How to Foster Informal Learning
A Cross-Cultural Study
Abstract
Abstract: This article explains how three different informal learning strategies are influenceable by feedback and if and how feedback further mediates the impact of the cultural value of power distance on informal learning. We also examined the three informal learning strategies regarding their stableness and separability. A survey at two different timepoints was administered to technical staff members from a globally acting company from the automotive industry within six different countries (N = 777). We tested our hypotheses by conducting a multigroup factor analysis based on a bootstrap-based structural equation model. Results showed informal learning strategies to be stable over time and cultures. Additionally, feedback availability showed to be a predictor of informal learning. The effect of power distance on informal learning is also mediated by feedback availability. The overall results show that informal learning strategies can be influenced differently in various countries.
Zusammenfassung: Dieser Artikel untersucht, wie informelles Lernen durch die Verfügbarkeit von Feedback begünstigt wird und wie diese Beziehung durch kulturell bedingte Machtdistanz beeinflusst werden kann. Ein indirektes Modell (N = 777) wurde entwickelt um zu untersuchen, ob unterschiedliche Formen des informellen Lernens über Zeit und Kulturen hinweg stabil und trennbar sind und ob eine mögliche vermittelnde Rolle von Feedback besteht. Die zugrundeliegenden Daten basieren auf einer Online-Befragung von technischen Mitarbeitern eines Automobilkonzerns, aus sechs unterschiedlichen Kulturen und zu zwei Zeitpunkten. Die untersuchten Hypothesen wurden mittels einer mehrgruppigen konfirmatorischen Faktorenanalyse und eines Bootstrap-basierten Strukturgleichungsmodells getestet und es zeigte sich, dass informelle Lernstrategien zeitlich stabil sind, dass Mitarbeiter aus unterschiedlichen Kulturen die gleichen informellen Lernstrategien nutzen, sowie dass die Verfügbarkeit von Feedback einen positiven Prädiktor darstellt. Der Einfluss der Machtdistanz auf informelles Lernen wird durch Feedback mediiert. Zusammenfassend zeigte sich somit, dass informelles Lernen in verschiedenen Ländern auf unterschiedliche Weisen begünstigt werden kann.
Informal Learning in a Globalized World
Competition among international companies has increased in recent years because of the demands of globalization (e. g., Marquardt & Berger, 2003), digitalization (e. g., Kluge et al., 2020), and the rise of new technologies (e. g., Paruzel et al., 2020). For companies to stay ahead of others, they must adjust the work processes of their employees to become faster and more flexible. Companies need to consider a wide range of practical adjustments to be able to hire, train, retain, and support their workforce, which is intercultural and often spread throughout the world (Marquard & Berger, 2003; McLean, 2001, 2016). To accomplish this goal, human resources development (HRD) must ensure that employees can convert the latest knowledge into the development of new and better technologies and services (Bartlett & Ghoshal 1998).
Learning with a focus on gathering new knowledge to improve work performance usually takes place in a classroom outside the regular working space or as a combination of a mixture of face-to-face and virtual formats based on a fixed structure of content, often planned far in advance (Cerasoli et al., 2018; Kauffeld & Paulsen, 2018). This does not align with the need to become faster in developing the most recent technologies and services as strict and mostly formal and commonly time-consuming learning formats and implementing them in their development and following implementation. This is a major reason for the frequent tardiness of formal formats (McGuire & Gubbins, 2010). Companies thus need to change how they guide their employees to develop technologies and services in the most time-efficient mode possible. Separation from work is no longer adequate (e. g., Kauffeld & Paulsen, 2018; Kortsch et al., 2019). In addition, up to 90 % of actual learning does not take place during formal training but while employees are on the job, as there are several types of learning outside the classroom (for an overview, see Wolfson et al., 2018). One of them is called informal learning (e. g., Cerasoli et al., 2018). Informal learning strategies are controlled by learners and can be highly effective as learners informally practice and improve their knowledge exactly when and how it is needed (Kortsch et al., 2019; Schaper & Sonntag, 2007). This can be differentiated into three different strategies: learning from others in a social context, learning from oneself, and learning from noninterpersonal sources (Noe et al., 2013). Informal learning plays a major role in addition to formal continuing education (Kauffeld, 2016; Decius & Schaper, 2021) because it reflects a flexible step toward knowledge transfer and employees getting involved with the content – which, in the end, creates a positive result for the company as transfer is related to both organizational outcome (e. g., knowledge and agility on tasks) and individual outcome (e. g., learning and performance) (Tannenbaum et al., 2010). Because of the very nature of informal learning, learners’ skills improve with the amount of time they spend on informal learning. Therefore, at the same time, it is important for informal learning strategies not to be a one-time occurrence but to persist over time to achieve the best possible outcome in the form of a transfer. This makes the long-term use of informal learning strategically relevant for companies as employees improve their knowledge and thus continuously perform better.
The nature of informal learning strategies can be influenced by different variables (e. g., Cerasoli et al., 2018): by the immediate supervisor or the team through feedback, which is defined as a way for others to comment on a certain action or result of a person’s work (London & Smither, 2002). Feedback can be seen as a valuable predictor of the internal reflections of learners and the informal learning strategies appearing in the workplace (Decius et al., 2019; Kortsch et al., 2019). Whether a feedback-oriented behavior is desired can be biased by someone’s socialization, which is influenced by their cultural affiliation (Kim & McLean, 2014).
Following the idea that a culture is defined not only by the geographical location of a country (Kim & McLean, 2014), values and interaction patterns must be considered when examining learners’ preferences for diverse forms of learning (Cseh & Crocco, 2020). One example is power distance (PD), which is defined as the extent to which unequal power distribution is accepted in a country (Hofstede et al., 2010). PD has been found to influence the self-directedness of learning and therefore the intrinsically steered usage of different informal learning strategies (e. g., Holtbrügge & Mohr, 2010; Kim, 2003; Kim & McLean, 2014). This makes PD highly relevant because organizations necessarily distribute power unequally (Farh et al., 2007), and also that employees could be reprehended for being self-organized or for choosing their own learning methods (Holtbrügge & Mohr 2010).
This study contributes to research investigating the influenceability of informal learning strategies over time, also regarding cultural differences and preferences. To date, authors have used various models to investigate informal learning strategies (e. g., Decius et al., 2021; Noe et al., 2013; Tannenbaum et al., 2010) and to discover how to influence these behaviors. Nevertheless, none of these models investigated the interplay of feedback availability and PD over time while also distinguishing the three types of informal learning.
Therefore, we pursue the following: We examine the understanding of informal learning strategies at the individual level, the organizational level, and the cultural level. We investigate the appearance of informal learning strategies and their stableness (1) over time and across cultures, how the appearance of informal learning strategies is impacted by feedback (2), and how feedback influences the impact of the cultural variable of PD on informal learning (3) (see Figure 1). Our study is based on data from the automotive industry, which has been transformed in recent decades by both globalization and technological progress and is therefore highly dependent on the rapid (informal) learning of its employees and the resulting performance results and is therefore very predestined for our investigations.
Theoretical Background and Development of Hypotheses
Informal Learning Strategies
Informal learning theory as such was developed by Marsick and Watkins (1990) and has expanded ever since. Informal learning has been described as not being primarily fostered and directed by the organization but rather initiated by the learner (Decius et al., 2019; Kortsch et al., 2019) outside the classroom (Noe et al., 2016). In this regard, it can be seen as the counterpart to formal learning formats that are predefined (Kauffeld, 2016; Kortsch et al., 2019) and that also differ from agile learning, which functions through partly formalized scrum-based learning units (Jungclaus & Schaper, 2021; Longmuß & Höhne, 2017). Informal learning is commonly utilized by persons intentionally reacting to a certain challenge at work or dealing with learned content that needs to be deepened for a certain situation or to solve a problem directly (Marsick & Neaman, 2018). To conduct informal learning, learners use certain informal learning strategies (e.g., Kortsch et al., 2019; Noe et al., 2013). According to Noe and colleagues, three different strategies of informal learning can be distinguished: (1) Informal learning without the help of others involves reflecting on one’s own performance and attempting new ways to improve is the first strategy which has to be distinguished. Here, learners spend time reflecting on how to improve their performance by experimenting (Noe et al., 2016). (2) The second strategy involves interacting with colleagues or mentors to receive feedback on ideas and performance and to develop strategies for performance improvement (Noe et al., 2013). (3) The third strategy is when learners pursue activities such as reading professional magazines or researching on the internet for useful resources and information; no social interaction is conducted (Noe et al., 2016).
Research on how informal learning can be promoted or inhibited is still fragmented (Cerasoli et al., 2018; Kortsch et al., 2019; Marsick & Neaman, 2018). Furthermore, it is not yet clear how cultural variables in particular can influence the interplay between individual and organizational factors which have an impact on informal learning.
Informal Learning Strategies in Different Cultures
The basic referent of the construct culture is a social group with a set of common values (Brown, 1998) that influence behavior in general and also at the workplace (e. g., Tsui et al., 2007; Watkins & Dirani, 2013). Values, as the principles guiding behavior and judgment, offer a stable opportunity to compare behavioral differences in cultures (e. g., Schein, 2004). Geert Hofstede’s model, which includes six cultural dimensions, is the most often used and most common model for this purpose (Jackson, 2020; Tung & Verbeke, 2010). He distinguished the value dimensions of PD, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence (Hofstede, 1980). These dimensions were already applied in various contexts (e. g., intercultural cooperation, teambuilding, and leadership), but also within the learning context which is considered here learning (e. g., Jeong et al., 2018; Richter et al., 2020; Tsui et al., 2007).
One of these dimensions, PD, is defined as the extent to which unequal power distribution is accepted in a country (Hofstede et al., 2010). Within the learning context, this dimension becomes highly relevant (Holtbrügge & Mohr, 2010; Kortsch et al., 2022) as the individual PD value leads to different preferences in learning styles: Learners who accept a high PD prefer learning styles that include less self-steering as this would include self-selected and not prescribed procedures to maintain the PD, and when getting taught, a teacher is the main source of information and additional or further search for information is not desired (Joy & Kolb, 2009).
When it comes to informal learning, researchers found out that PD also influences informal learning behavior (e. g., Holtbrügge & Mohr, 2010; Kim, 2003; Kim & McLean, 2014). Some informal learning strategies include a social exchange (i. e., learning from others in a social context) and others do not (learning from noninterpersonal sources), which might be preferred or not in different countries (Kim, 2003). It was found that people from countries with a lower PD engage in informal learning (e. g., exchange) more often than people from high PD countries; within high PD countries, exchange, whether with superiors or colleagues, is not tolerated (Jiacheng et al., 2010). Nevertheless, to date there has been a lack of research focusing directly on correlations between the values and informal learning strategies of employees in various countries (e. g., Kim & McLean, 2014; Noe et al., 2013).
To investigate whether cultures with different PD levels have the same understanding of informal learning, and how the intensiveness of the individual usage differs because of the PD values, we hypothesize the following:
Hypothesis 1a: Employees in high and low PD cultures use the same informal learning strategies.
Hypothesis 1b: The intensity of the usage of informal learning strategies differs between cultures with different PD values.
It was recently argued that the learning activities of all employees follow an individual developmental strategy (Poell & Van Der Krogt, 2014): Individuals plan their learning activities along their individual learning paths (Poell, 2017) and pursue their individual goals. This, on the other hand, according to the goal-setting theory (Locke & Latham, 1990), is a highly relevant predictor for a subsequent transfer, which therefore leads to a possible better outcome for companies in terms of applied advanced knowledge.
Thus, the choice of learning strategies depends on the learning theme the individual follows (Kauffeld & Paulsen, 2018). Therefore, the preference for individual informal learning strategies is assumed to be strategic and, at least, relatively stable over time and occur several times within the measured period. Thus, we propose the following:
Hypothesis 2: The use of informal learning strategies is (relatively) stable over time.
Feedback Availability as an Antecedent for Informal Learning Strategies
Behaviors in certain situations can promote or hinder informal learning (Cerasoli et al., 2018). Feedback and its availability (FBA) have often been regarded separately from other forms of support (London & Smither, 2002) and are primarily useful after something learned has been applied (Kortsch et al., 2019), to give either a positive or negative interpretation of the learner’s behavior. Research has shown that feedback is one of the most effective methods of promoting learning and improving the learner’s ability to actually use the learned content (Decius et al., 2019). The results of receiving feedback have proved to be positive for learning (Janssens et al., 2017) and to promote the process of informal learning strategies (Lohman, 2005): Within small and medium-sized companies, the availability of feedback was found to be a good predictor for different types of reflective and interpersonal informal learning strategies: Learning from oneself and from and with others and learning together (Doornbos et al., 2008; Kortsch et al., 2019). Nevertheless, these findings must be strengthened and widened for the here looked at the scope of an internationally operating company.
Therefore, we assume the following:
Hypothesis 3a: Feedback availability promotes informal learning strategies.
Although we presume that all informal learning strategies can be impacted by feedback, one of them might stand out. The most socialized strategy of informal learning, which is learning from others in a social context, has been clearly connected to feedback (Sparr et al., 2017). This strategy is based on interpersonal action as opposed to learning from noninterpersonal sources or learning from oneself (Noe et al., 2013) but can still be separated from feedback, as an observation of actions in a social interaction can also lead to the derivation of improvement strategies. This does not necessarily require feedback from the person being observed. Because of the connection between values and preferences in receiving feedback, this study proposes a greater impact of feedback on the informal learning strategy of learning from and with others than on the other two informal learning strategies:
Hypothesis 3b: Feedback availability has the strongest influence on learning from others in a social context.
The Interplay of PD, Feedback Availability, and Informal Learning Strategies
PD expresses the emotional distance between employees and superiors as well as between subordinates and their superiors. The emergence of different informal learning strategies can thus be influenced (depending on the social desirability of, for example, social exchange or showing initiative), as this dimension impacts employees’ relationships with peers and supervisors and their self-engagement with learning (Kim & McLean, 2014).
However, PD might not explain a negative or positive appearance of informal learning strategies since it is not a clear action but an action-influencing variable. This is where feedback again comes into play. As stated above, feedback can be an effective way to facilitate learning on a certain topic and to use new abilities or knowledge (Decius et al., 2019). If we match these facts with cultural differences, we can deduce the positive or negative effects of PD on informal learning strategies. On the one hand, feedback generally promotes informal learning strategies (Cerasoli et al., 2018; Decius et al., 2019; Kortsch et al., 2019; Tannenbaum et al., 2010); on the other hand, feedback as a form of social exchange is not desired in high PD countries. In some cultures, feedback can promote the informal learning strategies desired; in others, feedback leads to the opposite (e.g., Kim & McLean, 2014).
Therefore, countries with a low PD and a positive attitude toward social exchange might respond to feedback with the practical applications of informal learning strategies. For countries with a high PD, Lehmann (2009) argued there is one-way and top-down communication between supervisors and employees (Bochner & Hesketh 1994). This might lead to a negative impact on the motivation to seek feedback or change in general.
In addition to the national culture and identity, a company’s learning culture should not be neglected as this culture might overrule cultural preferences (Richter & Kauffeld, 2020). A so-called learning culture reflects the importance of learning in a company and specifies how learning is promoted within the organization (Kortsch & Kauffeld, 2019; Kortsch et al., 2019). If the headquarters of a company is located in a country that exemplifies a learning culture promoting feedback, this is also transferred to the countries into which they expand. Since the power of the learning culture overruling cultural preferences has not yet been fully examined (Richter & Kauffeld, 2020), we assume that the provision of feedback can influence the impact of the PD dimension on the informal learning strategies.
Therefore, we state the following:
Hypothesis 4: Feedback availability mediates the impact of PD on informal learning strategies.
Method
Procedure
Data were collected from technical employees of a German globally acting company (GAC) within the sales departments of six international locations: the United Kingdom (UK), China (CN), Spain (ES), Switzerland (CH), the United States of America (USA), and Australia (AU). All participants were asked to complete an online questionnaire at two different timepoints after participating in technical training about the technical features of a new hybrid SUV, namely, immediately afterward and 4 – 6 weeks after the training.
Participants
1,459 employees were invited to participate voluntarily, and a total of 777 participants responded at t1 (NUK = 170, NES = 163, NCN = 150, NAT = 119, NUSA = 97, NAU = 78); 444 responded at t2 (NUK = 115, NES = 143, NCN = 51, NAT = 38, NUSA = 26, NAU = 71).
Whenever possible, we used the data from all participants. However, to ensure a representative dataset over t1 and t2, we used only the highest responding countries for t2, including the UK, Spain, and China, which had a total of 444 responses for t1 and t2. This resulted in a total return rate of 53 % for t1 and 30 % for t2 and, in total, a dropout rate of 42.9 %. About 98 % of the participants were male, and the overall average age fell into the category “25 to 34 years.” Since correct answers had to be ensured, all participants were provided a questionnaire in their mother tongue and/or the official language of their respective countries.
Data Analysis
For statistical analyses, we used R (version 3.5.1, R Core Team, 2018) with the lavaan package (version 0.6 – 2, Rosseel, 2012). To test hypotheses 1a and 1b – whether employees in different cultures use the same three informal learning strategies, and whether the relevance of the informal learning strategies differs between cultures – we conducted a multiple-group confirmatory factor analysis (CFA) and an analysis of variances at t1 and t2 in R (Hirschfeld & Van Brachel, 2014). Hypotheses 2, 3a, 3b, and 4 were investigated using an SEM.
Measurements
Informal Learning Strategies
All three informal learning strategies were assessed using the measurement scale constructed by Noe and colleagues (2013), which consisted of 9 items (3 items for each of the three informal learning strategies) to measure the probands’ self-steered learning activities after the training took place. All participants were asked to indicate on a 5-point Likert-like scale (1 = very seldom to 5 = very often) how often they engaged in different learning behaviors to perform their jobs better during a typical work week. A sample item for learning from others in a social context (ILS_SO) is “interacting with my supervisors.” A sample item for learning from oneself (ILS_SE) is “reflecting on how to improve my performance,” and a sample item for learning from noninterpersonal sources (ILS_NI) is “reading professional magazines and vendor publications.” The Cronbach’s α values (please see Table 1) were satisfying overall.
Feedback Availability
The employees were asked about the feedback within their company. To collect this data, we used 3 items from the revised Learning Transfer System Inventory Version 4 (LTSI) by Bates and colleagues (2014). A sample item from this scale is “I get a lot of advice from others about how to do my job better.” The participants answered on a range from 1 (I do not agree) to 5 (I totally agree). The Cronbach’s α for this scale was good for both evaluation times (αt1 = 0.88, αt2 = 0.92).
The data were collected using validated and reliable scales. When necessary, they were translated according to the procedure suggested by Bates et al. (2007).
Power Distance
The countries examined here exhibit different power distance values, which range from high (China = 80) to medium (Spain = 57 and USA = 40) and low (Australia = 36, UK = 35 and Switzerland = 34). The values were then added following the model of cultural differences (Hofstede et al., 2010).
Results
Separability of Constructs
To demonstrate the validity and separability of all constructs (i. e., three informal learning strategies and feedback), we performed three confirmatory factor analyses (CFAs) based on the dataset of t1. A one-factor model assumed all constructs to be inseparable (Model 1); a two-factor model assumed two different factors (i. e., informal learning and feedback, Model 2) to be inseparable; and a four-factor model separated all four constructs, as in this study (i. e., three informal learning strategies and feedback, Model 3). Model 3 had a good fit (Δχ² = 242.275, Δ df = 48, p < .001), and the following fit indices: comparative fit index (CFI) = .95, root mean square error of approximation (RMSEA) = .08, standardized root mean square residual (SRMR) = .05, and Tucker-Lewis Index (TLI/NNFI) = .92) according to Hu and Bentler (1999). The model fits of Model 1 (CFI = .64, RMSEA = .19, SRMR = .11, TLI = .56) and Model 2 (CFI = .83, RMSEA = .13, SRMR = .08, TLI = .79) were worse. Additional model comparisons revealed that the model fit of Model 3 was superior to Model 1 (Δχ² = 1202.709, Δdf = 54, p < .001) and to Model 2 (Δχ² = 577.102, Δdf = 53, p < .001, CFI = .83, RMSEA = .13, SRMR = .08).
The loadings for the items of the dimension “learning from oneself” ranged from .56 to .61; the loadings for the dimension “learning by social interaction” ranged from .62 to .87; the loadings for the next dimension, “learning from other sources” resulted in values between .67 to .99; and the loadings for the dimension of “feedback availability” ranged from .83 to .97.
Correlations
Table 1 shows the descriptive statistics, reliability estimates, and correlations. As expected, the results were relatively constant for all variables of t1 and t2. The correlations were similar for t1 and t2, and correlations of between 0.45 (learning from others in a social context and learning from noninterpersonal sources) and 0.52 (learning from others in a social context and learning from oneself) for the informal learning strategies showed medium effect sizes (Cohen, 1992). All other correlations between the other variables (feedback and PD) had a medium effect as well. All correlations were significant at the .01 level.
Hypothesis Testing
To test hypothesis 1a, which concerned the invariance of informal learning strategies across cultures, we calculated a multiple group CFA in R (Hirschfeld & Van Brachel, 2014). Because of the small sample sizes from Switzerland, the United States of America, and Australia (N < 150), only the three countries with several answers greater than N = 150 were included in the analysis of the first hypothesis: UK (PD = 35), Spain (PD = 57), and China (PD = 80). The results of this CFA were satisfactory (Δχ² = 242.275, Δdf = 48, p < .001, CFI = .95, RMSEA = .08, 90 % CI [.06, .10], SRMR = .05) and indicated that the same factorial structure of the informal learning was found in all three countries. In sum, these high loadings (most loadings are higher than .60) speak for a convergent validity and interpretability (Kline, 1997). At the same time, our results point only toward a partial metric measurement invariance for all groups, as just the dimensions “learning from oneself” and “learning from other sources” have the same loadings for more than half the items (Vandenberg & Lance, 2000).
The second step compared this baseline model with a model that assumed the item loadings were equal across the three countries. This model differed significantly from the baseline model (Δχ² = 39.12, Δdf = 12, p < .001). We concluded that the structure of the informal learning strategies was stable over the three different cultures analyzed (UK, Spain, and China), but that they differed in the loadings. Hypothesis 1a is partly supported.
To investigate hypothesis 1b, we included all countries and compared the means of the three informal learning strategies between all six countries with analyses of variance at t1 and t2. Each analysis of variance was significant (p < .001) (see Table 2). Particularly noteworthy are the mean values for Spain (PD = 57), as they are the highest for t1: 3.99 (learning from others in a social context). Meanwhile, the lowest values for t1 were in the UK (PD = 35), and learning from noninterpersonal sources had a value of 2.51. For t2, the highest values were found for Spain and learning from others in a social context (4.08). The lowest values (2.56) were found for the UK and learning from noninterpersonal sources (PD = 35). Bonferroni-adjusted post-hoc tests showed that the UK’s mean values for all three strategies were significantly lower than those of China at t1 and t2 (all p < .001). Hypothesis 1b is thus supported.
Furthermore, we used lavaan to model the assumptions of hypotheses 2, 3a, 3b, and 4. We used the function SEM with the option “bootstrap” (N = 1000) to bootstrap the estimates of the standard errors (SE). In particular, we modeled all connections between the three informal learning strategies from t1 to t2, on the assumption that the autoregression paths had the highest coefficients (hypothesis 2). Furthermore, we modeled the direct effect of feedback (t1) on informal learning strategies (t2) (hypotheses 3a and 3b) and the indirect effect of PD on feedback (t1) about informal learning strategies (t2) (hypothesis 4). To control for correlations of feedback (t1) and informal learning strategies (t1), we also included these connections in the model. Furthermore, we controlled the direct effect of PD on informal learning strategies (t1) and informal learning strategies (t2) and the correlations of the error variances of the informal learning strategies scales’ respective items from t1 and t2. The results of the SEM are depicted in Figure 2. The overall fit of the model relies on the bootstrap procedure (1,000 bootstrap runs) and showed a good overall fit (Δχ² = 379.03, Δdf = 172, p < .001, CFI = .97, SRMR = .05, RMSEA = .05).
For hypothesis 2, the autoregressive paths from learning from oneself (β = 0.44, SE = .11, p < .001), learning from others in a social context (β = 0.89, SE = .11, p < .001) and learning from noninterpersonal sources (β = 0.85, SE = .07, p < .001) were each the highest regression coefficients. Thus, hypothesis 2, which assumed the stability of the informal learning strategies, was supported by the results – except for the transition effect between learning from oneself and learning from others in a social context.
One significant path was found for hypotheses 3a and 3b: Feedback (t1) had a significant effect on learning from others in a social context (t2) (β = 0.1, SE = .06, p < .05). The effects of feedback on learning from oneself (t2) (β = 0.08) and on learning from noninterpersonal sources (t2) (β = 0.05) were not significant. Thus, hypothesis 3a was partly supported. Because of the finding that feedback (t1) only influenced learning from others in a social context (t2), hypothesis 3b was supported as well.
The results were partly in agreement with hypothesis 4, which proposed that the effect of PD on informal learning strategies was mediated by feedback availability. Only the indirect effect of PD over feedback (t1) on learning from others in a social context (t2) was significant (β = 0.06, SE = .002, p < .05). For direct effects of PD on the three informal learning strategies, the effect on learning from noninterpersonal sources (β = 0.09, SE = .001, p < .05) was significant. Thus, for learning from others in a social context, the effect of PD on this form of learning was fully mediated by feedback availability. Therefore, hypothesis 4 was partly supported.
Discussion
The first hypothesis was based on the theory of cultural values (Hofstede, 1980) and examined whether the use informal learning strategies differ from country to country. Complementing former research (e. g., Flynn et al., 2006; Jeong et al., 2018; Kim & McLean, 2014), this multicultural study reveals that different informal learning strategies are also correlated with different PD values.
Additional analyses of variance revealed that in China, the country with the highest PD value (80), employees showed the least mean scores for informal learning after the UK, which is a country with a low PD value (35). The results for China were to be assumed as we examined cultural conditions, but the results in the UK were at least a bit surprising. This result might be explained, for example, by the high level of the indulgence value (69) and the individualism level (89) which also shape this country (Hofstede et al., 2010). Within a country that is described as indulgent, a great deal of value is placed on leisure, enjoying life, and having fun (Hofstede, 2011), which might have led to further learning not being given high priority.
These results formed the basis for further hypothesis testing. We examined the stability of the three informal learning strategies over time. Two of the strategies with high autocorrelations and a medium correlation for the third strategy supported learning path theory: No matter whether formal or informal, their learning always follows an individual and stable developmental strategy (Poell & Van Der Krogt, 2014), which is also strengthened through the research on protean careers as elements of a protean career orientation are part of the elements of human needs for growth and meaning (Hall et al., 2018).
To go into further detail about how to enhance informal learning, we obtained insight that feedback availability has its greatest influence on learning from others in a social context. The results of the final hypotheses revealed that feedback mediates the effect of PD on learning from others within a social context. Our examined assumption was based on the assumption that a company’s learning culture could possibly overrule cultural preferences (Richter & Kauffeld, 2020). The last hypothesis was partially supported, since only one mediation of PD via feedback availability could be shown for learning from others in a social context.
One explanation for the slightly weakened significance of the cultural variable could be that the company, and thus also the internal learning culture, is characterized by a very low PD of 35 (Hofstede et al., 2010) because of the company headquarters in Germany. This culture thrives on social exchange, support across hierarchical levels, and a participative communication style through which the learning culture is also carried by the management into the other cultures, where it is exemplified.
Theoretical Implications
This study had several theoretical implications. The first and most significant contribution was to earlier knowledge about the fact that culture does matter for HRD (e. g., Marquardt & Berger, 2003; Ruona et al., 2003). This underlines the former statements that learning and personnel development have to change to help meet the challenges deriving from a globalized world (e. g., Cseh & Crocco, 2020). PD and some of its effects on workplace learning had been previously investigated for intraorganizational knowledge sharing in the USA and China (Jiacheng et al., 2010) and for its impact on managerial work values in Russia, the USA, China, and Japan (Ralston et al., 2008). In addition to former studies, which focus on employees from only one or two countries (e. g., Kim, 2003), whenever possible because of the data basis (all hypotheses except hypothesis 1a), we used a larger variety by comparing six countries (the USA, China, the UK, Spain, Australia, and Switzerland), and we expanded the knowledge of the impact of PD by investigating the three different forms of informal learning strategies to clearly distinguish between the different types.
Furthermore, to our knowledge, this was the first study to show that preferences in informal learning strategies were stable in different cultures and over time. Thus, this study supplemented former knowledge about cultural values (e. g., Hofstede et al., 2010; House et al., 2004; Trompenaars & Hampden-Turner, 2008) and their detailed influence on employees’ workplace learning behavior (e. g., Kim & McLean, 2014). In detail, we examined the stability of the three informal learning strategies over time. Two of the strategies with high autocorrelations and a medium correlation for the third strategy supported the learning path theory. Nevertheless, follow-up studies should include further variables in addition to this knowledge. It should be investigated, for example, how these stable preferences could become irrelevant or weakened by environmental factors and the availability of learning opportunities (Tews et al., 2017). This line of thought was already raised by Cerasoli and colleagues (2018), who determined that only a small part of the variance of informal learning is explained by their model or the individual/personal factors in it.
We also expanded on former knowledge about the predictive nature of feedback as feedback was considered as a component of the informal learning process (e. g., Decius et al., 2019) by defining its availability and informal learning as explicit process steps. Our results were comparable to those derived from studies looking at smaller companies (e. g., Kortsch et al., 2019), and we showed that feedback supports the outcome of informal learning. In addition, we used two times of measurement. The results speak for a causal relation, which must be investigated further, for example, through experimental designs of research.
Because of the study’s intercultural setup, the results contribute to the research on different countries’ values and the connection to FBA in the context of informal learning. Former research (Kim & McLean, 2014), which also complemented informal learning strategies within the context of PD, had to be widened.
The overall results indicate that a generally applicable tailoring of feedback to ensure engagement with learned content and to improve the usage of informal learning strategies is not possible (Marsick & Neaman, 2018). Cultural preferences must be considered, and the application of feedback formats must be considered separately for different cultures.
Practical Implications
Informal learning occurs outside a classroom. It is unstructured and embedded within a practical context (Livingstone, 2001; Manuti et al., 2015), for example, after formalized training. And it is initiated and self-directed by the employees (Livingstone, 2001). We showed that employees’ use of informal learning strategies is stable over time, which assumes that employees are learning strategically. Our results support the strategic character of informal learning, for practice. To set up a sustainable environment for informal learning, HRD should try to evaluate the individual learning strategies of employees to align them with company goals. If social exchange strategies are preferred, long-term investments should be made in work and learning models that enable social exchange according to formal learning formats and in general. For this purpose, additional time slots must be planned that can be used for question-based mentoring or coffee breaks to informally connect. If, on the other hand, self-isolated learning is preferred, rooms and the corresponding access to learning platforms should be made available by the state. Furthermore, internal databases to gather informal knowledge, such as articles, videos, and recordings, should be built up.
In addition, formal learning should be rethought as well: In many technology companies, it will not be possible to completely dispense with formal training formats in the future, as certain standards must always be complied with. To provide new and needed information more quickly, the “how” should be designed in a more agile manner. Agile learning frameworks can be used to do so as they base on modularity, incrementality, and flexibly: Shorter but steered learning units (sprints) ensure an easier modification to outdated content (Jungclaus & Schaper, 2021; Longmuß & Höhne, 2017).
We strengthened the conviction that culture is ever-present and, therefore, important for HRD to consider (e. g., Cseh & Crocco, 2020). To be more precise, if the concepts of HRD promote the goal of increasing informal learning after formal training in different cultures, our findings on PD should be considered for those setting up new and internationally relevant HRD measures. We found that measures designed to promote informal learning are not equally applicable everywhere: If learning from noninterpersonal sources is one of the declared goals of the new measure, the specific level of PD plays an important role, as it has a direct influence. Employees from high PD countries are not likely to automatically learn from sources with no interpersonal interaction. High PD means that the instructions of superiors have considerable weight (Hofstede et al., 2010). Therefore, this learning strategy can be promoted more directly by leading figures, e. g., through a mentoring relationship. Within such a relationship, supervisors in high PD values who are aware of their position can set learning goals for computer-based learning, give the order to research something in technical literature, and keep track of the completion of the task.
We investigated feedback as another factor that can positively influence the emergence of informal learning. This assumption was also largely confirmed. This finding is particularly interesting when informal learning is already practiced, but HRD managers are looking for further methods to support the emergence of informal learning. This is especially noteworthy for learning from and with others. Holding institutionalized feedback talks after the application of newly learned knowledge is very helpful, and it promotes the further emergence of social exchanges on a problem or topic. These pairings then establish a plan for regular feedback. This possibility of supporting the emergence of informal learning is very easy to implement, and the cornerstone of this strategy can already be built into concepts for formal learning by HRD managers.
Last but not least, we found that the effect of PD was fully mediated by feedback availability for one informal learning strategy. In practice, this means that feedback is an important tool for internationally operating companies with different PD values. Thus, informal learning from and with others can be encouraged by providing feedback. In concrete terms, this means that giving and getting feedback could be applied to raise awareness on the topic of feedback. This is particularly relevant for managers, as it has been shown that they play a major role in this process (Kortsch & Kauffeld, 2019; Kortsch et al., 2022). Further, companies should encourage a vivid learning climate through feedback-based collaboration and team learning in general. Again, feedback loops based on the agile learning framework can be used frequently to institutionalize feedback (Longmuß & Höhne, 2017).
Limitations and Future Research
Although the study was well planned, some limitations leading to future research topics must be discussed. First, there were limitations regarding the cultural aspects. Even though this study stated that culture has no geographical boundaries, we utilized Hofstede’s model, which initially states the assumption of geographically assigned nationality to define the values of the different countries. However, since the countries were distinguished by values rather than locations, this limitation can be overlooked. Geert Hofstede’s work is a prominent and current theory in the field, cited by scholars engaging in research on culture and learning (e. g., Richter et al., 2020), it has been criticized several times in general (e. g., Maull et al., 2001; McSweeney, 2002; Oyserman et al., 2002) as other models describe cultural values and differences on a more variant basis (e. g., Hall & Hall, 1990; House et al., 2004; Trompenaars & Hampden-Turner, 2008; Tung & Verbeke, 2010). Nevertheless, this model is still the most common way to describe cultural differences (Eckhardt, 2002; Tung & Verbeke, 2010), and the recently established applicability within an international learning context (Richter et al., 2020) cannot be denied. To differentiate more precisely, we have always relied on the exact cultural values of each country. Further research should consider the possibility of different cultural values within one country. Furthermore, we focused on PD as an important variable within the context of learning. Nevertheless, we cannot exclude a statistical bias as all individuals from one country were assigned the same PD value. Therefore, the variance of this dimension cannot represent the actual variance of the individual probands. Further research should apply multilevel study designs to control for the use of aggregated values.
Methodological limitations must also be mentioned. Self-reports may lead to common method bias since the possible variance could be attributed to the measurement method and not to the questions that the researchers are actually interested in (Podsakoff et al., 2012). We used only validated and reliable scales (Noe et al., 2013; Bates et al., 2014) to address this limitation. The questionnaires were used in several languages and countries, and the analysis revealed comparable results, which also argues against common method variance problems. Further, the Chinese participants reported informal learning behavior as a high PD country. The topic of social desirability can play a role here, as failure (in this case not conducting informal learning) can be understood as being unable to conduct the expected tasks (Bedford & Hwang, 2003). We tried to counteract this by ensuring anonymity, but recommend that this factor be included in future studies. Furthermore, the data used were from employees of a single company, which produces limited generalizability. Since the results were interpreted through values, the results may have been derived from the companies’ cultures throughout different cultures. Additionally, the present sample was collected from employees at a typical GAC of the automotive industry, which supports the generalizability of these findings. Considering these facts, the sample used herein was a representative base for technical staff members of the automotive industry. For future research, employees from different companies should be included to underline generalization and to provide a balanced sample of both genders. Also, the selection of the time interval between the measurement timepoints requires further consideration. Regarding the intervals between the surveys and the different starting times, the company’s framework conditions applied. To rule out any influence from other training measures as far as possible, we had to base our survey on the training calendars of the individual countries. Future studies should consider expanding the intervals to provide even better evidence of causality. Also, the dropout rate of the participants must be addressed; this study had an overall dropout rate of 42.9%, which could be explained by the different countries’ learning styles within the GAC and different peak phases in sales during our evaluation. As a result, the data from participants who answered at both times (N = 444 employees) were used for all hypotheses except hypothesis 1. Additionally, the correlations (see Table 1) were constant for both times of evaluation, which showed the validity of the dataset. With our dataset, we were able to confirm the stability and relevance of informal learning strategies for only three countries that differed in their PD (UK = 35, ES = 57, and CN = 80). Further studies should calculate this model with a sufficient sample from AT, the USA, and AU. Also, our testing of hypotheses based on this differentiation examined the intensity of the use of informal learning strategies. The results of the multiple-group CFA showed high but only partially congruent loadings, which speaks for interpretability (Kline, 1997), albeit for only a partial metric measurement invariance (Vandenberg & Lance, 2000). We therefore suggest that future studies try to replicate our findings. Last but not least, in our study, we assumed a positive influence of feedback and did not ask about the nature of feedback. In follow-up studies, it would be desirable to distinguish between positive and negative feedback and the active/nonactive type of receiving feedback.
Conclusion
In these challenging and globalized times we live in, HRD has now reached a critical point, as more and more challenges come up from ever-faster developments (Torraco & Lundgren, 2020). One of the most difficult challenges was, and still is, the task of helping a company to transform from its national and domestic orientation to a global orientation (Claus, 2006). This would ensure fast learning and development of GAC’s employees around the world. Workplace learning must be broadened and rethought. Learners who expand their learning habits beyond formal learning through informal learning will be at an urgent competitive advantage (Van Breda-Verduijn & Heijboer, 2016).
To support international HRD in this process, we found informal learning to be occurring over time, and we observed that employees from different cultures use the same informal learning strategies. The value of PD was shown to directly influence the appearance of feedback availability. It was mediated by this variable while influencing learning from and with others. These results were the first to be confirmed based on an intercultural and longitudinal study.
To cope with the challenges of our times, international HRD is urged to move forward in promoting the different informal learning strategies – better too early than too late. This study provides initial guidance in this regard.
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