Employee mobility intentions within a regional industry

Faculty of Engineering, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden Master of Science in Industrial Management and Engineering

June 2020

Employee mobility intentions within a

regional industry

A study on high-tech employees’ perceived opportunities and

preferences for mobility within a regional industry

ii This thesis is submitted to the Faculty of Engineering at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Industrial Management and Engineering. The thesis is equivalent to 20 weeks of full time studies.

The author declare that she is the sole author of this thesis and that she has not used any sources other than those listed in the bibliography and identified as references. She further declares that she has not submitted this thesis at any other institution to obtain a degree.

Contact Information: Author: Anna Wendel E-mail: anwh15@student.bth.se University advisor: Martin Svensson

Department of Industrial Economics

Faculty of Engineering

Blekinge Institute of Technology SE-371 79 Karlskrona, Sweden

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BSTRACT

Background

As digitalization and the Internet of Things (IoT) evolves in a rapid pace, the need for engineers, IT specialists and software developers introduce an increasing shortage of skilled workers. Managing the existing workforce is a challenge, increasing the interest in the antecedents and implications of their mobility activities, i.e. if, why and where they leave their organization. Employee mobility has been broadly viewed as a disadvantage for firms losing valuable human capital, increasing the implementation of retention strategies. On the other hand, an increasing amount of studies argue that mobility between organizations in related industries within a region is a source of knowledge spillovers. It is argued to enhance innovation and productivity, as well as regional economic growth by facilitating access to new external ideas and capabilities. There is a gap in the research on employees’ perception of opportunities and preferences that leads them to engage in mobility within the regional high-tech sector instead of other types of mobility.

Objectives

The purpose of this thesis is to increase the understanding of what factors influence the mobility of employees within the regional high-tech industry. Therefore, the perceived opportunities and preferences for different types of mobility are investigated. The aim is that the findings will facilitate the management of employee mobility and maximize the local organizations’ joint

human capital.

Method

A quantitative survey study is conducted, collecting data from two high-tech organizations operating within the same geographical region. The collected data includes individual, organizational and external factors, as well as the intentions for turnover and considerations for different types of mobility. SPSS is used to statistically test what factors are associated with high-tech employees’ opportunities and preferences for mobility within the regional industry.

Results

High-tech personnel perceive most opportunities for alternative employment within the high-tech industry in another region, while finding another job within the region is perceived more difficult. If employees considered leaving their current organization, most would prefer to take a job within the regional high-tech industry. Satisfaction with pay, training opportunities and supervisors in the current job have a reducing effect on the intention to leave the organization, while perception of having alternative employment opportunities have an increasing effect on both turnover intention and for considering mobility within the same industry. No significant model for predicting the preference for mobility within the same region was found in this study.

Conclusions

The majority of high-tech personnel already prefer mobility within the regional industry if they were leaving their current job, but there is a mismatch with the perceived opportunities for this type of mobility. Actions towards matching the opportunities with the preferences are expected to result in benefits for the region by increasing the local overall knowledge base, provide the organizations with more opportunities to attract highly skilled workers locally, and increase employees’ job satisfaction and performance through better job-matches.

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AMMANFATTNING

Bakgrund

På grund av den växande digitalisering och Internet of Things (IoT)- utvecklingen ökar behovet av ingenjörer, IT-specialister och mjukvaruutvecklare. Detta leder i sin tur till en ökad brist av kvalificerad arbetskraft på alla professionella nivåer. Hanteringen av den existerande arbetskraften är en utmaning som leder till ett ökat intresse för de anledningar och implikationer som följer deras mobilitet. Att anställda byter jobb ses som en nackdel då företag förlorar värdefull personal, vilket ökar implementationen av strategier för att behålla dessa inom organisationen. Å andra sidan finns det ett ökande antal studier som konstaterar att regional mobilitet mellan organisationer i relaterade branscher är en källa för kunskapsspridning. Det hävdas öka innovationen och produktiviteten överlag, samt öka regionens ekonomiska tillväxt genom att främja tillgången till externa idéer. Det finns en brist på forskning om de möjligheter och preferenser som leder fram till mobilitet inom en regional tekniksektor framför någon annan typ av mobilitet.

Syfte

Syftet med studien är att öka förståelsen för vilka faktorer som påverkar högkvalificerade arbetstagares mobilitet inom en regional teknikindustri. Därför kommer möjligheter och preferenser för olika typer av mobilitet att undersökas. Målet är att resultaten ska främja organisationers hantering av personalmobilitet och på så vis maximera det gemensamma

kompetenskapitalet mellan teknikföretag aktiva inom samma region. Metod

En kvantitativ enkätstudie är gjord för att samla in data från två high-tech företag aktiva inom samma region. Insamlade data inkluderar information om individuella, organisatoriska och externa faktorer, samt avsikter att sluta och benägenhet för att engagera sig i olika typer av mobilitet. SPSS används för att statistiskt testa vilka faktorer som är associerade med de möjligheter och preferenser som teknikpersonal upplever för mobilitet inom den regionala industrin.

Resultat

Teknikpersonal upplever generellt störst möjligheter för annan anställning inom tekniksektorn i en annan region, medan minst möjligheter upplevs finnas inom regionen. Om de skulle lämna sin organisation, föredrar de att ta ett annat jobb inom den regionala high-tech sektorn. Att vara nöjd med sin lön, träningsutveckling och sina handledare eller chefer har en minskande effekt på avsikten att sluta sitt jobb, medan uppfattningen om att ha andra anställningsmöjligheter har en ökande effekt på avsikten att sluta, och på övervägandet för mobilitet inom samma industri. Studien kunde inte hitta någon signifikant modell som beskriver vilka faktorer som påverkar preferensen för mobilitet inom samma region.

Slutsatser

Majoriteten av teknikpersonal föredrar redan mobilitet inom den regionala tekniksektorn om de skulle lämna sin nuvarande organisation, men bilden av möjligheterna för denna typ av mobilitet möter inte preferenserna. Att vidta åtgärder för att matcha möjligheterna med preferenserna förväntas resultera i regionala fördelar genom att öka den lokala kunskapsbasen, öka organisationernas möjligheter att attrahera kvalificerad arbetskraft lokalt, och som följd av bättre jobbmatchningar, fler arbetstagare som är mer nöjda med sina jobb och presterar bättre.

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CKNOWLEDGEMENTS

First and foremost, I would like to thank my supervisor from Blekinge Institute of Technology, Martin Svensson. Thank you for providing the guiding and support I needed during this thesis process. I would also like to thank the studied companies for their valuable insights and to all participants in the survey that allowed me to conduct this thesis work.

Finally, I would also like to thank all those close friends and family that have encouraged me through this whole time and been supporting in different ways.

Thank you! Anna Wendel

Blekinge Institute of Technology

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ONTENTS

2.1.1 Region and Industry employee mobility ... 5

DETERMINANTS OF EMPLOYEE MOBILITY ... 5

2.2.1 Individual characteristics ... 7

2.2.2 Organizational characteristics ... 9

2.2.3 External factors ... 12

2.2.4 Mobility intention ... 13

RESEARCH MODEL AND HYPOTHESES ... 13

3 METHOD ... 16

THE RESEARCH SETTING ... 16

RESEARCH DESIGN AND STRATEGY ... 16

DATA COLLECTION ... 17

3.3.1 Survey design ... 18

3.3.2 Measures ... 19

3.3.3 Sampling ... 23

3.3.4 Reliability and validity ... 24

DATA ANALYSIS ... 25 3.4.1 Reliability ... 26 3.4.2 Normality ... 27 3.4.3 Multicollinearity ... 28 3.4.4 Analysis ... 28 3.4.5 Model selection ... 29

4 RESULTS AND ANALYSIS ... 33

SURVEY PRESENTATION ... 33

PERCEIVED ALTERNATIVE EMPLOYMENT OPPORTUNITIES ... 34

4.2.1 Perceived alternative employment opportunities and organization ... 34

4.2.2 Perceived alternative employment opportunities and demographic factors ... 35

4.2.3 Perceived alternative employment opportunities and specific knowledge ... 36

4.2.4 Perceived alternative employment opportunities and organizational factors ... 38

MANAGING EMPLOYEE MOBILITY ... 38

4.3.1 Turnover intention ... 38

4.3.2 Consideration of engaging in different types of mobility... 41

JOB SATISFACTION AS A MEDIATOR ... 44

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5 DISCUSSION ... 46

RESEARCH QUESTIONS AND HYPOTHESES ... 46

RELIABILITY ... 48

MODEL SELECTION ... 49

THE SURVEY ... 50

RESULTS AND THEORETICAL IMPLICATIONS ... 51

5.5.1 Perception of alternative employment opportunities ... 51

5.5.2 Turnover intention ... 52

5.5.3 Consideration for mobility within the same industry and region ... 54

5.5.4 Organizational differences ... 56

6 CONCLUSION ... 57

CONCLUDING SUMMARY ... 57

IMPLICATIONS AND FUTURE WORK ... 58

7 REFERENCES ... 61

APPENDIX A: SURVEY RESULTS ... 65

APPENDIX B: RESULT TABLES ... 67

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IST OF FIGURES

Figure 1. Research model as described and proposed by Lambert and Hogan (2009) ... 6

Figure 2. Research model as described and proposed by Kim et al. (2005) ... 9

Figure 3. Suggested theoretical research model for employee mobility intentions ... 13

Figure 4. Proposed model for employees’ consideration for different mobility alternatives ... 14

Figure 5. The distribution of answers in the three categories for firm- and industry specific skills ... 26

Figure 6. Distribution of education level in the sample ... 33

Figure 7. Survey answers regarding perceived alternative employment opportunities ... 34

Figure 8. Turnover intention for full sample and per organization ... 38

Figure 9. Consideration of different mobility types ... 41

Figure 10. Consideration of different mobility types with adjusted categories ... 42

Figure 11. Job satisfaction as a mediator between pay and turnover intention ... 44

Figure 12. Job satisfaction as a mediator between firm-specific knowledge and turnover intention .... 45

Figure 13. Responses to measures of organization aspects ... 65

Figure 14. Responses to measures of organization aspects. Organization 1 ... 65

Figure 15. Responses to measures of organization aspects. Organization 2 ... 66

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IST OF TABLES

Table 1. Measurement aspects, their origin and reliability of scales ... 20

Table 2. Distribution of the survey in both organizations ... 24

Table 3. Reliability of scales measured by Cronbach Alpha ... 27

Table 4. Inter-correlations among the control variables ... 30

Table 5. Final regression models for turnover and mobility intentions ... 32

Table 6. Descriptive statistics for control variables ... 33

Table 7. Spearman’s correlation for perceived firm-specific knowledge and PAEO ... 36

Table 8. Spearman’s correlation for perceived industry-specific knowledge and PAEO ... 37

Table 9. Regression models for turnover intention with standardized ß- coefficients ... 39

Table 10. Independent samples test for regression factors, grouped by org ... 40

Table 11. Logistic regression for intra-industry mobility consideration ... 42

Table 12. Group statistics for PAEO based on the considered mobility type ... 44

Table 13. Summary of hypotheses related to RQ1 ... 46

Table 14. Summary of hypotheses related to RQ2 ... 46

Table 15. Independent Samples Test PAEO, grouped by org ... 67

Table 16. Independent Samples Test, grouped by education level ... 67

Table 17. One-way ANOVA for PAEO, grouped by age ... 68

Table 18. One-way ANOVA for PAEO grouped by tenure. Welch’s test in parentheses ... 68

Table 19. ANOVA for PAEO, grouped by years in industry. Welch's test in parentheses ... 69

Table 20. ANOVA for PAEO, grouped by years in region. Welch's test in parentheses ... 69

Table 21. Group statistics and independent samples tests for the perception of holding firm-specific knowledge, grouped by skills learned in org ... 70

Table 22. Group statistics and independent t-test for PAEO, grouped by skills learned in org ... 70

Table 23. Group statistics and independent t-tests for the perception of holding industry-specific knowledge, grouped by learned skills in industry ... 71

Table 24. Group statistics and independent t-tests for PAEO, grouped skills learned in industry ... 71

Table 25. Model summary for turnover intention ... 72

Table 26. Coefficients table for TI regression model with collinearity statistics ... 72

Table 27. Mann-Whitney U Test for TI, pay and job satisfaction, grouped by org ... 72

Table 28. Correlation matrix TI and IVs ... 73

Table 29. Independent samples test for organizational factors, grouped by org ... 73

Table 30. Unique correlations with TI when controlling for other IVs ... 74

Table 31. Reduced regression model: mobility consideration within industry ... 74

Table 32. Regression model: mobility consideration within region ... 74

Table 33. Independent samples t-test for years in region and org, grouped by mobility consideration within the region ... 75

Table 34. Independent samples t-test for years in region and org, grouped by mobility consideration in a different region ... 75

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BBREVIATIONS

Human resource management Independent variable

Organization

Perceived alternative employment opportunities Standard deviation

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EFINITIONS

Advanced, modern technology of high complexity, especially within the area of information and communication technology Industry with high patenting behavior

The act of both leaving a job and taking another job The intention to leave the current job

Mobility between different industries Mobility between different regions Mobility within an industry

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NTRODUCTION

The first chapter includes an overview of the research area and a motivation of the research problem. The objectives for the study and the research questions seeking answers are also presented.

Background

An organization’s human capital is all its employees and their competencies, skills, educations and experience (Aslam Memon et al. 2009). The human capital an organization holds is viewed as the most important factor for gaining competitive advantage and maximize performance. Developing, employing and retaining the right high-skilled human capital is crucial and a great challenge for organizations. Unlike technology or other physical resources, human capital is more difficult both to acquire, transfer or create (Yazdani 2008 cited in Aslam Memon et al. 2009).

As digitalisation and the Internet of Things (IoT) evolves in a rapid pace, along with the field of smart development, connectivity and communication, the need for highly skilled professionals such as engineers, IT specialists and software developers grow just as fast (Bonnaud et al. 2019). This introduces another challenge concerning the increasing shortage of skilled workers at all professional levels in the labor market. This has become an international concern where most countries report a shortage of skilled technical personnel and have been for years (Steenkamp et al. 2017). There are different theories on the reasons and solutions for this problem, such as attracting and retaining more engineering students until graduation by investing in motivational and supporting strategies and strengthen student communities. Another equally important aspect is how the existing human capital should be managed, increasing the interest in the determinants and implications of their mobility, i.e. if, why and where they are going. Turnover is defined as leaving the current organization, while mobility is both the act of leaving one organization and joining another (Wynen et al. 2013).

Employee mobility has been broadly viewed as a disadvantage for firms losing valuable human capital, affecting performance negatively and implicating replacement costs in the form of recruitment and training (Campbell et al. 2011). In technology-intensive industries, some studies find it crucial to retain employees to gain competitive advantage (Shoaib et al. 2009; Aguenza & Mat Som 2012), leading to a large focus on identifying factors that affect why employees leave, with the intention of constructing and implementing strategies to strengthen employee retention practices.

On the other hand, there are an increasing amount of studies stating that inter-firm mobility, i.e. employees moving between different external firms, is a source of knowledge spillovers. It is argued to enhance overall innovation and productivity, as well as regional economic growth by facilitating access to new external ideas and capabilities (Singh and Agrawal 2011; Saxenian 1996; Braunerhjelm et al. 2015). Early empirical studies showed the relationship between mobility and knowledge flows, e.g., Song et al. (2003) that investigated ideas from inventors that moved from another employer, and Rosenkopf and Almeida (2003) who found that the flow of knowledge was greater between firms with more employee mobility between them. This also leads to an increased interest on identifying the factors influencing employees’ intentions for engaging in different types of mobility.

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organization (Sammarra et al. 2013) or social relations and connections at other organizations (Kaiser et al. 2011).

A shortage of human capital holding relevant skills leads to an increased interest and research focus of the mobility of employees holding these. The benefits found from facilitating specific mobility leads to an interest in understanding these antecedents and implications, which opens up for questions regarding what factors enables and influences the preferences for mobility.

Problem Discussion

As described in the previous section, employee mobility is often viewed harmful for organizations, but an increasing amount of studies have instead focused on the benefits that can follow different types of mobility. Beyond the innovation- and productivity enhancing effects from managing mobility, by not doing so, the results are worse job-matches between employees and firms, as well as a less experienced workforce (Eliasson 2006). Boschma et al. (2014) claims that mobility itself does not enhance regional growth, but the mobility in related industries within a region leads to a more effective labor market caused by improved job matching. By eliminating obstacles and thereby facilitating intra-regional mobility, knowledge diffusion and increased spillovers from knowledge networks can be enhanced. Intra-industry mobility can be considered a source of costs for employers, but also a source of knowledge transfers. The following trade-off for firms is whether to locate far away from other firms within the same industry to reduce own spillover losses, or in close proximity to take advantage of the knowledge-transfer gains from inter-firm mobility. There exists evidence of both negative and positive effects, but for innovative firms operating within the same industry, the benefits of mobility are expected to cancel out the costs (Dindaroglu 2010).

There has been less studies conducted with the intention to manage mobility instead of reducing it, as well as investigating the factors surrounding employees’ ability to switch firms (Marx et al. 2009), i.e. the obstacles and opportunities for mobility. The general shortage of skilled human capital within high-tech industries, as well as the replacement costs for training and development provides motive for involved organizations to retain this among them. To do this, Meier & Hicklin (2008) means that employee mobility should be managed instead of minimized – suggesting that organizations need to understand what factors determines mobility. When discussing high technology, or high-tech, industries, organizations and employees in this study, emphasis lie on the use and knowledge of advanced, modern technology of high complexity (Steenhuis & De Bruijn 2006), especially within the area of information and communication technology.

There are different types of mobility, i.e. between sectors, within sectors, within regions, between firms and different combinations of them. Different types of mobility are influenced by different factors (Simonen et al. 2016), motivating further research of the factors influencing mobility especially within the high-tech sector within a region, even though separately or different combinations may been researched previously with other motives. To be able to experience the benefits from mobility within a regional industry, organizations must understand what factors affect this type of mobility in particular. The results will help organizations manage and facilitate employee mobility among them, and the following knowledge spillovers may have societal benefits by enhancing the regional growth (Singh & Agrawal 2011).

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that they may not be competing for the same customers, but they need the same type of human capital. There is evidence of the benefits from mobility between non-competing firms, when firms hire or lose employees to a potential cooperator, a client or provider, the network ties between them can be strengthened and their performance enhanced (Deepak et al. 2008). Based on the general problem with accessing human capital with the relevant technical competencies, in combination with the evident benefits of local mobility, an objective to understand which factors facilitate this mobility is evolving. Ultimately, although turnover itself is mostly considered a disadvantage for organizations in terms of the replacement costs it is implying, intra-regional mobility between organizations in related industries capitalizes on some of these costs by retaining highly skilled employees within the broader setting of the regional high-tech sector. The costs of losing employees to other industries or regions is viewed larger than within the regional sector, where the human capital keeps developing and increases the overall regional knowledge.

Objective

Based on the previous problem discussion, the purpose of this thesis is to increase the understanding of the factors influencing the regional mobility of employees within the high-tech industry. The aim is that the findings will facilitate the management of employee mobility and maximize the joint human capital among the organizations within the regional industry. To

do this, the following research questions will be answered:

1. What is the perception of high-tech workers’ opportunities for mobility within the regional industry?

2. Which factors are important for managing employee mobility, in particular, among organizations operating within the same regional high-tech industry?

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HEORETICAL BACKGROUND

This chapter aims to provide a theoretical background to the research area by presenting the general domain of employee mobility, followed by the more specific regional and industry perspectives. The section will also explain the investigated determinants for mobility and lastly, the proposed research model for explaining mobility intentions is presented.

Employee mobility

Employee mobility is concerned with job switching within or between organizations and can be voluntary (e.g. a promotion) or involuntary (e.g. lay-off, downward mobility) (Ng et al. 2010). There are empirical evidence suggesting that employee mobility is increasing and that life-long careers in the same organization is becoming less common, caused by both increasing lay-offs and an increased incentive for employees to acquire different work experiences. What this mobility means for organizational performance and innovation is divided. On one hand, employee mobility is considered a threat to organizations’ competitive advantage (Palomeras & Melero 2010), on the other hand, it is considered important for organizations in spreading knowledge and enhancing innovation, as well as for the growth of the broader societal knowledge capital (Ng et al. 2010).

Many organizations’ competitive advantage is based on their ability to innovate, which can be constrained by only using existing internal knowledge (Singh & Agrawal, 2011). By acquiring external resources, ideas from different sources can be combined to increase the innovative ability. This suggests that mobility provides organizational benefits, which some researchers has been claiming for years (e.g. Saxeian 1996; Arrow 1962 in Singh & Agrawal, 2011). Several similar studies have found mobility as a source of knowledge spill-overs and innovation (e.g. Corredoira & Rosenkopf 2010; Kaiser et al. 2011; Rosenkopf & Almeida 2003; Singh & Agrawal 2011; Song, Almeida, & Wu 2003).

Rosenkopf & Almeida (2003) investigated knowledge flows following both employee mobility and alliances between firms, predicting from theory that both would have the same effect. However, the analysis only found evidence of knowledge flows caused by inter-firm mobility, suggesting that the mobility of individual employees has a larger effect on the knowledge transfers between firms than organizational-level alliances.

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2.1.1 Region and Industry employee mobility

There are several different types of mobility, among them are mobility within regions and industries. Braunerhjelm et al. (2020) conducted a study showing that the mobility of knowledge workers has a strong positive effect on innovation. In addition, the results showed that the regional factor had a large effect on the innovation following mobility, meaning that knowledge workers moving within a region had a larger effect on firm innovativeness than those moving between regions. The results can be used by firms for decisions on where to locate their business and suggests that job-matching and knowledge spillovers can be enhanced by facilitating mobility within regions. The study also showed that the type of industry influences the effect of mobility where firms operating in innovative industries, i.e. with high patenting behavior, benefits the most, suggesting that facilitating flexibility in the labor market should be considered a strategy for higher growth.

Even though mobility has been viewed as a driver of regional development due to the following knowledge spillovers and increased overall learning, Boschma et al. (2014) argued that mobility does not enhance regional growth per se. In contrast, mobility between firms in a region might constrain the development of human capital due to increased employee poaching between firms which reduces the willingness to invest in human capital development (Eliasson 2006). However, mobility between skill-related industries is shown to increase regional growth due to increased matching of skills, meaning that regional growth is higher when mobility between technologically related firms is facilitated over other mobility. Saxeian (1996) strongly argues that the overall regional effect on performance from high-tech employee mobility is innovation enhancing, by exploring Silicon Valley as an example. She showed that the close proximity to local firms facilitated mobility, allowed for individuals’ paths to cross several times, relationships and networks to be built and in turn, larger knowledge spread and enhanced innovation. Instead of investing in retention strategies, firms in Silicon Valley general accepted the occupational mobility, gave leavers their blessings and welcomed them back in the future with their new capabilities. This attitude and non-retaining setting are argued to be the main reasons for the large regional innovational success. The same conclusions on the relationship between innovation and regional mobility are also found in later studies (Marx et al. 2009, Kaiser et al. 2015).

Wynen et al. (2013) investigated the determinants of interorganizational mobility within the U.S federal government, i.e. what made employees leave their current organization and what made them take another job within the government instead of outside. The idea was that facilitating mobility among them would build competencies and infuse new ideas into the organization, and that the opportunities for employees to improve their skills and careers by moving within the public service would retain them from leaving to another sector. This indicates that there exist benefits for both organizations and employees from facilitating mobility among organizations in a certain sector and reducing the outside losses of human capital.

Determinants of employee mobility

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that says that employees choose to leave or stay depending on factors influencing both their desirability and ease of movement. In agreement, Ng et al. (2007) claims that employees assess both their preferences and perceived opportunities when making a mobility decision. Since preferences and opportunities to move varies between different individuals and organizations, there may be many different combinations leading to an intention for mobility. In previous studies, preferences for mobility, i.e. the desire to start searching for new opportunities, is often found to be related to dissatisfaction with the job or organization (e.g. Delfgaauw 2007b; Ng et al. 2007).

Several studies have recognized that it is not enough to only investigate what factors influence whether an employee leaves, since these factors might vary depending on the employee’s destination after leaving (Fields et al. 2011; Kirschenbaum & Weisberg 2002). Fields et al. (2011) argue that only considering the decision to leave might be the reason why the results from previous studies varies so much regarding the predictors of mobility. Considering this, Wynen et al. (2013) investigated mobility as two steps, both the intention to leave the current employer and then where employees intend to go after leaving.

There are many different perspectives on employee mobility, and many different types of models and frameworks trying to explain it. Several previous studies, as well as a meta-analysis by Griffeth et al. (2000), show that the variables used to predict employee mobility can be broadly grouped into individual, organizational and external characteristics (Moynihan & Landuyt 2008; Wynen et al. 2013; Moynihan & Pandey 2007; Lambert & Hogan 2009; Fields et al. 2011; Kirschenbaum & Weisberg 2002). Some studies use the demographics and/or external characteristics as control variables in their analyses. The organizational aspects are sometimes divided into job characteristics and organizational characteristics during the analysis, distinguishing between aspects of the job or tasks performed, and the work context such as workplace culture (e.g. Griffeth et al. 2000; Moynihan & Landuyt 2008; Lambert & Hogan 2009). Figure 1 below presents a research model proposed by Lambert and Hogan (2009) including the demographic, work environment and external factors, with job satisfaction as a mediator, influencing turnover intention and in turn, turnover.

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A description of all possible determinants of employee mobility are beyond the scope of this study. Instead, a sample of individual, organizational and external characteristics are selected from previous research based on their relevance for employee turnover intention, in combination with their relevance for the decision to move within or outside the regional industry. This means that work environment, demographic factors and alternative employment opportunities are included in the study, complemented with factors especially relevant for mobility within the regional industry. These factors are more comprehensively described in the following sub-sections.

2.2.1 Individual characteristics

Individual characteristics and preferences influence employees’ mobility choices, meaning that the availability of employment options is a necessary but not a sufficient condition to motivate employees to accept a job mobility opportunity (Ng et al. 2007).

Fields et al. (2011) showed that individual aspects such as age, tenure and family situation influenced mobility differently depending on if the new job is different or similar as the current. Taking a similar job with a different employer is caused by not being satisfied with the conditions of the organization, but with the type of work. This type of mobility is more likely for younger employees and those with shorter tenure because they have less relation to the organization. One risk following inter-organizational mobility is that the former employer might not allow the employee back at a later point in time. Taking a different type of job with a different employer indicates that the employee might be unsatisfied with both the job and the organization. The risks with this kind of mobility is even greater, thus less likely for employees with family responsibilities and for employees with longer tenures. This suggests that demographic factors have different effects on different types of mobility.

Based on previous studies on demographic factors that influences employee mobility, the following are investigated in this study:

Age

Several studies support the fact that increasing age is expected to generally reduce employee mobility intention (e.g. Wynen et al. 2013; Moynihan & Landuyt 2008; Kirschenbaum & Weisberg 2002). The idea is, to some extent, that with increasing age, more people have family responsibilities and stability becomes a more important factor. The increasing reluctance for change as age increases also affect the intention to, in case of turnover, take a job within the current geographical region (Moynihan & Landuyt 2008). Also, over an individual’s lifetime, as age increases, many find a job-match that they are satisfied with, which decreases their intention to search for and take another job (Lambert et al. 2001). The mean age for employees in technical occupations such as IT-specialists or software- and system developers, was around 40 years in 2018 (SCB 2020a).

Gender

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Education

Higher level of education is expected to increase mobility intention, Moynihan and Landuyt (2008) found that employees with a college degree or higher were more likely to show intention of leaving the organization than those with lower educational level. The idea is that employees with higher education have gathered more human capital and have more opportunities to find other employment, i.e. perceive more opportunities for alternative employment. Fields et al. (2011) share the same view and suggests that higher education might make the employee more attractive to external organizations.

Years working in an organization

The intention to engage in any kind of external mobility is expected to reduce as number of years working at an organization increases (Moynihan Landuyt 2008; Wynen et al. 2013). Possible reasons could be that feeling of loyalty increases, that a good job match was found or caused by a combination of increased age and family responsibilities. However, as number of years at an organization increases, so does the number of years working in a particular industry, which might influence the mobility decision in case of turnover (Neal 1995).

Years working in an industry

Wynen et al. (2013) conducted a study showing that an increasing number of years working for the federal government had a negative effect on the intention to leave the federal government but a positive effect on the intention to leave the current organization. The possible explanation presented was that increased time in a setting leads to an increased need to switch organization in order to grow and improve skills.

This is not necessarily applicable on an industry but according to Neal (1995), how long an employee has worked in the given industry negatively affects the level of pay resulting from switching to another industry, which is assumed to affect the employee’s mobility choice by increasing the intention to keep working in the same industry.

Years located in a geographic region

How many years an employee has spent in the same geographical region influence his or her intention to take a new job outside the region (Moynihan & Landuyt 2008). Increasing number of years in the same region is expected to reduce the intention to take a new job outside the geographical region. The idea is that increased time in a region, in combination with increased age, results in higher valuation of stability and grows a larger reluctance for change among employees.

Perceived firm-specific human capital

Another aspect of the determinants for mobility are the skills and capabilities held by the employee. An employee’s decision to switch jobs is affected by the general or specific human capital they have acquired (Wright et al. 2018). The perception is that holding a lot of firm-specific skills can retain employees by having their skills more valued at the current firm, which reduces the opportunity to gain economic advantages through inter-firm mobility (Sammarra et al. 2013). Because of this, organizations may try to manage mobility among knowledge workers by managing the general or firm-specific development they invest in. In contrast, rapid employee turnover might lead employers to reduce the development and training opportunities available for the workforce to minimize costs (Eliasson 2006).

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and form a view with what they know, believing that other firms operate in the same way. Also, perceiving skills as firm-specific might lead employees to feeling stuck and less satisfied at work.

Perceived industry-specific human capital

Neal (1995) considered the effect of skills specific to an industry rather than a firm, on employee mobility. The results suggest that the amount of industry-specific experience, possibly related to the number of years working in the industry, negatively affects the pay level from switching to another industry, which is assumed to affect a mobility choice. Knowledge workers with occupation-specific knowledge are more likely to engage in inter-firm mobility when they are not perceived to be tied to the specific organization, i.e. when they do not hold firm-specific knowledge as well (Sammarra et al. 2013).

2.2.2 Organizational characteristics

Even though factors often overlap or fit into several categories, one way to divide organizational factors is into job characteristics, human resource management (HRM) practices and work environment, where the two latter are factors concerned with the organizational setting (Moynihan & Landuyt 2008; Kim 2005). Figure 2 below shows such a division in a research model proposed by Kim (2005).

Figure 2. Research model as described and proposed by Kim et al. (2005)

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Experiences from a current job influences an employee’s expectation on other jobs (Delfgaauw 2007a). Since jobs at organizations within the same industry tend to share characteristics, poor experiences with some aspects of an employee’s current job may not only influence the decision to stay or leave a job, but also whether to remain in the same industry.

A sample of organizational factors were included in this study based on their relevance for employee mobility shown in previous studies to increase the accuracy of the research model. Most importantly, the factors that showed importance for the particular decision to stay or leave the regional industry were included to be able to answer the research questions. The following factors are considered and used in this study:

Job characteristics

Autonomy

Autonomy in the workplace is concerned with employees’ level of independence to carry out their work, which could be regarding schedules, how certain tasks should be done or involvement in decision-making (McKnight et al. 2009; Lambert et al. 2001). Employees that experience greater autonomy are expected to be more satisfied with their job and have less intention to leave (Fields et al. 2011; Moynihan & Landuyt 2008; Kim 2005). In addition, greater freedom and autonomy to make decisions and influence the own work enhances an intrapreneurship-friendly organizational culture (Menzel et al. 2007). A broad definition of intrapreneurship is that it is entrepreneurial activities conducted within an organization. Dissatisfaction with autonomy might lead employees searching for another organization with greater support for intrapreneurial activities or lead to an increased intention to leave for self-employment since it offers more freedom and independence (Millán et al. 2013).

Workload

Greater workload is associated with stress and is positively related to turnover (Moynihan & Landuyt 2008), assuming that employees that experience excessive workload will have greater intent to leave.

Whether stress and workload are factors related to the nature of the current job or organization is divided in previous studies. Fields et al. (2011) considers stress as a factor related to the nature of the current job which indicates that dissatisfaction with workload would lead employees to search for a different type of job, that may be more likely to find in a different industry. Similarly, Delfgaauw (2007a) also found that employees that decide to leave their current organization due to the work pressure are more likely to leave for a different industry that is more likely to have different conditions. However, employees might still search for the same type of job at another organization if they believe that the workload and following stress-level will be a better match (Fields et al. 2011). Wynen et al. (2013) considers that the amount of workload may be related to the current organization, thus only affecting the decision to leave the current organization, and not where to go to.

Job satisfaction

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their job are less likely to express intention to leave (McKnight et al. 2009). However, Fields et al. (2011) found that employees that change jobs perceive more positive outcomes than those that does not, including higher job satisfaction. According to Millán et al. (2013) there’s a significant relationship between job satisfaction and self-employment, and employees with lower job satisfaction at the current organization is more likely to express intention for self-employment (Brockhaus 1980).

HRM policies

HRM policies are often related to job satisfaction and in turn, employee mobility. Pay, career- and training opportunities are some policies shown to influence satisfaction and turnover intentions (Wynen et al. 2013; Moynihan & Landuyt 2008).

Pay

The perception that an increased pay could be gained at another organization may lead employees to seek other jobs (Fields et al. 2011). However, a higher pay level or satisfaction with the pay at their current job is assumed to make employees’ less likely to search for another job or to get better offers from other employers, i.e. less alternative employment opportunities with equally good benefits. In turn, employees are expected to have lower turnover intention (Wynen et al. 2013; Fields et al. 2011). In addition, Fields et al. (2011) found that employees that believed that “the pay is good” had higher overall job satisfaction, and employees that perceive their salary as fair have lower turnover intent (Moynihan & Landuyt 2008). In case employees leave their current organization due to unsatisfaction with the pay, it is suggested that they are more likely to take a job in another industry where the pay-level is more likely to be different (Delfgaauw 2007a).

Career opportunities

Perceived opportunities for career advancements within the current organization is assumed to reduce turnover intention caused by the increased feeling of recognition and career improvement (Moynihan & Landuyt 2008; Wynen et al. 2013). Generally, it is important for employees to feel as their career goals are receiving support, and advancement opportunities are strongly associated with job satisfaction (Moynihan & Pandey 2007). Lack of such opportunities has a positive effect on turnover (Kim 2005) and Moynihan and Landuyt (2008) found that employees that had received a promotion based on merit in the past 2 years where less likely to have intention to leave the organization.

Training

12 Work environment

Employees’ work environment has a large impact on their attitude towards their job (Lambert & Hogan 2009). The work environment reflects how organizational culture interacts with individual employee perspectives (Moynihan & Landuyt 2008). This type of factors are strongly associated with the current organization, indicating that work environment factors will mainly influence the overall turnover intention, and not where to go (Wynen et al. 2013). Delfgaauw (2007a) argues that aspects that are primarily influenced by the organization rather than the industry will decrease the intention to leave for a job in a different industry.

Supervisory practices

Support from supervisors is expected to have a large impact on employees’ job satisfaction and in turn, their decision to stay or leave the organization (Wynen et al. 2013). The perception of having supervisors that show support and concern might influence employees’ well-being and impact their satisfaction and commitment to the organization, thus reducing turnover intention (Fields et al. 2011). Listening to and encouraging employees, as well as supporting risk-taking and tolerate failure is important for enhancing innovative behavior of potential intrapreneurs (Alpkan et al. 2010; Menzel et al. 2007). Dissatisfaction with supervisors might lead employees searching for another organization with greater support for intrapreneurial activities or lead to an increased intention to leave for self-employment (Brockhaus 1980).

Voice

Voice can be described as the degree to which employees perceive that they can contribute to organizational decision making and express their opinions even if they contradict the dominant view in the organization (Lambert & Hogan 2009; Moynihan & Landuyt 2008). Employees that are able to participate in decision-making processes gets a better understanding of project objectives and more opportunities to influence their work environments (Kim 2005; Moynihan & Pandey 2007). Experiencing having a voice in organizational decisions generally increases employees’ job satisfaction and thus indirectly reduces turnover intention (Lambert & Hogan 2009; Moynihan & Landuyt 2008). In addition, to be able to express ideas or thoughts that contradicts the majority is important to support intrapreneurship in the organization (Menzel et al. 2007).

2.2.3 External factors

There exist several external factors that may influence employee mobility decisions such as economic conditions, industry growth etc. (Ng et al. 2007). Here, only one external factor is included and further studied:

Perceived alternative employment opportunities (PAEO)

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employment opportunities could be influenced by the firm- or industry-specific skills held, due to the perception that their skills are more valued at the current firm or industry (Sammarra et al. 2013) and that these specific skills limits their perceived external opportunities for employment (Raffiee & Coff 2016).

2.2.4 Mobility intention

An employee’s intention to leave the current organization is not independent of where he or she will go to (Wynen et al. 2013), meaning that both the intention to leave and where to is interesting to study. Turnover intention rather than actual employee turnover is primarily used in previous studies (e.g. Samad 2006; Hassan 2014; Wynen et al. 2013; Moynihan & Landuyt 2008; Valcour & Tolbert 2003). Employees decide to switch job in advance, so according to attitude-behavior theory (Fishbein & Ajzen 1975 in Samad 2006 and Lambert et al. 2001), the intention to leave is a close predictor of leaving. A significant relationship between turnover intention and actual turnover has been found by Lambert et al. (2001). Also, mobility intention can be easier to measure than the actual mobility because employees that left might be hard to reach (Lambert & Hogan 2009).

Research model and hypotheses

The research model in Figure 3 below proposes that mobility intention is influenced by individual, organizational and external characteristics. The model presents job satisfaction as a mediating variable, suggesting that it explains some of the relations between the individual and organizational factors, and turnover intention.

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Individual and organizational factors influence employees’ job satisfaction, which in turn affects the decision to leave the current organization, causing the factors to have an indirect effect on turnover intention. However, there is also direct effects between the individual and organizational factors and different mobility intentions. Which specific or general skills held by the employee is expected to influence the perceived alternative employment alternatives that, in turn, have a direct effect on the turnover intention, as well as where to go (e.g. perceived ease of alternative employment within the regional industry increases employees’ consideration for this type of mobility). Figure 4 presents the different types of mobility for which considerations will be investigated in this study.

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From the explanatory variables of mobility leading to the research model in Figure 3 and 4, the hypotheses below are tested in this study. By testing the hypotheses regarding employees’ perceived opportunities and what factors influences their intention to move within the regional high-tech industry, the aim is to answer the research questions.

RQ1. What is the perception of high-tech employees’ opportunities for mobility within the regional industry?

H1. Higher education level is associated with higher levels of perceived alternative employment

opportunities

H2. The perception of holding specific knowledge is associated with lower levels of perceived

alternative employment opportunities.

RQ2. Which factors are important for managing employee mobility, particularly among organizations operating within the same regional high-tech industry?

H3. The perception of holding specific knowledge decreases employees’ intentions for mobility H4. The perception of having alternative employment opportunities increase employees’

intention for mobility

H5. Increased satisfaction with aspects of the current job decreases employees’ intention for

mobility

H6. Job satisfaction mediates the relation between individual and organizational characteristics,

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3 M

ETHOD

This chapter describes and explains how the study has been planned and conducted. The research design and the methods used for the data collection and analyses are presented and motivated.

The research setting

For confidentiality reasons, the identity of the studied companies is not disclosed in this study, leaving them mentioned as Organization 1 and Organization 2 throughout the report. The studied organizations are operating in a competitive setting regarding their human capital. The organizations operate in a technologically advanced and innovative industry in which the human capital is a valuable and necessary asset in order to be in the front edge, delivering new, innovative products and services to their customers. The firms have a customer-supplier relationship but uses the same type of human capital where they both benefit from employing employees that left the other organization. By having an open policy regarding mobility between organizations within the region and industry, they might benefit from returning employees that now have obtained valuable knowledge they would not have if they did not engage in mobility.

The region (Blekinge county) in which they operate holds a few large actors competing for the same competence. The five most common occupations in the county includes service mechanics and carpenters for men, and assistant nurses and preschool teachers for women (SCB 2020a), indicating a limited access to technically skilled workers.

During this thesis work (January-June 2020), there is an international situation with the COVID-19 pandemic. This weakens the economy, leading to a large number of dismissals, bankruptcies and worries throughout the country and the world as the stock markets fell over 30 percent in February-March (SCB 2020c). Although the high-tech industry may not be the worst affected sector, it is suspected to make employees more risk averse and affect their intention to engage in mobility.

Research Design and Strategy

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The research strategy is the more detailed plan for how to answer the research questions. The choice of research strategy is depending on the types of research questions, the study’s objective and time- and resource limitations (Saunders et al. 2019). The design is a quantitative study, which opens up for several different research strategies to be used, e.g. the case study strategy. A case study could provide valuable empirical insights from a real business context, but often requires a mixed- or multi-method research design, using a combination of different strategies, e.g. records and documentation, interviews, observation and questionnaires, which is very time consuming. This study uses two different organizations that opens up for a multiple case study, but that also opens up for more challenges with replication because it is not expected that these two organizations show the same results and using a single case often represents an extreme or unique case which is not the aim of this study. Therefore, neither a single or a multiple case study aligns particularly well with the objective or research questions of this study. Other quantitative research strategies are the survey- or experiment research strategy. An experiment research strategy often has some expected outcome and predictive research questions that are going to be tested. The research questions in this study are instead openly defined to learn more about what factors influence employees’ perceptions and intentions, where the use of a questionnaire allows for large quantitative and standardized data to be collected on peoples’ thoughts and opinions without identifying the respondent (Blomkvist & Hallin 2015). Other alternatives that were considered within the survey strategy were structured observations or interviews but was considered too time consuming and also a threat against the anonymity of the study. The survey study also allows for statistical analyses to be conducted on the collected data to find relationships between the factors and answer the research questions which made it a suitable strategy for this study (Saunders et al. 2019).

Data Collection

The data collection is conducted using a survey at two organizations to retrieve the data needed to answer the research questions, e.g. asking employees about their experiences regarding their job and organization, their intention to switch jobs, and about their preference regarding region or industry in these decisions.

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Material such as research papers and articles were gathered from Google Scholar and Summon@BTH for the theoretical background on employee mobility and evaluated according to the following

1. the relevance in terms of objective and context in relation to the study’s research questions

2. the value in terms of methodology, theory and reasoning, as well as the quality of the source and possible bias.

From this literature review, several relevant predictors of mobility intention were selected, which are described and motivated in the previous section 2. Theoretical Background, as well as in the later section 3.3.2 Measures.

3.3.1 Survey design

As described above, the study uses a survey strategy for the primary data collection which, when using a questionnaire, allows for standardized data collection and following statistical analyses. This is important for the objectives of the study since the aim is to, through the examination of relationships between different factors, draw conclusions representative for the target population. Since previous research has been used to identify relevant factors to analyze in relation to employee mobility, standardized questions can be asked, which provides further rationale to the use of a questionnaire (Saunders et al. 2019). Due to time limitations, the study collects cross-sectional data, i.e. describes the relationship between the chosen factors and mobility intention at a particular point in time (Hair et al. 2014). This limits the opportunity to follow how the relationships and intention for mobility evolves over time. However, the use of cross-sectional data in this study is appropriate given that the respondents that show turnover intention at one point might have left the organization the next time the data is collected and, above all, if the respondents was followed over time, they would need to be identified and the anonymity would be threatened (Saunders et al. 2019). This would in turn threaten the reliability of the study since the respondents might not answer truthfully about their intentions to leave their organization if they did not believe their identity was confidential.

The survey was created with Microsoft Forms (Microsoft 2020). Other alternatives were considered that provided several additional features, but they also logged the respondents IP addresses. Since it was crucial that the respondents could not be tracked, these were not used. In addition, licenses to use MS Forms was provided through BTH and was experienced by the author.

The questionnaire is provided with clear instructions to reduce misunderstandings. It was also pilot tested with a few students from different educational programs and reviewed by supervisors at the organization and at BTH to refine the questions and to increase the chance of respondents interpreting the questions as intended. The pilot test is used to find out how clear the instructions and questions were and any additional comments about the questions or the layout (Höst et al. 2006). Due of the nature of the research, the respondents will be asked questions regarding their current position, employer and consideration of switching jobs. This kind of information can be sensitive for some to answer, especially if they consider the confidentiality to be uncertain – this might lead to biased or inaccurate responses. To reduce this risk, self-completed questionnaires are used where the respondents completes it without involvement from the researcher and the respondents are ensured that their answers will only be used in a summarized way (Saunders et al. 2019).

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questionnaire but the web-based allows for a larger distribution sample during the time period for the thesis.

This study has adopted and adapted questions from previous studies, which is more efficient than creating new questions and allows reliability to be assessed (Saunders 2019). Using existing questions also allows for comparison with previous research. However, other research studies have other purposes than this one, indicating the importance to assess each question to ensure that it is in line with this study’s objectives. Most of the questions in the survey are closed-ended (Saunders et al. 2019). Only two open-ended questions are added as follow up questions to learn what knowledge or skills the respondent have learnt in their organization or industry. These questions were optional to answer due to the possibility that the respondents may not perceive to have learnt anything from the organization or industry. Open-ended questions are more time-consuming to analyse, which is why only two are included. For those, the importance of allowing the respondents to elaborate on their answers are considered to outweigh the additional analysing time.

The order and flow of the questions are considered when constructing the questionnaire to increase the quality and number of full responses (Blomkvist & Hallin 2015). The questions were grouped in an order that make sense to the respondents, but placed in randomized order, i.e. not all scale items after one another, in order to increase reliability when the respondents must consider their answer carefully and are not able to predict what the next answer “should” be. Since Microsoft Forms do not offer such randomization feature, it was done manually and then a mapping was done between the grouped questions and the corresponding question in the questionnaire. The invitation was sent via e-mail with a short introduction of the researcher and what the answers would be used for. To maximize the response rate (Höst et al. 2006), the respondents are welcomed with an introduction letter that more thoroughly explains the purpose of the study and its anonymous and voluntary nature, as well as ensuring that the answers only will be used for this thesis work alone.

3.3.2 Measures

The survey was designed to collect both opinion-, behavioural- and attribute variables from the respondents (Saunders et al. 2019). This is because data on employee opinions on job characteristics, human resource management practices, work environment, alternative employment opportunities will be used to find associating or predicting relationships with mobility considerations and their behavioural intentions to act on these considerations. The demographical, or attribute, data can then be used to assess how representative the sample is and to find potential differences depending on factors such as gender, age and education level (Blomkvist & Hallin 2015).

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for further refinement of employees’ opinions, but because of the majority of previous studies using the five-point scale and the assessment that a scale with more points might make it difficult to distinguish between the different points, it was decided that a five-point scale was more appropriate for this study (Saunders et al. 2019).

To measure the different concepts, or constructs, several rating questions are combined into scales where the attributes are inferred by different indicating questions (Saunders et al. 2019). In this study, some scales are adopted as a whole, others are adapted where only a subset

is included in the questionnaire due to the difference in objectives between the different studies. For some aspects, questions from studies using a single-question measure is added to the construct because of its better fit to the study objective, e.g. workload and pay used two scale items validated in one study and one single-question measure from another study, and turnover intention used two different scales which both shared the scale item “I will probably look for a different job in the next year”, see the Adapted from column in Table 1 below. The Reliability

column in the table shows the Cronbach alpha for the full scales measured by previous studies and describes in parentheses if any scale items were excluded from this study. Modifying the scales may alter their reliability, making it even more important to measure the reliability of the adapted scales before analysing the data (Pallant 2007).

Table 1. Measurement aspects, their origin and reliability of scales

Constructs/

Aspects Items/Questions Adapted from Reliability Cronbach

alpha

Demographics

Age Kim (2005)

Gender

Education level SCB (2019a)

In what organization are you employed?

How long have you been working at

your current organization? Kim (2005) How long have you been working in

the high-tech industry?

How long have you been working in the geographical region (“Blekinge län”)? SCB (2019b); Moynihan and Landuyt (2008) Perceived firm-specific skills

How useful do you think your

knowledge or skills which you learned from this job would be for other jobs if you move to another workplace in the

same industry and occupation? Raffiee and Coff (2016)

Perceived industry-specific skills

How useful do you think your

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Construct Items/Questions Adapted from Reliability

Job

characteristics

Autonomy

There is a lot of autonomy in my job, i.e. my job permits me to decide on my own how to go about doing the work

Hackman and Oldman (1974) 0,66 Hackman & Oldman (1974) 0,81 Nesheim et al. 2017 This job gives me considerable

opportunity for independence and freedom to how I do the work This job denies me any chance to use my personal initiative or judgement in carrying out the work

Workload

My workload is reasonable Wynen et al. (2013); Moynihan and Landuyt (2008)

Single measure I feel that the number of requests,

problems or complaints I deal with is more than expected

Kirmeyer and Dougherty (1988) adapted from Caplan et al. (1975) and Kahn et al (1964) in Moore (2000) 0,8(-2) I feel that the amount of work I do

interferes with how well it is done

Job Satisfaction

All in all, I am satisfied with my job _{Hackman and}

Oldman (1974) 0,76 (-2) I am generally satisfied with the kind

of work I do in this job

HRM practices

Pay

I think my level of pay is fair _{Hackman and} Oldman (1974) in

Kim (2005) 0,89 (-1) I am generally satisfied with the

amount of pay and fringe benefits I receive

Salaries in this organization are competitive with similar jobs in the region

Moynihan and

Landuyt (2008) _measure Single Career opportunities

This organization provides me with a fair opportunity for advancement or

promotion Balfoue and _{Wechsler (1996) in}

Kim (2005) 0,83

I can see opportunities for

advancement in this organization Training

In this organization, I have received the

training necessary to stay up to date _{Kim (2005)}

0,89 This organization takes an interest in

my career development and advancement

My development needs are being

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Construct Items/Questions Adapted from Reliability

Organization characteristics

Supervisory practices

My supervisor(s) is very concerned about the welfare of subordinates

Fields et al. (2011) _(this 0,81 subset) My supervisor(s) is competent in doing

their job

My supervisor(s) goes out of their way to praise good work

Voice

The work atmosphere in my

organization encourages honest and

open communication Moynihan and

Landuyt (2008) 0,53 (-1) My ideas and opinions count at work

People who challenge the status quo are valued at this organization

Perceived alternative employment opportunities

How easy would it be for you to find another job with another employer that is as good or better than your current job?

Mueller, Boyer, Price and Iverson (1994) in Lambert and Hogan (2009)

Turnover intention

I often think about quitting this job Porter, Crampton and Smith (1976) in Kim (2005) 0,78 Kim (2005) 0,92 Moore (2000) (-2) I will probably look for a different job

in the next year Jackson et al. (1987) and Mitchel (1981) in Moore (2000);

Porter, Crampton and Smith (1976) in Kim (2005)

I will still be with this company five

years from now Jackson et al. (1987) and Mitchel (1981) in Moore (2000)

Mobility consideration

If I considered leaving my organization

within the next year, it would be to: Wynen et al. (2013)

Individual