Research Paper for Pre-MSc TOM
What is the influence of managerial perceived workload on their
work design behaviour when implementing a new Smart
Manufacturing Technology?
Final version
Research Project 1B: The role of managers in shaping work design By
Hunter Joosting Bunk Kleine Pelsterstraat 2-6, 9711 KN Groningen +31643239876 h.h.joosting.bunk@student.rug.nl S4183061 University of Groningen Faculty of Economics and Business
Abstract
With the emergence of smart manufacturing technologies, new questions regarding work design emerged as well. High-quality work design is supported in research, but it remains unclear what factors influence the work design behaviours of work designers. Therefore, in this paper we have shed some light on the effect of managers’ perceived workload on their work design behaviour in a smart manufacturing setting. To develop an understanding in this topic we have conducted a multiple-case study, of which qualitative data was collected and analysed. In the results, a trend was noticed where all respondents with a higher perceived workload tend to design less enriched work, but one should recognize the fact that various other variables are influential in work design behaviour.
Introduction
Manufacturing paradigms are facing dramatic changes as a consequence of the industry 4.0 technological revolution (Cagliano, Canterino, Longoni, & Bartezzaghi, 2019). Smart manufacturing is considered the central dimension of this technological revolution, in which new technologies are enabling connectivity and intelligence, and ultimately differentiates the industry 4.0 concept from previous industrial stages (Frank, Dalenogare, & Ayala, 2019). New technological features like connectivity, intelligence, and technological complexity are challenging organizations to reshape the work environment, working activities, and – eventually – the organization of the factories (Maghazei & Netland, 2017). However, empirical evidence on how smart manufacturing technologies and organizational design interplay is till now limited (Cagliano et al., 2019).
The fear regarding upcoming technologies and the associated changes in the work environment result from an unclear comprehension of the role of the human actor in future manufacturing processes, as technologies can lead to the substitution of tasks that were previously executed by humans (Rauch, Linder, & Dallasega, 2020; Waschull, Bokhorst, Molleman, & Wortmann, 2020). Technologies with a high degree of task substitution are so-called technology-centred automation systems, in which human work has a temporary character and of which the conceivable final state is complete automation (Hirsch-Kreinsen, 2014). Research on industry 4.0, however, shows a clear trend towards more human-centred systems, in which the operator is seen as a qualified and valuable resource (e.g. Longo, Nicoletti, & Padovano, 2017; Romero, Stahre, et al., 2016). Human-centred systems are characterized by the cooperation of machines with humans, allowing the operator to be in control of the work process and the technology and fostering the utilization of human competencies (Romero, Stahre, et al., 2016). This causes that with smart manufacturing technologies, job characteristics like autonomy take on a new meaning and become increasingly important for work design (Parker & Grote, 2020).
Work design can be defined as ‘the content and organization of one’s work tasks, activities, relationships, and responsibilities’ (Parker, 2014: 662). Typically, the way that work is designed is highly influenced and controlled by technological and organizational choices of managers (Waschull et al., 2020). The latter are inter alia choices regarding the reorganization of potentially substituted human tasks into different jobs, thereby also changing job characteristics (Waschull et al., 2020). Job characteristics are the different dimensions of a job, which can lead to various personal and organizational outcomes (Hackman & Oldham, 1976). Research suggests that implementors often do not consider the effect of introducing a new technology on job characteristics, while high-quality work design could deliver various positive outcomes (Parker & Grote, 2020). Work design behaviour is the way that individuals design work, by making different choices regarding work design (Parker, Andrei, & Van den Broeck, 2019). Although these work design choices must be made, managers might often rather unconsciously accept the status quo or make decisions automatically, failing to give work design specific consideration (Parker & Grote, 2020).
influence an individual’s job performance (Bakker & Demerouti, 2017), studying its influence on managerial work design behaviour could potentially lead to interesting insights. This is why we aim to answer the following question: When implementing smart manufacturing
technologies, what influence does managerial (perceived) workload have on their work design behaviour?
The research on when, how, and why managers engage in work design is still considered to be in its infancy (Parker et al., 2017). To contribute to the currently available literature on this topic, this paper will provide new insights in explaining certain managerial work design behaviour. In this multiple-case study, we specifically aim to find out how managerial workload affects work design behaviour during the implementation of new technologies. We do this by analysing the already available literature and combining this with semi-structured interviews at 10 manufacturing companies.
The paper is organized as follows. First, we will review relevant literature to define smart manufacturing technologies, work design behaviour, and workload, so their relations become clearer. Following up will be the methodology, describing how the case study was designed. Then the findings will be presented, after which these are discussed in relation to the existing literature. Finally, we will conclude with implications for theory and practice, the limitations of this study, and suggestions for future research.
Theoretical framework
Smart Manufacturing Technologies and work design
As mentioned, manufacturing industries are facing dramatic technological changes as a result of the fourth industrial revolution. With the implementation of new technologies, job demands can change drastically. The potential effects on work design could be that cognitive demands increase or that workloads decrease because tasks become (partially) automated (Parker & Grote, 2020). The more knowledge is gathered about how and why technology affects work design, the more we will gain insights into how to optimize the technology’s benefits (Parker & Grote, 2020).
This technological revolution is initiating a paradigm shift from independent automated and human activities towards systems characterized by the cooperation of machines with humans, designed not to replace the skills and abilities of humans, but rather assist humans in being more efficient and effective (Romero, Bernus, Noran, Stahre, & Berglund, 2016). The implementation of a technology to automate or to augment a specific task or function leads to decisions regarding the required skills and abilities of workers. It may result in some tasks getting augmented or substituted, meaning that management has choices to make to organize remaining human tasks into different jobs (Waschull et al., 2020). Their behaviour regarding these work design choices is elaborated in the next section.
Work Design Behaviour
Motivational theories (e.g. Hackman & Oldham, 1976) and theories from a stress perspective (e.g. Bakker & Demerouti, 2017; Karasek, 1979) propose that high-quality work design is characterized by ensuring reasonable levels of job demands together with enriched job characteristics like autonomy, skill variety, and social support. Literature stresses the importance of high-quality, or enriched work design, as this can be very beneficial for organizational performance (e.g. Cordery & Parker, 2012; Hackman & Oldham, 1976; Parker et al., 2019). An enriched work design is one where the employee has, for example, the job autonomy to make decisions, a variety of tasks, and an opportunity to use and develop their skills (Parker et al., 2019). A vast amount of research has shown that enriched work design affects various factors like stress, job satisfaction, performance, absenteeism, accidents, team innovation, company financial revenue, and more (Humphrey, Nahrgang, & Morgeson, 2007). Poor-quality work design is often associated with job simplification, which is characterized by jobs in which mental work is often allocated to managers and engineers, and other remaining jobs encompass physical, often routinized tasks that are tightly controlled (Waschull et al., 2020).
Research suggests that managers’ knowledge about work design is usually limited to motivational or stress-reduction approaches, with relatively little understanding of the learning and performance benefits of well-designed work (Parker & Grote, 2020). Various antecedents are shown to be influential in shaping individual work design behaviour. KSAs (knowledge, skills, abilities), motivation, and opportunities of managers and others in formal decision-making positions are influential in their work design decisions (Parker et al., 2017). An example of this could be managers’ knowledge about work design options, or their personal desire to design high-quality jobs.
As mentioned, we argue that managers’ work design behaviour can be seen as their individual job performance. Next to knowledge, skills, ability, and motivation, opportunity is argued to be crucial in individual performance (Blumberg & Pringle, 1982). Opportunity can be seen as the certain composition of different forces surrounding a person and his or her task which are not in this person’s direct control and are enabling or constraining his or her task performance (Parker et al., 2017). Examples of opportunities could be organisational policies or time. These could potentially affect an individuals’ workload, which we elaborate next.
Managerial workload
The term workload is defined in literature as a part of job demands, which is an independent variable for measuring stress sources (Karasek, 1979). Physical, psychological, social, or organizational aspects of the job that require sustained physical and/or psychological effort are aspects that form job demands, which in turn are associated with certain physiological and/or psychological costs (Bakker & Demerouti, 2017).
turn, respectively positively and negatively related to job performance (Bakker & Demerouti, 2017). This shows that workload can have both positive and negative consequences regarding job performance.
Decisions made by executives who are under significant job demands will closely reflect their functional background, educational experiences, age and tenure, as well as their psychological dispositions (Hambrick, Finkelstein, & Mooney, 2005). Next to this, executives under high job demands have so much performance pressure that they can simply not afford to be comprehensive in their analyses or search for solutions (Hambrick et al., 2005).
In this paper, we have chosen to not engage in the complex procedure of formally distinguishing ‘objective’ and ‘subjective’ workload. The managers’ experienced degree of difficulty or challenge in their job is what will be used to assess their workload. This way, we look purely at perceived workload, independently of personal characteristics and other factors, of which we recognize that they could be influential in perceived workload.
Linking the topics
The complexity of implementing a smart manufacturing technology is found to be coupled to certain work design choices to be made by managers (Cagliano et al., 2019). For organizations applying new technologies, designing enriched work is beneficial for individual and organizational outcomes (Parker & Grote, 2020). To design enriched work, opportunity is a factor that is crucial (Blumberg & Pringle, 1982). This opportunity factor is, among other influences, affected by workload. Managers that are under high workload engage in limited research to make choices (Hambrick et al., 2005), which could constrain enriching work design behaviour. Because of this potential interplay, we argue that a manager’s perceived workload that he or she experiences while implementing a smart manufacturing technology could affect his or her work design behaviour. This idea is visualized in the conceptual framework in Figure 1.
Figure 1: Conceptual framework
Methodology
Research design
This research conducts a multiple case-study, in which qualitative data will be analysed to answer the research question. The case study is a research strategy which focuses on understanding the dynamics present within a single setting, in this case being the implementation of smart manufacturing technologies (Eisenhardt, 1989). Central to building theory from multiple case-studies is that each case is a distinct analytic unit, which serves as a replication, contrast, and extension to the emerging theory (Eisenhardt & Graebner, 2007). The unit of analysis in this study are the managers that are involved in implementing a smart manufacturing technology. We studied the dynamics of six managers’ work design behaviours and their perceived workload in different companies and smart manufacturing settings, each manager being a different case. Because the relationship between these factors could differ for each manager, a multiple case-study is the most suitable research design.
Case selection & research setting
We have investigated implementation processes at multiple manufacturing companies that were already completed. The reason that manufacturing companies were selected, is because these are the focus in Industry 4.0. The manufacturing companies that were involved are all operating worldwide and are particularly interesting to investigate because they recently implemented a new technology in their manufacturing processes which can be considered as a smart manufacturing technology. The focal managers are selected for being involved in the implementation of this particular technology. They are the formal decision-makers when it comes to work design and are for that reason suitable for this case study. An overview can be found in table I.
Table I - Case overview
Company Case Interviewee position Type of project Organization Y I1 Manager Production &
Assembly
Predictive maintenance Organization Y I2 Manager Maintenance &
Production engineering
Predictive maintenance
Organization X I3 Production Manager Performance behaviour monitoring Company Z I4 Project Manager Maintenance
Company X I5 Commissioning & Start-up Manager
Predictive maintenance Company X I6 Project process licenser Predictive maintenance Data collection
technology and its implementation process. All interviews were held in Dutch, to overcome possible language barriers and to exclude possible language-based bias. All were recorded and transcribed word-by-word. The interviews were all scheduled to take around 1 hour.
Data analysis
The interviews were transcribed and analysed to be able to get a structured overview of the collected data. The coding scheme as presented in Table II was created to come up with starting codes relating to the concepts and variables stated in the research question. These starting codes then served as a guideline with which all separate cases could be read and analysed. Quotes from the interview relating to the listed starting codes were written down in Excel as descriptive codes (first-order codes). These descriptive codes then were assigned a second-order code (e.g. ‘reason for implementation’, or ‘stress level’). After analysing and summarizing each case, all relevant information was combined for the cross-case analysis. After reducing overlap in second-order codes, we were left with 22 distinctive codes. Finally, third-order themes were derived based on the second-order quotes. An excerpt of the coding tree is included in Appendix A.
Table II - Coding scheme
# Concepts & variables
#2 Starting codes Definition A Smart
manufacturing technology
A1 Technological characteristics
Characteristics of the networked information-based technologies for manufacturing enterprises (Cagliano et al., 2019)
A2 Implementation complexity
Complexity of implementing the technology (Frank et al., 2019) A3 Industry 4.0 base
technologies
Internet of Things (IoT), cloud services, big data and analytics (Frank et al., 2019)
B Managers’ work design behaviour
B1 Changes in job/task characteristics
Changes in work characteristics as defined by Morgeson & Humphrey (2006)
B2 Decisions regarding work design
Decisions around the content and organization of one’s work tasks, activities, relationships, and responsibilities (Parker, 2014) B3 Workers' level of job
demands
Demands like high workloads, emotional demands, physical demands, and role ambiguity (Bakker & Demerouti, 2017; Barnes & Van Dyne, 2009)
B4 Workers’ autonomy to make decisions
The degree to which the job provides substantial freedom, independence,
B5 Workers’ variety of tasks
Refers to the degree to which a job requires employees to perform a wide range of tasks on the job (Morgeson & Humphrey, 2006) B6 Workers’ opportunity
to use and develop skills
E.g. training, support, feedback from job or superiors.
B7 Individual factors Individual factors like current job autonomy and openness to change (Parker et al., 2019) C Managers’ perceived workload C1 Experienced degree of difficulty or challenge in job
Challenging stressors: demands that cost effort but that potentially promote personal growth and achievement (Bakker & Demerouti, 2017)
C2 Motivation Characterized by work
engagement, commitment, flourishment, etc. (Bakker & Demerouti, 2017)
C3 Strain Characterized by exhaustion, job related anxiety, health complaints, etc. (Bakker & Demerouti, 2017)
Findings
First, we present the within-case analyses, in which we outline the gathered data relevant to the research question. Afterwards, the cases are compared to identify differences and similarities across the collected data.
Case I1
Implementing the technology caused changes in workers’ job characteristics. For example, the technology was introduced to prevent the manufacturing plant from idling by telling operators what to do according to a mathematical model instead of having them intervene in the process: “Because the operators would normally intervene, but we said: you shouldn't intervene anymore, the model tells you what to do. That's pretty strange for an operator, because he thought he had to intervene. But he didn't do anything, and it came neatly back within his bandwidth, his window. And if he hadn't, he would've made adjustments, and now he hasn't made adjustments.” (I1). This and other examples indicate less enriched work caused by the technology and work design decisions of the manager.
In terms of workload, the manager indicates that in projects stress levels always peak and that there can be some conflict within day-to-day tasks and innovation projects, but he sees this as a part of the job and thus does not mind. This causes that we cannot notice a clear direct correlation between the manager’s work design behaviour and his perceived workload.
Case I2
uh, you have to be more process-driven, so you have to understand the production process, what is behind it and which process characteristics are critical to get the right quality.” (I2). Overall the manager showed that he involved workers in the implementation process and has facilitated various learning opportunities for them. The size of the workforce went down, but the job complexity went up as higher educated workers were needed. This, therefore, indicated higher-quality work design decisions.
The manager is motivated by the process of implementing new improvements. He mentioned that sometimes making decisions takes longer due to the organizational structure and that stress levels peak during some projects. He further indicated that the workload affects the amount of time that he spent on considering work design: “Well you have, um, you're working on a change project, but you also have the financial responsibility for your week, um, just your standard work you have to do and your planning and you have this and that. I'd rather spend sometimes, an hour or two more on a project like that. Just explain that clearly and train it so that it lands better in production or starts better. Now sometimes you feel like you have to scramble things to get it ready.” (I2). This suggested that the manager’s work design behaviour is negatively impacted by his workload in a project.
Case I3
The manager actively tries to get the machine operators involved in problem-solving, having them actively involved in increasing performance: “First of all, you make those machine operators think [by introducing whiteboard] so they don't say, ‘it's [machine] broken and I'm in the coffee corner and I will hear it when it’s working again’, but they have to start thinking for themselves.” (I3). Next to this, an employee was hired to create Standard Operating Procedures and to provide performance feedback on team leaders as well as operators. These characteristics all point out high-quality work design behaviour, as the manager is improving various job characteristics (e.g. variety of tasks, feedback, etc.).
Furthermore, the interviewee stated that he views the project as one of many that are part of his daily tasks: “That process is never-ending. I'm always looking for other possibilities, that's how you go forward.” (I3). Because of this, no clear link between work design behaviour and workload could be drawn.
Case I4
The technology aims to predict the right moment for machine maintenance. “Smart technology has much more of a supporting role than a replacement role.” (I4). The manager indicates that he is not particularly encouraging responsibility for the workers, but that he is mostly interested in their current performance and progress in the project.
Case I5
Operator decision autonomy decreased because of the project: “They are not allowed to change much in terms of settings, they really have to do what is asked of them. Low autonomy.” (I5). However, the necessary skills that were asked for increased: “This was taken into account in the sense that we had estimated that this learning curve would be large. So, that a lot was expected of the operators and that we also needed experienced and good operators to understand that. And we also organised the guidance of operators well.” (I5). The case indicates an overall low degree of enriching work design behaviour.
I4 indicates being very free in his decisions, as he is part of an external company that was hired for implementing the technology. About his workload, he stated: “Very high, very high. ...the amount of work, complexity of the work. There was a lot of pressure on the project, so that always translates.” (I5). Apart from the project, however, he perceives a very low workload. Although this is the case, he does not think that he would spend more time considering the job characteristics of operators if his workload was lower.
Case I6
To ensure that employees have the necessary skills, training is encouraged. I6 in principle is not advocating routinized work: “But I feel that, at least in the Dutch organization, this kind of routine manual labour, day in, day out. In every team. That, that's actually not suitable. That's not the right way to work with operators.” (I6). On the other hand, he indicates not taking the operator’s wishes into account often: “But the role of that operator and what that means for the operator. That's something that... Those changes for the operator, you don't look at those changes so much. It's much more like I have to take the operator with me, but what it means to him, that's funny. You don't really do very much with that.” (I6). This indicates less enriching work design decisions and behaviour.
In terms of workload, I6 indicated that the implementation process caused a lot of stress and pressure, especially in the final stages. He stated that he would not have spent more time considering job characteristics if his workload was reduced. Although this case’s workload can be considered high and its work design behaviour less enriching, a change in workload would not directly cause a change in work design behaviour.
Cross-case analysis
What catches attention, is that in almost all cases tasks become more complex, pushing employees to be more engaged in problem solving. Accordingly, training employees and having them involved in the technology implementation process shows to be a recurring trend in the analysed data as well. In case I1, I4, I5, and I6, fewer signs of active consideration of work design were noticed, leading to less enriching work design behaviour.
Discussion and Conclusion
In this paper, we have aimed to answer the following question: When implementing new smart
manufacturing technologies, what influence does managerial (perceived) workload have on their work design behaviour? From the analysis of the gathered data, various interesting
insights have emerged. We found that half of the respondents have perceived an increase in workload due to the implementation of the smart technology, where the other half did not indicate an increase therein. Connecting this with the work design characteristics, we saw that in most cases no clear direct link could be established between these two variables.
In terms of work design behaviour, we saw a couple of clear trends. The goal of all cases for implementing the smart manufacturing technology is the same: increase of cost-efficiency. Research has suggested that this motivates managers to design less enriched work (Parker et al., 2017). In almost all cases the technology led to a higher task complexity for employees, as more educated employees are needed to work with it. This most probably explains why most managers actively engaged in training and development of employees. Next to this, the involvement of the employees in the implementation process is shown to be a popular method to get them committed to the goals set by management. These characteristics indicate high-quality job design. However, in case I5 & I6 we can see that overall, the changes in job characteristics indicate a poorer quality of work design. The differences between the work characteristics in each case can possibly be explained by other direct and indirect ways in which work design is shaped, being higher-level external context (global/international, national, and occupational factors), organizational context, local work context, and individual factors (Parker et al., 2017).
Our findings indicate that overall, task complexity and the opportunity to develop skills increased due to the implementation of smart manufacturing technologies. In most of the cases, the quality of work design did not seem to be directly affected by workload. We do believe, however, that other influences could be at play here, like the function of the manager in the implementation of the smart manufacturing technology. In case I5 and I6, for example, the interviewees had a temporary role for the implementation of the project. This might cause other work design behaviour, as organizational influences differ (Parker et al., 2017).
With this research paper, we have contributed insights on the work design behaviour of managers in a smart manufacturing technology setting. As research on when and how managers engage in work design is quite limited (Parker et al., 2017), this paper contributes to the available literature by providing insights in the relationship between managerial perceived workload and work design behaviour. Furthermore, the importance of the managers’ role in the specific project is expected to be of influence as well, as we expect that a different level of engagement might occur as a project manager moves to another project after finishing. This might be an interesting direction for future research. In case I3, there were no clear deadlines: the manager simply wanted to increase the efficiency of the plant and views the implementation process as an ongoing business improvement process. We therefore expect that the absence of clear deadlines in project form might lead to lower workload levels, which could enable the manager to spend more time on work design consideration. For that reason, research on the relationship between these variables could deliver interesting insights as well.
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Appendix A
Excerpt of coding tree. # Responde
nt
Starting code Data reduction (1st order code) 2nd order code 3rd order theme B4 Comp X I1 Workers’ autonomy to make decisions
I5: 'En die werkverdeling, die rolverdeling. Ja, dat hebben we aan de
teams van operators zelf overgelaten.' Employee involvement Implementation process B2 Comp Z I1 Decisions
regarding work design
I4: 'Absoluut en ik denk ook dat het, door hen medeverantwoordelijk te maken dat zij kar worden van deze nieuwe manier van onderhoud plegen, zeg maar dat dat al helpt in het bedenken, dan werp je het niet in hun schoot van dit is de nieuwe manier van werken. Succes ermee. Wij hebben het samen bedacht, dat dit past eigenlijk ook het best bij hun functie en hun rol binnen het bedrijf.'
B6 Comp Z I1 Workers’ opportunity to use and develop skills
I4: 'Ja wat je kunt doen, zeg maar, is een workshop of op dat soort manieren om ze erbij te betrekken en te kijken wat je allemaal mogelijk maken met elkaar.'
C1 Comp Z I1 Experienced degree of difficulty or challenge in job
I4: 'Ja ik denk dan dat je iedereen de gelegenheid moet geven om zijn idee te delen en met die ideeën aan de gang te gaan en daar een plan van maken. Ja dan krijg je veel meer mensen mee. Dat is een andere manier van omgaan. '
A 2
Org X I1 Technological complexity
B1 Org X I1 Changes in job/task characteristics
I3: 'De eerst opmerking was, 'jezus, moeten we dat er ook nog bij doen'. Dan is het de truc om zelf ook op de vloer te lopen en het te verkopen. Zo van: gasten, ik verwacht heel veel van jou namelijk, ik ga zeggen dat je zo hard mogelijk moet draaien. Maar je hebt hier nu een middel om aan te geven dat het niet lukt. Omdat je machine kapot is of weetikveel wat je hebt.'
Employee involvement B4 Org Y I1 Workers’ autonomy to make decisions
I1: 'We hebben ook een stukje verbetercultuur in ons bedrijf zitten. Dus ze [operators] komen zelf ook met ideeën van 'he dit helpt mij bij mijn productieproces, zodat ik beter kan produceren'.'
B2 Org Y I1 Changes in job/task characteristics
I1: 'Een operator ziet het ook als een tool die hem helpt om goede kwaliteit te produceren. De procesoperators op dat niveau zien het niet als bedreiging, maar als een stuk ondersteuning.'
B2 Org Y I2 Decisions regarding work design
I2: 'Ofwel er moet personeel uit of niet, of we gaan het herverdelen en dan vragen we van wat zijn jullie wensen hierin? Zodat iedereen daar ook zijn eigen verantwoordelijkheid in neemt, dat is heel belangrijk he in verandermanagement.'
B6 Comp X I1 Workers’ opportunity to use and develop skills
I5: 'Integratie en training van de operators is een hele belangrijke, en van de onderhoudsafdeling.' Employee training B6 Comp X I2 Workers’ opportunity to use and develop skills
I6: 'Belangrijk is dat de vaardigheden die daarvoor nodig zijn gewoon goed getraind worden bij de mensen.'
B6 Comp X I2 Workers’ opportunity to use and develop skills
I6: 'En ook dat mensen zo vroeg mogelijk al getraind moeten gaan worden.'
B6 Org X I1 Workers’ opportunity to
I3: 'Dat hebben we dus allemaal ingericht, dus toen konden we die mensen opleiden en ook zeggen van deze man is vakvolwassen of deze man heeft nog begeleiding nodig.'
use and develop skills B6 Org Y I1 Workers’ opportunity to use and develop skills
I1: 'Nou was het in dit geval, als je zegt ik ga automatiseren, en ze hebben nog nooit automatisering gedaan, dan neem je ze mee in een stuk training. Dan laten we zien van waar willen we heen, hoe ziet het er uit, en wat helpt het jou in je job.'
B4 Org Y I2 Workers’ autonomy to make
decisions
I2: 'Dan zit er dus per processtappen zit er dan opleiding met documentatie. Die moeten ze doornemen. En daarna moeten ze dat uitleggen aan hun leidinggevende of hun job trainer op de locatie. Dus dan aan de hand van de machines. En op een gegeven moment wordt het afgetoetst. Dus we hebben eigenlijk helemaal de inhoudelijke, specifieke kennis van die machines dat is eigenlijk inhoudelijk opleidingsprogramma van organisatie Y zelf.'
B6 Org Y I2 Workers’ opportunity to use and develop skills
I2: 'Nou, we hebben een opleiding 2.0. De vakoperators hebben extern met ROC een opleiding, 6 / 7 jaar geleden is dat geïntroduceerd. Uh, die gaan ook dat ze beter voorbereid worden op hun onderhoudstaken, en dat ze wiskunde natuurkunde op een hoger niveau krijgen. Dus dat is een jaar of half jaar aan opleiding. En dan worden ze nog een praktijkopleiding samen met de onderhoudsdienst, krijgen ze dan intern'
B2 Comp X I1 Decisions regarding work design
I5: 'Voor de operators is het dus dat ze veel minder aan de machines
zelf mogen veranderen.' Decision autonomy Job characteristics
B4 Comp X I1 Workers’ autonomy to make
decisions
I5: 'Er is heel veel rekening gehouden met wat mag een operator wel of niet doen met de machines. En dat is toch echt tot een minimum beperkt. Dus aan allerlei instellingen, zeg maar, mag een operator helemaal niet komen.'
B4 Comp X I1 Workers’ autonomy to make
decisions
B1 Comp X I2 Changes in job/task characteristics
I6: 'Maar dat is bijvoorbeeld wel iets, waar we heel veel discussie over hebben gehad met de operators, want die waren dat gewend om dat allemaal lekker zelf te doen en een beetje heen en weer te roepen met mekaar. En dat gaat nu automatisch. '
Decision autonomy B4 Comp X I2 Workers’ autonomy to make decisions
I6: 'Om dingen goed voor elkaar te krijgen dus, dan gaan ze aan de ene kant dingen aanpassen mechanisch dingen verzetten, ander standje geven en settings aanpassen. En dat zijn dingen….. Daar hebben we wel last van gehad.'
B2 Org Y I1 Decisions regarding work design
I1: '..zodat niet de operator via OCAPs (out of control action plans) maar eigenlijk via een wiskundig model: nu moet je dit doen, nu moet je dat doen'
B3 Comp Z I1 Workers' level of job
demands
I4: 'En stimuleer jij ook die verantwoordelijkheid bij de maintenance
engineers? Dus dat zij veel vrijheid krijgen? En hoe stimuleer je dat?'
'Die vrijheid stimuleer ik niet perse. Wel dat ze resultaat halen in wat ik graag zou willen zien. In dit project zal ik dan vooral vragen hoeveel is er al gebeurd? Wat voor voortgang heb je? Welke acties lopen er, dat soort zaken.'
Job demands
B4 Org X I1 Workers’ autonomy to make
decisions
I3: 'Met name met dat performance bord. 'Dit is jullie machine, ik stel jullie verantwoordelijk voor alles wat eraf komt. Dus julie zijn
verantwoordelijk voor het product, dus als dat fout is dan weet ik jou te vinden.' Heel plat gezegd. […] Eh ja, dus ze hebben wel
daadwerkelijk echt de verantwoordelijkheid. Jij bent de
machinevoerder, dus jij bent verantwoordelijk voor het product dat er af komt.'
B1 Comp X I1 Changes in job/task characteristics
I5: 'Ze moeten veel meer met mekaar afstemmen in handelen, de werkplekken zijn inderdaad meer afhankelijk van elkaar.'
Interdepende ncy
B2 Comp X I1 Decisions regarding work design
I5: 'Ja, er wordt veel meer gewerkt met alarmen en met foutmeldingen
B2 Org X I1 Decisions regarding work design
I3: 'Ten tweede, teamleiders zijn nu verplicht om drie keer in de shift rond te lopen, langs alle machines en dus je contactmomenten
vergroot. Daarmee heb je ook een soort people engagement. Als je altijd chagrijnig bent (zo'n machinevoerder) en die reageert nergens op, is er vaak wat aan de hand, of je thuissituatie niet in orde. Je creëert dan ook weer contactmomenten. Op die manier hebben is geen verrassing meer.'
B3 Org X I1 Workers' level of job
demands
I3: 'Mijn beeld was dat die mensen [teamleiders] alleen maar boven achter een computertje zaten. En ik wil dat die mensen op de vloer lopen en daar de mensen aan coachen om altijd uit te dagen, draai maar harder.'
B5 Org X I1 Workers’ variety of tasks
I3: 'Dus toen hebben wij een whiteboard geïntroduceerd. En daar staat ook in met een zevental KPIs, waarbij twee op veiligheid, twee
