Introduction
The Covid-19 pandemic severely accelerated the extent (or intensity; Gajendran & Harrison, Reference Gajendran and Harrison2007; Raghuram, Hill, Gibbs, & Maruping, Reference Raghuram, Hill, Gibbs and Maruping2019) of remote work and when viewed alongside the rapid technological advancements that simplifies remote work, many argue that we have entered a ‘new world of work’ where alternative work arrangements is becoming the norm (Gajendran, Ponnapalli, Wang, & Javalagi, Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Spreitzer, Cameron, & Garrett, Reference Spreitzer, Cameron and Garrett2017). That people work some days from home and some days at the workplace has become commonplace and the development of information communication technologies has enabled workers to easily connect with colleagues or clients remotely (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022). Many organizations, however, are debating whether they should continue with remote and flexible work arrangements after the Covid-19 pandemic (Neeley, Reference Neeley2021), partly because of the uncertainty regarding the effect of remote work on employee performance (Mutiganda et al., Reference Mutiganda, Wiitavaara, Heiden, Svensson, Fagerström, Bergström and Aboagye2022). Individual work performance is the basic building block that entire economies are built on (Kim & Ployhart, Reference Kim and Ployhart2014) and is considered one of the most crucial dependent variables in work and organizational psychology research (Campbell & Wiernik, Reference Campbell and Wiernik2015). Yet, there is limited empirical research on mechanisms linking remote work to employee performance and previous research indicate that effects of remote work on various employee outcomes (including work performance) seem to operate through different mechanisms (e.g., Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Gajendran & Harrison, Reference Gajendran and Harrison2007; Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Gillet, Huyghebaert-Zouaghi, Austin, Fernet, & Morin, Reference Gillet, Huyghebaert-Zouaghi, Austin, Fernet and Morin2021).
One potential key mechanism is work motivation, which is a fundamental component of work performance (Cerasoli, Nicklin, & Ford, Reference Cerasoli, Nicklin and Ford2014). However, research on how work motivation is impacted by remote work and how work motivation impacts work performance in remote work settings is lacking and scholars have called for more research on the topic (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022). Furthermore, leaders are in a unique position to influence their employees’ motivation and performance (e.g., Deci, Olafsen, & Ryan, Reference Deci, Olafsen and Ryan2017; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023; Montano, Reeske, Franke, & Hüffmeier, Reference Montano, Reeske, Franke and Hüffmeier2017; Slemp, Kern, Patrick, & Ryan, Reference Slemp, Kern, Patrick and Ryan2018), however, the increase of alternative work arrangements (e.g., remote work) has created a new context for leadership (e.g., Bell, McAlpine, & Hill, Reference Bell, McAlpine and Hill2023; Kniffin et al., Reference Kniffin, Narayanan, Anseel, Antonakis, Ashford, Bakker and Vugt2021). As such, there is a need to better understand the motivational implications of various leadership behaviors in remote work settings (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022).
The aim of the current study was to examine the impact of remote work intensity (RWI) on work motivation and individual work performance and explore the mediating role of perceived leadership behaviors in these relationships. Following prior research, RWI is defined as ‘the frequency or amount of time spent working remotely typically assessed in terms of days/hours per week or percent of work week’ (Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024, p. 2). The current study is grounded in self-determination theory (SDT; Ryan & Deci, Reference Ryan and Deci2017), which is a broad theory of human motivation and outlines key antecedents and consequences of different types of motivation. SDT provides a comprehensive motivational framework that can elucidate mechanisms linking RWI to employee performance via perceived leadership behaviors that enhance or diminish autonomous work motivation (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Slemp et al., Reference Slemp, Kern, Patrick and Ryan2018).
Our study makes three main contributions to the literature. First, it sheds light on the motivational and performance implications of RWI and the role of different leadership behaviors in these relationships. Researchers argue that the ‘new normal’ way or working with a high degree of alternative work arrangements, such as remote work, has created a new context for leadership (Bell, McAlpine, & Hill, Reference Bell, McAlpine and Hill2023; Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Kniffin et al., Reference Kniffin, Narayanan, Anseel, Antonakis, Ashford, Bakker and Vugt2021). In the current study, we focus on remote and hybrid work in Norway, which have seen a substantial increase RWI in the last couple of years. Reports indicate an increase from 10% in 2019 to approximately 42% in 2024 of employees in Norway that sometimes or usually work remotely (Eurostat, 2025). This substantial increase in remote and hybrid work arrangements comes with unique challenges for leaders, such as helping their followers maintain a shared awareness of the current hybrid configuration (i.e., who is working where and when), structure work tasks to optimize competencies when workers are onsite and off-site, a need to pay more attention to employees’ needs and well-being, avoid imbalances in employees’ access to resources (e.g., technology), and visibility of contributions (Bell, McAlpine & Hill, Reference Bell, McAlpine and Hill2023). Leaders in remote and hybrid work settings will also need to devote more attention to structure and planning aspects of work to optimize the use of employees’ skills and competencies to attain desired outcomes (Morgeson, DeRue, & Karam, Reference Morgeson, DeRue and Karam2010).
In the current study we examine the impact of RWI on three leadership behaviors with known motivational implications‒need-supportive, controlling, and laissez-faire leadership behaviors‒and in turn how RWI and leadership behaviors relate to employee motivation and individual work performance over time. Examining leadership behaviors in remote and hybrid work settings through the lens of SDT (Deci, Olafsen, & Ryan, Reference Deci, Olafsen and Ryan2017)‒focusing on leadership behaviors that either facilitate employees’ autonomy, sense of belonging, and provide a clear structure (i.e., need-supportive behaviors), or undermines employees’ autonomy, competence, and relatedness through exaggerated monitoring, control, or avoidance behaviors‒provides an ideal framework well-aligned with the challenges leaders may face in remote and hybrid work settings.
Second, research has shown that RWI can be a positive and negative force; however, we need a better understanding of the different pathways leading to positive or negative effects on work performance (Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023). Through SDT we can elucidate mechanisms linking RWI to work performance via perceived leadership behaviors that enhance or diminish autonomous work motivation, thus, providing a more nuanced understanding of the pathways linking RWI to work performance (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022).
Third, most of our current understanding of the links between RWI and employee outcomes in general (including performance) is based on research prior to or during the Covid-19 pandemic (e.g., Allen, Golden, & Shockley, Reference Allen, Golden and Shockley2015; Gajendran & Harrison, Reference Gajendran and Harrison2007; Hackney, Yung, Somasundram, Nowrouzi–Kia, Oakman, & Yazdani, Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022; Raghuram et al., Reference Raghuram, Hill, Gibbs and Maruping2019). These previous studies show that RWI can have beneficial effects on proximal outcomes, such as perceived autonomy and work-life conflict, while not being detrimental for the quality of workplace relationships with supervisors or colleagues (Allen, Golden, & Shockley, Reference Allen, Golden and Shockley2015; Gajendran & Harrison, Reference Gajendran and Harrison2007). RWI may also be beneficial for more distal outcomes, such as job satisfaction and performance (Gajendran & Harrison, Reference Gajendran and Harrison2007), however, the effects of RWI on work performance and productivity seem to depend on the type of remote work arrangement or policy; non-mandatory remote or hybrid work seems to be beneficial whereas mandatory remote or hybrid work seem to have detrimental effects (Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022). However, researchers argue that we have entered a new world of work where alternative work arrangements (e.g., remote work) are the norm (Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Kniffin et al., Reference Kniffin, Narayanan, Anseel, Antonakis, Ashford, Bakker and Vugt2021; Leonardi, Parker, & Shen, Reference Leonardi, Parker and Shen2024) and previous research have shown differences in relations when comparing data before and during the pandemic (Allen, Grelle, Lazarus, Popp, & Gutierrez, Reference Allen, Grelle, Lazarus, Popp and Gutierrez2024; Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022; Mutiganda et al., Reference Mutiganda, Wiitavaara, Heiden, Svensson, Fagerström, Bergström and Aboagye2022). This new world of work will result in a greater reliance on technology in general, change the way workers relate to each other with an increase in virtual interactions and less face-to-face interactions, rewire social networks at work, and impact how workers identify themselves in organizations (Leonardi, Parker, & Shen, Reference Leonardi, Parker and Shen2024). Thus, there is a need for knowledge based on data collected after the Covid-19 pandemic that reflects relations in the new world of work.
Remote work and leadership effectiveness
Early studies on leadership and remote work that focused on physical/spatial distance and its impact on leadership effectiveness showed that increased distance had a negative effect on the leader-follower quality, follower satisfaction, and individual task performance (Podsakoff, MacKenzie, & Bommer, Reference Podsakoff, MacKenzie and Bommer1996). Previous findings also showed that increased distance strengthened the positive relation between charismatic leadership and organizational performance, that the positive relation between transformational leadership and individual work performance was stronger in close than distant conditions, and that the positive relation between contingent reward leadership and individual work performance was stronger in distant than close conditions (Antonakis & Atwater, Reference Antonakis and Atwater2002). More recent findings indicate that various constructive forms of leadership behaviors (i.e., task-, relational-, and change-oriented) generally were positively related to work performance, whereas, destructive leadership behaviors, such as laissez-faire leadership, were negatively related work performance in highly remote and virtual settings (Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023). Prior research has also shown that empowering leadership, which is similar to SDTs conceptualization of need support, had a greater impact on individual and team performance in remote work settings and these relations were mediated by virtual collaboration behaviors (Hill & Bartol, Reference Hill and Bartol2016).
Despite these previous findings we still have a limited understanding of the pathways linking RWI to work performance and many argue that RWI exert its influence through different pathways (e.g., Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Hill, Axtell, Raghuram, & Nurmi, Reference Hill, Axtell, Raghuram and Nurmi2022; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023). Furthermore, several scholars have argued that we need a balanced view on the opportunities and risks of remote work, and that we need to examine an expanded range of mediators (and moderators) and examine relationships over time (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Hill, Axtell, Raghuram, & Nurmi, Reference Hill, Axtell, Raghuram and Nurmi2022; Purvanova & Kenda, Reference Purvanova and Kenda2018).
Leadership through the lens of self-determination theory
In the current study we used SDT (Ryan & Deci, Reference Ryan and Deci2017), which provides a comprehensive motivational framework to understand different pathways linking RWI to employee motivation and performance through its impact on perceived leadership behaviors. SDT proposes that satisfaction of the three basic psychological needs ‒ autonomy (feeling ownership of one’s actions), competence (feeling efficient in accomplishing personally important tasks), and relatedness (feeling secure and accepted in one’s relationships) ‒ are crucial for individual motivation and functioning (Ryan & Deci, Reference Ryan and Deci2017). Given the importance of the basic psychological needs for individual motivation and functioning, any environmental condition, including leadership behaviors, that nurture the satisfaction of these needs will help drive individual motivation and functioning (Deci, Olafsen, & Ryan, Reference Deci, Olafsen and Ryan2017; Huyghebaert‐Zouaghi, Morin, Ntoumanis, Berjot, & Gillet, Reference Huyghebaert‐Zouaghi, Morin, Ntoumanis, Berjot and Gillet2023). Research on need-supportive interpersonal behaviors have supported this core assumption in SDT across various populations and cultures (e.g., Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Olafsen, Halvari, & Frølund, Reference Olafsen, Halvari and Frølund2021; Reeve & Cheon, Reference Reeve and Cheon2021; Slemp et al., Reference Slemp, Field, Ryan, Forner, Van den Broeck and Lewis2024, Reference Slemp, Kern, Patrick and Ryan2018; Vansteenkiste, Ryan, & Soenens, Reference Vansteenkiste, Ryan and Soenens2020). However, the impact of need-supportive behaviors in remote work settings is yet to be examined (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022).
Within SDT, interpersonal behaviors that directly support followers’ need satisfaction are called need-supportive behaviors (Ryan & Deci, Reference Ryan and Deci2017). Autonomy, competence, and relatedness support have been acknowledged within SDT as particularly important for employees’ need satisfaction, wellbeing, and performance (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Slemp et al., Reference Slemp, Field, Ryan, Forner, Van den Broeck and Lewis2024; Tafvelin & Stenling, Reference Tafvelin and Stenling2018). When managers understand the employees’ perspectives, acknowledge their feelings, encourage exploration and curiosity, provide a meaningful rationale, and provide opportunities for choice, they display autonomy support. When managers provide clear and understandable guidelines and expectations, instill a sense of competence, and provide relevant feedback to employees, they display competence support. Managers who show a genuine interest in their employees and their employees’ well-being by spending a considerable amount of time, energy, and resources on them are displaying relatedness support (Slemp et al., Reference Slemp, Field, Ryan, Forner, Van den Broeck and Lewis2024; Tafvelin & Stenling, Reference Tafvelin and Stenling2018).
Managers can also engage in controlling interpersonal behaviors, which in contrast to need-supportive behaviors, are characterized by pressure to make employees think, feel, and behave in certain ways, which undermines need satisfaction and promotes controlled motivation (Deci, Olafsen, & Ryan, Reference Deci, Olafsen and Ryan2017; Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022). Controlling managers rely on forceful language and let their own perspective overrun the employees’ perspectives via intrusion and pressure (Reeve, Reference Reeve2009).
Laissez-faire leadership comprises passive behaviors characterized by avoidance, inaction, and not being present as a leader (Skogstad, Einarsen, Torsheim, Aasland, & Hetland, Reference Skogstad, Einarsen, Torsheim, Aasland and Hetland2007). When leaders formally occupy a leadership position but have more or less abdicated from the responsibilities and duties assigned to them, they display laissez-faire leadership (Lewin, Lippitt, & White, Reference Lewin, Lippitt and White1939). This avoidant type of (non-)leadership is present when leaders delay or avoid making decisions, do not provide feedback or rewards, and make no attempts to motivate or recognize their employees or satisfy their needs. Laissez-faire leadership thus implies a lack of presence and not meeting legitimate expectations from employees or superiors (Skogstad et al., Reference Skogstad, Einarsen, Torsheim, Aasland and Hetland2007). Laissez-faire leadership has generally been linked to negative employee outcomes, such as ill-being and poor performance (Fosse, Skogstad, Einarsen, & Martinussen, Reference Fosse, Skogstad, Einarsen and Martinussen2019; Gagné et al., Reference Gagné, Forest, Vansteenkiste, Crevier-Braud, van den Broeck, Aspeli, Bellerose, Benabou, Chemolli, Güntert, Halvari, Indiyastui, Johnson, Molstad, Naudin, Ndao, Olafsen, Roussel, Wang and Westbye2015; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023; Skogstad et al., Reference Skogstad, Einarsen, Torsheim, Aasland and Hetland2007; Skogstad, Hetland, Glasø, & Einarsen, Reference Skogstad, Hetland, Glasø and Einarsen2014). That leadership behaviors have motivational implications are well documented and research indicate that need-supportive leadership behaviors have a positive impact on employees’ motivation and performance, whereas controlling and laissez-faire leadership behaviors generally have a weak or negative impact on employee motivation and performance (Deci, Olafsen, & Ryan, Reference Deci, Olafsen and Ryan2017; Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Slemp et al., Reference Slemp, Kern, Patrick and Ryan2018).
Motivation through the lens of self-determination theory
SDT conceptualizes motivation as a multidimensional construct that consists of three overarching categories, amotivation, extrinsic motivation, and intrinsic motivation (Gagné et al., Reference Gagné, Forest, Vansteenkiste, Crevier-Braud, van den Broeck, Aspeli, Bellerose, Benabou, Chemolli, Güntert, Halvari, Indiyastui, Johnson, Molstad, Naudin, Ndao, Olafsen, Roussel, Wang and Westbye2015). Amotivation is characterized by an absence of motivation, often accompanied by a sense of meaninglessness and lack of competence in relation to an activity. Extrinsic motivation refers to engaging in an activity for instrumental reasons, such as avoiding punishment or receiving rewards (external regulation), boosting self-esteem or avoiding guilt or shame (introjected regulation), or striving towards a personally valued goal or identifying with the meaning of an activity (identified regulation). These different types of extrinsic motivations vary in their degree of internalization ranging from complete non-internalization (i.e., external regulation), to partial but controlled internalization (i.e., introjected regulation), to a high degree of internalization (i.e., identified regulation). Intrinsic motivation refers to engagement in an activity for its own sake and for the inherent enjoyment and interest in the activity. Meta-analytic findings indicate that intrinsic and identified work motivation are related to desirable employee outcomes (e.g., wellbeing and performance), that the less internalized types of motivation (external and introjected regulation) have weak or near zero relations to desirable employee outcomes, and that amotivation is linked to undesirable employee outcomes (Van den Broeck, Howard, Van Vaerenbergh, Leroy, & Gagné, Reference Van den Broeck, Howard, Van Vaerenbergh, Leroy and Gagné2021). The specific meta-analytic correlations with work performance and proactivity were higher for identified regulation (0.35 and 0.33, respectively) and intrinsic motivation (0.30 and 0.39, respectively) than introjected regulation (0.28 and 0.27, respectively), external regulation (0.04 and 0.20, respectively), and amotivation (−0.28 and −0.11, respectively).
The present study
Prior research indicates that RWI can have positive or negative effects on work performance (e.g., Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Hackney et al., Reference Hackney, Yung, Somasundram, Nowrouzi-Kia, Oakman and Yazdani2022; Mutiganda et al., Reference Mutiganda, Wiitavaara, Heiden, Svensson, Fagerström, Bergström and Aboagye2022), however, we still have a limited understanding of the pathways linking RWI to employee performance outcomes. The overarching aim of the current study was to examine direct and indirect effects of RWI on employee motivation and individual work performance through various types of leadership behaviors firmly grounded in SDT (Ryan & Deci, Reference Ryan and Deci2017) to better understand why RWI have positive or negative effects.
In the current study, we focus on individual work performance, which is defined as ‘things that people actually do, actions they take, that contribute to the organization’s goals’ (Campbell & Wiernik, Reference Campbell and Wiernik2015, p. 48). We operationalize individual work performance through the work-role performance model developed by Griffin, Neal and Parker (Reference Griffin, Neal and Parker2007), which focuses on three dimensions; proficiency on core tasks, proficiency in adapting to changes, and being proactive in instituting new methods or solutions. These aspects of individual work performance have been highlighted as particularly important in remote work settings, which are characterized by higher uncertainty (e.g., increased unpredictability and uncertainty about what activities are needed to be successful) and interdependence among people, systems, and technology, which can impact task proficiency, adaptability, and proactivity (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022).
In the current study we address the following research question ‘How does RWI influence employees’ work motivation and individual work performance, and what role do leadership behaviors play as mediators in these relationships?.’ Based on previous findings on the effects of RWI on employee performance (e.g., Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Hill & Bartol, Reference Hill and Bartol2016; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023; Mutiganda et al., Reference Mutiganda, Wiitavaara, Heiden, Svensson, Fagerström, Bergström and Aboagye2022) and findings in the SDT literature on the effects of need support and motivation on work performance (e.g., Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Slemp et al., Reference Slemp, Kern, Patrick and Ryan2018; Van den Broeck et al., Reference Van den Broeck, Howard, Van Vaerenbergh, Leroy and Gagné2021), we expect that if RWI positively impact employees perceived need support from their manager, it will also have positive indirect effects on autonomous motivation (i.e., types of motivation with high degree of internalization) and individual work performance. In contrast, if RWI positively impact perceptions of controlling leadership behaviors (e.g., excessive monitoring), it will indirectly impact external and introjected regulations (i.e., controlled types of motivation with low or no internalization), which in turn will have weak or near-zero relations with work-role performance. Finally, if RWI positively impact perceptions of laissez-faire leadership behaviors (e.g., avoiding decision-making), it is expected to result in higher amotivation and lower work performance.
Methods
Participants and procedure
We used three waves of data from the REMOTE panel (Olafsen, Bentzen, Stenling, & Tafvelin, Reference Olafsen, Bentzen, Stenling and Tafvelin2023), which at baseline consisted of data from 3633 workers in Norway assessed repeatedly over a 12-month period in 2022. The time lag was approximately 3 months between each wave of data collection. RWI, leadership behaviors, and control variables were assessed at wave 1, work motivation at wave 2, and individual work performance at wave 3. We employed the following inclusion criteria for the study: employees (not holding a manager position) who across all waves had the opportunity for remote work, did not change job or manager, who worked at least 50% ≈ 19 hours) per week, which resulted in a sample of 512 employees (222 women, 290 men). The age of the sample ranged from 24 to 70 years (M = 51.8, SD = 10.3) and tenure at their current workplace was on average 14.5 years (SD = 11.0). Of the participants, 56.0% worked in the private sector, and 80.9% had a higher education degree.
Measures
All variables were assessed based on the respondents’ experiences over the past 4 weeks.
Remote work intensity (RWI)
Following prior research (e.g., Gajendran & Harrison, Reference Gajendran and Harrison2007; Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024; Raghuram et al., Reference Raghuram, Hill, Gibbs and Maruping2019) we assessed RWI, which refers to time spent working remotely. Participants reported on their average number of weekly working hours over the past 4 weeks, and the average number of these working hours spent working remotely. RWI was calculated as the proportion of remote work hours of total work hours.
Leadership behaviors
Need-supportive behaviors were assessed using the 12-item Need Support at Work Scale (NsU-WS; Tafvelin & Stenling, Reference Tafvelin and Stenling2018), which consists of three 4-item subscales capturing employees’ perceptions of their managers autonomy (e.g., My manager tries to understand my perspective before stating his/her opinion), competence (e.g., My manager provides me with the support I need to develop at work), and relatedness (e.g., My manager shows that he/she really listens to what I have to say) support. Evidence of reliability, factorial validity, and criterion-related validity of the NsU-WS have been reported in previous research (Tafvelin, Lundmark, von Thiele Schwarz, & Stenling, Reference Tafvelin, Lundmark, von Thiele Schwarz and Stenling2023; Tafvelin & Stenling, Reference Tafvelin and Stenling2018).
Employees perceptions of their managers controlling behaviors were assessed with the four-item Controlling Teacher Questionnaire (CTQ; Jang, Reeve, Ryan, & Kim, Reference Jang, Reeve, Ryan and Kim2009) modified to the work context by changing the stem from ‘My teacher…’ to ‘My manager….’ An example item of the CTQ is ‘My manager tries to control everything I do.’ Evidence of reliability (coefficient alpha > .70) and discriminant validity through negative correlations with perceived autonomy support have been reported in previous research (Cheon, Reeve, & Moon, Reference Cheon, Reeve and Moon2012; Jang et al., Reference Jang, Reeve, Ryan and Kim2009).
Laissez-faire behaviors were assessed with a four-item scale from the Multifactor Leadership Questionnaire 5X-short (Bass & Avolio, Reference Bass and Avolio1995). An example item is ‘My manager avoids getting involved when important issues arise.’ Evidence of reliability and validity have been reported in previous research (e.g., Antonakis, Avolio, & Sivasubramaniam, Reference Antonakis, Avolio and Sivasubramaniam2003). Responses on all leadership items were provided on a 5-point Likert scale from 1 (never/almost never) to 5 (always).
Work motivation
The 19-item Multidimensional Work Motivation Scale (MWMS; Gagné et al., Reference Gagné, Forest, Vansteenkiste, Crevier-Braud, van den Broeck, Aspeli, Bellerose, Benabou, Chemolli, Güntert, Halvari, Indiyastui, Johnson, Molstad, Naudin, Ndao, Olafsen, Roussel, Wang and Westbye2015) was used to measure six types of work motivation grounded in SDT. Following the stem ‘Why do you or would you put efforts into your current job?,’ the MWMS contains items capturing employees’ amotivation (3 items, e.g., ‘I don’t know why I’m doing this job, it’s pointless work.’), external regulation-social (3 items, e.g., To get others’ approval (e.g., supervisor, colleagues, family, clients …)”, external regulation-material (3 items, e.g., ‘Because others will reward me financially only if I put enough effort in my job (e.g., employer, supervisor …),’ introjected regulation (4 items, e.g., ‘Because otherwise I will feel ashamed of myself’), identified regulation (3 items, e.g., ‘Because putting efforts in this job aligns with my personal values’), and intrinsic motivation (3 items, e.g., ‘Because I have fun doing my job’). Evidence of reliability, factorial validity, convergent validity, and discriminant validity have been reported in previous research (e.g., Gagné et al., Reference Gagné, Forest, Vansteenkiste, Crevier-Braud, van den Broeck, Aspeli, Bellerose, Benabou, Chemolli, Güntert, Halvari, Indiyastui, Johnson, Molstad, Naudin, Ndao, Olafsen, Roussel, Wang and Westbye2015; Howard, Gagné, Morin, & Van den Broeck, Reference Howard, Gagné, Morin and Van den Broeck2016). Responses were provided on a 7-point Likert scale ranging from 1 (not at all) to 7 (completely).
Individual work performance
Nine items were used to measures individual work performance (Griffin, Neal, & Parker Reference Griffin, Neal and Parker2007) in the form of individual task proficiency (3 items, e.g., ‘Carried out the core parts of your job well’), individual task adaptivity (3 items, e.g., ‘Adapted well to changes in core tasks’), and individual task proactivity (3 items, e.g., ‘Initiated better ways of doing your core tasks’). Evidence of reliability and validity of the work-role performance subscales have been reported in previous research (Griffin, Neal, & Parker Reference Griffin, Neal and Parker2007). Responses were provided on a 5-point Likert scale ranging from 1 (very little) to 5 (a great deal).
Control variables
In the structural model, we included age (years), sex (male or female), education (highest completed degree), sector (public or private), and tenure at the current workplace (years) as control variables.
Statistical analysis
Mplus version 8.10 (Muthén & Muthén, Reference Muthén and Muthén1998-2017) was used to perform the statistical analysis. First, we used Bayesian structural equation modeling (BSEM; Muthén & Asparouhov, Reference Muthén and Asparouhov2012) to examine the factorial validity of the scales. In contrast to the traditional independent clusters model confirmatory factor analysis (ICM-CFA) specification with zero cross-loadings and zero residual correlations, BSEM allows for approximate zeros for the cross-loadings and residual correlations. Replacing exact zeros with approximate zeros better reflect substantive theories, acknowledges the fallible nature of indicators of the constructs, and can reduce bias in the parameter estimates (Asparouhov, Muthén, & Morin, Reference Asparouhov, Muthén and Morin2015; Muthén & Asparouhov, Reference Muthén and Asparouhov2012). Zero-mean, small-variance priors were used on the cross-loadings and residual correlations in all models with a prior specification indicating that 95% lies between ±0.20. Standardized indicators and factors were used in the analyses and thus a cross-loading or residual correlation of 0.20 is considered small. BSEM analysis in Mplus uses Markov Chain Monte Carlo (MCMC) algorithms with the Gibbs sampler to estimate the models. Two MCMC chains and a minimum of 50,000 iterations were used for estimating the posterior distributions. The first half of the iterations (a minimum of 25,000) was discarded as burn-in iterations. We also used a thinning of 5, which means that every 5th sampled value was stored. The potential scale reduction factor (PSRF) was used to assess model convergence and a PSFR around 1 is considered evidence of convergence (Gelman et al., Reference Gelman, Carlin, Stern, Dunson, Vehtari and Rubin2014).
Second, to reduce model complexity we saved plausible values (Asparouhov & Muthén, Reference Asparouhov and Muthén2010) to obtain factor score estimates of the latent variables from the measurement models and used these in the structural model to examine direct and indirect effects. We generated 100 sets of plausible values and used the mean point estimate across these 100 sets in the structural models.
Model fit of the BSEM models was assessed with the posterior predictive p (PPP) value and its 95% confidence interval (CI). A PPP value around 0.50 with a symmetrical 95% CI is considered an excellent model fit, whereas a PPP value larger than 0.10 was considered an acceptable model fit (Cain & Zhang, Reference Cain and Zhang2019). For the specific parameters (e.g., factor loadings, latent factor correlations, regression coefficients, indirect effects) a 95% credibility interval (CrI) is generated and used to interpret the parameter estimates; if the 95% CrI does not include zero, it is considered a credible parameter estimate (Muthén & Asparouhov, Reference Muthén and Asparouhov2012; Zyphur & Oswald, Reference Zyphur and Oswald2015). Furthermore, in the measurement models we used the prior-posterior predictive p (PPPP) value (Asparouhov & Muthén, Reference Asparouhov and Muthén2017) to examine whether the minor parameters (i.e., cross-loadings and residual correlations with zero mean and small-variance priors) can be assumed to come from a N (0, τ2) distribution and test the hypothesis that these parameters are approximately zero. A PPPP value larger than 0.05 indicates that the minor parameters are not outside of the N (0, τ2) distribution and can be assumed to be approximately zero (Asparouhov & Muthén, Reference Asparouhov and Muthén2017).
Results
Measurement models and preliminary analyses
In the first set of analyses, we examined measurement models of leadership behaviors, work motivation, and individual work performance separately by using BSEM. Factor score estimates of the latent variables from these three measurement models were saved and used in the structural model. The leadership behavior measurement model consisted of three parcels (i.e., mean scores of each subscale for autonomy support, competence support, relatedness support) for a latent need-supportive behaviors factor. The latent factors for controlling and laissez-faire behaviors consisted of four items each. Model fit was excellent (PPP = 0.516, 95% CI [−34.997, 36.048]) and the PPPP value of 0.669 supports the assumption that the minor parameters are approximately zero. The standardized factor loadings were strong (> 0.74) and the latent factor correlations ranged from −0.511 to 0.631.
The measurement model for work motivation consisted of six latent factors (amotivation, external regulation-material, external regulation-social, introjected regulation, identified regulation, intrinsic motivation) and 19 items. Model fit was excellent (PPP = 0.537, 95% CI [−59.582, 55.929]) and the PPPP value of 0.816 supports the assumption that the minor parameters are approximately zero. The standardized factor loadings were strong (> 0.70) and the latent factor correlations ranged from −0.350 to 0.572.
The measurement model for individual work performance consisted of three latent factors and 9 items and showed excellent model fit (PPP = 0.487, 95% CI [−28.303, 29.754]) and the PPPP value of 0.620 supports the assumption that the minor parameters are approximately zero. The standardized factor loadings were strong (> 0.70) and the latent factor correlations ranged from 0.229 to 0.594.
Bivariate correlations between the study variables and reliability estimates (ω) are presented in Table 1. The correlations between the leadership variables, motivation, and performance variables were generally in the expected direction, except for the positive correlations between amotivation, external regulation-material, and proactivity. Omega reliability estimates were generally satisfactory and ranged from 0.795 to 0.905.
Table 1. Bivariate correlations between the study variables
Note: NS = Need support, CB = controlling behaviors, LF = laissez-faire, Amot = amotivation, ExMa = external regulation-material, ExSoc = external regulation-social, IJ = introjected regulation, ID = identified regulation, IM = intrinsic motivation, Prof = proficiency, Adapt = adaptability, Proac = proactivity, RWI = remote work intensity, Tenure = tenure at workplace (years), Age = age (years), Sex (coded as male = 0, female = 1), Educ = highest completed degree (0 = compulsory school, 1 = high school, 2 = university bachelor’s degree, 3 = university master’s degree or higher), Sector (coded as 0 = public, 1 = private), ω = omega reliability estimates.
Structural model
The structural model showed excellent model fit (PPP = 0.462, 95% CI [−52.550, 57.909]) and the results from the main structural model are displayed in Fig. 1. RWI had a positive effect on need support (β = .114, 95% CrI [0.028, 0.200]), a negative effect on controlling behaviors (β = −.090, 95% CrI [−0.176, −0.004]), and a negative effect on laissez faire behaviors (β = −.090, 95% CrI [−0.177, −0.003]). RWI did not have credible direct effects on work motivation or performance.
Figure 1. Structural model showing credible direct effects (i.e., the 95% credibility interval did not include zero).
Need support had a positive effect on intrinsic motivation (β = .339, 95% CrI [0.229, 0.445]), adaptivity (β = .207, 95% CrI [0.091, 0.321]), and proactivity (β = .232, 95% CrI [0.112, 0.347]). Controlling behaviors had a negative effect on intrinsic motivation (β = −.117, 95% CrI [−0.244, −0.009]) and a positive effect on external regulation-social (β = .249, 95% CrI [0.140, 0.356]), external regulation-material (β = .305, 95% CrI [0.202, 0.406]), and amotivation (β = .308, 95% CrI [0.205, 0.407]). Laissez faire behaviors had a positive effect on proactivity (β = .145, 95% CrI [0.016, 0.263]).
Intrinsic motivation had a positive effect on adaptivity (β = .207, 95% CrI [0.096, 0.316]) and proactivity (β = .170, 95% CrI [0.057, 0.282]), whereas amotivation had a negative effect on proficiency (β = −.141, 95% CrI [−0.251, −0.028]) and a positive effect on proactivity (β = .138, 95% CrI [0.023, 0.525]). All direct effects on work motivation and individual work performance are shown in Tables 2 and 3.
Table 2. Direct effects (standardized coefficients) of RWI, perceived leadership behaviors, and work motivation on work performance
Note: RWI = remote work intensity, LL = lower limit, UL = upper limit, CrI = credibility interval.
Table 3. Direct effects (standardized coefficients) of RWI and perceived leadership behaviors on work motivation
Note: RWI = remote work intensity, LL = lower limit, UL = upper limit, CrI = credibility interval.
Indirect effects
Credible indirect effects and their 95% credibility intervals are displayed in Table 4. We focus the reporting on credible indirect effects involving RWI, however, the Online Supplements contain results pertaining to all indirect effects from RWI to individual work performance (Tables S1–S3), from RWI to work motivation (Table S4), and from perceived leadership behaviors to individual work performance (Tables S5–S7). Most of the credible indirect effects of RWI on motivation and/or individual work performance involved need-supportive leadership behaviors. RWI had a positive indirect effect on adaptability (ab = 0.028, 95% CrI [0.006, 0.062]) and proactivity (ab = 0.020, 95% CrI [0.003, 0.051]) through need-supportive leadership and intrinsic motivation. RWI also had a positive indirect effect on adaptability (ab = 0.064, 95% CrI [0.012, 0.144]) and proactivity (ab = 0.083, 95% CrI [0.018, 0.180]) through need-supportive leadership. RWI had a positive indirect effect on intrinsic motivation (ab = 0.123, 95% CrI [0.029, 0.238]) through need-supportive leadership. RWI also had a negative indirect effect on external regulation-social (ab = −0.069, 95% CrI [−0.159, −0.003]) and external regulation-material (ab = −0.087, 95% CrI [−0.190, −0.004]) through controlling leadership behaviors.
Table 4. Credible indirect effects
Note: RWI = remote work intensity, ab = indirect effect, CrI = credibility interval.
In summary, the results indicate that RWI primarily was linked to perceptions of higher need-supportive leadership behaviors, which in turn was related to higher intrinsic motivation, adaptability, and proactivity, as well as perceptions of lower levels of controlling leadership behaviors and extrinsic regulation.
Discussion
The current study contributes to the growing body of literature on remote work by examining its implications for leadership behaviors, work motivation, and performance through the lens of SDT (Ryan & Deci, Reference Ryan and Deci2017). We found that RWI was positively associated with need-supportive leadership behaviors and negatively associated with controlling and laissez-faire leadership behaviors. Need-supportive leadership was linked to higher intrinsic motivation, which in turn positively influenced adaptability and proactivity. Conversely, controlling leadership behaviors were associated with higher levels of external regulation and amotivation, which had weak or negative effects on performance. Laissez-faire leadership, surprisingly, showed a positive relationship with proactivity, suggesting that some employees may thrive in environments with minimal managerial intervention. Intrinsic motivation positively predicted work adaptability and proactivity, while amotivation negatively predicted proficiency but had a paradoxical positive effect on proactivity. The study also revealed that RWI had an indirect positive effect on motivation and performance through need-supportive leadership, underscoring the importance of this interpersonal leadership style in remote work settings. Additionally, the findings challenge earlier assumptions that remote work weakens leadership effectiveness by highlighting how need-supportive leadership can still thrive in remote work settings. However, the study did not find strong direct effects of RWI on motivation or performance, indicating that leadership behaviors may serve as crucial mediators. The findings offer valuable insights into the role of perceived leadership behaviors in shaping employee motivation and performance outcomes in remote work settings.
RWI and leadership behaviors
In the current study we examined three leadership behaviors: need-supportive, controlling, and laissez-faire. The positive link between RWI and need-supportive leadership suggests that remote environments may encourage leaders to adopt more need-supportive practices, potentially due to the increased emphasis on trust and empowerment in remote settings. However, the study’s findings challenge some earlier research suggesting that spatial distance can diminish leadership effectiveness (Antonakis & Atwater, Reference Antonakis and Atwater2002; Podsakoff, MacKenzie, & Bommer, Reference Podsakoff, MacKenzie and Bommer1996). This discrepancy may be attributed to changes in leadership practices post-pandemic, as managers have become more adept at fostering engagement in remote and virtual work environments (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Hill & Bartol, Reference Hill and Bartol2016; Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023).
One unexpected finding is that laissez-faire leadership was positively associated with proactivity. Traditionally, laissez-faire leadership has been linked to negative employee outcomes, such as disengagement and reduced performance (Höddinghaus, Nohe, & Hertel, Reference Höddinghaus, Nohe and Hertel2023). However, our findings suggest that in remote settings, reduced managerial intervention might provide employees with more autonomy, thus fostering proactivity. Previous findings suggest that there might be a dark and bright side to laissez-faire leadership and that the impact of laissez-faire leadership can differ as a function of various contextual conditions and moderators (e.g., Yang, Reference Yang2015; Zhang, Wang, & Gao, Reference Zhang, Wang and Gao2023). The time lag between assessments may also play a role in this unexpected relationship. It was approximately 6 months between the assessments of perceived leadership and individual work performance, which is a relatively short time. Hence, the experience of laissez-faire leadership may have triggered proactive behaviors at work that were maintained over the study period.
Motivation and work performance implications
The findings reinforce SDT’s assertion that need-supportive leadership enhances intrinsic motivation, which in turn predicts higher adaptability and proactivity. This confirms prior research indicating that employees with higher intrinsic motivation are more likely to exhibit self-driven behaviors essential for navigating the uncertainties of remote work (Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Van den Broeck et al., Reference Van den Broeck, Howard, Van Vaerenbergh, Leroy and Gagné2021).
However, the study’s findings on amotivation present an interesting paradox. While amotivation negatively predicted proficiency, it positively predicted proactivity. This suggests that employees experiencing amotivation may attempt to regain a sense of purpose through proactive behavior, possibly by seeking out meaningful tasks or redefining their roles (e.g., by engaging in job crafting; Olafsen et al., Reference Olafsen, Marescaux and Kujanpää2025). This unexpected finding merits further investigation into whether certain types of employees respond to amotivation by proactively reshaping their work environments. Previous findings based on cross-sectional survey and interview data indicate that career expectations and job crafting may mitigate dysfunctional effects of amotivation at work (Masood, Karakowsky, & Podolsky, Reference Masood, Karakowsky and Podolsky2022). It is also likely that the time lag between assessments had an impact on the amotivation-proactivity relationship; the 3-month time lag between the assessments of motivation and individual work performance may have been short enough for employees to initiate and maintain a more proactive approach to work. Although recent meta-analytic evidence on the temporal relationship between work motivation and job performance did not indicate a moderating effect of time-lag (when comparing 1–6 months to 7–12 months) between assessments, none of the included studies assessed amotivation (Wang, Luan, & Ma, Reference Wang, Luan and Ma2024). Hence, the role of time in the amotivation-proactivity relationship and potential mediators and moderators of this relationship requires further exploration.
Limitations and future research
Despite its valuable contributions, the study has several limitations. First, the reliance on self-reported data introduces the potential for common method bias (Podsakoff, Podsakoff, Williams, Huang, & Yang, Reference Podsakoff, Podsakoff, Williams, Huang and Yang2024), as employees’ perceptions of leadership behaviors, motivation, and performance were all measured from a single perspective. Although the temporal distance between measurement points can mitigate common method bias, future research could incorporate multi-source data, such as peer or supervisor evaluations, to provide a more objective assessment of performance outcomes.
Second, while the study examined relationships over time, it did not explicitly examine changes over time. While it identifies relationships between RWI, leadership, motivation, and performance, it remains unclear how these relationships evolve. Using for example repeated assessments and a latent growth modeling approach (cf. Stenling, Ivarsson, & Lindwall, Reference Stenling, Ivarsson and Lindwall2017) could provide deeper insights into how leadership adaptation in remote settings influences motivation and performance over extended periods.
Third, in the current study we focused on RWI, which has been shown to impact multiple employee outcomes (e.g., Gajendran & Harrison, Reference Gajendran and Harrison2007; Gajendran et al., Reference Gajendran, Ponnapalli, Wang and Javalagi2024), however, we did not account for the degree of virtuality in remote work. Prior research has shown that remote work exists on a spectrum, with hybrid models offering different motivational and performance dynamics compared to fully remote setups (Hill et al., Reference Hill, Axtell, Raghuram and Nurmi2022). Despite the importance of RWI for employee outcomes, it only reflects a subdimension of dispersion (i.e., spatial dispersion). Other relevant subdimensions, such as technology dependence, temporal dispersion, and quality of the remote work environment may also have motivational and performance implications in remote and hybrid work settings in addition to RWI. Future studies should differentiate between various remote work configurations and include multiple subdimensions of remote and hybrid work to refine our understanding of how leadership effectiveness varies across these models.
Fourth, the study’s findings suggest that higher RWI may encourage more need-supportive leadership, but it is unclear whether this effect is contingent upon the organization’s broader support structures or the nature of the work itself (cf. Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Hill et al., Reference Hill, Axtell, Raghuram and Nurmi2022). Future research could explore whether certain job roles, work characteristics, or organizational factors moderate the relationship between RWI and leadership behaviors.
Future research should further explore the conditions under which remote work enhances or undermines motivation and performance. Specifically, examining how individual differences (e.g., personality traits, self-regulation capacity) interact with RWI and leadership behaviors would provide deeper insights into personalized remote work strategies. Furthermore, the unexpected positive effect of laissez-faire leadership and amotivation on proactivity needs further exploration in future longitudinal studies to gain a better understanding of when and how such effects unfold over time. Future studies should also differentiate between empowering productive autonomy and neglectful leadership (cf. Wong & Giessner, Reference Wong and Giessner2018) and continue to explore what some refer to as paradoxical leadership (e.g., leadership that comprises both agentic and communal aspects of leadership simultaneously; cf. Fürstenberg, Alfes, & Kearney, Reference Fürstenberg, Alfes and Kearney2021; Purvanova & Kenda, Reference Purvanova and Kenda2018) to clarify these effects in remote work settings.
Implications for practice
The findings have significant implications for organizations seeking to optimize remote work strategies. The study underscores the importance of need-supportive leadership in remote settings, suggesting that organizations should invest in leadership development programs that emphasize autonomy, competence, and relatedness support (Slemp, Lee, & Mossman, Reference Slemp, Lee and Mossman2021). Such leadership development programs should also include training to be able to recognize and mitigate the risks of controlling behaviors while fostering a work environment that enhances employees’ intrinsic motivation. Organizations and managers can also help mitigate the potential negative effects of laissez-faire leadership and amotivation on proactive behaviors, for example by helping employees with career planning and providing opportunities for job crafting.
Proactive behaviors have been highlighted as crucial in the new world of work characterized by increased use of remote and hybrid work (Allen et al., Reference Allen, Grelle, Lazarus, Popp and Gutierrez2024; Gagné et al., Reference Gagné, Parker, Griffin, Dunlop, Knight, Klonek and Parent-Rocheleau2022; Leonardi et al., Reference Leonardi, Parker and Shen2024). Although organizations can help employees and managers excel in remote work by facilitating their learning and training of new skills and expertise, workers themselves must also be proactive in developing and practicing these competencies (Leonardi et al., Reference Leonardi, Parker and Shen2024). However, our findings indicate that engaging in need-supportive leadership behaviors contributes to increases in autonomous or self-determined work motivation, which in turn increases the likelihood that employees will engage in proactive behaviors.
Furthermore, an increased use of remote and hybrid work requires new communication and technical skills, likely increases the importance of cross-cultural competence at work, and challenges the traditional boundaries between home and work. Organizations and managers thus need to identify how they are affected by different forms of remote and hybrid work to be able to address the unique challenges the organization and workers face. Factors such as the organizations psychological, social, temporal, technological, and structural dynamics should be accounted for during planning to minimize the pitfalls and reap the benefits of remote and hybrid work (Leonardi et al., Reference Leonardi, Parker and Shen2024).
Conclusion
This study offers a compelling examination of how RWI predicts leadership behaviors, work motivation, and performance. By grounding the analysis in SDT, it provides a theoretically rich perspective on the mechanisms underlying remote work effectiveness. While the findings highlight the benefits of need-supportive leadership in remote settings, they also raise intriguing questions about the role of laissez-faire leadership and the paradoxical effects of amotivation on proactivity. Addressing these gaps through further research will be crucial in shaping the future of remote work practices.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/jmo.2025.10056.
Andreas Stenling is an associate professor at the Department of Psychology at Umeå University, and the Department of Sport Science and Physical Education at the University of Agder. His research interests span the fields of health, sport, and work psychology, public health, and gerontology. Stenling is currently involved in several research projects focused on leadership, motivation, and health, and he has a keen interest in research design and statistics.
Susanne Tafvelin is an associate professor at the Department of Psychology at Umeå University. She is an expert on leadership theory and research including constructive and destructive leadership, leadership training, and transfer of leadership training. Tafvelin is the current editor-in-chief of the Scandinavian Journal of Work and Organizational Psychology.
Marte Bentzen works as an associate professor at the Department of Sport and Social Sciences, The Norwegian School of Sport Sciences and at the School of Business at the University of South-Eastern Norway. Bentzen is involved in various research projects on topics related to motivational adherence, recovery, and mental health in contexts such as sport and para-sport, rehabilitation, and work.
Anja H. Olafsen is a professor at the School of Business at the University of South-Eastern Norway. Her research focuses on antecedents and consequences of employees’ work motivation, job and need crafting, job recovery, and how technology and flexible working arrangements impact the boundaries between work and leisure. She is currently involved in several research projects on these topics.
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