Measuring Digital Work in (German) Employee Surveys: An Overview and Proposal of Systematization

Marx, Charlotte K.; Abendroth, Anja-Kristin; Meyer, Sophie-Charlotte; Reimann, Mareike; Tisch, Anita Article Measuring Digital Work in (German) Employee Surveys: An Overview and Proposal of Systematization Journal of Contextual Economics – Schmollers Jahrbuch Provided in Cooperation with: Duncker & Humblot, Berlin Suggested Citation: Marx, Charlotte K.; Abendroth, Anja-Kristin; Meyer, Sophie-Charlotte; Reimann, Mareike; Tisch, Anita (2022) : Measuring Digital Work in (German) Employee Surveys: An Overview and Proposal of Systematization, Journal of Contextual Economics – Schmollers Jahrbuch, ISSN 2568-762X, Duncker & Humblot, Berlin, Vol. 142, Iss. 1, pp. 67-92, https://doi.org/10.3790/schm.142.1.67 This Version is available at: https://hdl.handle.net/10419/292609 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Contextual Economics 142 (2022), 67–92 Duncker & Humblot, Berlin Measuring Digital Work in (German) Employee Surveys: An Overview and Proposal of Systematization By Charlotte K. Marx,* Anja-Kristin Abendroth,** Sophie-Charlotte Meyer,*** Mareike Reimann,**** and Anita Tisch***** Abstract Innovative measurements in representative surveys are needed to draw meaningful conclusions about the prevalence of digital work and its consequences for employees’job demands and resources. Since the digitalization of work encompasses a variety of technological developments and possible implications for employment, there are many different approaches to its operationalization. Within this article, we (1) provide a scheme for classifying different approaches to measuring digital work, (2) apply this scheme to nine different representative German employee surveys that operationalize digital work, and (3) evaluate the measurement of digital work by discussing the advantages and limitations of the different approaches. We identify three approaches to measuring digital work: equipment-based, contentbased, and opinion-based. Besides the advantages and disadvantages of these approaches, we discuss the state of the art in measuring digital work and whether it would make sense to create a standardized set of questions. JEL Code: O33 Keywords: Technological Change, Digitalization, Methodology, Employee Surveys, Germany 1. Introduction The digitalization of work is currently one of the most important issues in public and scientific debates. Particularly since the beginning of the COVID-19 pandemic in 2020, there has been an increase in work communication via digital means and in working from home facilitated by digital technology (e.g., messaging and videoconferencing tools) (Bolisani et al. 2020; Waizenegger et al. 2020; Kleinert et al. 2021). The digitalization of work includes * Department of Sociology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany. The author can be reached at [email protected]. ** Department of Sociology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany. The author can be reached at [email protected]. *** Federal Institute for Occupational Safety and Health, Friedrich-Henkel-Weg 1–25, 44149 Dortmund, Germany. The author can be reached at [email protected]. **** Department of Sociology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany. The author can be reached at [email protected]. ***** Federal Institute for Occupational Safety and Health, Friedrich-Henkel-Weg 1–25, 44149 Dortmund, Germany. The author can be reached at T[email protected]e. Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 more than digital communication, however; it also incorporates the implementation of different types of digital technologies in workplaces and work processes (Gray and Rumpe 2015; Hirsch-Kreinsen 2016; Govers and van Amelsvoort 2019). Previously, researchers often operationalized the digitalization of work by collecting information on the availability of computers and internet access in the workplace, whereas more recent studies increasingly implement more differentiated and detailed evaluations of technology and equipment, including how these tools are used for specific tasks and the consequences of their use. With an increasing number of studies implementing questionnaire modules concerning digital work, the resulting measurements and data are becoming more heterogeneous. On the one hand, this can be advantageous in terms of examining such an extensive and multi-factorial concept. On the other hand, the complexity of these measurements and data also increases. This article contributes to the disentangling and reflection of the approaches now being used to measure digital work within employee surveys. First, we briefly summarize the debate concerning the digitalization of work and its relation to job quality. Second, we propose a scheme that identifies different approaches to measuring digital work. Moreover, we apply this scheme to nine representative German studies that operationalize digital work and consider working conditions or job quality. Here, we also look at already existing results of the studies and how they relate to theoretical debates on the importance of digital work for job quality. Finally, we evaluate the approaches and discuss their advantages and limitations. Regarding the broadness of the digitalization phenomenon in particular, it is important to acquire and maintain an overview of the different types of operationalization and to place different approaches and their immanent goals within the context of current debates. In this way, we can evaluate the coverage of different aspects of digital work and consider what specific conclusions can be drawn by applying different measurements. In addition, a systematization of measurements reveals gaps in the operationalization of digital work and provides an instrument for assessing and integrating measurements within the context of research in this field. We focus on German employee studies for two reasons: First, in Germany, the digitalization of work and its consequences for industrial sectors (“Industrie 4.0”, Warhurst and Hunt 2019, 3), as well as for the labor market and the workforce (“Arbeit 4.0”, Becka, Enste and Ludwig 2019, 342), is a widely discussed topic (Hirsch-Kreinsen 2016). From an international perspective, this discussion is therefore often referred to as the German debate, with Germany being regarded as a trailblazer (Warhurst and Hunt 2019). Increased funding for projects that concern the digitalization of work in Germany has further increased the integration of measurements in more and more employee surveys designed to provide new insights into the digital transformation of work. The strong focus on digitalization and the multitude of such surveys enables us to compare the various approaches being undertaken to operationalize digital work. Second, the specific focus on German surveys allows us to investigate the operationalization of digitalization in a homogeneous setting. Because contextual factors represent an important influence on the development of the digitalization of work and society, the industrial setting (Žwaková 2018), qualification structure (Caselli and Coleman 2001), labor market policies (Berger and Frey 2016), and country-specific discourse (Marenco and Seidl 2021) play an important role in this development and add to the already complex conceptualization of digitalization. In order to reduce this variety of Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch68 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 factors, we look at one specific context and limit our focus to the operationalization of digitalization within studies undertaken in Germany. However, we invite international researchers to contribute to our findings and extend the debate. 2. Theoretical Perspectives on Digital Work and Its Relation to Job Quality According to the international literature on technological development and change, digitalization comprises the increasing dissemination of digital technologies as well as its impact on organizational and societal processes (Legner et al. 2017; Govers and van Amelsvoort 2019). The predicted structural consequences have been thoroughly discussed, albeit in a rather techno-determinist sense, assuming that new technologies structure and impact work (Winner 1977; Attewell and Rule 1984; Dafoe 2015). Some authors propose a socio-technical perspective to digital work (Trist and Bamforth, 1951; Trist 1953; Emery [1959] 2016), implying that the technical and the social subsystems within work organizations interact with and complement each other in the execution of tasks and work processes (Govers and van Amelsvoort 2019). Hence, the systems are not determinate but are dependent on one another (Fischer and Herrmann 2011). As an example, Fischer and Herrmann (ibid.) mention communication systems that ease the communication between team members but also allow employees to contribute to the (further) development of such systems. This type of interaction between human action and technologies is also claimed by the socio-material perspective (see, e.g., Orlikowski 2000; Orlikowski and Scott 2014), which emphasizes human agency in shaping technological structures and implies that engaging employees in the use of technologies affects the way in which technology can be integrated into work processes and organizations (Orlikowski 2000). Research in this context also emphasizes the role of the organizational and structural context in the dissemination and impact of these technologies (e.g., flexibility going along with the adoption of technologies as well as the possibility to enhance skills for employees) (Hirsch-Kreinsen 2016; Arntz, Gregory and Zierahn 2019). To understand the relationship between technologies and various aspects of job quality in particular, the techno-stress model understands the dissemination and use of digital work technologies as a stressor which leads to employees’strain (Tarafdar, Pullins and Ragu-Na- than 2015). Whereas the techno-stress model takes only the demanding aspects of digital work technologies and, thus, a downgrading of job quality into account, the integration into the already existing and widely used Job-Demands-Resources Model (J D-R Model; Bakker and Demerouti 2007; Day, Scott and Kelloway 2010; Day et al. 2012) allows for the classification of these technologies as a job demand but also as a job resource for employees (Day et al. 2012). Similar to the techno-stress model, aspects like a constant availability, ICT hassles or the possibility to monitor employees are seen as demanding factors. From a resource perspective, the support by digital technologies in an assisting way, as well as strengthening of already existing resources is of major importance and even upgrades job quality being associated with lower stress and strain (Day et al. 2012). Though these two concepts focus on information and communication technology, these thoughts can also be transferred to automation and algorithmic technologies. Here, digital monitoring and evaluation or digital assistance systems can on the one side be a demanding factor and im- Measuring Digital Work in (German) Employee Surveys 69 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 pact job quality in a negative way by e. g., leading to lower job performance, commitment, and external control (Posey et al. 2011; Jeske and Santuzzi 2015; Martin, Wellen and Grimmer 2016; Siegel, König and Lazar 2022). On the other side, they can also ease especially physically demanding working conditions or lead to more transparency in performance evaluation (Sharma and Sharma 2017; Wood et al. 2019; Wood 2021). Technologies and technical systems thus serve as support systems for employees’performance of repetitive or physically demanding tasks and allow for more complex decision making, which might even enhance job control for workers (Hirsch-Kreinsen 2016). Overall, the different theoretical perspectives on digital work comprise a wide range of possible scenarios in terms of the effects on job quality. This situation opens up many reference points for empirical research and the operationalization of digital work in different studies and employee surveys. 3. Proposal of Systemization Based on the state of research on digital work and our review of existing studies, we propose a scheme to classify different approaches for measuring digital work in employee surveys (see Figure 1). As a basis for developing a scheme, we refer to classical guidelines of survey methodology (Bhattacherjee 2012; Groves et al. 2009). The first step in an empirical research process is to define the subject to be measured, as based on a theory or a general goal. The underlying abstract theoretical constructs’intention is a translation into concrete terms (Bhattacherjee 2012; Groves et al. 2009). Thus, the definition and theoretical concept constitute the first level within our scheme (cf. Figure 1). The concept of digital work, the study interest, and the study design each play a role in designing the operationalization of digital work. Whereas some concepts of digital work are quite simple and unidimensional, others are harder to grasp. The concept may include many dimensions like the use and extent of different technologies, their integration in the work organization or the relevance for employees’ working conditions. The precise and concrete definition is affected by the (underlying) perspective on digital work –whether it is the focus on the diffusion of specific technologies at work, the impact on employees’daily working lives and work organization as a result of the interaction between technologies and human work, or how employees perceive the implementation of digital technologies at work. Moreover, differences regarding the dimension of work are of interest, as are their assumed consequences: specific occupations, work tasks, skills or qualification processes, work resources, or work demands. The operationalization of digital work is also connected to the study interest; for example, an explanation of social inequalities and polarization or capturing working conditions may lead to different approaches to this phenomenon. The study interest can also be related to different concepts of digital work. In addition to the concept of digital work and the study interest, the study design plays a role in shaping the operationalization of digital work. The digitalization of work unites a multitude of technologies and processes, so the implementation, type, and use of these technologies varies considerably across structural components such as jobs, branches, or the size of the company (Hirsch-Kreinsen 2016; Holler 2017; Brockhaus et al. 2020; Reimann, Abendroth and Diewald 2020). Moreover, the focus and structure of measuring digital work differ depending on whether the research survey is integrated Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch70 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 into an already existing study, how much space it takes up, or whether the study has been designed for the purpose of capturing digital work only. Thus, the operationalization of digital work is likely to depend on how one designs the study. Practically, these matters are often interrelated. For instance, if digital work is surveyed as only one part/module of a comprehensive study, the operationalization of digital work must be adapted to the (already existing) study interest and sample. Thus, the concept of digitalization depends on both these factors. Conversely, the study interest and sample may also be linked and designed according to the concept of digital work. At the second level, questionnaires are designed that reflect the underlying definition or concept of digital work by translating them into indicators or items and verbalizing variables (Bhattacherjee 2012; Groves et al. 2009 on the operationalization of surveys or survey modules shown in Figure 1 in this article). The many different types of operationalization reflect differences in the theoretical conceptualization of digital work but also different foci on specific aspects of digitalization as well as survey-specific populations (such as different cohorts or employees). First, understanding technology as structure in the rather technic-centered perspective, one type of operationalization focuses on the dissemination of specific digital technologies at work. We summarize these approaches under the term work equipment-based approach. Second, the work content-based approach aims to measure the work content or functioning technologies that are used (e.g., automation processes or digital communication), since technologies can be used for different purposes in line with the concept of socio-technical systems. Third, we distinguish opinion-based approaches, which ask directly about the perceived “impact”of the digitalization of work often without referring to a specific technology. Even though this operationalization does not allow for causal inferences with regard to the implications of digitalization for the employees’work situation, opinion-based approaches are particularly useful in describing the perceptions and attitudes of different employee groups or, in a longitudinal perspective, perceptual changes. Theoretically, this approach is also more technically centered and can be located, for example, in techno-stress approaches (Tarafdar, Pullins and Ragu-Nathan 2015). Finally, most surveys also allow researchers to examine the various impacts of digitalization, that is, how job quality, work-life balance, or health relates to the use of digital work equipment, to digital work content, or to opinions about the implications of the digitalization of work. Such impacts may include transformations in the way work is organized within establishments, as well as changes on the occupational or the employee level (Govers and van Amelsvoort 2019). In terms of impact, we distinguish between an individual assessment of the impact of digital work and an empirical analysis of the consequences of digital work without an individual assessment. Assessment includes particular questions about how individuals perceive working with specific digital work equipment or how they evaluate digital work content. The direct connection between specific equipment or content and the employee’s assessment distinguishes this strategy from the opinion-based measurement of digitalization, which asks for the employee’s perceived general consequences of digital work. In contrast to the other two clearly distinct categories, opinion-based assessments are not always clear-cut. Consequences offer the potential to analyze the impact of digital work equipment or digital work content on, for instance, working conditions, educational attainment, health, or different aspects of job quality, which are independently included in most Measuring Digital Work in (German) Employee Surveys 71 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 surveys but do not rely on the respondents’subjective evaluations of digital work. Although the occupational, organizational, and national contexts are not displayed in Figure 1, we acknowledge that these factors shape the diffusion of specific technologies for work equipment and work content, as well as the opinions about digital work and their impact. Our purpose is to develop a scheme for classifying the existing measurements of digital work. When these measurements are used to answer specific research questions about digital work, such influences need to be considered. 4. Measurements of Digital Work in German Studies 4.1 Selection of Studies We review large studies conducted in Germany in order to identify the different instruments used to measure digital work. We select studies to be analyzed based on the following criteria: (1) they have to focus on aspects of digitalization at the workplace specifically, not only on the private or general use of a certain technology; (2) they have to include working conditions, aspects of job quality, or workplace characteristics as part of their surveys; (3) they have to provide information about the general survey design and/or the specific questionnaires covering digitalization; and (4) the sampled respondents have to cover a large part of the working population and to be representative of the respective population. Overall, nine surveys meet these criteria (see Table 1). Six out of the nine studies are employee surveys [BAuA-AZB, DiWaBe, DGB-Index “Gute Arbeit”, LEEP-B3, LPP, Bertelsmann Stiftung study “Stand der digitalen Transformation in Deutschland”]. The other studies have different target groups: they focus on households [SOEP] or on specific birth Figure 1. Scheme of Measuring Digital Work Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch72 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 cohorts [NEPS, lidA], but questions about digital work are received by working respondents only.1 Most of the studies have, up to now, included the comprehensive measurement of digitalization only once (the exceptions being LPP and NEPS). DiWaBe and LEEP-B3 also include retrospective questions on different aspects of digitalization.2For detailed descriptions of all the studies considered, see Table 1. 4.2 Implementation Within the Studies 4.2.1 Definition and Theoretical Concept: Concept of Digital Work, Study Interest, and Study Design Except for NEPS, which clearly refers to the digitalization of work in terms of socio-tech- nical systems (Friedrich et al. 2022), the studies do not refer to a theoretical concept of digital work for their operationalization. Consequently, we can only reflect on the theoretical concept by including questionnaires and published research. In most of the studies, the questionnaire has a specific focus, particularly in relation to the study interest (see Table 1). Based on the study interest, as well as the operationalization of digitalization, it can be inferred that all studies understand the implementation of technology as part of the work environment and in its interaction with employees’work tasks and conditions. This line of inquiry indicates an understanding of digital work as a theoretical concept of socio-technical systems. However, there are some gradations in their understanding of technology as a determinant of employees’working conditions. Some surveys, DiWaBe, lidA, BAuA-AZB and Bertelsmann Stiftung study, look at the digitalization of work by focusing on the implementation of specific technologies or the digitalization in general affecting employees rather than their integration in the work process (see Table 2 in the Appendix). The first two surveys focus on the economic, social, work organizational, and health-related consequences of digital technologies in the workplace and concentrate on the implementation and diffusion of specific technologies. The BAuA-AZB and the DGB-Index focus primarily on working conditions and the quality of work and look specifically at employees’perception of digital work in terms of their work demands and resources. The Bertelsmann Stiftung study is interested specifically in employees’perspectives on the digital transformation at their workplace and on working conditions, with a focus on capturing their perceptions about the status and implementation of digital work in their companies. The DGB-Index, LEEP-B3, NEPS, LPP take an interactive perspective by looking at the integration of digital work technologies into work processes (see Table 3 in Appendix). The LEEP-B3, which has a special interest in employment relationships and organizational inequality regimes, asks primarily about the application of digital work technologies in the work process and about the interactions with digital technologies among different groups of employees, as well as the consequences of their use when it comes to job demands and resources. In the NEPS, the emphasis is on technology as part of work tasks and changes in tasks and work, which reflects the study’s interest in the 1For some studies, an English translation of the questionnaire was available. The other questionnaires were translated by the authors (BAuA-AZB, Bertelsmann Stiftung study, DiWaBe, DGB- Index, lidA, SOEP). 2We, however, did not include this as a criterion. Measuring Digital Work in (German) Employee Surveys 73 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Table 1. Overview of the Surveys Reviewed] Survey BAuA-AZB Bertelsmann Stiftung study DGB-Index DiWaBe LEEP-B3 lidA LPP NEPS SOEP Responsibility BAuA Bertelsmann Stiftung, Kantar DGB ZEW, IAB, BAuA, BIBB Bielefeld University (cooperation with IAB) Bergische University Wuppertal BMAS, IAB IAB, WZB DIW Survey type Panel Cross-sectional Cross-sec- tional (repeatedly) Cross-sectional Panel Cohort study, panel Panel Cohort study, panel Panel Survey date (digitalization measured) 2019 2019 2016 2019 2018/19 2018 2014/15, 2018/19 2019/20, 2020/21, 2021/22 (in field) 2019 Study interest Long-term consequences of the changing working environment and a continuous reporting about working hours; focus on the design of working hours, conditions and health State of digitalization within establishments from the employees’perspective; perception of the digital transformation in daily work and at the workplace Investigation of the quality of work from the employees’perspective; in 2016, focus on digitalization Social, work organizational, and health-re- lated consequences of digital technologies at the workplace Organizational inequalities and interdependencies between work and private life; data from employers and employees about the workplace context, working conditions, and private life of employees Long-term effects of work on health and employment participation of elder workers Examination of personnel management, individual job quality, and corporate success Investigation of the development of competencies and education for different cohorts over the life span Household survey for innovative research projects; module about digitalization in 2019 Sample Working population at least 15 years old and working at least 10 hours per week German employees Employees Employees (sample based on IAB-ZEW Arbeitswelt 4.0 sample) Employees born after 1960 within large establishments (> 500 employees) Employee cohorts born in 1958 or 1965 Employees within establishments with at least 50 employees Adults born between 1944 and 1986 (Study cohort 6) and born in 1995/1996 (Study cohort 4) Households Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch74 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 ferences between sectors when all of the technologies used at work were combined (Reimann, Abendroth and Diewald 2020). However, distinguishing different types of digital work technologies reveals important differences: ICT are mainly used by high-skilled employees in higher positions working in business-related, financial or information service sectors (Arnold et al. 2016; DGB 2016; Friedrich et al. 2021; Tisch et al. 2021). Otherwise, networked digital technologies and algorithmically controlled work processes, and the use of machines or tools are mainly prevalent in the production sector and in jobs performed by manual or low-skilled workers (Arnold et al. 2016; DGB 2016; Friedrich et al. 2021; Gensler and Abendroth 2021; Tisch et al. 2021; Marx, Abendroth and Meyer 2022). However, it is not only the extent and type of use, which differs according to employment characteristics. The relationship with employees’working conditions and well-being is also uneven. Results based on the DGB-Index show that digital work is related to higher levels of autonomy and a better work-life balance only for employees working under favorable conditions (DGB 2016). In the LPP, respondents with a lower qualification report of less physical strain, but also less demands for skills and competencies and a higher fear of job loss (Arnold et al. 2016). Similar tendencies could be found in the NEPS data (Friedrich et al. 2021). 5. Discussion and Further Research Different theoretical concepts suggest increasing and new work demands in digital working environments supported by first empirical studies (e.g., Arnold et al. 2016; Borle et al. 2021; Friedrich et al. 2021; Gensler and Abendroth 2021; Meyer et al. 2021; Meyer and Hünefeld 2021; Kersten and Junghanns 2022; Marx, Abendroth and Meyer 2022). The digitalization phenomenon, however, includes a variety of technologies and processes such as ICT and automation technologies in all kinds of industries and work organizations. Thus, the definition of digitalization is challenging when it comes to measuring it. For this reason, it is important to get an overview of the state of art in measuring digital work, especially because of its increasing implementation in employee surveys. In Germany, research concerning the digitalization of work has been strongly encouraged and funded within recent years, leading to a variety of approaches to operationalize digital work in large (employee) surveys. In this article, we bring together theoretical considerations and representative German studies that integrate questions about the digitalization of work. Doing so, we aim to present an overview of the existing approaches and develop a theoretical scheme for classifying different approaches to measuring digital work. Reflecting different theoretical and conceptual considerations, we identify three different approaches to measure digital work: work equipment–based, work content–based, and opinion-based. Within the nine German employee studies considered, all of these approaches are implemented to some degree, although they differ with regard to their focus on specific approaches. Each of these approaches can be advantageous for measuring various aspects of digitalization. Looking specifically at the implementation and use of work equipment helps to gain detailed knowledge about the dissemination of digital technologies in different sectors and occupations. Moreover, it depicts the state of technological development, showing which technologies are practically implemented and widespread within firms and the workforce, and not limited to theoretical discussions. Indeed, research results based on the surveys in- Measuring Digital Work in (German) Employee Surveys 81 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 troduced show that ICT are already widespread whereas the use of algorithmics or artificial intelligence are more prevalent for a selective group of workers (Arnold et al. 2016; DGB 2016; Borle et al. 2021; Friedrich et al. 2021; Giering and Kirchner 2021; Marx, Reimann and Ribbat 2021; Tisch et al. 2021). However, asking about specific technologies or work equipment may also risk obtaining relatively group-specific results; for example, robots are probably more widespread within production facilities than among administrative occupations. In addition, asking about specific technologies may lead to erroneous answers, since one cannot assume that all employees know the exact technology they are working with or the precise terminology that is used for that technology (Giering et al. 2021). The measurement of digital work by means of a work content-based approach allows for considering the application of technology in work processes and tasks. This type of operationalization can improve our understanding of the work tasks performed with or supported by digital technologies as well as the extent to which they are part of everyday working life and processes. For example, the results based on the NEPS considered in this paper show that ICTare used for different purposes, ranging from work-related communication, searching for information or for collecting and preparing data (Friedrich et al. 2021). In addition, changes in work tasks and content can be monitored in the long term. Work content-based approaches might also be easier to investigate because they are supposed to capture how work tasks and content are permeated by digital technologies and do not require questions about specific technologies such as algorithms or artificial intelligence, which are difficult for employees to identify. A good example for this is included in the SOEP Innovation Sample as Giering et al. (2021) show that asking for a specific technology can be biased. By comparing responses to a direct question on the use of AI and indirect questions about tasks integrating AI, they find that employees might not be aware that this technology is part of their work. The broad query of work content, however, can also be a disadvantage when it comes to certain research questions, since it is not associated with a specific technology and may therefore mask differences in terms of technology’s impact, leading to an “aggregation bias”and the mutual cancellation of its effects or a “cover up.”Moreover, only pre-assumed relationships can be considered, and an exploration of possible new impacts is difficult. Whereas the two above-mentioned approaches to operationalize digital work focus on the implementation and use of technology, the opinion-based approach aims to depict the employees’subjectively perceived impact of digitalization. Thus, employees’perception can be used to capture sentiment regarding this topic. However, this approach is highly subjective and represents an opinion rather than an actual association. The results from the opinion-based approach point to various concerns being prevalent like a higher amount of work or strain but also positive aspects like work relief or more work-life balance due to the digitalization (DGB 2016; Meyer and Backhaus 2022). Overall, our proposed distinction between work equipment-based, work content-based, and opinion-based approaches is not entirely selective. Small differences in the wording may have led to questions being classified in a different category. This issue also seems to reflect the complexity and ambiguity of the subject of digitalization. However, this can also be a major strength of surveys, which combine several approaches since it is possible to compare different measurements of digitalized work. Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch82 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Most of the studies integrate questions about digital work in their existing surveys which sets the study interest but also restricts a wide-ranging integration of questions simply because of time and space restrictions for surveys. Nevertheless, our systematization of existing approaches to measure digital work shows that there is not (yet) one ideal way with all three approaches having strengths and weaknesses. However, the question arises whether it would be useful to create a more uniform set of questions to measure digitalization. On the one hand, standardization allows for comparability, which would be particularly fruitful at the beginning of this development to avoid a complete defibration of operationalization and to make the results more accessible to other researchers. In addition, it could also be interesting to take a closer look at statistical metrics provided by standardized measurements to facilitate meta-analyses regarding the digitalization of work. Such meta-analyses can be used to maintain credible results in social science research (Tong and Guo 2019). On the other hand, the use of standardized measurement risks failure to apprehend rapid technological developments. Thus, the questions should be quickly adaptable and capture technological developments and processes rather than specific technologies, such as mail or specific communication software or apps, which could become outdated within a few years (an example being the BlackBerry smartphone). Such coarse measurements could in turn disadvantage the collection of specific and detailed information. As already evident in existing research, it is not sufficient to understand and capture the digitalization of work as an over-reaching phenomenon that affects heterogeneous groups of employees in the same way. Rather, digitalization seems to be stratified according to categories such as occupations, qualifications, and industry sectors (Autor, Levy and Murnane 2003; Hirsch-Kreinsen 2016). Thus, standardization of questions should be implemented on this level or at least be applicable and adaptable to these differences to avoid missing important information for specific groups. Moreover, these considerations do not yet consider country-specific differences in the development of digital work (see, e.g., Berger and Frey 2016; Žwaková 2018), which may also be challenging when it comes to standardizing survey instruments. Additional measurements that consider the different designs of or collaborations with the same technology (e.g., the interactivity or adaptivity of robots or algorithmic work control) might also be required. Comparisons and linkages with qualitative or process-generated data on digital work might help to further evaluate and improve existing measurements in large-scale surveys. Finally, with an increasing number of theories and approaches trying to explain potential developments in the labor market and in working conditions because of new technologies, it is increasingly important to integrate theory-based operationalization to test and advance the theoretical considerations. The comparison of the study results shows the importance of integrating digital work within employee surveys, since it is no fringe phenomenon in today’s working world but already widespread. Thus, it is part of the working environments affecting employees’work and family lives, as well as their health. Further research to measure the impact of digital work on employment to examine the consequences for job quality can be realized by integrating this topic within panel surveys to measure the trends over time. Measuring Digital Work in (German) Employee Surveys 83 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Limitations of this Study Although our proposed scheme for classifying different measurement approaches should help to structure the debate on digitalization, certain limitations must be acknowledged. We intentionally decided to focus on German studies that involve large sample sizes and are representative of specific groups of employees. In doing so, we aim to increase the comparability of different approaches for measuring digital work because these studies provide a relatively homogeneous setting in which the different operationalizations are implemented. However, owing to these selection criteria, we omit studies of rather technically focused disciplines, smaller case studies that focused on a specific phenomenon, and international studies that involve other contextual factors. This also applies to the study subject and the study design, as well as to differences in the factors or foci at each level of the theoretical scheme. None of the studies specifically defines a concept of digital work, but the operationalization is often interrelated with the study interest. The study designs are similar, since we focus on representative employee studies that mostly cover a large part of the labor force,3which might also explain why these studies cover a wide range of technologies and applications of both ICT and automation. Thus, including more heterogeneous study designs and foci might enrich our scheme, since the foci and bases of operationalization might be expanded or even more distinct. Thus, further research is needed to compare studies that are conducted in a more heterogeneous and/or international setting and further expose what is missing within the scheme or research in general. Conclusion The operationalization of digital work has increasingly found its way from smaller case studies to larger employee surveys. As the digitalization of work encompasses a wide range of technologies, work processes and developments in the labor market and employment relationships, there are no established and standardized ways to measure digital work (yet). Our work provides an overview over approaches of operationalization and the advantages and limitations they imply. We offer a systemization and reflection of ways to measure digital work within employee surveys and apply it to nine German employee surveys. The result of our work can help researchers working with data on digital work to assess and frame their research results, but it also helps to further develop existing surveys. References Arnold, D., S. Butschek, S. Steffes, and D. Müller. 2016. Digitalisierung am Arbeitsplatz: Bericht. (Forschungsbericht /Bundesministerium für Arbeit und Soziales, FB468). Nürnberg: Bundesministerium für Arbeit und Soziales; Institut für Arbeitsmarkt- und Berufsforschung der Bundesagentur für Ar- 3Such similarity applied to the survey mode as well. The ability to survey detailed technological processes also depends on the survey mode and the feasibility of implementing a complex questions structure. It is easy to apply various filters within computer-assisted surveys because the respondents receive only applicable questions, whereas paper-and-pencil questionnaires or half-standardized interviews may be somewhat overwhelming. All studies used standardized, mainly computer-assisted methods, which explains why we did not find big differences in the complexity of the questionnaires. Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch84 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 beit (IAB); Zentrum für Europäische Wirtschaftsforschung (ZEW) GmbH; Universität Köln. Accessed on January 17, 2023. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-47712–7. Arntz, M., K. Dengler, R. Dorau, T. Gregory, M. Hartwig, R. Helmrich, F. Lehmer, B. Matthes, A. Tisch, S. Wischniewski, and U. Zierahn. 2020. Digitalisierung und Wandel der Beschäftigung (DiWaBe): Eine Datengrundlage für die interdisziplinäre Sozialpolitikforschung. Datenreport und Forschungspotenzial. Dokumentation 20–02. Mannheim, ZEW. Arntz, M., T. Gregory, and U. Zierahn. 2019. Digitalization and the Future of Work: Macroeconomic Consequences. ZEW Discussion Paper No. 19–024. Mannheim, ZEW. Attewell, P. and J. Rule. 1984. “Computing and Organizations: What We Know and What We Don’t Know.”Communications of the ACM 27 (12): 1184–92. Autor, D. H., F. Levy, and R. J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.”Quarterly Journal of Economics 118 (4): 1279–333. Bakker, A. B., and E. Demerouti. 2007. “The Job Demands-Resources Model: State of the Art.”Journal of Managerial Psychology 22 (3): 309–28. BAuA Arbeitszeitbefragung. 2019. Scientific Use File. Version 1. Dortmund, Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. https://doi.org/10.48697/baua.azb19.suf.1. Becka, D., P. Enste, and C. Ludwig. 2019. “Zur Wirkungsmessung digitaler Transformationsprozess in Arbeitswelten.”ARBEIT 28 (4): 341–62. Berger, T. and C. B. Frey. 2016. Structural Transformation in the OECD: Digitalisation, Deindustrialisation and the Future of Work. OECD Social, Employment and Migration Working Papers No. 193. Accessed on April 11, 2022. https://www.oecd-ilibrary.org/social-issues-migration- health/structural-transformation-in-the-oecd_5jlr068802f7-en. Bhattacherjee, A. 2012. Social Science Research: Principles, Methods, and Practices. Textbooks Collection. USF Tampa Library Open Access Collections. Accessed on January 17, 2023. http://scholar commons.usf.edu/oa_textbooks/3. Bolisani, E., E. Scarso, C. Ipsen, K. Kirchner, and J. P. Hansen. 2020. “Working from Home During COVID-19 Pandemic: Lessons Learned and Issues.”Management & Marketing. Challenges for the Knowledge Society 15 (SI): 458–76. Borle, P., F. Boerner-Zobel, S. Voelter-Mahlknecht, H. M. Hasselhorn, and M. Ebener. 2021. “The Social and Health Implications of Digital Work Intensification: Associations Between Exposure to Information and Communication Technologies, Health and Work Ability in Different Socio-Economic Strata.”International Archives of Occupational and Environmental Health 94 (3): 377–90. Brockhaus, C. P., T. S. Bischoff, K. Haverkamp, T. Proeger, and A. Thonipara. 2020. “Digitalisierung von kleinen und mittleren Unternehmen in Deutschland –ein Forschungsüberblick.”Göttinger Beiträge zur Handwerksforschung 46. Volkswirtschaftliches Institut für Mittelstand und Handwerk an der Universität Göttingen. Broszeit, S., P. Grunau, and S. Wolter. 2016. LPP –Linked Personnel Panel 1415. Arbeitsqualität und wirtschaftlicher Erfolg: Längsschnittstudie in deutschen Betrieben (Datendokumentation der zweiten Welle). FDZ-Datenreport, 06/2016. Nürnberg, Institut für Arbeitsmarkt- und Berufsforschung. Accessed on April 11, 2022. https://fdz.iab.de/187/section.aspx/Publikation/k160922302. Caselli, F. and W. J. Coleman. 2001. “Cross-Country Technology Diffusion: The Case of Computers.” American Economic Review 91 (2): 328–35. Dafoe, A. 2015. “On Technological Determinism: A Typology, Scope Conditions, and a Mechanism.” Science, Technology, & Human Values 40 (6): 1047–76. Measuring Digital Work in (German) Employee Surveys 85 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Day, A., S. Paquet, N. Scott, and L. Hambley. 2012. “Perceived Information and Communication Technology (ICT) Demands on Employee Outcomes: The Moderating Effect of Organizational ICT Support.”Journal of Occupational Health Psychology 17 (4): 473 –91. Day, A., N. Scott, and E. K. Kelloway. 2010. “Information and Communication Technology: Implications for Job Stress and Employee Well-Being”.InResearch in Occupational Stress and Well-being: New Developments in Theoretical and Conceptual Approaches to Job Stress, Vol. 8, edited by P. L. Perrewé and D. C. Ganster, 317–50. Bingley: Emerald Group Publishing Limited. DGB. 2016. Report 2016 –Schwerpunkt: Digitalisierung der Arbeitswelt. Accessed on June 4, 2021. https://index-gute-arbeit.dgb.de/++co++76276168-a0fb-11e6-8bb8-525400e5a74a. Emery, F. (1959) 2016. “Characteristics of Socio-Technical Systems.”In The Social Engagement of Social Science: A Tavistock Anthology,Volume 2: A Tavistock Anthology—The Socio-Technical Perspective, edited by E. Trist, H. Murray, and B. Trist, 157–86, Philadelphia: University of Pennsylvania Press. Fischer, G. and T. Herrmann. 2011. “Socio-Technical Systems: A Meta-Design Perspective.”International Journal of Sociotechnology and Knowledge Development 3 (1): 1–33. Friedrich, T. S., M.-C. Laible, C. Müller, R. Pollak, S. Schongen, B. Schulz, and B. Vicari. 2022. Digitalisierung der Arbeitswelt –ein Messinstrument der NEPS-Erwachsenenbefragungen. NEPS Survey Papers 92. Bamberg. Friedrich, T. S., M.-C. Laible, R. Pollak, S. Schongen, B. Schulz, and B. Vicari. 2021. “Grasping Digitalization in the Working World. An Example from the German National Educational Panel Study.” Soziale Welt 72 (4): 415–52. Gensler, E. and A. Abendroth. 2021. “Verstärkt algorithmische Arbeitssteuerung Ungleichheiten in Arbeitsautonomie? Eine empirische Untersuchung von Beschäftigten in großen deutschen Arbeitsorganisationen.”Soziale Welt 72 (4): 514–50. Giering, O., A. Fedorets, J. Adriaans, and S. Kirchner. 2021. Artificial Intelligence in Germany: Employees Often Unaware They Are Working with AI-Based Systems. DIW Weekly Report Nr. 48/ 2021. Berlin: DIW. Accessed online on January 17, 2023. http://hdl.handle.net/10419/248514. Giering, O. and S. Kirchner. 2021. “Künstliche Intelligenz am Arbeitsplatz. Forschungsstand, Konzepte und empirische Zusammenhänge zu Autonomie.”Soziale Welt 72 (4): 551 –88. Govers, M. and P. van Amelsvoort. 2019. “A Socio-Technical Perspective on the Digital Era: The Lowlands View.”European Journal of Workplace Innovation 4 (2): 142–59. Gray, J. and B. Rumpe. 2015. “Models for Digitalization.”Software & Systems Modeling 14 (4): 1319–20. Groves, R. M., F. J. Fowler, Jr., M. P. Couper, J. M. Lepkowski, E. Singer, and R. Tourangeau. 2009. Survey Methodology. Second Edition. Hoboken, NJ: Wiley. Grzymek, V. and O. Wintermann. 2020. Wie digital sind die Unternehmen in Deutschland? Ergebnisse einer repräsentativen Befragung unter Erwerbstätigen. Bertelsmann Stiftung/Kantar. Accessed on June 4, 2021. https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/wie-digital- sind-die-unternehmen-in-deutschland. Häring, A., H. Schütz, M. Kleudgen, C. Brauner, L. Vieten, A. Michel, and A. M. Wöhrmann. 2020. Methodenbericht und Fragebogen zur BAuA-Arbeitszeitbefragung 2019. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Hirsch-Kreinsen, H. 2016. “Digitization of Industrial Work: Development Paths and Prospects.”Journal for Labour Market Research 49 (1): 1–14. Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch86 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Holler, M. 2017. Verbreitung, Folgen und Gestaltungsaspekte der Digitalisierung in der Arbeitswelt. Auswertungsbericht auf Basis des DGB-Index Gute Arbeit 2016. Berlin: Institut DGB-Index Gute Arbeit. Accessed on June 4, 2021 https://index-gute-arbeit.dgb.de/++co++1c40dfc8-b953–11e7– 8dd1–52540088cada. Jeske, D. and A. M. Santuzzi. 2015. “Monitoring What and How: Psychological Implications of Electronic Performance Monitoring.”New Technology, Work and Employment 30 (1): 62–78. Kersten, N. and G. Junghanns. 2022. “Informationsüberflutung am Arbeitsplatz. Gesundheitliche Beschwerden im Längsschnitt der BAuA-Arbeitszeitbefragung.”Zentralblatt für Arbeitsmedizin, Arbeitsschutz und Ergonomie 72 (5): 206–18. Kleinert, C., A.-C. Bächmann, B. Schulz, B. Vicari, and M. Ehlert. 2021. Für wen brachte Corona einen Digitalisierungsschub? Veränderungen in der Nutzung digitaler Technologien während der COVID- 19-Pandemie. NEPS Corona &Bildung, 6. Bamberg: Leibniz-Institut für Bildungsverläufe. Legner, C., T. Eymann, T. Hess, C. Matt, T. Böhmann, P. Drews, A. Mädche, N. Urbach, and F. Ahlemann. 2017. “Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community.”Business & Information Systems Engineering 59 (4): 301–8. Marenco, M. and T. Seidl. 2021. “The Discursive Construction of Digitalization: A Comparative Analysis of National Discourses on the Digital Future of Work.”European Political Science Review 13 (3): 391–409. Martin, A. J., J. M. Wellen, and M. R. Grimmer. 2016. “An Eye on Your Work: How Empowerment Affects the Relationship Between Electronic Surveillance and Counterproductive Work Behaviours.”The International Journal of Human Resource Management 27 (21): 2635–51. Marx, C., A.-K. Abendroth, A.-C. Bächmann, M. Diewald, L. Lükemann, S. M. Melzer, E. Peters, and M. Reimann. 2020. Employee and Partner Surveys Wave 3 of the Linked-Employer-Employee-Panel (LEEP-B3) Project (DFG –373090005): Organizational Inequalities and Interdependencies between Capabilities in Work and Personal Life: A Study of Employees in Different Work Organizations. Technical Report. Bielefeld: Bielefeld University. Marx, C. K., A.-K. Abendroth, and S.-C. Meyer. 2022. Automatische Datenspeicherung von Arbeitsschritten und das Wohlbefinden von Beschäftigten. Ergebnisse einer Befragung in deutschen Großbetrieben. baua: Bericht kompakt. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Marx, C. K., M. Reimann, and M. Ribbat. 2021. Führung digital: Anforderungen und Ressourcen bei Führungskräften. baua: Bericht kompakt. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Meyer, S.-C. and N. Backhaus. 2022. Wie nehmen Beschäftigte den technologischen Wandel wahr? Ergebnisse der BAuA-Arbeitszeitbefragung. baua: Bericht kompakt. Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Meyer, S.-C., M. Hartwig, A. Tisch, and S. Wischniewski. 2021. Veränderte Arbeitsanforderungen in der digitalisierten Arbeitswelt. Unterschiede nach Digitalisierungsgrad des Arbeitsmittels. baua: Bericht kompakt. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Meyer, S.-C. and L. Hünefeld. 2021. “Work-Related ICT Use and Work Intensity: The Role of Mobile Devices.”Soziale Welt 72 (4): 453–82. NEPS. 2022. Documentation of Starting Cohort Adults SC(6). Version 13.0.0. Accessed on May 30, 2023. https://www.neps-data.de/Data-Center/Data-and-Documentation/Start-Cohort-Adults/ 105157-NEPSSC61300. https://doi.org/10.5157/NEPS:SC6:13.0.0. Orlikowski, W. J. 2000. “Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations.”Organization Science 11 (4): 404–28. Measuring Digital Work in (German) Employee Surveys 87 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Orlikowski, W. J. and S. V. Scott. 2014. “What Happens When Evaluation Goes Online? Exploring Apparatuses of Valuation in the Travel Sector.”Organization Science 25 (3): 868–91. Pattloch, D., L. Menze, L. Vieten, J. Nold, N. Backhaus, I. Entgelmeier, L.-K. Stein, S. Zink, A. Tisch, C. Brauner-Sommer, A. Michel, and A. M. Wöhrmann. 2021. Datendokumentation des Scientific Use File der BAuA-Arbeitszeitbefragung 2019. Dortmund: Bundesanstalt für Arbeitsschutz und Arbeitsmedizin. Posey, C., B. Bennett, T. Roberts, and P. B. Lowry. 2011. “When Computer Monitoring Backfires: Invasion of Privacy and Organizational Injustice as Precursors to Computer Abuse.”Journal of Information System Security 7 (1): 24–47. Reimann, M., A.-K. Abendroth, and M. Diewald. 2020. How Digitalized is Work in Large German Workplaces and How is Digitalized Work Perceived by Workers? A New Employer-Employee Survey Instrument. IAB-Forschungsbericht, 8|2020. Accessed on November 9, 2021. http://doku.iab.de/for schungsbericht/2020/fb0820.pdf. Ruf, K., J. Mackeben, T. Haepp, S. Wolter, and P. Grunau. 2020a. Linked Personnel Panel (LPP) – Version 1819 v1. Forschungsdatenzentrum der Bundesagentur für Arbeit (BA) im Institut für Arbeitsmarkt- und Berufsforschung (IAB).Nuremberg : IAB. Ruf, K., J. Mackeben, T. Haepp, S. Wolter, and P. Grunau. 2020b. LPP –Linked Personnel Panel 1819 –Quality of Work and Economic Success: Longitudinal Study in German Establishments (Data Documentation on the Fourth Wave). FDZ-Datenreport, 11/2020 (en). Nuremberg : IAB. Sharma, A. and T. Sharma. 2017. “HR Analytics and Performance Appraisal System: A Conceptual Framework for Employee Performance.”Management Research Review 40 (6): 684–97. Siegel, R., C. J. König, and V. Lazar. 2022. “The Impact of Electronic Monitoring on Employees’Job Satisfaction, Stress, Performance, and Counterproductive Work Behavior: A Meta-Analysis.”Computers in Human Behavior Reports 8: 100227. https://doi.org/10.1016/j.chbr.2022.100227. SOEP Innovation Sample (SOEP-IS). 2020. Data from 1998–2019. Accessed on March 29, 2022. https://www.diw.de/sixcms/detail.php?id=diw_01.c.818852.en#c_818854Frageboegen_tab. SOEP-IS Group. 2021. SOEP-IS 2019 –Fragebogen für die SOEP-Innovations-Stichprobe. SOEP Survey Papers 953, Series A –Survey Instruments (Erhebungsinstrumente). Berlin: DIW Berlin/SOEP. Accessed on April 11, 2022. https://www.diw.de/de/diw_01.c.821972.de/publikationen/soepsurvey papers/2021_0953/soep-is_2019_____fragebogen_fuer_die_soep-innovations-stichprobe.html. Steinwede, J., J. Ruiz Marcos, and M. Kleudgen. 2020. Methodenbericht. lidA Welle 3. Bonn: infas Institut für angewandte Sozialwissenschaft GmbH. Accessed on January 17, 2023. https://arbeit.uniwuppertal.de/fileadmin/arbeit/Publikationen/ Methodenbericht_lidA_W3. Tarafdar, M., E. B. Pullins, and T. S. Ragu-Nathan. 2015. “Technostress: Negative Effect on Performance and Possible Mitigations.”Information Systems Journal 25 (2): 103 –32. Tisch, A., N. Backhaus, M. Hartwig, S.-C. Meyer, and S. Wischniewski. 2021. “Digitalisierung und Arbeitsbedingungen.”In Datenreport 2019 –ein Sozialbericht für die Bundesrepublik Deutschland, edited by Statistisches Bundesamt, Wissenschaftszentrum Berlin für Sozialforschung &Bundesinstitut für Bevölkerungsforschung, 189–196. Bonn: Bundeszentrale für politische Bildung. Tong, G. and Guang Guo. 2019. “Meta-Analysis in Sociological Research: Power and Heterogeneity.” Sociological Methods & Research 51 (2): 566–604. Trist, E. L. 1953. Some Observations on the Machine Face as a Socio-Technical System. London: Tavistock Institute. Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch88 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Trist, E. L. and K. W. Bamforth. 1951. “Some Social and Psychological Consequences of the Long Wall Method of Coal-Getting.”Human Relations 4 (1): 3–38. Waizenegger, L., B. McKenna, W. Cai, and T. Bendz. 2020. “An Affordance Perspective of Team Collaboration and Enforced Working from Home During COVID-19.”European Journal of Information Systems 29 (4): 429–42. Warhurst, C. and W. Hunt. 2019. The Digitalisation of Future Work and Employment: Possible Impact and Policy Responses. JRC Working Papers on Labour, Education and Technology 2019–05. European Commission: Joint Research Centre. Winner, L. 1977. Autonomous Technology: Technics-out-of-Control as a Theme in Political Thought. Cambridge: MIT Press. Wood, A. J. 2021. Algorithmic Management: Consequences for Work Organisation and Working Conditions. JRC124874. European Commission. Seville. Accessed on October 20, 2022. https://joint-re search-centre.ec.europa.eu/system/files/2021–05/jrc124874.pdf. Wood, A. J., M. Graham, V. Lehdonvirta, and I. Hjorth. 2019. “Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy.”Work, Employment and Society 33 (1): 56 –75. Wuppertal University. 2018. Home. lidA Study. Wuppertal: Bergische Universität. Accessed on June 4, 2021. https://arbeit.uni-wuppertal.de/en/study.html. zukunft der arbeit. 2020. Wie digital sind die Unternehmen in Deutschland?. Accessed on November 11, 2021. https://www.zukunftderarbeit.de/2020/01/31/unternehmen-digital/. Žwaková, M. 2018. “The Conditions for Digitalization and Industry 4.0 Development in Selected European States.”Journal of Applied Economic Sciences 13 (2): 484–97. Zweck, B. and A. Glemser. 2020. SOEP-IS 2019 –Survey Report on the 2019 SOEP Innovation Sample. SOEP Survey Papers 902, Series B –Survey Reports (Methodenberichte), Berlin: DIW/SOEP. Accessed on April 11, 2022. https://www.diw.de/de/diw_01.c.806669.de/publikationen/soepsurvey papers/2020_0902/soep-is_2019_____survey_report_on_the_2019_soep_innovation_sam ple.html. Appendix Table 2. Work Equipment Surveyed Within the Studies] Work Equipment Studies ICT AI DiWaBe, SOEP Big Data DiWaBe Blockchain-based data DiWaBe Checkout systems DiWaBe, SOEP Computer (single item) DiWabe, lidA, SOEP Internet of Things DiWaBe Internet of Services DiWaBe ICT (laptop, smartphone, tablet, computer) combined in one question BAuA-AZB, DGB-Index, LPP, NEPS Measuring Digital Work in (German) Employee Surveys 89 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44 Table 2 (Continued) Work Equipment Studies ICT Laptop (single item) DiWaBe, lidA, SOEP Self-controlled or self-learning computer systems (combined in one question) NEPS, SOEP Smartphone (single item) DiWaBe, lidA, SOEP Tablet (single item) DiWaBe, lidA, SOEP Virtual or Augmented Reality DiWaBe Machinery, devices, tools Computer-based vehicles and transportation (e.g., cars, bus) DiWaBe Dataglasses lidA Diagnostic devices DiWaBe, LPP, SOEP Intelligent equipment or machinery Bertelsmann Mobile devices and tools DiWaBe, LPP Production- or process-technologies; automation technologies BAuA-AZB, NEPS (Stationary or mobile) robots DGB-Index, DiWaBe, LEEP-B3, LPP, SOEP Scanner SOEP Stationary production machines and devices BAuA-AZB, DiWaBe, LEEP-B3 Supporting electronic devices (e. g., data glasses, diagnostic devices, scanner) combined in one question DGB-Index Technical equipment Bertelsmann 3D-print DiWaBe Example: “You told us that you use information and communication equipment at work. Does this include the following computerized tools: 1: desktop PC, 2: laptop, 3: smartphone, 4: tablet 5: POS systems 6: [something] else (open category)?”(DiWaBe; Arntz et al., 2020) Table 3. Work Content Surveyed Within the Studies] Work Content Studies Administration of data bases LPP Automatic feedback SOEP Automatic data storage LEEP-B3, SOEP Automatic work instructions, instructed work process DGB-Index, DiWaBe, LEEP- B3, SOEP Collect or prepare data with spreadsheet programs LPP Collecting information online LPP, NEPS Communication via mail (single question) LEEP-B3, lidA, LPP Ch. K. Marx, A.-K. Abendroth, S.-Ch. Meyer, M. Reimann, A. Tisch90 Journal of Contextual Economics 142 (2022) 1 OPEN ACCESS | Licensed under CC BY 4.0 | https://creativecommons.org/about/cclicenses/ DOI https://doi.org/10.3790/schm.142.1.67 | Generated on 2023-10-13 12:13:44