Remote working intensity in knowledge work: Associations with informal workplace learning, basic psychological needs satisfaction, job satisfaction, and turnover intention

Remote working intensity in knowledge work: Associations with informal workplace learning, basic psychological needs satisfaction, job satisfaction, and turnover intention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Remote working intensity in knowledge work: Associations with informal workplace learning, basic psychological needs satisfaction, job satisfaction, and turnover intention Ilmari Juho Aleksi Puhakka, Petri Nokelainen, Eija Lehtonen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5683479/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Vocations and Learning → Version 1 posted You are reading this latest preprint version Abstract The increase in remote working has changed the way both employees and organizations view work in an already tumultuous landscape of the IT sector. In this study, we surveyed Finnish employees ( n = 266) from an international IT sector company in 2022 after the remote working mandates were lifted. We firstly examined how remote working intensity (RWI) was associated with informal workplace learning, basic psychological needs satisfaction (BPNS, including autonomy, competence, and relatedness), work engagement, job satisfaction, and turnover intention (RQ1). Second, we investigated using structural equation modeling (SEM) how informal workplace learning, BPNS, and RWI were associated with work engagement and well-being outcomes (job satisfaction, and turnover intention) (RQ2). Finally, we wanted to know whether work engagement mediated the previous relationships (RQ3). Results for RQ1 were generally against our expectations as RWI was associated only with relatedness satisfaction (negatively). SEM results (RQ2) generally matched our expectations as autonomy and relatedness satisfaction, and work engagement were positively related to job satisfaction and negatively to turnover intention. Furthermore, work engagement was a positive mediator for the relationships of informal workplace learning and outcomes, and autonomy and outcomes (RQ3). Informal learning was thus interestingly related to job satisfaction and turnover intention but only via work engagement. The results imply that RWI is not distinctly beneficial or detrimental for learning and well-being at work, however, having some office days per week supports relatedness satisfaction, which in turn relates to positive work outcomes. Furthermore, high work engagement can allow informal learning activities to positively influence work well-being. remote work basic psychological needs satisfaction informal learning work engagement job satisfaction turnover intention Figures Figure 1 Figure 2 Introduction During the last few years, remote and multilocational work have become a prevalent way of working in knowledge work. In particular, knowledge work in the technology and IT consulting sector has been extensively impacted due to the fact that work in these sectors is often not constrained by physical locations. Remote and multilocational work have been studied in relation to well-being and efficiency for over two decades but research has rapidly increased in recent years during and after COVID-19 pandemic (see Vacchiano et al., 2024 ). Nevertheless, the current widespread remote working that is not anymore forced by the pandemic, differentiates from the previous research context in various ways. The availability, acceptability, and utilization of remote working in knowledge work is unprecedented. Pre-pandemic studies or research on forced remote working thus do not necessarily apply to current and forthcoming realities in work (Torres & Orhan, 2023 ). Remote working has been a common practice in the IT sector, however, the extent of which employees are allowed to work remotely, that is weekly days working remotely (i.e. remote working intensity; RWI), has come under increased scrutiny after the extensive remote working continued following the lifting of pandemic restrictions. In terms of learning and well-being at work, remote working can be seen to influence factors related to social interactions, performance, and managerial work (e.g., Mühlenbrock et al., 2023 ; Rigolizzo, 2023 ; Zajac et al., 2022 ). As majority of learning at work is considered to happen informally (Cerasoli et al., 2018 ) or, in other words, through work (Billett, 2010 ), important sources of this learning, such as model learning and feedback (Decius et al., 2019 ) differ substantially between remote and onsite working. The decrease of informal interaction (e.g., pre-meeting talks; Allen et al., 2014 , water cooler conversations; Lin & Kwantes, 2015 ) which occurs more naturally in face-to-face settings also influence informal learning as the exchange of relevant information often takes place in these auxiliary interactions in addition to formal meetings. While these interactions can be supported via deliberate planning, facilitation, and the use of digital tools (see e.g., Andrade et al., 2024 ), they require additional effort from organizations, leaders, and employees. Pertinent to IT sector knowledge work, digital tools can support individual learning processes (e.g., self-directed learning; Lemmetty & Collin, 2020 ) depending on job description and tasks. While collaborative efforts are needed to produce team learning and actualize informal learning in context, individual informal learning practices (e.g., experimentation and reflection) can be maintained by the concentration that remote working provides. In addition to commonly researched job attitudes (e.g., job satisfaction and turnover intention), which measure more outcome-oriented dimensions of well-being (Sessa & Bowling, 2020 ), remote working can affect more antecedent and process related factors (see Tynjälä, 2013 ) such as perceptions of work engagement (Bakker & Demerouti, 2017 ) and the satisfaction of basic psychological needs for autonomy, competence, and relatedness (Ryan & Deci, 2017 ). These factors, alongside informal learning, link generally positively to job satisfaction and negatively to turnover intention (e.g., Cerasoli et al., 2018 ; Mazzetti et al., 2023 ; Van den Broeck et al., 2016 ). While existing literature provides mixed results nevertheless leaning to favoring remote working, current widespread remote working can contribute to these factors and their relationships in ways yet studied. Previous studies indicate positive effects, particularly of multilocational work (i.e., working at other locations than office for some but not all the time), to well-being (e.g., Bloom et al., 2022 ; Charalampous et al., 2019 ; Felstead & Heneken, 2017; Gagné et al., 2022 ; Gajendran et al., 2024 ), including higher job satisfaction, perceived autonomy, and lower attrition, while also negative aspects such as higher social isolation and work-home spill-over are highlighted. Consequently, the effects of remote working on learning and well-being at work are complex and may differ from those observed prior to the increase in remote working and during the COVID-19 forced remote working. These changes can be seen to impact IT sector knowledge work more modestly than other sectors, in addition to possible countermotion towards on-site working. The autonomy and digital preparedness of IT sector employees and organizations provide a stable and lasting context to examine the influence of RWI for the future working life. It is thus important to model associations among learning, well-being, and remote working in this context. This study aims to, first, investigate the relationships that remote working intensity (RWI, i.e., weekly days working remotely) have on learning and well-being at work in white-collar post-pandemic multilocational working context. Second, since work-related research conducted before the increase in remote work may not fully apply to the current state (Torres & Orhan, 2023 ), we wanted to model the associations between informal workplace learning and well-being at work and reflect on the pre-pandemic studies (e.g., Puhakka et al., 2021 ) while including RWI in the mix. Specifically, we wanted to investigate how basic psychological needs satisfaction (BPNS; Ryan & Deci, 2017 ), informal workplace learning (Decius et al., 2019 ), and work engagement (Bakker & Demerouti, 2017 ) are connected to job satisfaction and turnover intention (Judge & Kammeyer-Mueller, 2012 ), and whether RWI is a significant predictor in the model. The present study presents three research questions: RQ1) “Is RWI associated with informal workplace learning, BPNS, work engagement, job satisfaction, and turnover intention?”, RQ2) “How informal workplace learning, BPNS, and RWI are associated with work engagement, job satisfaction, and turnover intention when modeled simultaneously”, and RQ3) “Does work engagement mediate the relationship between informal workplace learning and well-being outcomes, and between BPNS and well-being outcomes?”. Theoretical framework The following sections describe the theoretical framework alongside with the set hypotheses related to bivariate RWI associations (RQ1) and SEM of the study variables (RQ2 and RQ3). Remote working intensity Remote working is defined here as working away from the primary workplace. Research on comparing remote and onsite working has indicated both benefits and challenges of remote working. This is particularly relevant to knowledge work (i.e., work that requires extensive formal education and continuous learning, involves design and planning in addition to self-managing tasks, and produces knowledge as a primary outcome; Pyöriä, 2005 ), as this type of work can be often performed remotely. Tasks requiring intensive concentration can be more effectively accomplished working remotely (Golden & Gajendran, 2019 ), while the decrease in social interaction can challenge relatedness and increase various biases (Schinoff et al., 2020 ). As multilocational working has become more common, it is relevant to focus on whether the weekly time spent working remotely (i.e., RWI) has an impact on work, workplace learning, and well-being at work. Measuring this kind of RWI and its associations with different work-related factors is a way to examine the benefits and hindrances of remote working (see Gajendran et al., 2024 ). Informal workplace learning and remote working In this study, informal workplace learning is conceptualized using the octagon model of informal workplace learning (Decius et al., 2019 ) which includes four components: Experience/action, feedback, reflection, and intention to learn. Each of these four components contain two dimensions totaling up to eight and thus representing the octagon name of the model. Experience/action component refers to employees engaging in new experiences by either doing (trying and applying their own ideas) or observing (model learning). Feedback component includes direct feedback and vicarious feedback in which the employee is an active participant (i.e., asking for feedback). Reflection addresses anticipatory reflection and subsequent reflection. Finally, intent to learn refers to learning in order to further one's own career or learning to solve problems at work faster. The reduced number of face-to-face encounters in remote work settings influences informal learning regarding its social dimensions (Zajac et al., 2022 ). Remote work context may offer less rich and more restricted communication due to diminished social support and fewer learning cues and opportunities, which reduces opportunities for informal workplace learning (Mühlenbrock et al., 2023 ). These social dimensions are important in informal workplace learning as learning at work can be seen to happen majorly not only through work, but often in collaborating and participation in “communities of practice” (Lave & Wenger, 1991 ). In relation to the octagon model (Decius et al., 2019 ), particularly challenging dimensions in remote work are model learning and feedback. These dimensions require additional effort and planning to be successful and the probability of them happening by chance is lower in remote working compared to onsite working. For example, providing feedback remotely has been perceived as more demanding due to the need to assess the appropriate communication channels and the risk of misinterpretation (Jansson & Kangas, 2025 ). An important venue for informal learning in knowledge work are meetings and different collaborative sessions. As meetings have moved to remote settings, the need and use of digital skills and digitalization of learning and professional development in work has increased drastically compared to the discussion of this digitalization before the pandemic (Wallin et al., 2020 ). Companies in the IT sector currently utilize extensive digital tools to communicate and collaborate remotely (e.g., Jackson et al., 2022 ). Digital technologies provide flexible access to interaction, enabling learning through online meetings and discussion forums, and enrich interaction through chat, screen sharing, and emojis (Karhapää et al., 2024 ). These have been utilized commonly by virtual teams (i.e., teams that operate predominantly remotely and geographically dispersed; Dulebohn & Hoch, 2017 ), which can connect people irrespective of temporal and locational factors. Team learning in these settings can be supported by, for instance, agreed upon goals, ability to experiment independently, and an environment of trust (Dixon, 2017 ). Nonetheless, the lack of local encounters can lead informal communication to be less spontaneous and more siloed as forming close relationships in remote settings is more challenging (Begemann et al., 2024 ). As virtual teams have been previously utilized mainly by multinational companies with specific populations and roles, their characteristics and the support provided by organization are likely to differ from employees or teams who have been working onsite or multilocationally before the expansion of remote working. In addition to purely interactive actions (e.g., feedback and model learning), informal learning happens through more individually initiated processes such as reflection (Decius et al., 2019 ). These processes can benefit from less interruptions provided by remote working, though reflection can also be a collaborative process which promotes team learning (e.g., Faller et al., 2020 ). While individual reflection can benefit from remote working, a high RWI can impede opportunities to participate in and engage with communal activities at work, even when addressed in multilocational work environments. We thus expect that RWI is negatively associated with informal workplace learning (H1). Basic psychological needs satisfaction and remote working Basic psychological needs (i.e., autonomy, competence, and relatedness) are a central part of the Self-Determination Theory (SDT; Ryan & Deci, 2017 ) which is a theory that considers motivation in the context of psychological growth and well-being. SDT has been utilized accordingly in the contexts of educational psychology (see Ryan & Deci, 2020 ) and work (Gagné et al., 2022 ). Based on the theory, well-being and learning at work are affected by motivation and how self-determined the actions are (Ryan & Deci, 2000). These different types of motivation include, from the most optimal to the least, intrinsic motivation (doing an activity for the satisfaction of the activity itself), extrinsic motivation (doing an activity to reach a separate outcome), and amotivation (lack of motivation). Essential to producing and supporting intrinsic motivation and autonomous types of extrinsic motivation is the satisfaction of the basic psychological needs of autonomy, competence, and relatedness (Ryan & Deci, 2000). This BPNS is also connected to various positive outcomes in the work context, such as job satisfaction, positive affect, and work engagement (see Van den Broeck et al., 2016 ). Autonomy refers to individual’s need to self-regulate their experiences and actions, more specifically to the need for an internal perceived locus of causality providing self-endorsed behavior (Ryan & Deci, 2017 ). Competence refers to the need for feeling effectance and proficiency, while relatedness refers to the need to connect to other people and the need for belonging. These basic psychological needs and their satisfaction can be thought to play a relevant role in multilocational and remote working contexts as they consider aspects of work that are influenced by the changes in both individual and communal domains resulting from increased RWI. In general, remote working can have both positive and negative effects on need satisfaction (see Gagné et al., 2022 ). In terms of autonomy, remote working can provide opportunities for better autonomy satisfaction, particularly when managers don’t leave remotely working employees on their own but provide an appropriate balance of autonomy support and control (Pianese et al., 2023 ). On the other hand, more intense monitoring from the management and increased home-work conflicts can have a negative influence on autonomy satisfaction (Gagné et al., 2022 ). Nonetheless, pre-pandemic studies have found a positive association between RWI and perceived autonomy (Gajendran et al., 2024 ). Meta-analytic results from the studies done during pandemic yielded similar (non-significant) positive associations, however, the effect sizes were small. The current study population consists of IT sector knowledge workers of an organization enabling remote working policies; thus the participants likely have some previous experience on remote working. Due to these characteristics and based on the previous research, we expect that RWI is positively associated with autonomy satisfaction (H2a). Compared to autonomy, competence satisfaction has not been studied in relation to remote working except for rare cases (Brunelle & Fortin, 2021 ). Brunelle and Fortin ( 2021 ) found a positive association of teleworking (i.e., working multilocationally or fully remotely) and competence satisfaction, however, they did not investigate RWI but compared fully office-bound employees and multilocational/remote workers. Competence satisfaction can be linked to productivity, on which studies have found remote working to have both positive (Choudhury et al., 2021 ; Gajendran et al., 2024 ) and negative (e.g., Barroro et al., 2023) effects. Gagné et al. ( 2022 ) listed increased role clarity and self-efficacy as positive effects that remote working can have on competence satisfaction, while information overload and technical issues can hamper competence satisfaction. In addition, increased isolation can also lower the awareness about other team members’ work and competence as such and in relation to (Morrison-Smith & Ruiz, 2020 ). Nevertheless, the studies often compare remote/multilocational and office work, not RWI (e.g., Gajendran et al., 2024 ). RWI, particularly in organizations which enable remote and multilocational working, can be expected to support competence satisfaction because employees can organize their work in ways that utilize the best of both remote and onsite settings. Furthermore, as forced remote working was already lifted when the data were collected, we expect that employees had influence in choosing their RWI. Based on these factors and previous research, we expect that RWI is positively related to competence satisfaction (H2b). Working at home has been associated with lower perceived relatedness (e.g., Peijen et al., 2024) and higher perceived isolation (Gajendran et al., 2024 ). In addition to isolation, remote working can hinder the creation and maintaining support networks and meaningful collegial relationships (Gagné et al., 2022 ). Social interactions are at the core of relatedness satisfaction (Chen et al., 2015 ), thus remote working challenges relatedness by limiting work related social interactions. Spontaneous encounters in work are more likely to happen in the office. Examples of these include pre-meeting talks (Allen et al., 2014 ) and “water cooler conversations” (Lin & Kwantes, 2015 ) which happen more naturally in face-to-face settings and facilitate not only learning and information sharing but also relationship and team building. Increase in remote working and the technologies emerging from this increase can have certain benefits as well. Gagné et al. ( 2022 ) highlight that increased remote working technologies can provide possibilities to connect with people across time and space. Research on virtual teams has indicated that appropriate use of digital tools and technologies can support the lack of physical presence in virtual teams (Dulebohn & Hoch, 2017 ). Tools can provide flexible access to and enrich remote interaction (Karhapää et al., 2024 ) and are increasingly utilized in the IT sector to communicate and collaborate remotely (e.g., Jackson et al., 2022 ). Employees’ expectations of the interactions and relationships in virtual versus multilocational or office-based teams differ nonetheless, with the latter experiencing varying levels of physical encounters compared to fully spatial separation. Following this and based on previous research, we expect that RWI is negatively associated with relatedness satisfaction (H2c). Work engagement and remote working According to the Job Demands-Resources model (JD-R; Bakker & Demerouti, 2017 ), work engagement can be described as a relatively stable, positive, and fulfilling work-related state of mind including vigor, dedication, and absorption. In addition to previous research indicating a positive link between high RWI and work engagement (e.g., Nagata et al., 2021 ; Rodríguez-Modroño, 2022 ), knowledge work can benefit from the concentration and perseverance enhanced by remote working (Allen et al., 2015 ; Choudhury et al., 2021 ). On the other hand, work engagement is linked to relatedness and social interaction at work (Gerards et al., 2018 ; Van den Broeck et al., 2016 ), which can suffer from high RWI due to increased perceived isolation (Gajendran et al., 2024 ). Studies conducted during early parts of COVID-19 pandemic indicate that work engagement in remote work is high (e.g., Mäkikangas et al., 2022 ), however, recent meta-analytic findings have found weak and non-significant associations between RWI and work engagement in pre- and during pandemic studies (Gajendran et al., 2024 ). Examining whether RWI influences work engagement similarly to what pre-pandemic and during pandemic studies found is important to evaluate differences between limited, forced, and wider applied RWI in knowledge work. As IT sector knowledge work is expected to include tasks requiring high levels of concentration, we expect RWI and work engagement to have a positive relationship (H3). Job satisfaction, turnover intention, and remote working Job satisfaction and turnover intention are commonly researched job attitudes that refer to general well-being and ill-being at work (Judge & Kammeyer-Mueller, 2012 ). Job satisfaction refers in this study to a global affective component of job satisfaction compared to more specific facets, such as satisfaction with work itself or supervision (Bowling & Hammond, 2008 ). The affective dimension of job satisfaction has been the focus of research since the 1990s with recent research aiming towards emotions, thus further linking job satisfaction to well-being in both work and life context (Judge et al., 2020 ). Turnover intention, on the other hand, refers to “...a conscious and deliberate willfulness to leave the organization” (Tett & Meyer, 1993 , p. 262) and can be used as a proxy for actual turnover. Job satisfaction has been considered as an antecedent to turnover intention, particularly the changes in both (Chen et al., 2011 ), however, there are findings indicating that turnover intention can emerge from more positive or neutral origins (e.g., high competence satisfaction in a competitive job market; see Van den Broeck et al., 2016 ). Turnover intentions can be seen as an outcome of JD-R model processes (e.g., Collie, 2023 ), or on the opposite end of continuum from job satisfaction (Puhakka et al., 2021 ). While studies conducted in the 90s and early 2000s provided mixed results and discussion about the relationship between remote work and job satisfaction (see e.g., Golden, 2006 ), recent studies have connected remote working to higher job satisfaction and lower turnover intention resulting from, for example, higher flexibility (e.g., Felstead & Henseke, 2017 ). A recent meta-analysis on studies conducted pre-pandemic and during pandemic found that RWI and remote work use (i.e., users vs. non-users of remote work) are both positively associated with job satisfaction and negatively with turnover intentions (Gajendran et al., 2024 ). Additionally, multilocational work seems to provide certain benefits compared to fully remote work (e.g., better access to informal communication; Singh & Sant, 2023 ). Based on the previous research we expect that, when examining bivariate associations, RWI is positively associated with job satisfaction (H4a) and negatively with turnover intention (H4b). Modeling learning and well-being at work While the knowledge of bivariate relationships that RWI has on learning and well-being factors is important for theory and practice, there is additional value in modeling interrelations between learning and well-being factors. This is due to the complexity of these work-related phenomena and relationships, for which modeling techniques (e.g., Structural Equation Modeling, SEM; Hair et al., 2010 ) enable multivariate estimation of these latent variable (i.e., unobserved and indirectly measured concepts) and their relationships while accounting for measurement error in observed variables (Hair et al., 2021 ). Although BPNS and informal workplace learning have been widely studied in work contexts (see e.g., Manuti et al., 2015 ; Van den Broeck et al., 2016 ), utilizing them to model the interrelations between BPNS, informal workplace learning and work well-being has been less common (e.g., Puhakka et al., 2021 ). For instance, the broader frameworks of well-being (e.g., JD-R; Bakker & Demerouti, 2017 ) and learning at work (e.g., 3-P model; Tynjälä, 2013 , Octagon model; Decius et al., 2019 ) often do not include BPNS (Nokelainen et al., 2023 ), or both well-being and learning factors together. Modeling work well-being with informal workplace learning and relevant job outcomes can thus increase our understanding of these interrelated factors and their influence in work context. The increase in remote working introduces a novel component into the model, since remote working can have influence on several variables (e.g., increase in autonomy, decrease in informal interaction) and alter relationships between, for example, BPNS and job satisfaction (Brunelle & Fortin, 2021 ). We set the following hypotheses regarding to the model of workplace learning and well-being in multilocational knowledge work (RQ2): First, based on previous research (e.g., Felstead et al., 2015 ; Susomrith & Coetzer, 2019 ) we expect that informal workplace learning is positively associated with work engagement (H5a) and job satisfaction (H6a), and negatively associated with turnover intention (H7a). Second, based on previous research (e.g., Puhakka et al., 2021 ; Van den Broeck et al., 2016 ) we expect that BPNS has positive associations with work engagement (H5b) and job satisfaction (H6b), and a negative association with turnover intention (H7b). Finally, we expect based on previous research (Kim, 2017 ; Mazzetti et al., 2023 ; Yalabik et al., 2017 ) that work engagement is positively associated with job satisfaction (H6c) and negatively with turnover intention (H7c). Regarding RQ3, we test a mediation effect of work engagement (M) on the expected relationships between informal workplace learning (X1) and job satisfaction (Y1)/turnover intention (Y2) (H8a), and BPNS (X2) and job satisfaction (Y1)/turnover intention (Y2) (H8b) which is indicated by previous research (Ali Abadi et al., 2023 ; Bakker & Demerouti, 2017 ). RWI is included in the model to assess the effect of RWI on work engagement, job satisfaction, and turnover intention when the effects of BPNS and informal workplace learning are accounted for. Visualization of the SEM and hypotheses related to RQ2 and RQ3 is presented in Fig. 1 . Methods Participants and procedure The sample included 266 Finnish white-collar employees from an international IT (software consultant) company, which provides services such as strategic consulting, service design, software development, AI and analytics solutions, as well as cloud and integration services. The company invests in employees´ well-being and has received recognitions and awards in this regard. The respondents recorded responses to at least one scale. The average age of the respondents was 39.7 years, and they had on average 15.9 years of work experience. The most common titles of the respondents were Software Designer ( n = 42, 15.8%), Data Engineer ( n = 28, 10.5%), Project Manager ( n = 25, 9.4%), Manager ( n = 21, 7.9%), and Integration Specialist ( n = 19, 7.1%). Most of the participants ( n = 162, 60.9%) identified as a man (woman n = 96, 36.1%, other/prefer not to tell n = 8, 3.0%). Most had completed higher-level university degree ( n = 170, e.g., master’s degree, ISCED 7, 63.9%), next frequent was lower-level university degree ( n = 70, e.g., bachelor’s degree, ISCED 6, 33.8%) followed by secondary level degree ( n = 20, 7.7%). Only a few had doctorate or equivalent degree (ISCED 8, n = 6, 2.3%) and none had only basic level education. Online survey (Limesurvey) was used to collect demographic information and responses to validated questionnaires from study participants. Link to the survey was distributed via the company’s internal channels to personnel ( N = 1040, response rate = 25.6%). The survey was active for three weeks and during that time two reminders were sent. Some participants ( n = 18) answered the survey during Spring 2022 as a part of a substudy, while the remaining ( n = 248) answered in Autumn 2022. Measures We measured RWI from three different perspectives. We asked about the participants’ current RWI: “On average, how many days during the week you currently work remotely (e.g., at home, in public spaces, etc.?)”, RWI before COVID-19 pandemic : “On average, how many days during the week have you worked remotely (e.g., at home, in public spaces, etc.)”, and preferred RWI: “On average, how many days during the week would you prefer to work remotely (e.g., at home, in public spaces, etc.)?”. Kendall’s tau-b (τ b ; Kendall & Gibbons, 1990 ) correlations showed significant associations between these different RWI perspectives. The high correlation between preferred RWI and current RWI (τ b = .76) suggests that employees enjoy considerable autonomy in shaping their current working arrangements and that the organization supports flexible remote work practices. Additionally, the positive associations between pre-pandemic RWI and both current (τ b = .26) and preferred (τ b = .34) RWI indicate that employees’ historical remote work patterns influence their current behaviour and future preferences. This may reflect a longer-term, internalized preference for remote work as well as the organization’s responsiveness to evolving work modalities. From now on in the text, RWI refers to current RWI, which is used in the analysis to measure remote working intensity. In addition to questions about demographic information and RWI, validated questionnaires were used in the survey to measure work well-being and informal learning (see Table 1 and Appendix). Table 1 Survey scales Scale Citation Measure Items Response scale Informal workplace learning: Short measure for white-collar workers Decius et al. ( 2023 ) Informal workplace learning 8 1 = strongly disagree … 5 = strongly agree Basic Psychological Needs Scale-Revised (adapted) Schultz et al. ( 2015 ) Autonomy, competence, and relatedness satisfaction 12 1 = strongly disagree … 5 = strongly agree Ultra-short measure for work engagement Schaufeli et al. ( 2019 ) Work engagement 3 1 = never … 5 = always Michigan Organizational Assessment Questionnaire Job Satisfaction Subscale Bowling & Hammond ( 2008 ) Job satisfaction 3 1 = strongly disagree … 5 = strongly agree Turnover Intention Scale Bothma & Roodt ( 2013 ) Turnover intention 4 1 = never … 5 = always Statistical analysis Survey data were analyzed using the R statistical computing environment (R Core Team, 2020 ). Preliminary analyses included testing the univariate and multivariate normality of dependent variables (univariate skewness and kurtosis, and multivariate kurtosis; Finney & DiStefano, 2013 ) using the MVN package (Korkmaz et al., 2014 ). First, means, standard deviations, Revelle’s omega reliabilities (see McNeish, 2018 ), and Kendall’s Tau-b correlations (Kendall & Gibbons, 1990 ) were calculated using the psych package (Revelle, 2024 ). Bivariate Tau-b correlations were used to answer RQ1. To answer RQ2 and RQ3, hypothesized associations of the variables were examined using SEM (Hair et al., 2010 ). The SEM analysis was conducted using the lavaan package (Rosseel, 2012 ). The analyzed SEM model was constructed based on previous research (e.g., Mazzetti et al., 2023 ; Puhakka et al., 2021 ; Van den Broeck et al., 2016 ). In the model, informal workplace learning and the satisfaction of every basic psychological need (autonomy, competence, relatedness) were positively related to work engagement and job satisfaction and negatively related to turnover intention. In addition, we analyzed whether work engagement (M) mediates the associations between informal workplace learning (X1) and job satisfaction (Y1) and turnover intention (Y1), as well as BPNS (X2) and job satisfaction (Y1) and turnover intention (Y2). Finally, we included current RWI in the model to assess the influence of remote working. Preliminary analysis Of the participants who started the survey ( n = 286), 258 responded to all scales (full respondents) and eight responded to at least one scale (partial respondents). Multivariate and univariate normality of dependent variables were violated (multivariate kurtosis = 8.33, p < .001). Due to these deviations, we used full information maximum likelihood (FIML) approach and an estimator with robust (Huber-White) standard errors and a scaled test statistic (MLR) in the SEM analysis. The final data used in the analysis thus included both full and partial respondents ( n = 266). For SEM analysis, model fit was assessed by the scaled chi-squared test and by examining the following robust fit indices: Comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Commonly used cut-off values for excellent fit are the following: CFI ≥ .95, TLI ≥ .95, RMSEA ≤ .06, SRMR ≤ .08 (Hu & Bentler, 1999 ). Measurement model For RQ2 and RQ3, a CFA of the original measurement model which included latent variables of informal workplace learning (8 items), satisfaction of basic psychological need for autonomy (4 items), competence (4 items), and relatedness (4 items), work engagement (3 items), job satisfaction (3 items), and turnover intention (4 items) resulted in not positive definite covariance matrix. This often occurs when items or constructs are highly collinear, necessitating model adjustments such as removing correlated items or modifying factor specifications. After inspection, the fourth item in turnover intention scale (TI4: “How often do you look forward to another day at work?”; see Appendix) was identified as the issue and was removed. Following this a CFA for the previous model without TI4 showed a poor fit: χ 2 (356) = 694.33, p < .001; RMSEA = .063 (90% CI = .056 – .071, p = .002); CFI = .906; TLI = .892; SRMR = .068. Factor loadings ranged from .35 to .91. To improve the model, the item with the distinctly low factor loading (.35), the first item of the intent to learning -dimension of the informal workplace learning scale (IWL_I2: “I want to learn something new at work for myself because then I can pursue my career at the company”) was removed and one modification based on the examination of modification indices was applied: Allowing the residual covariance between the fourth item of autonomy satisfaction scale (AUT4: “I feel I have been doing what really interests me in my job”) and the second item of work engagement scale (WE2: “I am enthusiastic about my job”). The modified measurement model had an acceptable fit: χ 2 (328) = 569.85, p < .001; RMSEA = .055 (90% CI = .047 – .064, p = .132); CFI = .932; TLI = .921; SRMR = .066. Factor loadings ranged from .48 to .91, indicating generally acceptable to strong relationships between items and their respective latent constructs. Results RQ1: Bivariate associations between RWI and study variables Table 2 presents the means, standard deviations, and Kendall’s Tau-b (τ b ) correlations for the study variables. The results showed that RWI was not significantly associated with informal workplace learning which did not support our H1. RWI was negatively associated with relatedness satisfaction supporting our H2c (τ b = .18, p < .001). Autonomy and competence satisfaction were, however, not associated with RWI, which was against H2a and H2b. Furthermore, RWI was not significantly associated with work engagement (H3), job satisfaction (H4a), or turnover intention (H4b) contrary to our expectations. Table 2 Descriptive statistics, omega reliabilities, and bivariate Kendall’s Tau-b correlations M SD ω 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. IWL 4.00 0.53 .76 — 2. Experience/Action 3.97 0.73 — .55 — 3. Feedback 3.70 0.81 — .64 .30 — 4. Reflection 3.95 0.75 — .64 .34 .37 — 5. Intent to learn 4.36 0.66 — .50 .16 .29 .29 — 6. BPNS 4.03 0.58 .89 .34 .36 .28 .18 .17 — 7. Autonomy 4.05 0.69 .83 .32 .30 .23 .17 .22 .67 — 8. Competence 4.16 0.69 .91 .26 .28 .21 .18 .14 .58 .35 — 9. Relatedness 3.87 0.81 .90 .25 .28 .22 .12 .10 .67 .42 .27 — 10. Work engagement 3.61 0.75 .82 .34 .26 .29 .21 .23 .45 .47 .35 .29 — 11. Job satisfaction 4.37 0.66 .85 .29 .23 .28 .13 .20 .58 .54 .35 .48 .49 — 12. Turnover intention 2.28 0.73 .70 −.23 −.20 −.20 −.09 −.17 −.46 −.46 –.26 −.35 −.45 −.61 — 13. Age 39.73 8.04 — .00 .01 .03 .05 −.09 .04 −.07 .13 .04 .03 −.01 .08 — 14. Gender — — — −.14 −.09 −.17 −.03 −.09 −.06 −.09 .03 −.08 −.03 −.07 .04 .06 — 15. Education — — — .01 .01 .04 .02 −.03 .13 .10 .12 .07 −.02 .08 .00 .02 −.13 — 16. Work experience 15.90 8.32 — .01 .02 .02 .06 −.08 .04 −.08 .16 .04 .04 −.03 .08 .78 .00 −.01 — 17. RWI 3.06 1.64 — .05 −.06 .05 .07 .09 −.11 −.05 .01 −.18 .05 −.08 .02 −.01 .02 −.11 −.02 Note . Bolded values indicate statistical significance ( p |.06| weak association, τ b > |.19| moderate association, τ b > |.33| strong association (Walker, 2003 ); BPNS = basic psychological needs satisfaction, IWL = informal workplace learning, RWI = remote working intensity, ω = reliability (Revelle’s omega; see McNeish, 2018 ). RQ2: SEM of study variables The hypothesized SEM showed acceptable fit to the data: χ 2 (353) = 618.01, p < .001; RMSEA = .056 (90% CI = .048 – .063, p = .111); CFI = .926; TLI = .915; SRMR = .067. Similarly to the measurement model, in the SEM model, we also allowed the correlation between the unexplained (error) parts of the fourth autonomy satisfaction item (AUT4: “I feel I have been doing what really interests me in my job”) and the second work engagement item (WE2: “I am enthusiastic about my job”) based on modification indices. Standardized SEM results are presented in Table 3 and statistically significant paths are visualized in Fig. 2 . Informal workplace learning was positively related to work engagement (β = .19, p = .021; H5a) but not to job satisfaction (H6a) or turnover intention (H7a). Autonomy satisfaction was positively related to work engagement (β = .61, p < .001; H5b) and job satisfaction (β = .32, p = .001; H6b), and negatively related to turnover intention (β = −.28, p = .032; H7b). Competence satisfaction, contrary to our expectations, was not related to work engagement (H5b), job satisfaction (H6b), or turnover intention (H7b). Relatedness satisfaction was positively related to job satisfaction (β = .31, p < .001) and negatively related to turnover intention (β = −.36, p = .001), supporting H6b and H7b. Work engagement was positively related to job satisfaction (β = .42, p < .001; H6c) and negatively related to turnover intention (β = −.33, p = .002; H7c). RWI was not a significant predictor of work engagement, job satisfaction, nor turnover intention, which was in accordance with the bivariate results from RQ1. However, the negative association between RWI and turnover intention, while marginal (β = −.11, p = .061), indicated that when examining the relationships that BPNS, informal workplace learning, and work engagement have on turnover intention simultaneously, higher RWI seems to relate to lower turnover intention. This differed from the weak positive bivariate correlation between RWI and turnover intention (τb = .02, p = .61). Table 3 Standardized direct, indirect, and total effects of the SEM model β SE p Direct effects to work engagement Informal workplace learning .19* .08 .021 Autonomy satisfaction .61*** .09 < .001 Competence satisfaction .09 .07 .215 Relatedness satisfaction −.01 .06 .939 Remote working intensity .05 .05 .309 Direct effects to job satisfaction Informal workplace learning −.05 .08 .509 Autonomy satisfaction .32** .10 .001 Competence satisfaction −.01 .06 .875 Relatedness satisfaction .31*** .07 < .001 Work engagement .42*** .10 < .001 Remote working intensity .03 .04 .549 Direct effects to turnover intention Informal workplace learning .18 .11 .096 Autonomy satisfaction −.28* .13 .032 Competence satisfaction –.01 .08 .972 Relatedness satisfaction −.36*** .10 < .001 Work engagement −.33*** .11 .002 Remote working intensity −.11 .06 .061 Indirect effects to job satisfaction via work engagement Total indirect .38*** .08 < .001 Specific indirect from informal workplace learning .08* .03 .020 Specific indirect from autonomy satisfaction .26*** .08 < .001 Specific indirect from competence satisfaction .04 .03 .229 Specific indirect from relatedness satisfaction .00 .03 .939 Indirect effects to turnover intention via work engagement Total indirect −.29** .10 .002 Specific indirect from informal workplace learning −.06* .03 .044 Specific indirect from autonomy satisfaction −.20** .07 .007 Specific indirect from competence satisfaction −.03 .03 .256 Specific indirect from relatedness satisfaction .00 .02 .939 Total effects to job satisfaction .95*** .07 < .001 Total effects to turnover intention −.76*** .08 < .001 Note. β = standardized estimate, SE = standard error, *** p < .001, ** p < .01, * p < .05. RQ3: Mediation effects of work engagement There was a significant positive indirect effect from informal workplace learning (X1) via work engagement (M) to job satisfaction (Y1) (β = .08, p = .020) and negative indirect effect from informal (X1) workplace learning via work engagement (M) to turnover intention (Y2) (β = −.06, p = .044), which was in line with our H8a. Similarly, indirect effect from autonomy satisfaction (X2) via work engagement (M) to job satisfaction (Y1) (β = .26, p < .001) and negative indirect effect from autonomy satisfaction (X2) via work engagement (M) to turnover intention (Y2) (β = −.20, p = .007) were found supporting H8b in terms of autonomy. In summary, the SEM findings show that informal workplace learning boosts work engagement, which in turn raises job satisfaction and reduces turnover intention. Further, autonomy and relatedness satisfaction are key predictors of job satisfaction and turnover intention: Autonomy satisfaction increases work engagement and job satisfaction while lowering turnover intention, and relatedness satisfaction enhances job satisfaction and reduces turnover intention. Indirect effects through work engagement were observed for both informal workplace learning and autonomy satisfaction. Summary of the study hypotheses and whether the results support them are listed in Table 4 . Table 4 Study hypotheses and support from analysis Hypothesis Association Expected association Observed bivariate association Observed predictor in SEM analysis Support for hypothesis RQ1 H1 RWI-IWL − + (NS) NA no H2a RWI-AUT + − (NS) NA no H2b RWI-COM + − (NS) NA no H2c RWI-REL − − NA yes H3 RWI-WE, RWI->WE + + (NS) + (NS) partial H4a RWI-JS, RWI->JS + − (NS) + (NS) no H4b RWI-TI, RWI->TI − + (NS) − (NS) no RQ2 H5a IWL->WE + + + yes H5b BPNS->WE + + + yes H6a IWL->JS + + − (NS) no H6b BPNS->JS + + + yes H6c WE->JS + + + yes H7a IWL->TI − − + (NS) no H7b BPNS->TI − − − yes H7c WE->TI − − − yes RQ3 H8a Mediation1 +/− NA + yes H8b Mediation2 +/− NA − yes Note. “+” indicates positive association, “−“ indicates negative association, bolded symbols indicate observed associations analyzed for hypotheses. NS = non-significant, NA = not analyzed, RWI = remote work intensity, IWL = informal workplace learning, AUT = autonomy, COM = competence, REL = relatedness, WE = work engagement, JS = job satisfaction, TI = turnover intention, BPNS = basic psychological needs satisfaction. Discussion This study aimed, first, to investigate the association between RWI, informal learning activities, and well-being at work, and second, to examine potential differences in the modeling of learning and well-being at work compared to pre-pandemic studies and reviews (e.g., Felstead et al., 2015 ; Puhakka et al., 2021 ; Van den Broeck et al., 2016 ). The results revealed unexpected findings regarding RWI, as RWI had significant (negative) bivariate association only with relatedness satisfaction, while the modeling results between workplace learning and well-being generally aligned with our expectations. Table 4 summarizes the expected associations based on the hypotheses, observed associations, and whether the results provide support for the set hypotheses. The results considering RWI (RQ1) provided little support for the H1-H4 hypotheses. Regarding the observed weak relationship between RWI and informal workplace learning (H1), a possible explanation pertains to the use of digital collaboration and communication tools in supporting learning when remote working (Jackson et al., 2022 ; Karhapää et al., 2024 ). Furthermore, employees in technology and IT service sectors have been likely familiar with various tools and skills necessary for learning and working remotely. Only the feedback component had a negative (non-significant) correlation with RWI, indicating that while asking for feedback is somewhat challenging with higher RWI, more individual learning processes can benefit from remote working. Further research should examine the factors that contribute to successful high RWI work in terms of informal workplace learning. Studies have indicated significant challenges but also opportunities arising from RWI in learning and development at work (e.g., Authors, submitted for publication), in addition to the suggestions to remedy the challenges, for example, via leadership and management (see Mühlenbrock et al., 2023 ). It is important to note that RWI was measured by a self-reported average number of remote working days per week, which can mask other ways that remote working influences informal learning at work. For example, variation in work task characteristics, requirements for competence development, and distribution of individual vs. collaborative work can influence the relationships between RWI and learning. For BPNS, only relatedness satisfaction was negatively correlated with RWI (H2c), emphasizing the impact of RWI on relatedness, community, and social aspects of work. This aligns with results that relatedness satisfaction was experienced more on office days compared to working at home (Peijen et al., 2024). This also implies that while flexibility and the use of digital tools can enable high RWI without significant negative effects on informal workplace learning, the satisfaction of relatedness suffers. Considering the lack of associations between RWI, autonomy and competence satisfaction, one possible explanation could be the organizational culture of the company: Since current RWI in this organization aligned closely with preferred RWI (τ b = .76), this alignment can support BPNS for employees working with both high and low RWI. Furthermore, autonomy and competence satisfaction can be considered to have a less linear relationship (e.g., more of a U-shape) with RWI compared to relatedness satisfaction. In addition, as remote working can have both positive and negative effects on BPNS (Gagné et al., 2022 ), the increase in RWI can be seen to influence particularly relatedness, as social interactions are central in relatedness satisfaction (Chen et al., 2015 ). These results and their focus on relatedness align with other research indicating that higher RWI can influence relatedness and social interaction that in turn have an impact on learning and well-being both work in general (Lemmetty, 2024 ) and in work interaction (Authors, submitted for publication). In terms of work engagement, the association between RWI and work engagement was weak in both bivariate correlations and in our SEM. This aligns with the generally weak association found in Gajendran et al. ( 2024 ) meta-analysis. Previously mentioned flexible organizational culture related to remote working and weaker linearity of the relationship between RWI and work engagement can explain this result. Further investigations of the ways that work engagement can be supported in high or low RWI should be undertaken, with focus on structures and practices that employees and organizations can utilize. For job satisfaction and turnover intention, bivariate correlations with RWI were nonsignificant but in SEM, RWI was marginally negatively related to turnover intention. These results align more with studies reporting ambiguous relationships between remote working and these job attitudes (e.g., Golden, 2006 ), compared to more recent research synthesis (e.g., Gajendran et al., 2024 ). The autonomy in choosing the levels of RWI can explain these weak correlations, as employees’ job satisfaction and turnover intention are less dependent on their preference for RWI. As our sample was limited to a single IT sector organization, the present study results encourage further research on the factors that boost or enable higher RWI to increase job satisfaction and decrease turnover intentions, or on the other hand, which organizational characteristics make employees satisfied and committed to organization regardless of their RWI. Similarly to RWI and informal workplace learning, the way remote working was surveyed in the present study (average weekly days working remotely) may ignore certain impacts that remote working has on well-being. Factors such as time, distance from the office, and the locations where remote working occurs, for instance, can moderate the relationships between RWI and well-being. Further qualitative and mixed method studies could shed light on these possible interactions. The results mostly supported our hypotheses regarding RQ2 and RQ3, which indicates that the increase in multilocational and remote working has not fundamentally changed the dynamics of learning and well-being in knowledge work. In addition, RWI was not a significant predictor of work engagement, job satisfaction, nor turnover intentions, agreeing with the bivariate correlations. An interesting result from the SEM analysis considers the association of informal workplace learning to job satisfaction and turnover intention. While being associated with only work engagement directly, informal workplace learning had indirect effects on both job satisfaction (positive) and turnover intention (negative). This result follows previous studies suggesting that, for example, workplace learning opportunities do not directly relate to job attitudes when examined with BPNS (Puhakka et al., 2021 ). These results indicate that while BPNS can have higher impact on job attitudes compared to informal workplace learning, informal workplace learning can still enhance job satisfaction and lower turnover intention but only via work engagement. This mediating effect of work engagement has been observed in other studies between work related variables, such as informal learning and career identity (Susomrith & Coetzer, 2019 ). BPNS and work engagement have also been considered to play a role in the processes of the JD-R model (Bakker & Demerouti, 2017 ; Van den Broeck et al., 2008 ). The present study results suggest that informal workplace learning, and other learning-related factors can influence these processes as well. Regarding BPNS, autonomy satisfaction was a significant positive predictor of job satisfaction and work engagement and negative predictor of turnover intention. Furthermore, while considered to have more of a secondary influence on some outcomes compared to autonomy (Ryan & Deci, 2017 ), relatedness satisfaction had associations to job satisfaction and turnover intention comparable to autonomy satisfaction. Increased remote and multilocational work could have influence on this increased importance of relatedness satisfaction on work attitudes. The strong direct and indirect effects of autonomy satisfaction on job satisfaction and turnover intention follow the idea of autonomy and autonomy satisfaction being prominent factors in work well-being (Ryan & Deci, 2017 ). This can be particularly important in knowledge work as flexibility and variability of work tasks enable increased autonomy and possibilities to influence one’s own work benefit both development and performance of employees. Going forward, it would be interesting to explore how autonomy and relatedness satisfaction are emphasized across different work populations and relationships of outcomes, in addition to how remote and multilocational working influence these processes. One distinct result, which was contrary to our expectations, was that competence satisfaction had no associations with work engagement, job satisfaction, or turnover intention. This can stem from the previously mentioned availability of digital resources but also from the notions that autonomy and relatedness satisfaction have higher impact especially on turnover intention and work engagement compared to competence satisfaction (see Van den Broeck et al., 2016 ). Competence satisfaction can also have more complex associations to job satisfaction and turnover intention based on different populations and working cultures. For instance, the technology sector is considered to have high voluntary and nonvoluntary turnover rates and thus, career paths and expectations for career development can differ from the traditional preferences of permanent, stable positions in relatively few organizations during career. Indications of the presence of these more complex phenomena include for example results from the studies that link competence satisfaction (Puhakka et al., 2021 ; Van den Broeck et al., 2016 ) or subjective career satisfaction (Lehtonen et al., 2022 ) to higher turnover intention. Practical implications The results imply that organizations should provide autonomy and encourage informal workplace learning (e.g., by improving feedback culture) to improve employees’ well-being at work. The study results highlight particularly the role of relatedness satisfaction in current multilocational work and encourage employees to work at least some days per week in office. Also, focusing on and developing the work community, alongside with relatedness increasing policies and activities can support relatedness satisfaction while allowing employees autonomy to work multilocationally. Enhancing work engagement via proactive management and job crafting methods (see e.g., Bakker et al., 2023 ) can support learning and well-being by influencing various levels and pathways within work. While organizations and sectors differ in their utilization of digital tools and preparedness for remote working, getting familiarized with remote working preferences of both individual employees, but also teams can provide organizations with important information to discuss and reach an agreement that considers the needs from all parties. This supports the alignment of current and preferred RWI and can thus improve learning and well-being outcomes for all employees regardless of their RWI. Based on these results, we emphasize that broad conclusions on remote working being distinctly superior or inferior compared to on-site office working should be avoided and instead, individualized examination of organization or team level factors conducted. Amid the discussions on forcing employees back to offices from remote working, the acknowledgement of context- and individual-related factors on the success of multilocational work is highly important for the organizations and employees. Limitations and further research directions Certain limitations are warranted due to the study’s sample and data characteristics. Firstly , the cross-sectional nature of the survey data used in the analysis prevents making causal interpretations of the results. Secondly , the study participants were white-collar employees from a single company in the IT sector, where continuous learning and development are essential, thus further investigations in different industries and work cultures are needed to obtain a more comprehensive picture of current knowledge work. IT sector employees are expected to be skilled in using digital tools and thus the impact of increasing need and utilization of digital skills and digitalization of learning and professional development in work (Wallin et al., 2020 ) can have limited effect compared to employees in other fields. Thirdly , while the sample size was sufficient for the mediation SEM analysis, internal consistency values of the turnover intention scale were marginal and further covariances created issues with analysis that required item omission. Due to these issues with the scale, interpretations regarding turnover intention results should be made with caution. Fourthly , some of the intercorrelations of the informal workplace learning questionnaire were quite low, which calls for caution when interpreting the results from informal workplace learning. Future studies could use the longer version to enable more in-depth examination of different components and should also further investigate the validity of the shortened version of the questionnaire. Fifthly , remote working was measured only by self-reported average frequency of remote working days per week. This hides the possible variations of RWI over time or based on work tasks and phases, as well as possible moderating factors that might influence the relationship between remote work frequency and study variables (e.g., available technology, remote working spaces, ergonomics). Nevertheless, the observed weak or nonexistent associations between RWI and informal workplace learning and well-being indicators (apart from relatedness satisfaction) raise questions about the magnitude of the impact of RWI in knowledge work. Longitudinal research on the effects of remote and multilocational work on well-being and learning at work should be undertaken to investigate these possible prevailing and multifaceted effects. Conclusion The importance of relatedness satisfaction in relation to RWI encourages the companies and organizations to invest in community culture, particularly of those who work (and prefer to work) frequently remotely. In general, the organizations should provide autonomy and encourage informal workplace learning (e.g., improving feedback culture) to improve employees’ well-being at work. Theoretically, the results indicate that BPNS has relatively higher impact on job satisfaction and turnover intention in knowledge work context over informal workplace learning. Nevertheless, informal workplace learning can still enhance job satisfaction and lower turnover intention through high work engagement. Taken together the study results imply that intensity of remote working is not distinctly beneficial or detrimental for employee learning and well-being, however, having some office days is important to experiencing relatedness which is positively associated with job satisfaction and negatively with turnover intention. Declarations Participants were informed about the study and gave their consent to participate before filling out the survey. Funding : This study was supported by [anonymized] [grant number: XXXXXX]. Ethics approval : Before data collection,a favorable statement for the study was obtained from [anonymized]. 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Business and Society Review , 127 (S1), 283–298. https://doi.org/10.1111/basr.1226 Additional Declarations Competing interest reported. Non-financial interests: Petri Nokelainen is an editorial board member of Vocations and Learning. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Vocations and Learning → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5683479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443176150,"identity":"b4b6b947-375d-4041-9ba0-354d9fd441bc","order_by":0,"name":"Ilmari Juho Aleksi Puhakka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNnYQaQDEEgzMjA0MNkAWkKowwKOFGVVLmgRYyxk8WhiYYQyIlsMSYM4ZPDr4mLkTP1cUMOQZ3G5+bDij5nwdkNHAcKAAn8N4N0sCnVFscOeYceKGY7clDO4cBGrB6xfeDZINBgyJG24kGB98wAbUciOxgfkDfi2bf0K0pH8++ODfObAWQrZsg9qSY5y4se0AcVosGwwkiiVv5BQbzuxLlpwJ1HIAnxb59t7NNxv+2OTx3UjfLNnzzY4fyHj44MAf3FqgQCIBhXuAoAYgSCCoYhSMglEwCkYuAABRVFT4okE7ewAAAABJRU5ErkJggg==","orcid":"","institution":"Tampere University","correspondingAuthor":true,"prefix":"","firstName":"Ilmari","middleName":"Juho Aleksi","lastName":"Puhakka","suffix":""},{"id":443176151,"identity":"2d1cec74-1ad4-41d0-bb0b-39866884da5b","order_by":1,"name":"Petri Nokelainen","email":"","orcid":"","institution":"Tampere University","correspondingAuthor":false,"prefix":"","firstName":"Petri","middleName":"","lastName":"Nokelainen","suffix":""},{"id":443176152,"identity":"c01b59f7-bc24-4da0-972e-b184e69d55a3","order_by":2,"name":"Eija Lehtonen","email":"","orcid":"","institution":"Tampere University","correspondingAuthor":false,"prefix":"","firstName":"Eija","middleName":"","lastName":"Lehtonen","suffix":""}],"badges":[],"createdAt":"2024-12-20 11:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5683479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5683479/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12186-025-09377-2","type":"published","date":"2025-09-18T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80777583,"identity":"2852e23f-cbbf-4b4b-b3d8-008e414e41c7","added_by":"auto","created_at":"2025-04-17 03:41:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112263,"visible":true,"origin":"","legend":"\u003cp\u003eStudy hypotheses for RQ1, RQ2, and RQ3\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. + = \u003c/em\u003eexpected positive association, − = expected negative association. H8a and H8b consider mediation: Hypothesis H8a proposes that Work Engagement mediates (M) the relationships between Informal Workplace Learning (X1) and Job Satisfaction (Y1) and Turnover Intention (Y2), as well as (H8b) between Basic Psychological Needs Satisfaction (X2) and Job Satisfaction (Y1) and Turnover Intention (Y2).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5683479/v1/030767d0284425b9648810eb.png"},{"id":80776925,"identity":"34db4783-0a51-4d03-99a7-e30de7ce2269","added_by":"auto","created_at":"2025-04-17 03:33:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107047,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the results for RQ1, RQ2, and RQ3\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Only statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05) paths and estimates are displayed. τb = Kendall’s Tau-b correlation coefficient. Estimates for RQ2 and RQ3 are standardized (β).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5683479/v1/7702062302e5591372d0e755.png"},{"id":91733551,"identity":"8825dfa8-df1d-420b-b4d3-2d8b7dcadecb","added_by":"auto","created_at":"2025-09-19 16:49:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683479/v1/6869f840-6a22-44da-9c66-35ea2aa81a2f.pdf"},{"id":80776923,"identity":"989f271f-e462-4b33-bfd6-365d8d4f6f20","added_by":"auto","created_at":"2025-04-17 03:33:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18130,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5683479/v1/48c29c861df45548b51d6209.docx"}],"financialInterests":"Competing interest reported. Non-financial interests: Petri Nokelainen is an editorial board member of Vocations and Learning.","formattedTitle":"Remote working intensity in knowledge work: Associations with informal workplace learning, basic psychological needs satisfaction, job satisfaction, and turnover intention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDuring the last few years, remote and multilocational work have become a prevalent way of working in knowledge work. In particular, knowledge work in the technology and IT consulting sector has been extensively impacted due to the fact that work in these sectors is often not constrained by physical locations. Remote and multilocational work have been studied in relation to well-being and efficiency for over two decades but research has rapidly increased in recent years during and after COVID-19 pandemic (see Vacchiano et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the current widespread remote working that is not anymore forced by the pandemic, differentiates from the previous research context in various ways. The availability, acceptability, and utilization of remote working in knowledge work is unprecedented. Pre-pandemic studies or research on forced remote working thus do not necessarily apply to current and forthcoming realities in work (Torres \u0026amp; Orhan, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Remote working has been a common practice in the IT sector, however, the extent of which employees are allowed to work remotely, that is weekly days working remotely (i.e. remote working intensity; RWI), has come under increased scrutiny after the extensive remote working continued following the lifting of pandemic restrictions.\u003c/p\u003e \u003cp\u003eIn terms of learning and well-being at work, remote working can be seen to influence factors related to social interactions, performance, and managerial work (e.g., M\u0026uuml;hlenbrock et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rigolizzo, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zajac et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As majority of learning at work is considered to happen informally (Cerasoli et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or, in other words, through work (Billett, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), important sources of this learning, such as model learning and feedback (Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) differ substantially between remote and onsite working. The decrease of informal interaction (e.g., pre-meeting talks; Allen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, water cooler conversations; Lin \u0026amp; Kwantes, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) which occurs more naturally in face-to-face settings also influence informal learning as the exchange of relevant information often takes place in these auxiliary interactions in addition to formal meetings. While these interactions can be supported via deliberate planning, facilitation, and the use of digital tools (see e.g., Andrade et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), they require additional effort from organizations, leaders, and employees. Pertinent to IT sector knowledge work, digital tools can support individual learning processes (e.g., self-directed learning; Lemmetty \u0026amp; Collin, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) depending on job description and tasks. While collaborative efforts are needed to produce team learning and actualize informal learning in context, individual informal learning practices (e.g., experimentation and reflection) can be maintained by the concentration that remote working provides.\u003c/p\u003e \u003cp\u003eIn addition to commonly researched job attitudes (e.g., job satisfaction and turnover intention), which measure more outcome-oriented dimensions of well-being (Sessa \u0026amp; Bowling, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), remote working can affect more antecedent and process related factors (see Tynj\u0026auml;l\u0026auml;, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) such as perceptions of work engagement (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the satisfaction of basic psychological needs for autonomy, competence, and relatedness (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These factors, alongside informal learning, link generally positively to job satisfaction and negatively to turnover intention (e.g., Cerasoli et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mazzetti et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile existing literature provides mixed results nevertheless leaning to favoring remote working, current widespread remote working can contribute to these factors and their relationships in ways yet studied. Previous studies indicate positive effects, particularly of multilocational work (i.e., working at other locations than office for some but not all the time), to well-being (e.g., Bloom et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Charalampous et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Felstead \u0026amp; Heneken, 2017; Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including higher job satisfaction, perceived autonomy, and lower attrition, while also negative aspects such as higher social isolation and work-home spill-over are highlighted.\u003c/p\u003e \u003cp\u003eConsequently, the effects of remote working on learning and well-being at work are complex and may differ from those observed prior to the increase in remote working and during the COVID-19 forced remote working. These changes can be seen to impact IT sector knowledge work more modestly than other sectors, in addition to possible countermotion towards on-site working. The autonomy and digital preparedness of IT sector employees and organizations provide a stable and lasting context to examine the influence of RWI for the future working life. It is thus important to model associations among learning, well-being, and remote working in this context.\u003c/p\u003e \u003cp\u003eThis study aims to, first, investigate the relationships that remote working intensity (RWI, i.e., weekly days working remotely) have on learning and well-being at work in white-collar post-pandemic multilocational working context. Second, since work-related research conducted before the increase in remote work may not fully apply to the current state (Torres \u0026amp; Orhan, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we wanted to model the associations between informal workplace learning and well-being at work and reflect on the pre-pandemic studies (e.g., Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) while including RWI in the mix. Specifically, we wanted to investigate how basic psychological needs satisfaction (BPNS; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), informal workplace learning (Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and work engagement (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) are connected to job satisfaction and turnover intention (Judge \u0026amp; Kammeyer-Mueller, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and whether RWI is a significant predictor in the model.\u003c/p\u003e \u003cp\u003eThe present study presents three research questions: RQ1) \u0026ldquo;Is RWI associated with informal workplace learning, BPNS, work engagement, job satisfaction, and turnover intention?\u0026rdquo;, RQ2) \u0026ldquo;How informal workplace learning, BPNS, and RWI are associated with work engagement, job satisfaction, and turnover intention when modeled simultaneously\u0026rdquo;, and RQ3) \u0026ldquo;Does work engagement mediate the relationship between informal workplace learning and well-being outcomes, and between BPNS and well-being outcomes?\u0026rdquo;.\u003c/p\u003e"},{"header":"Theoretical framework","content":"\u003cp\u003eThe following sections describe the theoretical framework alongside with the set hypotheses related to bivariate RWI associations (RQ1) and SEM of the study variables (RQ2 and RQ3).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRemote working intensity\u003c/h2\u003e \u003cp\u003eRemote working is defined here as working away from the primary workplace. Research on comparing remote and onsite working has indicated both benefits and challenges of remote working. This is particularly relevant to knowledge work (i.e., work that requires extensive formal education and continuous learning, involves design and planning in addition to self-managing tasks, and produces knowledge as a primary outcome; Py\u0026ouml;ri\u0026auml;, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), as this type of work can be often performed remotely. Tasks requiring intensive concentration can be more effectively accomplished working remotely (Golden \u0026amp; Gajendran, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while the decrease in social interaction can challenge relatedness and increase various biases (Schinoff et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As multilocational working has become more common, it is relevant to focus on whether the weekly time spent working remotely (i.e., RWI) has an impact on work, workplace learning, and well-being at work. Measuring this kind of RWI and its associations with different work-related factors is a way to examine the benefits and hindrances of remote working (see Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInformal workplace learning and remote working\u003c/h3\u003e\n\u003cp\u003eIn this study, informal workplace learning is conceptualized using the octagon model of informal workplace learning (Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which includes four components: Experience/action, feedback, reflection, and intention to learn. Each of these four components contain two dimensions totaling up to eight and thus representing the octagon name of the model. Experience/action component refers to employees engaging in new experiences by either doing (trying and applying their own ideas) or observing (model learning). Feedback component includes direct feedback and vicarious feedback in which the employee is an active participant (i.e., asking for feedback). Reflection addresses anticipatory reflection and subsequent reflection. Finally, intent to learn refers to learning in order to further one's own career or learning to solve problems at work faster.\u003c/p\u003e \u003cp\u003eThe reduced number of face-to-face encounters in remote work settings influences informal learning regarding its social dimensions (Zajac et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Remote work context may offer less rich and more restricted communication due to diminished social support and fewer learning cues and opportunities, which reduces opportunities for informal workplace learning (M\u0026uuml;hlenbrock et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These social dimensions are important in informal workplace learning as learning at work can be seen to happen majorly not only through work, but often in collaborating and participation in \u0026ldquo;communities of practice\u0026rdquo; (Lave \u0026amp; Wenger, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). In relation to the octagon model (Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), particularly challenging dimensions in remote work are model learning and feedback. These dimensions require additional effort and planning to be successful and the probability of them happening by chance is lower in remote working compared to onsite working. For example, providing feedback remotely has been perceived as more demanding due to the need to assess the appropriate communication channels and the risk of misinterpretation (Jansson \u0026amp; Kangas, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn important venue for informal learning in knowledge work are meetings and different collaborative sessions. As meetings have moved to remote settings, the need and use of digital skills and digitalization of learning and professional development in work has increased drastically compared to the discussion of this digitalization before the pandemic (Wallin et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Companies in the IT sector currently utilize extensive digital tools to communicate and collaborate remotely (e.g., Jackson et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital technologies provide flexible access to interaction, enabling learning through online meetings and discussion forums, and enrich interaction through chat, screen sharing, and emojis (Karhap\u0026auml;\u0026auml; et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These have been utilized commonly by virtual teams (i.e., teams that operate predominantly remotely and geographically dispersed; Dulebohn \u0026amp; Hoch, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which can connect people irrespective of temporal and locational factors. Team learning in these settings can be supported by, for instance, agreed upon goals, ability to experiment independently, and an environment of trust (Dixon, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nonetheless, the lack of local encounters can lead informal communication to be less spontaneous and more siloed as forming close relationships in remote settings is more challenging (Begemann et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As virtual teams have been previously utilized mainly by multinational companies with specific populations and roles, their characteristics and the support provided by organization are likely to differ from employees or teams who have been working onsite or multilocationally before the expansion of remote working.\u003c/p\u003e \u003cp\u003eIn addition to purely interactive actions (e.g., feedback and model learning), informal learning happens through more individually initiated processes such as reflection (Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These processes can benefit from less interruptions provided by remote working, though reflection can also be a collaborative process which promotes team learning (e.g., Faller et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While individual reflection can benefit from remote working, a high RWI can impede opportunities to participate in and engage with communal activities at work, even when addressed in multilocational work environments. We thus expect that RWI is negatively associated with informal workplace learning (H1).\u003c/p\u003e\n\u003ch3\u003eBasic psychological needs satisfaction and remote working\u003c/h3\u003e\n\u003cp\u003eBasic psychological needs (i.e., autonomy, competence, and relatedness) are a central part of the Self-Determination Theory (SDT; Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) which is a theory that considers motivation in the context of psychological growth and well-being. SDT has been utilized accordingly in the contexts of educational psychology (see Ryan \u0026amp; Deci, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and work (Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on the theory, well-being and learning at work are affected by motivation and how self-determined the actions are (Ryan \u0026amp; Deci, 2000). These different types of motivation include, from the most optimal to the least, intrinsic motivation (doing an activity for the satisfaction of the activity itself), extrinsic motivation (doing an activity to reach a separate outcome), and amotivation (lack of motivation). Essential to producing and supporting intrinsic motivation and autonomous types of extrinsic motivation is the satisfaction of the basic psychological needs of autonomy, competence, and relatedness (Ryan \u0026amp; Deci, 2000). This BPNS is also connected to various positive outcomes in the work context, such as job satisfaction, positive affect, and work engagement (see Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAutonomy refers to individual\u0026rsquo;s need to self-regulate their experiences and actions, more specifically to the need for an internal perceived locus of causality providing self-endorsed behavior (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Competence refers to the need for feeling effectance and proficiency, while relatedness refers to the need to connect to other people and the need for belonging. These basic psychological needs and their satisfaction can be thought to play a relevant role in multilocational and remote working contexts as they consider aspects of work that are influenced by the changes in both individual and communal domains resulting from increased RWI.\u003c/p\u003e \u003cp\u003eIn general, remote working can have both positive and negative effects on need satisfaction (see Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In terms of autonomy, remote working can provide opportunities for better autonomy satisfaction, particularly when managers don\u0026rsquo;t leave remotely working employees on their own but provide an appropriate balance of autonomy support and control (Pianese et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, more intense monitoring from the management and increased home-work conflicts can have a negative influence on autonomy satisfaction (Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nonetheless, pre-pandemic studies have found a positive association between RWI and perceived autonomy (Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meta-analytic results from the studies done during pandemic yielded similar (non-significant) positive associations, however, the effect sizes were small. The current study population consists of IT sector knowledge workers of an organization enabling remote working policies; thus the participants likely have some previous experience on remote working. Due to these characteristics and based on the previous research, we expect that RWI is positively associated with autonomy satisfaction (H2a).\u003c/p\u003e \u003cp\u003eCompared to autonomy, competence satisfaction has not been studied in relation to remote working except for rare cases (Brunelle \u0026amp; Fortin, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Brunelle and Fortin (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found a positive association of teleworking (i.e., working multilocationally or fully remotely) and competence satisfaction, however, they did not investigate RWI but compared fully office-bound employees and multilocational/remote workers. Competence satisfaction can be linked to productivity, on which studies have found remote working to have both positive (Choudhury et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and negative (e.g., Barroro et al., 2023) effects. Gagn\u0026eacute; et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) listed increased role clarity and self-efficacy as positive effects that remote working can have on competence satisfaction, while information overload and technical issues can hamper competence satisfaction. In addition, increased isolation can also lower the awareness about other team members\u0026rsquo; work and competence as such and in relation to (Morrison-Smith \u0026amp; Ruiz, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, the studies often compare remote/multilocational and office work, not RWI (e.g., Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). RWI, particularly in organizations which enable remote and multilocational working, can be expected to support competence satisfaction because employees can organize their work in ways that utilize the best of both remote and onsite settings. Furthermore, as forced remote working was already lifted when the data were collected, we expect that employees had influence in choosing their RWI. Based on these factors and previous research, we expect that RWI is positively related to competence satisfaction (H2b).\u003c/p\u003e \u003cp\u003eWorking at home has been associated with lower perceived relatedness (e.g., Peijen et al., 2024) and higher perceived isolation (Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition to isolation, remote working can hinder the creation and maintaining support networks and meaningful collegial relationships (Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Social interactions are at the core of relatedness satisfaction (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), thus remote working challenges relatedness by limiting work related social interactions. Spontaneous encounters in work are more likely to happen in the office. Examples of these include pre-meeting talks (Allen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and \u0026ldquo;water cooler conversations\u0026rdquo; (Lin \u0026amp; Kwantes, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) which happen more naturally in face-to-face settings and facilitate not only learning and information sharing but also relationship and team building.\u003c/p\u003e \u003cp\u003eIncrease in remote working and the technologies emerging from this increase can have certain benefits as well. Gagn\u0026eacute; et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlight that increased remote working technologies can provide possibilities to connect with people across time and space. Research on virtual teams has indicated that appropriate use of digital tools and technologies can support the lack of physical presence in virtual teams (Dulebohn \u0026amp; Hoch, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Tools can provide flexible access to and enrich remote interaction (Karhap\u0026auml;\u0026auml; et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and are increasingly utilized in the IT sector to communicate and collaborate remotely (e.g., Jackson et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Employees\u0026rsquo; expectations of the interactions and relationships in virtual versus multilocational or office-based teams differ nonetheless, with the latter experiencing varying levels of physical encounters compared to fully spatial separation. Following this and based on previous research, we expect that RWI is negatively associated with relatedness satisfaction (H2c).\u003c/p\u003e\n\u003ch3\u003eWork engagement and remote working\u003c/h3\u003e\n\u003cp\u003eAccording to the Job Demands-Resources model (JD-R; Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), work engagement can be described as a relatively stable, positive, and fulfilling work-related state of mind including vigor, dedication, and absorption. In addition to previous research indicating a positive link between high RWI and work engagement (e.g., Nagata et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rodr\u0026iacute;guez-Modro\u0026ntilde;o, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), knowledge work can benefit from the concentration and perseverance enhanced by remote working (Allen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Choudhury et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, work engagement is linked to relatedness and social interaction at work (Gerards et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which can suffer from high RWI due to increased perceived isolation (Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies conducted during early parts of COVID-19 pandemic indicate that work engagement in remote work is high (e.g., M\u0026auml;kikangas et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), however, recent meta-analytic findings have found weak and non-significant associations between RWI and work engagement in pre- and during pandemic studies (Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Examining whether RWI influences work engagement similarly to what pre-pandemic and during pandemic studies found is important to evaluate differences between limited, forced, and wider applied RWI in knowledge work. As IT sector knowledge work is expected to include tasks requiring high levels of concentration, we expect RWI and work engagement to have a positive relationship (H3).\u003c/p\u003e\n\u003ch3\u003eJob satisfaction, turnover intention, and remote working\u003c/h3\u003e\n\u003cp\u003eJob satisfaction and turnover intention are commonly researched job attitudes that refer to general well-being and ill-being at work (Judge \u0026amp; Kammeyer-Mueller, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Job satisfaction refers in this study to a global affective component of job satisfaction compared to more specific facets, such as satisfaction with work itself or supervision (Bowling \u0026amp; Hammond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The affective dimension of job satisfaction has been the focus of research since the 1990s with recent research aiming towards emotions, thus further linking job satisfaction to well-being in both work and life context (Judge et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Turnover intention, on the other hand, refers to \u0026ldquo;...a conscious and deliberate willfulness to leave the organization\u0026rdquo; (Tett \u0026amp; Meyer, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, p. 262) and can be used as a proxy for actual turnover. Job satisfaction has been considered as an antecedent to turnover intention, particularly the changes in both (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), however, there are findings indicating that turnover intention can emerge from more positive or neutral origins (e.g., high competence satisfaction in a competitive job market; see Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Turnover intentions can be seen as an outcome of JD-R model processes (e.g., Collie, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or on the opposite end of continuum from job satisfaction (Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile studies conducted in the 90s and early 2000s provided mixed results and discussion about the relationship between remote work and job satisfaction (see e.g., Golden, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), recent studies have connected remote working to higher job satisfaction and lower turnover intention resulting from, for example, higher flexibility (e.g., Felstead \u0026amp; Henseke, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A recent meta-analysis on studies conducted pre-pandemic and during pandemic found that RWI and remote work use (i.e., users vs. non-users of remote work) are both positively associated with job satisfaction and negatively with turnover intentions (Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, multilocational work seems to provide certain benefits compared to fully remote work (e.g., better access to informal communication; Singh \u0026amp; Sant, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on the previous research we expect that, when examining bivariate associations, RWI is positively associated with job satisfaction (H4a) and negatively with turnover intention (H4b).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModeling learning and well-being at work\u003c/h2\u003e \u003cp\u003eWhile the knowledge of bivariate relationships that RWI has on learning and well-being factors is important for theory and practice, there is additional value in modeling interrelations between learning and well-being factors. This is due to the complexity of these work-related phenomena and relationships, for which modeling techniques (e.g., Structural Equation Modeling, SEM; Hair et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) enable multivariate estimation of these latent variable (i.e., unobserved and indirectly measured concepts) and their relationships while accounting for measurement error in observed variables (Hair et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough BPNS and informal workplace learning have been widely studied in work contexts (see e.g., Manuti et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), utilizing them to model the interrelations between BPNS, informal workplace learning and work well-being has been less common (e.g., Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, the broader frameworks of well-being (e.g., JD-R; Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and learning at work (e.g., 3-P model; Tynj\u0026auml;l\u0026auml;, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Octagon model; Decius et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) often do not include BPNS (Nokelainen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or both well-being and learning factors together. Modeling work well-being with informal workplace learning and relevant job outcomes can thus increase our understanding of these interrelated factors and their influence in work context. The increase in remote working introduces a novel component into the model, since remote working can have influence on several variables (e.g., increase in autonomy, decrease in informal interaction) and alter relationships between, for example, BPNS and job satisfaction (Brunelle \u0026amp; Fortin, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe set the following hypotheses regarding to the model of workplace learning and well-being in multilocational knowledge work (RQ2): First, based on previous research (e.g., Felstead et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Susomrith \u0026amp; Coetzer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) we expect that informal workplace learning is positively associated with work engagement (H5a) and job satisfaction (H6a), and negatively associated with turnover intention (H7a). Second, based on previous research (e.g., Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) we expect that BPNS has positive associations with work engagement (H5b) and job satisfaction (H6b), and a negative association with turnover intention (H7b). Finally, we expect based on previous research (Kim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mazzetti et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yalabik et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that work engagement is positively associated with job satisfaction (H6c) and negatively with turnover intention (H7c).\u003c/p\u003e \u003cp\u003eRegarding RQ3, we test a mediation effect of work engagement (M) on the expected relationships between informal workplace learning (X1) and job satisfaction (Y1)/turnover intention (Y2) (H8a), and BPNS (X2) and job satisfaction (Y1)/turnover intention (Y2) (H8b) which is indicated by previous research (Ali Abadi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). RWI is included in the model to assess the effect of RWI on work engagement, job satisfaction, and turnover intention when the effects of BPNS and informal workplace learning are accounted for. Visualization of the SEM and hypotheses related to RQ2 and RQ3 is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and procedure\u003c/h2\u003e \u003cp\u003eThe sample included 266 Finnish white-collar employees from an international IT (software consultant) company, which provides services such as strategic consulting, service design, software development, AI and analytics solutions, as well as cloud and integration services. The company invests in employees\u0026acute; well-being and has received recognitions and awards in this regard. The respondents recorded responses to at least one scale. The average age of the respondents was 39.7 years, and they had on average 15.9 years of work experience. The most common titles of the respondents were Software Designer (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42, 15.8%), Data Engineer (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28, 10.5%), Project Manager (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25, 9.4%), Manager (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, 7.9%), and Integration Specialist (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19, 7.1%). Most of the participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;162, 60.9%) identified as a man (woman \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96, 36.1%, other/prefer not to tell \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 3.0%). Most had completed higher-level university degree (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;170, e.g., master\u0026rsquo;s degree, ISCED 7, 63.9%), next frequent was lower-level university degree (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70, e.g., bachelor\u0026rsquo;s degree, ISCED 6, 33.8%) followed by secondary level degree (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20, 7.7%). Only a few had doctorate or equivalent degree (ISCED 8, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, 2.3%) and none had only basic level education.\u003c/p\u003e \u003cp\u003eOnline survey (Limesurvey) was used to collect demographic information and responses to validated questionnaires from study participants. Link to the survey was distributed via the company\u0026rsquo;s internal channels to personnel (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1040, response rate\u0026thinsp;=\u0026thinsp;25.6%). The survey was active for three weeks and during that time two reminders were sent. Some participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18) answered the survey during Spring 2022 as a part of a substudy, while the remaining (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;248) answered in Autumn 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eWe measured RWI from three different perspectives. We asked about the participants\u0026rsquo; \u003cem\u003ecurrent\u003c/em\u003e RWI: \u0026ldquo;On average, how many days during the week you currently work remotely (e.g., at home, in public spaces, etc.?)\u0026rdquo;, RWI \u003cem\u003ebefore COVID-19 pandemic\u003c/em\u003e: \u0026ldquo;On average, how many days during the week have you worked remotely (e.g., at home, in public spaces, etc.)\u0026rdquo;, and \u003cem\u003epreferred\u003c/em\u003e RWI: \u0026ldquo;On average, how many days during the week would you prefer to work remotely (e.g., at home, in public spaces, etc.)?\u0026rdquo;. Kendall\u0026rsquo;s tau-b (τ\u003csub\u003eb\u003c/sub\u003e; Kendall \u0026amp; Gibbons, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) correlations showed significant associations between these different RWI perspectives. The high correlation between preferred RWI and current RWI (τ\u003csub\u003eb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.76) suggests that employees enjoy considerable autonomy in shaping their current working arrangements and that the organization supports flexible remote work practices. Additionally, the positive associations between pre-pandemic RWI and both current (τ\u003csub\u003eb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.26) and preferred (τ\u003csub\u003eb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.34) RWI indicate that employees\u0026rsquo; historical remote work patterns influence their current behaviour and future preferences. This may reflect a longer-term, internalized preference for remote work as well as the organization\u0026rsquo;s responsiveness to evolving work modalities. From now on in the text, RWI refers to \u003cem\u003ecurrent\u003c/em\u003e RWI, which is used in the analysis to measure remote working intensity.\u003c/p\u003e \u003cp\u003eIn addition to questions about demographic information and RWI, validated questionnaires were used in the survey to measure work well-being and informal learning (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Appendix).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResponse scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal workplace learning: Short measure for white-collar workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecius et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInformal workplace learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;strongly disagree \u0026hellip; 5\u0026thinsp;=\u0026thinsp;strongly agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic Psychological Needs Scale-Revised (adapted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchultz et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutonomy, competence, and relatedness satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;strongly disagree \u0026hellip; 5\u0026thinsp;=\u0026thinsp;strongly agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltra-short measure for work engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchaufeli et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWork engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;never \u0026hellip; 5\u0026thinsp;=\u0026thinsp;always\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMichigan Organizational Assessment Questionnaire Job Satisfaction Subscale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBowling \u0026amp; Hammond (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;strongly disagree \u0026hellip; 5\u0026thinsp;=\u0026thinsp;strongly agree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurnover Intention Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBothma \u0026amp; Roodt (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;never \u0026hellip; 5\u0026thinsp;=\u0026thinsp;always\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSurvey data were analyzed using the \u003cem\u003eR\u003c/em\u003e statistical computing environment (R Core Team, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Preliminary analyses included testing the univariate and multivariate normality of dependent variables (univariate skewness and kurtosis, and multivariate kurtosis; Finney \u0026amp; DiStefano, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) using the \u003cem\u003eMVN\u003c/em\u003e package (Korkmaz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). First, means, standard deviations, Revelle\u0026rsquo;s omega reliabilities (see McNeish, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Kendall\u0026rsquo;s Tau-b correlations (Kendall \u0026amp; Gibbons, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) were calculated using the \u003cem\u003epsych\u003c/em\u003e package (Revelle, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Bivariate Tau-b correlations were used to answer RQ1. To answer RQ2 and RQ3, hypothesized associations of the variables were examined using SEM (Hair et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The SEM analysis was conducted using the \u003cem\u003elavaan\u003c/em\u003e package (Rosseel, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The analyzed SEM model was constructed based on previous research (e.g., Mazzetti et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the model, informal workplace learning and the satisfaction of every basic psychological need (autonomy, competence, relatedness) were positively related to work engagement and job satisfaction and negatively related to turnover intention. In addition, we analyzed whether work engagement (M) mediates the associations between informal workplace learning (X1) and job satisfaction (Y1) and turnover intention (Y1), as well as BPNS (X2) and job satisfaction (Y1) and turnover intention (Y2). Finally, we included current RWI in the model to assess the influence of remote working.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary analysis\u003c/h2\u003e \u003cp\u003eOf the participants who started the survey (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;286), 258 responded to all scales (full respondents) and eight responded to at least one scale (partial respondents). Multivariate and univariate normality of dependent variables were violated (multivariate kurtosis\u0026thinsp;=\u0026thinsp;8.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Due to these deviations, we used full information maximum likelihood (FIML) approach and an estimator with robust (Huber-White) standard errors and a scaled test statistic (MLR) in the SEM analysis. The final data used in the analysis thus included both full and partial respondents (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;266).\u003c/p\u003e \u003cp\u003eFor SEM analysis, model fit was assessed by the scaled chi-squared test and by examining the following robust fit indices: Comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Commonly used cut-off values for excellent fit are the following: CFI\u0026thinsp;\u0026ge;\u0026thinsp;.95, TLI\u0026thinsp;\u0026ge;\u0026thinsp;.95, RMSEA\u0026thinsp;\u0026le;\u0026thinsp;.06, SRMR\u0026thinsp;\u0026le;\u0026thinsp;.08 (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement model\u003c/h2\u003e \u003cp\u003eFor RQ2 and RQ3, a CFA of the original measurement model which included latent variables of informal workplace learning (8 items), satisfaction of basic psychological need for autonomy (4 items), competence (4 items), and relatedness (4 items), work engagement (3 items), job satisfaction (3 items), and turnover intention (4 items) resulted in not positive definite covariance matrix. This often occurs when items or constructs are highly collinear, necessitating model adjustments such as removing correlated items or modifying factor specifications. After inspection, the fourth item in turnover intention scale (TI4: \u0026ldquo;How often do you look forward to another day at work?\u0026rdquo;; see Appendix) was identified as the issue and was removed. Following this a CFA for the previous model without TI4 showed a poor fit: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(356)\u0026thinsp;=\u0026thinsp;694.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; RMSEA\u0026thinsp;=\u0026thinsp;.063 (90% CI\u0026thinsp;=\u0026thinsp;.056 \u0026ndash; .071, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002); CFI\u0026thinsp;=\u0026thinsp;.906; TLI\u0026thinsp;=\u0026thinsp;.892; SRMR\u0026thinsp;=\u0026thinsp;.068. Factor loadings ranged from .35 to .91. To improve the model, the item with the distinctly low factor loading (.35), the first item of the intent to learning -dimension of the informal workplace learning scale (IWL_I2: \u0026ldquo;I want to learn something new at work for myself because then I can pursue my career at the company\u0026rdquo;) was removed and one modification based on the examination of modification indices was applied: Allowing the residual covariance between the fourth item of autonomy satisfaction scale (AUT4: \u0026ldquo;I feel I have been doing what really interests me in my job\u0026rdquo;) and the second item of work engagement scale (WE2: \u0026ldquo;I am enthusiastic about my job\u0026rdquo;). The modified measurement model had an acceptable fit: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(328)\u0026thinsp;=\u0026thinsp;569.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; RMSEA\u0026thinsp;=\u0026thinsp;.055 (90% CI\u0026thinsp;=\u0026thinsp;.047 \u0026ndash; .064, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.132); CFI\u0026thinsp;=\u0026thinsp;.932; TLI\u0026thinsp;=\u0026thinsp;.921; SRMR\u0026thinsp;=\u0026thinsp;.066. Factor loadings ranged from .48 to .91, indicating generally acceptable to strong relationships between items and their respective latent constructs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRQ1: Bivariate associations between RWI and study variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the means, standard deviations, and Kendall\u0026rsquo;s Tau-b (τ\u003csub\u003eb\u003c/sub\u003e) correlations for the study variables. The results showed that RWI was not significantly associated with informal workplace learning which did not support our H1. RWI was negatively associated with relatedness satisfaction supporting our H2c (τ\u003csub\u003eb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Autonomy and competence satisfaction were, however, not associated with RWI, which was against H2a and H2b. Furthermore, RWI was not significantly associated with work engagement (H3), job satisfaction (H4a), or turnover intention (H4b) contrary to our expectations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics, omega reliabilities, and bivariate Kendall\u0026rsquo;s Tau-b correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eω\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. IWL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Experience/Action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Reflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Intent to learn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. BPNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Work engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Job satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. Turnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash;.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15. Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16. Work experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17. RWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026minus;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u0026minus;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026minus;.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"20\"\u003e\u003cem\u003eNote\u003c/em\u003e. Bolded values indicate statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). Kendall\u0026rsquo;s tau-b correlation (Kendall \u0026amp; Gibbons, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) cutoffs: τ\u003csub\u003eb\u003c/sub\u003e \u0026gt; |.06| weak association, τ\u003csub\u003eb\u003c/sub\u003e \u0026gt; |.19| moderate association, τ\u003csub\u003eb\u003c/sub\u003e \u0026gt; |.33| strong association (Walker, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); BPNS\u0026thinsp;=\u0026thinsp;basic psychological needs satisfaction, IWL\u0026thinsp;=\u0026thinsp;informal workplace learning, RWI\u0026thinsp;=\u0026thinsp;remote working intensity, ω\u0026thinsp;=\u0026thinsp;reliability (Revelle\u0026rsquo;s omega; see McNeish, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRQ2: SEM of study variables\u003c/h2\u003e \u003cp\u003eThe hypothesized SEM showed acceptable fit to the data: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(353)\u0026thinsp;=\u0026thinsp;618.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; RMSEA\u0026thinsp;=\u0026thinsp;.056 (90% CI\u0026thinsp;=\u0026thinsp;.048 \u0026ndash; .063, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.111); CFI\u0026thinsp;=\u0026thinsp;.926; TLI\u0026thinsp;=\u0026thinsp;.915; SRMR\u0026thinsp;=\u0026thinsp;.067. Similarly to the measurement model, in the SEM model, we also allowed the correlation between the unexplained (error) parts of the fourth autonomy satisfaction item (AUT4: \u0026ldquo;I feel I have been doing what really interests me in my job\u0026rdquo;) and the second work engagement item (WE2: \u0026ldquo;I am enthusiastic about my job\u0026rdquo;) based on modification indices.\u003c/p\u003e \u003cp\u003eStandardized SEM results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and statistically significant paths are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Informal workplace learning was positively related to work engagement (β\u0026thinsp;=\u0026thinsp;.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.021; H5a) but not to job satisfaction (H6a) or turnover intention (H7a). Autonomy satisfaction was positively related to work engagement (β\u0026thinsp;=\u0026thinsp;.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; H5b) and job satisfaction (β\u0026thinsp;=\u0026thinsp;.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001; H6b), and negatively related to turnover intention (β = \u0026minus;.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032; H7b). Competence satisfaction, contrary to our expectations, was not related to work engagement (H5b), job satisfaction (H6b), or turnover intention (H7b). Relatedness satisfaction was positively related to job satisfaction (β\u0026thinsp;=\u0026thinsp;.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and negatively related to turnover intention (β = \u0026minus;.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), supporting H6b and H7b. Work engagement was positively related to job satisfaction (β\u0026thinsp;=\u0026thinsp;.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; H6c) and negatively related to turnover intention (β = \u0026minus;.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002; H7c). RWI was not a significant predictor of work engagement, job satisfaction, nor turnover intention, which was in accordance with the bivariate results from RQ1. However, the negative association between RWI and turnover intention, while marginal (β = \u0026minus;.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.061), indicated that when examining the relationships that BPNS, informal workplace learning, and work engagement have on turnover intention simultaneously, higher RWI seems to relate to lower turnover intention. This differed from the weak positive bivariate correlation between RWI and turnover intention (τb\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.61).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized direct, indirect, and total effects of the SEM model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effects to work engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInformal workplace learning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.19*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAutonomy satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.61***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCompetence satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRelatedness satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRemote working intensity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effects to job satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInformal workplace learning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAutonomy satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCompetence satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRelatedness satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.31***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWork engagement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRemote working intensity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effects to turnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInformal workplace learning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAutonomy satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCompetence satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRelatedness satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWork engagement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRemote working intensity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effects to job satisfaction via work engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal indirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from informal workplace learning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.08*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from autonomy satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from competence satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from relatedness satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effects to turnover intention via work engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTotal indirect\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from informal workplace learning\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from autonomy satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from competence satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSpecific indirect from relatedness satisfaction\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effects to job satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.95***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effects to turnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e β\u0026thinsp;=\u0026thinsp;standardized estimate, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard error, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRQ3: Mediation effects of work engagement\u003c/h2\u003e \u003cp\u003eThere was a significant positive indirect effect from informal workplace learning (X1) via work engagement (M) to job satisfaction (Y1) (β\u0026thinsp;=\u0026thinsp;.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020) and negative indirect effect from informal (X1) workplace learning via work engagement (M) to turnover intention (Y2) (β = \u0026minus;.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.044), which was in line with our H8a. Similarly, indirect effect from autonomy satisfaction (X2) via work engagement (M) to job satisfaction (Y1) (β\u0026thinsp;=\u0026thinsp;.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and negative indirect effect from autonomy satisfaction (X2) via work engagement (M) to turnover intention (Y2) (β = \u0026minus;.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007) were found supporting H8b in terms of autonomy.\u003c/p\u003e \u003cp\u003eIn summary, the SEM findings show that informal workplace learning boosts work engagement, which in turn raises job satisfaction and reduces turnover intention. Further, autonomy and relatedness satisfaction are key predictors of job satisfaction and turnover intention: Autonomy satisfaction increases work engagement and job satisfaction while lowering turnover intention, and relatedness satisfaction enhances job satisfaction and reduces turnover intention. Indirect effects through work engagement were observed for both informal workplace learning and autonomy satisfaction. Summary of the study hypotheses and whether the results support them are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy hypotheses and support from analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected association\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved bivariate association\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObserved predictor in SEM analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupport for hypothesis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-IWL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-AUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus; (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-COM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus; (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-REL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-WE, RWI-\u0026gt;WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epartial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-JS, RWI-\u0026gt;JS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus; (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWI-TI, RWI-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus; (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWL-\u0026gt;WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPNS-\u0026gt;WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWL-\u0026gt;JS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus; (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPNS-\u0026gt;JS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWE-\u0026gt;JS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIWL-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+ (NS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBPNS-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWE-\u0026gt;TI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediation1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+/\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMediation2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+/\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e \u0026ldquo;+\u0026rdquo; indicates positive association, \u0026ldquo;\u0026minus;\u0026ldquo; indicates negative association, bolded symbols indicate observed associations analyzed for hypotheses. NS\u0026thinsp;=\u0026thinsp;non-significant, NA\u0026thinsp;=\u0026thinsp;not analyzed, RWI\u0026thinsp;=\u0026thinsp;remote work intensity, IWL\u0026thinsp;=\u0026thinsp;informal workplace learning, AUT\u0026thinsp;=\u0026thinsp;autonomy, COM\u0026thinsp;=\u0026thinsp;competence, REL\u0026thinsp;=\u0026thinsp;relatedness, WE\u0026thinsp;=\u0026thinsp;work engagement, JS\u0026thinsp;=\u0026thinsp;job satisfaction, TI\u0026thinsp;=\u0026thinsp;turnover intention, BPNS\u0026thinsp;=\u0026thinsp;basic psychological needs satisfaction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed, first, to investigate the association between RWI, informal learning activities, and well-being at work, and second, to examine potential differences in the modeling of learning and well-being at work compared to pre-pandemic studies and reviews (e.g., Felstead et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The results revealed unexpected findings regarding RWI, as RWI had significant (negative) bivariate association only with relatedness satisfaction, while the modeling results between workplace learning and well-being generally aligned with our expectations.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the expected associations based on the hypotheses, observed associations, and whether the results provide support for the set hypotheses. The results considering RWI (RQ1) provided little support for the H1-H4 hypotheses. Regarding the observed weak relationship between RWI and informal workplace learning (H1), a possible explanation pertains to the use of digital collaboration and communication tools in supporting learning when remote working (Jackson et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karhap\u0026auml;\u0026auml; et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, employees in technology and IT service sectors have been likely familiar with various tools and skills necessary for learning and working remotely. Only the feedback component had a negative (non-significant) correlation with RWI, indicating that while asking for feedback is somewhat challenging with higher RWI, more individual learning processes can benefit from remote working. Further research should examine the factors that contribute to successful high RWI work in terms of informal workplace learning. Studies have indicated significant challenges but also opportunities arising from RWI in learning and development at work (e.g., Authors, submitted for publication), in addition to the suggestions to remedy the challenges, for example, via leadership and management (see M\u0026uuml;hlenbrock et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is important to note that RWI was measured by a self-reported average number of remote working days per week, which can mask other ways that remote working influences informal learning at work. For example, variation in work task characteristics, requirements for competence development, and distribution of individual vs. collaborative work can influence the relationships between RWI and learning.\u003c/p\u003e \u003cp\u003eFor BPNS, only relatedness satisfaction was negatively correlated with RWI (H2c), emphasizing the impact of RWI on relatedness, community, and social aspects of work. This aligns with results that relatedness satisfaction was experienced more on office days compared to working at home (Peijen et al., 2024). This also implies that while flexibility and the use of digital tools can enable high RWI without significant negative effects on informal workplace learning, the satisfaction of relatedness suffers.\u003c/p\u003e \u003cp\u003eConsidering the lack of associations between RWI, autonomy and competence satisfaction, one possible explanation could be the organizational culture of the company: Since current RWI in this organization aligned closely with preferred RWI (τ\u003csub\u003eb\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.76), this alignment can support BPNS for employees working with both high and low RWI. Furthermore, autonomy and competence satisfaction can be considered to have a less linear relationship (e.g., more of a U-shape) with RWI compared to relatedness satisfaction. In addition, as remote working can have both positive and negative effects on BPNS (Gagn\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the increase in RWI can be seen to influence particularly relatedness, as social interactions are central in relatedness satisfaction (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These results and their focus on relatedness align with other research indicating that higher RWI can influence relatedness and social interaction that in turn have an impact on learning and well-being both work in general (Lemmetty, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and in work interaction (Authors, submitted for publication).\u003c/p\u003e \u003cp\u003eIn terms of work engagement, the association between RWI and work engagement was weak in both bivariate correlations and in our SEM. This aligns with the generally weak association found in Gajendran et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) meta-analysis. Previously mentioned flexible organizational culture related to remote working and weaker linearity of the relationship between RWI and work engagement can explain this result. Further investigations of the ways that work engagement can be supported in high or low RWI should be undertaken, with focus on structures and practices that employees and organizations can utilize.\u003c/p\u003e \u003cp\u003eFor job satisfaction and turnover intention, bivariate correlations with RWI were nonsignificant but in SEM, RWI was marginally negatively related to turnover intention. These results align more with studies reporting ambiguous relationships between remote working and these job attitudes (e.g., Golden, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), compared to more recent research synthesis (e.g., Gajendran et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The autonomy in choosing the levels of RWI can explain these weak correlations, as employees\u0026rsquo; job satisfaction and turnover intention are less dependent on their preference for RWI. As our sample was limited to a single IT sector organization, the present study results encourage further research on the factors that boost or enable higher RWI to increase job satisfaction and decrease turnover intentions, or on the other hand, which organizational characteristics make employees satisfied and committed to organization regardless of their RWI.\u003c/p\u003e \u003cp\u003eSimilarly to RWI and informal workplace learning, the way remote working was surveyed in the present study (average weekly days working remotely) may ignore certain impacts that remote working has on well-being. Factors such as time, distance from the office, and the locations where remote working occurs, for instance, can moderate the relationships between RWI and well-being. Further qualitative and mixed method studies could shed light on these possible interactions.\u003c/p\u003e \u003cp\u003eThe results mostly supported our hypotheses regarding RQ2 and RQ3, which indicates that the increase in multilocational and remote working has not fundamentally changed the dynamics of learning and well-being in knowledge work. In addition, RWI was not a significant predictor of work engagement, job satisfaction, nor turnover intentions, agreeing with the bivariate correlations. An interesting result from the SEM analysis considers the association of informal workplace learning to job satisfaction and turnover intention. While being associated with only work engagement directly, informal workplace learning had indirect effects on both job satisfaction (positive) and turnover intention (negative). This result follows previous studies suggesting that, for example, workplace learning opportunities do not directly relate to job attitudes when examined with BPNS (Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These results indicate that while BPNS can have higher impact on job attitudes compared to informal workplace learning, informal workplace learning can still enhance job satisfaction and lower turnover intention but only via work engagement. This mediating effect of work engagement has been observed in other studies between work related variables, such as informal learning and career identity (Susomrith \u0026amp; Coetzer, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). BPNS and work engagement have also been considered to play a role in the processes of the JD-R model (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The present study results suggest that informal workplace learning, and other learning-related factors can influence these processes as well.\u003c/p\u003e \u003cp\u003eRegarding BPNS, autonomy satisfaction was a significant positive predictor of job satisfaction and work engagement and negative predictor of turnover intention. Furthermore, while considered to have more of a secondary influence on some outcomes compared to autonomy (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), relatedness satisfaction had associations to job satisfaction and turnover intention comparable to autonomy satisfaction. Increased remote and multilocational work could have influence on this increased importance of relatedness satisfaction on work attitudes. The strong direct and indirect effects of autonomy satisfaction on job satisfaction and turnover intention follow the idea of autonomy and autonomy satisfaction being prominent factors in work well-being (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This can be particularly important in knowledge work as flexibility and variability of work tasks enable increased autonomy and possibilities to influence one\u0026rsquo;s own work benefit both development and performance of employees. Going forward, it would be interesting to explore how autonomy and relatedness satisfaction are emphasized across different work populations and relationships of outcomes, in addition to how remote and multilocational working influence these processes.\u003c/p\u003e \u003cp\u003eOne distinct result, which was contrary to our expectations, was that competence satisfaction had no associations with work engagement, job satisfaction, or turnover intention. This can stem from the previously mentioned availability of digital resources but also from the notions that autonomy and relatedness satisfaction have higher impact especially on turnover intention and work engagement compared to competence satisfaction (see Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Competence satisfaction can also have more complex associations to job satisfaction and turnover intention based on different populations and working cultures. For instance, the technology sector is considered to have high voluntary and nonvoluntary turnover rates and thus, career paths and expectations for career development can differ from the traditional preferences of permanent, stable positions in relatively few organizations during career. Indications of the presence of these more complex phenomena include for example results from the studies that link competence satisfaction (Puhakka et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van den Broeck et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) or subjective career satisfaction (Lehtonen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to higher turnover intention.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eThe results imply that organizations should provide autonomy and encourage informal workplace learning (e.g., by improving feedback culture) to improve employees\u0026rsquo; well-being at work. The study results highlight particularly the role of relatedness satisfaction in current multilocational work and encourage employees to work at least some days per week in office. Also, focusing on and developing the work community, alongside with relatedness increasing policies and activities can support relatedness satisfaction while allowing employees autonomy to work multilocationally. Enhancing work engagement via proactive management and job crafting methods (see e.g., Bakker et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) can support learning and well-being by influencing various levels and pathways within work. While organizations and sectors differ in their utilization of digital tools and preparedness for remote working, getting familiarized with remote working preferences of both individual employees, but also teams can provide organizations with important information to discuss and reach an agreement that considers the needs from all parties. This supports the alignment of current and preferred RWI and can thus improve learning and well-being outcomes for all employees regardless of their RWI. Based on these results, we emphasize that broad conclusions on remote working being distinctly superior or inferior compared to on-site office working should be avoided and instead, individualized examination of organization or team level factors conducted. Amid the discussions on forcing employees back to offices from remote working, the acknowledgement of context- and individual-related factors on the success of multilocational work is highly important for the organizations and employees.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and further research directions\u003c/h2\u003e \u003cp\u003eCertain limitations are warranted due to the study\u0026rsquo;s sample and data characteristics. \u003cem\u003eFirstly\u003c/em\u003e, the cross-sectional nature of the survey data used in the analysis prevents making causal interpretations of the results. \u003cem\u003eSecondly\u003c/em\u003e, the study participants were white-collar employees from a single company in the IT sector, where continuous learning and development are essential, thus further investigations in different industries and work cultures are needed to obtain a more comprehensive picture of current knowledge work. IT sector employees are expected to be skilled in using digital tools and thus the impact of increasing need and utilization of digital skills and digitalization of learning and professional development in work (Wallin et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) can have limited effect compared to employees in other fields. \u003cem\u003eThirdly\u003c/em\u003e, while the sample size was sufficient for the mediation SEM analysis, internal consistency values of the turnover intention scale were marginal and further covariances created issues with analysis that required item omission. Due to these issues with the scale, interpretations regarding turnover intention results should be made with caution. \u003cem\u003eFourthly\u003c/em\u003e, some of the intercorrelations of the informal workplace learning questionnaire were quite low, which calls for caution when interpreting the results from informal workplace learning. Future studies could use the longer version to enable more in-depth examination of different components and should also further investigate the validity of the shortened version of the questionnaire. \u003cem\u003eFifthly\u003c/em\u003e, remote working was measured only by self-reported average frequency of remote working days per week. This hides the possible variations of RWI over time or based on work tasks and phases, as well as possible moderating factors that might influence the relationship between remote work frequency and study variables (e.g., available technology, remote working spaces, ergonomics). Nevertheless, the observed weak or nonexistent associations between RWI and informal workplace learning and well-being indicators (apart from relatedness satisfaction) raise questions about the magnitude of the impact of RWI in knowledge work. Longitudinal research on the effects of remote and multilocational work on well-being and learning at work should be undertaken to investigate these possible prevailing and multifaceted effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe importance of relatedness satisfaction in relation to RWI encourages the companies and organizations to invest in community culture, particularly of those who work (and prefer to work) frequently remotely. In general, the organizations should provide autonomy and encourage informal workplace learning (e.g., improving feedback culture) to improve employees\u0026rsquo; well-being at work. Theoretically, the results indicate that BPNS has relatively higher impact on job satisfaction and turnover intention in knowledge work context over informal workplace learning. Nevertheless, informal workplace learning can still enhance job satisfaction and lower turnover intention through high work engagement. Taken together the study results imply that intensity of remote working is not distinctly beneficial or detrimental for employee learning and well-being, however, having some office days is important to experiencing relatedness which is positively associated with job satisfaction and negatively with turnover intention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eParticipants were informed about the study and gave their consent to participate before filling out the survey.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003c/em\u003e This study was supported by [anonymized] [grant number: XXXXXX].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e:\u0026nbsp;\u003c/em\u003eBefore data collection,a favorable statement for the study was obtained from [anonymized].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. I.P. and E.L. performed the data collection. I.P. prepared and analyzed the data. The first draft of the manuscript was written by I.P. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey data used in this study will be anonymized and submitted to the Finnish Social Science Data Archive 10 years after the end of the research project (2033) and opened for further research use.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli Abadi, H., Coetzer, A., Roxas, H., \u0026amp; Pishdar, M. (2023). Informal learning and career identity formation: The mediating role of work engagement. \u003cem\u003ePersonnel Review\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(1), 363\u0026ndash;381. https://doi.org/10.1108/PR-02-2021-0121\u003c/li\u003e\n\u003cli\u003eAllen, T. 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Promoting virtual, informal learning now to thrive in a post‐pandemic world. \u003cem\u003eBusiness and Society Review\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(S1), 283\u0026ndash;298. https://doi.org/10.1111/basr.1226\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"remote work, basic psychological needs satisfaction, informal learning, work engagement, job satisfaction, turnover intention","lastPublishedDoi":"10.21203/rs.3.rs-5683479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5683479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increase in remote working has changed the way both employees and organizations view work in an already tumultuous landscape of the IT sector. In this study, we surveyed Finnish employees (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;266) from an international IT sector company in 2022 after the remote working mandates were lifted. We firstly examined how remote working intensity (RWI) was associated with informal workplace learning, basic psychological needs satisfaction (BPNS, including autonomy, competence, and relatedness), work engagement, job satisfaction, and turnover intention (RQ1). Second, we investigated using structural equation modeling (SEM) how informal workplace learning, BPNS, and RWI were associated with work engagement and well-being outcomes (job satisfaction, and turnover intention) (RQ2). Finally, we wanted to know whether work engagement mediated the previous relationships (RQ3). Results for RQ1 were generally against our expectations as RWI was associated only with relatedness satisfaction (negatively). SEM results (RQ2) generally matched our expectations as autonomy and relatedness satisfaction, and work engagement were positively related to job satisfaction and negatively to turnover intention. Furthermore, work engagement was a positive mediator for the relationships of informal workplace learning and outcomes, and autonomy and outcomes (RQ3). Informal learning was thus interestingly related to job satisfaction and turnover intention but only via work engagement. The results imply that RWI is not distinctly beneficial or detrimental for learning and well-being at work, however, having some office days per week supports relatedness satisfaction, which in turn relates to positive work outcomes. Furthermore, high work engagement can allow informal learning activities to positively influence work well-being.\u003c/p\u003e","manuscriptTitle":"Remote working intensity in knowledge work: Associations with informal workplace learning, basic psychological needs satisfaction, job satisfaction, and turnover intention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 03:33:09","doi":"10.21203/rs.3.rs-5683479/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef7e4426-6d80-412d-9002-09e2682efaa6","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-19T16:49:11+00:00","versionOfRecord":{"articleIdentity":"rs-5683479","link":"https://doi.org/10.1007/s12186-025-09377-2","journal":{"identity":"vocations-and-learning","isVorOnly":false,"title":"Vocations and Learning"},"publishedOn":"2025-09-18 00:00:00","publishedOnDateReadable":"September 18th, 2025"},"versionCreatedAt":"2025-04-17 03:33:09","video":"","vorDoi":"10.1007/s12186-025-09377-2","vorDoiUrl":"https://doi.org/10.1007/s12186-025-09377-2","workflowStages":[]},"version":"v1","identity":"rs-5683479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5683479","identity":"rs-5683479","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}