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Journal of Management Development Motivation exchange rate: the real value of incentives Yundong Huang,

Article information: To cite this document: Yundong Huang, (2018) "Motivation exchange rate: the real value of incentives", Journal of Management Development, Vol. 37 Issue: 4, pp.353-362, https://doi.org/10.1108/JMD-08-2017-0255 Permanent link to this document: https://doi.org/10.1108/JMD-08-2017-0255

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https://doi.org/10.1108/JMD-08-2017-0255

Motivation exchange rate: the real value of incentives

Yundong Huang Department of Management, Arthur J. Bauernfeind College of Business,

Murray State University, Murray, USA

Abstract Purpose – The purpose of this paper is to describe how individuals compare the value of different types of incentive. This is similar to comparison in the financial field for currency exchange. The proposed exchange rate between incentives is referred to here as motivation exchange rate (MER). Design/methodology/approach – Survey-based empirical data were analyzed to test for the existence of MER, based on 330 samples collected from a number of organizations. Partial least square-based structural equation modeling was applied to test the research model, specifically examining the exchange rate between intrinsic motivation and monetary incentives. Findings – Individuals place their own value on job enjoyment. The present analysis suggests that MER exists between intrinsic motivation and monetary incentive and varies among individuals. Originality/value – The motivation literature has shown that individuals have their own preferences for different types of incentives. When studying employees’ working behavior, scholars have traditionally focused on preferred incentives. However, the present study reveals that these predominant incentives may change when employees are sufficiently compensated by other types of incentive; how much is “sufficient” depends on MER, as elaborated here. Keywords Motivation, Organizational behaviour, Quantitative methods, Motivation exchange Paper type Research paper

1. Introduction In organizations, incentives are provided to motivate employees. According to Deci and Ryan (1985), work motivations can be classified into two basic categories: extrinsic factors, such as salaries, bonuses, and health insurance; and intrinsic factors, related, for instance, to enjoyment derived from the job. When given opportunities to have the same workload, therefore, employees will tend to maximize every type of incentive (e.g. higher salary, more interesting job). In many cases, however, employees must sacrifice one type of motivation for another. For example, to work at an enjoyable job, an individual may have to accept a lower salary. Conversely, individuals may choose to work at an unpleasant job in exchange for better pay.

In this sense, there should be an exchange rate among types of motivations to measure the extent to which an employee is willing to trade one type of incentive for another. In this study, we call this the motivation exchange rate (MER). In particular, the current study measures this exchange rate in relation to monetary income and job enjoyment. It was assumed that the value of a given incentive would vary for different employees. Those who value job enjoyment more highly would seem less likely to trade off this intrinsic motivation.

This study therefore used quantitative methods to answer a simple question: Does everyone have his or her own price for job enjoyment? In organizations, individuals may evaluate job incentives on the basis of entirely different systems, such as personal values, experience, or goals (Vroom, 1964; Deci and Ryan, 2008). In many cases, individuals perform this exchange and evaluation process ambiguously and unconsciously, especially when the motivation is in an uncountable form (Deci and Ryan, 2008).

2. Literature review and hypothesis development Work motivation can be defined as “a set of energetic forces that originate both within as well as beyond an individual’s being, to initiate work-related behavior and to determine its

Journal of Management Development

Vol. 37 No. 4, 2018 pp. 353-362

DOI 10.1108/JMD-08-2017-0255

Received 12 August 2017 Revised 5 February 2018 Accepted 14 March 2018

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0262-1711.htm

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form, direction, intensity, and duration” (Pinder, 1998). In business contexts, this motivation is triggered by something, an incentive or a reward, that employers provide to their employees in exchange for their work. The most common incentive is money, organizations may introduce other forms of incentives, such as promotion, job tenure, and health benefits operation. The enjoyment derived from doing a job is itself a form of motivation, which Deci and Ryan (1985) classified as intrinsic motivation.

Scholars have previously observed that incentives are valued differently by different individuals. In early studies, McClelland and Atkinson (1948) found that hierarchies of motivation vary among individuals. Which ones are preferred depends on how individuals learn to associate their needs and feelings. Higher levels of motivation dominate lower levels, while lower levels still have a moderating effect.

According to Vroom’s (1964) expectancy theory, individuals tend to prefer certain rewards over others, depending on the cognition between the expected rewards and the individual’s goal. This cognition, “valence,” refers to an individual’s feelings about a specific reward. However, the theory does not explain how individuals compare rewards when a positive valence exists for multiple types of rewards.

In discussing equity theory, Adams (1965) listed six incentives, “outcomes,” that an individual considers in estimating whether his or her “inputs” (job effort, skills, etc.) are equal to the outcomes. Equity theory noted the multi-motivator issue but paid it insufficient attention. Instead of assigning weights to different incentives, it identified predominant inputs and outcomes that can be compared with a reference, such as a coworker. Whether a non-predominant reward affects the comparison does not appear to have been explored.

In contemporary studies, several more variables, such as job autonomy and self-efficacy, have been found to affect an individual’s preferences regarding incentives (rewards). Vandercammen et al. (2014) found that organizations can influence intrinsic motivation by changing the job autonomy level. Similarly, Nie et al. (2015) found job autonomy was highly associated with intrinsic motivation, but they did not compare it to other types of rewards. Thus, it cannot be concluded that when autonomy is high, individuals prefer intrinsic rewards over other rewards. The only hint is from a study by Dysvik et al. (2013) that showed that high autonomy can lead to high intrinsic motivation, but low extrinsic motivation (measured as monetary reward).

Cherian and Jacob’s (2013) review of the 2000-2012 literature concluded that self-efficacy would improve intrinsic motivation. Katz et al.’s (2014) empirical study on the education field yielded similar results. Malik et al. (2015) revealed that self-efficacy could really change the motivation preference. Their quantitative analysis showed that job autonomy was positively correlated with intrinsic motivation but not with extrinsic motivation (measured as monetary reward).

In summary, a review of the literature revealed that under certain circumstances, one type of incentive will be more valuable than another. But “How much more?” is a question not answered. To fill this gap, the current study proposes a new concept, MER, to describe how individuals evaluate different kinds of incentives in the same ways that individuals assess the value of monetary currency by comparing it to the value of other monetary currencies. This quantitative study focuses on the exchange rate between intrinsic motivation (enjoyment of doing a job) and monetary incentives (pay), formulated as:

MER ¼ ðmonetary incentiveÞ=ðintrinsic motivationÞ:

Note that the MER is not the exchange rate between an individual’s current monetary incentives and intrinsic motivation but between types of incentives considered to be of the same value. In other words, the MER measures the extent to which individuals are willing to exchange one type of incentive for another.

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The current study argues that individuals evaluate one incentive by comparing it to another, i.e. for a given person, job enjoyment is worth a certain amount of money. It further argues that an employee will be willing to switch to an unpleasant/boring job if that employee receives adequate monetary compensation. What constitutes “adequate” depends on the exchange rate of the two incentives in the employee’s mind.

Does a MER exist? It is easy to assume that individuals have their own price for enjoyment, but proving this depends on analysis of real data. The study’s first two hypotheses test the existence of a MER. Because this exchange rate is not directly measurable, a model was designed to measure the MER indirectly by examining the moderating effect of the MER for incentives and job satisfaction.

Organizational behavior literature confirms the positive effects of both monetary incentives and intrinsic motivation on job satisfaction (Kuvaas, 2006; Jang, 2008; Masvaure et al., 2014; Olafsen et al., 2015). If a MER exists, as defined here, it should have some effect on the link between incentives and job satisfaction. If an individual considers his or her intrinsic motivation more important, the effect of intrinsic motivation on job satisfaction tends to be stronger. If the moderating effect is significant, this indicates a new dimension in motivation research. On this basis, the following hypothesis was advanced:

H1. A high MER will strengthen (positively moderate) the correlation between intrinsic motivation and job satisfaction.

Similarly, if an individual considers monetary incentives less important, the effect of money on job satisfaction should be weaker. To examine this alternative aspect of a MER’s moderating effect on the correlation between motivation and its outcomes (such as job satisfaction), the following hypothesis was advanced:

H2. A high MER will weaken (negatively moderate) the correlation between monetary incentive and job satisfaction.

The explanatory power of the first two hypotheses relied on the assumption that both money and intrinsic motivation increase job satisfaction. In general, the literature supports this assumption. For instance, Jang (2008) provided empirical evidence from the hotel industry that both intrinsic motivation and extrinsic rewards (such as money) are significantly associated with job satisfaction. Olafsen et al. (2015) reported a positive correlation between monetary incentives and job satisfaction. This finding was supported by Huang (2016). Thus, the following hypotheses were advanced:

H3. Intrinsic motivation is positively associated with job satisfaction.

H4. Monetary incentives are positively associated with job satisfaction.

The research model can be summarized as follows (Figure 1).

Motivation Exchange Rate (MER)

Intrinsic Motivation

Monetary Incentive

Job Satisfaction H3 +

_ +

Moderating Effect

Figure 1. Research model

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3. Methodology Observation data were collected by an anonymous online survey powered by Google Docs. Approximately 2,000 e-mail invitations were distributed between April 2013 and January 2014. The target population was contacts at the Institutional Review Board (IRB) at Texas A&M International University, Twitter followers of WorldAcademic, employees at CAZL LLC. and the Journal of Risk Analysis and Crisis Response. Although affiliated with these institutions, subjects were anonymized and unidentifiable. In total, 330 valid responses were returned (response rate 16 percent).

Intrinsic motivation was measured by question items proposed by Warr et al. (1979) and Kuvaas and Dysvik’s (2009) scale. Monetary incentives were measured directly by asking: “How much compensation did you earn last year from your current job? (If you are not currently employed, please refer to your most recent job.)” Job satisfaction was measured using Brayfield and Rothe’s (1951) well-cited measurement scales.

The MER was measured by the following question: “Suppose your company/institution offers you an opportunity to work in a new job or position with which you are not at all familiar. You are told that the rank and the workload of the new position are similar to those of your current job. If your company is willing to pay you more for this job, how much would be acceptable?” In other words, this study used the minimum amount of compensation required to switch to an unknown job to measure the individual’s MER level. Here, “unknown job” refers to a job that the subject has never done before and for which the subject has no knowledge of the duties. See more in the discussion and limitations sections.

Consider that an employee has an intrinsic motivation level of X for his or her current job and is receiving B($) as a monetary incentive. The current study assumes that employees have no intrinsic motivation with respect to an unknown job. A($) represents the minimum monetary compensation required by the individual to switch to an unknown job, i.e. the employee’s willingness to sacrifice all of his or her current intrinsic motivation for a higher extrinsic motivation A($). The difference between A($) and B($) is the level of extrinsic motivation considered equivalent in value to the employee’s enjoyment of his or her current job. In this case, the MER is not the ratio of B/X; instead, MER ¼ (A−B)/X.

The lower boundary of the MER is zero. In this case, employees may still be motivated by intrinsic motivations, but they regard their enjoyment of a job as worth nothing. The MER does not have an upper boundary, but it could be so high that it loses its meaning and individuals regard their enjoyment in a job as priceless. So, they will not give up even a little amount of enjoyment for money, no matter how much they are offered.

The data were analyzed by partial least square (PLS)-based structural equation modeling. WarpPLS software was used to perform this analysis. Linear PLS regression was applied to all tests of the hypotheses.

A group of variables was used as control variables: job autonomy (Karim, 2017), self-efficacy (Aldridge and Fraser, 2016), organizational commitment (Peng et al., 2016), age, gender, and education.

4. Model assessment Descriptive statistics and sample characteristics are set out in Table I.

Latent variables were created using confirmatory factor analysis. Relationships between indicators and latent variables were already defined. Table II shows combined loading and cross-loadings of all indicators. Note that monetary incentives and the MER are not latent variables. Convergent validity is good if the question items associated with each latent variable are understood as intended by the subjects. To ensure acceptable convergent validity, indicator loadings should be higher than 0.5 and significant at the 0.01 level (Hair, 2009). On that basis, some indicators (intrinsic motivation question item 2 and job satisfaction question item 2) were removed from the model. In addition, some

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indicators (marked R) show negative values, indicating that they were reversed items in the questionnaire.

Table III shows that all p-values associated with indicator weights were lower than 0.05. For all indicators, the variance inflation factor (VIF) was lower than 5. Indicator weight-loading

Variable Total sample 330

Gender Male 44.5%; Female 55.5% Age 33.7 (σ ¼ 10.8) Full-time work experience 10.5 (σ ¼ 10.7) Part-time work experience 2.5 (σ ¼ 4.0) Years of service 6.0 (σ ¼ 6.3) Less than high school 0% High school 3% Some college 13% 4-year college degree 30% Graduate or professional training 55%

Table I. Descriptive statistics

IM JS S error p-value

IM01 (0.638) −0.349 0.047 o0.001 IM03 (0.676) −0.039 0.047 o0.001 IM04 (0.538) −0.383 0.047 o0.001 IM05 (0.605) −0.151 0.047 o0.001 IM06 (0.731) −0.329 0.047 o0.001 IM07 (0.756) 0.25 0.047 o0.001 IM08 (0.689) 0.463 0.047 o0.001 IM09 (0.712) 0.392 0.047 o0.001 JS01 0.002 (0.801) 0.047 o0.001 JS03 −0.01 (0.833) 0.047 o0.001 JS04 0.116 (0.879) 0.047 o0.001 JS05 0.013 (0.684) 0.047 o0.001 JS06R −0.166 (0.631) 0.047 o0.001 Notes: IM, Intrinsic motivation; JS, job satisfaction. Loadings in parentheses

Table II. Loadings and

cross-loadings of indicators

IM JS p-value VIF WLS ES

IM01 0.177 0 o0.001 1.481 1 0.113 IM03 0.188 0 o0.001 1.587 1 0.127 IM04 0.149 0 o0.001 1.435 1 0.08 IM05 0.168 0 o0.001 1.566 1 0.102 IM06 0.203 0 o0.001 1.716 1 0.148 IM07 0.209 0 o0.001 2.604 1 0.158 IM08 0.191 0 o0.001 2.646 1 0.132 IM09 0.197 0 o0.001 2.087 1 0.141 JS01 0 0.269 o0.001 1.787 1 0.216 JS03 0 0.28 o0.001 2.483 1 0.233 JS04 0 0.296 o0.001 2.869 1 0.26 JS05 0 0.23 o0.001 1.427 1 0.158 JS06R 0 0.212 o0.001 1.321 1 0.134 Notes: IM, Intrinsic motivation; JS, job satisfaction

Table III. Indicator weights

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signs (WLS) were applied to check whether an indicator was making a negative contribution to the R2 of its latent variable because this might be a sign of Simpson’s paradox. (It is recommended that all indicator WLS values should be positive). Effect sizes (ES) are detailed in Table III. An indicator’s ES should be higher than 0.02; otherwise, it would be considered too weak to correlate to the latent variable, regardless of its p-value (Kock, 2013). Convergent validity assessments in Tables II and III show that question items associated with each latent variable were understood as intended by the subjects.

Reliability was assessed using composite reliability and Cronbach’s α, see Table IV. As conservative criteria, all composite reliability and Cronbach’s α coefficients were greater than 0.7. (1.0 for non-latent variables), indicating that responses to all question items were stable and consistent across different subjects.

Table V shows a correlation matrix of all the variables that were used, including the control variables. The numbers in parentheses are the square roots of the average variance extracted (AVE). For each latent variable, the square root of the AVE should be higher than any of the correlations involving that latent variable (Kock, 2013). For non-latent variables, the square root of AVEs is 1.

Table V also shows that many of the correlations among variables were highly significant, and this indicated potential collinearity issues. Therefore, Table VI includes full collinearity checks, VIF test. As shown in Table VI, VIF scores were all smaller than 5, indicating an acceptable level of multicollinearity for this assessment model.

1 2 3 4 5 6 7 8 9 10

1 SE (0.797) 2 Age 0.088 (1) 3 Education 0.001 0.303** (1) 4 MER 0.225** 0.037 0.018 (1) 5 JA 0.407** 0.18* 0.095 0.184** (0.884) 6 Gender 0.021 −0.061 0.071 0.097 0.054 (1) 7 IM 0.659** 0.161* 0.031 0.54** 0.484** 0.125 (0.671) 8 Money 0.137 0.281** 0.119 −0.186** 0.127 0.012 0.125 (1) 9 JS 0.478** 0.243** 0.009 0.192** 0.516** 0.048 0.539** 0.199** (0.771) 10 OC 0.164 0.12 0.123 0.241** 0.397** 0.041 0.379** 0.024 0.463** (0.691) Notes: SE, Self-efficacy; JA, job autonomy; IM, intrinsic motivation; JS, job satisfaction; OC, organizational commitment. *po0.05; **po0.01

Table V. Correlation matrix and square roots of AVEs

SE Age Education MER JA Gender IM Money JS OC MER*IM MER*EM

VIF 2.023 1.290 1.163 1.857 1.593 1.045 3.126 1.503 1.983 1.462 1.396 1.394 Notes: SE, Self-efficacy; JA, job autonomy; IM, intrinsic motivation; JS, job satisfaction; OC, organizational commitment

Table VI. Variance inflation factor

IM Money MER JS

Composite Reliability 0.87 1.00 1.00 0.88 Cronbach’s α 0.82 1.00 1.00 0.83 Notes: IM, Intrinsic motivation; JS, job satisfaction

Table IV. Variable coefficients

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5. Results and conclusion PLS path coefficients and statistical significance levels are shown in Figure 2. The symbol ** refers to po0.01; * refers to po0.05. Figure 2 shows only the key variables in the research model; the control variables are hidden. Table VII shows all the path coefficients, including the control variables.

H1 was designed to test the existence of the MER. If an individual regards intrinsic motivation as valuable or important, he or she should be more sensitive to changes in intrinsic motivation. In other words, the MER should positively moderate the correlation between intrinsic motivation and job satisfaction. Because the PLS regression results show a statistically significant moderating effect ( β ¼ 0.11, po0.01), H1 was supported.

H2 was designed to examine the other component of the MER definition: If an individual considers monetary incentives less valuable or important, he or she will be less sensitive to changes in monetary incentives. Because the MER negatively moderates the correlation between monetary incentives and job satisfaction ( β ¼ −0.08, po0.05), H2 was supported.

The MER was observed to have moderating effects on intrinsic motivation and monetary incentives, indicating that if an individual regards intrinsic motivation as more valuable than money, he or she will be more sensitive to changes in intrinsic motivation and less sensitive to changes in monetary incentives. Together, H1 and H2 confirmed the existence of the MER. In other words, individuals assign different prices to job enjoyment.

H3 and H4 were also supported, and this is consistent with the organizational behavior literature. The finding that intrinsic motivation is positively associated with job satisfaction

Motivation Exchange Rate (MER)

Intrinsic Motivation

R2 = 0.33 Monetary Incentive

Job Satisfaction 0.55**

0.17**

–0.08* 0.11**

Moderating Effect

Correlation Notes: *p< 0.05; **p< 0.01

Figure 2. Results of

research model

Variable Path coefficient

Intrinsic motivation 0.55** Monetary incentive 0.17** Intrinsic motivation × MER 0.11** Monetary incentive × MER −0.08* (Age) 0.13* (Gender) 0.02 (Education level) 0.14* (Organizational commitment) 0.33** (Self-efficacy) 0.21** (Job autonomy) 0.22** Notes: Variables in parenthesis are control variables. *po0.05; **po0.01

Table VII. Path coefficients

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( β ¼ 0.55, po0.01) means that job enjoyment leads to higher job satisfaction. Monetary incentives are also positively associated with job satisfaction ( β ¼ 0.17, po0.01), although the path coefficient is smaller. This means that higher monetary payment leads to higher job satisfaction, but intrinsic motivation is more effective than money.

6. Discussion The current study proposed that individuals evaluate and compare different incentives in a similar way as they would currency exchange rates. The exchange rate between two incentives is referred to here as the MER.

The two hypotheses (H1 and H2) regarding the existence of a MER were both supported. This finding indicates that each employee’s job enjoyment has a (monetary) price, and that each individual’s price is different. This seems obvious, but the motivation literature indicates that no quantitative study has previously explored this.

These results imply that even an employee who regards intrinsic motivation as the most important reason to work may still be willing to work for money if the amount is considered adequate. Therefore, the predominant incentive may be changed. This suggests that organizations not focus on any single type of incentive, even one favored by employees.

When a sufficient amount of one reward is added, the motivation from other rewards will be relatively reduced (change of predominant incentive). It means, in accordance with expectance theory, that the valence could be changed by the amount of the reward. Expectance theory states that people expect to earn only rewards that can help them pursue their personal goals – a positive valence (Miner, 2005). However, the current study implied another possible direction: people set their goals based on what they can earn from a job.

This finding could provide empirical support for the self-determinate theory (SDT), which argues that individuals may pursue extrinsic aspirations (life goals) as a substitute for true need satisfaction (Deci and Ryan, 2008). To explore this proposition further, future studies should include personal goals and test both expectance theory and the SDT with the MER.

Future studies should also investigate the determinants of an individual’s MER, i.e. why individuals favor money, job security, enjoyment, or other rewards. If an individual’s MER can be estimated, organizations will be better equipped to motivate their employees and to be more cost-efficient when offering incentives.

7. Limitations The measurement of the MER may be affected by omitted-variable bias. The current study assumes that individuals have no intrinsic motivation for a job that they have never done before and have no knowledge of the associated duties. This may not apply to individuals with high openness to experience, a personality identified in the five-factor model (FFM) (McCrae and Costa, 1985).

According to the FFM, some individuals have high curiosity: they are interested in learning and exploring new things (Barrick and Mount, 1991). For them, an unknown job may be very attractive, rather than neutral. Whether this attractiveness should be defined as intrinsic motivation is debatable because intrinsic motivation is the enjoyment of doing a job (Deci and Ryan, 1985). Therefore, the expected enjoyment may or may not be defined as intrinsic motivation before an individual starts a job.

Future research should focus on this issue and include personality factors, especially openness to experience. People’s openness may be used to adjust the calculation of the MER.

The current study is also subject to under-coverage bias, i.e. the population may be inadequately represented in the sample. The data were collected from an online questionnaire, and the respondents were invited contacts.

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As a result, the study sample shows some common features that may not apply to the population. For example, most of the respondents were highly educated; however, a literature review conducted by Squires et al. (2015) found that education level is not correlated with job satisfaction.

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https://www.emeraldinsight.com/action/showLinks?doi=10.1108%2FJMD-08-2017-0255&crossref=10.1111%2Fj.1744-6570.1991.tb00688.x&citationId=p_3

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