Job analysis for the contemporary workplace
University of Amsterdam
Faculty of Economics and Business Master Business Administration Track Leadership and Management Master Thesis
Author: Anne Erkelens
Student number: 10025324
Date: 29-08-2015
A mixed method research showing the important factors of changing tasks
ABSTRACT This mixed method study researched what the important factors of changing tasks are. For job analysis to be useful in the contemporary workplace it needs to consider the change in jobs. Firstly by knowing what triggers change creates an understanding of change and makes it possible to predict change. Moreover, insight in what change in tasks actually happens shows practical implications for job analysis information that helps in making inferences based on the information. The quantitative study found a negative impact of non-core jobs on job satisfaction of primary school teachers. Secondly, a general trend found in the quantitative and qualitative study is that tasks seem to grow in amount and time spent. Growth in time is mostly caused by forced change. Growth in amount is however initiated by the employees based on their need for growth and favorable work conditions. Thirdly, the content of tasks change as well, mostly by employees themselves. Providing freedom to change tasks to lessen misfits between demands and resources and moreover to make tasks and the job more interesting should be part of job design.
Table of contents
Introduction 3 Theoretical framework 6 Job analysis 6 Forces of change 9 Job satisfaction 12 Task types 14 Methodology 23 Quantitative study 25 Procedure 25 Sample 26 Measures 27 Data analysis 30 Results 30 Implications 37Theoretical framework part two 39
Job crafting 39 Extra-role behavior 41 Qualitative research 44 Procedure 44 Sample 45 Data collection 45 Data analysis 45 Results 46 Discussion 52
Practical implications and further research 56
Limitations and further research 57
Conclusion 59
References 61
Appendix A: List of tasks 69
Appendix B: Interview protocol 72
Introduction
An important human resource (HR) practice is job analysis. Job analysis is a process to create a clear image of what tasks and responsibilities a job holds and what skills, knowledge and abilities are needed to perform the job (Werbel & DeMarie, 2005). This job information forms the basis for most other HR practices like job design, selection and performance management (Sanchez & Levine, 2012). On the other hand, job analysis as an HR practice receives a lot of critique and its usefulness for an organization is questioned. Strategic human resource management (SHRM) addresses the critical function that human resource management (HRM) has in organizational effectiveness (Boxall & Purcell, 2000). To be effective HR strategy and practices need to fit with the business strategy, with the external environment and the practices need to complement each other (Boxall & Purcell, 2000; Lengnick-Hall, Lengnick-Hall, Andrade & Drake, 2009). However, the business environment is a volatile environment. Job analysis and its uses are criticized because of the incapability to deal with this dynamic environment (Sanchez & Levine, 2012; Singh, 2008).
Innovation and the use of technology cause a faster change in jobs and work is more often organized in a team based structure and employees get more authority. Moreover, the requirements to do a job change and job analysis needs to keep up with these changes by acknowledging these forces and incorporating changing activities in the analysis (Singh, 2008). Job analysis should not be just about the activities in the job and the requirements that are needed for those activities but it should also analyze change factors and activities outside the job borders. As well as taking into consideration the changes inside the job borders. Employees are defining their own job by undertaking action to change their tasks and work (Wrzesniewski & Dutton, 2001). This ‘job crafting’ is a method of redesigning jobs from the bottom up (Demerouti, 2014). Changing ones job, changes the work itself and employees become job designers which influences job analysis since the information about a job changes (Tims & Bakker, 2010). An other trend in the contemporary business environment is the growth in extra-role behavior. Employees perform tasks outside the formal job description that help the organization and its goals (Somech & Drach-Zahavy, 2000; Stoner, Perrewe &
Munyon, 2011). This behavior leads mostly to positive organizational outcomes and positive performance evaluations for the employee. It does have a dark side by creating pressure on the employee since they feel it is expected from them (Bolino, Turnley, Gilstrap & Suazo, 2010).
These factors of change show that a job is not a static entity (Singh, 2008). The last movement in job analysis research focusses on taking the context into consideration (Sanchez, 1994). This does create broader job definitions but it does not take into account the constant change (Nelson, 1997). Work is an important factor in peoples’ life and job satisfaction is related to life satisfaction (Judge & Klinger, 2008; Saari & Judge, 2004). As work changes the influence on employees changes. For job analysis to be useful it needs to find out what is important to look at regarding change and its influence on employees. Both positive and negative influences of change in work on employees should be monitored. If job analysis collects this information other HR practices like finding a person-job fit, selection and training practices can build upon this and use it to create an efficient human capital pool in a volatile business environment.
This research looks at tasks, change in tasks and how changes influence employees and job information. Tasks are verifiable and observable parts of a job and they give job relevant information. Analyzing tasks creates valid information that is useful for making inferences (Harvey & Wilson, 2000). This study researches tasks and their influence in a quantitative way and in a qualitative way. For the quantitative study a framework of task types is built in which tasks are divided based on their characteristics that influence job satisfaction. The influence of each task type on job satisfaction is tested as well as the influence of growing and shrinking tasks. Considering tasks that grow or shrink over time it is possible to see which changes effect satisfaction and in which direction. This relationship is studied by using a dataset of tasks and task change provided by primary school teachers. The quantitative study creates a detailed image of the teaching job but moreover forms an important basis for finding general trends. Considering the results of the quantitative study, the qualitative study uses interviews to discover more different types of change. It focusses more on
where change comes from, what the response on the trigger is and how tasks change. The research question addressed in this study is:
What are the important factors of changing tasks?
This question is directed to find a way to make job analysis useful in the contemporary workplace. Firstly, the influence of tasks and changing tasks on employees is researched which has as goal showing the tasks that are important to employees. A second contribution to the usefulness of job analysis is the focus on why tasks change and how tasks change. Creating insight into factors that initiate change helps to understand the change. Whereas, the understanding of how tasks change creates a basis for practical implications in job analysis information and helps making inferences based on job analysis.
Starting with an overview of the literature concerning job analysis, the environmental strains on job analysis and works towards an understanding of job satisfaction and a categorization of tasks. This leads up to the hypothesis used for the quantitative part of this research. Subsequently, the research method, the data analysis and results of the quantitative study are reported. Followed by the interpretation and implications of the results. Based on these, job crafting behavior and extra-role behavior are explained in more depth. Followed by the introduction of the qualitative study and the results of this part are presented. The discussion connects the implications of the two studies and links the results to the theory. Moreover, the limitations and fsuggestions for future research are discussed. Lastly, in the conclusion the research question is answered.
Theoretical framework
Change in work environment and jobs forms a strain on the usefulness of job analysis and therefore job analysis is often criticized. Being aware of change is not enough, monitoring the influence of change creates more in-depth job information and considering change makes job analysis useful again. To understand the value of monitoring change for job analysis, several theoretical concepts are explained. Job analysis and its history as a human resource practice is discussed. Moreover, the forces of the changing environment are discussed. After which, the concept of job satisfaction and its sources are discussed. Followed by the creation of task types based on the Job Characteristics Model which leads to several hypothesis.
Job analysis
As mentioned, job analysis is seen as a basis for (all) other human resource (HR) practices (Sanchez & Levine, 2012; Singh, 2008). It is “the process through which one
gains an understanding of the activities, goals and requirements demanded by a work assignment” (Sanchez & Levine, 2012, p. 398). Getting the facts about a work
assignment creates criteria for selecting employees that fit with the work assignment or helps find specific training to complement employees’ skills and knowledge (Singh, 2008). Job analysis has a long history and different definitions have been used over time.
First of all, it is often described as a process of finding and documenting information about a job (Sackett & Laczo, 2003; Sanchez & Levine, 2012). This has been the focus of research in the past which was mostly directed towards finding accuracy in job analysis to create job descriptions (Morgeson & Campion, 2000). The emphasis lay on creating the best job information collection tools (Siddique, 2004; Singh, 2008). In earlier research job analysis information was based on the premises that it is a ‘true score’ which means it is stable over time and thus accuracy is gained by getting information from multiple sources and across time (Morgeson & Campion, 2000). Inaccuracy occurs because the information is based on human judgement, which is prone to rater reliability and validity issues (Morgeson & Campion, 1997). Reliability
of information is mostly obviated by inter-rater tests and intra-rater tests to come to an agreement of the information. An approach that is widely used to create validity is asking experts whether the descriptions given actually cover the job (Sanchez & Levine, 2012).
As the research developed, job analysis became more than just information gathering. Doing something with the information became part of the term. The focus shifted from the methodological research to the inference based models (Morgeson & Campion, 2000; Sanchez & Levine, 2000). These models start with the premises that job analysis is there to find out what is done in a job, without the focus on totally accurate information. Morgeson and Campion (2000) describe two parts of a job, firstly the job description which are the tasks and duties of a job and secondly job specification which describes the knowledge, skills, abilities (KSAs) and other characteristics needed to perform the job. These two parts emphasize the distinction between job-oriented and worker oriented information (Morgeson, Delaney-Klinger, Mayfield, Ferrara & Campion, 2004). Job-oriented information includes the tasks and procedures in a job. Worker-oriented information is more focussed on the abilities an employee needs to perform the job. This information is mostly a rating of a degree of reasoning or decision making skills that employees need to posses. Job-oriented information is observable and no subjective inference has to be made to describe a task or procedure which lowers inaccuracy (Morgeson & Campion, 1997; Morgeson et al., 2004). The inference based models argue that from those two parts of information inferences can be made. For example, the described KSAs could be used in selection systems or training programs (Sanchez & Levine, 2000).
As job analysis theory evolved even further the term ‘work analysis’ came into existence. This term is supposed to encompass the boundarylessness of jobs and creating a broader view on work (Sanchez, 1994). Work analysis is thus defined as a broader term since it encompasses the exploration of work role requirements and it takes into account the broader context of work roles. Work role requirements contain both the worker- and the job-oriented information (Morgeson & Dierdorff, 2010). These three features, worker-oriented information, job-oriented information and context
information, are examined in work analysis. The work-oriented information can be based on tasks or responsibilities. Tasks are elements that “include actions, the object of
the action, and the purpose or result of the action.” (Morgeson & Dierdorff, 2010, p. 8).
Responsibilities are more broad in that they describe related activities and behaviors that together lead to accomplishing a goal. Worker-oriented information is based on the characteristics that are needed to perform the work, these are the knowledge, skills, abilities and other characteristics an employee has and needs.
The term work analysis is supposed to deal with the change towards more open organizational structures with cross-functional work and self-managing teams (Sanchez, 1994). It does this by taking into account the work context, the third descriptor which describes circumstances that have an effect on the work. Work context can be divided into three context forms. Firstly the physical context which reflects the work space and the conditions therein, for example lightening, exposure to chemicals and physiological demands. Second is the social context that indicates the relationships between workers, for example how they communicate and their interdependence. Thirdly is the task context, which are the conditions under which the tasks are performed, for example the level of autonomy, task significance and the resources available. All this information leads in the end to a specified job description (Morgeson & Dierdorff, 2010).
Taking the context into consideration does however not deal with the problem that jobs are not a static entity (Nelson, 1997; Singh, 2008). The critique on job analysis has been focussed on the rigid formulation of job descriptions and the incapability of taking into account that there are forces of change and that jobs are not a static entity (Sackett & Laczo, 2003; Sanchez & Levine, 2012; Singh, 2008). Work analysis might make broader job descriptions but the question remains whether this encompasses the flexibility and change in jobs (Nelson, 1997). For job analysis to be useful in a changing environment it is important to know what changes in the environment and which changes are important for employees and the organization. Therefore, in the next chapters the forces of change are discussed as well as the components that lead to job satisfaction.
Forces of change
The last two decades there has been an increasing attention to forces that influence the workplace, jobs and organizational structures and systems and with that the design of HRM practices (Burke & Ng, 2006; Cascio, 1995; Ryan & Wessel, 2015). In the following paragraphs these forces of change are discussed and their impact on HRM. The changes are organized around six themes and these themes are interrelated (Ryan & Wessel, 2015).
The first theme is globalization. Globalization refers to the boundaryless marketplace, a global market place for products, services and also employees (Burke & Ng, 2006; Cascio, 2003). Companies invest and produce all over the world and this results into worldwide competition. The most useful asset in this competition are the capabilities and knowledge of the workforce because production, structures and systems can be copied around the world (Cascio, 2003). Moreover, not only companies can compete throughout the world, with globalization and the openness of borders it is also possible for employees to work anywhere, creating a global labor force. Companies in their turn use this to select and attract the highest skilled people from anywhere in the world (Burke & Ng, 2006). A globalized labor market increases the diversity of the labor force, which for example influences the perception of fairness (Ryan & Wessel, 2015). Globalization also causes international cooperation within the company and work teams are becoming multinational (Guevara & Ord, 1996). Companies and employees therefore need to deal with different legal systems and international and national laws (Grant, Fried, Parker & Frese, 2010). Technology breaks down barriers and makes globalization possible (Burke & Ng, 2006).
The second theme of change is technology. Not only does technology break down barriers it also influences the nature of work. The development of machines and computers makes manual labor superfluous. Technology reduces costs and increases efficiency and quantity. This change results in the deskilling of employees, the craftsman disappears and employees are encouraged to keep up with the technological developments (Burke & Ng, 2006). This is not all, technology also created a revolution in communication systems (Cascio, 2003). This makes it possible to communicate all
the time with people all over the world and the need to travel for meetings has decreased (Burke & Ng, 2006). The new communication systems also changes the communication in organizations from face-to-face communication to virtual communication (Burke & Ng, 2006; Cascio, 2003). Physical presence becomes obsolete and with that a shared physical context misses which can create biased perceptions. Moreover, when coming together in the same physical context, people can appear totally different from their virtual being (Guevara & Ord, 1996). Lastly, technology changes the way we process and save information. Large databases make knowledge sharing easier and it creates information systems that help organize and group all sorts of information creating for example a Human Resource Information System (Cascio, 2003). Technology and globalization are trends that influence the structure of work and that ask attention from the workforce to deal with those changes which could be a problem considering the demographic composition.
The third theme of change is the demographic composition of the workforce. Already mentioned is the increasing ethical diversity as a result of globalization (Burke & Ng, 2006; Cascio, 2003). Companies need to manage diversity to deal with discrimination (Burke & Ng, 2005) and to keep a culturally diverse workforce motivated (Cascio, 2003). Furthermore, the workforce is aging as the population ages (Guevara & Ord, 1996; Howard & Ulferts, 2007). This trend is even more enhanced because the inflow of younger workers is less than the outflow of older employees (Burke & Ng, 2006; Howard & Ulferts, 2007). Organizations have to deal with the following implications. They are losing part of the knowledge of their workforce which leads to companies promoting the retention of the aging workforce (Burke & Ng, 2006). However, older employees are more inclined to have health problems and an inflexible attitude towards change and technology (Burke & Ng, 2006). There is also a gap between the needed technological skills and the actual skills of older workers, which increases the need for training to update the skills (Rizzuto, 2011). Moreover, the young employees that do enter the labor market are different from the older employees, which is specified in the next theme of change.
The fourth theme reflects the change in workers attitude. While the baby boomers (born between 1945 and 1960) are slowly leaving the labor market, generation X (born between 1960 and 1976) now have to share the labor market with the net generation (born between 1980 and 1995), the children of the baby boomers (Burke & Ng, 2006). This generation is characterized with high ambition and they dare to have a voice, want to move fast and do not restrain themselves from moving on. They are used to diversity and are more tolerant, social and collaborative. Moreover, they grew up with technology and are highly proficient in using the latest technology (Burke & Ng, 2006). It is not just age but also the different characteristics that come with different generations that creates diversity in the workforce. Organizations need to get used to those new employee characteristics and may need to change organizational structures. Not only the workers change, the sort of work changes as well, which reflects the fifth theme of change. There is an increase in service and knowledge work. Service economy took the place of the manufacturing economy in developed countries and employs around 70% of the workforce (Ryan & Wessel, 2015). Service is a product that is intangible, not easily standardized and it cannot be stored which makes it different from tangible goods. Furthermore, service work extends the interpersonal relationships and interactions to outside the organization which creates more interdependence and uncertainty (Grant & Parker, 2009). Managing service employees is different from managing manufacturing employees and companies need to change for example processes like evaluation (Ryan & Wessel, 2015). Moreover, with the increase in technology knowledge workers came into existence. Knowledge workers are “those
who produce, apply, and distribute knowledge” (Burke & Ng, 2006). Knowledge, like
service, is an intangible product and even less visible than service. Measuring performance is therefore a new challenge and managing knowledge workers to improve their performance has to be thought through.
The sixth and last theme of change is the shift in relationship between
employer and employee. First of all, permanent employment relationships are no longer
the trend. Having multiple jobs during working life is not an exception anymore (Cascio, 2003; Grant & Parker, 2009). There is a shift from organizational commitment
to occupational commitment, where people do stay in the same profession but not necessarily in the same organization (Rousseau, 1997). This means that the workforce of a company is a lot more temporary and flexible and hiring new employees probably happens on a more regular basis. As well as it creates the need to think about how to retain employees and creating jobs that deal with the flexibility of employees (Grant & Parker, 2009) Moreover, the contracts between employer and employee become more individualized and employees negotiate their own contract that includes their preferences with regard to working hours, payment and also personal development (Ryan & Wessel, 2015).
Concluding, there has been a lot of change that influences the nature of work and this asks for a change in management views and practices (Barley & Kunda, 2001). Because the nature of work changed, it is important to take a step back from looking at organizing and start to analyze work again. Knowing what people do is important, but as mentioned before this is criticized since jobs are not static (Singh, 2008). Moreover, it is important to know how changes influence employees. For example service work creates an increase in emotional labor (Barley & Kunda, 2001). To get a sense of what is important with regard to employee attitudes about work the next section discusses job satisfaction and its sources.
Job satisfaction
Job satisfaction is a long standing concept of employee attitudes. Locke defined it as “a
pleasurable or positive emotional state resulting from an appraisal of one’s job or job experiences.” (as cited in Kurland & Hasson-Gilad, 2015, p. 59). It is often described as
a form of well-being (van de Voorde et al., 2012) and is seen as a subjective experience or perception of work or as an attitude or psychological response to work (Fisher, 2010; Hulin & Judge, 2003; Kurland & Hasson-Gilad, 2015). Spector (1997) simply puts it as “the extent to which people like (satisfaction) or dislike (dissatisfaction) their jobs.” (p. 2).
Job satisfaction is related to an array of other concepts. Firstly, a positive relationship between job and life satisfaction is found. The spillover model that
indicates that job satisfaction and life satisfaction complement each other is found as a dominant model of this relationship. Research suggests that this relationship is circular (Judge & Klinger, 2008; Saari & Judge, 2004). Job satisfaction and job performance is also a relationship that is often researched. Research on this topic is mostly about ‘a happy worker is a productive worker’ (Zelenski, Murphy & Jenkins, 2008). The assumption is that positive emotions, employees that are happy or satisfied with their job, are likely to create a productive worker. However, there seem to be interaction effects with for example the instructions given and positive or negative emotions. Even though this relationship has a controversial research past, in the last twenty years of research a positive correlation is found between satisfaction and performance (Hulin & Judge, 2003; Judge & Klinger, 2008; Saari & Judge, 2004; Zelenski et al., 2008). Other correlations that research supports are a negative correlation between job satisfaction and turnover and retirement intentions (Alniaçik, Alniaçik, Erat & Akçin, 2013; Kurland & Hasson-Gilad, 2015).
There are different views on where job satisfaction comes from. For example, The Cornell Model focusses on wide social and economic context of the job and how this influences individuals’ satisfaction. Locke, however, proposes with his Value-Percept Model that satisfaction is dependent on the match between the values of an individual and job outcomes. An employee can put high value on pay and than high pay leads to high satisfaction (Hulin & Judge, 2003; Judge & Klinger, 2008).
Moreover, instead of focussing on values there are theories that focus on needs of employees. A well known Needs Theory is from Herzberg. Herzberg, Mausner and Snyderman created the Two-factor Theory of motivation (Kurland & Hasson-Gilad, 2015; Lundberg, Gudmundson & Andersson, 2009). As the name proposes there are two factors that influence motivation and job satisfaction. The first are hygiene factors, which are the needs necessary for basic survival, such as salary but also rewards, company policy and interpersonal relations. These factors are more contextual related than job related. The second factor Herzberg refers to, is more intrinsic to the job and is called the motivational factor. These factors satisfy growth needs and are for example recognition, responsibility and promotion. Herzberg’s theory moreover mentioned that
the hygiene factors are there to reduce dissatisfaction but not necessarily create satisfaction. However, motivational factors do create satisfaction but when they are not met it does not cause dissatisfaction (Lundberg et al., 2009).
In the theory about employee well-being the Job Demands-Resources Model (job D-R model) is a dominant approach. This model focusses on the characteristics of the job instead of on the employee’s preferences. Job demands are the parts of the job that demand attention from employees and that could cause strain if an employee cannot comply. The second set is job resources which are the part of the job that make work possible and help the employee in fulfilling the demands and even stimulate growth and development (Hu, Schaufeli & Taris, 2011; Li, Jiang, Yao & Li, 2013; Tims & Bakker, 2010). Moreover, job resources can bring balance to high demands, eliminating the negative effects of job demands on well-being and job satisfaction. Furthermore, when both demands and resources are high this could create a higher level of work engagement (Hu et al., 2011).
A fifth model that also focusses on the job and which shows different factors needed for job satisfaction is the Job Characteristics model (JCM) of Hackman and Oldham (1976). In short, this model proposes that a job needs five intrinsically motivating characteristics to lead to job satisfaction. This model is discussed in more detail in the next section. It is used as the basis for developing different task types since it offers the possibility to divide tasks into categories based on the five characteristics. Satisfaction about the work itself seems to be dependent on the intrinsic properties of a job. A job is a collection of multiple tasks and these are different on aspects as autonomy or meaningfulness of the task (Taber & Alliger, 1995). The JCM used as the basis for developing different task types since it offers the possibility to divide tasks into categories based on five characteristics.
Task types
This research focusses on tasks and how these influence employee satisfaction. Using tasks as job analysis information is a valid measure. Tasks are verifiable anchors and observable which makes it easy to create valid information in contrast with for example
abstract behavioral aspects of a job. Moreover, tasks give specific and job-relevant information which makes them useful for inferences being made based on the job analysis data (Harvey & Wilson, 2000).
However, there are many tasks in a job and not all tasks influence people the same way (Schyns & Croon, 2006; Shikdar & Das, 2003). It appears moreover, that to design a job that is motivating, it is important to use motivational tasks (Wong & Campion, 1991). Furthermore, research suggests that overall job satisfaction is only partially predicted by individual task properties, separating tasks thus gives better insight in the facets of job satisfaction (Taber & Alliger, 1995). Therefore, a classification of tasks is created in this research. These task types are, as mentioned, based on the JCM of Hackman and Oldham (1976). This theory uses five job characteristics and the influence of those on job satisfaction is researched extensively. Moreover, these characteristics form the basis of the nature of tasks, which is found to be important for the motivation and job satisfaction of employees (Hulin & Judge, 2003; Taber & Alliger, 1995).
Job Characteristics Model
Hackman and Oldham (1976) argue that the quality of work became more important over the years and this resulted into work redesign. In their study leading to the development of the JCM they research characteristics of jobs that lead to better quality of work. They build their model on four theories.
The first theory they pull in is the Motivation-Hygiene Theory of Herzberg that was also mentioned before. This theory postulates (as cited in Hackman & Oldham, 1976) that there are motivators, factors intrinsic to the work like recognition and responsibility, that cause satisfaction and hygiene factors, extrinsic to the work like policies and leadership practices, that cause dissatisfaction. Important to take from this theory is the use of the two factors. Since hygiene factors do not create satisfaction but only reduce dissatisfaction, enriching a job with solely the use of hygiene factors does not create satisfaction. Satisfaction is created by enriching a job with motivational factors (Hackman & Oldham, 1976; Lundberg et al., 2009).
A second theory they consider important is Activation Theory researched by Berlyne (1967) and Scott (1966) (as cited in Hackman & Oldham, 1976). Under-activation has negative effects on work effectiveness, so Under-activation should lead to more work effectiveness. To use this theory in its full extent Hackman and Oldham (1976) see, however, two problems. The first has to do with the measurement of activation and the role of individual preferences in the optimal level of activation. The second problem they indicate has to do with adaptation to change and the role of familiarity in levels of activation. Although they spot those problems they stipulate the use of job rotation to support activation and keep motivation high.
Thirdly, Hackman and Oldham (1976) use Socio-Technical Systems Theory in the assembly of their model. This theory is important because of its focus on the interdependencies of individuals, their work and the social surroundings wherein they work. This theory states that the quality of work is increased by taking these interdependencies into consideration by for example creating an autonomous work group where the members have the authority to make decisions on a whole working process, from planning to execution and see the outcomes for them and the organization.
The fourth and maybe the most important theory is the interactive approach by Turner and Lawrence (1965), which is where they get the concept job characteristics from. Job characteristics can influence employee performance, motivation and satisfaction (Hackman & Lawler, 1971). There are four core dimensions mentioned in this earlier research: variety, task identity, autonomy and feedback which positively influence employee attitudes and behaviors at work one of them being job satisfaction (as cited in Faunce, 1965; in Hackman & Lawler, 1971 and in Hackman & Oldham 1967).
Considering these four theories Hackman and Oldham (1976) create the JCM. They describe five core job dimensions that influence three different psychological states of employees that lead to positive work outcomes such as satisfaction and performance (Humphrey, Nahrgang & Morgeson, 2007). The first core job dimension is skill variety which takes note of the different activities in a job and the different skills
and capabilities that are required from an employee. This dimension clearly takes into account the Activation Theory which postulates that variety in a job is important (Hackman & Oldham, 1976; Morgeson & Humphrey, 2006). The second core dimension is task identity which is the degree to which the job is a complete process of work, with a beginning and an end where the outcome is visible. Here, the basis lies in the Socio-Technical Theory that presumes that planning work from beginning to end helps motivate employee (Hackman & Oldham, 1976; Morgeson & Campion, 2003). Socio-Technical Theory also assumes that it is important to see what the influence of work is. This is discernible in the third dimension: task significance which implicates the impact of the job on other people, their work or lives. The fourth core dimension is
autonomy which necessitates the interdependence and discretion the job provides and
thus the freedom employees have in the planning and execution of their work. This dimension combines the motivation factors of the Two Factor Theory which is responsibility in work and the socio-technical aspect of the importance of autonomy in creating motivation (Hackman & Oldham, 1976; Pulfrey, Darnon & Butera, 2013). The fifth and last dimension is feedback which refers to the information employees receive about their performance or effectiveness in carrying out their jobs. Which again refers to the importance of the intrinsic factors of work from the Two Factor Theory (Hackman & Oldham, 1976; Hulin & Judge, 2003; Northcraft, Schmidt & Ashford, 2011).
Connecting these job characteristics to the three psychological states Hackman and Oldham (1976) explain that the first three core dimensions together create the first psychological state experienced meaningfulness. Which refers to the degree of experienced meaningfulness, valuableness and worth of a job by the employee. Tapping into the use of skills of people creates meaningfulness. As well as the completion of a job creates meaningfulness. Task significance contributes to meaningfulness since employees see the significant impact of their work on others (Humphrey et al., 2007). Autonomy leads to experienced responsibility, which is the feeling of being personally responsible for the outcomes of the work. Autonomy makes that the work outcomes are dependent on the efforts of employees and thus creates responsibility (Hackman & Oldham, 1976; Humphrey et al., 2007). The third psychological state that leads to
positive work outcomes is the knowledge of results. This refers to “the degree to which
the individual knows and understands, on a continuous basis, how effectively he or she is performing the job.” (Hackman & Oldham 1976: 257), which is fostered by feedback
(Hackman & Oldham, 1976; Humphrey et al., 2007).
These psychological states result into certain outcomes. Especially, high performance, high job satisfaction and high internal motivation and low absenteeism and turn over (Fried & Ferris; 1987; Hackman & Lawler, 1971; Hackman & Oldham, 1976). All in all, the nature of work and tasks is strongly correlated with job satisfaction (Hulin & Judge, 2003; Taber & Alliger, 1995).
In their theory, Hackman and Oldham (1976) describe the individual need of growth-strength as an important moderator of the JCM. People with a high need for personal growth are more stimulated by jobs that offer potential and that are highly motivating. Thus, when tasks have the five job characteristics, people with high growth needs experience higher motivational states and the relation between the characteristics and the outcomes like job satisfaction is stronger (Hulin & Judge, 2003; de Jong, van der Velde & Jansen, 2001). Growth-need-strength is found to specifically positively moderate the positive relation between skill variety and autonomy and job satisfaction (de Jong et al., 2001). In the development of the task types, the five characteristics help to create task types based on their nature. Moreover, the influence of growth-need-strength on job satisfaction is in this case tested separately.
Creating task types
Based on job characteristics theory, tasks differ in their influence on people depending on characteristics and therefore create different work outcomes (Acuña, Gómez & Juristo, 2009; Langfred & Moye, 2004; Schyns & Croon, 2006). Even though the theory is four decades old and extensively researched and criticized, it is still used to design jobs (Cohen, 2013). Taking this theory, tasks can be divided into different types depending on their characteristics. This research contributes to existing theory of task types by creating a framework of four task types. Two types are based on job characteristics theory, the third type is created by considering growth-need strength
theory and a fourth type is created to add to the job characteristics theory by including the contemporary trend of extra-role behavior.
The framework is created as such that is general so that it can be used for all sorts of jobs, it is a basic framework for designing jobs. Creating task types is necessary because of two reasons. Firstly, analyzing each task individually is a lot of work, therefore dividing a job into smaller parts but clustering those parts, in this case in task types, is a more enduring form and analyzing and responding to change is easier (Cunningham, 1996). Secondly, since the types are related to the characteristics they are related to job satisfaction. This creates a hierarchy of tasks that have a high influence on job satisfaction and tasks that have less influence on job satisfaction (Taber & Alliger, 1995). Following now are the different types and the hypothesis concerning these types and their relation to job satisfaction.
Core tasks are in-role behavior job tasks that are high on the five job characteristic dimensions.
Firstly, these tasks form a part of the formal in-role behavior of a job. These are behaviors that are directly connected to the organizational goals and activities (Stoner et al., 2011). A teacher teaches, a surgeon operates and a recruiter select people. Secondly, these tasks are categorized based on the fact that they lead to the three psychological motivational states so that they lead to the highest possible satisfaction. This means that the five job characteristics are highly important for those tasks since they lead to the psychological motivational states. Research found that skill variety is important in predicting job satisfaction (Dodd & Ganster, 1996; Zeffane, 1994), the category core tasks is thus an assembly of multiple tasks that vary from each other. For example, writing vacancies and doing an interview with a prospective employee are total different tasks but are both in-role behavior tasks of a recruiter. An other important aspect of core tasks is that they have high task identity since this is proven to influence job satisfaction (Stepina, Perrewe, Hassell, Harris & Mayfield, 1991). The in-role core tasks are mostly tasks that form a whole process or are part of a process that has a beginning and an end. Writing a vacancy can be the start of a selection process and interviewing and selecting a new employee is the end of this process. Core tasks are
moreover high on autonomy, employees are given the freedom to execute the task themselves. This creates a feeling of responsibility which in turn leads to work motivation (Langfred & Moye, 2004). As the tasks are part of the formal in-role job behaviors employees receive feedback on these tasks. Feedback is important in motivation since it gives a sense of importance of the results (Humphrey et al., 2007) and feedback creates a focus on the specific task (Northcraft et al., 2011). Based on these arguments the following hypothesis are formed:
Hypothesis 1: Core tasks relate positively to satisfaction. Hypothesis 1a: Core tasks that grow relate positively to satisfaction. Hypothesis 1b: Core tasks that shrink relate negatively to satisfaction.
Non-core tasks are in-role behavior job tasks that are low on the five job characteristics dimensions
Firstly, these non-core tasks are in-role behavior tasks but they differ from core tasks in that they do not necessarily ask attention of the employee’s specific skills. These non-core tasks could be described as tasks that surpass a job and are more general, like attending meetings, and doing administration. Moreover, these non-core in-role tasks could be described as mindless tasks: “tasks that are low in cognitive
difficulty and performance pressures.” (Elsbach & Hargadon, 2006, p. 475). These
mindless tasks have a negative effect on the psychological states described in the JCM and are less motivating than other tasks which creates negative outcomes on job attitudes and job satisfaction (Elsbach & Hargadon, 2006). An important difference is that non-core tasks score low on task significance. Even though, multiple studies pointed out that task significance is a poor predictor of job satisfaction (Dodd & Ganster, 1996; Fried & Ferris, 1987; Humphrey et al., 2007). When looked at as sole indicator of job performance it does have a high effect on experienced meaningfulness of the job (Grant, 2008) indicating that it should be an important aspect of tasks. However, in-role tasks like administration do not have impact on other people their lives indicating a low task significance. Moreover, the autonomy on those tasks is low, mostly people are obliged to attend meetings and have to do administration work with
standardized methods which creates a lack in feeling responsible for the work. Feedback on those tasks consists of whether the task is done or not which is not assumed to be high quality feedback and the impact on the employee is low (Northcraft et al., 2011). Considering these points the following hypothesis are formulated:
Hypothesis 2: Non-core tasks relate negatively to satisfaction. Hypothesis 2a: Non-core tasks that grow relate negatively to satisfaction. Hypothesis 2b: Non-core tasks that shrink relate positively to satisfaction.
These two categories do not cover all tasks. A third type that is created is self-development tasks.
Self-development tasks is a special category that specifically describes tasks focussed on self-development and learning.
In the JCM Hackman and Oldham (1976) propose that employee with a high need for growth-strength have a higher correlation between job characteristics and job satisfaction (Hackman & Oldham, 1976; Hulin & Judge, 2003). Based on the assumption that people with high desires for personal development “want their jobs to
contribute to their personal growth” (Hulin & Judge, 2003, p. 262). The need for
growth-strength is thus seen as a moderating effect on the relation between job characteristics and job satisfaction. There are however tasks in a job that specifically deal with self-development or learning. It is interesting to see whether these tasks also have a direct relationship with job satisfaction. Assuming that people have a desire for personal development tasks related to this desire result in positive job satisfaction. Therefore, the following hypothesis are tested:
Hypothesis 3: Self-development tasks relate positively to satisfaction. Hypothesis 3a: Self-development tasks that grow relate positively to satisfaction. Hypothesis 3b: Self-development tasks that shrink relate negatively to satisfaction.
Than there is one more type that is created for this model. This is to cover the extra-role behavior tasks that employees tend to take on.
Extra tasks are extra-role behavior job tasks.
Firstly, extra-role behavior is defined as: “those behaviors that go beyond specific role
a unit, in order to promote organizational goals.” (Somech & Drach-Zahavy, 2000).
These are tasks that are not job specific but neither are they done by every surgeon, teacher or recruiter. Some employees for example take place in the participation council, or involve themselves in organizing a company party or excursion. Furthermore, doing those tasks is mostly voluntary since they are not formally described in job requirements. Even though, they are not formal tasks they are important for the organization (Belogolovsky & Somech, 2010; Somech & Drach-Zahavy, 2000). These extra tasks are connected to the JCM because these are tasks that add to employees’ skill variety in that they are doing something else than their job normally requires them to do. Moreover, as the definition implies these tasks have a (high) impact on others and the organization indicating a high level of task significance. Moreover, the voluntary nature makes that the autonomy on these tasks is high. Since these characteristics are satisfied in the extra tasks, high job satisfaction is expected. Which is already a topic in research; the correlation between extra-role behavior and satisfaction seems to be a resilient relationship (Bowling, 2010). These points lead to the following hypothesis:
Hypothesis 4: Extra tasks relate positively to satisfaction. Hypothesis 4a: Extra tasks that grow relate positively to satisfaction. Hypothesis 4b: Extra tasks that shrink relate negatively to satisfaction.
Figure 1. Schematic view of hypothesis
Core tasks +
Satisfaction
Core tasks grown +
Core tasks shrunk
-Non-core tasks
-Non-core tasks grown
-Non-core tasks shrunk +
Self-development tasks +
Self-development tasks grown + Selfdevelopment tasks shrunk
Methodology
This study focusses on the influences of tasks and changing tasks on job satisfaction. The study is split in two parts: a quantitative study to discover the influence of task types and changing tasks on job satisfaction among primary school teachers and a qualitative case study to get insight in the factors that influence change in tasks and how tasks actually change. This second study is exploratory in nature where it tries to find new insights and create an understanding of changing tasks and the influences on this change (Saunders, Lewis & Thornhill, 2009).
In this research a complementarity approach to mixed methods is used. This means that the two studies investigate different aspects (Bryman, 2008). The two methods combine deductive and inductive research approaches which Saunders and colleagues (2009) see as advantageous. Deduction, the dominant approach, tests the hypothesis derived from the theory. Induction focusses more on the development of theory (Saunders et al. 2009).
By combining the two methods a more complete image of tasks and changing tasks can be established (Bryman, 2008). Since the quantitative data does not offer depth with regard to where change comes from the qualitative data can add to this. For the hypothesis forming of the quantitative part and creating interview topics a deductive approach is used. Theory forms a basis for the hypothesis and the qualitative research tests the theory. Moreover, theory helps with operationalizing concepts used in the qualitative study. Using theory for the development of interview topics makes that the answers can be set against the theory and this makes it possible to see where the theory lacks in explaining the experience of respondents (Saunders et al., 2009).
Moreover, mixing the methods brings more utility to the results (Bryman, 2008). This is helpful for bringing back job analysis as a useful HR tool. By seeing the influence of tasks on job satisfaction and moreover knowing where change in tasks comes from, job analysis can incorporate the influence of change and again be useful in a volatile environment. This is supported by an inductive approach. This creates an open vision for the context in the interviews and helps to come to new insights and emerging questions. As well as by using the results from the quantitative research to find topics
that could be explored further in the interviews. The combination creates a basic idea of the theory and clearly structures the operationalization of concepts while it also gives the opportunity to gain understanding about the context and have a flexible structure (Saunders et al. 2009).
The two methods are used sequentially starting with the quantitative study. In this research the studies have equal weight since they research different parts that are mixed afterwards in reaching a conclusion (Leech & Onwuegbuzie, 2009). Figure 2 shows the research model which indicates the topics of the quantitative and qualitative studies and how these relate to each other.
Figure 2. Research model
Sources of change
Task types
Changing tasks Satisfaction
Study 1 Quantitative study Study 2
Qualitative case study
!
Quantitative study
This chapter describes the research method of the quantitative part of this research. The data collection procedure is explained and the sample is described in detail. Moreover, the measures that are used are operationalized.
Procedure
For the quantitative part of this study, the data used is collected by TNO with the
mooiwerk tool they developed in collaboration with the University of Twente. This
Dutch tool is developed to create better insight in tasks and whether these tasks fit with an employee. It is developed to be filled out in a workshop setting whereby the participants are guided through the steps of the tool by an instructor. The tool can be used for all sorts of jobs and its main focus is to create insight for the employees whether their tasks fit with what they want, need and with what they are capable of (Dorenbosch, 2013). This interactive method of involving employees creates awareness of possible (mis)fits and creates a proactive vision of creating and reaching goals (van Wingerden, Derks, Bakker & Dorenbosch, 2013).
In the workshop, participants are asked to follow the steps of the tool. Starting with providing some descriptive information about for example their age, tenure and job satisfaction. This is followed by the first step wherein they have to fill out the tasks in their job. The participants can fill out a maximum of twelve tasks and are asked to only fill out one task in each entry box. The second step lets the participants think about how these tasks relate to each other based on the percentage of time they spend on the tasks. They have to group the tasks into large, medium and small tasks and indicate for each group how much time is spent in total for this group. This is done by labeling each group with a percentage of time spent on all the tasks in the group whereby the percentages need to add up to 100. The third step puts the tasks on post-its and the participants are asked to position the post-its in a matrix. This matrix is based on two axes. One axis indicates whether the task has been in the job since the start or is a new task. The other axis indicates whether the task grew or shrunk over time. In the following step participants can indicate risk and strength factors in their work and match
these factors to the tasks. This shows which tasks are risk tasks, two-faced tasks, empty tasks and beautiful tasks. Next, participants are asked to indicate whether they want to keep doing the task and if they think they can still manage doing the task. Only the descriptive data and the first three steps are used in this research.
Sample
In this case primary school teachers completed the tool in a workshop where they had an instructor guiding them through the steps of the tool. All teachers work for a school that falls under the umbrella of Stichting Katholiek Onderwijs (SKO), which is a foundation for Catholic primary education. All schools are located in and around
Enkhuizen, Noord-Holland. Although the schools belong to the same foundation, there
are some differences in views on teaching by the directors and teachers in the different schools. Teachers participated between January and March 2015.
The sample holds 136 primary school teachers from nine schools. Seven respondents did not fill out teaching as a task and are eliminated from the study, this brings the sample back to 129 teachers. Moreover, some teachers did not fill out the demographic questions, these are coded as missing values. This brings down the usable number of respondents even more which is shown in table 1 (featured in the results section) which reports the means, standard deviations and correlations of some of the demographic data as well as it shows the N of every variable excluding the missing values on that variable.
The sample holds 17 male and 99 female teachers (N = 116) between the age of 25 and 65 (M = 46.00, SD = 11.10, N = 114). The tenure of the teachers with their current employer varies between 1 and more than 20 years with a mean around the 11 to 15 years of tenure (M = 5.353, SD = 1.440, N = 116). The teachers work between 8 and 60 hours (M = 28.681, SD = 9.567, N = 116). 94% of the participants finished an applied science (HBO) education (M = 5.965, SD = .349, N = 115). An overview of these descriptives can be found in table 1 in the results section. For the final sample the respondents that had missing data on the variables included in the regression analysis are also eliminated, bringing the sample back to 113 primary school teachers.
Measures
The tool is developed for general use and not all parts of the data collected are used in this study. The measures used in this research are described below.
Satisfaction. In this study this is the dependent variable. The teachers filled out
a single item about satisfaction with their job. They answered the question: “To what
extent are you, taken everything together, satisfied with you work/job?”. Answer options
are on a 5-point Likert scale with 1 as very unsatisfied and 5 very satisfied. A single item for the variable satisfaction is not always appreciated in research and often criticized (Wanous, Reichers & Hudy, 1997). This critique is mostly focussed on the reliability of the measure. When there is a scale of multiple questions about satisfaction, reliability of the questions can be estimated by tests. The final score than indicates if the respondent has been consistent in answering the questions. A single item measure is thus said to lack internal consistency reliability. However, Wanous and colleagues (1997) find in their meta-analysis that a single item is reliable as the correlation they found between single items and scales averaged around .67. Moreover, differences between single item measures had no influence on the results while differences in scales do moderate the results arguing that a single item measure has a higher robustness (Wanous et al., 1997). Using a single item has several advantages. Starting with a practical advantage, space in the survey could be limited. Moreover, people might get frustrated with filing out lists of questions that seem the same reducing the face validity. A statistical error of a scale is that mostly the average is used as scale score. If somebody has a mean of three and answered all items with a three this scale score gives the right reflection. However, if the different items are answered with different scores ending with a mean of three the variety of the scores is lost and the scale score does not reflect the answers (Wanous et al., 1997). Using a single item for job satisfaction is not without problems, but is a valid measurement.
Task types. The respondents named all the tasks their job holds, with a
maximum of twelve tasks. The researchers coded those tasks into the four task types: core tasks, non-core tasks, self-development tasks and extra tasks. This is done
following the theoretical framework about tasks described above. A few examples of tasks per task type are indicate below:
-
Core tasks are in-role behavior job tasks that are high on the five job characteristic dimensions.-
For example: teaching, preparation, correction work, parent contact.-
Non-core tasks are in-role behavior job tasks that are low on the five job characteristics dimensions.-
For example: meetings, administration, reports, e-mail.-
Self-development tasks is a special category that specifically describes tasks focussed on self-development and learning.-
For example: training, reading specialized literature.-
Extra tasks are extra-role behavior job tasks.-
For example: committees, mathematics or reading coordinator, participation council, organizing school camp.During the coding the researchers kept a list of all the tasks put into each type. This list is provided in appendix A.
In a meeting beforehand the researchers talked through some tasks they were expecting to come by such as teaching, correcting work, administration and organizing activities. The researchers made a preliminary classification by using the created task types that are based on the JCM and in-role and extra-role behavior. During the coding, close contact was kept and the researchers argued over tasks that they did not expect or were unclear. For example, sometimes definitions were unclear, the Internet and a friend who is a primary school teacher helped in defining occupation specific terms. To illustrate, teachers named the task administratie parnassys, after consultation of the Internet it became clear that parnassys is a student follow system and this task is thus an administration task. Moreover, certain abbreviations were used to indicate tasks, with help of the primary school teacher the task IB was defined as an internal mentor (Dutch:
intern begeleider) which is classified as an extra task since there is only one IB’er in a
school. An other problem the researchers came across was that some teachers put tasks together that other teachers filled out as separate tasks, even though the instructions ask
to only fill out one task per field. For example, meetings and commissions are sometimes put together as one task however these fall into separate types. For this task is decided that it is classified as non-core tasks and not as extra tasks. Since it is assumed that all teachers participate in a general school meeting but not all teachers participate in committees, meeting is the main task.
All task types are measured in amount indicating the number of tasks in that task type. As mentioned, participants could fill out a maximum of twelve tasks. Furthermore, the task types are measured in time spent in percentage on the type which indicates the total percentage of time spent on this type.
Change in tasks. As mentioned, teachers put each task in the matrix dividing
them into grown and shrunk tasks. With this the teachers indicated whether the tasks take up more time over the period they have been doing the task. As the tasks have been split up in the task types, the researchers counted the grown and shrunk tasks in each type. This way each type is split into two variables indicating weather the tasks from the total of the type are grown and shrunk. Again, these variables are split up into amount and time spent variables.
The control variables used in this study include the respondents’ age and working hours. Both these variables are measured with single items. Age is recoded from the birth year to age in years. As table 1 shows age and both the tenure variables correlate positively and significantly. This indicates that either one of those variables tell the same information about the sample therefore it is useless to include all three. Age is chosen as control variable since it is a continuous variable and the tenure variables are measured on a 7-point Likert scale which makes age a more precise measure. Working hours are indicated by the respondents by filling out a number. Gender is not included since 85% of the sample is female, which is due to the fact that the job of primary school teacher is female dominated. In other populations this would give a skewed image of the population, however in teaching this is a general image. The same holds for education, to be a primary school teacher you are required to have finished an applied science degree (HBO) and with 94% of the sample having indeed
finished an applied science degree, education level does not make a difference among the participants.
Data analysis
Data obtained with the TNO tool is analyzed with IBM’s Statistical Package for the Social Science (SPSS Statistics) version 21 for Mac. Correlation and multiple regression analysis are used to test the hypothesis. Correlation analysis is used to see if multicollinearity could take place in the regression analysis. Control variables used are age and working hours.
Results
Descriptive statistics
Means, standard deviations and correlations are reported in tables 1-5. All correlations of variables that are also in a regression model together are lower than .70 and multicollinearity tests indicate that none of the models has a multicollinearity problem. All variables could thus be included in the regression analysis. To keep the tables easier to read they are split up into smaller tables picturing the correlations per task type.
Table 1, containing the descriptive statistics of the control variables, shows that the primary school teachers in this sample are relatively satisfied with a mean of almost 4 on a scale of 5 (M = 3.826, SD = .625). Table 1 also indicates that there is a positive significant correlation between gender and work hours indicating that male teachers indicate longer work hours (r = .393, p < .01). An other interesting correlation is the significant negative correlation between age and education, indicating that older teachers have a lower education (r = -.234, p < .05).
Table 2, reporting the descriptive statistics of core tasks, shows that teachers have on average 5 core tasks (M = 4.681, SD = 1.384) on which they spent on average 61% of their time (M = 61.221, SD = 14.674). Of these core tasks, on average 3 have grown (in time spent on the task) over time (M = 3.000, SD = 1.642) and on average teachers spent 35% of their time on those grown tasks (M = 34,743, SD = 21.908). Of the core tasks 1,5 task shrunk over time (M = 1.664, SD = 1.279) and teachers spent on average 26% of their time on the shrunken core tasks (M = 25.761, SD = 22.978). The correlations between the different task variables are all logical, that is to say as the amount of core tasks is higher than there are more grown core tasks and more shrunken core tasks. An interesting correlation is the positive and significant correlation between age and grown core task indicated with time spent on these tasks (r = .282, p < .01). Older teachers spent more time on core tasks that have grown. Moreover, they also
spent less time on shrunken core tasks as there is a negative significant correlation between age and the variable ‘core tasks shrunk over time’ (r = -.266, p < .01). Furthermore, it appears that working more hours a week does not mean that teachers spent more time on core tasks. This is indicated by the significant negative correlation between the variable ‘core tasks time’ and work hours (r = -.251, p < .01)
Table 3, depicting the descriptive statistics of non-core tasks, shows that teachers have on average 2.5 non-core tasks (M = 2.611, SD = 1.129). They spent on average 20% of their time on non-core tasks (M = 20.274, SD = 12.065). Teachers have on average 2 non-core tasks that have grown over time (M = 1.823, SD = 1.046) and they spent an average of 16% of their time on these grown non-core tasks (M = 16.363,
SD = 11.624). Only 1 non-core tasks shrinks over time (M = .779, SD = .842) and
teacher spent on average 4% of their time on shrunken non-core tasks (M = 3.912, SD = 6.260). Again, the correlations between the task variables are all logical. Table 3 also indicates that there is a significant negative correlation between the amount of non-core tasks and job satisfaction (r = -.195, p < .05). Moreover, grown non-core tasks also have a significant negative correlation with job satisfaction (r = -.210, p < .05). An other interesting correlation is the positive significant correlation between work hours and the variable ‘non-core tasks time’ (r = .186, p < .05), indicating that work hours grow when
teacher spent more time on non-core tasks. The ‘grown non-core tasks time’ variable also has a positive significant correlation with work hours, showing that the time spent on non-core tasks that grow increases work hours.
Table 4, reporting the descriptive statistics of self-development tasks, shows that teachers have on average 1 self-development task (M = .802, SD = 1.815). Mostly they have this self-development task from the beginning (M = .640, SD = .748) and on average self-development tasks grow over time (M = .586, SD = .694). Table 5 also indicates that male teachers have more new self-development tasks (r = .271, p < .01). Both self-development tasks that grew and shrunk over time have a significant positive correlation with self-development tasks that are old (r = .585, p < .01, r = .527, p < . 01). Moreover, self-development tasks that grew over time correlate positively and significantly with self-development tasks that are new (r = .381, p < .01), indicating that teachers spend more time on self-development tasks that are new.
Table 5, containing the descriptive statistics of extra tasks, indicates that teachers have an average of 3 extra tasks (M = 2.867, SD = 1.810). Extra tasks take on average 16% of a teachers time (M = 15.912, SD = 13.884). On average 2 extra tasks grow over time (M = 2.212, SD = 1.661) and teachers spent 13% of their time on these grown tasks (M = 13.398, SD = 13.217). Interesting is that the range of time spent on extra tasks and grown extra tasks is very wide, there are teachers who have none extra
