally, organizational practices account for significantly more variance in qualitative job insecurity than quantitative job insecurity, while personal factors and organizational social indicators explain a similar amount of variance in qualitative and quantitative job insecurity. Results suggest that gathering personal and organizational resources is more important than removing demands in terms of reducing job insecurity; having access to more resources in an attempt to diminish job insecurity is especially functional in countries high in individualism, gross domestic product, and egalitarianism, or low in power distance, national unemployment rate, and income inequality.
This study empirically investigates the contingent effect of STARA (Smart Technology, Artificial Intelligence Robotics and Algorithms) awareness on perceived job insecurity for employees working in industries that are expected to face high degrees of automation. More specifically we empirically investigate the moderating role of learning culture and authoritative culture on the relationship between STARA awareness and job insecurity, among 291 individuals. Our findings show a significant positive relationship between STARA awareness and perceived job insecurity. An authoritarian organizational culture further enhances the effects of STARA awareness on perceived job insecurity. Contrary to expectations, the moderating effect of retraining expectations was not statistically supported.
Objective
The objective was to determine the effect of two head-mounted display (HMD) augmented reality (AR) devices on muscle activity and eye strain of electric utility workers. The AR devices were the Microsoft HoloLens and RealWear HMT-1.
Background
The HoloLens is an optical see-through device. The HMT-1 has a small display that is mounted to the side of one eye of the user.
Method
Twelve power plant operators and 13 manhole workers conducted their normal procedural tasks on-site in three conditions: HoloLens, HMT-1, and “No AR” (regular method). Duration of test trials ranged up to 30 s for operators and up to 10 min for manhole workers. Mean and peak values of surface electromyographic (sEMG) signals from eight neck muscles were measured. A small eye camera measured blink rate of the right eye.
Results
In general, there were no differences in sEMG activity between the AR and “No AR” conditions for both groups of workers. For the manhole workers, the HoloLens blink rate was 8 to 11 blinks per min lower than the HMT-1 in two tasks and 6.5 fewer than “No AR” in one task. Subjective assessment of the two AR devices did not vary in general.
Conclusion
The decrease in blink rate with the HoloLens may expose utility manhole workers to risk of eye strain or dry-eye syndrome.
Application
HMD AR devices should be tested thoroughly with respect to risk of eye strain before deployment by manhole workers for long-duration procedural work.
Industrial production is still widely sustained by human operators. However, the design of human–machine interaction often does not foster the motivation to learn more about their machine or system. This may decrease operators’ ability to flexibly adjust their decision making and problem-solving skills to the current production context. Motivation to learn could be attained by a motivating socio-technical design of assistance systems, but suitable and context-specific design strategies are lacking. In the present study, a systematic literature review of motivation theories in education, at the workplace, and in system design was carried out. The resulting 16 theories were integrated into a conceptual model of motivating assistance system design in industrial production. In this model, learning motivation results from the satisfaction of the needs for autonomy, competence, and relatedness, which in turn is mediated through the design of the system (including interface, task, and behavior). Moreover, this process is subject to moderating influences from job characteristics, personal variables, and factors concerning the respective work domain. Strategies for motivational design are derived from the model, and an example from the discrete processing industry is used to illustrate how the model could be applied to design assistance systems in this domain. Finally, the procedures for theory selection and model development are discussed, theoretical and practical implications are derived, and alternative strategies of instilling motivation are considered.
German companies have been affected by a new wave of digitalization during the past years, and this has led to responses both in the way production is organized and in the goals of the German industrial policy. The coordinated response is widely referred to as “Industry 4.0” and is intended to support German industries in the increasingly fierce competition for global leadership in manufacturing. Simultaneously, a social science debate about changes in work and in employee training began and continues to this day. Far-reaching predictions of fundamental change circulate, especially concerning the organization of work and worker competence requirements. Other issues include also the needs and opportunities of firm-based competence development at work as well as the role and the uses of digital media. In recent years, more empirical studies have become available for clarifying open questions, and this paper presents three main results from one such study based on case investigations in 10 German industrial firms. First, digitalization does not change industrial production work radically. There is no general trend of upskilling, downgrading, or reskilling. A moderate trend favoring upgrading is observable, however. Thus, traditional workforce competences are not becoming “obsolete.” Rather, in the context of automation, they are being complemented by other skills including new technical qualifications in information technology as well as the ability to take a more theoretical approach to problem-solving in process optimization. Second, our results confirm more cautious assessments of the need for accelerated continuing vocational training. They also make it clear that the potential for increased learning opportunities in digital work and for the increased use of digital media in continuing vocational training has been overestimated. Learning opportunities seem to decrease rather than increase with digital work. Moreover, the use of digital media in continuing vocational training is limited due to organizational, financial, and cultural constraints and due to the lack of knowledge about the effectiveness of digital learning environments. Third, a number of organizational measures are needed to manage change. One measure would be to integrate new skill requirements into a binding institutional curriculum for education and training. Another measure would be to make on-the-job learning opportunities a central aspect of how work is organized.
People design and use technology for work. In return, technology shapes work and people. As information communication technology (ICT) becomes ever more embedded in today’s increasingly digital organizations, the nature of our jobs, and employees’ work experiences, are strongly affected by ICT use. This cross-disciplinary review focuses on work design as a central explanatory vehicle for exploring how individual ICT usage influences employees’ effectiveness and well-being. We evaluated 83 empirical studies. Results show that ICT use affects employees through shaping three key work design aspects: job demands, job autonomy, and relational aspects. To reconcile previous mixed findings on the effects of ICT use on individual workers, we identify two categories of factors that moderate the effects of ICT use on work design: user-technology fit factors and social-technology fit factors. We consolidate the review findings into a comprehensive framework that delineates both the work design processes linking ICT use and employee outcomes and the moderating factors. The review fosters an intellectual conversation across different disciplines, including organizational behavior, management information systems, and computer-mediated communication. The findings and proposed framework help to guide future research and to design high quality work in the digital era.
Zusammenfassung
Die Einführung neuer digitaler Technologien wie digitale Werkerassistenzsysteme (z. B. Datenbrillen und Tablets) im Produktionsbereich führen zu Veränderungen der Arbeitswelt. Bestehende Modelle der Arbeitsgestaltung betrachten Technologien als Antezedenzien für die Arbeitsgestaltung. Die Theorie der sozialen Gestaltung von Technologien nimmt an, dass unterschiedliche Akteure Einfluss auf die Gestaltung und Verbreitung von Technologien im betrieblichen Kontext nehmen. Bisher ist kaum bekannt, welche Erwartungen unterschiedliche Akteure an die Anwendung und Gestaltung digitaler Werkerassistenzsysteme stellen. Ziel dieses Beitrags der Zeitschrift Gruppe. Interaktion. Organisation. (GIO) ist es, die Sichtweisen relevanter Akteure im Technologieentwicklungsprozess zu erfassen und basierende darauf Handlungs- und Gestaltungsempfehlungen für den Einsatz digitaler Werkerassistenzsysteme in der betrieblichen Praxis zu geben. Hierfür wurden 76 halbstrukturierte Interviews mit Wissenschaftlern, politischen Akteuren und betrieblichen Praktikern durchgeführt und inhaltsanalytisch ausgewertet. Als Ergebnis dieser Studie ist ein Kategoriensystem für die Einteilung von Anwendungsmöglichkeiten digitaler Werkerassistenzsysteme in der Produktion entstanden, das einen Ausgangspunkt für betriebliche Entscheidungsprozesse bei der Technologieeinführung darstellen kann. Die identifizierten Chancen und Risiken beim Einsatz von digitalen Werkerassistenzsystemen stellen Ansatzpunkte für die Technologiegestaltung und die Technologieimplementierung im Unternehmen bereit.
We propose a central role for work design in understanding the effects of digital technologies. We give examples of how new technologies can – depending on various factors ‐ positively and negatively affect job resources (autonomy/control, skill use, job feedback, relational aspects) and job demands (e.g., performance monitoring), with consequences for employee well‐being, safety, and performance. We identify four intervention strategies. First, work design choices need to be proactively considered during technology implementation, consistent with the sociotechnical systems principle of joint optimization. Second, human‐centred design principles should be explicitly considered in the design and procurement of new technologies. Third, organizationally‐oriented intervention strategies need to be supported by macro‐level policies. Fourth, there is a need to go beyond a focus on upskilling employees to help them adapt to technology change, to also focus on training employees, as well as other stakeholders, in work design and related topics. Finally, we identify directions for moving the field forward, including new research questions (e.g., job autonomy in the context of machine learning; understanding designers’ work design mindsets; investigating how job crafting applies to technology); a re‐orientation of methods (e.g., interdisciplinary, intervention studies); and steps for achieving practical impact.
The inclusion of employees with disabilities in production is an issue that has rarely been addressed by scientists from the manufacturing sector. In this article, we examine to what extent the trend towards Industry 4.0 offers potential for the inclusion of people with disabilities in Production 4.0. First, we examine relevant legal foundations and restrictions in Europe and in more detail in Austria, Italy, and Norway. Next, based on a literature review, we examine which technological aids in the form of worker assistance systems derived from Industry 4.0 can make jobs in the manufacturing sector accessible for people with disabilities. Three types of assistance systems have been examined: sensorial aid systems, physical aid systems, and cognitive aid systems. In a concluding discussion of the results, we finally summarize the implications on management and policies as well as the potential and limitations of identified worker assistance technologies. On the one hand, the study is intended to draw the attention of researchers and industrial companies to new technological possibilities for the inclusion of people with disabilities in production. On the other hand, difficulties and grievances due to the legal foundations are pointed out to stimulate a critical discussion here as well.
It is not yet clear whether work redesigns actually affect individual-, team- or organizational-level performance. In a synthesis of this literature, we conclude there is good overall evidence, with the most promising evidence at the individual level. Specifically, our systematic review assessed whether top-down work redesign interventions affect performance and, if so, why (mechanisms) and when (boundary conditions). We identified 55 heterogeneous work redesign intervention studies, of which 39 reported a positive effect on performance, two reported a negative effect, and 14 reported mixed effects. Of five types of work redesign, the evidence that work characteristics can explain the effect of redesign interventions on performance was most promising for relational interventions, and participative and non-participative job enrichment and enlargement. Autonomous work group and system-wide interventions showed initial evidence. As to ‘why’ work redesigns enhance performance, we identified change in work motivation, quick response and learning as three core mechanisms. As to ‘when’, we showed that intervention implementation, intervention context (including alignment of organizational systems, processes and the work redesign) and person factors are key boundary conditions. We synthesize our findings into an integrative multilevel model that can be used to design, implement and evaluate work redesigns aimed at improving performance.
Due to mass customization product variety increased steeply in the automotive industry, entailing the increment of worker’s cognitive load during manual assembly tasks. Although worker assistance methods for cognitive automation already exist, they proof insufficient in terms of usability and achieved time saving. Given the rising importance of simulation towards autonomous production planning, a novel approach is proposed using human simulation data in context of worker assistance methods to alleviate cognitive load during manual assembly tasks. Within this paper, a new concept for augmented reality-based worker assistance is presented. Additionally, a comparative user study (N=24) was conducted with conventional worker assistance methods to evaluate a prototypical implementation of the concept. The results illustrate the enhancing opportunity
of the novel approach to save cognitive abilities and to induce performance improvements. The implementation provided stable information presentation during the entire experiment. However, with regard to the recentness, there has to be carried out further developments and research, concerning performance adaptions and investigations of the effectiveness.
Information and communication technologies (ICTs) have become a ubiquitous and integral aspect in our social lives and our work world. Smartphones, laptops, and other devices keep us easily connected to people and tasks across both work and home domains. Although heralded as time- and cost-saving initiatives that boost employee flexibility, unintended negative consequences of ICTs have led to a backlash felt in some quarters of the working world. However, these criticisms have not been explored in detail, and critical questions remain as to the seemingly paradoxical impact of ICT on worker health and functioning. In this chapter, we explore what we know about ICT paradoxes – what we refer to as the iParadox Triad related to autonomy, social connectivity, and productivity issues. We then explore whether the stress and well-being effects of the iParadox Triad can be understood through traditional occupational health psychology models. Finally, we develop a theoretical model that incorporates social psychology theory on the self and motivation to help guide future research in this area that can help address autonomy, connectivity, and productivity issues.
We propose a new work design model, SMART work design, that identifies five higher order categories of work characteristics, including stimulating work characteristics (task variety, skill variety, information processing requirements, and problem‐solving requirements), mastery work characteristics (job feedback, feedback from others, and role clarity), autonomous work characteristics (decision‐making autonomy, timing autonomy, and method autonomy), relational work characteristics (social support, task significance, and beneficiary contact), and tolerable work characteristics (low levels of: role overload, work–home conflict, and role conflict). Higher order confirmatory factor analysis of working participants provided initial evidence of this structure (Study 1, N = 1107), which was replicated in an additional dataset (Study 2, time 1, N = 709). To provide further evidence, we examined Study 2 data across three waves ( N = 573) to show that each higher order factor at time 1 predicted time 3 job satisfaction either directly or via the theorized time 2 mediators (challenge appraisals, work meaningfulness, fulfillment of relatedness needs, and activated negative affect). In Study 3 ( N = 108), employees' scores on specific higher order variables correlated with leader ratings of performance in the expected ways. The SMART work design model provides a unique integrating and multidimensional theory of work design that extends beyond existing models. The model can be used to facilitate the synthesis of research knowledge and guide scholars and practitioners to diagnose and address contemporary work design challenges.
Models of work design emerged in the twentieth century to address workplace changes created by the industrial revolution. However, the world of work is currently undergoing a new, profound revolution in terms of technological, demographic, and environmental changes, leading to a new economy, within which organizations and employees must function. The field of work design currently includes robust theories with a deep understanding of how work design affects employee outcomes, many with relevance to this new economy. However, the new economy also includes issues never before considered (e.g., algorithmic management and gig and lone work), and the field of work design must tackle the implications of these emerging issues. In this article, we review the general findings on work design and then examine a range of contextual, economic, technological, and diversity issues and their relevance to work design. We conclude with an agenda for future work design research and implications for work analysis and work design interventions and policies.
Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 11 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Including contributions from leading scholars at Harvard Business School, Yale, and MIT's Sloan School of Management, this book explores the ways that careers have changed for workers as their firms reorganize to meet global competition. As firms re-engineer, downsize, enter into strategic alliances with other firms, and find other ways to reduce costs, they frequently lay off workers. Job security has been replaced by insecurity and workers have been forced to take charge of their own career development in ways they have never done before. The contributors to the book analyse the implications for these workers, who now have "boundary less careers". While many find the challenge rewarding as they find new opportunities for growth, others are finding it difficult to adapt to new jobs in new locations. The book looks at policy issues that can provide safety nets for those who are not able to find a place in the new world of boundary less careers.
Extant research on job insecurity (JI) largely focuses on the individual level, rather than considering the process of JI at collective, team levels. But employees' worries and anxiety about potential job losses create affective job insecurity (AJI), which can converge over time in teams, especially following dramatic changes such as layoffs. Drawing on a multilevel theory of emergence in teams and uncertainty reduction theory, this study offers theoretical predictions of AJI convergence, as well as its potential influences on team functioning and outcomes. A four‐wave, post‐layoff survey of 468 employees and 91 supervisors confirms two major predictions. First, AJI convergence exists within teams over time, as established by a consensus emergence model. Second, teams with members who converge at a high level of AJI experience intrateam power struggles that impair both team performance and team proactivity. By theorizing about the phenomenon of AJI convergence, this study not only expands the notion of JI from the individual to the team level, noting its harmful effects on teams, but also highlights the need for both team members and team leaders to pay close attention to such convergence and its potentially detrimental effects following layoffs.
In the era of digital transformation, new technological foundations and possibilities for collaboration, production as well as organization open up many opportunities to work differently in the future. The digitization of workflows results in new forms of working which is denoted by the term Work 4.0. In the context of Work 4.0, digital assistance systems play an important role as they give users additional situation-specific information about a workflow or a product via displays, mobile devices such as tablets and smartphones, or data glasses. Furthermore, such digital assistance systems can be used to provide instructions and technical support in the working process as well as for training purposes. However, existing digital assistance systems are mostly created focusing on the “design for all” paradigm neglecting the situation-specific tasks, skills, preferences, or environments of an individual human worker. To overcome this issue, we present a monitoring and adaptation framework for supporting self-adaptive digital assistance systems for Work 4.0. Our framework supports context monitoring as well as UI adaptation for augmented (AR) and virtual reality (VR)-based digital assistance systems. The benefit of our framework is shown based on exemplary case studies from different domains, e.g. context-aware maintenance application in AR or warehouse management training in VR.
Understanding how Industry 4.0 (I4.0) technologies complement each other on one side, and human skills on the other, is an increasingly important concern for managers and policymakers alike: it is essential for the optimization of firm-level sociotechnical interactions as much as it is vital to smoothen the digital transition on a global scale. However, firm-level evidence on patterns of conjoint adoption of I4.0 technologies and their relationship with skills is still scant. The present work contributes to covering this gap by leveraging a large cross-sectional database of Italian firms. Distinguishing between digital and physical I4.0 technologies, we find that companies are primarily (though not exclusively) driven toward either the former or the latter category of technologies. Additionally, we find that both the adoption of digital I4.0 technologies and the adoption of physical ones lead to the upskilling of non-ICT employees alongside ICT-specialized personnel. However, small firms seem to be constrained in their ability to follow the same upskilling strategy as their larger counterparts. This may have key practical implications, especially in the context of recent industrial policies enacted at the European level.
Drawing on the job demand-resource (JD-R) model, the current study constructs a dual-pathway approach which aims to reveal the mixed impact of AI awareness on service innovative behavior. Specifically, this paper argues that AI awareness will both increase employees' emotional exhaustion, inhibiting their service innovative behaviors (strain pathway) and stimulate employees' intrinsic motivation, promoting their service innovative behaviors (motivation pathway). Meanwhile, employees’ future orientation will buffer the strain pathway and strengthen the motivation pathway. Multisource data (n = 317) from China support the proposed theoretical framework. Implications for theory and practice are discussed.
Self-determination theory has shaped our understanding of what optimizes worker motivation by providing insights into how work context influences basic psychological needs for competence, autonomy and relatedness. As technological innovations change the nature of work, self-determination theory can provide insight into how the resulting uncertainty and interdependence might influence worker motivation, performance and well-being. In this Review, we summarize what self-determination theory has brought to the domain of work and how it is helping researchers and practitioners to shape the future of work. We consider how the experiences of job candidates are influenced by the new technologies used to assess and select them, and how self-determination theory can help to improve candidate attitudes and performance during selection assessments. We also discuss how technology transforms the design of work and its impact on worker motivation. We then describe three cases where technology is affecting work design and examine how this might influence needs satisfaction and motivation: remote work, virtual teamwork and algorithmic management. An understanding of how future work is likely to influence the satisfaction of the psychological needs of workers and how future work can be designed to satisfy such needs is of the utmost importance to worker performance and well-being. Technology is changing the nature of work by enabling new forms of automation and communication. In this Review, Gagné et al. describe how self-determination theory can help researchers and practitioners to shape the future of work to ensure that it meets the psychological needs of workers.
Robots are increasingly used in a variety of work environments, but surprisingly little attention has been paid to how robots change work. In this comparative case study, we explore how robotization changed the work design of order pickers and order packers in eight logistic warehouses. We found that all warehouses robotized tasks based on technological functionality to increase efficiency, which sometimes created jobs consisting of ‘left-over tasks’. Only two warehouses used a bottom-up approach, where employees were involved in the implementation and quality of work was considered important. Although the other warehouses did not, sometimes their work design still benefitted from robotization. The positive effects we identified are reduced physical and cognitive demands and opportunities for upskilling. Warehouses that lack attention to the quality of work may risk ending up with the negative effects for employees, such as simplification and intensification of work, and reduced autonomy. We propose that understanding the consequences of robots on work design supports HR professionals to help managing this transition by both giving relevant input on a strategic level about the importance of work design and advocating for employees and their involvement.
The exponential stability problem for impulsive systems subject to double statedependent delays is studied in this paper, where state-dependent delay (SDD) is involved in both continuous dynamics and discrete dynamics and the boundedness of it with respect to states is prior unknown. According to impulsive control theory, we present some Lyapunov-based sufficient conditions for the exponential stability of the concerned system. It is shown that the stabilizing effect of SDD impulses on an unstable SDD system changes the stability and achieves desired performance. In addition, the destabilizing effect of SDD impulses is also fully considered and the corresponding sufficient conditions are derived, which reveals the fact that a stable SDD system can maintain its performance when it is subject to SDD impulsive disturbance. As an application, the proposed result can be employed to the stability analysis of impulsive genetic regulatory networks (GRNs) with SDD and the corresponding sufficient conditions are proposed in terms of the model transformation technique and the linear matrix inequalities (LMIs) technique. In order to demonstrate the effectiveness and applicability of the derived results, we give two examples including impulsive GRNs with SDD and the impulsive controller design for the nonlinear system with SDD.
Purpose
The prevalence of artificial intelligence (AI) has considerably affected management and society. This paper aims to explore its potential impact on hospitality industry employees, bringing enlightenment to both employees and managers.
Design/methodology/approach
Data were collected from a survey of 432 employees who worked in full-service hotels in China. Structural equation modeling (SEM) was used to analyze the data.
Findings
Results presented a positive relationship between AI awareness and job burnout. No significant direct relationship was found between AI awareness and career competencies. Organizational commitment mediated the relationship between AI awareness and career competencies, as well as the relationship between AI awareness and job burnout.
Research limitations/implications
This study contributes to human resource management in the hospitality industry to theoretical and practical aspects. Theoretically, it enriched both career theory and fit theory. Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.
Practical implications
Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.
Originality/value
The study aims to analyze the impact of AI from a career perspective. It provided theoretical support and evidence for hotel managers for the effects of AI awareness on hotel employees. The study conveys a potential topic of concern that the hospitality industry may face in the future.
In diesem Buch werden die Herausforderungen bei der Einführung von informatorischen Assistenzsystemen in der Montage erörtert und umfangreiche Gestaltungsempfehlungen vermittelt.
Die industrielle Montage ist in Deutschland nach wie vor von hoher volkswirtschaftlicher Bedeutung. In der Industrie existieren zehntausende Montagearbeitsplätze. Diese verändern sich. Infolge hoher Wettbewerbsintensität und Innovationsdynamik entstehen immer mehr Produktvarianten, die zu einer Zunahme der Komplexität der Montagearbeit beitragen. Es stellt sich daher die Frage, wie mit dieser gestiegenen Komplexität umzugehen und wie diese seitens der Beschäftigten zu bewältigen ist, ohne dass es zu Überbeanspruchungen und einem Verfehlen von Produktivitätszielen kommt. Während Montage bislang vornehmlich als energetische Arbeit begriffen wurde, rücken künftig die kognitiven Anteile der Montagearbeit in den Vordergrund. In diesem Zusammenhang kommt dem Komplexitäts- und Informationsmanagement eine entscheidende Rolle zu: Während früher Strategien des Vereinfachens, z. B. durch fortschreitende Arbeitsteilung, im Vordergrund standen, gewinnen heute Strategien des Beherrschens der Komplexität an Bedeutung. Im Zentrum dieses Paradigmenwechsels stehen informatorische Assistenzsysteme, die den Beschäftigten im Montageprozess mit Informationsangeboten unterstützen und so die wachsende Komplexität sicher beherrschbar machen.
Die Herausgeber
Prof. Dr. Manfred Bornewasser
Studium der Psychologie und Soziologie, seit 1994 Professur an der Universität Greifswald am Institut für Psychologie, Arbeits- und Organisationspsychologie, aktuelle Schwerpunktthemen sind körperliche und mentale Belastungen und Beanspruchungen in Arbeitsprozessen sowie kognitive Ergonomie.
Prof. Dr.-Ing. Sven Hinrichsen
Studium des Wirtschaftsingenieurwesens, seit 2011 Professor für Industrial Engineering an der Technischen Hochschule Ostwestfalen-Lippe; wissenschaftliche Schwerpunkte: Gestaltung von manuellen und hybriden Montagesystemen sowie Entwicklung von informatorischen Assistenzsystemen.
Je höher die Komplexität eines Montagesystems ist, desto wichtiger ist es, den Beschäftigten Informationen so bereitzustellen, wie sie diese für eine effektive und effiziente Ausführung ihrer Tätigkeit benötigen. Die informatorische Montagesystemgestaltung beinhaltet dabei einerseits die Entwicklung und Bereitstellung von Montageinstruktionen, die über Assistenzsysteme ausgegeben werden. Andererseits ist die Anordnung und Kennzeichnung von Werkzeugen, Bauteilen, Ladungsträgern und Vorrichtungen Gegenstand der informatorischen Montagesystemgestaltung. Beide Teilbereiche sind eng miteinander verbunden und aufeinander abzustimmen. Dazu sind betriebsspezifische Richtlinien zu formulieren. Aufbauend auf diesen Richtlinien bedarf es einer Digitalisierungsstrategie, die das Ziel verfolgt, durchgängige digitale Prozessketten mit Hilfe von Montageassistenzsystemen zu realisieren.
Examining the determinants of technology acceptance has been a central interest across disciplines. The technology acceptance model (TAM) and its variants and extensions are the most popular theoretical frameworks in this line of research. Two model-based meta-analytical approaches, that is, meta-meta-analysis and conventional meta-analysis, are used to pool the correlations and to test the path relationships among the variables of the TAM. We find that the extended TAM, which we term the TAM Plus, prevails in the model fit testing and that the results of the pooled correlations and path coefficients estimated using the meta-meta-analysis and meta-analysis are generally consistent.
Experts have predicted that 57% of jobs within the OECD could be replaced by technology, due to advancements in automation, artificial intelligence, robotics, etc. Despite this, little is known about how employees view the future of work, and what influence this has on their job behaviors. Using a sample of 1516 employees (from the United States, Australia, and New Zealand) and structural equation modeling, we find that an employee's perceived threat of technological disruption had a significant effect on job insecurity and turnover intentions. Furthermore, we find strong support for a mediation effect from job insecurity. In addition, we included job mobility in our model and argued that those with greater job options might react more purposefully in response to job threats from radical technological changes. Overall, the perceived threat of technological disruption was found to differ across the three countries with Australia and New Zealand having similar modest levels, but with the United States sample reporting significantly higher levels. This paper provides a much-needed insight into how people view technology in relation to their own job security and willingness to stay in their current line of work.
Digitization and connectivity are hot topics for nearly every company today; numerous new technologies offer diverse options. In
this project, a specific technology − smart glasses − was implemented in a manufacturing company. The implementation process was
innovative, as the employees’ perspective was taken into account from the beginning, rather than solely designing the technological aspects
and involving the users after decisions were taken. Employees involved with the new technology were surveyed to take into account the employees’ expectations and fears regarding work design characteristics. This allowed us to customize features of the smart glasses, adapt the work organization, and inform employees about unclear points concerning the implementation process. Moreover, the competencies required for future work were analyzed using a comprehensive work analysis method. We report the results of two quantitative studies and summarize the lessons learned from this project, which can serve as a guideline for other companies.
