Amsterdam Business School
“The relation between incentives and job motivation, and the
influence of gender”
Name: Huub Spanjaards Student number: 10868615
Thesis supervisor: ir. S. van der Heide Date: 17-06-2016
Word count: 12035, 0
MSc Accountancy & Control, specialization Control
Statement of Originality
This document is written by student Huub Spanjaards who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
This study investigates the relation between incentives in general and the job motivation of employees. Moreover, it investigates if there is a difference between the different types of incentives and their effect on the job motivation. Finally, it looks at the impact of gender on the relation between incentives and job motivation. This study uses survey data from 106 pairs of operational employees and their immediate managers in various jobs, industries and countries. The self-determination theory is used, in order to explain the motivation concept. Other theories, such as agency theory, expectancy theory and gender stereotype theory, are used to construct the hypotheses. Using linear regression analysis and the process method of Andrew F. Hayes, several results are found. First of all, as hypothesized, the results indicate that incentives have a positive effect on the job motivation of employees. Second, as hypothesized, there is no statistical evidence that there are differences between the type of incentive and the impact on job motivation. Last, as hypothesized, the results indicate that gender does moderate the relationship between incentives and job motivation. Even though it was hypothesized that incentives have more effect on men than on women, the results show that women are much more sensitive for the use of incentives. In sum, this study demonstrates that incentives are a good way to motivate employees, it does not matter which type of incentive is used and that incentives are most useful to motivate the female employees.
Keywords: Incentives; Monetary incentives; Non-monetary incentives; Job motivation; Gender; Self-determination theory.
Contents 1 Introduction ... 6 2 Theoretical Framework ... 8 2.1 Definitions ... 8 2.1.1 Incentives ... 8 2.1.2 Job Motivation... 9 2.2 Hypothesis development ... 10
2.2.1 Incentives and job motivation ... 10
2.2.2 Different types of incentives and job motivation ... 12
2.2.3 Incentives, job motivation and the influence of gender ... 14
3 Method ... 15
3.1 Data collection and respondents ... 15
3.2 Variables ... 17 3.2.1 Incentives ... 17 3.2.2 Monetary Incentives ... 17 3.2.3 Non-Monetary Incentives ... 18 3.2.4 Job Motivation... 18 3.2.5 Gender ... 18 3.2.6 Control variables ... 18 3.3 Factor analysis ... 19 4 Results... 20 4.1 Descriptive statistics ... 20 4.2 Hypothesis 1 ... 22 4.3 Hypothesis 2A ... 23 4.4 Hypothesis 2B ... 25 4.5 Hypothesis 3 ... 26
5 Discussion ... 29
5.1 Future research ... 30
5.2 Limitations... 31
Appendix A. ... 33
Contacting people of my own network (Dutch) ... 33
Contacting the other part of the pair (Dutch) ... 34
Appendix B. ... 35 Appendix C. ... 36 Hypothesis 1 ... 36 Hypothesis 2A ... 37 Hypothesis 2B ... 38 References ... 39
1 Introduction
Most economists emphasize that incentives matter (Gneezy, Meier & Biel, 2011). Gneezy et al. (2011) indicate that the basic law of behavior is that higher incentives will lead to more motivation and a higher performance. But is this really the case? In the literature there are tremendous amounts of studies investigating the effects of incentives on the motivation of employees. Nevertheless, after decades of research the results are still ambiguous (Franco-Santos, Lucianetti, & Bourne, 2012). Some authors indicate the positive effects of incentives on motivation (Appelbaum & Kamal, 2000; Cameron & Pierce, 1994; Fang & Gerhart, 2012; Locke et al., 1980; Markovic, 1997; Welch, 2001). On the other hand other authors show the negative effects of incentives on motivation (Deci, 1971; Dugar, 2013; Kohn, 1993; Kunz & Linder, 2012;). Therefore the first aim of this study is to provide clarity on the relation between incentives and the job motivation of employees.
The second aim of this study, is to investigate if there is a difference between the type of incentive (monetary or non-monetary) and their effect on the job motivation. Deci (1971) suggests that not all types of rewards exhibit the same relations with motivation and work effort, therefore it is interesting to find out if there are differences among the different types of incentives. There are authors that indicate that monetary incentives are the best option to motivate employees (Gneezy & Rustichini, 2000; Hansen, 1980). But on the other hand there are also authors that indicate that non-monetary incentives are a better option to motivate employees than monetary incentives (Ariely et al., 2009; Frey, 2007).
The last aim of this study is to explore the impact of gender on the relation between incentives and job motivation. According to Paarsch and Shearer (2004) economists have long been interested in measuring and explaining differences in labor-market performance between men and women. They indicate that attention in these researches has focused on the productivity differences between men and women. The focus in this study is on motivational differences between men and women. In literature there are several studies signaling (significant) differences between men and women regarding motivation (Gneezy et al., 2003; Migheli, 2015; Rusillo & Arias, 2004). Therefore the following research question is constructed:
“Does someone’s gender affect the relation between incentives and job motivation?”
By answering this question this study contributes to the literature by providing clarity on the relation between incentives and job motivation, it gives an answer to the question whether there is a difference between the types of incentives in relation with job motivation and it provides
insight into the influence of gender on this relation. Therefore this study responds to Bonner and Sprinkle’s (2002, p.325) call for more research to sort out the relations among incentives, task attractiveness, motivation, effort, and performance. Furthermore, this study responds to Kunz and Linder’s (2012, p.615) call for more research into rewards from a broader perspective.
Moreover, answering this question has a practical relevance. In the current knowledge economy, with increasing globalization and global competition, human capital is often seen as a vital source for sustained competitive advantage (Sheehan, 2012; Wilden, Gudergan, & Lings, 2010). As of today, it is widely recognized that the motivation and engagement of an employee depends on the perceived feeling of being fairly rewarded for one’s skills, knowledge and contribution (Dahlqvist & Matsson, 2013). According to Bhattacharya and Mukherjee (2009), this does not simply mean paying high wages and offering attractive benefits. These authors state that employees in the 21th century are searching for something more beyond just monetary rewards. As every (for-profit) company strives to make as many profit as possible and to achieve competitive advantage, insights in the effects of different types of incentives on job motivation are relevant.
To summarize, in figure 1 the research model of this study is given. In this model incentives are the independent variable, job motivation is the dependent variable and gender is the moderator.
Figure 1: Research model
The remainder of this study will be structured as follows. Section two contains the theory and hypothesis development. In section three the research methodology is discussed. Section four provides an overview of the empirical results. Finally, the conclusion, future research and research limitations are discussed in section five.
Gender
Job motivation Incentives
2 Theoretical Framework
In this section the definitions of incentives and job motivation will be discussed. Furthermore the hypotheses of this study will be constructed, based on existing literature and different theories such as the expectancy theory, agency theory, theory of psychological contracts and theory of rational economic.
2.1 Definitions
2.1.1 Incentives
Until now the definitions incentives and job motivation have passed a number of times in this study, but what are they precisely? Incentives are suggested as a method for motivating and improving the performance of employees (Atkinson et al., 2001). According to Atkinson et al. (2001) these incentives could be divided in monetary terms such as bonuses or non-monetary terms such as recognition. It is important to be aware that the concept of non-monetary incentives does not necessarily mean that the provided incentive should have no financial value, it simply means that whatever is given, should not be just money (Silverman, 2004). It is also possible to make a distinction between extrinsic and intrinsic rewards (Dahlqvist & Matsson, 2013). Dahlqvist and Matsson (2013) indicate that extrinsic rewards are of monetary value, such as salary, wage-rise, bonuses or other monetary benefits. Whereas the intrinsic rewards are of a more intangible nature and are linked to the work task and are not of monetary value. Examples can be that the employee is increasing its decision making authority, is given more complex tasks or achieves a higher position within the organization hierarchy (Jacobsen & Thorsvik, 2002). Moreover, based on a research of KPMG (2007), who investigated which incentives are most frequently used by organizations for improving employee motivation, it seems that the following incentives are most frequently used:
- Offering financial rewards and bonuses; - Showing recognition;
- Providing career development opportunities; - Seeking workers opinion.
The incentive listed at the top is most frequently used and the incentive listed at the bottom is least frequently used. Moreover the first incentive is a monetary incentive, whereas the other incentives are non-monetary incentives. This research shows that most companies use financial rewards and bonuses to motivate their employees.
2.1.2 Job Motivation
Job motivation could be defined as an individual's degree of willingness to exert and maintain an effort towards the goals of an organization (Franco, Bennet & Kanfer, 2002). Moreover the self-determination theory of Ryan and Deci is used, which was developed in 1985 and expanded throughout the years, to explain more about motivation.
Figure 2: The self-determination theory (Ryan & Deci, 2000).
In figure 2 the self-determination theory is displayed. This theory defines more than one type of extrinsic motivation, next to intrinsic motivation and amotivation (Ryan & Deci, 2002). The first type of motivation is non-regulation, which falls within the amotivation category. With this type of motivation individuals either don’t act or act passively. The second type of motivation is external regulation, which falls within the extrinsic motivation category and is the most externally oriented motivational type. With this type of motivation individuals are only stimulated by receiving a reward or avoiding punishment. The third type of motivation is introjected regulation, which falls within the extrinsic motivation category and is a somewhat externally oriented motivational type. With this type of motivation individuals are stimulated by avoiding self-worth characteristics such as guilt and shame or to attain ego enhancements or feelings of worth. The fourth type of motivation is identified regulation, which falls within the extrinsic motivation category and is a somewhat internally oriented motivational type. With this type of motivation individuals are stimulated by activities which they find valuable or meaningful. The fifth type of motivation is integrated regulation, which falls within the extrinsic motivation category and is an internally oriented motivational type. With this type of motivation individuals are stimulated because there is congruence between the personal endorsed values, goals, and needs. The last type of motivation is intrinsic regulation, which falls within the intrinsic
motivation category. With this type of motivation individuals are stimulated because they find the activity itself pleasurable and self-fulfilling.
Moreover the self-determination theory of Ryan and Deci (2000) distinguishes autonomous motivation from controlled motivation. When individuals are motivated autonomously they engage in an activity because they find it interesting, they do the activity voluntary (Gagné & Deci, 2005). The earlier mentioned identified regulation, integrated regulation, and intrinsic regulation are classified under autonomous motivation. On the other hand, if individuals are controlled motivated, they participate in an activity because it involves a sense of pressure or a sense of forced engagement (Gagné & Deci, 2005). The external regulation and introjected regulation are classified under controlled motivation. Furthermore Gagné and Deci (2005) indicate that autonomous and controlled motivation stand in contrast to amotivation, because the autonomous and controlled motivation are both intentional and the amotivation involves a lack of intention and motivation. Moreover, both autonomous and controlled motivation can result in high involvement in an activity (Vansteenkiste et al., 2010). However, Vansteenkiste et al. (2010) indicate that the self-determination theory stresses the importance of autonomous motivation above controlled motivation. Autonomous motivation is, compared to controlled motivation, superior about well-being, job satisfaction, and performance (Ryan & Deci, 2000). Because of the variety of advantages of autonomous motivation, organizations should focus on creating autonomous motivation over controlled motivation (Ryan & Deci, 2008).
2.2 Hypothesis development
2.2.1 Incentives and job motivation
As mentioned in the introduction there is no consensus in the literature regarding the effects of incentives on the motivation of employees. For example, Fang and Gerhart (2012) find in their study no support for the concern that monetary incentives will lead to decreasing interest and motivation for employees. On the contrary, they find that monetary incentives lead to higher levels of motivation. Likewise, Locke et al. (1980) state that money is the crucial incentive and that no other incentive or motivational technique comes even close to the effects of money. Furthermore, Welch (2001) indicates that monetary incentives are one of the most powerful tools in an organizational arsenal to motivate employees. All these authors find that monetary incentives lead to a higher job motivation.
On the other hand there are also authors who find that monetary incentives do not lead to a higher job motivation. For example, Kohn (1993) argues that just because too little money can irritate and demotivate does not mean that more and more money will bring about increased motivation. Moreover, Deci (1971) finds in his study that when money was used as an external reward, motivation tended to decrease. Kunz and Linder (2012), found that monetary rewards have a harmful effect on the motivation of employees.
The same discrepancy in literature holds for the non-monetary incentives. For example, Cameron and Pierce (1994) find that verbal rewards (non-monetary) have a positive effect on motivation. Furthermore Appelbaum and Kamal (2000) find in their study that a lot of non-monetary incentives, such as job enrichment and job recognition, will lead to a higher job motivation. Moreover Markovic (1997) concludes that in order for employees to be truly satisfied and motivated in their jobs, they must feel that they are contributing, learning and enjoying themselves. All these authors find that non-monetary incentives lead to a higher job motivation.
On the other hand there are also authors who find that non-monetary incentives do not lead to a higher job motivation. For example, Dugar (2013) finds in his article that non-monetary incentives could also lead to employees with opportunistic behavior and therefore are not really motivated.
To develop the first hypothesis, expectancy theory, agency theory and the theory of psychological contracts are used. The expectancy theory is developed by Vroom (1964). This theory is the most used theory to explain employee motivation (Purvis, Zagenczyk, & McCray, 2015). The expectancy theory of Vroom (1964) is a basis for motivation and it consists of three different components. The first component is valence. Vroom defined this concept as all possible affective orientations toward outcomes, and it is interpreted as the importance, attractiveness, desirability, or anticipated satisfaction with outcomes. The second component is instrumentality. Vroom defined this concept as an outcome-outcome association, and it has been interpreted not only as a relationship between an outcome and another outcome but also as a probability to obtain an outcome. The last component is expectancy. Vroom defined expectancy as a subjective probability of an action or effort leading to an outcome or performance. In formula the expectancy theory is displayed as: Motivation = Valence x Instrumentality x Expectancy. Moreover, the expectancy theory of Vroom predicts that employees in a company will be motivated when they believe that more effort assigned to a task will yield better job performance and better job performance will lead to rewards. In order to have things happened
as mentioned before, it is necessary to choose rewards that are valued by that employee (Vroom, 1964).
The next theory that is relevant in developing the first hypothesis is the agency theory. According to Eisenhardt (1989), the agency theory is one of the leading theories in explaining the use of incentives. She indicates that the agency theory is broadened with the so-called agency problem. This agency problem occurs when multiple parties that are working together have different goals and different labor divisions. Specifically, the agency theory attempts to describe the relationship between one party, the principal, and the other, the agent. The difference between the two that causes a problem is that the principal will try to maximize the value of the firm, whereas the agent wants to maximize his own utility. The agents’ utility is determined by his salary, minus the effort that he puts into his job, whereas the principals optimal profit is represented by the highest possible output minus the lowest possible salary for his employee. In other words, agency theory presumes employees’ self-interest is to some extent different from the interest of the organization. Therefore, managers should use incentives to make sure their employees work in a way that contributes to the overall organizational objectives (Jensen & Meckling, 1992). Agency theory also suggests, in line with expectancy theory, that people want to perform better if they expect to receive a reward for good performance, because better performance will then personally benefit them (Bonner & Sprinkle, 2002).
The last theory is the theory of psychological contracts (Rousseau, 1995). A psychological contract refers to perceptions of the explicit and implicit promises made in the exchange agreement between employer and employee. The psychological contract consists of three elements: perceived employee obligations, perceived employer (organization’s) obligations and perceived fulfillment/violation of employer obligations (Freese & Schalk, 2008). If an organization is able to deliver on the ‘promise’ to his employees, employees are more committed, healthier and have less turnover intentions (Bredwell, 2008; Conway, Guest, & Trenberth, 2011). In other words, if an organization delivers on his promises, employees will be more motivated. This results in the following hypothesis:
H1: Incentives have a positive effect on job motivation.
2.2.2 Different types of incentives and job motivation
As indicated earlier, Deci (1971) suggest that not every type of incentive will lead to the same level of job motivation. A study conducted by Hansen (1980) concludes that using monetary incentives leads to a higher job motivation than using non-monetary incentives. Moreover, Gneezy and Rustichini (2000) indicate that offering high financial rewards leads to motivated
employees. In these studies it becomes clear that monetary incentives are the best option to motivate employees.
On the other hand, the research of Ariely et al. (2009) found that excessive monetary rewards can lead to detrimental performances, compared to non-monetary rewards. Moreover, Frey (2007) concludes that non-monetary incentives, such as awards, are better than monetary incentives. In these studies it becomes clear that non-monetary incentives are the best option to motivate employees.
To develop the second hypotheses, the rational economic theory and the theory of hierarchy of needs are used. According to the rational economic theory, an employer should always provide cash incentives, because employers will never be able to perfectly predict the preferences of all their employees (Shaffer & Arkes, 2009). Furthermore they indicate that cash should always be favored, because it is inherently more fungible than any non-cash incentive. This is confirmed by a research of Hein and Alonzo (1998). They asked employees to indicate their preference among the following types of incentives: $1500 cash, a travel award worth $1500, or a merchandise award worth $1500, 79% of the respondents chose to receive the cash.
The theory of hierarchy of needs is developed by Maslow in 1943 and is expanded throughout the years. According to this theory, there are five different motivational needs, often depicted as hierarchical levels within a pyramid. He divided the five different motivational needs into basic needs and growth needs. The basic needs consists of physiological/biological needs, safety needs, social needs, and esteem needs, whereas the growth needs consists of self-actualization needs. Non-monetary incentives are necessary to gain safety needs and social needs. Wallace and Zeffane (2001) indicate in their study that employees depend on incentives like money as the main factor of motivation, because according to Maslow’s hierarchy of needs, money is a unique reward that can satisfy different needs such as physiological need for food. Based on these two theories and prior literature, it seems that there are no differences between the use of monetary incentives and the use of non-monetary incentives and their effect on the job motivation of employees. Although Deci (1971) indicates that not all types of rewards exhibit the same relations with motivation and work effort, there is no evidence for this based on the two theories and prior literature. This results in the following hypotheses:
H2A: Monetary incentives have a positive effect on job motivation. H2B: Non-monetary incentives have a positive effect on job motivation.
2.2.3 Incentives, job motivation and the influence of gender
The theory behind gender motivation differences is the theory of gender stereotype (Arnania-Kepuladze, 2010). This theory is based on the idea of the differences between men and women, i.e. on the existence of gender stereotypes, which can be traced back to historical context of men’s hegemony proceeding from the perception of priority based on sex differences and collaterality of qualitative differences of human beings. According to this theory, a “typical” men and a “typical” women have different psychology, possibilities, values, interests, social
predestinations, roles, needs and, therefore, are motivated differently.
Moreover, as mentioned in the introduction, several studies found (significant) differences between men and women regarding motivation. Rusillo and Arias (2004), for example, explain in their study that men have a greater degree of extrinsic motivational orientation whereas women show a greater intrinsic motivation regarding academic goals. In other words, women are much more motivated from the inside and they do not need incentives to get motivated. This is confirmed by the research of Migheli (2015). He concludes that women are not extremely sensitive to incentives, because of the fact that women tend to accomplish the assigned task as well as they can, regardless of the incentive scheme. He therefore concludes that employers should reduce incentives for women, as the net marginal gain is much lower for women than for men and women are much more intrinsically motivated. Moreover, a research conducted by Gneezy et al. (2003), has as main finding that using tournament incentives (the person that performs best, gets the highest incentive) has a significant influence on men, but has no influence on women. These studies indicate that the use of incentives has more effect on men, than on women. This results in the following hypothesis:
3 Method
This section starts with explaining the research method of this study and the manner in which the respondents are collected. Moreover, this section gives some characteristics of the respondents such as gender and organizational tenure. Furthermore, this section explains how the variables of this study are measured and what the reliability of these variables is. This section ends with a factor analysis.
3.1 Data collection and respondents
The research method of this study is a survey, with newly collected data. The reason for this is that publicly available archival data for testing the hypotheses is not available. Respondents of this survey are operational employees and their supervisory manager. These respondent pairs had to meet three criteria: (1) the employees had to carry out operational activities within their organization; (2) the employees must have worked in their current functions for at least one year; and (3) the organization must have performance metrics for employees’ performance.
This study is part of a project where more researchers take part in. Each researcher had to deliver at least 6 pairs of respondents, to be allowed to join the project. The search of respondents began with contacting people within my own network. This contacting was done using email and by asking the respondents if they were willing to participate in an online survey (see Appendix A). In some cases the operational employee came from my network and in other cases the supervisory manager came from my network. Operational employees who agreed to participate, provided their manager’s contact details. Managers who agreed to respond to the survey provided the contact details of employees who met the pre-specified criteria. Therefore it was possible to contact the other part of the pair, which was also done using email (see Appendix A). In this email the name of the other part of the pair was clearly mentioned.
The survey was completed by 106 pairs of respondents. This sample is quite broad and diverse, which could be seen by the nationalities of the respondents. Within these 106 pairs there are respondents from Azerbaijan, Belgium, Cyprus, England, Russia, South Africa, Turkey and Ukraine, but most of the respondents are from China and the Netherlands. Moreover, the operational employees consists out of 51% men and 49% women. For the managers this is 69% men and 31% women. On average, the operational employees have been working for 6 years in their organization and have been employed in their current function for 5.5 years. The managers have, on average, been working for 9,5 years in their organization and have been employed in their current function for 7 years. These figures suggest that the respondents are well informed
about their job characteristics. Table 1 summarizes the most important information of the respondents.
Table 1: Respondent characteristics.
To ensure that the respondents who have filled in the questionnaire provide an unbiased sample of the group of individuals who received the questionnaire, a response analysis is carried out. This response analysis is only carried out for the employees who filled in the questionnaire and not for the managers who filled in the questionnaire, because in this study only the data filled in by the employees is used (see Appendix B). The reason that only the data filled in by the
employees is used, is because in this study the job motivation of employees is investigated, not the job motivation of the managers. The response analysis consists of checking for non-response bias by comparing early and late respondents based on the mean date of filling in the
questionnaire. This response analysis shows that there are significant differences found between the early and late respondents in relation with the non-monetary incentives variable, although this significance level is only slightly significant (p<.05). With exception of the non-monetary incentives variable, there are no additional significant differences found between the early and late respondents on the other three variables. According to Berg (2005) it is important to carry out a response analysis, because there are differences in analyzing a non-random sample and analyzing a random sample. A non-random sample does not provide the same valid grounds for generalizing about a population than a random sample does. Therefore it is positive that the results show that non-response bias is not a major concern in this sample.
In addition to the response analysis, also a missing value analysis is conducted. This missing value analysis is useful to check if there are values missing in the sample and if this is the
case, it is also useful to check if there is some pattern that explains which values are missing. It is a cause of concern if values are missing in a non-random manner. In this study there are no missing values within the independent and dependent variables, but there are some missing values within the moderator and control variables. However, these values are missing in a random manner and therefore these results support that non-random missing values is not a major concern in this sample.
3.2 Variables
3.2.1 Incentives
One of the independent variables in this study is incentives in general. This variable consists of two monetary incentive items and two non-monetary incentive items. The monetary incentive items are potential salary increases and potential bonuses or extras, the non-monetary incentive items are increasing chances of promotion and increasing authority within the organization (see Appendix B). The operational employees rated these items on a seven-point fully anchored Likert scale: (1) totally disagree, (2) disagree, (3) moderately disagree, (4) neutral, (5) moderately agree, (6) agree, (7) totally agree. The variable incentives is calculated by taking the mean of the item scores. To check the reliability of the scale, the Cronbach’s alpha is measured. In general, the lower limits of acceptability for exploratory research of the Cronbach’s alpha is around 0.50 to 0.60 (Nunnally, 1978). The Cronbach’s alpha of this variable is 0.885, which is high. This alpha could even be slightly increased by deleting the fourth item (see this item in Appendix B), but given the already high value of the alpha there is no need for this.
3.2.2 Monetary Incentives
Another independent variable in this study is monetary incentives. As mentioned in the previous chapter, monetary incentives are incentives that literally consist only out of money (Silverman, 2004). This variable is measured with a well-established instrument developed by Moers (2006). As can be seen in the Appendix B, this variable is measured with two items. The operational employees rated these items on a seven-point fully anchored Likert scale: (1) totally disagree, (2) disagree, (3) moderately disagree, (4) neutral, (5) moderately agree, (6) agree, (7) totally agree. The variable monetary incentives is calculated by taking the mean of the item scores. The Cronbach’s alpha of this variable is 0.820, which is satisfactory.
3.2.3 Non-Monetary Incentives
The last independent variable in this study is non-monetary incentives. As mentioned in the previous chapter, non-monetary incentives certainly could have financial value, as long as it is not only money what is given (Silverman, 2004). Just as the variable monetary incentives, this variable is measured with the instrument developed by Moers (2006). This variable is also measured with two items (shown in the Appendix B). The operational employees rated these items on a seven-point fully anchored Likert scale: (1) totally disagree, (2) disagree, (3) moderately disagree, (4) neutral, (5) moderately agree, (6) agree, (7) totally agree. The non-monetary incentives are calculated by taking the mean of the item scores. The Cronbach’s alpha of this variable is 0.796, which is adequate.
3.2.4 Job Motivation
The dependent variable in this study is job motivation. As mentioned in the previous chapter, job motivation is an individual's degree of willingness to exert and maintain an effort towards the goals of an organization (Franco, Bennet & Kanfer, 2002). This variable is measured with an instrument developed by Groen et al. (2015). It consists out of three items, which could be seen in the Appendix B. The operational employees rated these items on a seven-point fully anchored Likert scale: (1) totally disagree, (2) disagree, (3) moderately disagree, (4) neutral, (5) moderately agree, (6) agree, (7) totally agree. Job motivation is calculated by taking the mean of the item scores. The Cronbach’s alpha of this variable is 0.860, which is high. This alpha could even be increased slightly by deleting the second item (see this item in Appendix B), but given the already high value of the alpha there is no need for this.
3.2.5 Gender
The moderator in this study is gender. This variable is measured with an open question in which respondents could indicate if they are a men or a women. The question can be seen in the Appendix B.
3.2.6 Control variables
The reason to include control variables is to figure out if the independent variables caused the change in the dependent variable or if the change was due to (one of the) control variables (Field, 2013). The control variables in this study are education level and organization type. Education level is categorized in seven different groups: (1) primary education, (2) lower vocational education, (3) intermediate general education, (4) intermediate vocational education, (5) higher general education, (6) higher vocational education/BSc and (7) scientific
education/MSc or higher. Organization type is categorized in two groups: (1) profit organization and (2) non-profit organization. Organization type in particular is an interesting control variable, because according to Light (2002) employees in nonprofit organizations display stronger
motivations and higher satisfaction linked to the meaningfulness of their work.
3.3 Factor analysis
In the previous paragraph, the different variables are introduced and the Cronbach alpha’s of the variable was given, if relevant. This paragraph shows if the different items reflect on the incentives variables and the job motivation variable. This is done by creating a rotated component matrix, which is shown in Table 2.
Table 2: Rotated component matrix.
Table 2 shows two different factors out of the items, namely the incentives variable and the job motivation variable. Moreover, it is possible to see if some items correlate stronger with another construct than the one it is supposed to reflect on. According to Stevens (2002), items correlate strong with another construct if the value is greater than 0.40. In Table 3 only the values above 0.40 are displayed. Therefore, Table 2 shows that based on the value of 0.40, there are no items with a strong correlation with another construct. In other words, it means that there is not an item that is supposed to reflect on the incentives variable, but also correlates strong with the job motivation variable.
4 Results
In this section the descriptive statistics for the most important variables are given. Moreover, this section provides a correlation matrix to look at the relation between the different variables. Finally, this section ends with answering the hypothesis constructed in section 2 and testing each hypothesis for robustness by alternative specifications of the model.
4.1 Descriptive statistics
To get a more complete picture of the data in the research sample, a table with the summary statistics of each variable filled in by the employees is created (shown in Table 3).
Table 3: Descriptive statistics.
Table 3 shows that the respondents selected a minimum value of 1 for the incentives, the monetary incentives and the non-monetary incentives. Moreover this table shows that the respondents selected a maximum value of 7 for these three variables. The average value of these three variables is around the 4.5, which could be seen in the mean column. The job motivation variable has a minimum value of 2.67 and a maximum value of 7. The average value of this variable is 5.9686. The standard deviation for the incentives, the monetary incentives and the non-monetary incentives is between 1.59 and 1.72, whereas the standard deviation for the job motivation is lower than 1. Finally, this table confirms the fact that there are no missing values within the independent and dependent variables, because all 106 respondents have selected a value. This could be seen in the N column.
To get an even better overview of the data in the research sample, also a correlation matrix is constructed (shown in Table 4). This correlation matrix is useful to give an indication of the bivariate relations between the variables.
Table 4: Correlation matrix.
In this correlation matrix both the Pearson correlation and the Spearman correlation are displayed. Below the diagonal is the Pearson correlation and above the diagonal the Spearman correlation. The Pearson correlation is useful when the data is parametric and the Spearman correlation is useful when the data is non-parametric. In this study the data is non-parametric, which means that the Spearman correlation is most useful. Table 4 shows that the different incentive variables correlate positively significant with the job motivation variable, this is the case in both the Pearson and Spearman correlation. The Pearson correlation between the incentives and the job motivation is 0.399, between the monetary incentives and the job motivation is 0.392 and between the non-monetary incentives and the job motivation is 0.358. The Spearman correlation between the incentives and the job motivation is 0.320, between the monetary incentives and the job motivation is 0.271 and between the non-monetary incentives and the job motivation is 0.342. Apparently, the three incentive variables and the job motivation variable are not mutually exclusive. If the sum of the incentives increases, also the job motivation increases. This also holds for the correlation between the different incentives variables itself. The three incentive variables are not mutually exclusive, they even correlates strong with each other. According to Field (2013), variables correlates strong with each other if they have a value of 0.7 or higher. It is not surprisingly that the incentive variables correlates strong with each other, because they have much in common. In contrary, the gender, education and organization type
variables do not have a significant relation with job motivation, this is the case in both the Pearson and Spearman correlation.
4.2 Hypothesis 1
H1 predicted that incentives in general have a positive effect on the job motivation of employees. This hypothesis can be tested with a simple cause-effect relationship and because of the fact that the independent variable is measured on an ordinal scale, the test that was undertaken is a linear regression. The first two interesting values are the R Square (0.161) and the Adjusted R Square (0.136). These two values are useful to give an indication of the variance in the dependent variable that is explained by the independent variable and the control variables. In this model, it means that between 13% and 16% of the job motivation of employees is explained by the incentives in general, the education of employees and the type of organization the employees are working. This value is pretty high, because in this model there are not many variables and it means that these few variables explains between 13% and 16% of the job motivation of employees. To ensure that the R Square and Adjusted R Square explain a significant proportion of the variance in the job motivation variable, also an ANOVA test is executed. In this case the 13 - 16% job motivation variance explained by the incentives and the control variables is significant (p<0.01). Moreover, the Durbin-Watson statistic is provided. This value is useful to assess the assumption of no serial correlation. In general, the closer the Durbin-Watson statistic is to 2, the better this value is. If the value is less than 1 or higher than 3 this is a real cause of concern (Field, 2009). According to Field (2009) this concern of serial correlation is that the independent variables seems to be significant, when they may not be significant. In this model the value is 1.677, which indicates that there is no serial correlation.
Finally, we are testing whether the incentives have a significant positive effect on the job motivation of employees. Before we can look into the relationship between incentives and job motivation, it is important to look at the assumptions of a linear regression. In the data it is important that the incentive variable and job motivation variable are normally distributed. As can be seen in Appendix C under hypothesis 1, the histograms of both variables are not normally distributed. Moreover, a Kolmogorov-Smirnov test is conducted and with both variables it gives a significant score which indicates a deviation from normality. Therefore, in addition to the normal regression also a robust regression with the bootstrap function is executed. The outcome of these two regressions can be seen in Table 5.
Table 5: Regressions.
In Table 5 it becomes clear that there is not much difference between the normal regression and the robust regression. In both regressions there is a positive relation between incentives and job motivation. This could be seen in the B column, because if the incentives increases with a value of 1, than the job motivation of employees increases with a value of 0.221. Moreover it is important to check if this relation is significant. In both regressions it is significant (p<0.01). Therefore it can be concluded that the incentives have a positive effect on the job motivation of employees, and therefore H1 is supported. The control variables, both do not have a significant relation with the job motivation of employees.
To check for the robustness of these results, the regression is also executed with alternative specifications of the model. As mentioned in section 3, the Cronbach’s alpha of the incentives variable and job motivation variable could be slightly increased by deleting some items. For the incentive variable this was item 4 and for the job motivation variable this was item 6 (see Appendix B). Therefore, the regression is executed again only with the incentives variable and job motivation slightly different. The results with this alternative model showed practically similar results as before. The incentives still have a significant positive relation with job motivation, only this relation is slightly weaker in this model.
4.3 Hypothesis 2A
H2A predicted that the monetary incentives have a positive effect on the job motivation of employees. This hypothesis can be tested with a simple cause-effect relationship and because of the fact that the independent variable is measured on an ordinal scale, the test that was undertaken is a linear regression. The first two interesting values are the R Square (0.159) and the Adjusted R Square (0.134). These two values are useful to give an indication of the variance in the dependent variable that is explained by the independent variable and the control variables. In this model, it means that between 13% and 16% of the job motivation of employees is explained
by the monetary incentives, the education of employees and the type of organization the employees are working. This value is pretty high, because in this model there are not many variables and it means that these few variables explains between 13% and 16% of the job motivation of employees. To ensure that the R Square and Adjusted R Square explain a significant proportion of the variance in the job motivation variable, also an ANOVA test is executed. In this case the 13 - 16% job motivation variance explained by the incentives and the control variables is significant (p<0.01). Moreover, the Durbin-Watson statistic is provided. This value is useful to assess the assumption of no serial correlation. In this model the value is 1.663, which indicates that there is no serial correlation.
Finally, we are testing whether the monetary incentives have a significant positive effect on the job motivation of employees. Before we can look into the relationship between monetary incentives and job motivation, it is important to look at the assumptions of a linear regression. In the data it is important that the monetary incentives variable and job motivation variable are normally distributed. In hypothesis 1 it became clear that the job motivation variable is not normally distributed and as can be seen in Appendix C under hypothesis 2A, the histogram of the monetary incentive variable is also not normally distributed. Moreover, a Kolmogorov-Smirnov test is conducted and with the monetary incentive variable it gives a significant score which indicates a deviation from normality. Therefore, in addition to the normal regression also a robust regression with the bootstrap function is executed. The outcome of these two regressions can be seen in Table 6.
Table 6: Regressions.
In Table 6 it becomes clear that there is not much difference between the normal regression and the robust regression. In both regressions there is a positive relation between the monetary incentives and job motivation. This could be seen in the B column, because if the monetary incentives increases with a value of 1, than the job motivation of employees increases with a value of 0.207. Moreover it is important to check if this relation is significant. In both regressions
it is significant (p<0.01). Therefore it can be concluded that the monetary incentives have a positive effect on the job motivation of employees, and therefore H2A is supported. The control variables, both do not have a significant relation with the job motivation of employees.
To check for the robustness of these results, the regression is executed again only with the job motivation variable slightly different. The results of the regression with alternative specifications, showed practically similar results as before. The monetary incentives still have a significant positive relation with job motivation, only this relation is slightly weaker in this model.
4.4 Hypothesis 2B
H2B predicted that the non-monetary incentives have a positive effect on the job motivation of employees. This hypothesis can be tested with a simple cause-effect relationship and because of the fact that the independent variable is measured on an ordinal scale, the test that was undertaken is a linear regression. The first two interesting values are the R Square (0.129) and the Adjusted R Square (0.103). These two values are useful to give an indication of the variance in the dependent variable that is explained by the independent variable and the control variables. In this model, it means that between 10% and 13% of the job motivation of employees is explained by the non-monetary incentives, the education of employees and the type of organization the employees are working. This value is still pretty high, because in this model there are not many variables and it means that these few variables explains between 10% and 13% of the job motivation of employees. To ensure that the R Square and Adjusted R Square explain a significant proportion of the variance in the job motivation variable, also an ANOVA test is executed. In this case the 10 - 13% job motivation variance explained by the non-monetary incentives and the control variables is significant (p<0.01). Moreover, the Durbin-Watson statistic is provided. This value is useful to assess the assumption of no serial correlation. In this model the value is 1.692, which indicates that there is no serial correlation.
Finally, we are testing whether the non-monetary incentives have a positive effect on the job motivation of employees. Before we can look into the relationship between non-monetary incentives and job motivation, it is important to look at the assumptions of a linear regression. In the data it is important that the non-monetary incentives variable and job motivation variable are normally distributed. In hypothesis 1 it became clear that the job motivation variable is not normally distributed and as can be seen in Appendix C under hypothesis 2B, the histogram of the non-monetary incentive variable is also not normally distributed. Moreover, a Kolmogorov-Smirnov test is conducted and with the monetary incentive variable it gives a significant score
a robust regression with the bootstrap function is executed. The outcome of these two regressions can be seen in Table 7.
Table 7: Regressions.
In Table 7 it becomes clear that there is not much difference between the normal regression and the robust regression. In both regressions there is a positive relation between the non-monetary incentives and job motivation, as hypothesized. This could be seen in the B column, because if the non-monetary incentives increases with a value of 1, than the job motivation of employees increases with a value of 0.185. Moreover it is important to check if this relation is significant. In both regressions it is significant (p<0.01). Therefore it can be concluded that the non-monetary incentives have a positive effect on the job motivation of employees, and therefore there is support for H2B. The control variables, both do not have a significant relation with the job motivation of employees.
To check for the robustness of these results, the regression is executed again only with the job motivation variable slightly different. The results of the regression with alternative specifications, showed practically similar results as before. The non-monetary incentives still have a significant positive relation with job motivation, only this relation is slightly weaker in this model. Moreover, the relation between the non-monetary incentives and job motivation is in this model less significant.
4.5 Hypothesis 3
H3 predicted that incentives have more effect on men than on women regarding the job motivation. This hypothesis consists of an independent variable, a dependent variable and a moderating variable. In this case, the independent variable is the incentives, the dependent variable is the job motivation and the moderator is gender. According to Field (2013), the best option to test this kind of hypotheses, is by using the Process method of Andrew F. Hayes. This test is some kind of a regression, but it has several advantages over using the normal regression:
(1) it will center predictors, (2) it computes the interaction term automatically and (3) it adds the slope analysis. The output of this test is shown in Table 8.
Table 8: Process method
Table 8 indicates that there is no relation between gender and job motivation. Moreover, this table indicates that incentives has a positive relation with job motivation (0.2074) and this relation is significant (p<0.01), which we have already seen in the first hypothesis. Finally, Table 8 shows a significant interaction effect (p<0.05), indicating that the relationship between incentives and job motivation is moderated by gender. To interpret this moderation effect, we look at the slope analysis produced by the Process method. This output is shown in Table 9.
Table 9: Slope analysis
In Table 9 the first row represents the women and the second row represents the man. This is based on the respondent characteristics of Table 1, which states that the respondents in this sample are divided in 51% men and 49% women. Moreover, this table shows the results of two different regressions: the regression for incentives as a predictor of job motivation (1) when respondents are women and (2) when respondents are men. When the respondents are women, this has a positive effect of 0.3406 on the relationship between incentives and job motivation. Moreover, this positive relationship is significant (p<0.01). On the other hand, when the respondents are men, it seems that there is no relationship between incentives and job motivation. Therefore, it cannot be concluded that incentives have more effect on men than on women regarding the job motivation. In other words, there is no support for H3.
Another advantage of the Process method is that it also generates data for plotting a graph. This method calculated that, for example, when incentives are low and the respondent is a man, the predicted value of job motivation is 6. When incentives are high and the respondent is a women, the predicted value of job motivation is 6,39. And so on for every possible situation.
Therefore, to give a different view of the interaction between incentives, job motivation and gender, also a graph of interaction is constructed (shown in Figure 3). In this graph the incentives are divided in three different groups, low use of incentives, mean (average) use of incentives and high use of incentives. Whereas low use of incentives means below average use of incentives and high use of incentives means above average use of incentives. The Process method itself made this distinction. Job motivation is a number and gender is divided in men and women, where the blue line represents men and the green line represents women.
Figure 3: Graph of interaction.
Figure 3 shows that incentives have more effect on women than on men. When incentives are low, women have a lower job motivation than men. But on the other hand, when incentives are high, women have a higher job motivation than men. In other words, women are much more sensitive for the use of incentives than men are and this conclusion is opposite to what was expected in H3.
To check for the robustness of these results, the Process method is executed again only with the incentives variable and job motivation slightly different. In this model it means that for the incentive variable item 4 is deleted and for the job motivation variable item 6 is deleted (see Appendix B). The results of the Process method with alternative specifications, showed similar results. Also with the alternative specifications, the women are more sensitive for the use of incentives than men are.
5 Discussion
The first aim of this study was to provide a better insight in the relationship between the incentives and the job motivation of employees. Moreover, this study aimed at giving an answer if there are differences among the different types of incentives (monetary or non-monetary) and their effect on the job motivation of employees. The last aim of this study was to explore the impact of gender on the relation between incentives and job motivation. The analyses show a statistically positive effect of the incentives in general and the job motivation of employees. This finding is consistent with a large literature documenting the positive effects of incentives on the job motivation of employees (Fang & Gerhart, 2012). Furthermore, the analyses show no statistical evidence that there are differences between the different types of incentives and their relation with job motivation. This finding is also consistent with what was hypothesized, based on the literature, the rational economic theory and the theory of needs. Finally, there is statistical evidence that gender does moderate the relationship between incentives and job motivation. The finding that gender does moderate the relationship is consistent with what was hypothesized. Only the analyses show that incentives lead to a higher job motivation for women, than for men, whereas the expectation was conversely.
The results of this study provide some interesting insights into the use of incentives. Firstly, the results show that incentives in general has a positive effect on the job motivation of employees (H1). In practical terms, it shows that incentives are a good option for managers to motivate their employees. It also shows that it does not matter for the motivation of employees in which type of organization they are working or how high they are qualified. Profit organizations and non-profit organizations leads to the same level of motivational effort, and this also applies for primary education and scientific education/MSc or higher. These results are not very surprisingly, because the outcome is what was expected based on different theories.
Secondly, the results of this study also indicates that there are no differences in using different types of incentives regarding the job motivation of employees (H2). In practical terms, it shows that it does not matter for managers if they use monetary or non-monetary incentives to motivate their employees. This hypothesis also confirms that the type of organization and education of the employees has no influence on the job motivation of employees. These results are interesting, because it contradicts with the expectations of Deci (1971), who indicates that not every type of incentive will lead to the same level of job motivation.
Lastly, the results of this study show that the use of incentives has more influence on the female employees than on the male employees (H3). In practical terms, it shows that the female employees are much more sensitive for the use of incentives than the male employees are.
To conclude, the research question of this study was defined as “Does someone’s gender affect the relation between incentives and job motivation?”. The answer to this question is yes. As hypothesized, based on the theory of gender stereotype, the analyses show that gender affect the relation between incentives and job motivation. Based on prior literature the expectation was that the use of incentives has more influence on the job motivation of the male employees, than on the job motivation of the female employees. However, the analyses show that the female employees are much more sensitive to the use of incentive, than the male employees. In my opinion this is an interesting result and therefore gender is a variable that deserves more empirical attention in relation to incentives and motivation.
5.1 Future research
A number of directions for future research emerge from this study. In relation to the first hypothesis, future research on this subject could look at the influence of incentives on the different types of motivation. Based on the self-determination theory of Ryan and Deci (2002), as mentioned in section 2, there are six different types of motivation. Therefore, it could be an interesting study to look at the influence of incentives on each individual type of motivation.
In relation to the two second hypotheses, future research on this subject could make a subdivision within the monetary incentives and a subdivision within the non-monetary incentives. According to Bucklin and Dickinson (2001) examples of monetary incentives can include cash bonuses, stock options and profit-sharing, whereas according to Silverman (2004) examples of non-monetary incentives can include reserved car parking space, time off from work and flexible work schedules. Therefore, it could be interesting to look at the effects of each different subordinate type of incentive on the job motivation of employees.
In relation to the last hypothesis, future research on this subject could concentrate on identifying and testing different moderators of the incentives–job motivation link that may explain the many different findings with regard to this relationship. As indicated by Bonner and Sprinkle (2002), there is a huge call for more research concerning incentives, task attractiveness, motivation, effort, and performance. An example of a moderator for future research could be to focus on other countries or specific industries in relation to incentives and job motivation. For example, a comparison of incentive practices within car dealerships in the Netherlands and the
United States found a significant negative effect of using monetary compensation in Dutch firms, whereas this finding was insignificant in US firms (Jansen et al., 2009). Testing whether the results hold under different conditions, obtains a broader empirical foundation and makes the results more generalizable. Another example of a moderator for future research could be to focus on age in relation to incentives and job motivation. For example, Del Vecchio and Wagner (2011) found that the use of incentives has more effect on the motivation of salespersons less than 25 years old, than on the motivation of salespersons between 25 and 44 years or salespersons older than 55 years. They therefore suggest that age is a variable worth more conceptual and empirical attention with reference to examining the relationship between incentives and motivation.
5.2 Limitations
In interpreting the results of this study, some limitations must be acknowledged. The first limitation of this study is concerning the sample size. According to Blasius & Thiessen (2012), a sample with the size around 100 respondents is relatively small. Therefore, any inferences from the results must be drawn cautiously. Another limitation of this study is regarding the potential for measurement errors, which is often associated with survey data that relies on perceptions of the respondents (Kasprzyk, 2005). According to him, measurement errors affects the consistency of the parameter estimation of the structural model and its standard errors. In this study, measurement errors may occur in measuring the job motivation variable. For instance, the job motivation is measured only with self-reported motivation, instead of investigate whether other people in the company thinks the respondent is motivated.
Despite these limitations, this study also has some strengths. An important strength of this study is that the sample is strictly randomly selected. This means that the respondents in this sample have a lot of different nationalities and are operating in a lot of different industries, which increases the generalizability of the findings. Another strength of this study is that several tests, such as a response analysis and a missing value analysis, have been used to check for potential bias. Normally, using the survey method to collect data creates this response bias and non-random missing values, in this study these two things are not a major concern. Moreover, additional analyses have been done to check for the robustness of the results. The robustness checks showed almost all similar results.
To conclude, the design of the study showed some strengths and some weaknesses. Where on the one hand the sample is relatively small, is on the other hand the sample strictly
study, there is no potential bias regarding the response and missing values. In other words, the design of this study is of sufficient quality.
Appendix A.
Contacting people of my own network (Dutch) Hallo ….,
Zoals je misschien wel weet ben ik momenteel bezig met mijn Masteropleiding Accountancy & Control. Om deze master succesvol af te ronden, moet ik vanaf februari gaan beginnen aan het schrijven van een scriptie. Vanuit school heb ik de kans aangeboden gekregen om een onderzoek te gaan doen, waarbij ik gebruik mag gaan maken van de verzamelde data van een
docent-onderzoeker (welke dan ook mijn begeleider is gedurende dit traject). In ruil hiervoor moet ik “nieuwe” data aanleveren, waarmee zij dan weer aan de slag kan. Vandaar ook deze mail, dus ondanks dat je er misschien geen zin in hebt is het voor mij van belang dat ik mensen aan kan leveren die de enquête in willen vullen.
Ik heb een bepaald aantal personen nodig welke een enquête (ongeveer 15 minuten) willen invullen. Deze personen moeten minimaal één jaar werkzaam zijn in de huidige functie en er moet bij deze functie gebruik worden gemaakt van zogenaamde prestatie indicatoren, om te kunnen meten hoe er gepresteerd wordt. Deze prestatie indicatoren komen veelal aan bod in een functionering- of eindejaargesprek, dus bijna iedereen heeft er wel mee te maken. Daarnaast moet de leidinggevende, die dus de prestatie indicatoren gebruikt, ook een enquête invullen. Als je zelf in een leidinggevende functie zit en je de prestatie van bepaalde werknemers meet aan de hand van prestatie indicatoren, dan is dit natuurlijk ook goed!
Nu is mijn vraag eigenlijk of je voldoet aan de hierboven gestelde “eisen” (dus minimaal één jaar werkzaam bent binnen de huidige functie en of er bij deze functie gebruik wordt gemaakt van prestatie indicatoren) en bereidt bent om de enquête in te vullen? Er moet dus ook gevraagd worden aan de leidinggevende of deze de enquête in zou willen vullen.
Graag hoor ik voor vrijdag 20 november of je mij zou kunnen helpen? Als dit zo is dan ontvang je nog een mail met wat extra informatie.
Alvast bedankt!
Contacting the other part of the pair (Dutch) Beste …,
Via … heb ik vernomen dat u bereid bent om de enquête in te vullen, hiervoor dank.
Hiermee helpt u mij bij een eerste stap richting het behalen van mijn Master of Science diploma aan de Universiteit van Amsterdam.
De enquête heeft betrekking op prestatie-indicatoren en andere werkgerelateerde zaken en duurt ongeveer 15 minuten om in te vullen. De enquête wordt rond 8 december gestuurd en wordt niet door mij, maar door mijn docent-begeleider (Bianca Groen) gestuurd.
Nogmaals dank. Met vriendelijke groet, Huub Spanjaards.
Appendix B.
A. Incentives (completed by the employee)
My manager attaches very high importance to the performance indicators in: 1. Determining potential salary increases
2. Determining potential bonuses or extras 3. Increasing my chances of promotion
4. Increasing my authority within the organization
B. Monetary incentives (Moers, 2006; completed by the employee)
My manager attaches very high importance to the performance indicators in: 1. Determining potential salary increases
2. Determining potential bonuses or extras
C. Non-monetary incentives (Moers, 2006; completed by the employee)
My manager attaches very high importance to the performance indicators in: 3. Increasing my chances of promotion
4. Increasing my authority within the organization
D. Job motivation (Groen et al., 2015; completed by the employee) To what extent do you agree with the following statements:
5. I find it positive to always meet everything that is expected of me in my work 6. It satisfies me to always meet everything that is expected of me in my work 7. I find it important to always meet everything that is expected of me in my work
E. Gender (completed by the employee) 8. What is your gender?
