characteristics affect decisions regarding work
design
By
Arjen Eisen
4175212
University of Groningen
Faculty of business and economics
ABSTRACT
The rise of smart manufacturing technologies brings huge opportunities for work and society. However, the effects of these new technologies are not pre-defined and deterministic. The effect of these technologies is strongly dependent on factors like the managers' choices and the way it is implemented. During the implementation of these new technologies, work design is an important aspect. In this research paper, a study has been performed at 4 different organizations that have implemented smart manufacturing technologies. With this study, there is tried to answer the question if there is a link between managerial characteristics and decisions regarding work design. The characteristics which were examined were gender, age and mindset of the managers. From the results of the study, there could be suggested that there is a link between mindset and the decisions regarding work design. However, to draw hard conclusions further research is advised to make the results more reliable.
Keywords:
Smart manufacturing, Work design, decisions-making
Supervisor: S.A. Waschull
INTRODUCTION
With respect to the future of production systems, the adoption of industry 4.0 is getting more and more popular in the more advanced countries (Chiarello et al., 2018). With the application of smart manufacturing (Kagermann et al., 2013), industry 4.0 involves the implementation of digital technologies in the manufacturing systems, to gather real-time data and analyse that data, to provide information about the process (Lee et al., 2015; Wang et al., 2016). These new technologies will not only affect the current production processes, but they can also affect the current work design of individuals (Waschull et al., 2020). As technology can substitute human tasks, work design around these technologies will change. This can be by simplifying or enriching jobs (Hirsch-Kreinsen, 2016). As studies from Parker and Grote (2020) have shown, smart manufacturing technology has the potential to affect the job characteristics. Nevertheless, the effects of new technologies on individual work design often are not deterministic and depends on several influences, like the managerial decisions and the way the new technology is designed (Clegg & Corbett, 1986; Parker & Grote, 2020; Waschull et al., 2020). Earlier research by Parker et al. (2019) shows that job simplification has a negative effect on both individuals and organizations. However, despite this evidence, simplified jobs remain rather common (Felstead et al., 2010; Kawakami et al., 2014; Vidal, 2013). This leads to the question of why managers design work this way. In this study, therefore, the focus lies on how personal characteristics of managers like mindset, gender and age influence their decision-making.
From several studies (Clegg & Corbett, 1986; Waschull et al., 2020), there can be concluded that managerial decisions can greatly affect individual work design. Jobs can be enriched or simplified (Hirsch-Kreinsen, 2016), or even jobs can be substituted (Frey & Osborne, 2017). However, there is little known about how managerial decision-making is affected by the personal characteristics of a manager.
The contribution of this research is to broaden the understanding about how personal characteristics like mindset, gender and age affect the work design decisions of managers. To do this, the research was performed by studying multiple cases wherein managers implemented smart manufacturing technologies. By doing this, the study is trying to answer the question:
The remainder of this paper will be structured as follows: The first section consists of a literature review on the effects of Smart manufacturing technologies, what differences there can be between managers and work design. The second section will give further insight into the methodology which is being used to perform this study. After the methodology, one section is being used to explain the results that were found during the study. And in the last section, the findings will be discussed, and the conclusions will be made according to the discussion.
LITERATURE REVIEW
In the following section, the most important insights of the research question will be discussed according to data which has been gathered from previous studies. These insights will then be used to give a conceptual framework of the research.
Smart technology and its impact on work design
A positive or negative effect of new technology on work design is based on 5 categories. These five categories are job autonomy and control, skill variety and use, job feedback, relational aspects and job demands. Collectively these categories of job characteristics cover the most important aspects of work design as well as the Job Characteristics Model (JCM) as designed by Hackman & Oldham (1976). New technology can lead to both simplified and enriched jobs (Hirsch-Kreinsen, 2016). However, the effect of new technologies on work design, as well as the changes they bring are not deterministic (Clegg & Corbett, 1986; Parker & Grote, 2020). These effects variate between organizations, due to the work design decisions of an organization (Parker & Grote, 2020). After the industrial revolution, the key way to work design was achieving efficiency by creating simplified jobs (Parker et al., 2019). but evidence of negative effects of job simplification for both individuals and organizations showed that high quality work design consists of enriched work characteristics (Parker et al., 2019). Despite this evidence, several studies (Felstead et al., 2010; Kawakami et al., 2014; Vidal, 2013) showed that simplified jobs remain rather common. This leads to the question of why managers design work this way how differences between managers influence their decision making.
Individual influences of managers
Mindsets
Earlier research suggests that to process complex and conflicting information, individuals often use simplifying systems which makes the ability to organize and make sense of the world (S. Taylor & Crocker, 1981; L. E. Williams et al., 2009). Because of the complex and conflicting information individuals have to process, it is impossible to intercept every detail, resulting in a huge amount of selectivity in the information processing (A. J. Crum et al., 2013). A mindset is therefore defined as a cognitive screen that selectively organizes the information, and orientates an individual toward a way of understanding and experiencing the information so he can correspond his actions (A. J. Crum et al., 2013). Mindsets are a familiar aspect of the human mind, nevertheless, they are very important, since the mindset individuals tend to use, have a tremendous effect on a person’s behaviour (Liberman et al., 2004) and judgement (Taylor & Gollwitzer, 1995). A persons mindset is developed and influenced by factors from outside like culture, religion and trusted people (Crum & Zuckerman, 2017). Typically there are known two mindsets: a fixed mindset – which believes that peoples characteristics are fixed and cannot be changed (Dweck, 2006). The other type is a growth mindset – which tends to believe that peoples characteristics are changeable and can be developed over time (Dweck, 2006). Following these mindsets, managers with a growth mindset also tend to have more faith that their employees can develop their skills than managers with a fixed mindset (Dweck, 2006). People with these mindsets also continuously try to improve by learning from other people. Altogether Dweck (2006) argued that managerial mindsets affect the success of an organization as well as factors like the motivation, productivity and job satisfaction of employees.
Age of the manager
Gender of the manager
Not only everyday life is affected by differences between genders, but business as well (Gernreich & Exner, 2018). For instance, compared to men, woman tend to be more influenced by how their decisions affect others, thinking more in a network (Tetlock, 1985). Often they prefer to create an environment where everyone can give input to the decision-making (Patel & Buiting, 2013). Also, in earlier research on decision-making, man based their decisions on rules, while woman gave further clarification with their decisions. This led to those female managers solving problems in a more creative way that satisfied all stakeholders more equally (Dawson, 1995).
Conceptual framework
From the studied theory, there is expected that differences in personal characteristics of managers do affect the decision-making of the managers. As stated before, the mindset is a cognitive screen which organizes information on which a person handles. As Crum (2017) stated, mindsets are influenced and developed by factors from outside, which means that they are developable over time. Therefore, it is expected that due to age the mindset of a manager can variate from others. Also as previously mentioned, a woman tends to be more open to input from others in their decision making (Patel & Buiting, 2013). That is why there is expected that gender also influences a persons’ mindset and eventually their decision-making regarding work design. Combining these insights results in the following conceptual framework in Figure 1.
METHODOLOGY
Research design
This paper is based on qualitative research in combination with a multiple case study. The qualitative research is an approach to understand, analyse and explain events within management at the social or organizational level (Delattre et al., 2009). A popular method for examining qualitative data is a case study since these create a better understanding of the subject by examining the samples in-depth (Yin, 2014). The unit of analysis is the managers’ work design decisions. Beforehand, little is known about the effect of the managers their characteristics. This makes qualitative research in combination with a multiple case study fitting best with the research question. This is because an advantage of this method is that it gives a better understanding of how things happened and not only at what happened (Maxwell & Kaplan, 2005). Instead of getting samples of information from a high population, qualitative research gives the opportunity to get more detailed information from a smaller population (Patton, 2014). This makes it possible that with fewer but in-depth interviews of managers in multiple different cases, data can be gathered about the influences of managers their characteristics on their work design decisions.
Case selection & research setting
The four contacted organizations are all manufacturing companies from various industries. Mainly the research tries to answer how work design decisions of managers are affected by their characteristics during the implementation of smart technologies. Therefore, it was very important to find organizations which are known with these technologies. In Table 1, an overview can be found of the selected organizations.
TABLE 1
Overview organizations Number of
interviewees Number of employees
Implemented technology
Company X 2 500 MES/QC
Company Y 2 160 CAD/CAM
The study tries to focus on how personal characteristics like age and gender influence managers their work design decisions. However, the managers willing to participate in the interviews were all male managers. The age of these managers variates between 37 and 63 years old. An overview of the interviewees can be found in Table 2.
TABLE 2 Overview interviewees
Organization Function Age Gender Interviewee 1 Company X Commissioning & Start-up manager 46 yr. Man
Interviewee 2 Company X Process licenser 44 yr. Man
Interviewee 3 Company Y Manager technical department 63 yr. Man
Interviewee 4 Company Y CEO 44 yr. Man
Interviewee 5 Company W Manager production, engineering &
maintenance 52 yr. Man
Interviewee 6 Company W Manager production 37 yr. Man
Data collection
To gather important data, semi-structured interviews have been conducted. The semi-structured interview is most often used as an interview format for qualitative research. (DiCicco-Bloom & Crabtree, 2006). The reason that this format is so popular is that it has been proven both versatile and flexible (DiCicco-Bloom & Crabtree, 2006). Some of the main advantages of the semi-structured interview method are that the method enables reciprocity between the interviewer and the participant (Galletta, 2013) and enables the interviewer to react to the answers of the participant by asking follow-up questions (Rubin & Rubin, 2005).
Operationalisation of the concepts
The conducted interviews were split into six main topics: (1) background information of the company, (2) the project, (3) the mindsets and work design behaviour, (4) the work design changes, (5) individual influences and (6) the involvement of the process executioners.
As stated before, the research question is “how do differences in manager characteristics affect work design during the implementation of smart technologies?”. From this, three main concepts can be derived. These are (1) Smart Manufacturing technology implementation, (2) individual characteristics of managers and (3) work design behaviour. These three main concepts have been operationalized through more specific questions, which are displayed in Appendix B.
Data analysis
To evaluate the transcribed data which has been received from the interviews, qualitative data must be analysed by coding the data systematically. This has been done by identifying certain themes or patterns (Hsieh & Shannon, 2005). These codes then can be used to identify which concepts of the obtained data can be seen as relevant (Strauss & Corbin, 2008). For this qualitative research, a coding tree will also be used wherein the relevant concepts will be categorized.
TABLE 3 Coding tree
Theoretical background Codes
A Smart manufacturing technology implementation
A1 Reason for implementation
A2 Objectives B Individual characteristics of managers B1 Result judgement B2 Vision on humans
B3 Openness for new technology B4 Personality
B5 Mindset B6 Age B7 Gender C
Work design decisions
C1 Employee perspective C2 Effect on employees C3 End-user involvement C4 Results C5 Affected employees C6 Decision making
RESULTS
In the following section, each case will be analysed. The interviewee, its gender and age can be found back in table 2. The supporting data for the cases can be found in Appendix C
Case 1 – Commissioning & start-up manager
Case 2 – Process licenser
For this manager, there was a clear mindset. This was that repetitive jobs should not exist anymore in The Netherlands. This is because he believed that repetitive jobs still are necessary, but that they can and should be automated. This resulted in that the work of the operators shifted from physical work to cognitive work. During this project, he also involved the process executioners in making decisions around the technology. However, he stated that during implementation he did not think about how his decisions affected the work of the process executioner.
Case 3 – Manager technical department
This manager, just as in case two had the clear mindset that when possible, you should automate as much as possible. His ultimate criteria for the implementation of new technology were simplification of the work, so there would be fewer errors. From the beginning of the project, this manager involved the process executioners in his decision-making process. Results of the project were that the autonomy of the operators was reduced, and the feedback of the machine increased. Also, the physical job demand reduced, and the cognitive job demand increased. Nevertheless, in this case, the manager tries to maintain autonomy for the workers by making use of job rotation. In this way, the autonomy of the workers only decreases slightly. Also, he is convinced that workers can gain more autonomy by following courses so they can do different tasks.
Case 4 – CEO
Case 5 – Manager production, engineering & maintenance
For this case, it was again clear what his mindset was. In his project with predictive maintenance was a form of automation, namely the quality control of their product. It was also clear this manager seemed this form of automation as very important to reduce human error. With this project also, just as with the other cases, the autonomy of the process executioner reduced. This was because the maintenance program now informs the process executioner when to undertake actions. The feedback from the machines again also increased.
Case 6 – Production manager
The interviewee from case 6 again showed signs that in his mindset automation should be increased. In the project, he also involved the process executioners and even schools them externally. In his case, he would like to see more preventive maintenance by use of sensors in the production lines. At his department, they introduced autonomous maintenance, which means that the operators now are more responsible for maintenance at the production line. However, this maintenance must be performed according to standard procedures. This results in that the autonomy does not increase. The feedback of the machines then again does increase, and the cognitive demand of the operators also increases while the physical demand decreases.
Cross-case analysis
Individual characteristics
As seen in Table 2, all participants of the interviews were men variating between 37 and 63 years old. What was interesting to see is that all the managers showed a combination of a fixed-and a growth-mindset. They did want to automate as much as possible to prevent human error in the production process. However, they did involve the process executioners in their decision-making process, which is typical for a growth-mindset. Despite variations in age, their vision on human labour within their organizations seems to be that workers are no longer needed to do simple repetitive jobs because these can be automated.
Work design decisions
the feedback from the machines increased in most of the cases. This feedback was in combination with the reduction of autonomy, as this feedback from the machines gives the process executioners signal when to act. So, from the results, there can be said that most managers chose for job simplification by reducing the autonomy of the workers. However, there were two managers (case 3 and 6) which tended more to job enrichment. What is notable about this is that case 3 and 6 both have the same mindset as the other participants. However, their age differs most from the average age as well as their decisions regarding work design.
DISCUSSION & CONCLUSION
The results from the interviews showed that overall between the participants there was a common mindset. One that is a combination of a fixed- and a growth mindset. This showed because they did try to automate as much as possible to prevent human error, but also involved the process executioner in their decision-making process. From the work design decisions, it seems that there is a slight variation in the decisions. For example, case 3 and 6 tried to simplify the jobs as less as possible by making sure the workers could find autonomy in other tasks. This while the other cases tried to reduce autonomy as much as possible.
Interpretation of results
From the research, it is hard to draw conclusions based on gender. This is because the interviews were all male. It was interesting to see that the common mindset between the participants was that automation of the production process should be as high as possible. However, since there were no female participants, it is not possible to draw hard conclusions about this characteristic.
decisions, this could also be found back in the fact that they tried to maintain autonomy for the workers while the others reduced it as much as possible. In 2013 Crum et al. stated that a mindset is a cognitive screen to organize information and act on this information. This in combination with the differing work design decisions by case 3 and 6, indicates that differing mindsets do influence decisions regarding work design. Case 3 and 6 showed that they had a mindset in which they do think that people can develop themselves over time. Their decisions were corresponding to this by therefore giving the workers more autonomy.
What was interesting to see in perspective to the influence of age on decision making, was that the two managers who tried to keep the jobs enriched were very different in age. They differed 26 years in age. Also, the other managers between 37 and 63 years old made different decisions. This would mean that age did not influence the managers' mindsets and thus his decision-making. This is in contradiction to what Kirchner (1958) & Surwillo (1964) claimed that age contributes to the quality of decision-making.
Limitations
For this research, seven interviews were conducted at four different organizations. This has led to previous insights about how the participants their characteristics could be linked to their work design behaviour. However, to draw hard conclusions for the research question the sample size of interviews is too small. This makes it not possible to generalize the outcomes of this research. Also, the variety of participants is low. The interviewees were all man which makes it impossible to draw conclusions on the effects of gender. Also, the variety in age was high. This makes conclusions regarding the effects of age not reliable.
Theoretical and practical implications
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Appendices
Appendix A, Interview questions
Background information
1.1. What is your role in the company, and your responsibilities? 1.2. What is your previous working experience?
1.3. Could you briefly describe your company in terms of: • Industry
• Products and services
• Types of market served (B2B/B2C) • Number of employees
• Type of production processes
1.4. What study have you followed/completed?
1.5. How do you characterize the overall organizational culture and manager-employee interaction inside the company?
Information on the smart manufacturing project
2.1.Could you describe the smart manufacturing project/program, and the implemented technologies?
2.2.What were the main project activities?
2.3.*What is/was your role, and your daily tasks in the project?
2.4. Who else is/was involved in the design or implementation, what are/were their roles? Who has/had what responsibility?
2.5. Have you had any previous training and experience regarding the implementation of such technologies?
2.6. What was the main reason for the choice of the technologies adopted/implemented? 2.4. Which were/are the main objectives and motivations for the project?
2.7. Do/did you have all the resources necessary to make the project a success?
Unwrapping mindset and work design behaviour
3.1. How do you judge a good design and/or implementation of the (smart manufacturing) technology?
3.2. What are the ultimate criteria for success (of the implementation)? How do you meet these? 3.3. To ensure the technology helps enhance productivity, what else needs to be in place,
beyond technology that works?
3.4. What is your general ‘vision’ or perspective on the role of humans in the factory? 3.5. What ‘human’ considerations have you made in the project?
3.6. What have you done to take into account the perspective of employees, can you name examples?
3.7. Who was responsible for the consideration of human factors (their work, tasks, skills) during the project?
3.8. Have you involved the user of the new technology in the development/implementation project of the new technology, how?
3.9.a Questions to ask in case of designers of a technology: What considerations have you made about how the technology will be used in the work? What processes do you have for thinking about how the system will actually work when implemented?
3.9.b Question to ask in case of implementors of a technology: What role/staffing issues does the new technology give rise to? What skill requirements? Will people work in the same way as now? If not, what will be different?
3.10. What functions do you/did you allocate to a person rather than a machine/software? How do you make these decisions?
Work design changes
4.1. What employee group was/is most affected by the implementation?
(When technology not yet implemented):
4.2. When the technology is finally in place, how might their work design change? 4.3. Will people work in the same teams in the same roles/ different?
4.4. If more detail is needed, ask more specific questions:
Will levels of autonomy change? What control will the person have relative to the machine?
Will the person get feedback from the machine/system? Will job demands change?
Will the person need to develop new skills? Did the job complexity change?
What about social interaction, will people interact more or less with others?
(When technology is already implemented):
4.5. a. How did the work design change of X due to the implementation of the technology? b. How were these changes communicated to the involved stakeholders/ employees? 4.6. Do people work in the same teams, and in the same roles or different ones?
4.7. Were any of these changes unexpected?
4.8. If more detail is needed, ask more specific questions:
Will levels of autonomy change? What control will the person have relative to the machine?
Will the person get feedback from the machine/system? Will job demands change?
Will the person need to develop new skills? Did the job complexity change?
Individual influences
Demographics:
5.1. What is the average age in your department? 5.2. What is your age and sex (male/female)?
5.3. What is the distribution of male/female in your department?
5.4. Do you consider yourself as a person open to change, conservative or somewhere in the middle?
Workload:
5.5. How do you generally feel in terms of the workload that you have when not in the implementation process? (e.g. high, low, cognitive, physical, emotional)
5.6. Did your workload change when your got involved in implementing the smart manufacturing technology? (physical, cognitive, emotional). If yes, in what way?
5.7. Was this change in workload reversed when the implementation process was completed? 5.8. Do you think that you would spend more time to think about the change in task/job characteristics when your workload decreases?
Personality characteristics:
5.9. How would you describe your own personality and actions in a working environment in terms of: (If applicable please provide an small example to point it out)
1) Conscientiousness (Dutch: zorgvuldig, georganiseerd)
a) Are you aware of your actions and are you motivated to achieve ambitious goals?
b) Are you tidy, planned and well organized?
2) Emotional Stability/ Neuroticism (Dutch: angstig, humeurig)
a) Can you work in a stressed, emotional situation and how do you handle this? b) Are you quickly fearful that things don’t work as you want to?
3) Extraversion (Dutch: Extravert, sociaal)
4) Agreeableness (Dutch: Betrouwbaar, aardig, sensitief) a) Are you involved with your employees?
b) Are you sensitive, trustworthy/do employees trust you and are they willing to tell their concerns/ work problems?
5) Openness (Dutch: Nieuwsgierig, creatief)
a) How approachable are you for others, can you listen well to others?
b) Do you like new technical innovation and are you always looking for new opportunities?
c) Are you creative/ imaginative and do you have some unconventional ideas and beliefs?
5.10 Can you rate yourself from 1 till 5 on each of these five factors?
1= Not applicable; 3= Neutral ;5=Fully applicable (Please encircle the number) Conscientiousness 1 2 3 4 5
Neuroticism 1 2 3 4 5
Extraversion 1 2 3 4 5 Agreeableness 1 2 3 4 5
Openness 1 2 3 4 5
5.11 Do you think that your personality has an influence in the way you contribute in the decisions-making on work design choices? (Can you give an example?)
Including the process executer:
5.11. Did you include the process executer (the technology user) during the development/implementation of the technology?
5.12. Why did or did you not do this?
5.13. From experience, what do you think the effect will be of including the process executer in the design process?
What is your opinion on designing a process and then never changing the process?
Empowerment (encouraged by the manager):
5.14. Did the operators improve or attain new competences after the implementation of the smart technology?
5.15 Are the operators currently (after the implementation of the new technology) challenged to fully utilize the skills they possess and why so?
5.16 a) Do you share goals, information-processing and problem-solving activities with operators? (after the implementation of new technology)
b) If the above is the case (or partly), How do you facilitate and communicate this to the operators?
5.17 How do you monitor the operator’s participation/involvement regarding this goal. 5.18 Do you check whether the operators feel involved and how?
5.19 Do you recognize improvement contributions made to the new implementation by operators?
5.20 a) Did you facilitate team development, to the operators, when the new technology was in the implementation phase?
b) how did you facilitate team development? and do you still facilitate this?
5.21 How do encourage self-management and group decisions autonomy to operators since the implementation of the new technology?
5.22 a) did you facilitate employee training and skill development during the implementation?
b) Do you still facilitate this and do you play an encouraging role in development and training of the operators?
Wrapping up the interview
5.23. Do you consider the implementation a success and why? 5.24. What were negative outcomes of the project?
Appendix B – Supporting data results section
Supporting data on smart manufacturing technology implementation
Code(s) Quote(s)
Reason for implementation I2: “Because it is capable of high speed and high quality.”
I3: “Our current production line needed to be replaced because
some parts reached their end of the life cycle.”
I4: We had a machine line from Italy, which was around 12-13
years old. It still functioned okay, but we expected it to give problems near 2020”
Objectives I1: “Capacity expansion, so producing more and being capable
to fulfil the market”
I1: “Mainly to reduce the sensitivity too human error” I2: “I think mainly, reducing the sensitivity to human error.” I6: “Well, the whole investment was focused on reducing from
8 to 5 workers per team”
Supporting data on individual characteristics of managers
Code(s) Quote(s)
Openness for change I2: “I am definitely a person open to change, but I am not a
person which is first with all the new gadgets”
I2: “Especially in the beginning phase of implementing you
need the younger operators”
I3: “I loved it when they came up with the plans for a new
production line”
I4: “I often get the reproach for being too fast in new
technology and innovation”
I6: “I am open for it, but I am also critical” I7: “Well I think I am quite open for it”
Mindset I2: “I believe that repetitive jobs do not fit within Dutch
organizations. It still is necessary, but you could automate it”
I3: “Everything which can be automated, should be automated” I4: “The boys in production, don’t want repetitive jobs, they
I5: “Before you had operators who performed actions based on
a couple of checks, but now you want to follow the process automatically and advise actions on basis of maths”
I6: “I think that in the future we should act more on predictive
maintenance. Because robots also do have sensors. With these sensors, we can gather data and see if internally parts are wearing out”
Result judgement I1: “Yes I see it as a success because it runs at the predefined
capacity”
I2: “Yes I definitely see it as a success, since we delivered a
line conform budget with high performance and quality”
I3: “Yes, I see a product which needs less handling and has
higher quality”
I4: “Yes, because all goals have been succeeded” I5: “A project is sustainable if you can also maintain it” I6: “At the end, you have a product submission warrant which
you sign when everything is delivered”
Supporting data on work design decisions
Code(s) Quote(s)
Decision making I1: “Yes, but it has to be systematic and well thought out”
I3: “That was actually the job of the project manager”
Employee
perspective/effect on employees
I1: “Yes, autonomy as less as possible”
I2: “Yeah that is a funny one, we did not really think about
that”
I2: “Physically it reduced, but they have to be more alert” I2: “We tried to make sure operators get clear signals now
when breakdowns occur”
I3: “Physically it stayed the same, cognitively the work
increased”
I3: “The workers get feedback from the machine. When
something happens, a light will go on”
happens it will shut down automatically”
I3: “These workers rotate between tasks because you cannot do
these 8 hours a day. Then you must rotate between tasks. In other tasks, they can get more autonomy”
I4: “Now it is mostly knowledge and fast estimating what the
machine is going to do while in the past it was more physical”
I5: “In the past, we saw that operators need to act, but
nowadays that is not necessary because the model says so”
I6: “At the old production lines, craftmanship was needed, but
with the new lines you need to understand the process more” End-user involvement I1: “The maintenance department and the operators thought
along in the design”
I1: “We tried to motivate them to report issues as much as
possible, so we could fix them along the way”
I2: “Yes, but in a way, it is not up to them to decide, in that
way it was obligated”
I2: “On a certain moment we just trained the order change for
a week”
I2: “We worked with representatives of each discipline that
represented his department”
I3: “From the start on we involved the personnel from the
machine and got the information”
I3: “Before everything was finished, every worker got his
chance to express himself”
I3: “If the workers want to do more than they are doing now,
they can follow internal training”
I4: “But we did involve the people themselves”
I4: “So, in essence, it was with the little things that the workers
could influence”
I5: “You have got to involve them in training them how such a
system works”
I5: “We have got an idea box, for when an operator has got an
I6: “I am very active in communication with the operators. I
spend much time at the shop floor”
I6: “With the change management part, you have got to involve
them
Appendix C -- Operationalization of the most important concepts
Concept Interview question
Smart manufacturing
technology implementation
2.7. Could you describe the smart manufacturing project/program, and the implemented technologies?
2.6. What was the main reason for the choice of the technologies adopted/implemented?
2.7. Which were/are the main objectives and motivations for the project?
Individual characteristics
of managers
3.1. How do you judge a good design and/or implementation of the (smart manufacturing) technology?
3.4. What is your general ‘vision’ or perspective on the role of humans in the factory?
5.2. What is your age and sex (male/female)?
5.4. Do you consider yourself as a person open to change, conservative or somewhere in the middle?
5.11 Do you think that your personality has an influence in the way you contribute in the decisions-making on work design
choices?
Work design behaviour
3.6. What have you done to take into account the perspective of employees, can you name examples?
3.8. Have you involved the user of the new technology in the development/implementation project of the new technology, how?
