Self-development in the twenty-first century: An exploratory analysis of the relationship between new work characteristics and informal workplace learning

This article in the journal “Gruppe. Interaktion. Organisation. (GIO)” presents the results of an exploratory study on the relationship between new work characteristics and informal workplace learning (IWL). New ways of working drastically shaped modern workplaces, but their association with workplace learning, a key driver of organizations’ success, remains unclear—little is known about whether and under which circumstances new work characteristics are related to workplace learning. Drawing on Conservation of Resources theory, we tested if new work characteristics (i.e., digitalization, flexibility, dissolution of boundaries, participation, and relevance of work) are positively related to IWL. Moreover, we assumed that learning climate positively influences the strength of the relationship between new work characteristics and IWL. We conducted an online survey involving 291 participants to test our hypotheses. We found a positive overall relationship between new work characteristics and IWL. Examining the new work characteristics in more detail, relative importance analysis showed that this overall relationship seems to be driven by relevance of work, followed by participation and dissolution of boundaries. Digitalization and flexibility showed only weak relationships with IWL. Contrary to our assumption, learning climate did not strengthen the new work characteristics-IWL relationship. Our study introduces new work characteristics as an antecedent of IWL and speaks to their overall benefits for IWL. However, our findings suggest that new work might not be studied as a unified concept, but rather separately for its different characteristics. We hope to inspire further research to help organizations and employees to capitalize on the effects of new work characteristics.


Introduction
The nature of work is and will keep changing as we move further into the 21st century, but its implications for organizations and employees remain blurry (Barley et al. 2017).These complex changes of work environments are often coined under the term "new work" and can comprise diverse and skill acquisition, as well as performance (Cerasoli et al. 2018;Decius et al. 2021).IWL is assumed to fit well to the rapid changes in the world of work (Kortsch et al. 2021;Decius et al. 2022;Gerards et al. 2020;Noe et al. 2014) because it is triggered by challenges at work and thus takes place directly in the work process (Marsick and Volpe 1999;Segers et al. 2018).Due to its work-integrated nature, IWL therefore does not require overcoming transfer barriers as exist with formal learning (Blume et al. 2010;Grossman and Salas 2011).
Despite IWL's assumed central role for coping with changing work environments, the relationship to new work characteristics remains unclear.While work design has played an important role as a vehicle for workplace learning (Parker 2014;Taris and Kompier 2004;Wielenga-Meijer et al. 2010), we need to know more about how major shifts in the nature of work may alter learning, such as the implementation of digital technologies (Parker and Grote 2022), agile project management (Sharp and Lang 2018), or flexibility in working relations (Gerards et al. 2020).This evolution in work is expected to motivate employees to learn with a growing volume and intensity (Kyndt and Baert 2013), to remain competitive in their work capabilities and employability throughout their careers.Moreover, organizations may not be well-equipped to handle the challenges of new work, if there is little to say about its consequences (Barley et al. 2017).Grounded in Conservation of Resources (COR) theory (Hobfoll et al. 2018), this study therefore explores if and which characteristics of new work are related to IWL.In line with Demerouti (2022), we therefore consider new work characteristics as organizational resources that can be invested according to the resource gain principle of COR theory to enhance IWL and skill development.
However, the relationship between new work and IWL might be influenced by organizational conditions: Although organizations might not be able to explicitly control IWL due to its lack of formal structure (Tannenbaum and Wolfson 2022), organizations can nevertheless create a supporting environment for IWL (Cerasoli et al. 2018;Hilkenmeier et al. 2021;Zia et al. 2022).Specifically, we argue based on the principle of resource caravan passageways of COR theory (Hobfoll 2011)-according to which organizations can pave the way to resource gain of individuals and groups-that the new work characteristics-IWL relationship depends on how organizations allow and enable their employees to learn (Fischer et al. 2018).Organizational learning climate represents this idea well, as this construct depicts the facilitation and appreciation of learning and how mistakes are handled within an organization (Nikolova et al. 2014).We therefore test if learning climate strengthens the new work characteristics-IWL relationship.This contributes to a better understanding of when new work characteris-tics may impact IWL and allows for a more fine-grained view at the consequences of new work characteristics and how organizations can contribute to them in their favor.Our findings may support organizational decision makers, HR practitioners, and employees address current and future learning demands in the new world of work.

The concept of new work characteristics
The concept of new work is manifold, and it can comprise different meanings and approaches.It ranges from an understanding of new work as fostering psychological empowerment of employees up to a diverse set of new work activities, such as agile project management or open office concepts (Junker et al. 2022;Schermuly and Geissler, 2021).In our study, we focus on new work characteristics (Poethke et al. 2019(Poethke et al. , 2023) ) as conceptualization for investigating their relation to IWL, that can be applied to a broad range of jobs and employees.By using new work characteristics instead of specific new work interventions, such as barcamps or hackathons (see Schermuly and Geissler 2021, for an overview), we can be more inclusive of bluecollar work, for instance, as those new work interventions are still predominantly introduced to highly skilled whitecollar work (Kortsch et al. 2023).
We follow the approach of Poethke et al. (2019) in assuming five central new work characteristics: digitalization, flexibility, dissolution of boundaries, relevance of work, and participation.While digitalization describes the use of and dependency on information and communication technology (Poethke et al. 2019; also see Harteis 2018; or Ifenthaler 2018), flexibility refers to both time and place of work that employees can adjust (Demerouti et al. 2014).Digitalization and flexibility are accompanied by a dissolution of boundaries between work and private life (Poethke et al. 2019).Relevance of work describes employees' subjective feeling in how far they see their work as meaningful and significant (Rosso et al. 2010;Spreitzer 1995).Finally, participation refers to an active involvement in corporate decision-making at different organizational levels beyond autonomy over one's own job (Mende et al. 2015).What makes those new work characteristics stand apart is their bundling of drastically changed working conditions, especially accelerated by new technological developments (Poethke et al. 2019).We will outline our reasoning on why new work characteristics are likely to be related to IWL.Our research model is depicted in Fig. 1.Demerouti (2022) argues that under certain conditions, the changing nature of work, particularly digitalization and automation, can be a job resource and contribute to employee growth and well-being.These conditions include that new work characteristics are designed to support the work of employees, that employees retain control over the use of technology, and that job demands remain manageable.For example, as long as dissolution of boundaries between work and private life (Poethke et al. 2019) is not perceived as a constraint or stressor but, on the contrary, allows for better time compatibility through more flexibility, this can be a valuable resource for employees.

New work as a resource for learning
We assume that new work characteristics in their role of resources lead to IWL and base this assumption on COR theory (Hobfoll et al. 2018).COR theory principally states that individuals and groups strive to gain, maintain, and protect resources.Resource losses are perceived by the individual or group to be more salient than resource gains; however, resource gains increase in value in high-resourceloss situations (Hobfoll et al. 2018).The resource investment principle of COR theory states that resource investment is necessary to protect against resource loss, to recover from resource losses that have already occurred, and to realize resource gains.In order to enable employees to cope with challenges of the new world of work, organizations need to invest resources-in this case, the investment is in the provision of new work characteristics that support employees in the sense of Demerouti (2022).In short, the resources invested are positive working conditions in the new world of work.This investment should pay off for employ-ees: Resource gain and protection against resource loss, respectively, are reflected in competence acquisition through work-related learning processes, as skilled and empowered employees are better prepared to cope with changes in the world of work (Noe et al. 2014;Tannenbaum and Wolfson 2022).Thus, the organization's resource investment in new work should result in resource gains in terms of IWL among employees.In the long run, this may result in a positive resource gain spiral, where organizational resources (e.g., competitiveness) and employee resources (e.g., employability) reinforce each other (Hobfoll 2001;Hobfoll et al. 2018).Therefore, it is important to investigate the relationships between new work characteristics with IWL.Referring to COR theory, we hypothesize: H1 There is a positive relationship of new work characteristics, namely (a) digitalization, (b) flexibility, (c) dissolution of boundaries, (d) participation, and (e) relevance of work, with IWL.
Another principle of COR theory is that of resource caravans, which states that resources do not occur individually but in packs or caravans (Hobfoll et al. 2018).In terms of resource gains, according to Hobfoll (2011), "[c]aravan passageways for organizations are the environmental conditions that support, foster, enrich, and protect the resources of individuals, sections or segments of workers, and organizations in total" (pp. 118-119).In terms of new work, we suggest that the five new work characteristics often occur together and act as reinforcing resources: Dissolution of boundaries, for instance, goes hand in hand with more flexibility, and stronger participation leads to a higher perception of relevance of work (see Poethke et al. 2019).As a guiding condition, digitalization influences the entire new work design (Parker and Grote 2022).We therefore assume that new work as a whole is related to IWL.However, due to a lack of previous research, it is unclear which of the new work characteristics have the strongest link with IWL.In our study, we therefore examine the differential associations of new work and IWL, following this exploratory question: RQ1 Which new work characteristics are most strongly related to IWL?

The moderating role of learning climate
Organizations can do more than just invest resources in new work; they can also affect how resource investment impacts employees' IWL.According to the COR principle of resource caravan passageways, resources exist in a context of ecological conditions that can enhance or hinder resource building (Hobfoll et al. 2018).We argue that learning climate as a key condition is likely to strengthen the proposed new work characteristics-IWL relationship, as it is "one of the most prevalent mechanisms through which organizations provide support for employee learning" (Nikolova et al. 2016).Learning climate refers to employees' shared perceptions of organizational policies and practices that enhance or hamper their learning behavior (Mikkelsen and Grønhaug 1999;Nikolova et al. 2014).It comprises employees' perceptions of how well their organizations facilitate learning, appreciate learning, and provide safety when errors occur (Nikolova et al. 2014;Rausch et al. 2017).Research revealed positive links between learning climate and learning outcomes (Berson et al. 2015;Nikolova et al. 2016) as well as employees' emotional demands and intention to leave their organization (Nikolova et al. 2014).When organizations do not facilitate and appreciate learning and do not view failures as learning opportunities, employees are less likely to engage in IWL, and to gain results from learning (Ellinger and Cseh 2007;Hilkenmeier et al. 2021;Putz et al. 2013).Therefore, we examine if learning climate moderates the assumed positive relationship between new work characteristics and IWL.We hypothesize: H2 Learning climate strengthens the positive relationship of new work characteristics with IWL.

Procedure and participants
Participants were invited via social media to take the online survey between November 2018 and February 2019.There was no incentivization.In total, 291 participants answered our survey.59.5% were female.The majority was between 26 and 30 years old (21.6%), followed by 31-35 years and 36-40 years (each 16.2%), and 46-50 years (11.3%).Most participants (61.9%) hold a high school diploma as their highest educational qualification.The highest vocational qualification reported by most participants (36.4%) was a bachelor's degree, followed by no vocational training (29.6%).Most participants were employees (56.7%), followed by lower management (20.3%) and middle management (17.5%).Most participants had more than 20 years of work experience (31.6%), followed by 11-20 years (26.1%).They have worked on average 3.31 years (SD = 1.96) for their current organization.Most organizations had between 1001 and 10,000 employees (35.1%) and were in the communication and information sector (39.2%).Most participants classified their job as office work (69.1%), followed by social work (12.7%), and industrial work (2.7%).

Measures
All variables were assessed with validated psychometric scales.Participants evaluated items on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree).All measures showed at least acceptable scale reliabilities.
New work characteristics were measured with the Measurement Instrument for the Assessment of Central Aspects of the New Way of Work (Poethke et al. 2019).It measures five dimensions with five items each: digitalization, flexibility, dissolution of boundaries, participation, and relevance.Example items and reliabilities are as follows: For digitalization "I depend on digital media (e.g., internet, email) to complete my work tasks" (CA = 0.79); for flexibility "I can flexibly arrange my working hours" (CA = 0.82); for dissolution of boundaries "I take professional phone calls outside regular working hours" (CA = 0.81); for participation "My suggestions for change will be considered in my organization" (CA = 0.75); and for relevance "I feel that my work is important" (CA = 0.89).Cronbach's Alpha for the overall scale was 0.85.
Informal workplace learning was measured with 24 items in eight subscales (Decius et al. 2019)   ).The measure has three dimensions (facilitation, appreciation, and error avoidance climate) with three items per dimension.We combined all items to a single score for learning climate.The items for error avoidance have been reversed.A sample item was "In my organization, employees who make an effort to learn new things receive appreciation and respect".Cronbach's Alpha was 0.88 for the overall learning climate scale, 0.79 for facilitation, 0.81 for appreciation, and 0.91 for error avoidance climate.
We statistically controlled for employees' gender, because new ways of working might differ between male and female employees (Lindgren and Packendorff 2006).We also considered years of work experience and organizational tenure as control variables.More experienced employees may receive more support for workplace learning (Harteis et al. 2015).They may also have more control over the work environment, allowing them to better create learning opportunities (Cerasoli et al. 2018).

Results
Intercorrelations between study variables are shown in Table 1.To test our hypotheses, we conducted a series of multiple regression analyses predicting IWL by new work characteristics and our set of control variables.First, we had a look at the overall relationship between new work characteristics and IWL based on their combined scores (irrespective of their respective dimensions, see Table 4).1There was a significant and positive overall relationship between new work characteristics and IWL, β = 0.39, p < 0.001.The overall model accounted for 18% of the variance of IWL.
For a more fine-grained analysis regarding the different new work characteristics, we conducted a multiple regression analysis predicting IWL (combined score) by the five new work characteristics and our set of control variables (see Table 2).There were only significant positive relationships between digitalization and IWL (H1a), β = 0.11, p = 0.03, as well as dissolution of boundaries (H1c), β = 0.12, p = 0.04, and relevance (H1e), β = 0.35, p < 0.001, respectively.We did not find a significant relationship between flexibility and IWL (H1b), β = 0.03, p = 0.61, and participation and IWL (H1d), β = 0.06, p = 0.36.This model accounted for 23% of the variance of IWL.
As some of the new work characteristics were significantly related to each other (see Table 1), we used relative importance analysis in addition to multiple regression analysis (Tonidandel and LeBreton 2011).Relative importance analysis partitions explained variance among predictors to   work (RS-RW = 55.71%),participation (RS-RW = 18.53%), dissolution of boundaries (RS-RW = 13.12%),digitalization (RS-RW = 8.61%), and flexibility (RS-RW = 4.03%).Similarly to the traditional multiple regression analysis results, the majority of the predicted variance in IWL is attributed to relevance of work.Contrary to the traditional multiple regression analysis results above, participation was among the most important predictors of IWL.We conclude that the non-significant relationship between participation and IWL in traditional multiple regression is due to the high correlation between participation and relevance of work (see Table 1 again).
We then tested the interaction effect of learning climate and new work characteristics on IWL.For that purpose, we entered learning climate and the interaction term of learning climate and the overall new work characteristics score as predictors into the model (see Table 4).The predictors were mean-centered before entering the model.Learning climate was not significantly related to IWL, β = 0.05, p = 0.42, in the regression model without the interaction term.There was no significant relationship between the interaction term and IWL, β = 0.02, p = 0.65.Thus, our data do not support H22 .

Discussion
Our study aimed to shed light on the relationship between new work characteristics, learning climate, and IWL.Findings show a positive overall relationship between new work characteristics and IWL.Despite the medium-sized overall relationship, this study also highlighted differential relationships between specific new work characteristics and IWL.Contrary to expectations, learning climate did not moderate the new work characteristics-IWL relationship.

Theoretical implications and limitations
Employees increasingly experience changing work demands and must try adapting to them (Richter et al. 2020).One strategy to maintain work capability is using IWL, as it directly supports coping with current work tasks (Decius 2020).Our findings show a positive overall relationship between new work characteristics and IWL, indicating that there are benefits of new work characteristics.This supports the COR rationale (Hobfoll et al. 2018) and the argumentation of Demerouti (2022) that new work characteristics may be a resource to be invested in order to receive IWL (and possibly resulting increases in competence) as a resource gain.This also matches the premise that work design contributes to employees' learning and learning outcomes, stated in models as the work design growth model (Parker 2017), the presage, process, and product (3-P) model of workplace learning (Tynjälä 2013), or-more specifically for IWL-the antecedents, processes, and outcomes (APO) model of IWL (Decius et al. 2021).It also empirically supports the assumption that IWL fits well to the changing nature of work (Kortsch et al. 2021;Gerards et al. 2020;Noe et al. 2014).However, due to the cross-sectional design in our study, we can only assume that new work characteristics lead to IWL, and not vice versa.Although our reasoning suggests that new work characteristics precede IWL, we cannot exclude that this relationship could also be reversed.Employees who seek more learning opportunities may also find ways to incorporate new work characteristics in their workplaces.Also, the assumed mechanisms according to COR theory, such as the resource gain principle and resource caravans (Hobfoll et al. 2018) can only be tested with longitudinal study designs.Future studies thus could shed more light on how new work characteristics unfold their potential influence on IWL.For instance, new work characteristics may come with stronger learning demands (Kubicek et al. 2015), that may mediate the relationship new work characteristics score on IWL and also for the separate dimensions of new work characteristics interacting with the overall learning climate score on IWL.We did not find any significant relationships for either of those combinations.Statistical results are available upon request.
to IWL.Additionally, alternative explanations such as job crafting (Wrzesniewski and Dutton 2001;Zhang and Parker 2019) could explain the potential reciprocal relationship between work demands and IWL (Decius et al. 2023d;De Lange et al. 2010).Based on our reasoning, we would also expect that new work characteristics act as an antecedent to other learning approaches and hope to inspire future research for other learning approaches, such as formal learning and self-regulated learning, as well.
However, the overall new work characteristics-IWL relationship seems to be driven by the relationship between relevance of work and IWL in particular.Our findings add that new work might not be best investigated as a unified concept, but separately for the different new work characteristics.It especially highlights the role of employees' motivation, driven by work design including the perceived relevance of their work, for their efforts to learn and develop (see Deci and Ryan 2002;Parker 2017).According to relative importance analysis, digitalization and flexibility were less important than the other new work characteristics and appear theoretically and practically less relevant.Given the central role of relevance of work, we call for more research on the specific mechanism of how relevance of work could trigger IWL.Furthermore, some new work characteristics may also work as job demands rather than resources.Although we found a positive relationship between dissolution of boundaries and IWL in our study, others found a positive relationship with presenteeism, suggesting that it could also act as a demand (Poethke et al. 2023).Investigating new work characteristics individually, rather than in an overall concept, may help to better understand differential associations with work-related outcomes.
Interestingly, learning climate did not influence the positive relationship between new work characteristics and IWL.We conclude that the identified new work characteristics-IWL link is robust against whether or not learning is facilitated and appreciated, or errors are welcomed or not as learning opportunities.According to our results, the organizational environment does not appear as a powerful source to strengthen the link between new work characteristics and IWL.The robustness of this relationship implies that it may not be easy for organizations to handle the effects of new work characteristics for employees.Indeed, IWL is hard or even impossible to control for organizations (Cerasoli et al. 2018) and our findings indicate that this is also true for implicit means, such as learning climate.We need more studies to better understand why and when new work characteristics are related to IWL, given that there seems to be a positive and robust relationship.However, in line with previous research on learning support and IWL (Crans et al. 2021;Decius et al. 2021;Hilkenmeier et al. 2021), we found a direct effect of learning climate on IWL, supporting the assumption that employees with more resources will acquire new resources more efficiently (Nikolova et al. 2016).
We relied on employees' self-ratings only, which may raise methodological concern regarding common method bias (Podsakoff et al. 2012).Given that we identified a positive relationship between new work characteristics and IWL, we suggest that future studies utilize longitudinal designs and add supervisor ratings or observations to replicate our findings.However, employees' perceptions of their workplace in terms of learning opportunities also vary widely, even when conditions are inherently the same (Coetzer 2007), so self-assessments are likely to remain an important component of IWL research.

Practical implications and conclusions
Our findings have implications for workplaces where new work characteristics are increasingly taking hold.Organizational decision-makers, HRD professionals, and employees may not be afraid of new work characteristics per se, as we identified a positive relationship to IWL.The new work characteristics-IWL link suggests that organizations and HRD professionals can expect an increase of learning and may want to capitalize on that effect.Although our results are primarily based on white-collar employees who indicated their job as office work, IWL may be particularly important for employees with learning barriers to participation in formal training (Decius 2020); we interpret this as a good sign for provoking learning, especially for groups that are traditionally not in the scope of systematic HRD, such as employees in small and medium sized enterprises (Jeong et al. 2018) and less-qualified employees (Decius et al. 2019;Kyndt et al. 2011).Most participants in our study work in the communication and information sector and we cannot exclude that the sector may influence the strength of the new work characteristics-IWL relationship.Future studies may want to explicitly explore the potential influence of the sector to derive more specific practical suggestions for certain sectors.
Learning climate did not strengthen the new work characteristics-IWL link.Unfortunately, this implies that organizations cannot accelerate the benefits of new work characteristics for learning by improving their learning climate.However, we did find a direct relationship between learning climate and IWL which is in line with previous research (see Cerasoli et al. 2018;Decius/Graßmann & Creon 2023a;and Kyndt and Baert 2013, for systematic overviews).We thus conclude that organizations should still improve their organizational policies and practices to support learning, whether or not new work characteristics are apparent.
In summary, our study contributes by showing the potential benefits of new work characteristics for employees' IWL.This relationship seems to be robust against learning climate.New work appears as a diverse concept of related but different characteristics and more studies investigating them would help HRD navigating through the chances and challenges of new work with a more comprehensive evidence base.

Fig. 1
Fig. 1 Research model with three items each: trying/applying own ideas (e.g., "I try a different method to solve new tasks at work."), model learning (e.g., "I look at how others work in the company to improve my work."),direct feedback (e.g., "I ask my supervisor when I am not sure how well I worked."),vicarious feedback (e.g., "I ask my colleagues about the methods and tricks they use at work."), anticipatory reflection (e.g., "Before starting a new task, I think about how I can do my work best."),subsequent reflection (e.g., "When I have finished a new task, I think about what I still could do better next time."),K

Table 1
Means, standard deviations, and correlations (Nikolova et al. 2014insic intent to learn (e.g., "I want to learn something new for myself because then I can solve problems at work faster."),andextrinsicintent to learn (e.g., "I want to learn something new at work for myself because then I can pursue my career at the company.").Following conceptual and empirical recommendations(Decius et al. 2023c), which suggested to consider IWL on a superordinate level, we combined all items to a general single score for IWL, similar to what has been done in recent studies (e.g.,Decius et al. 2023b).In this way, we reduced the complexity of the computational model, as the question of our study did not include differential relations for the separate IWL facets.Cronbach's Alpha was 0.90.Learning climate was measured with nine items(Nikolova et al. 2014

Table 2
(Tonidandel and LeBreton 2015)k characteristics (separate dimensions) predicting IWLTonidandel and LeBreton 2011).We used the webbased tool RWA Web(Tonidandel and LeBreton 2015)with 10,000 bootstrapping replications and set alpha at 0.05.Results are summarized in Table3.The confidence intervals of the raw weights revealed that each new work characteristic explained a statistically significant amount of variance in IWL.All five new work characteristics combined explained 19% of the variance of IWL.The relative order of new work characteristics predicting IWL was relevance of

Table 3
Relative importance analysis results (predicting IWL) Raw weights (RW) represent the percentage of variance each new work characteristic explains in IWL and will sum to R 2 .Rescaled weights (RS-RW) reflect the proportion of variance that each new work characteristics accounts for relative to the variance explained by the full model and will sum to 100%