The German Version of the Hybrid Work Characteristics Scale
Abstract
Abstract.Introduction: To account for fast-paced developments at work, hybrid work characteristics (HWCs) were introduced. To measure them, an English instrument was developed by Xie et al. (2019). HWCs encompass more than one work characteristics domain such as the task, social, or contextual domain and include boundarylessness, multitasking, the demand for constant learning, and non-work-related interruptions and are associated with employee attitudes and well-being. Objectives: We validated a German translation of the HWC scale. Method: Using employee samples from Germany (N = 391) and the United Kingdom (N = 400), we assessed the quality of the German translation. Results: The German version was internally consistent, showed an acceptable model fit, and reached a scalar level of measurement invariance. The HWCs are related to employee attitudes and well-being. Conclusion: We recommend the use of the German translation of the HWC scale, as our results support its reliability and validity.
Today, the way of working in Germany is changing fundamentally (e.g., Maier et al., 2020), driven forward by fast-paced technology developments and globalization. Thereby, our constantly evolving working life becomes increasingly diffusive, dynamic, and is characterized by disruptions (e.g., Korunka & Kubicek, 2017; Okhuysen et al., 2013). Because of these changes in the world of work, classical assessments of job design such as the Work Design Questionnaire (Morgeson & Humphrey, 2006) do not depict completely all relevant work characteristics. Up to now, work design research focused on three clearly delimitable domains: the task, the social, and contextual domain. Task characteristics stem from how the work itself is done, social characteristics stem from the social environment at work, and contextual characteristics stem from a broader physical and organizational environment (Morgeson & Humphrey, 2006). However, current workplaces are characterized by an ever-increasing degree of digitalization. For example, the on-going COVID-19 pandemic and digitalization have led many employees to work remotely (Trougakos et al., 2020), which makes the boundaries between private and professional life more diffusive. Moreover, the increasing use of information and communication technology has magnified multitasking. Characteristics of modern work cannot be easily classified as exclusively task-related, social, or contextual any longer. Xie et al. (2019) chart new terrain in work design by extending classical work design characteristics by hybrid work characteristics (HWC) that possess features from more than one domain. Thereby, they deepen our understanding and add an enhanced perspective on work design in modern times. They propose a scale measuring HWC, which enables an investigation of current workplaces and helps to derive recommendations concerning work design. In Germany, a scale measuring HWC is especially much needed, as the everyday working life of many employees in Germany is characterized by fast-paced changes due to digitalization (Kauffeld & Maier, 2020; Schlicher et al., 2020). Therefore, the aim of this study was to validate a German version of the HWC scale (Xie et al., 2019).
Previous research identified four HWCs: boundarylessness, multitasking, demand for constant learning, and non-work-related interruptions. These work characteristics are hybrid in terms that they share multiple domains from classical work design frameworks such as the Work Design Questionnaire (WDQ) (Morgeson & Humphrey, 2006); for example, they may share task- and context-related aspects that define them as hybrid. Boundarylessness describes employees’ perceptions of connectedness to their work apart from their physical workplace. It is defined as “the extent to which clear temporal or/and geographical boundaries between one’s work and non-work domains have dissolved” (Xie et al., 2019, p. 481). It reflects the diffusive nature of work time and space, for example, prolonged work hours. With an increase of digitalization, boundarylessness becomes more and more predominant (e.g. Field & Chan, 2018). Multitasking describes the juggling of numerous tasks at the same time in a high-velocity work environment (Bluedorn et al., 1999) and has become a widespread requirement. Multitasking takes the polychronic nature of work into account and is defined as “the need to accomplish multiple task goals in the same general time period and regularly prioritize competing demands” (Xie et al., 2019, p. 481). Demand for constant learning describes the need to continually update and upgrade knowledge and skills to deal with increasing demands and uncertainty at work (Xie et al., 2019). It is defined by the extent to which an employee’s work requires them to “engage in continuous learning of new technologies, knowledge, methods, and applications to stay on top of the latest developments” (Xie et al., 2019, p. 481). Non-work-related interruptions at work are, contrary to work-related interruptions, under-investigated, even more disruptive, and are less likely to benefit employees or the organization (Xie et al., 2019). They are defined as “nonwork-related incidents or occurrences that impede or delay jobholders as they attempt to make progress on work tasks” (Xie et al., 2019, p. 481). After being interrupted due to non-work-related issues, the return to the initial work task may take more time and effort because different cognitive schemes may be involved. Next to personal communication, the increased use of information technology in various forms such as e-mail or instant messaging leads to more interruptions at work (Garrett & Danziger, 2007; Russell et al., 2007).
Hybrid Work Characteristics and Employee Attitudes and Well-Being
Work characteristics can potentially be perceived as enriching challenge stressors in terms of potentially promoting achievement or as depleting hindrance stressors in terms of a potential threat to employees’ achievement (LePine et al., 2004; Xie et al., 2019). In their model, Xie et al. (2019) predicted that boundarylessness, multitasking, and the demand for constant learning are challenge stressors and that non-work-related interruptions are a hindrance stressor. Furthermore, they predicted that these hybrid work characteristics are associated with work-related attitudes such as job satisfaction and occupational commitment as well as psychological and physiological well-being indicators such as emotional exhaustion and somatic health symptoms (Xie et al., 2019). To establish criterion validity, we follow Xie et al. (2019) and test these predictions in our sample. We expect that boundarylessness, multitasking, and the demand for constant learning will be positively related to job satisfaction and occupational commitment and positively related to emotional exhaustion and somatic health symptoms. Non-work-related interruptions at work are expected to correlate negatively with job satisfaction and occupational commitment and positively with emotional exhaustion and somatic health symptoms.
The Original Work Characteristics Scale
For the construction of the original items, Xie et al. (2019) jointly proposed a definition of each work characteristic based on the literature and informational interviews with employees across a wide range of jobs. Then, three researchers independently generated 10 items per work characteristic, items were discussed and wordings were reconciled. The item pool was pretested (N = 194 employees) to refine the items based on these results. Next, internal consistencies and convergent validities were investigated in a pilot study (N = 182 employees) and criterion validity, predictive, and discriminant validities in the following two studies (N = 432 and N = 388 employees in North America). The questionnaire’s intended use is to assess employees’ perceptions of their situation at work, specifically HWC in their workplace.
The Present Study
To enable the measurement of HWC in Germany, in the first step, we translate the HWC scale by Xie et al. (2019) into German. Then, we examine how these work characteristics are associated with employee attitudes and well-being. We assume that these relationships are similar in Germany and in English-speaking cultures because of similar values (e.g., rather individualistic than collectivistic according to the Culture Index by Hofstede, 2001). To assess the validity and investigate measurement invariance between the German and English version, we collect a German sample and a UK sample with the following advantages: We collect the samples simultaneously at the same point of time which increases comparability and is important when investigating technology-related topics that are prone to fast-paced changes. Using these two samples, we are able to investigate measurement invariance to determine whether the four HWC scales function the same way in Germany and English-speaking countries. By doing so, we not only test the applicability of the German version of HWC but also replicate Xie et al.’s (2019) original findings (relationships with attitudes and well-being) in another English-speaking country (United Kingdom).
Apart from existing scales in German-speaking countries such as the Instrument for the Assessment of Central Aspects of the New Way of Work (Poethke et al., 2019), Scale for assessing the degree of work-related Digitalization (Görs et al., 2019), Mental Stress and Strain Assessment in Digital Work (Hagemann et al., 2021), or the Intensification of Job Demands Scale (Kubicek et al., 2015), the German version of the HWC has distinctive features: First, the HWC scales reflect the significant changes occurring in work design and add value to work design literature by the innovative perspective on the hybrid nature of work characteristics. Second, the HWCs are not only applicable to digitalized workplaces only; instead, they can be applied to all jobs, and measurement will not be biased as to the degree of digitalization. Third, the German version of the HWC allows direct comparisons of German-based studies with studies using the original English version. Such comparisons will provide invaluable information about what work design knowledge might be generally applicable across cultures and what are specific to the German cultural context.
Materials and Methods
Using the online survey tool Qualtrics, we collected a German sample and a UK sample, among employees with a regular job, to assess measurement invariance via the survey provider clickworker.de in December 2019. The survey included an informed consent, the HWC scale, measures of the dependent variables, and demographics. Participation in this study was remunerated with 1.80€, which corresponded to slightly more than the minimum wage in both countries (average completion time: 10 min). Participants answered the items in relation to their regular employment apart from clickworker.de, and they reported a variety of jobs (e.g., engineer or lawyer). Data, code, and supplemental materials are available on the Open Science Framework https://osf.io/rmt95/. Statistical analyses were conducted using SPSS 28 and R 4.1.3 with packages ccpsyc (Fischer & Karl, 2019), lavaan (Rosseel, 2012), and semTools (Jorgensen et al., 2021).
Sample
We used Xie et al.’s (2019) sample sizes as an orientation and defined a sample of N = 400 as our goal, which is also in line with Bühner’s (2011) recommendations concerning the feasibility of confirmatory factor analysis (CFA) and rules of thumb (n = 20 per item). The final German sample consisted of N = 391 employees, of which 61.9% were men, 37.3% women (0.8% did not answer), and the participants’ average age was 36.95 years (SD = 11.0). On average, they worked 36.8 h per week (SD = 9.1). The final UK sample consisted of N = 400 participants, of which 52% were female, 47% male (1% did not answer), and the participants’ average age was 35.55 years (SD = 10.5). On average, they worked 32.5 h per week (SD = 12). In both samples, a small number of participants had to be excluded because they did not finish the survey, which was considered as a withdrawal of consent (n = 13 in the German sample, n = 15 in the UK sample), or did not give their consent (n = 1 in the German sample) or were unemployed or self-employed or listed crowdwork as their job (n = 2 in the German sample, n = 6 in the UK sample).
Measurement
In the following, we describe the instruments used for the validation of the German scale, which required the use of translated versions. For the UK sample, we used the original scales.
Hybrid Work Characteristics
The original HWC by Xie et al. (2019) was translated into German following the procedures recommended by Douglas and Craig (2007) with two independent translations which are discussed with a third expert. The scale consists of four subscales: (a) boundarylessness, (b) multitasking, (c) demand for constant learning, and d) non-work-related interruptions. Sample items are “My job is not limited to my place of work. Rather, I am "on call" no matter where I am since I stay connected via the cell phone or internet” (boundarylessness); “My job requires me to regularly prioritize competing demands” (multitasking); “My job requires me to continually learn new technology, techniques, and ideas” (demand for constant learning); and “In my job, I often stop tasks I am working on to respond to NON-work-related questions from other colleagues” (non-work-related interruptions). Items were responded on a 7-point rating scale (1 = very inaccurate to 7 = very accurate). Table 1 contains the German items.
Employee Attitudes
Job satisfaction was measured with three translated items (van Dick et al., 2001) from the scale by Hackman and Oldham (1980). A sample item was “All in all, the job I have is great,” and items were responded on a 7-point rating scale (1 = strongly disagree to 7 = strongly agree). The reliability was satisfactory (α = .86 in the German sample, α = .90 in the UK sample). Occupational commitment was measured with five translated items (Felfe et al., 2002) from a scale by Meyer et al. (1993). A sample item was “My profession is important to my self-image,” and items were responded on a 7-point rating scale (1 = strongly disagree to 7 = strongly agree). The reliability was sufficient (α = .72 in the German sample, α = .85 in the UK sample).
Employee Well-Being
Emotional exhaustion was measured with five translated items (Büssing & Perrar, 1992) from a scale by Maslach and Jackson (1986). A sample item was “I feel emotionally drained from my work,” and items were responded on a 5-point rating scale (1 = rarely to 5 = very often). The reliability was satisfactory (α = .87 in the German sample, α = .90 in the UK sample). Somatic health symptoms were measured with six translated items from a scale by Caplan et al. (1975). A sample item was “I got tired for no reason,” and items were responded on a 5-point rating scale (1 = rarely to 5 = very often). The reliability was satisfactory (α = .85 in the German sample, α = .86 in the UK sample).
Results
Quality and Factor Structure of the German Translation
Table 2 contains the reliabilities reported by Xie et al. (2019) and our German and UK samples. The Cronbach’s alpha values of the German translation are lower than the reliabilities of the original scale but reached an acceptable to good level (DeVellis, 2012). Table B in the online supplemental materials contains further reliability indices.
Using confirmatory factor analysis (R package lavaan by Rosseel, 2012), we tested the factor structure and goodness of fit of our translation (Table 3). According to Hu and Bentler (1999), the model fit reached an acceptable level with regard to most fit indices (except for the overall χ2 test of model fit which was significant): χ2/df (129) was < 3.0, RMSEA < .10 (moderate), CFI > .90, and standardized root-mean-square residual (SRMR) < .09 (Table 3). The factor loadings are displayed in Table A in the online supplemental materials. We assessed discriminant validity using the HTMT2 criterion which is the heterotrait–monotrait ratio of correlations relying on the geometric mean (Roemer et al., 2021). Discriminant validity is achieved, when HTMT2 values are below .85 (Henseler et al., 2015), which was fulfilled in the German translation and in the UK sample (Table 4). In Table 5, alternative models (second-order and bifactor models) are displayed. The examination of modification indices indicated the highest values for the covariance of Item B1 with Items B2 and B5; however, for theoretical reasons, we do not recommend a modification of the model (fit indices displayed in Table 5).
To assess measurement invariance (Table 6), we applied the following criteria (Chen, 2007): For loading invariance, ΔCFI ≤ .01 and ΔSRMR ≤ .03 or ΔRMSEA ≤ .015 indicate negligible differences and therefore measurement equivalence (ΔCFI ≤ .01 and ΔSRMR ≤ .01 or ΔRMSEA ≤ .015 for intercept and residual invariance), while CFI serves as the main criterion. According to the recommendations by Chen (2007), when sample sizes are equal across groups and N > 300, these criteria can be used to assess measurement invariance. Moreover, we examined an effect size of the degree of nonequivalence between the groups on the item level: The effect size measure for differences in mean and covariance structures (dMACS) represents the differences in the intercepts and slopes across two groups (Nye & Drasgow, 2011; Nye et al., 2019). Additionally, we report further commonly used criteria such as χ2 difference tests (nonsignificance indicating measurement invariance) and the Bayesian information criterion (BIC; the lowest value of BIC indicating the appropriate level of measurement invariance) in Table 6.
Following these recommendations, our translation reached the level of strict scalar invariance which means that the factor structure is equivalent in both samples (configural invariance), as well as the factor loadings (metric invariance), the intercepts (scalar invariance), and residuals (strict invariance). In two simulation studies, Nye et al. (2019) discuss cutoffs for small, medium, and large dMACS effect sizes. According to these studies, values below .20 (Study 1) or below .40 (Study 2) might be considered small, depending on context and practical importance issues (greater values indicating larger misfit). dMACS was low to medium and therefore acceptable for all subscales (Fischer & Karl, 2019; Nye et al., 2019). In summary, there is a low item-level misfit, thereby supporting the notion of measurement equivalence. The results are displayed in Table C in the online supplemental materials.
Relationships With the Outcome Variables
In the German and UK samples, the results of the regression analyses of the relationships between the HWC and employee attitudes and well-being were mostly consistent with Xie et al.'s (2019) results. We followed their approach exactly and conducted four blockwise regression analyses with occupational commitment, job satisfaction, emotional exhaustion, and somatic health symptoms as dependent variables and boundarylessness, multitasking, demand for constant learning, and non-work-related interruptions as independent variables (Block 2), controlling for age, gender, education, and tenure (Block 1) because they are likely to affect attitudes and well-being and are commonly controlled for (e.g., Ganster & Rosen, 2013; McEwen, 2007). In the German sample, boundarylessness was positively related to occupational commitment and positively related to somatic health symptoms, but not related to job satisfaction and emotional exhaustion. Multitasking was positively related to occupational commitment and emotional exhaustion, whereas not related to job satisfaction and somatic health symptoms. Demand for constant learning was positively related to job satisfaction and occupational commitment and negatively related to emotional exhaustion and somatic health symptoms. Non-work-related interruptions were negatively related to job satisfaction and occupational commitment and positively related to emotional exhaustion and somatic health symptoms. All regression coefficients are shown in Table 7 (consistencies with Xie et al., 2019 in bold). We conducted these regression analyses also in the UK sample (see Table E in the online supplemental materials). Correlations of all variables under investigation are displayed in Table D in the online supplemental materials.
Discussion
With the HWC, Xie et al. (2019) introduced a modern and powerful instrument to assess hybrid work characteristics, which allows to detect and depict the fast-paced changes in our everyday working lives. Especially in Germany, a highly digitalized country (Kauffeld & Maier, 2020; Schlicher et al., 2020) and also in light of the COVID-19 pandemic, which caused an abrupt change of working conditions (Diab-Bahman & Al-Enzi, 2020), such a measure which takes factors into account that were overseen in the past was strongly needed and is now presented through our German version of the HWC. HWCs are associated with occupational commitment and well-being, which makes them critical factors in work design. With the HWC, Xie et al. (2019) extended classical work design approaches by considering factors that touch multiple work design domains simultaneously.
Our German translation is ready for use in German-speaking organizations to assess boundarylessness, multitasking, demand for constant learning, and non-work-related interruptions in the workplace and, consequently, as a basis to shape work differently as needed on the basis of emerging work characteristics to ensure employees’ well-being and health. Our translation is internally consistent, has an adequate model fit, and reached a scalar level of measurement invariance.
Concerning the relationships of the HWC with employee attitudes and well-being, the data did not support all hypotheses; however, they are mostly consistent with Xie et al.’s findings. We conclude that boundarylessness is positively associated with occupational commitment and negatively with well-being (i.e., increased somatic health symptoms but not emotional exhaustion). Multitasking is positively associated with occupational commitment and negatively with well-being (i.e., increased emotional exhaustion). Interestingly, in Germany, the demand for constant learning is not only positively related to job satisfaction and occupational commitment but, contrary to English-speaking countries, may buffer the adverse effects on well-being, as it is associated with decreased emotional exhaustion and somatic health symptoms. Xie et al. (2019) acknowledge that the demand for constant learning may be perceived as an advantage but may also become a demand. In our sample, the demand for constant learning was rather perceived as an advantage. This might be due to German law, which allows employees to take educational leave (“Bildungsurlaub”) consisting of five paid days off per year for educational purposes. This regulation might change the German employees’ view of extra education as a right as compared to employees living in countries without the right of educational leave. Non-work-related interruptions are, as hypothesized, positively related to job satisfaction and occupational commitment and negatively related to well-being, which indicates that interruptions are not perceived as microbreaks.
Limitations
One item of the original scale from the German version (Item b1) was associated with high modification indices (covariances with Items B2 and B5). However, due to theoretical reasons, we do not recommend a modification of the model or scale.
Moreover, the data have been collected before the pandemic. With an increase of remote work and working from home across a variety of jobs during and after the pandemic, employees may have gained more personal experience with the phenomena described by the HWC. On the other side, an advantage of the data collection before the pandemic is that the data are not biased by the stressful situation of the pandemic in the private domain apart from the working life and may better reflect ordinary working situations than data collected during the pandemic.
Conclusion
Based on our results, we recommend the use of the scale in Germany, as it has shown to be a reliable and valid instrument to measure HWC, which enables an investigation of current workplaces and helps to derive recommendations concerning jobs in modern times.
The authors thank Nina Dragon, Lina Strickling, and Berit Thesing for their assistance with the data collection and organization.
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