Relating Voluntary Turnover with Job Characteristics, Satisfaction and Exhaustion

Relating Voluntary Turnover with Job Characteristics, Satisfaction and Work Exhaustion — An Initial Study with Brazilian Developers

Tiago Massoni Nilton Ginani UFCGCampina GrandeBrazil massoni@dsc.ufcg.edu.br nilton.ginani@ccc.ufcg.edu.br Wallison Silva Zeus Barros UFCGCampina GrandeBrazil wallison.silva@copin.ufcg.edu.br zeus.barros@splab.ufcg.edu.br  and  Georgia Moura UniNassauCampina GrandeBrazil georgiaio@hotmail.com
Abstract.

High rates of turnover among software developers remain, involving additional costs of hiring and training. Voluntary turnover may be due to workplace issues or personal career decisions, but it might as well relate to Job Characteristics, or even Job Satisfaction and Work Exhaustion. This paper reports on an initial study which quantitatively measured those constructs among 78 software developers working in Brazil who left their jobs voluntarily. For this, we adapted well-known survey instruments, namely the JDS from Hackman and Oldham’s Job Characteristics Model, and Maslach et al.’s’ Burnout Measurement. In average, developers demonstrated low to moderate autonomy (, on a 1–7 scale) and satisfaction (), in addition to moderate exhaustion () before leaving their jobs, while experiencing high task significance (). Also, testers reported significantly lower job satisfaction than programmers. These results allow us to raise interesting hypotheses to be addressed by future studies.

Turnover, JCT, Job Satisfaction, Work Exhaustion
journalyear: 2019copyright: acmcopyrightconference: ; May 2019; Torontoprice: 15.00doi: http://dx.doi.org/xx.xxxx/xxxxxxx.xxxxxxxisbn: 978-1-xxxx-xxxx-9/19/01ccs: Software and its engineering Software development process management

1. Introduction

As the largest IT market in Latin America — also World’s 9th largest — Brazil’s software industry encompasses around 4,800 companies producing software or providing software-related services, generating, to the local economy, more than US$8.6 billion (as of 2016 (ABES, 2017)). In this scenario, high staff turnover becomes critical (PayScale, 2017). Software companies face low retention, generating significant costs, due to the time to find other professionals and training new hires (Lingo Live, 2016; Moore, 2000; McKnight et al., 2009).

Similarly to many other areas, software developers may voluntarily leave their current job for many reasons; it is commonly believed the primary motives are better career options or financial improvement, as well as adverse workplace conditions (McKnight et al., 2009). However, job characteristics (and the developer’s emotional response to them) might influence developers in deciding to leave for another company; some of these characteristics may be related to job dissatisfaction and burnout as well (Jason Thatcher and Yongmei Liu and Lee Stepina and Joseph Goodman and Darren Treadway, 2006; Gregorio Robles and Jesus Gonzalez-Barahona, 2006). Most studies, such as the one carried out by McKnight et al. (McKnight et al., 2009), relate job characteristics or exhaustion to turnover intention, not actual voluntary turnover.

In this paper, we describe an initial study with 78 Brazilian software developers who left their last job voluntarily, aiming to explore the relationship between actual turnover and Job Characteristics, Satisfaction and Work Exhaustion. The survey is primarily based on the Job Characteristics Theory (JCT) (Hackman and Oldham, 1976) whose primary instrument, the Job Diagnostic Survey (JDS), is adapted to collect data about the respondent’s last job, assessing five job core characteristics before they moved to the next (their current) job. Besides, the survey includes items on Job Satisfaction (Hackman and Oldham, 1980) and Work Exhaustion (Job Burnout) (Maslach and Jackson, 1981). Through mailing lists for software developers, we received 102 answers, from which a sample of 78 left their last job voluntarily.

Developers reported moderate Work Exhaustion and Job Satisfaction. These results agree with research evidence that exhaustion and dissatisfaction are recurrent for technology professionals in general (Moore, 2000). Also, they often show lack of autonomy, with 52.6% of inferior assessments (scores lower than 4). Previous research work shows that, in general, autonomy negatively correlates with turnover intention (McKnight et al., 2009; Dysvik and Kuvaas, 2013). On the other hand, mostly positive scores were observed for Job Significance and Skill Variety. Significance and Variety seem to be expected by the professional for many development jobs, regardless of which organisation they work for.

The objective of this initial study is to characterise the job which developers chose to quit voluntarily, aiming at understanding the rationale behind turnover; this could help management to foster more collaborative and healthier work environments. Also, we raise some hypotheses to be considered in future studies, either with this sample of Brazilian developers or in other contexts.

2. Background

Developer Turnover. Software companies are often faced with low retention. Due to the high demand for highly skilled professionals, new jobs are frequently available, and these specialised skills generate increasing business costs; teams and companies must find qualified substitutes and train new hires (Mockus, 2010). Turnover is then a significant concern in our software-driven society, having a dramatic impact on project success. (Tracy Hall and Sarah Beecham and June Verner and David Wilson, 2008; Hira et al., 2016). Foucault et al.’s work (Falleri et al., 2015) present evidence that constant changes in human resources in companies generate negatively impact software quality. Even reports from open-source projects present a high turnover rate, with dire consequences for project evolution (Hira et al., 2016; Lin et al., 2017).

Academics and industry leaders have tried to address this problem by observing antecedents and consequences of IT workers leaving their jobs voluntarily. A systematic review study, for instance, mapped 70 conceptually distinct turnover drivers for those professionals, as categorised into five classes – individual,job-related, psychological, environmental and organisational factors (Ghapanchi and Aurum, 2011). Although incentives like salary and promotion are deemed as critical for leaving a development job, other determinants, such as job autonomy, perceived workload and satisfaction have their relevance reported by research subjects. As an example, Jo Ellen Moore (Moore, 2000) assesses a high turnover intention among technology professionals manifesting work exhaustion.

Despite its importance, most research relating job-related and psychological factors with turnover focuses on turnover intention, in which professionals provide data about the probability of them quitting the current job (McKnight et al., 2009; Mockus, 2010).

Job Characteristics Theory. In 1975, Oldham and Hackman (Hackman and Oldham, 1976) constructed the original version of the Job Characteristics Theory (JCT), aiming at improving work design by measuring the effect of job characteristics on attitudes and behaviours of workers. According to the final version of the theory (Hackman and Oldham, 1980), five core characteristics (Skill Variety, Task Identity, Task Significance, Autonomy and Feedback) should predict three psychological states (work with meaning, responsibility for results and knowledge of results), leading to several favorable personal and work outcomes – in summary, high motivation, satisfaction and effectiveness.

The numeric relationship between these concepts is conveyed in the Motivating Potential Score (MPS), an index of the ”degree to which a job (…) is likely to prompt favourable personal and work outcomes” (Hackman and Oldham, 1976). Giving the theory support, the Job Diagnostic Survey (JDS) is used to assess the constructs. The JDS directly measures jobholders’ perceptions through Likert-based scales – for instance, 17 items measure the five core characteristics.

Nearly 200 studies with the JDS have been meta-analytically reviewed (Fried and Ferris, 1987; DeVaro et al., 2007), with evidence indicating the correlational results are reasonably valid. The model has though endured criticism, in particular concerning its prediction capabilities and the mediating role of the three psychological state variables. Nevertheless, the theory is still recognised as a useful framework for improving motivation and satisfaction at work, being still in use for IT-related jobs (Magalhaes, 2017; McKnight et al., 2009).

Work Exhaustion. Maslach and Jackson (Maslach and Jackson, 1981) define work exhaustion (job burnout) as a psychological syndrome of emotional exhaustion, depersonalization (negative or detached behaviour toward others), and diminished personal accomplishment. Many careers may experience burnout, but IT professionals can be particularly susceptible; several studies suggest the prevalence of work overload (Sethi et al., 1999; Cook, 2015).

3. Method

We structure the study in terms of two research questions:

RQ1: What are the perceived job characteristics, burndown and satisfaction in the job they voluntarily quit? We aim to assess the average scores for each JCT core characteristic (Skill Variety, Task Identity, Job Significance, Autonomy, Job Feedback), Work Exhaustion and Job Satisfaction.

RQ2: How do those values relate to Job Position and Degree Level? Here we analyse the scores as compared with collected demographic data: Job Position (Developer, Tester, and Manager), and Degree Level (Undergraduate, Graduate, and M.Sc. or PhD).

3.1. Study Participants

The participants are software developers, working either in public or private companies in Brazil. To participate, the developer must have worked in at least two paid jobs, because the questions are directed to who have moved to a second job; all items apply only to their previous job. Also, they were asked to specify their position within the team at the time.

We sent the invitation with an access link for an online form with informed consent 111All study’s material and data are available as a Zenodo repository (Massoni et al., 2019). to two online groups of software developers, four mailing lists, and to about 40 directed people, who served as a hub to pass on the survey to colleagues, as a convenience sample. The survey remained open to collect answers from December 15th, 2017 to February 12th, 2018.

3.2. Procedure and Measurement

The JDS instrument covers five job core characteristics, encompassing 17 items, while Job Satisfaction is measured with three Likert-scale items (Hackman and Oldham, 1980). Work Exhaustion is assessed with four items, as adapted from Maslach et al.’s Burnout Measurement (Maslach and Jackson, 1981), asking about feelings of exhaustion before and after a day at the job, or during job tasks  (Moore, 2000; McKnight et al., 2009). The full questionnaire was preceded by demographic questions, such as e-mail, position in the last job and Degree Level at the time. Moreover, question ordering was random, to minimise grouping bias. All item require 7-scale answers.

The survey was applied, at first, to a small sample of 15 participants, who were asked to provide feedback about the items. Reliability of the Portuguese version of this questionnaire was established with Cronbach’s , which showed values of at least . After fixing the detected issues (understandability and ambiguity), we carried out the final study. For the JCT core characteristics, we calculate, for each participant, the Motivating Potential Score (MPS), which is given as follows: . According to this formula, low values on either Autonomy or Feedback compromise the score, since these core characteristics are expected to promote responsibility and knowledge about the results. We used RStudio (RStudio Team, 2015) as the analytical tool for the R language.

4. Results and Discussion

4.1. Research Question 1

Table 1(a) presents the average scales and standard deviation for each JCT core characteristic, along with the average MPS. If MPS scores are classified as low (for scores below ), moderate () and high (¿), the average MPS () is moderate to high, however presenting significant standard deviation ().

JCT Core Char. Items Average Stand. Dev.
Skill Variety 4 4.68 1.70
Task Identity 3 4.29 1.49
Task Significance 3 5.15 1.41
Autonomy 4 3.75 1.49
Feedback 3 4.16 1.51
MPS - 81.31 62.75
(a) JCT Results
Concept Items Average Stand. Dev.
Work Exhaustion 4 4.0 1.63
Job Satisfaction 3 4.08 1.48
(b) Satisfaction and Burndown Results
Table 1. Average scores.

Autonomy received the lowest scores regarding the developers’ last job, from which they left voluntarily. Considered as the desire to be self-directed, which is related to independence in work, inferior assessments were reported by most participants (52.6% of answers below ). This result suggests most professionals lacked autonomy in the job they were willing to leave. Previous research work shows that, in general, autonomy negatively correlates with turnover intention (McKnight et al., 2009; Dysvik and Kuvaas, 2013). As displayed in Table 2, items regarding Autonomy received the lowest average scores in the study (Q6 and Q12). Furthermore, most professionals seemed to receive moderate feedback from their tasks in their last job – software professionals are reported to benefit from honest and throughout feedback (Cambridge University Press, 2014). On the other hand, at least two JCT core characteristics were positively assessed, on average: Job Significance and Skill Variety. Previous research has found a weak relationship between those aspects and turnover intention (McKnight et al., 2009).

Item Average Stand. Dev.
Q14. My last job was one where many people, in the organisation or outside, could be affected by how well my work was done 5.62 1.61
Q19. My last job itself was very significant and important in that it facilitated or enabled other people’s work 4.92 1.73
Q16. My last job was important in that the results of my work could significantly affect other peoples’ ability to do their work 4.90 1.63
Q12. I could usually do what I wanted on the last job without consulting my direct supervisor 2.96 1.78
Q6. In my last job, I usually did not have to refer matters to my direct supervisor for a final decision 3.69 1.85
Table 2. JCT items with highest and lowest scores.

Regarding Job Satisfaction, Table 1(b) shows a moderate average score from its three items, , with low standard deviation (), suggesting developers, although not reporting a significantly negative experience, either did not feel inspired or stimulated by the job. Lower scores usually convey disapproval on the way they worked, with the position they held, or the company they worked for. Although this study does not allow us to report on the precise influence of job satisfaction on the decision of leaving the job, data indicates a likely negative relationship (less satisfied developers tend to look for another job). This is reinforced by the current high demand for software developers, making them less cautious about their current job when looking for a more satisfying position.

In terms of Work Exhaustion, 25% of the participants answered they felt emotionally exhausted at least once a week (9% reported they felt like this every day), 29% reported feeling physically exhausted in a weekly basis, and half of the respondents reported feeling tired in the morning, before starting a work day in their last job. In psychology, work exhaustion is linked to psychiatric disorders such as the burnout syndrome, severely hampering motivation for work, often making professional to look for a potentially less stressful job. Nevertheless, there is evidence that work exhaustion is recurrent for technology professionals in general (Moore, 2000).

4.2. Research Question 2

In the study, most participants are aged between 25 and 30 years old (56.4%). While age is an objective question, Job Position was asked as an open-ended space-limited question, from which we manually classified as a programming position (66.7%), testing position (21.8%) and management position (11.5%). Regarding Degree Level, around 7% are professionals with no diploma yet, and more than 59% are at least graduated. Almost one-third of the sample holds M.Sc. or PhD degrees.

We compared average MPS, Job Satisfaction and Work Exhaustion scores between the three groups of job positions. For MPS and Exhaustion, difference results do not present statistical significance (the null hypotheses with corresponding p-values are shown in Table 3(a), by using Kruskal-Wallis variance tests (Kruskal and Wallis, 1952)). On the other hand, the test rejects the null hypothesis for Job Satisfaction (assuming confidence level=), suggesting a difference among the groups for satisfaction; by applying a Tukey post-hoc test, we see a slightly significant difference in satisfaction between programmers and testers (with adjusted p-value=). Research evidence (Zhang et al., 2010) suggests quality assurance activities could be less challenging than design and programming tasks, which could be a potential explanation for such results. Also, we found no significant difference in any measurement when it comes to Degree Level (Table 3(b)) – developers seem to report similar scores for all concepts, despite their education status.

Null Hypothesis Kruskal-Wallis
p-value
MPS 0.59
Job Satisfaction 0.03
Work Exhaustion 0.25
(a) Kruskal-Wallis hypothesis test for Job Positions.
Null Hypothesis Kruskal-Wallis
p-value
MPS 0.16
Job Satisfaction 0.16
Work Exhaustion 0.14
(b) Kruskal-Wallis hypothesis test for Degree Levels.
Table 3. Hypothesis test results.

5. Conclusion

In this paper, we performed an initial study on assessing Job Characteristics, Satisfaction and Work Exhaustion from software developers who left their last job voluntarily. Seventy-eight developers located in Brazil responded to the survey, whose instrument was adapted from the JCT (Hackman and Oldham, 1980) and Maslach et al.’s Burnout Measurement (Maslach and Jackson, 1981). As the next step, we intend to compare these results with more recently developed survey instruments related to job characteristics, like the Work Design Questionnaire (WDQ)  (Morgeson and Campion, 2003).

For Work Exhaustion, Job Satisfaction, Autonomy, Feedback, Task Identity, the respondents gave more than 60% of negative or moderate scores, while more positive scores were found for Job Significance and Skill Variety. Some hypotheses could be further investigated: despite a positive perception about the variance of skills or significance of the produced software, developers might look for other jobs due to other factors. Work Exhaustion may be a critical factor for quitting software companies – 25% of the participants reported emotional exhaustion at least once a week, while 9% reported feeling physically exhausted in a weekly basis. Also, the lack of autonomy seems to be an important aspect. We intend to carry out qualitative studies with the same participants, to further inquire into the reasons for the job change. Similarly, Job Satisfaction was perceived as moderate to low for almost 40% of the sample, suggesting a bad feeling or negative emotions were noticeable from their last job experience.

Furthermore, we investigated the relationship between the measured constructs and demographic data, namely Job Position and Degree Level. No significant differences were observed in most groups; only a slight statistical difference between programmers and testers, regarding Job Satisfaction, was detected. This hypothesis may be subject to further research: are Quality Assurance (QA) professionals less satisfied with their jobs, if compared to programmers? Or, if this is true, are they leaving their QA jobs for programming positions?

While only a few studies have examined motivation with actual turnover, it is hoped that additional studies of software developers will be conducted to more firmly determine the boundaries of generalizability. Scientific evidence on this matter could guide companies in avoiding the loss of key developers and its damaging consequences.

\balance

References

  • (1)
  • ABES (2017) ABES. 2017. Brazilian Software Market: Scenarios and Trends. http://central.abessoftware.com.br/Content/UploadedFiles/Arquivos/Dados 2011/ABES-Publicacao/Mercado-2017.pdf. Last accessed on 2019-01-23.
  • Cambridge University Press (2014) Cambridge University Press. 2014. Five Reasons Why Feedback May Be The Most Important Skill. http://www.cambridge.org/elt/blog/2014/03/17/five-reasons-feedback-may-important-skill/. Last accessed on 2019-01-31.
  • Cook (2015) Sara Cook. 2015. Job Burnout Of Information Technology Workers. International Journal of Business, Humanities and Technology 5, 3 (2015), 1–12.
  • DeVaro et al. (2007) Jed DeVaro, Robert Li, and Dana Brookshire. 2007. Analysing The Job Characteristics Model: New Support from a Cross-section of Establishments. The International Journal of Human Resource Management 18, 6 (2007), 986–1003.
  • Dysvik and Kuvaas (2013) Anders Dysvik and Bård Kuvaas. 2013. Perceived Job Autonomy and Turnover Intention: The Moderating Role of Perceived Supervisor Support. European Journal of Work and Organizational Psychology 22, 5 (2013), 563–573.
  • Falleri et al. (2015) Matthieu Foucault Falleri, Marc Palyart, Xavier Blanc, Gail Murphy, and Jean-Rémy. 2015. Impact of Developer Turnover on Quality in Open-Source Software. In 10th Joint Meeting on Foundations of Software Engineering (JMFSE). 829–841.
  • Fried and Ferris (1987) Yitzhak Fried and Gerald R. Ferris. 1987. The Validity of The Job Characteristics Model: A Review and Meta-Analysis. Personnel psychology 40, 2 (1987), 287–322.
  • Ghapanchi and Aurum (2011) Amir Hossein Ghapanchi and Aybuke Aurum. 2011. Antecedents to IT personnel’s intentions to leave: A systematic literature review. Journal of Systems and Software 84, 2 (2011), 238 – 249.
  • Gregorio Robles and Jesus Gonzalez-Barahona (2006) Gregorio Robles and Jesus Gonzalez-Barahona. 2006. Contributor Turnover in Libre Software Projects. In IFIP International Conference on Open Source Systems. 273–286.
  • Hackman and Oldham (1976) J Richard Hackman and Greg R. Oldham. 1976. Motivation Through The Design of Work: Test of a Theory. Organizational behavior and human performance 16, 2 (1976), 250–279.
  • Hackman and Oldham (1980) J. Richard Hackman and Greg R. Oldham. 1980. Work Redesign. Addison-Wesley.
  • Hira et al. (2016) Anandi Hira, Shreya Sharma, and Barry Boehm. 2016. Calibrating COCOMO (R) II for Projects with High Personnel Turnover. In IEEE/ACM International Conference on Software and System Processes (ICSSP). 51–55.
  • Jason Thatcher and Yongmei Liu and Lee Stepina and Joseph Goodman and Darren Treadway (2006) Jason Thatcher and Yongmei Liu and Lee Stepina and Joseph Goodman and Darren Treadway. 2006. IT Worker Turnover: An Empirical Examination of Intrinsic Motivation. SIGMIS Database 37, 2-3 (2006), 133–146.
  • Kruskal and Wallis (1952) J. Kruskal and W. Wallis. 1952. Use of Ranks in One-criterion Variance Analysis. J. Amer. Statist. Assoc. (1952).
  • Lin et al. (2017) Bin Lin, Gregorio Robles, and Alexander Serebrenik. 2017. Developer Turnover in Global, Industrial Open Source Projects: Insights from Applying Survival Analysis. In 12th International Conference on Global Software Engineering (ICGSE). 66–75.
  • Lingo Live (2016) Lingo Live. 2016. The Cost of Turnover for Software Engineers, ON 6-17-16. https://www.lingolive.com/cost-of-turnover-for-software-engineers/.
  • Magalhaes (2017) Cleyton Magalhaes. 2017. Toward Understanding Work Characteristics in Software Engineering. SIGSOFT Softw. Eng. Notes 41, 6 (2017), 1–6.
  • Maslach and Jackson (1981) Christina Maslach and Susan E. Jackson. 1981. The Measurement of Experienced Burnout. Journal of Organizational Behavior 2, 2 (1981), 99–113.
  • Massoni et al. (2019) Tiago Massoni, Nilton Ginani, Wallison Silva, Zeus Barros, and Georgia Moura. 2019. Relating Voluntary Turnover with Job Characteristics, Job Satisfaction and Work Exhaustion – An Initial Study with Brazilian Developers. Zenodo.
  • McKnight et al. (2009) D Harrison McKnight, Brandis Phillips, and Bill C. Hardgrave. 2009. Which Reduces IT Turnover Intention The Most: Workplace Characteristics or Job Characteristics? Information & Management 46, 3 (2009), 167–174.
  • Mockus (2010) Audris Mockus. 2010. Organizational Volatility and Its Effects on Software Defects. In 18th International Symposium on Foundations of Software Engineering (FSE). 117–126.
  • Moore (2000) Jo Ellen Moore. 2000. One Road to Turnover: An Examination of Work Exhaustion in Technology Professionals. Mis Quarterly 24, 1 (2000), 141–168.
  • Morgeson and Campion (2003) Frederick P. Morgeson and Michael A. Campion. 2003. Work Design. American Cancer Society, 423–452.
  • PayScale (2017) PayScale. 2017. Occupational Outlook Handbook, 2016-17 Edition, Software Developers. https://www.payscale.com/data-packages/employee-loyalty/full-list. Last accessed on 2019-01-31.
  • RStudio Team (2015) RStudio Team. 2015. RStudio: Integrated Development Environment for R. http://www.rstudio.com/
  • Sethi et al. (1999) Vikram Sethi, Tonya Barrier, and Ruth C. King. 1999. An Examination of the Correlates of Burnout in Information Systems Professionals. Inf. Resour. Manage. J. 12, 3 (1999), 5–13.
  • Tracy Hall and Sarah Beecham and June Verner and David Wilson (2008) Tracy Hall and Sarah Beecham and June Verner and David Wilson. 2008. The Impact of Staff Turnover on Software Projects: The Importance of Understanding What Makes Software Practitioners Tick. In ACM SIGMIS CPR Conference on Computer Personnel Doctoral Consortium and Research. 30–39.
  • Zhang et al. (2010) Xihui Zhang, JS Dhaliwal, and Mark L. Gillenson. 2010. Organizing software testing for improved quality and satisfaction. Journal of Information Technology Management 21, 4 (2010), 1–12.
Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
""
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
   
Add comment
Cancel
Loading ...
334909
This is a comment super asjknd jkasnjk adsnkj
Upvote
Downvote
""
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters
Submit
Cancel

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test
Test description