Employee Concerns Related to Technological Changes in Future Work: A Network Analysis of Organizational Work Factors in Norway

Participants and procedure

This cross-sectional survey was administered to a panel of Norwegian employees by a market research company. Participants were recruited from the market research company’s existing panel of individuals residing in Norway who were currently employed. Inclusion criteria were that participants must be currently employed, live in Norway, and be 18 years or older. Efforts were made to match the sample to the known distributions in the Norwegian population of working individuals regarding age (18–49 = 52% and ≥50 = 48%), gender (male, 51%, female, 49%), education level (primary/secondary = 40%, university level = 60%), and sector of employment (public = 43%, private = 57%). The survey was administered online, with participants completing the questionnaire at a time convenient for them. A total of n = 441 individuals responded to the survey. The data were collected in October 2024 and subsequently anonymized before being shared with the researchers.

Ethical considerations

Respondents gave informed consent by accepting the voluntary nature of participation before the online survey commenced and were free to cease participation at any time. Due to online anonymity and a limited set of background information, the researchers could not identify who was responding to the survey. The project, therefore, followed the requirements of the Norwegian Centre for Research Data (NSD).

Delphi study

The instruments used in this study were derived from a three-round Delphi study conducted in Norway and Denmark to forecast future challenges and opportunities in the Nordic work environment over the next 10–15 years (Christensen et al. 2021). The Delphi study surveyed experts from various backgrounds using open-ended questions in the first round, then structured the responses into seven key drivers of change: technology, demography, globalization, environment, skills/competence, political/social/cultural developments, and other statements. In subsequent rounds, Delphi participants rated approximately 220 statements on agreement, likely impact, and likelihood of occurrence of events in the statements. They identified potential benefits and risks tied to emerging trends, leading to conclusions that informed future action plans for various stakeholder groups (Christensen et al. 2021).

For this study, six work and organizational psychology experts evaluated the 220 statements generated from the Delphi study in 2021, eliminating overlaps and unclear items. Only statements unanimously highlighted by all experts were retained, supplemented by one item on lifelong learning from the literature, resulting in 43 final statements (see Supplementary file; Table 1). This study focused not only on technology and the work environment but also included items on demography, globalization, new work arrangements, skills, political/social/cultural developments, and other factors deemed relevant. After two rounds of discussions, disagreements were resolved, and consensus items were included, reflecting the complex interplay of technological, demographic, global, environmental, and social factors shaping the future of work.

Instruments

Technological concerns (5 items) explored how employees perceive the impact of technology on their work (e.g., Technology will, to a great extent, determine the organization of work). Organizational work environment (14 items) assessed the psychosocial aspects of the work environment, focusing on stress, health, and job-related factors (e.g., There will be increased work pressure and time pressure). Organizational work conditions (13 items) focused on the practical aspects of the work environment, including questions on working hours and time-related boundarylessness (e.g., Employees will experience increased demands for flexibility and adaptability, for example, regarding working hours). Organizational work demands (11 items) focused on the demands placed on employees, including skill requirements and efficiency pressures (e.g., More tasks will need to be solved with fewer resources). All survey questionnaire items in this study used a 5-point Likert scale ranging from “Strongly disagree (1)” to “Strongly agree (5)” as response options.

Statistical analyses

Statistical analyses were performed in RStudio (R Core Team 2024). Items extracted from the Delphi method were submitted to exploratory factor analysis (EFA) to define and validate acceptable measurement models of the three organizational work factors (organizational work environment, conditions, and demands) before estimating the network models. This data reduction approach ensured precise operationalization and construct definitions. The sample data were split into two halves: Sample 1 (n = 172) was used for EFA, and the hold-out Sample 2 (n = 269) was used for confirmatory factor analysis and network modeling. The following guidelines were used for item and factor selection, including Kaiser–Guttman eigenvalues greater than 1, scree plot test, and parallel analysis. Before performing the EFA, the items’ correlation matrix was inspected, and the assumptions of sampling adequacy were investigated. The EFA was performed using principal factor solution with the fa function in the psych package (Revelle 2022). Missing data in Sample 1 ranged between 4.6% and 6% and were therefore neither replaced nor imputed.

Measurement models obtained from the EFA using Sample 1 were validated in Sample 2 to confirm the underlying factor structures using the cfa function in the lavaan package (Rosseel 2012). Missing data in Sample 2 ranged between 2.2% and 4.8% and were therefore neither replaced nor imputed since the full information maximum likelihood method for handling missing data is commonly applied in CFA models. The CFA model fit was evaluated using multiple indices. A standardized root mean square residual (SRMR) value less than 0.08 was considered indicative of acceptable fit (Browne & Cudeck 1993; Hu & Bentler 1999). Root mean square error of approximation (RMSEA) values less than or equal to 0.06 were interpreted as indicating close fit, with values up to 0.08 reflecting acceptable fit, particularly when the upper bound of the 90% confidence interval was close to or below 0.08 (Hu & Bentler 1999). Comparative fit index (CFI) and Tucker–Lewis index (TLI) values greater than 0.90 were considered adequate fit, and greater than 0.95 were taken to indicate excellent model fit (Hu & Bentler 1999).

Having established precise operationalizations and construct definitions, we proceeded to estimate network models. Prior to estimating the combined network of technological concerns about the future of work, organizational work environment, conditions, and demands, we estimated a network model of only technological concerns in future work to understand how items of technological concerns about the future of work influence each other. Network components in this study were the questions measuring concerns related to technological changes in the future work environment, conditions, and demands referred to as nodes. Putative causal associations between nodes are referred to as edges (Borsboom & Cramer 2013; Epskamp, Borsboom & Fried 2018; Epskamp & Fried 2018). Statistically, edges represent partial correlations between two nodes, controlling for all the other nodes (Epskamp, Borsboom & Fried 2018; Epskamp & Fried 2018). A correlation matrix of the five technological concerns was computed and used as input to estimate a Gaussian graphical model (GGM), which estimates pairwise associations (Epskamp, Borsboom & Fried 2018). Graphical LASSO (i.e., Least Absolute Shrinkage and Selection Operator) was used to regularize parameters resulting from the GGM to avoid spurious connections. The graphical LASSO algorithm calculates the edges between nodes by computing partial correlations between them and then applying an L1 penalty to mask off smaller edges. This returns a sparse network consisting of only edges that are likely to be observed above chance. The network was estimated using qgraph (Epskamp et al. 2012), glasso (Friedman, Hastie & Tibshirani 2019), and bootnet (Epskamp, Borsboom & Fried 2018).

The stability of the network model was assessed using correlation stability (CS) coefficients via the bootnet package (Epskamp, Borsboom & Fried 2018). The CS coefficient reflects the proportion of cases that can be dropped while retaining a correlation of ≥ 0.70 with the original centrality metrics (95% confidence interval [CI]). We used 1,000 case-dropping bootstraps and followed the recommended guidelines of CS ≥ 0.25 as acceptable, and > 0.50 as preferred for stable networks (Epskamp, Borsboom & Fried 2018). The expected influence (EI) metric was calculated to identify the most central technological concerns in the network. The EI is the sum of a node’s connections, weighted by their sign, reflecting both the strength and direction of its associations (Robinaugh, Millner & McNally 2016). Similar to the EI, we computed the bridge expected influence (BEI), which measures a node’s connections to nodes in other communities in the combined network model to identify “bridge nodes” linking technological concerns to organizational factors. Higher BEI indicates stronger bridging effects across communities. Further descriptions of the network analysis are contained in the Supplementary file.

Exploratory Factor Analyses: Sample 1

Network structure of technological concerns about the future of work

Figure 1A shows a visualization of the network structure of technological concerns about the future of work. Technological concerns about the future of work were positively related to each other. The network model highlighted the strongest connection between alienation and job insecurity, followed by alienation and solitary work, suggesting that employees who are concerned about technological structuring as a threat to job security are also more likely to be concerned about feeling alienated in the workplace. The feeling of alienation is also linked with concerns about working in isolation due to technological structuring in the future of work.

Figure 1

The next strongest connections were between concerns about work organization by technology and lifelong learning, and work organization by technology and solitary work. On the one hand, the network model suggests that employees’ concerns about work organization by technology in the future of work are linked with the need for continuous learning to adapt in the workplace. On the other hand, concerns about work organization by technology are also linked with increased working in isolation. Finally, the network model highlighted another interesting mediation relationship in which concerns about working in isolation were a mediator in the relationship between work organization by technology and the feeling of alienation due to technological structuring in the future of work.

Figure 1B illustrates estimates of the EI in the network model. Concerns about the feeling of alienation in the workplace showed the highest EI. This means that, feeling alienated due to technological structuring in the future of work organization was the biggest employees’ concern, which was also mostly associated with other technological concerns, whereas concerns about lifelong learning were the least connected in the network model. Figures S1A and S1B in the Supplementary file display stability analyses and centrality indices for the network model.

Combined network structure of technological concerns about the future of work, organizational work environment, conditions, and demands

A depiction of the combined network structure is contained in Figure 2A. Figure 2B shows the BEI values of technological concerns about the future of work with respect to organizational factors (work environment, conditions, and demands). The BEI plot illustrates how much different technological concerns (on the y-axis) bridge connections with different organizational factors based on their EI (x-axis), while accounting for all relationships in the combined network. Overall, technological concerns about the future of work showed much higher BEI on organizational work demands and environment communities than the organizational work conditions community. This implies that employees’ technological concerns about the future of work are strongly connected with employees’ perceptions of increased work demands and a less favorable work environment.

Figure 2

Specifically, concerns about work organization by technology (0.50) and lifelong learning (0.32) showed the highest BEI in organizational work demands, respectively. This means that employees see work organization by technology and the need for continuous learning as closely tied to increased workload demands. Correspondingly, concerns about the need for continuous learning (0.18), followed by concerns about work organization by technology (0.10), showed the highest BEI values in relation to organizational work conditions, which dominated the organizational work environment. Furthermore, technological concerns about solitary work (0.35) and the feeling of alienation (0.34) dominated the organizational work environment. Thus, employees’ concerns about working in isolation and the feeling of alienation are tied to the effect on the work environment. Figures S2A and S2B in the Supplementary file display stability analysis and centrality indices for the combined network model.

Strengths and limitations of the study

The study has strengths and limitations that should be considered when interpreting the findings. One of the strengths of the study is its use of a a novel application of psychological network analysis to explore the complex relationships between technological concerns and organizational work factors, offering a comprehensive understanding of their associations. Another strength is that the study developed its survey based on the findings of a Delphi study about the Nordic work environment to enhance its relevance and ensure that the analysis addresses practical and policy issues. While the lack of validated survey scales is a limitation, the use of Delphi and network analysis represents an important initial step to gather relevant information, given the uncertainty surrounding the future of work. Moreover, the study used multiple data reduction techniques to validate the measurement models of organizational work factors, thus strengthening the rigor of construct operationalization in the study. The study is also limited by its cross-sectional design, which restricts the ability to draw causal inferences about the relationships between variables. Additionally, the sample is drawn exclusively from Norwegian employees, which may limit the generalizability of the findings to other cultural or national contexts, despite Norway’s position as a technologically advanced and progressive nation. Finally, the study’s reliance on self-report data may introduce common method variance, potentially inflating the observed relationships between variables. To avoid this bias, we have defined unfamiliar terms and used procedures such as not using vague concepts in the included items by keeping questions specific, simple, and concise, not double-barreled with complicated syntax.

Study implications for practice, policy, and future research

The practical relevance of the study findings for occupational health and safety practice and policy highlights the need for organizations to proactively manage the impacts of technological changes in the future work environment. This includes addressing concerns related to alienation, continuous learning, and job insecurity, even though currently a lot of discourse on technology advancements seems to focus on the impending loss of jobs, which is a concern to many stakeholders (Acemoglu et al. 2022).

Although the concept of alienation has been a central theme in critical theory and broader critical social sciences, its presence within mainstream work and organizational psychology has been relatively limited. However, it is important to note that changes in technology, automation, and new ways of working (such as remote and hybrid models) can make people feel more alone, less in control, and diminished meaning at work, which are all key contributors to the feeling of alienation at work. Critical perspectives in the social sciences have begun to introduce such concepts into organizational research, providing more understanding of power, control, and employee subjectivity. For instance, Hornung (2024) examines the multifaceted nature of power in organizations, providing a framework that could be applied to analyze how technological structuring may contribute to alienating work conditions. Similarly, Matthijs Bal et al. (2024) explore the psychological implications of automation and AI through the lens of cultural and organizational “fantasies,” pointing to how these technologies may both mask and reproduce alienating tendencies. Tommasi et al. (2025) extend this critical perspective into emerging digital environments such as the metaverse, emphasizing the need to address the socio-psychological risks of technologically mediated work. Our current and future applied organizational research focuses on understanding how technological concerns will shape the future of work and their consequences for employees’ feelings of loneliness and alienation as socially and technologically embedded phenomena in work organizations by examining how they emerge through interactions between technological change and key organizational dynamics such as work organization, control, deliberative democracy, trust, and power.

Policies should promote hybrid work models that balance flexibility with opportunities for social interaction. Organizations should implement digital collaboration tools, foster inclusive workplace cultures, and provide training to help employees adapt to new technologies. Organizations must prioritize creating environments that foster human connections and offer support systems to mitigate the negative effects of technological structuring. Additionally, organizational policies should prioritize employee well-being by incorporating regular feedback mechanisms to assess and mitigate feelings of isolation. By proactively managing these concerns, organizations can enhance employee engagement, productivity, and overall job satisfaction in a technologically evolving work environment.

The concerns also highlight that heavy tech adoption increases demand for cognitive and interpersonal skills such as reskilling or upskilling, collaboration, creativity, and stakeholder management, while reducing the need for routine administrative tasks like scheduling and budgeting (Alekseeva et al. 2021; Alekseeva et al. 2024). Organizations must adapt to these shifting skill requirements to train employees to use advanced technology to augment their daily task completion at work, not to replace them. Policymakers should develop new legislation that addresses the “technological work environment” and encourages collaboration between employers and employees to tackle challenges in human–technology interaction.

Furthermore, while occupational health and safety interventions often focus on isolated workplace issues, our findings suggest that the future work environment is a complex system made up of interconnected organizational, psychological, and social factors. Future research should examine these interactions, rather than breaking the system into separate parts. Of course, these studies are necessary, but they are not enough since they might lead to tunnel vision in work and organizational psychology research. A holistic approach will help ensure that important relationships between various organizational factors are not overlooked.