“Smart” work characteristics and outcomes
for a Smart Industry context: A theoretical
review
Milou M.P. Habraken
s2823403
RESEARCH MASTER THESIS
Research Master in Economics & Business, spec. HRM & OB
First supervisor: Prof. Dr. O. Janssen
Second supervisor: Dr. T. Vriend
University of Groningen Faculty of Economics & Business
Department of Human Resource Management and Organisational Behaviour
“Smart” work characteristics and outcomes for a Smart
Industry context: A theoretical review
Milou M.P. Habraken
University of Groningen, The Netherlands
Abstract
This paper tackles two existing shortcomings within the current body of work related to Smart Industry in the field of human resource management (HRM) – a lacking theoretical basis and a narrow focus on skills. To do so, focus is put on work design, hence the characteristics of work and its outcomes. In light of the job characteristics model (JCM), this paper constructs an overview of the current developments surrounding work design and addresses the development of “smart” work characteristics and outcomes. Based on the overview and the pillars of Smart Industry it is expected that six dominant characteristics (task significance, task identity, autonomy, feedback from job, skill variety and social support) will remain important within Smart Industry. Given that Smart Industry places greater emphasis on synergies, a configuration approach is adopted towards work design, specifically focused on bundling the following four work characteristic categories – task, knowledge, social and contextual. Finally, the job resource linked to smart industries, constructed on the basis of the job demands-resources model (JD-R model), gives rise to the expectation that the under-examined characteristics virtual work and equipment use will grow in importance within Smart Industry resulting in propositions regarding their relationships with outcomes.
Keywords: Smart Industry, Human Resource Management (HRM), Job characteristics model (JCM),
Preface
Smart Industry is happening right now and, like prior industrial revolutions, is bringing about change. The introduction of tools such as smart machines or augmented reality and an increase in digitization as well as connectivity are possibilities with which organisations are now faced. It is with respect to the impact of these changes, specifically in the field of human resource management (HRM), where the PhD project which I started in December 2015 at the University of Twente – ‘Smart Human Resource Management’ – centres on. It is also given the start of this project why the thesis for the research master at the University of Groningen, which lies before you – entitled “Smart” work characteristics and outcomes for a Smart Industry context: A theoretical review – is focused on the phenomenon Smart Industry.
Consequently, I would like to express my gratitude towards both universities for their collaboration, making it possible for me to combine the graduation of my research master with my PhD position. Additionally, I would like to give special thanks to Onne Janssen for supervising this master thesis.
Milou Habraken
Table of contents
Introduction p. 5
The employment debate – current state and taken perspective p. 6
Work design – before Smart Industry p. 8
Background and adopted theory p. 8
JCM and developments p. 9
Identified work characteristics p. 10
Graphical illustrations p. 12
Conclusion p. 14
Work design – for Smart Industry p. 14
Smart Industry p. 14
Dominant characteristics p. 15
Task and knowledge characteristics p. 16
Social characteristics p. 16
Configuration theory p. 17
Synergistic relationships p. 17
Smart Industry work design configurations p. 18
Under-examined characteristics p. 19
Job resource and demand for smart industries p. 19
Virtual work p. 21
Equipment use p. 22
Discussion p. 24
Contributions p. 24
Theoretical and practical implications p. 25
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Introduction
Being phrased as not just a mere vision but the future as well as the world’s fourth industrial revolution, Smart Industry or Industry 4.01 – characterised by technology, digitization and connectivity – is gaining a growing amount of attention. Through actions such as events, websites and reports, explicit emphasis is placed on Smart Industry. It even reawakened the discussion surrounding the possibility of massive unemployment, as did previous major technological changes, which stresses the impact that Smart Industry has with respect to work and more broadly the field of human resource management (HRM).
Despite the indicated amount of attention that is being directed towards Smart Industry, the current body of work related to this phenomena within the field of HRM is plagued with two problems: (a) a lacking fundamental theoretical basis and (b) a narrow focus on skills. Papers or reports to date have been crucial in the sense that they introduced what Smart Industry entails, created awareness for this development and presented relevant questions as well as statistics (e.g. 47 percent of total US employment are estimated to be at risk for computerisation; Frey & Osborne, 2013). However, what current reports are missing is a theoretical approach towards the HRM related issues being discussed in connection with Smart Industry. Additionally, the main interest in the field of HRM has been focused on the question of which skills become necessary. It is highlighted that Smart Industry will require adaptations towards the needed skills. Skills in the following areas are for instance expected to increase in importance: ICT, communication, problem-solving and creativity (e.g. Berger & Frey, 2015; FME, 2015; Levy & Murnane, 2013; Ten Have, Van Rhijn & Van Wijk, 2014). Although this issue is highly relevant, it prevents the obtainment of a broader understanding towards the problem of what Smart Industry does to work?
Consequently, the purpose of this paper is to address these shortcomings. Specifically, the current paper focuses on work design, which is the characteristics of work and its outcomes, as it provides a broader, more holistic approach to the question of what Smart Industry does to work. That is, work design extends the current focus on skills, a knowledge aspect of work, through its incorporation of task, social and contextual aspects of work. Furthermore, work design is connected to an extensive body of literature resulting in a rich amount of theoretical background in order to contribute to the provision of a theoretical basis. To address the characteristics and outcomes of work within the context of Smart Industry, insights into the existing characteristics and their effects is essential. Therefore, apart from tackling the mentioned shortcomings, the goal of the paper is, in light of the job characteristics model, to (1) create an overview of the current developments regarding work design and its associated outcomes and subsequently to (2) address the development of “smart” work characteristics and outcomes on the basis of the configuration
1 Although varying in label and origin, the concepts of Smart Industry and Industry 4.0 largely overlap. In addition,
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theory (Miller, 1981) and the job demands-resources model (JD-R model; Bakker & Demerouti, 2007). In short, the paper adopts the job characteristics model (JCM; Hackman & Oldham, 1976) and uses it as a starting point for the creation of a retrospect of past research on work characteristics and outcomes. Given the goal to address the development of “smart” work characteristics and outcomes, highly cited, outline as well as exemplary papers were analysed (instead of a full literature review) to capture the additions to the JCM for constructing the overview of the current developments regarding work design. This overview, the pillars of Smart Industry, the configuration theory and the JD-R model are used to address the development of “smart” work characteristics and outcomes.
Besides adding to the body of literature on work design via the previous two goals, the paper aims to make the following two contributions. First, the paper seeks to contribute to the provision of a theoretical basis towards HRM related issues being discussed in connection with Smart Industry. Secondly, by breaking away from the main HRM issue of interest to date and addressing the development of “smart” work characteristics and outcomes, the paper seeks to contribute to a broader understanding of what Smart Industry does to work. As the prior mentioned employment discussion impacts the necessity of investigating the design of work for Smart Industry, a short overview of the current state and taken perspective regarding this debate will be presented before proceeding to the theoretical argument.
The employment debate – current state and taken perspective
Smart Industry again awakens the discussion surrounding the possibility of massive unemployment, as did previous major technological changes (e.g. Kool, Van Est, Van Keulen & Van Waes, 2015). Consequently, given the focus of this paper on work design issues within the context of Smart Industry, it is of essence to highlight which perspective is taken here. The next part therefore shortly covers the existing discussion and clarifies the adopted view within this debate
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Belonging to the stream questioning the stated consensus (i.e. downward spiral) are, for instance, Brynjolfsson and McAfee (2014) and Ford (2015). These authors argue that machines, more than in the past, will be replacing people. According to them the balance between job creation and job loss has shifted to the latter. Brynjolfsson and McAfee (2014) substantiate their position by highlighting the inability of individual/employee skills and organizations to keep pace with technical change, resulting in the visibility of what these authors term ‘the great decoupling’. In other words, a continuing trend of increasing labour productivity but a drop in labour demand. Despite the above, Brynjolfsson and McAfee (2014) suggest that a doomsday scenario is preventable if businesses start racing with machines instead of against them. They propose that we are simply not being creative enough, which implies that it takes more creativity to pose the question ‘how can a machine and human interact to do something currently unknown and produce value’ compared to the question ‘how can I have a machine take over certain tasks’ (Bernstein & Raman, 2015). Ford (2015), on the other hand, indicates that robots and other forms of automation are going to consume much of the base of the job skills pyramid while, in addition, the top tier will not remain a safe haven due to developments in artificial intelligence applications. Therefore, a larger number of people will be fighting for ever smaller number of jobs unless a guaranteed basic income is realised. Adding to the more pessimistic side of future employment are the results of the study conducted by Frey and Osborne (2013), in which 47 percent of total US employment are estimated to be at risk for computerisation, as well as the many replication studies (e.g. Baert & Ledent, 2015; Heijne & Witteman, 2014; Pajarinen & Rouvinen, 2014; Schattorie, De Jong, Fransen & Vennemann, 2014). Frey and Osborne’s (2013) paper, however, is limited to the substitution effect of computerisation. It neglects technology’s role in the creation of jobs, the impact of societal forces and/or its solution to existing problems such as the aging population (Baert & Ledent, 2015; Heijne & Witteman, 2014; Pajarinen & Rouvinen, 2014; Schattorie et al., 2014; Van Est, 2015).
Unlike those viewing the future more gloomy2, Miller and Atkinson (2013) uphold a brighter perspective (i.e. upward spiral). They state that the pessimists assume a completely wrong link between technological change and employment. The main reason Miller and Atkinson (2013) provide for the fact that robots will not leave us massively unemployed is that human wants are close to infinite and, hence, as long as that is the case there will also be a continuing need for labour. Another positive minded is Bainbridge (2015) who, besides the argument that technology can also create jobs and bring down barriers of entry, states that people increasingly provide the competitive edge as “competition lies in the quality of service that only people can deliver because people are prepared to pay a little more for quality service and positive interaction” (Bainbridge, 2015, p.81). Davenport and Kirby (2015) likewise offer a less grim outlook as, in their view, human work can flourish when we reframe automation into augmentation; augmentation “means
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starting with what humans do today and figuring out how that work could be deepened rather than diminished by a greater use of machines” (Davenport & Kirby, 2015, p.60). To some extent this view fits with Brynjolfsson and McAfee’s (2015) statement that we are not being creative enough. Furthermore, Schouteten (2015) highlights the fact that technology in itself does not determine the function structure, and hence employment, but it is the combination and alignment with organisational design principles or organisational choice (i.e. the space/freedom to make decisions regarding the organisation of work).
The current paper adopts the perspective that the technological developments surrounding Smart Industry will not lead to massive unemployment. I acknowledge that (certain) jobs might drastically change but I do not agree with the outlook of a jobless future. Technology is not (yet) capable of outperforming us humans in every aspect (Bernstein & Raman, 2015). Even if this would not be the case, as well as for the aspects in which technology already outperforms us, there remains freedom in how to implement these developments. As Schouteten (2015) points out, technology in itself does not determine the future, but the decisions we make regarding them. Building on the aspect of choice is the possibility to stop viewing the developments as a means of diminishing current tasks and start seeing it as an opportunity. Linking both these statements to the current focus on work design implies that Smart Industry will lead to an equal amount or more employment when we design jobs according to the characteristics of Smart Industry instead of designing jobs in a tight manner. This is why in this paper I will attempt to contribute to the upward spiral discussion by proposing ways to design “smart” jobs based on the job characteristics model, supported by theoretical ideas related to the configuration theory and the JD-R model. The next section will create an overview of research on work design to aid the development of these work characteristics and outcomes for Smart Industry.
Work design – before Smart Industry
Background and adopted theory
The roots of contemporary approaches to work design can be traced back to the economic perspective of division of labour. That is, around the time of the first industrial revolution economists such as Smith (1776) and Babbage (1835) promoted the idea of breaking down jobs into simple tasks as a way to improve performance. Through the work of Taylor (1911), the concept of simplification - dubbed scientific
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is considered influential since it drew attention to the possibility of enhancing satisfaction; it served as the foundation for the interest in job enrichment. Research addressing the weaknesses of this theory eventually led to the job characteristics model (JCM) most fully articulated by Hackman and Oldham (1976). Other approaches to work design are further the sociotechnical systems theory (Trist & Bamforth, 1951) – which focuses on the interaction between the technical aspects or systems of the work and the people (i.e. the broader social environment) – and the social information processing perspective (Salancik & Pfeffer, 1978) which highlights the influence of social cues and, hence, implies that work characteristics are build up from social information instead of given factors (for more insights see e.g. Grant, Fried & Juillerat, 2011; Morgeson, Garza & Campion, 2013; Parker, Wall & Cordery, 2001).
As visible in the above background, various perspectives on work design exist. The current paper centres on the job characteristics model. This choice stems from the fact that I side with the more positive minded, or the upward spiral, regarding the two existing perspectives of the employment debate. As previously stated, within the context of Smart Industry, human work can flourish when we reframe automation (related to scientific management) into augmentation (related to job enrichment approaches). I therefore focus on the job enrichment approaches, specifically the JCM since it provides a clear framework regarding essential factors and outcomes while overcoming the problems of the motivator-hygiene theory.
JCM and developments
The original job characteristics model includes five job characteristics (skill variety, task significance, task identity, autonomy and feedback) and proposes that they give rise to three critical psychological states (experienced meaningfulness of the work, experienced responsibility for outcomes of the work and knowledge of the actual results of the work activities). Specifically, the theory highlights that the first three characteristics contribute to experienced meaningfulness, autonomy affects the level of experienced responsibility and feedback provides knowledge of the actual results. These psychological states, in turn, impact five outcomes – internal work motivation, performance, satisfaction, absenteeism and turnover (Hackman & Oldham, 1976). Hackman and Oldham (1976) further added growth need strength (GNS) as a moderator to their model. Research since then has examined the relations included in the JCM and proposed additions – new characteristics, outcomes and different mediating as well as moderating factors. Appendix I, II and III present an overview of the developments regarding work design3. Given the aim of
3 Although Parker et al. (2001) highlight the importance of group-level characteristics, literature on teams can be
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the present paper the overview incorporates highly cited, outline as well as exemplary papers4. In other words, Appendix I till III are meant as a clear retrospect of the developments surrounding work design, specifically the identification of essential characteristics and their respective relations, within the last (3rd) industrial revolution. The goal was thus not to create a full structured literature review. Consequently, a selection of articles was adopted as more would not have resulted in different propositions. The next parts address the identified work characteristics and the findings in the constructed graphical illustrations (Appendix II and III - Figures 1 and 2).
Identified work characteristics
The selected papers led to the identification of twenty-nine different work characteristics5. To help structure the examination of the meaning of the identified work characteristics, the paper adopts the categorisation proposed by Morgeson and Humphrey (2006). In the following paragraphs the twenty-nine characteristics are either discussed in their respective category highlighted by previous authors (task, knowledge, social or contextual characteristics) or in the unclassified category in case the specific characteristic was not included in Morgeson and Humphrey’s (2006) framework.
Task characteristics
The characteristics, task significance and task identity have and are still being defined in a similar fashion as the definitions adopted here: respectively “the degree to which a job influences the lives or work of others, whether inside or outside the organisation” and “the degree to which a job involves a whole piece of work, the results of which can be easily identified” (Morgeson & Humphrey, 2006, p.1323). Autonomy, on the other hand, has expanded regarding the number of freedom dimensions included; from latitude in scheduling and work method/procedure to freedom in decision making. The meaning of task variety however has become narrower, referring only to “the degree to which a job requires employees to perform a wide range of tasks on the job” (Morgeson & Humphrey, 2006 p.1323). The reference to the variety of equipment use has been omitted, probably due to the inclusion of the factor equipment use (see the category contextual characteristics for its definition). A transition in definition is also visible for the feedback construct. What started out as a rather broad term, (Hackman & Lawler, 1971) was reduced, by Hackman and Oldham (1976), to what is now considered as the task characteristic feedback from the job – “the degree to which carrying out the work activities required by the job results in the individual obtaining direct and
4 These either stem from one or more of the adopted outline papers (Grant et al., 2011; Morgeson et al., 2013; Parker
et al., 2001) and/or a Web of Science search (on June 27th, 2016) using the terms work/job AND characteristics with
year 2005-2016 or 2016 and to following refinements: article, management and business.
5 The factors dealing with others/task interdependence and friendship opportunities/social support have changed with
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clear information about the effectiveness of his or her performance” (Hackman & Oldham, 1976, p.258). Only to be extended again by reintroducing the concept of feedback from others (see social characteristics).
Knowledge characteristics
As with task significance and task identity, the characteristic skill variety has and is still being defined as follows “the extent to which a job requires an individual to use a variety of different skills to complete the work” (Morgeson & Humphrey, 2006, p.1323). For the remaining four knowledge characteristics the definition stated in Morgeson and Humphrey (2006) is adopted either because prior definitions are similar to this one or no other definition is available: problem solving reflects “the degree to which a job requires unique ideas or solutions and reflects the more active cognitive processing requirements of a job”, job complexity refers to “the extent to which the tasks on a job are complex and difficult to perform”, information processing reflects “the degree to which a job requires attending to and processing data or other information ” and specialization reflects “the extent to which a job involves performing specialized tasks or possessing specialized knowledge and skill” (Morgeson & Humphrey, 2006, p.1323-1324).
Social characteristics
As addressed above, the characteristic feedback from others is defined as “the degree to which others in the organisation provide information about performance” (Morgeson & Humphrey, 2006, p. 1324). The factor task interdependence has become narrower in meaning. It implies “the degree to which the job depends on others and others depend on it to complete the work” (Morgeson & Humphrey, 2006, p.1324), which is less broad compared to the factor dealing with others proposed by Hackman and Lawler (1971). This decrease in meaning, however, makes sense in consideration with the factor interaction outside the organisation – defined as “the extent to which the job requires employees to interact and communicate with individuals external to the organisation” (Morgeson & Humphrey, 2006, p.1324). The current characteristic social support – “the degree to which a job provides opportunities for advice and assistance from others” (Morgeson & Humphrey, 2006, p.1324) – has come about by incorporating earlier notions of supervisor and coworker social support (e.g. Karasek, 1979, obtained from Morgeson & Humphrey, 2006) and the construct of friendship opportunities proposed by Hackman and Lawler (1971).
Contextual characteristics
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reflect “the level of physical activity or effort required in the job” and ergonomics reflects “the degree to which a job allows correct or appropriate posture and movement” (Morgeson & Humphrey, 2006, p.1324)
Unclassified characteristics
The only identified definition regarding the characteristic attitudinal demands was the one proposed by Morgeson and Campion (2003, p.434) “the degree to which constant monitoring of work is required”. The same applies to the characteristic production responsibility “the extent to which an individual can make errors that can result in costly losses of output” (Morgeson & Campion, 2003, p.434) and emotion labour although here it was stated in Parker et al. (2001, p.423) “a requirement for individuals to manage their emotional expression in return for wage”. Finally, within the incorporated papers no definitions are available for the subsequent characteristics, yet most speak for themselves: role conflict, home-work conflict, skill and ability requirements, virtual work, time pressure, opportunity for skill acquisition, workday cycles and temporal horizon.
Graphical illustrations
Appendix II and III are graphical illustrations of the columns linked to the empirical findings (Appendix I). Appendix II presents the findings from a work characteristic (antecedent) perspective and Appendix III from an outcome perspective. In both figures the characteristics are classified based on the categorisation in Morgeson and Humphrey (2006). As stated previously, the characteristics belonging to the unclassified category were not present in Morgeson and Humphrey’s (2006) framework. Either because they were mentioned in a later paper or simply because they were not incorporated due to unknown reasons. The paper by Morgeson and Humphrey (2006) further provides results of direct correlations between their classified characteristics and various outcomes. However, given that these results were incorporated within the meta-analysis by Humphrey, Nahrgang and Morgeson (2007), Morgeson and Humphrey’s (2006) correlational findings were only stated for those factors not included in the study by Humphrey et al. (2007). Additionally, only relationships with a p value equal or less than .05 are presented in both figures. Due to the inclusion of meta-analysis the number of times a characteristic is studied is not addressed.
Correlational findings
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unclassified category were correlated to any outcome variable. Surprisingly, however, is the fact that the same applies for the characteristic equipment use which is one of the contextual characteristics.
Mediation effects
Considering the three psychological states proposed by Hackman & Oldham (1976), only experienced meaningfulness and responsibility operated as theorized in the JCM. Knowledge of results was not or only partially found to mediate the relation between feedback from job and outcome variables. Empirical support was further found for nine new mediators: two dimensions of psychological empowerment (meaning & competence), three knowledge characteristics (perceived value of knowledge, knowledge renewal & knowledge breadth), social worth and impact, work engagement and psychological contract fulfilment. Most of the studied effects made use of the five core characteristics. The majority of the effects are further linked to attitudinal outcomes and performance.
Moderation effects
Findings regarding the moderator proposed in the original job characteristics model (growth need strength) are conflicting (Morgeson et al., 2013). As a result GNS was not included in Appendix I and both figures. In later years, support for other individual moderators – conscientiousness, temporal focus, prosocial value – was found. Within the analysed papers, two non-individual moderators (i.e. production uncertainty and social intensity) were also present, hence in total five new moderators were identified. The relations, used to study the impact of a certain moderator, included the characteristics task significance, social support or autonomy. Regarding the outcomes side, they were linked to the variables performance, satisfaction, organisational commitment, role conflict, role ambiguity and/or turnover intentions.
Interactions and systems
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Conclusion
Based on the taken perspective within the employment debate, the JCM was adopted as starting point for the construction of an overview of the developments surrounding work design. Through the analysis of highly cited, outline as well as exemplary papers, additions to this model were captured. Specifically, twenty-four new work characteristics were identified as well as the introduction of a system which categorises the majority of the, in total twenty-nine, characteristics. Out of this extensive list of factors, the characteristics which stand out or dominate with respect to studies towards correlations with outcomes and research into mediators and/or moderators are the five core characteristics as well as the factor social support. Surprisingly, of all the characteristics connected to one of the categories proposed by Morgeson and Humphrey (2006), equipment use was found to be the only factor without any correlational findings regarding outcome variables. Another interesting finding is the fact that while mediating and moderating factors have received some attention, a scarce amount of research has focused on the topic of interactions and systems regarding work characteristics. The next section will build on these findings in order to address the goal of this paper that is the development of “smart” work characteristics and outcomes.
Work design – for Smart Industry
The previous section provided a background on and an overview of the current state of affairs regarding the inclusion and meaning of as well as empirical findings towards the topic of work design. The focus within this section is on the upcoming Smart Industry environment. It thus combines the previously outlined information with the characteristics of Smart Industry and the configuration as well as the JD-R model in order to address the development of “smart” work characteristics and outcomes. Within this process, the paper centres on the manufacturing industry since that is where the attention for Smart Industry currently lies. This does not imply that the service sector could not benefit from it.
Smart Industry
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Technological advancement = the level of complexity imbedded in technological solutions to support job tasks
Digitization intensity = the scope of which ‘things’ (whether analogue information or physical entities) are digitalised
Connectivity level = the extent to which machines and/or products are linked with each other, both inside and across company boarders
Like in today’s situation, a work floor in the context of Smart Industry comprises of machines which could be welding or transporting things. A differences between the current and upcoming situation, however, stems from the pillar digitization which leads to an increase in connectivity. That is, due to developments in sensors and information and communication technology (ICT) anything can turn into a digital entity. In so doing, machines and (sub)products can now collect, store, transmit and receive information. Consequently, machines, systems and (sub)products become able to interact with each other and humans. Allowing machines to become more flexible and make decisions independently. It also creates a whole lot of data. Think of machines signalling when there is a problem or hold up in the production system or indicating when its elements need to be replaced. With the introduction of sensors and improved communication methods, data surrounding for example health and safety issues could further be collected and reported, in short the options are manifold. Besides smart machines (i.e. intelligent, networked devices which can make decisions and perform tasks independently), technological advancements gives rise to the tools such as 3D printing and augmented reality – “refer to the integration of additional computer generated information into a real-world environment” (Paelke, 2014, p.1) – in the work floor.
Dominant characteristics
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Task and knowledge characteristics
By focusing on those factors which received an extensive amount of attention when considering their correlations with outcome variables and links with mediators and/or moderators, it becomes apparent that the included task and knowledge characteristics (tasks significance, task identity, autonomy, feedback from job and skill variety) together form the five core or original factors. Ever since their introduction in the job characteristics model these characteristics have remained essential as they have been included in multiple revisions and reviews of the JCM (e.g. Grant et al., 2011; Morgeson et al., 2013; Parker et al., 2001). It would thus be unlikely for them to suddenly become obsolete. In addition, as previously addressed within the context of Smart Industry, human work can flourish when reframing automation into augmentation. Smart Industry is thus viewed as an opportunity to enrich jobs, not as a means to diminish tasks. This, in turn is linked to one’s motivation and satisfaction (see part on work design background). In short, based on the adopted perspective in the employment debate, it is assumed that motivating people via work design will remain essential within the context of Smart Industry. Consequently, the five characteristics highlighted above are expected to stay important since the task and knowledge category together form the overarching motivational category (Morgeson & Humphrey, 2006), implying that the presence of these characteristics will result in more motivating and satisfying jobs. Based on the impact of the three pillars of Smart Industry, employees are furthermore expected to require some level of ICT skills besides skills related to one’s trade. In so doing, it supports the assumption that the characteristic skill variety will remain relevant. Finally, the three pillars of Smart Industry also support the existence of the other four characteristics. Consider, for instance, the relevance of task significance due to a shift towards production based on customer order stemming from the increase in flexibility or feedback from the job due to computer screens indicating the planned and actual production originating from the introduction of smart machines. Given the above, the following task and knowledge characteristics are thus expected to remain essential within a Smart Industry environment: (1) tasks significance, task identity, autonomy, feedback from job and (2) skill variety.
Social characteristics
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Industry leads to an increase in connectivity – “in the coming decade, a network centric approach to production will replace linear production processes with intelligent and flexible network approaches” (Huizinga et al., n.d., p.11). This emphasis on connectivity or networking regarding machines and systems could stimulate or trigger a greater emphasis on connectivity and networking between individuals. Consequently, given the above, the characteristic social support is expected to remain essential within the context of Smart Industry.
Configuration theory
The previous part addressed the importance of several separate work characteristics within the context of Smart Industry. The focus on individual work characteristics and its relations have been a common approach since the majority of the studies analysed adopt a universalistic or contingency approach. They either centre on linear relationships or on interactions of contingency variables, rather than a configurational approach which focuses on synergies between interdependent practices into a coherent system – a bundle of practices that are horizontally and vertically aligned (Delery & Doty, 1996). Yet, the Smart Industry pillar ‘connectivity’ emphasizes the increase in connections between components, whether they be machines, companies and/or humans, within the context of Smart Industry. In other words, there is an expected shift from isolated elements to interconnected or networked components. Additionally, Smart Industry itself is characterised by different pillars. Technology, digitization and connectivity operate together in order to yield a new environment. That is, in contrast to other industrial revolutions, Smart Industry does not stem from a change in a single field (i.e. water/steam power, electrical power and electronics/computers) but from the combination of developments in different areas (e.g. sensor technology, communication technology and data analysis). The Smart Industry phenomenon thus highlights a growing importance of synergies. This emphasis on synergies stemming from Smart Industry heightens the interest for as well as accentuates the adoption of the configurational approach in the context of work design. In short, based on the constructed overview, studies into synergies are hardly present in literature on work design. The phenomenon Smart Industry however places greater emphasis on the issue of synergies, thereby increasing the relevance of the configurational approach. Consequently, the following parts touch upon the configurational approach regarding the topic of work design by examining the holistic principle of the work characteristics categories proposed by Morgeson and Humphrey (2006) in the context of Smart Industry.
Synergistic relationships
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compared to the sum of each individual effect. When studying synergistic relationships, two forms can be distinguished: positive synergistic effects, or powerful connections, and negative synergistic effects, or deadly combinations (Delery, 1998). In case of the first form, the bundle or system of practices are said to work together, to enhance each other’s effectiveness. The latter form, however, implies to opposite, hence here the bundle or system of practices are said to work against each other (Delery, 1998). Although both types of synergistic relationships exist, the present paper focuses on the positive synergistic effects or powerful combinations. Bundles or systems of practices are likely constructed with the aim to enhance, rather than diminish, organisational effectiveness. Consequently, the current paper only raises expectations regarding enhancing synergies of the four work characteristics categories in the context of Smart Industry.
Smart Industry work design configurations
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also occur when there is alignment between what jobs in a Smart Industry environment give individuals, what they require of them, what the social context asks as well as gives these individuals and in what type of context everything takes place (e.g. equipment use & work conditions); hence a four-way interaction. Based on the constructed overview, these interactions are expected to have beneficial effects on behavioural, well-being, attitudinal and/or cognitive outcome variables. In sum,
Proposition 1: Within a Smart Industry context, synergies between the following work characteristic categories are needed:
(a) two-way interaction between task and knowledge characteristics
(b) three-way interaction between task, knowledge and social characteristics
(c) four-way interaction between task, knowledge, social and contextual characteristics (di) all synergies will have beneficial effects on behavioural, well-being, attitudinal and/or
cognitive outcomes, but
(dii) the four-way interaction will have more beneficial effects on behavioural, well-being, attitudinal and/or cognitive outcomes than the three-way and two-way interactions
Under-examined characteristics
Another conclusion is the fact that several identified characteristics have not been studied and, as a result, are not connected to any outcomes as observable in the constructed overview and graphical illustrations. The parts below focuses on these under-examined factors. Specifically, based on the job demands-resources model (JD-R model), I will argue which of these characteristics, with the exception of opportunity for skill acquisition, skill and ability requirements, role conflict and home-work conflict6, will bring opportunities for smart industries and how they will do so. The choice for the JD-R model stems from its fit with the adopted JCM and the fact that it facilitates the inclusion of a broad range of work characteristics. For those under-examined factors found to become more important, I will formulate propositions regarding their relationships with outcomes since understanding the effects of these relations will gain in relevance.
Job resource and demand for smart industries
According to the JD-R model, job resources refer to “those physical, psychological, social, or organizational aspects of the job that are either/or: functional in achieving work goals; reduce job demands and the associated physiological and psychological costs; stimulate personal growth, learning, and development”
6 Both skill factors are left out since they are viewed as referring more to the amount of personal resources an individual
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(Bakker & Demerouti, 2007, p.312). In so doing, the introduction of smart machines and tools such as 3D printing and augmented reality systems is considered a job resource for smart industries. These solutions/tools are physical aspects with the intention of being functional in achieving work goals. In addition, they can aid with the elimination of demands (e.g. prevent heavy lifting) and implementing them stimulates development due to their newness and focus on support. Job demands, in contrast refer to “those physical, psychological, social, or organizational aspects of the job that require sustained physical and/or psychological (cognitive and emotional) effort or skills” (Bakker & Demerouti, 2007, p.312). Consequently, the complexity imbedded in the introduced technological solutions can, in part, be considered a job demand. That is, it is expected that employees linked to certain technological tools will be required to perform certain tasks (e.g. fixing blue print malfunctions within 3D printing or connection errors within smart machines) which given the imbedded technological complexity of the tools, can be seen as a psychological aspect that requires sustained psychological (cognitive) effort or skills.
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tasks (e.g. fixing network errors) that can require sustained psychological (cognitive) effort or skills – this factor can also be related to the stated job demand. Consequently, depending on the adoption of a more general versus a more in-depth viewpoint, equipment use is either seen as a job resource or a job demand.
In sum, based on the JD-R model, job resources are essential in order to obtain beneficial outcomes. Considering the definition of job resources provided by Bakker and Demerouti (2007), the introduction of smart machines and tools such as 3D printing and augmented reality systems is viewed as a job resource for smart industries. This resource, in turn, is found to be related to the characteristics virtual work and equipment use on the basis of their definition. As a result, both virtual work and equipment use – depending on the above stated condition – are considered a job resource for Smart Industry and, hence, expected to become more important within the context of Smart Industry. Understanding the effects of their relations with outcomes will therefore gain in relevance. However, given the under-examined status of virtual work and equipment use, no relations with outcomes were found for these two characteristics. The next parts will therefore formulate propositions regarding those relationships
Virtual work
Due to the fact that the extent to which computer generated information (augmented reality) tools are implemented is viewed as a physical aspect of the job that is functional in achieving work goals and can stimulate personal development, virtual work can be classified as a job resource for Smart Industry. According to the job demands-resources model, job resources evoke a motivational process. Job resources are viewed as having motivational potential since they may play an intrinsic motivational role by fulfilling basic human needs and/or an extrinsic motivational role by fostering dedication to work tasks (Bakker & Demerouti, 2007). The motivational potential originating from job resources in turn is said to impact factors such as work engagement, organisational commitment and performance (Bakker & Demerouti, 2007). Virtual work may be extrinsically motivating due to its functionality, that means it can “visualize information directly in the spatial context were it is relevant” (Paelke, 2014, p.1) and in so doing foster dedication which increases the likelihood of goal attainment/performance. Additionally, virtual work may result in intrinsic motivation as it fosters the need for competence due to its development opportunity and can “guide the user through unfamiliar tasks” (Paelke, 2014, p.1), impacting factors such as engagement and commitment. Therefore, I suggest the following proposition for virtual work as a “smart” work characteristic for Smart Industry:
Proposition 2: Within a Smart Industry context, a higher level of virtual work will positively affect: (a) behavioural outcomes, specifically performance
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The relationships depicted in the above propositions are, however, expected to depend on three moderators. Firstly, the more employees adopt the augmented reality systems the more they will become proficient in dealing with this job resource. In such a case, a higher level of virtual work is expected to lead to more positive outcomes compared to a situation in which daily usage is absence. Consequently, the level of daily usage is seen as a moderator impacting the relationship between virtual work and outcomes. Specifically, an increase in daily usage is assumed to strengthen the stated relation. Secondly, Paelke (2014, p.3) indicates that “the appropriate visual complexity can be highly dependent on the user: e.g. for assembly instruction trained users might prefer abstracted outline visualizations that clearly outline the next step and provide little visual distraction, while novice users might prefer a realistic depiction that simplifies the visual recognition and matching of objects”. With the presence of a misfit between the available augmented reality tool and an individuals preferred level of visual complexity, employees are expected to consider the available augmented reality tool to a lesser extent as a job resource. As a result, the better the fit between the available augmented reality tool and an individuals preferred level of visual complexity the stronger the relationships stated under proposition 1 is assumed to be. Finally, the appropriate visualisation technique of the augmented reality system is dependent on, inter alia, the specific task (Paelke, 2014). As with the previous moderator, the existence of a mismatch is expected to cause employees to view the tool to a lesser degree as a job resource. Therefore, the better the match between the visualisation technique of the available system and the task at hand, the stronger the relationships stated under proposition 1 is expected to be. Proposition 3: Within a Smart Industry context, the positive relations between virtual work and behavioural
and attitudinal outcomes is moderated by:
(a) daily usage, specifically virtual work will have a stronger positive effect on behavioural and attitudinal outcomes when daily usage is high compared to low
(b) preference fit, specifically virtual work will have a stronger positive effect on above outcomes in case of a fit between system and individual preferences compared to a misfit (c) task relevance, specifically virtual work will have a stronger positive effect on above outcomes in case of a match between the visualization technique and the task at hand compared to a mismatch
Equipment use
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machines, 3D printing and augmented reality systems are a broad range of technologically complex equipment made possible within the context of Smart Industry. Each of these tools are introduced with the aim of being functional and some (e.g. smart machines) even possess the ability to reduce existing job demands. In so doing equipment use may be extrinsically motivating which leads to better performance via fostering dedication. Additionally, each of the mentioned tools has the potential to foster human needs (e.g. growth/learning or competence). As a result, equipment use may be intrinsically motivating impacting factors such as engagement and commitment. Thus,
Proposition 4: Within a Smart Industry context, a higher level of equipment use considered from a general viewpoint will positively affect:
(a) behavioural outcomes, specifically performance
(b) attitudinal outcomes, specifically work engagement and organisational commitment
Under the condition that equipment use is considered from a more in-depth perspective, this characteristic is seen as a job demand. Put simply, the level of equipment use in such a case has an impact on an individual’s chance of being confronted with tasks that can require sustained psychological (cognitive) effort or skills. Job demands, in turn are positively connected to the health impairment (strain) process of the job demands-resources model. More strain, subsequently, results in a depletion of energy and an increase in health problems which are likely to affect behavioural outcomes like performance and well-being outcomes like burnout/exhaustion (Bakker & Demerouti, 2007). In sum, equipment use can exhaust an individual’s mental resources (strain) causing a depletion of energy and an increase in health problems leading to above mentioned outcomes. Consequently,
Proposition 5: Within a Smart Industry context, a higher level of equipment use considered from an in-depth perspective, will:
(a) negatively affect behavioural outcomes, specifically performance (b) positively affect well-being outcomes, specifically burnout/exhaustion
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Proposition 6: Within a Smart Industry context, the relations between equipment use, under the second condition, and behavioural as well as well-being outcomes is moderated by an individual’s skills and abilities. The higher a person’s skills and abilities the weaker the relation will be.
Discussion
Over the years, an extensive body of work has developed with respect to the topic of work design. In contrast, the phenomenon Smart Industry has only recently gained attention. Despite its recent introduction, Smart Industry is considered the environment of the future and hence requires research with respect to its impact. The current paper does so, with specific focus to work design. In so doing it steps away from the main topic of interest ‘future skills’, gains a broader, more holistic approach to the question of what Smart Industry does to work and obtains a rich amount of theoretical background in order to contribute to the provision of a theoretical basis. To achieve such a theoretically grounded approach in addressing the development of “smart” work characteristics and outcomes, the first goal of the present paper was to construct an overview or retrospect of the literature surrounding work design. As a result, a considerable list of work characteristics was identified which can be linked to an equally large amount of outcomes. The constructed overview further depicted the presence of various mediators as well as moderators. Findings regarding interactions between work characteristics were, however, lacking. On the basis of this overview, in combination with the pillars of Smart Industry, the configurational approach and the JD-R model the second goal – addressing the development of “smart” work characteristics and outcomes – was tackled. This resulted in the expectation that the six characteristics which stood out within the constructed overview (task significance, task identity, autonomy, feedback from job, skill variety and social support) remain important within the context of Smart Industry. In addition, Smart Industry is highlighted as placing greater emphasis on the issue of synergies. Consequently, a configurational approach was adopted towards work design, resulting in the expectation that positive synergistic effects may occur when there are synergies between task, knowledge, social and contextual characteristics. Finally, based on the JD-R model I was able to add two “smart” work characteristics for the Smart Industry context and construct propositions with respect to their relationship with outcome variables.
Contributions
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placing emphasis on the examination of less examined directions based on the configuration theory and the JD-R model, the paper yields the following two contributions towards the body of literature on work design. Firstly, it makes a first move with respect to addressing synergies of work characteristics to build a holistic model of work design for Smart Industry. Secondly, it takes a step in the direction of developing “smart” work characteristics and in the process fills the existing gap with respect to lacking relations surrounding certain work characteristics, specifically virtual work and equipment use.
Theoretical and practical implications
With respect to theoretical implications, the indicated expectance of the remaining importance of six dominant characteristics first of all strengthens their presence within current work characteristics models or frameworks. Secondly, the greater emphasis on interactions within Smart Industry heightens the interest into the use of configuration theory and presents the opportunity to design synergies of work characteristics that build a holistic work design model. A direction which seems to be lacking in existing research on work design but, given the previous sentence, could require more theoretical attention. In addition, the expected growth of the characteristic virtual work will require moving this characteristic from the currently ‘unclassified’ category into one of the four existing work characteristic categories. Given its definition and close connection with the factor equipment use, it is proposed to shift virtual work towards the contextual characteristics category. Finally, the identification of a gap regarding the existence of relationships with outcome variables for some of the found work characteristics further implies the need for empirical research towards this aspect of the job characteristics model. Especially since two of the under-examined characteristics are expected to become more important within the context of Smart Industry. The main practical implication of the present paper, on the other hand, stems from its focus towards the upcoming phenomenon Smart Industry. That is, Smart Industry is not considered a mere vision but the future context for organisations. Consequently, attention should be paid towards this development in order to provide organisations with knowledge of its potential impact. Current paper does so through its focus on work design. Specifically it provides firms with insights into how to design jobs, that is which characteristics jobs should have, and which impact these could have to outcomes in the Smart Industry context.
Limitations
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the lack of research towards the characteristics equipment use and virtual work as well as the interactions between work characteristics. Support for the conclusion related to the factor equipment use, however, can be found in the paper by Morgeson et al. (2013) who highlight that equipment use is a largely unrecognised contextual characteristic. In addition, the implementation of augmented reality systems (virtual work) is only a recent option, linked to the emergence of Smart Industry, making the existence of extensive research towards virtual work less likely. Although the lack of interactions between work characteristics cannot be stated with full certainty, the moderate presence of contingency factors does make this conclusion more plausible. A configurational approach is more complex compared to the universalistic or contingency approach (Delery & Doty, 1996). Consequently, the existence of a moderate amount of contingency related research reduces the likelihood of having missed studies adopting a configurational perspective.
Research agenda
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References
Babbage, C. (1835). On the economy of machinery and manufactures. Knight
Baert, A. & Ledent, P. (2015). De technologische revolutie in België. Brussel: ING Economic Research. Retrieved January 18, 2016, from https://about.ing.be/Over-ING/Press-room/Press-article/De-technologische-revolutie-in-Belgie.htm
Bainbridge, S. (2015). In the future, what will people do? In T. Dolphin (Eds.), Technology, globalisation
and the future of work in Europe: Essays on employment in a digitised economy (pp. 80-85).
London: Institute for Public Policy Research (IPPR). Retrieved December 3, 2015, from http://www.ippr.org/publications/technology-globalisation-and-the-future-of-work-in-europe Bakker, A.B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of
Managerial Psychology, 22(3), 309-328.
Bechtold, J., Lauenstein, C., Kern, A., & Bernhofer, L. (n.d.). Industry 4.0 – The Capgemini Consulting
view: Sharpening the picture beyond the hype. Retrieved January 4, 2016, from
https://www.de.capgemini-consulting.com/resource-file-access/resource/pdf/capgemini-consulting-industrie-4.0_0.pdf
Berger, T., & Frey, C.B. (2015). Bridging the skills gap. In T. Dolphin (Eds.), Technology, globalisation
and the future of work in Europe: Essays on employment in a digitised economy (pp. 75-79).
London: Institute for Public Policy Research (IPPR). Retrieved December 3, 2015, from http://www.ippr.org/publications/technology-globalisation-and-the-future-of-work-in-europe Bernstein, A., & Raman, A. (2015). The great decoupling. Harvard Business Review, 93 (6), 66-74. Birtch, T.A., Chiang, F.F., & Van Esch, E. (2016). A social exchange theory framework for
understanding the job characteristics–job outcomes relationship: the mediating role of
psychological contract fulfillment. The International Journal of Human Resource Management,
27(11), 1217-1236.
Blanchet, M., Rinn, T., Von Thaden, G., & De Thieulloy. (2014). Industry 4.0: The new industrial
revolution – How Europe will succeed. Retrieved December 3, 2015, from
http://www.rolandberger.com/media/publications/2014-04-02-rbsc-pub-INDUSTRY_4_0_The_new_industrial_revolution.html
Brynjolfsson, E. & McAfee, A. (2014). The second machine age: Work, progress and prosperity in a time
of brilliant technologies. W.W. Norton & Company.
Butler, A., Grzywacz, J., Bass, B., & Linney, K. (2005). Extending the demands‐control model: A daily diary study of job characteristics, work‐family conflict and work‐family facilitation. Journal of
occupational and organizational psychology, 78(2), 155-169.
Chiaburu, D.S., & Harrison, D.A. (2008). Do peers make the place? Conceptual synthesis and meta-analysis of coworker effects on perceptions, attitudes, OCBs, and performance. Journal of
Applied Psychology, 93(5), 1082-1103.
Christian, M.S., Garza, A.S., & Slaughter, J.E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology, 64(1), 89-136. Davenport, T.H., & Kirby, J. (2015). Beyond automation. Harvard Business Review, 93 (6), 58-65. Davies, R. (2015). Industry 4.0: Digitalisation for productivity and growth. Retrieved April 26, 2016,
from
28
Delery, J.E. (1998). Issues of fit in strategic human resource management: Implications for research.
Human Resource Management Review, 8(3), 289-309.
Delery, J.E., & Doty, D.H. (1996). Modes of theorizing in strategic human resource management: Test of universalistic, contingency and configurational performance predictions. Academy of
Management Journal, 39(4), 802-835.
FME. (2015). Aan de slag met Smart Industry [White paper]. Retrieved December 9, 2015, from https://www.fme.nl/nl/whitepaper-slag-smart-industry
Ford, M. (2015). The rise of the robots: Technology and the threat of a jobless future. Basic Books. Frey, C.B., & Osborne, M.A. (2013). The future of employment: How susceptible are jobs to
computerization? Martin Publications.
Fried, Y., & Ferris, G.R. (1987). The validity of the job characteristics model: A review and meta-analysis. Personnel Psychology, 40(2), 287-322.
Grant, A.M. (2008). The significance of task significance: Job performance effects, relational mechanisms, and boundary conditions. Journal of Applied Psychology, 93(1), 108-124. Grant, A.M., Fried, Y., & Juillerat, T. (2011). Work matters: Job design in classic and contemporary
perspectives. In S. Zedeck (Eds.), APA handbook of industrial and organizational psychology,
Volume 1: Building and developing the organization (pp. 417-453). American Psychological
Association.
Hackman, J.R., & Lawler, E.E. (1971). Employee reactions to job characteristics. Journal of Applied
Psychology, 55(3), 259-286.
Hackman, J.R., & Oldham, G.R. (1976). Motivation through the design of work: Test of a theory.
Organizational Behavior and Human Performance, 16(2), 250-279.
Heijne, S., & Witteman, J. (2014, Oktober 1). 40 procent banen op de tocht door opkomst robot. De
Volkskrant. Retrieved January 18, 2016, from http://www.volkskrant.nl/
Herzberg, F., Mauser, B. & Snyderman, B.B. (1959). The motivation to work. Wiley
Huizinga, G., Walison, P., Bouws, T., Kramer, F., Van der Beek, H., Tops, P., … Grosfeld, T., (n.d.).
Rapport Smart Industry: Dutch industry fit for the future. Retrieved December 3, 2016, from
http://www.smartindustry.nl/documenten/
Humphrey, S.E., Nahrgang, J.D., & Morgeson, F.P. (2007). Integrating motivational, social, and
contextual work design features: a meta-analytic summary and theoretical extension of the work design literature. Journal of Applied Psychology, 92(5), 1332-1356.
Kiggundu, M.N. (1981). Task interdependence and the theory of job design. Academy of Management
Review, 6(3), 499-508.
Kool, L., Van Est, R., Van Keulen, I., & Van Waes, A. (2015). Inleiding. In R. Van Est & L. Kool (Eds.),
Werken aan de robotsamenleving: Visies en inzichten uit de wetenschap over de relatie
technologie en werkgelegenheid (pp. 21-32). Den Haag: Rathenau Instituut. Retrieved December
3, 2015, from https://www.rathenau.nl/nl/publicatie/werken-aan-de-robotsamenleving
Levy, F., & Murnane, R. (2013). Dancing with Robots: Human skills for computerized work. Washington, DC: Third Way NEXT. Retrieved December 3, 2015, from
http://www.thirdway.org/report/dancing-with-robots-human-skills-for-computerized-work Liden, R.C., Wayne, S.J., & Sparrowe, R.T. (2000). An examination of the mediating role of
29
Miller, D. (1981). Toward a new contingency approach: The search for organizational gestalts. Journal of
Management Studies, 18(1), 1-26.
Miller, B., & Atkinson, R.D. (2013). Are robots taking our jobs, or making them? Washington: The Information Technology & Innovation Foundation (ITIF). Retrieved January 18, 2016, from https://itif.org/publications/2013/09/09/are-robots-taking-our-jobs-or-making-them
Morgeson, F.P., & Campion, M.A. (2003). Work Design. In I.B. Weiner, W.C. Borman, D.R. Ilgen & R.J. Klimoski, Handbook of psychology, Volume 12: Industrial and organizational psychology (pp. 423-452). John Wiley & Sons.
Morgeson, F.P., Garza, A.S., & Campion, M.A. (2013). Work Design. In I.B. Weiner, N.W. Schmitt & S. Highhouse, Handbook of psychology, Volume 12: Industrial and organizational psychology (2nd
ed.) (pp. 525-559). John Wiley & Sons.
Morgeson, F.P., & Humphrey, S.E. (2006). The Work Design Questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of
Applied Psychology, 91(6), 1321.
Nahrgang, J.D., Morgeson, F.P., & Hofmann, D.A. (2011). Safety at work: a meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.
Journal of Applied Psychology, 96(1), 71-94.
Ohly, S., Sonnentag, S., & Pluntke, F. (2006). Routinization, work characteristics and their relationships with creative and proactive behaviors. Journal of Organizational Behavior, 27(3), 257-279. Paelke, V. (2014). Augmented reality in the smart factory: Supporting workers in an Industry 4.0.
environment. In Proceedings of the 2014 IEEE Emerging Technology and Factory Automation
(ETFA) (pp. 1-4). IEEE. Retrieved January 26, 2016, from
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7005252
Pajarinen, M. & Rouvinen, P. (2014). Computerization Threatens One Third of Finnish Employment. Finland: The Research Institute of the Finnish Economy (ETLA). Retrieved January 18, 2016, from https://www.etla.fi/en/publications/computerization-threatens-finnish-employment/ Parker, S.K., Wall, T.D., & Cordery, J. L. (2001). Future work design research and practice: Towards an
elaborated model of work design. Journal of Occupational and Organizational Psychology,
74(4), 413-440.
Pee, L.G., & Chua, A.Y.K. (2016). Duration, frequency, and diversity of knowledge contribution: Differential effects of job characteristics. Information & Management, 53(4), 435-446.
Peeters, M.A.G., & Rutte, C.G. (2005). Time-management behavior as a moderator for the job demand-control interaction. Journal of Occupational Health Psychology, 10(1), 64-75.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015).
Industry 4.0: The future of productivity and growth in manufacturing industries. Retrieved May 2, 2016, from https://www.bcgperspectives.com/search/?SearchQuery=industry%204.0
Salancik, G.R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23(2), 224-253.
Schattorie J., De Jong, A., Fransen, M., & Vennemann, B. (2014). De impact van automatisering op de
Nederlandse Arbeidsmarkt. Amstelveen: Deloitte. Retrieved December 16, 2015, from
http://www2.deloitte.com/content/dam/Deloitte/nl/Documents/deloitte-analytics/deloitte-nl-data-analytics-impact-van-automatisering-op-de-nl-arbeidsmarkt.pdf
Schouteten, R. (2015). Robotisering: het kan, maar moet het ook? Tijdschrift voor Arbeidsvraagstukken,
30
Shipp, A.J., Edwards, J.R., & Lambert, L.S. (2009). Conceptualization and measurement of temporal focus: The subjective experience of the past, present, and future. Organizational Behavior and
Human Decision Processes, 110(1), 1-22.
Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. W. Strahan & T. Cadell. Taylor, F.W. (1911). The principles of scientific management. Norton.
Ten Have, K., Van Rhijn, J.W., & Van Wijk, E.B. (2014). Dilemma’s in Smart Industry vragen om Smart
labour. Retrieved December 21, 2015, from
http://repository.tudelft.nl/view/tno/uuid%3A903c9ae8-4cd8-4151-884f-c3676efbd91d/ Trist. E.L., & Bamforth, K.M. (1951). Some social and psychological consequences of the longwall
method of coal-getting. Human Relations, 4(3), 3-38.
Van Est, R. (2015). Wij zijn de robots: Contouren van de maatschappelijke agenda voor het robotiseringsdebat. Tijdschrift voor Arbeidsvraagstukken, 31(2), 128-129.
Van der Zee, F. (2015). Technologie en arbeidsproductiviteit. In R. Van Est & L. Kool (Eds.), Werken
aan de robotsamenleving: Visies en inzichten uit de wetenschap over de relatie technologie en werkgelegenheid (pp. 93-110). Den Haag: Rathenau Instituut. Retrieved December 3, 2015, from:
https://www.rathenau.nl/nl/publicatie/werken-aan-de-robotsamenleving
