September 5, 2010

3rd ICBMR: Application of Planned Behavior Framework in Understanding Factors Influencing Intention to Leave among Alumnae of the Faculty of Economics University of Indonesia Year 2000-2003

Filed under: Organization Behavior,Proceeding — imams @ 9:55 pm

Please Cite: Salehudin, I. and Mukhlish, B.M. (2008) Application of Planned Behavior Framework in Understanding Factors Influencing Intention to Leave among Alumnae of the Faculty of Economics University of Indonesia Year 2000-2003. Proceeding of 3rd International Conference on Business and Management Research (ICBMR), Bali-Indonesia.

Application of Planned Behavior Framework in Understanding Factors Influencing Intention to Leave among Alumnae of the Faculty of Economics University of Indonesia Year 2000-2003

Imam Salehudin, SE

University of Indonesia

gsimam@gmail.com

Basuki Muhammad Mukhlish, SE

University of Indonesia

basukimukhlish@gmail.com

Abstract

Employee’s turnover or job-switching behavior has always been a major concern of every human resources manager in relation to human capital investments. Many researches have been done to analyze how and why people switched jobs. Although most of these researches use attitudinal approaches that linked attitude directly to behavior, Chandrashekaran et al. (2000) and Mitchell-Sablynski et al. (2001) has used intention as intervening variable in their job switching model.

This research uses Theory of Planned Behavior from Ajzen (2004) as framework to model intentions to leave among fresh graduates. Theory of Planned Behavior uses Attitude, Subjective Norms and Perceived Behavioral Control to predict intention which leads to behavior. Psychological contract is used as one attitudinal variable based on research done by Chay and Aryee (1994), while job-embeddedness is also used as the second attitudinal variable based on research done by Mitchell-Sablynski et al. (2001). Ease of Movement is used as behavioral control variable, both actual and perceived, based on separate researches done by Spencer and Steers (1980), Trevor (2001), and Malcolmson et. al (2005). While subjective norms is developed using the questions used by Ajzen (2004).

The respondent used in this research is 129 graduates randomly sampled from 1105 alumnus of the Faculty of Economics University of Indonesia year 2000-2003 using clusters sampling method. Structural Equation Modeling process is then used to test whether the data obtained from the survey supports the model proposed.

The result obtained from this research is that there is a significant negative relationship between both attitude construct and the intention to leave, in which higher Job Embeddedness and Psychological Contract would decrease the intention to leave. However, this research found that the relationship between Subjective Norms and Perceived Movement Capital to the Intention to Leave is not significant based on the data used in this research. Further research is recommended to confirm about the relationship between the Intention to Leave and the actual behavior of Job Switching, and to analyze the influence of Actual Movement Capital toward Perceived Movement Capital and the actual Job Switching Behavior.

Keywords: Planned Behavior, Intent to Leave, Fresh Graduate, Job-Embeddedness, Psychological Contract, Ease of Movement.


Backgrounds

Normally, employers would not want to see their employee quits their job, especially if the one quitting is a talented new prospect and she plans to move to the competing company. Most company sees employee turnover as a loss since most do spend a lot of money in order to attract, develop, maintain and retain their employees. Some would see this spending as a loss of investment since they thought of their expenditure as investments in their human capital. Like any other investments, companies would expect to get a corresponding return from that investment. Employee turnover would mean that the investments made for that employee stops generating returns. It could also prove crippling for the company, especially it the employee leaving is a key person and she switched to the company’s competitor.

Thus, minimizing employee turnover has always been one of the key performance indicators for human resources managers in most company. To try minimizing employee turnover, managers must understand it first. Although there has been plenty of researches done to understand the concept of employee turnover, there is still much to be learned and understood about why and how employees decide to quit their jobs.

Literature Review and Hypothesis

Various researches have been done to understand more about employee turnover and how it happens. Each of these researches have contributed toward understanding more about why and how employees decided to quit their jobs, so it is important to use this accumulated knowledge as the basis of this research.

Researches using attitudinal variables most often viewed the relationship between attitude and behavior as a direct relationship, in which attitude would correspond directly toward behavior. Most of these researches also used Job Satisfaction as their main attitudinal variable, such as Blumberg (1980), Hom and Kinicki (2001), and Trevor (2001). Although the result was most often mixed, Hom and Kinicki (2001) found that the attitudinal variables they used do not have direct relationship toward the actual Job Switching behavior.

Some researches have used intention as intervening variable in their job switching model. Chandrashekaran et al. (2000) found that salesman with higher intention to leave will quit their jobs faster, while Sablynski et al. (2001) found that attitudinal variables such as Job Satisfaction and Job Embeddedness would influences employee’s intention to leave, which in turn would influence the actual voluntary turnover. Thus, it can be concluded that intention to leave can be used as an intervening variable between attitude and behavior.

This research uses Theory of Planned Behavior from Ajzen (2004) as framework to model intentions to leave among fresh graduates. Theory of Planned Behavior has been used frequently to model behaviors in several fields of social science, such as marketing and psychology. The theory uses Attitude, Subjective Norms and Perceived Behavioral Control to predict intention which will leads to behavior.

In this research, there are two attitudinal variables used. Psychological contract is used as one attitudinal variable based on research done by Chay and Aryee (1994), while job-embeddedness is also used as the second attitudinal variable based on research done by Mitchell-Sablynski et al. (2001). Subjective Norms is developed from the concept used in the original model by Ajzen (2002). He proposed that Subjective Norms refers to an individual’s perceptions of other people’s opinions on whether or not he or she should perform a particular behavior, while perceived behavioral control refers to an individual’s perceptions of the presence or absence of the requisite resources or opportunities necessary for performing a behavior. Perceived Movement Capital is used as perceived behavioral control variable, based on separate researches done by Spencer and Steers (1980), Trevor (2001), and Malcolmson et. al (2005). Therefore, the following hypotheses are suggested based on previous researches above:

H1: Job Embeddedness has negative effect on Intention to Leave, thus alumnae with higher Job Embeddedness will experience less Intention to Leave.

Job Embeddedness can be broken down into three components, which is Fit, Links, and Sacrifice. Job Fit is the measurement on how strong employee feels that he belong to his job and organization. Job Links is the measurement on how much is the connection and interdependence between an employee and his work environments. Job Sacrifice is how big the sacrifice for the employee to leave his job and/or organization is.

The concept of Job Embeddedness is an alternative approach to the commonly used Job Satisfaction. Sablynski et. al. (2001) even postulates further that Job Embeddedness is better predictor of employee turnover, absenteeism, and job performance than Job Satisfaction. Strong Job Embeddedness would reduce employee’s intention to leave the same way strong Job Satisfaction would. However, weak Job Embeddedness would not encourage employees to quit like strong Job Dissatisfaction would. Weak Job Embeddedness would only means that the employee will be more susceptible to shocks and dissatisfactions.

H2: Relational Psychological Contract has negative effect on Intention to Leave, thus alumnae with Relational Psychological Contract will experience less Intention to Leave than alumnae with Transactional Psychological Contract.

Rousseau (1989) defined Psychological Contract as the employee’s perception of the reciprocal obligations existing with their employer; as such, the employee has beliefs regarding the organization’s obligations to them as well as their own obligations to the organization. Two dominant Psychological Contracts identified by past researches are relational and transactional contracts. MacNeil (1985) stated that a relational contract characterizes traditional employment relationship in which employees expects long-term relationships, experiences both monetizable and socio-emotional elements, has broad scope and is based on the values of good faith and fair dealing. In contrast, Rousseau and Parks (1993) stated that a transactional contract is characterized by short-term, purely monetizable agreements with limited involvement of each party in the lives and activities of the other, and has a narrow focus but a high degree of specificity.

Employees with transactional Psychological Contract would feel no compunction for quitting their jobs and pursue their career elsewhere, if a better career opportunity is present elsewhere. In the other hand, Chay and Aryee (1994) postulates that employees with relational Psychological Contract would not seek such opportunity and would even hesitate should any such opportunity is suddenly presented to them. Thus, employees with relational Psychological Contract should experience less intention to leave than employees with transactional Psychological Contract.

H3: Subjective Norms has negative effect on Intention to Leave, thus alumnae with stronger Subjective Norms would experience less Intention to Leave than alumnae with weaker Subjective Norms.

Ajzen (2002) stated that subjective norms refer to an individual’s perceptions of other people’s opinions on whether or not he or she should perform a particular behavior. Subjective Norms consists of Normative Believe and Motivation to Comply, and calculated my multiplying the two. Normative Believe is the perception of alumnus of whether their friends and family would disapprove of their action if they choose to quit, while Motivation to Comply is the extent to which the alumnus will act in regard of their disapprovals. Alumnae with higher Subjective Norms would then experience less Intention to Leave, since they would see that their friends and family would disapprove and would not act regardless of their disapproval.

H4: Perceived Movement Capital has positive effect on Intention to Leave, thus alumnae that perceive that they have better Movement Capital would experience more Intention to Leave than alumnae that perceive that they have less.

The concept of Ease of Movement has been found to be a contributing element to employee turnover. It consists of both individual and market-driven determinants. Movement Capital is the individual element of Ease of Movement. The underlying concept is that employees with better credentials will be more attractive in the general job market, thus have more opportunity to leave. Thus, dissatisfaction levels will matter more for voluntary turnover when people are better able to secure alternative employment by signaling the market that they are worth hiring. When they have little with which to signal competence, dissatisfaction will be less likely to matter since viable alternatives will be less likely to exist (Trevor, 2001). This research uses perceived Movement Capital rather than actual Movement Capital to predict alumnae’s Intention to Leave. Each alumnus would perceive their Movement Capital differently and is more likely to act on what they perceive and believe than what the actual reality is. Actual Movement Capital, however, does influence Perceived Movement Capital and the actual turnover behavior.

Five indicators of movement capital are used in this research. These indicators are obtained through exploratory Focus Group Discussion held at the beginning of this research. First is the Alma Mater or the origin of the Alumnae, which in this case is the Faculty of Economics University of Indonesia. This indicator is an indigenous indicator as education institution in Indonesia is divided into several tiers, in which graduates from top tier institutions should get better reception than graduates from the low tier institutions. Faculty of Economics University of Indonesia is considered a top tier educational institution. The second indicator is GPA. This is also an indigenous indicator as companies in Indonesia uses GPA as minimum requirements and screening criterion for fresh graduate employee recruitment. The third indicator is level of education, which is a classic movement capital indicator as employers would require certain level of education for a certain level of jobs and infer cognitive ability from it, thus alumnus with higher education would perceive themselves as more desirable to the employers than alumnus with lower education. The fourth indicator is work related skill certifications which related to perceived Movement Capital much the same as level of education. The last indicator is Job Experiences, which is the variety and extent of experiences in working in different positions. Employers require a certain amount of experience for certain levels of jobs and experience does improve job performance, thus alumnus with more experience would perceive themselves as more desirable to the employers than alumnus with little job experience. Overall, employees high in these elements of movement capital, should, by virtue of perceived opportunity, be more ready to act upon their dissatisfaction by leaving. Comprehensive model of the hypotheses above can be seen in the figure below.

Figure 1: Hypothesized Model of Intention to Leave

Methodology

Population and Sample

The population used for this research is the alumnae of the Faculty of Economics University of Indonesia year 2000-2003 who is currently working in an organization. Based on previous research by Veiga (1983), it is decided that the population would be limited to fresh graduates to contain the difference of the career dynamics inherent within a specific career stage. This would simplify the sampling process and reduce biases from unidentified variables.

The respondent used in this research is sampled using clustered random sampling. To perform the sampling, the researchers first obtained a list of alumnae from the Faculty of Economics University of Indonesia. Then, the alumnae are categorized by Year and Department. The respondent is the randomly chosen from each category in proportion to the size of each category. 430 alumnae are randomly chosen out of 1105 alumnae listed.

After choosing the respondent, the surveyor would then call each respondent to ask whether they are currently working and willing to answer the questionnaire via email. Out of 430 alumnae sampled, only 129 responded. Thus the respond rate for the survey is 30%. The respond rate is somewhat lower than our expectation because some of the contact information seems to be outdated, thus reducing the effective number of respondent.

Measures

There are a total of 40 indicators used in the questionnaire. Job Embeddedness is measured using 17 items in three different constructs, which is Job Fit (6 items), Job Links (4 items) and Job Sacrifice (7 items). Psychological Construct is measured using 3 items. Subjective Norms is calculated into a single measure using two items. Perceived Movement Capital is measured using 15 items divided into five different construct, which is Alma Mater, GPA, Level of Education, Certification of Skills, and Job Experience. The items are composed using 5 point Likert scale.

The reliability measurement obtained using Cronbach’s Alpha for the items are 0.805, which is good enough to be used for further analusis. The complete breakdown of the variables used in this research can be seen below in table 1.

No. Variables Symbol Constructs Symbol Indicators
1. Job Embeddedness ξ1 Job Fit X1 6 items
Job Links X2 4 items
Job Sacrifice X3 7 items
2. Psychological Contract ξ 2 Psychological Contract X4-X6 3 items
3. Subjective Norms ξ 3 Subjective Norms X7 2 items
4. Perceived Movement Capital ξ4 Alma Mater X8 3 items
GPA X9 3 items
Level of Education X10 3 items
Certification of Skills X11 3 items
Job Experiences X12 3 items
5. Intention to Leave η1 Intention to Leave Y1-Y3 3 items

Table 1: Variables and Constructs

Analyses

The method of analysis used in this research is Structural Equation Modeling with Lisrel 8.51 Full Version. The measurement model must be estimated first before the structural model can estimated using the data obtained in the survey. The mathematical equation below is derived from the constructs above to be used in the estimation of the measurement model in this research.

Equation 1: Measurement Model Equation

Figure 2 & 3: Standardized Loading Factor and t-value for the Measurement Model

The purpose of the measurement model is to identify invalid items and unreliable construct. As the rule of thumb, items with t-value less than 1.96 or standardized loading factor of less than 0.3 is considered invalid. Unreliable construct is identified by calculating construct reliability (CR) and variance extracted (VE). CR is calculated by dividing the square of total loading factors with the sum of it and the total error in that construct. VE is calculated by dividing the sum of squared loading factor with the sum of it and the total error in that construct. If CR is less than 0.7 or VE is less than 0.5 than the construct is considered unreliable and must be dropped.

Thus, from the measurement model, it is found that 2 item must be dropped because their Standardized Loading Factor is less than 0.5. Item PSYC03 is still retained because its loading factor is more than 0.3 and it is based on strong results in previous researches. Complete detail on the analysis can be seen in the table below,

No. Variables T-Value SLF Error CR VE Analysis Treatment
1 JFIT N/A* 0.83 0.3 0.84 0.72 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
2 JLINK -3.19 -0.29 0.91 N/A N/A SLF<0.5 Not Valid, drop item
3 JSAC 12.34 0.86 0.26 0.84 0.72 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
4 PSYC01 N/A* 0.96 0.09 0.83 0.73 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
5 PSYC02 2.07 0.19 0.96 N/A N/A SLF<0.5 Not Valid, drop item
6 PSYC03 4.69 0.45 0.32 0.83 0.73 SLF>0.3,

(Igbaria et. al, 1997)

Can be Accepted, Keep item
7 SNORM N/A* 1.15 -0.31 1.31 0.57 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
8 PMC1 N/A* 0.98 0.05 0.87 0.59 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
9 PMC2 10.96 0.73 0.47 0.87 0.59 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
10 PMC3 16.27 0.87 0.24 0.87 0.59 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
11 PMC4 6.62 0.52 0.73 0.87 0.59 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
12 PMC5 9.29 0.66 0.56 0.87 0.59 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
13 INTLE01 N/A* 0.82 0.32 0.89 0.72 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
14 INTLE02 13.5 0.86 0.27 0.89 0.72 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item
15 INTLE03 13.77 0.87 0.25 0.89 0.72 SLF>0.5, T-Value>1.96, CR>0.7, VE>0.5 Keep item

Table 2: Analysis on the result of the Measurement Model

The mathematical equation below estimates the relationships tested in the structural model based on the relationship mentioned in the research Hypotheses

Equation 2: Structural Model Equation

After estimating the measurement model and eliminating invalid items, the structural model can then be estimated. The estimation is done using the Maximum Likelihood method. Several error covariances are added based on the modification index, but is limited between items within a single construct. Suggestions of error covariance between items in different construct are disregarded.   The result obtained can be seen in figure 4 and 5.

Figure 4: T-Values for the Structural Model

Figure 5: Standardized Loading Factor for the Structural Model

Results

Goodness of Fit

Before doing the hypotheses testing, it is imperative to see whether the model used in this research is acceptable. There is several goodness of fit measurement that was established to measure whether the model tested is adequate for further analyses. It was also established that there is no single absolute measurement and satisfying the entire range of goodness of fit measurements would be difficult, thus satisfying the majority of the measurements would be enough to verify the acceptability of the model.

The result of goodness of fit measurements shows that the majority of measurement shows good model fitness while the rest shows only marginal model fitness. Thus, it can be concluded from the goodness of fit measurements above that the model used in this research is acceptable and further analysis can be done to test the hypotheses of this research on the results obtained from the model.

No. Measurement Result Standard Fitness
1 Chi-Square=

df=

N=

62.96

60

129

Chi-Square/df=<2 Good Fit
2 P-value 0.37201 >0.05 Good Fit
3 RMSEA 0.020 <0.05 Close Fit
4 P-Value for

Test of Close Fit

0.88 >0.05 Good Fit
5 ECVI S=1.42

M=0.98

I=6.76

M is small and

closer to S

Good Fit
6 AIC S=182.00

M=124.96

I=865.22

M is small and

closer to S

Good Fit
7 CAIC S=533.24

M=244.61

I=915.39

M is small and

closer to S

Good Fit
8 NNI

NNFI

CFI

IFI

RFI

0.91

0.97

0.98

0.98

0.88

>0.90

>0.90

>0.90

>0.90

>0.90

Good Fit

Good Fit

Good Fit

Good Fit

Marginal Fit

9 CN 152.10 >200 Marginal Fit
10 RMR 0.077 =<0.05 Marginal Fit
11 GFI 0.93 >0.9 Good Fit
12 AGFI 0.89 >0.9 Marginal Fit

Table 3: Measurements of Model Goodness of Fit

Hypotheses Test

The hypothesis test is done by analyzing the t-value and the sign of the loading factor of each path. Using the values obtained from the structural model, it can be concluded that Hypothesis 1 and 2 can be accepted because both path has t-value of more than 1.96. Both also have negative sign that confirms the hypothesis that both attitudes have negative effect toward Intention to Leave. Hypothesis 3 and 4 cannot be accepted because the t-value of their path is less than 1.96. In this case, the data does not support the hypothesis. The detailed hypothesis test can be seen below in table 4.

Hypotheses Path T-Value SLF Analyses
H1 JEMB–>INTLE -5.22 -0.45 Significant, Negative SLF. Data supports the Hypothesis
H2 PSYCO–>INTLE -4.27 -0.30 Significant, Negative SLF.Data supports the Hypothesis
H3 SNORM–>INTLE 1.20 0.08 Not Significant, Data does not support the Hypothesis
H4 PMC–>INTLE 0.97 0.02 Not Significant, Data does not support the Hypothesis

Table 4: Hypotheses Test

Discussion

Job Embeddedness and Psychological Contract

Statistical results show that there is significant negative relationship between the two attitude variables and the Intention to Leave. These findings support the first two research hypothesis. The first finding is that Job Embeddedness, which is how an employee feels attached about his job, does reduces employee’s intention to leave. This finding is consistent with an earlier research finding by Sablynski et. al. (2001), that employee with higher feeling of attachment to their job would exhibit less intention to leave than employee with less job embeddedness.

However, only two out of three construct is usable in this research since one construct, Job Links, is found not valid. The two remaining construct, Job Fit and Job Sacrifice, proved valid and reliable.  Job Links is found not valid because it has a standardized loading factor of less than 0.5, which is unacceptable. One plausible explanation for this is because that the population sampled in this research is fresh graduates, which graduated from college in less than five years ago and started working in their current company invariably less. Within this short span of employment, the respondent most probably does not have enough time to build sufficient Job Links to influence their Intention to Leave. In the other hand, Job Fit and Job Sacrifices does not require significant time to develop and influence employee intentions to leave.

The second finding is that tendency toward Relational Psychological Contract would have negative influence on an employee’s intention to leave. This finding is consistent with earlier finding of previous research by Chay and Aryee (1994). This can be explained that employee with a tendency toward Relational Psychological Contract would think of their employment relationship with their employes as a long term relationship. In viewing this, employees would not be actively seeking new jobs opportunities and might hesitate if offered one. All in all, the findings above confirms our first intuition that employee attitudes does plays a prominent role in determining whether an employee plans to leave his/her job or not.

Subjective Norms and Perceived Movement Capital

Statistical results show that the data used in this research does not supports the other two hypotheses. The first unsupported hypothesis is the relationship between Subjective Norms and Intention to Leave. It is found that the relationship between Subjective Norms and Employee Intention to Leave is statistically not significant and considered inconclusive. This finding contradicts the Planned Behavior Theory which suggested that Subjective Norms influences intention. This finding might be caused by the differential of compliance toward norms between one employee and the other. If this differential is indeed significant, further adaptation of the current questionnaire might be required to incorporate this differential within the questionnaire.

The second unsupported hypothesis is the relationship between Perceived Movement Capital and Intention to Leave. Although it is possible that higher perceived movement capital simply does not influences intention to leave, this may as well be cause by divergence of relationships between each of its constructs and employee’s Intention to Leave. Although there is no significant relationship between it and Intention to Leave, there are still possibilities that some of its component might have some influence toward Intention to Leave. Perceived Movement Capital consists of five different construct. Further breakdown of the relationship between Perceived Movement Capital and Intention to Leave is required to determine if any construct did have some relationship with Intention to Leave.

Future Research Directions and Limitation

This research has several limitations that can be explored in further research. The first is that this research limited its sample only to include fresh graduate as respondent. While this decision surely would exclude several biases and unnecessary variables, it would also reduce the validity of several job-related construct that requires sufficient time to develop. Some examples of this construct would be Job Links and Experience. Further research should be done, that includes respondents from other career stages. Hopefully, further research could identify and compare the differences of career dynamics between employees from different career stages.

The second limitation is that this research also limits the sample to the graduates of a specific faculty in a specific university. Further research should be done to include graduates from other University as well as other faculties. In this way, there would be a control groups to provide comparison of the statistical results with.

This research topic still has much room for expansion. There are still many aspects and angles of employee turnover that have not been researched. The future directions for this research are to include several variables such as actual movement capital and the actual turnover behavior. In this way, researcher can confirm whether employee intentions do indeed leads to employee behavior.

Conclusion and Suggestions

The conclusion of this research is that Employee Attitude, in this case Job Embeddedness and Psychological Contract, is confirmed as the dominant factor that affects Employee’s Intention to Leave. In this way, companies trying to reduce employee turnover can start by influencing their employee’s attitude. Employers can give better orientation and socialization of company’s culture and values to facilitate adaptation and increase Job Fit. Employers can also increase employee benefit or giving them more freedom to do their work to increase Job Sacrifice. Employers should also screens candidates in their employee selections to filter out candidates with transactional psychological contract and establish relational psychological contract with their employees through consistent human research policies and intensive communication with their employees.

As for educational institutions, this research confirms that attitude is also an important output of their service. Although further research would be requires, but it is safe to say that to some extent how fresh graduates develop their workplace attitude is largely influenced by their previous attitude in college. Thus, attitude should be stated explicitly in the curriculum as one of the expected output.

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