Halal Literacy: A Concept Exploration and Measurement Validation part 2 (Appendix)

Written by imams on May 23 2012

Author Details:

  • Imam Salehudin
  • Department of Management, Faculty of Economics University of Indonesia
  • imams@ui.ac.id, cc: gsimam@gmail.com

Original File

Abstract:

Muslim consumers have strict commandments which guides their consumption behavior. However, Muslim individuals may have different compliance regarding the commandments. This difference in compliance may be explained by difference in halal literacy. Halal literacy is the ability to differentiate permissible (halal) and forbidden (haram) goods and services which came from better understanding of Islamic laws (shariah). Thus, the purpose of this paper is to explore the concept of Halal Literacy as well as to develop and validate an instrument to measure Halal Literacy for Muslim consumers.

Halal literacy was measured using two methods. One method using six items of five point Likert self evaluation scale and the other using fifteen true-false test questions with an option to choose doesn’t know. Proportion of correct and incorrect was used as weights in scoring to represent the difficulty of items. Scoring results were then analyzed with Confirmatory Factor Analysis (CFA) usingWeighted Least Squaremethod to test construct validity. Scores were then used to classify cases into high, moderate and low Literacy groups. Self evaluation halal literacy and switching Intentions are compared between groups using ANOVA to determine concurrent validity.

Only ten out of fifteen items are considered valid using Confirmatory Factor Analysis. ANOVA showed that grouping of high, moderate and low literacy score can distinguish differences in perceived halal literacy and switching intentions between the groups. Post hoc tests and descriptive statistics revealed interesting non linear relationship between the halal literacy scores; self evaluated halal literacy and intentions to switch from products without halal labels.

Keywords: Halal Literacy; Halal Labels; Muslim Consumer; Measurement and Validation; Product Switching Intention

 

Appendix 1:  Summary of Items and Validation Result

A. ACTUAL HALAL LITERACY

Code

Items

Key

Validity

Which of the following can be considered halal…?
HL01 Beef slaughtered by ahlul kitab (Christian and Jews)

Daging sapi yang di sembelih oleh ahlul kitab

V

Valid

HL02 Medicine with ingredients containing pig fat

Obat yang salah satu bahan bakunya menggunakan lemak babi

X

Not Valid

HL03 Cakes using rum as an ingredient

Kue yang salah satu bahan bakunya adalah rum

X

Valid

HL04 Flesh of fish fed partly by feces

Daging ikan yang sehari-harinya diberi makan dengan kotoran

X

Not Valid

HL05 Flesh of animals died of old age

Daging hewan yang mati karena usia tua

X

Valid

HL06 To eat rang-rang ants

Makan semut rang-rang

X

Valid

HL07 To eat the flesh of bighal (mule)

Makan daging bighal (peranakan kuda dengan keledai)

X

Valid

HL08 Things harmful to ones health (such as cigarettes)

Semua benda yang memiliki pengaruh buruk bagi kesehatan (seperti rokok)

X

Not Valid

HL09 Meat of dhob’un or hyena

Daging dhob’un atau hyena

V

Valid

HL10 To eat shark’s fin

Makan sirip ikan hiu

V

Not Valid

HL11 To eat the flesh of a cattle slaughtered before it is one year old

Daging hewan ternak jika disembelih sebelum genap berumur satu tahun

V

Valid

HL12 To eat halal food used as offering in pagan rituals

Makanan halal yang telah dijadikan sesajen untuk upacara yang berbau syirik

X

Valid

HL13 To drink khamr, consumed as medicine

Minum khamr, jika dikonsumsi sebagai obat

X

Not Valid

HL14 Dining in a place that also serves alcoholic beverages

Makan/minum di tempat yang juga menyajikan minuman keras

X

Valid

HL15 Dining in a place that also serves haram foods, such as pork

Makan/minum di tempat yang juga menyajikan masakan haram seperti babi

X

Valid

Note:

V: True

X: False

 

B. HALAL LITERACY SELF EVALUATION

Code

Items: State the degree of your agreement with the following statements (5 point Likert scale)

SLF

T-value

Validity

C1

I  understand Islamic laws of halal and haram for food and drinks

Saya memahami hukum halal – haram untuk makanan dan minuman dalam Islam

0.66 7.93

Valid

C2

I feel capable to differentiate which food or drinks are permissible (halal) and which are forbidden (haram)

Saya merasa sudah mampu membedakan sendiri mana makanan atau minuman yang dihalalkan atau diharamkan oleh Islam

0.78 9.93

Valid

C3

I don’t know much about whether certain foods or drinks are permissible (halal) or forbidden (haram) [REVERSE ITEM]

Saya tidak banyak tahu mengenai hukum halal – haramnya suatu makanan atau minuman

0.66 8.23

Valid

C4

I feel that I need the help of someone else more knowledgeable to differentiate which food or drinks are permissible (halal) and which are forbidden (haram) [REVERSE ITEM]

Saya merasa butuh bantuan orang lain yang lebih paham untuk mengetahui mana produk yang halal dan mana yang haram.

0.46 5.45

Valid

C5

I feel that I know enough which foods or drinks are forbidden by Islam

Saya merasa sudah cukup mengetahui makanan dan minuman yang diharamkan oleh Islam

0.69 8.43

Valid

C6

I have enough knowledge to differentiate between permissible (halal) and forbidden (haram) stuffs

Saya punya cukup ilmu untuk membedakan antara barang yang halal dengan barang yang haram

0.75 9.36

Valid

Note: Underlined items are reverse items. All reverse items have undergone reverse coding before analysis.

Halal Literacy: A Concept Exploration and Measurement Validation part 1

Written by imams on May 23 2012

Author Details:

  • Imam Salehudin
  • Department of Management, Faculty of Economics University of Indonesia
  • imams@ui.ac.id, cc: gsimam@gmail.com

Original File

Abstract:

Muslim consumers have strict commandments which guides their consumption behavior. However, Muslim individuals may have different compliance regarding the commandments. This difference in compliance may be explained by difference in halal literacy. Halal literacy is the ability to differentiate permissible (halal) and forbidden (haram) goods and services which came from better understanding of Islamic laws (shariah). Thus, the purpose of this paper is to explore the concept of Halal Literacy as well as to develop and validate an instrument to measure Halal Literacy for Muslim consumers.

Halal literacy was measured using two methods. One method using six items of five point Likert self evaluation scale and the other using fifteen true-false test questions with an option to choose doesn’t know. Proportion of correct and incorrect was used as weights in scoring to represent the difficulty of items. Scoring results were then analyzed with Confirmatory Factor Analysis (CFA) usingWeighted Least Squaremethod to test construct validity. Scores were then used to classify cases into high, moderate and low Literacy groups. Self evaluation halal literacy and switching Intentions are compared between groups using ANOVA to determine concurrent validity.

Only ten out of fifteen items are considered valid using Confirmatory Factor Analysis. ANOVA showed that grouping of high, moderate and low literacy score can distinguish differences in perceived halal literacy and switching intentions between the groups. Post hoc tests and descriptive statistics revealed interesting non linear relationship between the halal literacy scores; self evaluated halal literacy and intentions to switch from products without halal labels.

Keywords: Halal Literacy; Halal Labels; Muslim Consumer; Measurement and Validation; Product Switching Intention


Background

Importance and Uniqueness of Muslim consumer

Islam is the religion with the second largest number of believers. Muslims (adherents of Islam) population was estimated to be 1.6 billion people concentrated in several regions such as the Middle East, Pakistan, South East Asia and parts of Africa as dominant majority; as well as growing presences as minorities in several countries such as India, Russia, USA and the EU (Pew Forum on Religion & Public Life, 2009). The estimated purchase power of the world Muslim populations was estimated to be 2.7 Trillion USD (JWT, 2007; Halal Journal, 2008). This sizable purchase power makes Muslims a good market segment for consumer goods -such as foods, fashions, beverage and pharmaceuticals- as well as services –such as financial, education and tourism services.

However, marketers must have sufficient understanding in order to target Muslim market segments. Muslim consumers possess different characteristics compared to other market segment. Islam is not only a religion, but also a way of life. Muslims have strict commandment regarding what they consume. Allah Subhanahu wa Ta’ala commands Muslims to consume only things that are Halal and good, “O Mankind! Eat of that which is Halal (lawful) and Thoyyib (good) on the earth, and follow not the footsteps of Satan. Verily, he is to you an open enemy.” (Al Qur’anul Karim, Al-Baqoroh, 2:168).

Halal, which is the opposite of haram, is a term to say that something is not forbidden to be consumed by the scriptures of Qur’an, by the saying of the prophet or by the ijma’ (consensus) of the ulama’ (Jusmaliani and Nasution, 2009). His Prophet, Muhammad Shollallohu alayhi wa salaam, also forbids his ummat (people) to avoid consuming things that are ambiguous whether it is Halal or haram, as narrated by Bukhari & Muslim: The Prophet Muhammad (peace be upon him) once said, “The Halal is clear and the Haram is clear; in between these two are doubtful (Syubhat) matters concerning which people do not know whether they are Halal or Haram. One, who avoids them, in order to safeguard his religion and his honor, is safe. Anyone who gets involved in any of these doubtful items, he may fall into the Haram. This case is similar to the one who wishes to raise his animals next to a restricted area, he may step into it. Indeed for every landlord there is a restricted area. Indeed the restrictions of Allah are Haram.” (Al-Utsaimin, 2005).

 

Difference in behavior regarding halal label

This commandment complicates foreign based marketers from non Muslim countries to really tap into this market segment.  However, even though the halal commandments do regulate the lives of all Muslims worldwide universally, the actual compliance may vary between individuals as well as between product contexts. Most researches covering this specific behavior usually focuses on attitudinal attributes based on Ajzen’s theory of planned behavior, i.e: attitudes, subjective norms and perceived behavioral control (Lada, Tanakinjal, and Amin, 2009). Other researches focused on religiosity and spiritual values that explained differences in compliance between individual Muslim consumer (Karim and Afiff, 2005; Kartajaya and Sula, 2006; Afiff and Astuti, 2009).

Their findings however, cannot fully explain differences of consumer compliance across different product contexts, in which the behavioral intentions of Muslim consumers regarding products with halal labels may not be necessarily consistent between product contexts.  The same person might have different behaviors across different product categories as one Muslim consumer may exhibit high involvement and compliance toward halal commandment when choosing canned foods, while purchasing over-the-counter medicines with less consideration.  Another example in the service sector is when a Muslim may choose very carefully where to eat out in order to avoid eating haram foods but is willing to put their savings in conventional banks.

This study proposes and explores an alternative concept to complement the attitudinal approach, by introducing a cognitive attribute to the behavioral model of Muslim consumers regarding halal labels. This study aims to highlights the importance of Halal Literacy in determining the behavioral intention of Muslim consumer in selecting products with halal labels. Halal literacy is defined as the correct awareness and understanding of individual Muslim regarding halal commandments. This proposition is in accordance to the verse “It is only those who have knowledge among His servants that fear Allah.”(Al Qur’anul Karim, Fatir, 35:28). Thus Muslims with better knowledge about the Halal commandments should be more involved and careful regarding the products they consume than less knowledgeable Muslims.

 

Literature Review

Literacy Concepts in Consumer Behaviors

The concept of literacy has been used frequently in previous researches to explain various consumer behaviors. However, since there have been no prior conceptualization of literacy in the context of Halal consumption behavior,  initial review of definitions and usage of Literacy concepts in other behavioral context would be required in order to build a foundation for the concept of Halal Literacy.

One concept of literacy that received much spotlight in consumer behaviors researches is the concept of Financial Literacy. Koonce et al. (2008); Hu, Malevergne and Sornette (2009); and Glaser and Weber (2007) used the Financial Literacy to explain various behavior of investors as consumer of financial services. Koonce et al. (2008) discovered that teenagers with better access of financial knowledge and information, either from their parents or from external sources, have greater tendencies to make long term financial planning as well as saving greater portion of their earnings. Hu, Malevergne and Sornette (2009) on the other hand, finds that investors with low financial literacy have greater vulnerability to optimism bias, both from themselves as well as bias caused by marketing efforts of fund managers as financial service provider.

Apart from Financial Literacy, the concept of literacy has also been used to explain perceptions and behaviors of consumers in a wide range of context. Media Literacy is another context of literacy which receives greater attention from scientist. Livingstone and Van der Graaf (2010) defined Media Literacy as the ability to access, understand and create messages at a variety of context. Narrower definition was used by Elma et al. (2010), which focused on ability to access and understand media messages, to explore the role of media literacy on how consumers perceived the ethics of media broadcasting corporations and trusts the neutrality of messages they contained.

In an entirely different context, Yamamiya et al. (2005) conducted a study which found that giving media literacy course would reduce the influence of media effect toward thin body image for teenage girls which may lead to various eating disorders. Other use of media literacy in health related consumer behavior includes a study by Primack et al. (2008) which explored the influence of media literacy toward student’s attitude toward anti-smoking advertising campaign.

In each of these studies, the concept of literacy was used instead of more generic cognitive concept such as awareness and involvement. Even though both of these concepts are related to literacy, they both represent a narrower concept of consumer cognition. Literacy is more than the state of awareness toward certain concepts or the motivation to seek more information on certain topics. Being literate means having capabilities to modify the behavior of oneself as result of greater understanding of certain specific topics.

Ingerman and Collier-Reed (2010) describes Literacy as having two component, Potential and Enactment. Potential literacy is made up of knowledge of a particular situation, personal engagement with a situation, and social engagement in the world. While Enactment requires a particular set of competencies in action, which together helps shape the situation: recognizing needs; articulating problems; contributing towards the process; and analyzing consequences. These definitions, albeit used originally in other contexts of literacy, may also be appropriate to be used to describe literacy in the context of halal consumptions.

 

Role of halal literacy in Islam

As explained earlier, the Prophet (peace be upon him) once said, “The Halal is clear and the Haram is clear; in between these two are doubtful (Syubhat) matters concerning which people do not know whether they are Halal or Haram…”. This hadith is the central foundation for the conceptualization of Halal Literacy for Muslim consumer. Based upon this hadits, everything can be categorized as permissible (Halal) and forbidden (Haram) in Islamic laws, with what is left over became doubful matters (Syubhat). Syubhat matters are matters concerning which people do not know whether they are Halal or Haram. Thus, in order to erase doubt, knowledge is required.

Islam commanded all of its believers to pursue religious knowledge; one of such is the knowledge of Halal and Haram. Ibnu Majah narrated, from Anas bin Malik, as well as other Companions, such as Ali bin Abi Thalib, ‘Abdullah bin ‘Abbas, ‘Abdullah bin ‘Umar, ‘Abdullah bin Mas’ud, Abu Sa’id Al-Khudriy, Al-Husain bin ‘Ali, and Jabir radhiyallahu’anhum, The Prophet Muhammad (peace be upon him) once said “Seeking (religious) knowledge is obligatory upon every Muslim (male and female)” (Ibnu Majah no. 224, in Al-Hilali, 2005a).

Islam places great importance toward the pursuit of knowledge as well as to those possessing knowledge. As mentioned in several verses in the Qur’an, such as in Al-Mujadilah, Allah subhanahu wa ta’ala said:”Allah will raise up, to (suitable) ranks and (degrees), those of you who believe and who have been granted knowledge. And Allah is well-acquainted with all you do.” (Al Qur’anul Karim, Al-Mujadilah 58:11). Also in Az-Zumar, Allah subhanahu wa ta’ala said: “Is he who payeth adoration in the watches of the night, prostrate and standing, bewaring of the Hereafter and hoping for the mercy of his Lord, (to be accounted equal with a disbeliever)? Say (unto them, O Muhammad): Are those who know equal with those who know not? But only men of understanding will pay heed” (Al Qur’anul Karim, Az-Zumar 39:9).

The verses above were further emphasized by hadith, as Tirmidzi narrated, Abu Umamah radhiyallohu’anhu relates that the Prophet (peace be upon him) once said, “A learned one is as much above an (ordinary) worshiper as I am above the least of you (Companions); and he added: Allah, His angels and all those in the heavens and in the earth, even the ants in their holes and the fish in the water, call down blessings on those who instruct people in beneficent knowledge” (Al-Hilali, 2005a)

Both verse and the hadith above shows how Islam place great importance to the pursuit of religious knowledge, by giving those who possess knowledge several degrees higher than those to do not (Ibnu Katsir, 2000). This is further emphasized by the statement of Allah that only men of understanding will pay heed and truly obey His commandments. How can men without understanding truly obey, since true obeisance only came after belief, and how can belief come without proper understanding? (Al Hilali, 2005a).

By pursuing religious knowledge, Muslims reduces Syubhat (doubts) by clarifying which are permissible (halal) and which is forbidden (haram). Without knowledge, everything is doubtful, and since The Prophet (peace be upon him) clearly forbid consuming doubtful matters, pursuing more religious knowledge regarding these doubtful areas would clear doubtful matter, thus making those matters initially forbidden to become permissible.

By pursuing religious knowledge regarding the legal nature of matters, Muslim can reduce doubtful matters and differentiate permissible matters from forbidden ones. Therefore, it can be concluded that in the context of Halal consumption behavior, Halal Literacy can be defined as the ability to differentiate permissible (halal) and forbidden (haram) goods and services which came from better understanding of Islamic laws (shariah). This newly conceptualized construct of Halal literacy is a potential variable in explaining the variance of compliance to halal commandment of Muslim consumers.

Measuring and Validating Halal Literacy

Generally, there are two ways to measure literacy in any context of behavior. One of which is by measuring self evaluation using attitudinal items that yields perceived literacy, while the other is by giving test based items     which yields actual literacy. Bandura (2003) posits that self evaluation of a competency or self efficacies might have greater influence on behavior than the actual competency of an individual, especially when the individual have lower self evaluation than the actual level of competence. However, self evaluation measurement might prove highly subjective since self assessment might involve numerous biases which may consciously or subconsciously affect measurement.

Glaser and Weber (2007) defined investor’s financial literacy as “investors’ ability to give an estimate of their own past realized stock portfolio performance” and discovered that investor’s self-rated and actual financial literacy are often mismatched, with investors overrate themselves most of the time. This finding was supported by findings of Hu, Malevergne and Sornette (2009) explained earlier.

These mismatches between self-rated and actual literacy may be caused by the limitation of individuals with low literacy to perform objective evaluations of their own competence level. This phenomenon has several different names which explain the same thing, such as the overconfidence bias or the metacognitive deficiency bias (Kruger and Dunning, 1999). Thus, in order to test for the existence of such bias in the concept of Halal Literacy, both self-reported and actual test-based measurements were used simultaneously.

 

Research Method

Data Set

This study employed 150 respondents obtained using purposive sampling. Criteria of sample used were Muslim with age between 19-25 years old. Selection of age group as criteria for sampling is based on differences which might occur between age groups or generations. This paper emphasizes on validating the measurement instrument with additional in-depth exploration and discussion regarding construct and concurrent validity of the items used for measuring Halal Literacy. Therefore, population representation is not the main concern thus the result of this study may not be generalized to the population.  However, this study may be used as foundation for further research concerning this particular research context.

 

Measurement and Scoring Technique

Halal literacy is measured using two different methods. The first method is using test based true-false questions with an option to choose doesn’t know. Twenty five test items for Halal Literacy were generated from the “Encyclopedia of Prohibitions” chapter on Food by Syaikh Salim bin Ied Al-Hilali (2005b), which lists all prohibitions in Islamic laws (Shariah) in details.  However, only fifteen passed the pre-test and employed in the final data collection.

The results of the test were then scored using +1 for correct answer, -1 for incorrect answer, and 0 for abstained answers. This scoring method would differentiate between truly literate and erroneous understanding of Islamic laws regarding food consumptions, as shown in Table 1. This would also eliminate guessing bias; since respondents were given option to stay abstain. Scoring results were then analyzed with Confirmatory Factor Analysis (CFA) usingWeighted Least Square method to test the construct validity. Standardized Loading Factor from the solutions was used as weight for the final score. Score norms were then calculated to classify cases into High, Moderate and Low Literacy groups. Proportion of correct and incorrect was also calculated in order to measure the difficulty of items.

Table 1: True-False-Abstain options and Scoring Method

Actual

Self-Perceived

Literate

Illiterate

Literate

Correct (+1)

Abstained (0)

Illiterate

Incorrect (-1)

Abstained (0)

Note: Scoring are (+1) if Correct; (-1) if Incorrect; (0) if Abstain

The second method is using 5-point Likert based scales to measure Self-Evaluated Halal Literacy. Six items was generated and all six passed the pre-test Alpha cronbach test. Result from the final data collection was analyzed with Confirmatory Factor Analysis (CFA) using Maximum Likelihood method. The validated items will then be correlated with Test-based Halal Literacy scores to check for convergent validity between the two measurements.

The validity of an instrument can be seen by more than one approach. The first approach to validity is the content validity, which sees the validity of an instrument as whether the instrument covered sufficient dimensions of the construct to be measured. Two components of content validity are the representativeness and relevance of the measurement instrument’s contents.

The second approach to validity is the construct validity, in which the validity of an instrument in seen as whether the results obtained from the tested instrument corresponds with the pattern of a particular theory about the construct intended to be measured (Domino and Domino, 2006). One method commonly used in analyzing this type of validity is by using a statistical method called confirmatory factor analysis. This method tests whether the data obtained from measurements can support the model developed from the theory of the construct to be measured (Chadha, 2009).

The third approach is the convergent validity, in which the validity of an instrument is seen as the correlation between the measurement results of an instrument with other instrument that measures the same construct and has passed the validity test (Chadha, 2009). The assumption underlying the validity of this is that if an instrument truly measures a certain construct, then the measurement results should be consistent with the results of tested instruments that measure the same construct.

While the fourth approach of validity is the criterion validity, which sees the validity of an instrument as the correlation between its result to the measurement result of other instruments which measure different constructs, but in theory corresponds to the construct intended to be measured (Anastasi and Urbina, 1997). If the result of the corresponding instruments is obtained simultaneously, then the validity is called concurrent validity.

Results from both measurements were then correlated with the intention to switch from products without halal label. The correlation analysis would show whether the two measurements have concurrent validity as well as which measurement have better concurrent validity. Scores from self-evaluated Halal Literacy and intentions to switch from products without halal label are then compared between groups based on the level of Tested Halal Literacy using ANOVA to gather more insight regarding possible relationships between the measurements. Confirmatory Factor Analysis (CFA) for the validation of the measurements were conducted using LISREL for WINDOWS 8.51 Full Version (Jőreskog dan Sőrbom, 2001), while ANOVA and descriptive analysis were performed using SPSS 15.0 for WINDOWS Full Version (SPSS Inc., 2006).

 

Analysis and Discussion

Descriptive Analysis

Halal literacy was measured using 15 true-false questions. Proportion of correct and incorrect answers was calculated for each item to measure item difficulties. Low difficulty items will have high proportion of correct answers and low proportion of incorrect ones. Vice versa, high difficulty items will have low proportion of correct answers and high proportion of incorrect ones. Typically, proportion of incorrect can be calculated simply by subtracting the proportion of correct answers to 100%. However, since in this case an option to be abstaining was given to eliminate guessing error, proportions of incorrect answers must be calculated separately.

Useful items should have moderate difficulties, since extremely hard or extremely easy items would yield less information and have weaker power to differentiate individual differences. Items with good difficulty should have proportion of correct between 10% and 90%; thus effectively differentiate the main 80% of population. Therefore, item HL09 can be considered as suboptimal as it has extremely high difficulty with proportion of correct answers of less than 10%. The remaining 14 items can be classified into Low, Moderate and High difficulty items based on their POC. Items with POC of less than 33% are considered High difficulty; while items with POC of more than 66% are considered Low difficulty items. Items with POC between 33% and 66% are considered as Moderate difficulty items. Complete description of item difficulties is shown in Table 2.

Table 2: Descriptive Statistics on Proportion of Correct and Incorrect

ITEMS

POC

POI

POA

Difficulty

HL01

64.67%

12.67%

22.66%

Moderate

HL02

85.33%

8.00%

6.67%

Low

HL03

68.00%

6.67%

25.33%

Low

HL04

10.67%

64.00%

25.33%

High

HL05

66.00%

11.33%

22.67%

Moderate

HL06

25.33%

23.33%

51.33%

High

HL07

18.67%

19.33%

62.00%

High

HL08

79.33%

0.67%

20.00%

Low

HL09

9.33%

46.67%

44.00%

Extremely High

HL10

46.00%

19.33%

34.67%

Moderate

HL11

36.00%

15.33%

48.67%

Moderate

HL12

68.67%

14.00%

17.33%

Low

HL13

40.67%

35.33%

24.00%

Moderate

HL14

28.00%

30.67%

41.33%

High

HL15

40.00%

22.00%

38.00%

Moderate

Note: N=150, Content of each item is shown in Appendix 1. POC: Proportion of Correct Answers; POI: Proportion of Incorrect Answers; POD: Proportion of Abstaining

Source: Data Processing

Construct Validity

The next step of analysis after calculating items difficulties are testing for the construct validity of the measurement items. Construct validity is tested using Confirmatory Factor Analysis (CFA) with Weighted Least Square (WLS) method of estimation. Maximum Likelihood can not be used to estimate the validity of this instrument since it uses true-false questions which yields nominal data.  Maximum Likelihood will be used in estimating the validity of the second instrument which uses 5 point Likert scale to measure Self Evaluation of Halal Literacy.

Initial test on the Test-based Halal Literacy score yields the following goodness of fit statistics: Chi-Square of 88.38; df of 90; p-value of 0.52865; and RMSEA of 0.0001. These results indicates that the model is already fit or supported by the data because there are no significant differences between covariance matrix obtained from the data and the one formulated from the model. A model is considered valid if it yields p-value of more than 0.05 or RMSEA of less than 0.08. Thus, the model can be considered fit by both criteria.

The next step is to validate items. Items are considered valid, if they have absolute t-value of above 1.96 (using confidence level of 95%). Only ten out of fifteen items were considered valid by this criterion. Thus, invalidated items are extracted from the measurement model and the calculation of latent variable scores used in further analysis. The complete result of the item validation is shown in Table 3.

Table 3: Item Parameters for Tested HL

ITEMS

SS

TV

RESULT

HL01

0.24

1.97

Valid

HL02

0.22

0.76

Not Valid

HL03

0.38

3.44

Valid

HL04

0.12

1.22

Not Valid

HL05

0.30

2.64

Valid

HL06

0.54

10.11

Valid

HL07

0.52

10.45

Valid

HL08

0.01

0.05

Not Valid

HL09

0.27

3.60

Valid

HL10

0.08

0.76

Not Valid

HL11

0.18

2.53

Valid

HL12

0.52

3.53

Valid

HL13

0.02

0.15

Not Valid

HL14

0.80

23.14

Valid

HL15

0.89

28.82

Valid

Note: N=150; SS: Standardized Solution; TV: T-Value

Source: Data Processing

 

Measurement model for the Self Evaluated Halal Literacy construct was estimated using the Maximum Likelihood method. Initial test on the Self Evaluated Halal Literacy items yields the following goodness of fit statistics: Chi-Square of 8.56; df of 7; p-value of 0.28566; and RMSEA of 0.039. These results also indicates that the model is already fit or supported by the data based both criteria explained earlier.

The next step is to validate items. Items in the Self Evaluated Halal Literacy instrument were evaluated using the same validity criterion as the Test-based instrument. All seven items were considered valid, even though covariance between errors must be added for C1 and C2 as well as for C5 and C6. Errors between items were assumed to be independent and covariance between items reduces their scope of measurement, since it means two or more items might overlap each other. Nonetheless, these violations of assumption were accepted as minor shortcoming of the item in question (Hair et al., 2009). The path diagram of the item validation is shown in Figure 1.

Note: Standardized Solutions (Left) and T-values (Right) of the Measurement Model

Figure 1: Path Diagram of Measurement Model for Halal Literacy Self Evaluation

Source: LISREL Data Processing

 

Norms

Good test instruments must have a certain score norms in order for users to be able to interpret results as well as comparing and analyzing results from different tests (Crocker and Algina, 1986). Therefore, after validating the measurement model, Norms were calculated in form of percentile rank to be used as base to classify distribution region (i.e. Upper, Middle or Lower distribution). The scores were grouped into three major groups, which are upper, middle and lower. In addition, two outlier groups were also categorized, which are top and bottom. These two outlier groups can be safely excluded from further validation. Detailed raw score and percentile for each group is shown in Table 4.

Table 4: Raw Score Norms

Distribution

Region

Raw

Score

Percentile

Rank

Score

Band

Percentile

Band

Top

3.65

95

More than 3.65

>95

Upper

2.34

80

1.37 – 3.65

65-95

Middle

0.78

50

1.37 – 0.25

35-65

Lower

(-0.56)

20

(-1.99) – 0.25

5-35

Bottom

(-1.99)

5

Less than (-1.99)

<5

Source: SPSS Data Processing

 

 

 

Concurrent and Convergent Validation

The last two measurement validity examined is the convergent and concurrent validity of the instrument. Convergent validity was measured by examining the correlation between scores from the test based Halal Literacy instrument with scores obtained from the self-evaluation Halal Literacy measurements. Concurrent validity, on the other hand, was measured by looking at the correlation between scores from both Halal Literacy measurements with measurement from theoretically-related construct, in this case the switching intention of Muslims from products without halal labels.

The correlations between the measurements scores was obtained from the standardized path coefficient between two constructs when processed in pairs while correlation significance was obtained from the t-value of the path between each pair (Hair et al., 2009). Result from correlating both Halal Literacy measurement scores indicated that both instruments have no significant positive correlation. The correlation even yields negative coefficient (r=-0.17, t=-1.51). This means that the instruments have poor convergent validity. This means that either the self evaluation or the test-based Halal Literacy instruments were inaccurate to measure Halal Literacy. This problem can be solved by examining the concurrent validity between the two instruments.

As explained earlier, concurrent validity can be measured by correlating an instrument with other instrument measuring theoretically related construct, in this case the switching intention of Muslims from products without halal labels. Results from correlating the scores from both Halal Literacy measurements with the switching intention, showed that test-based Halal Literacy instrument has significant positive correlation to Switching Intention (r=0.25, t=2.38). In contrast, self-evaluation Halal Literacy instrument produce significant negative correlation to Switching Intention (r=-0.19, t=-2.16).

Combining the result from the poor convergent validity with the mixed result from the concurrent validity, showed that test-based Halal Literacy instrument is the better instrument in explaining Switching Intentions. On the other hand, using self evaluation Halal Literacy may produce confusing result. This finding supports previous studies which found that consumers have difficulties in measuring their own level of competence, with self-rated and actual literacy often mismatched and a tendency for consumers to overrate themselves (Kruger and Dunning, 1999; Glaser and Weber, 2007; Hu, Malevergne and Sornette, 2009). The complete score correlations results for both purchase contexts are shown in Table 5.

Table 5: Correlation Result between Instruments and Constructs

Correlation

T – HL

SE – HL

BI

T-HL

1

SE – HL

-0.17 (-1.51)

1

BI

0.25 (2.38)*

-0.19 (-2.16)*

1

Note:

*) Significant at α =5%; t (0.05) = 1.96

T-HL: Tested Halal Literacy; SE-HL: Self Evaluated Halal Literacy; BI: Behavioral Intention to Switch from Products without Halal Label

Source: LISREL Data Processing

 

ANOVA Post-Hoc Analysis

Since the result from the convergent and concurrent validation analysis have yielded interesting results, this study follows up the results by performing ANOVA Post-Hoc Analysis in order to explore more about possible relationship between Actual Halal Literacy obtained from the test instrument, Self-Evaluated Halal Literacy and Switching Intention. Respondents were divided into three groups based on the score norms developed earlier (i.e. Lower, Middle and Upper). Mean score of Self Evaluation and Switching Intention were then analyzed and compared between the three groups.

Initial ANOVA test yields significant result for Switching Intention (F=6.77; Sig. 0.001) and non significant result for Self-Evaluation (F=0.43; Sig. 0.654). Thus, Actual Halal Literacy can predict Switching Intention but incapable to predict Self Evaluation. Deeper analysis may be obtained by examining the post-hoc analysis to see differences between each level of Halal Literacy. Result from post-hoc analysis is shown in Table 6.

Table 6: ANOVA Post-Hoc Analysis of Switching Intention and Self Evaluation Based on Halal Literacy Score Groups

Dependent Variable

Halal Literacy

Sig.

I

J

I-J

Switching

Intentions

Lower Middle

-0.13

0.00

**
  Upper

-0.13

0.01

*
Middle Upper

0.00

1.00

 
Self Evaluation Lower Middle

0.03

0.77

 
  Upper

-0.01

0.98

 
Middle Upper

-0.04

0.65

 

Note: *) Significant at 0.05; **) Significant at 0.01

Source: LISREL Data Processing

 

Result from post-hoc analysis reveals interesting relationship between Actual Halal Literacy and Switching Intention. Differences in Switching Intention was significant between Lower and Middle as well as between Lower and Upper Halal Literacy groups. On the contrary, difference between Switching Intention among Middle and Upper groups were not significant.  In confirmation to the initial ANOVA, there were no significant differences of Self Evaluation between all three groups of Halal Literacy.

Clearer understanding of these relationships may be better presented using a graphical representative. In order to obtain graphical representative of the data, standardized means of Switching Intention and Self Evaluation was calculated for each Halal Literacy group. The standardized means were then plotted on a chart to see differences between groups of Halal Literacy. Standardized means for each group is shown in Table 7 while the means plot is shown in Figure 2.

Table 7: Standardized Means of Switching Intention and Self Evaluation Based on Halal Literacy Score Groups

Halal Literacy

Switching Intentions Self Evaluation
Lower

0.91

1.01

Middle

1.04

0.97

Upper

1.04

1.02

Source: LISREL Data Processing

Figure 2: Means Plot of Switching Intention and Self Evaluation by Halal Literacy

Source: Microsoft Excel Data Processing

 

The means plot revealed clearer understanding of relationships between Halal Literacy, Self Evaluation and Switching Intention. It is shown that Halal Literacy differentiate consumer Switching Intention best at the lower group, in which consumers with low Halal Literacy score having less intention to switch from product without Halal Label than consumer with moderate to high literacy. This result may highlight the potential of using consumer education in increasing market share of halal industries in Muslim countries. Presumably, based on the result shown above, you don’t even have to increase the halal literacy of your consumer all the way to a high level, since increasing it to a medium level already suffice in significantly improving behavioral intention regarding halal label.

The next finding is that consumer with low literacy may develop overrated evaluation of themselves, which may impede intention to switch from products with halal label. This phenomenon is called overconfidence bias, which has been supported by findings in other context (Kruger and Dunning, 1999; Glaser and Weber, 2007; Hu, Malevergne and Sornette, 2009). Once again, focus is on consumer with lesser halal literacy since the effect of this bias is greater at the lower group than in the moderate and upper group. Thus, consumer education should be designed and targeted to focus on this group consumer.

 

Conclusion

There are several conclusions to be made in this study. The first conclusion is that both test-based and self-evaluation halal literacy have good construct validity. The second conclusion is that even though both instrument have good construct validity, only test based instrument have good concurrent validity with intention to switch from products without halal labels. The third is those consumers, especially the ones with low literacy, are prone to overrate their level of literacy thus may often have lesser involvement toward halal product. This may impede their intention to switch from products without halal labels.

It is important to note that the content of Halal Literacy instrument validated in this research is limited to foods and medicine related topics, while many other potential topics of halal literacy remains to be measured, such as halal literacy of financial transactions and clothing. Behavioral intentions of individual Muslim may vary between contexts and halal literacy may have different influence in other product contexts. Thus, further research validating halal literacy in product contexts other than foods and medicine is important.

This study delineates the importance of consumer education, through advertising or other marketing communication methods, to increase awareness and understanding of Muslim consumer about halal commandments and its implication to their daily live. Halal literacy may prove to have considerable role in influencing compliance behavior toward Islamic laws, especially the halal commandments. As Alloh subhanahu wa ta’ala said in the Qur’an Surah Al-Balad 90:4-10, “Verily We have created man into toil and struggle. Have We not made for him a pair of eyes; and a tongue, and a pair of lips; and shown him the two ways (obedience and disobedience)?” Thus He commanded Muslims to use their eyes, their ears, their lips, and most importantly their minds to struggle continuously to follow the path of obedience which define an individual as a true Muslim.

 

References

 

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Yamamiya, Y., Cash, T.F., Melnyk, S.E., Posavac, H.D., and Posavac, S.S. (2005) Women’s exposure to thin-and-beautiful media images: body image effects of media-ideal internalization and impact-reduction interventions. Body Image, 2(1), pp 74–80

‘PERCEIVED PURCHASE RISK IN THE TECHNOLOGICAL GOODS PURCHASE CONTEXT: AN INSTRUMENT DEVELOPMENT AND VALIDATION Part 2

Written by imams on May 23 2012

Imam Salehudin

Department of Management Faculty of Economics University of Indonesia

Jl. Prof. Dr. Sumitro Djojohadikusumo, UI-Depok Campus, Depok-West Java, 16424

Email: imams@ui.ac.id; gsimam@gmail.com

Original File

CONCLUSIONS

There are four conclusions drawn from the result of this study. First, each measurement model for all constructs was tested significant in both the purchase contexts. Therefore, it can be concluded that all the instruments have good construct validity. Recapitulation of the fitness measurement for all three instruments on both purchase contexts are shown in table 10 below.

Table 10: Recapitulation for Goodness of Fit Measurements

Fitness Measure

SMARTPHONE

NETBOOK

UPPR

MPPR

PI

UPPR

MPPR

UPPR

Target Value

χ2(df)

14.57

(10)

180.21

(157)

5.04

(4)

5.80

(4)

113.78

(94)

6.32

(3)

n.a

p-value

0.148

0.098

0.283

0.214

0.080

0.096

≥0.05

RMSEA

0.054

0.031

0.041

0.057

0.039

0.089

≤0.08

Source: Data Processing

Second, some items in both the Unidimensional and Multidimensional Perceived Purchase Risk measurement were invalidated in one or both purchase context. Invalidated items can not be used in the measurement and must be removed from the instrument. Only significant items can be included in the measurement for future use.

Third, the newly developed Multidimensional Perceived Purchase Risk measurement has good convergent and concurrent validity. Thus, the measurement can be considered to be ready for practical use within the purchase context of technological gadgets such as Smartphones and Netbooks. The instrument can be utilized by manufacturers and marketers of technology products in market surveys to map psychographic consumer segments of potential markets. This instrument can be used to measure the risk perceptions of consumers towards the purchase of existing products on the market and also new products about to be launched by the manufacturers.

Fourth, although the newly developed Multidimensional Perceived Purchase Risk instrument has weaker concurrent validity than the Unidimensional Perceived Purchase Risk instrument developed by Corbitt et al. (2003), the new instrument provide more comprehensive information. Apart from the level of risk perceived by the consumer purchases, the new instrument may also provide more detailed information to identify aspects which are considered high risk by a segment of consumers targeted by the marketer.

Identifying risk factor as perceived as high risk by consumer is important since the likelihood of a prospective purchaser to seek additional information will be higher when faced with purchasing decisions perceived to have a higher risk (Cox, 1967; Capon and Burke, 1977; Locander and Hermann, 1979; Lutz and Reilly, 1973). Information sought by prospective buyers will be the information that may alleviate the risk they perceive. Meanwhile, the likelihood of a prospective buyer to postpone or cancel the purchase will be even greater if he can not find the information he sought. Therefore, marketers can use the information obtained from this instrument to develop the best communication strategies to reduce the perceived purchase risk by prospective buyers.

 

ACKNOWLEDGMENTS

The author expresses utmost gratitude to his supervisor, Jahja Umar, PhD., for his guidance in completing the thesis from which this paper is born.

 

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APPENDIX: LIST OF ITEMS

Note: S for Smartphone; N for Netbook

CODE

ITEM

VALID

MULTIDIMENSIONAL PERCEIVED PURCHASE RISK MEASUREMENT

S

N

PR1

The offered product might not provide the performance that I require

Yes

Yes

Produk yang ditawarkan mungkin tidak dapat memberikan performa yang saya butuhkan

PR2

The operating speed of this product might rapidly decreases

Yes

Yes

Kecepatan operasi produk ini mungkin akan berkurang dengan cepat

PR3

This product might not support applications that I may need

Yes

Yes

Produk ini mungkin tidak dapat mendukung aplikasi yang akan saya butuhkan

PR4

The offered product might not have a stable / consistent performance

No

No

Performa produk yang ditawarkan mungkin tidak stabil/konsisten

PR5

The offered product might not deliver the benefits promised by the seller

Yes

No

Produk ini mungkin tidak dapat memberikan manfaat yang sudah dijanjikan penjual

PR6

The technology offered by the product might be rapidly out of date

Yes

No

Teknologi yang ditawarkan produk ini bisa cepat ketinggalan jaman

PR7

The feature of the product might not support my job mobility (size, weight or battery capacity)

Yes

No

Fitur produk ini bisa jadi tidak mendukung mobilitas kerja saya (ukuran, berat  atau umur batere)

 

CODE

ITEM

VALID

MULTIDIMENSIONAL PERCEIVED PURCHASE RISK MEASUREMENT

S

N

FR1

The price for this product might not worth the benefits I receive

No

No

Harga produk ini tidak sebanding dengan manfaat yang saya dapatkan

FR2

The product offered might be bought with a cheaper price elsewhere

No

No

Produk yang ditawarkan ini dapat saya beli ditempat lain dengan harga yang lebih murah

FR3

Might be other additional costs to be incurred before I can use this product properly (installation, upgrades, etc.)

Yes

Yes

Ada biaya tambahan lain yang harus dikeluarkan sebelum produk ini dapat saya gunakan (instalasi, upgrade, dsb)

FR4

This product might require great maintenance costs in order to stay durable

Yes

Yes

Produk ini membutuhkan biaya perawatan yang besar agar tidak cepat rusak

FR5

If it breaks, the cost of repair that I would have to pay for this product would be greater than other products

Yes

Yes

Jika rusak, saya harus membayar biaya reparasi yang lebih besar untuk produk dibanding produk yang lain

FR6

The offered product might be easily damaged, so I would have to buy a new product

No

No

Produk ini cepat rusak sehingga saya harus membeli produk yang baru

CODE

ITEM

VALID

MULTIDIMENSIONAL PERCEIVED PURCHASE RISK MEASUREMENT

S

N

CR1

Buying this product might cause me to expend valuable time to take care of matters related to this product.

Yes

Yes

Membeli produk ini dapat membuat saya menghabiskan waktu yang berharga untuk mengurus hal-hal yang terkait produk ini.

CR2

Buying this product might cause interference to my daily routine

Yes

Yes

Membeli produk ini dapat menyebabkan terjadinya gangguan pada rutinitas harian yang saya miliki

CR3

Buying this product might interfere with the work plan I’ve arranged for myself.

Yes

Yes

Membeli produk ini dapat mengganggu rencana kerja yang sudah saya susun.

CR4

Buying this product might lead to problems that hinders my work or class

Yes

Yes

Membeli produk ini dapat menyebabkan terjadinya masalah yang menghambat pekerjaan atau perkuliahan saya

CR5

Buying this product might create problems that inconveniences me

Yes

No

Membeli produk ini dapat menciptakan masalah yang dapat merepotkan diri saya

CR6

This product might require a lot of services and treatments that inconveniences me

No

No

Produk ini akan membutuhkan banyak servis dan perawatan yang akan merepotkan saya

 

CODE

ITEM

VALID

MULTIDIMENSIONAL PERCEIVED PURCHASE RISK MEASUREMENT

S

N

SR1

Buying this product might harm my personal image in the eyes of my friends

No

No

Membeli produk ini dapat menciderai image saya dimata teman-teman saya

SR2

Buying this product might make me feel anxious

No

No

Membeli produk ini dapat membuat saya merasa cemas

SR3

My friends would consider this product a cheap/inferior product

Yes

Yes

Teman-teman saya akan menganggap produk ini produk murahan

SR4

My friends would consider this product a mass-products (not exclusive)

Yes

Yes

Teman-teman saya akan menganggap produk ini produk pasaran (tidak eksklusif)

SR5

My friends would consider this product outdated

Yes

Yes

Teman-teman saya akan menganggap produk ini ketinggalan jaman

SR6

My friends will see me as incompetent if I buy this product

Yes

Yes

Teman-teman saya akan menganggap saya tidak kompeten jika saya membeli produk ini

SR7

I would feel embarrassed to be seen using this product in public places

Yes

Yes

Saya akan merasa minder jika menggunakan produk ini di tempat umum

SR8

The shape and color of this product does not fit with my self image

Yes

Yes

Bentuk dan warna produk ini tidak sesuai dengan image diri saya

CODE

ITEM

VALID

SATU FAKTOR PERCEIVED RISK MEASUREMENT

S

N

PRA I believe that buying the product offered has a big risk because the benefits promised by the seller might not necessarily be in accordance with the reality.

Yes

Yes

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena belum tentu manfaat yang dijanjikan oleh penjual sesuai dengan kenyataan.
PRB I believe that buying the product offered has a big risk because there is a possibility that the product offered might be of low quality.

Yes

Yes

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena ada kemungkinan bahwa produk yang ditawarkan ternyata memiliki kualitas rendah.
PRC I believe that buying the product offered has a big risk because it can make me experience financial losses.

Yes

Yes

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena dapat membuat saya mengalami kerugian finansial.
PRD I believe that buying the product offered has a big risk because it can reduce my reputation in the eyes of others.

Yes

No

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena dapat mengurangi reputasi saya dimata orang lain.
PRE I believe that buying the product offered has a big risk because it could be incompatible with the concept of self that I have.

Yes

No

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena bisa jadi tidak sesuai dengan konsep diri yang saya miliki.
PRF I believe that buying the product offered has a big risk because it can spend precious time that I have.

Yes

Yes

Saya meyakini bahwa membeli produk yang ditawarkan diatas memiliki resiko yang besar karena dapat menghabiskan waktu berharga yang saya miliki.
PRG Overall, I feel that buying the product offered involves a big risk.

Yes

Yes

Secara keseluruhan, saya merasa pembelian produk yang ditawarkan diatas memiliki resiko yang besar.

 

CODE

ITEM

VALID

PURCHASE INTENTION

S

N

PI1 I have the intention to buy the product offered

Yes

Yes

Saya memiliki niat untuk membeli produk yang ditawarkan.
PI2 I have expectations in the future to buy the product offered

Yes

Yes

Saya memiliki ekspektasi di masa depan untuk membeli produk yang ditawarkan.
PI3 There is a possibility for me in the near future to buy the product offered

Yes

Yes

Ada kemungkinan bagi saya dalam waktu dekat untuk membeli produk yang ditawarkan.
PI4 There is a possibility for me to recommend the product offered to my friends and family.

Yes

Yes

Ada kemungkinan bagi saya untuk merekomendasikan produk yang ditawarkan kepada teman dan keluarga saya.
PI5 If I want to buy this type of product, then I would look for the product described in the offering.

Yes

Yes

Jika saya ingin membeli produk, maka saya akan mencari produk yang dijelaskan dalam penawaran.

 

 


‘PERCEIVED PURCHASE RISK IN THE TECHNOLOGICAL GOODS PURCHASE CONTEXT: AN INSTRUMENT DEVELOPMENT AND VALIDATION Part 1

Written by imams on May 23 2012

PERCEIVED PURCHASE RISK IN THE TECHNOLOGICAL GOODS PURCHASE CONTEXT: AN INSTRUMENT DEVELOPMENT AND VALIDATION

Imam Salehudin

Department of Management Faculty of Economics University of Indonesia

Jl. Prof. Dr. Sumitro Djojohadikusumo, UI-Depok Campus, Depok-West Java, 16424

Email: imams@ui.ac.id; gsimam@gmail.com

Original File

ABSTRACT

Each purchase decision is most likely to be a risky decision. Woodside and DeLozier (1976) proposed that consumer purchase-related behaviors correspond to the perceived level of risk in the purchase. Therefore, understanding consumer’s perceived purchase risk is paramount for marketers –especially marketers of high risk products. This study intends to develop a valid and reliable instrument in measuring consumer’s perceived purchase risk using the concept of perceived risk by Peter and Ryan (1976). This study does not intend to infer conclusions regarding the population of respondents used in the research, but only conclusions regarding the sample of items used in the instrument.

The instrument was validated using two purchase context, smartphone and netbook purchase. An item is considered valid only if it tested valid in both contexts. The nomological validity of the instrument was tested using Confirmatory factor analysis as the primary method of analysis. Correlations between instruments were also tested to analyze convergent and concurrent validity of the instrument. This study employs LISREL for WINDOWS 8.51 Full Version (Jőreskog and Sőrbom, 2001) as software used for the analysis.

The result of this study is that all instrument used in the study have good nomological validity. However, some item were found to be not valid in at least one purchase context, thus was excluded from the measurement model. The newly developed instrument has better convergent validity, even though with slightly weaker concurrent validity than existing instrument.

Keywords: Instrument Validation, Perceived Purchase Risk, Technological Goods.

 

BACKGROUND

Generally in a planned purchase decision, rational buyers would only buy a certain product if the total benefit to be received from the purchase is greater than the total cost of the purchase. While for cases where there is more than one choice of products that offers the same functionality, the buyer would tend to choose the product with the greatest benefit/cost ratio (Perreault, Cannon and McCarthy, 2008).

However, in most cases the actual benefits of a product can only be known after the product is purchased and consumed. Meanwhile, the seller can promise a variety of benefits to prospective buyers that increase expectations but in reality might not be realized by the product. Therefore, in most purchase decision -especially for initial purchase decisions- consumers will generally face a certain degree of uncertainty whether the benefits to be received from the purchase will at least be equal to the benefit expected.

Simonson (1992) and Anderson (2003) concluded that when faced with purchasing situations perceived as uncertain or high-risk, potential buyers tends to delay or cancel their purchase to wait for other alternatives that are perceived to have lower risk. Simonson (1992) adds that consumers who experience greater anticipated regret will tend to choose a safe decision to purchase products that are already known and can be justified. One way for buyers justify a purchase is by looking at the brands or product prices as an indicator of quality or just buy the products sold in stores that have a high image quality (Tan, 1999).

Weber and Milliman (1997) concluded that a stable personality trait exists which influences how much risk a prospective buyer is willing to take. This personality trait determines the threshold of risk acceptable to the prospective buyer. If the perceived Purchase Risk by the prospective buyer is greater than the risk he is willing to bear, then he will not make the purchase. Conversely, if the Purchase Risk perceived by the prospective buyer is still within the limit he is willing to bear, then he would be willing to make the purchase. Thus it can be concluded that the consumer purchase decision is dependent to a certain level by how much risk (uncertainty) the consumer perceived (Weber and Milliman, 1997; Chuang and Lin, 2007).

Therefore, it is important for a marketer who wants to increase sales of its products to gain better understanding on how potential buyers perceive the uncertainty or the risk of purchasing the products being sold. With this understanding, a marketer can develop communication strategies that reduce the perceived purchase risk of prospective buyers, thereby reducing the likelihood of a prospective buyer to postpone or cancel the purchase.

 

LITERATURE REVIEW

The construct of perceived risk has several fundamental differences with the construct of consumer satisfaction, which have been more commonly used in market surveys and included in purchase decision-making models. Although both can be regarded as a factor influencing consumer purchasing decisions, the construct of consumer satisfaction is the result of cognitive and affective evaluation of the consumers towards their past experience of a certain purchase (Dube-Rioux, 1990). On the other hand, the construct of risk perception is basically a consumer expectation of a future purchase not yet experienced (Ha, 2002). Therefore, the construct of perceived risk can be used to predict purchase decisions for consumers who have never purchased a product (initial purchase) as well as consumers with prior experience of purchasing the product (repeat purchase), while customer satisfaction can not be used to predict the initial purchase of potential buyer. Thus, the construct of risk perception would be very beneficial for manufacturers who want to launch a new product and need information on the potential purchase of a target market that has never had the experience of buying a similar product.

Existing measurement instruments for Perceived Purchase Risk are generally composed of a number of questions that directly ask the overall perceived risk perception prospective buyers, although there has been some instruments that measures more than one dimension of risk perception (Jacoby and Kaplan, 1972). However, these measurements tend to be done with the limited theoretical assessment process and only measures perceptions as a unidimensional construct (Dowling, 1985; Tan, 1999, Corbitt, 2003; Tiangsoongnern, 2007). Meanwhile, only few recent studies uses multidimensional approach by doing the operational definition of constructs based on the findings of Jacoby and Kaplan (1972) to identify the dimensions of Perceived Purchase Risk (Chang and Chen, 2008; Kim, Kim and Hwang, 2009).

Jacoby and Kaplan (1976) identified at least six dimensions of consumers’ perceived purchase risk. Their finding has been confirmed by the findings of other researchers, thus obtained the following six dimensions of risk (Jacoby and Kaplan, 1976; Laroche et al., 2004; Chang and Chen, 2008; Kim, Kim and Hwang, 2009):

1)     Performance Risk: Consumer perceptions of risks that the functional attributes of the product can not satisfy their needs.

2)     Financial Risk: Consumer perceptions of risks that the purchase of the product will cause financial losses.

3)     Physical Risk: Consumer perceptions of risks that the product purchased can injure their physical wellbeing.

4)     Convenience Risk: Consumer perceptions of risks that the product purchased takes a lot of time and effort to repair and adjust before it can be used.

5)     Social Risk: Consumer perceptions of risks that the product purchased may adversely affect the views of others towards them.

6)     Psychological Risk: Consumer perceptions of risks that the product purchased will interfere with their view of themselves.

Each dimension of Perceived Purchase Risk may have different significance for different products or purchase context. For example, the perception of physical risk is more dominant than the social and financial risk in the purchase of over the counter medicinal products, while social risk perception is more dominant than physical and financial risks in the purchase of fashion products (Jacoby and Kaplan, 1976).

In certain purchase situation, some risk dimensions may not be needed to be measured. This is because each dimension is product-specific and independent among each other (Laroche et al, 2004). Focused Group Discussions conducted to explore the purchase decision in the context of laptops, netbooks and smartphones purchase discovered that prospective buyers does not place much importance in the dimension of physical risk as the products are perceived to have less impact on physical safety (Fuziah et al., 2010; Pratama et al., 2010). Meanwhile, the dimensions of Psychological and Social Risk can be combined into a single dimension as the Psycho-Social Risk dimension (Gewald et al, 2006). Thus, four dimensions identified above are included in this research as sub-factors for the construct of Perceived Purchase Risk.

Various measurement approaches have been used by in previous researches, thus selecting the measurement approach used in the study is also an important decision. Peter and Ryan (1976) developed the concept of expected utility of Bernoulli (1938) to formulate the concept of risk. He defines risk as a function of multiplying the probability of occurrence of an event with undesirable consequences to the expected magnitude of the undesirable consequence, thus obtained the following equation:

PR = Σ (PLi * ECi)                                (1)

PR = Perceived Risk

PL = Probability of Loss

EC = Expected Consequence

i = Risk Dimension

Based on the above formula, a prospective buyer will perceive that there is a substantial risk only if: (1) there is a great likelihood that losses will occur, and (2) the consequences of these losses are perceived important by prospective buyers. Conversely, if at least one component is perceived as insubstantial, then the Purchase Risk will also be perceived by the potential buyers as insubstantial.

This formulation of risk concept can be considered as more comprehensive in explaining the perception and behavior of buyers than the concept of risk perception that only considered the perceived probability of loss without taking into account the level of the subjective importance of the consequences of a loss. Therefore, measurement approach of risk perception using two components of risk -probability perceptions and expectations of the consequences- should be more valid in measuring risk perceptions and explain the behavior of potential buyers. However, no research using this approach to measure perceptions of risk have been observed. Therefore, this research is interested in developing the measurement of Perceived Purchase Risk based on the approach by Peter and Ryan (1976) and comparing it with measurements based on the approach that has been used previously.

Newly developed instruments should be tested first for its validity before it can be used in practical applications such as market surveys. The validation test consists of construct, convergent and concurrent validity (Anastasi and Urbina, 1997; Domino and Domino, 2006). Meanwhile, the purchase contexts selected for the validation is the purchase context of technological items or gadgets, such as: laptops, netbooks, and smartphones. The purchase context selection is based the characteristic of the product category in which technological products have a lot of product variety, with new products introduced regularly and rapidly, and usually is quite expensive. Thus, purchase decision for technological items, such as netbooks and smartphones, can be considered as risky decisions in which Perceived Purchase Risk may play a significant role in the purchase decision.

In order to develop valid measurement for Perceived Purchase Risk for all contexts of products and purchases, it is necessary to test the validity of the instrument in more than one the contexts of different products. Therefore, two product contexts were selected for the validation, which are Netbook and Smartphones. Thus, the in order to be considered valid, the items would have to be valid in both product contexts. Accordingly, based on the literature reviews above, the following measurement model of Perceived Purchase Risk was obtained:

Figure 1: Measurement Model for Multidimensional Perceived Purchase Risk

RESEARCH METHOD

The validity of an instrument can be seen by more than one approach. The first approach to validity is the content validity, which sees the validity of an instrument as whether the instrument covered sufficient dimensions of the construct to be measured. Two components of content validity are the representativeness and relevance of the measurement instrument’s contents.

The second approach to validity is the construct validity, in which the validity of an instrument in seen as whether the results obtained from the tested instrument corresponds with the pattern of a particular theory about the construct intended to be measured (Domino and Domino, 2006). One method commonly used in analyzing this type of validity is by using a statistical method called confirmatory factor analysis. This method tests whether the data obtained from measurements can support the model developed from the theory of the construct to be measured (Chadha, 2009).

The third approach is the convergent validity, in which the validity of an instrument is seen as the correlation between the measurement results of an instrument with other instrument that measures the same construct and has passed the validity test (Chadha, 2009). The assumption underlying the validity of this is that if an instrument truly measures a certain construct, then the measurement results should be consistent with the results of tested instruments that measure the same construct.

While the fourth approach of validity is the criterion validity, which sees the validity of an instrument as the correlation between its result to the measurement result of other instruments which measure different constructs, but in theory corresponds to the construct intended to be measured (Anastasi and Urbina, 1997). If the result of the corresponding instruments is obtained simultaneously, then the validity is called concurrent validity.

Data collection process yields 159 respondents for Smartphone purchase and 141 respondents for Netbook purchase. Data was collected from undergraduate students, with an age range between 19 and 23 and monthly expenditures between Rp.500.000 and Rp.1.000.000. Gender proportion between respondents of Smartphone is 36% male and 64% female, while proportion for Netbook is 44% males and 56% females. Ownership proportion between respondents of Smartphone is 58% owners and 42% non-owners, while proportion for Netbook is 73% owners and 27% non-owners.

Purchase Intention was selected as validation construct for testing concurrent validity of Unidimensional and Multidimensional Perceived Purchase Risk instrument. Selection is based on the results of previous studies which concluded that significant relationship exists between risk perceptions and purchasing decisions (Weber and Milliman, 1997; Chuang and Lin, 2007; Simonson, 1992; Anderson, 2003).

Purchase Intention is defined as the propensity of consumers to buy a particular item. In the context of a planned purchase, purchase intention is the result of consumer evaluation of the elements of consideration, whether is favorable and unfavorable towards the purchase. The following is a summary of the operational definition of the measurement variables used in this study:

Table 1: Operational Definition of Research Variables

Construct

Sub-factor

Operational Definition

Items

Unidimensional Perceived Purchase Risk

(7 item)n.a.Consumer perceptions of the probability of occurrences of events that can harm them as a result of purchasing a particular product.X1 – X7Multidimensional Perceived Purchase Risk

(27 item)Performance

RiskConsumer perception of risks that the functional attributes of the product can not satisfy their needs.X8 – X14Financial

RiskConsumer perception of risks that the purchase of the product will cause financial losses.X15-X20Convenience

RiskConsumer perception of risks that the product purchased takes a lot of time and effort to repair and adjust before it can be used.X21-X26Psychosocial

RiskConsumer perceptions of risks that the product purchased can interfere with their own view of themselves or negatively affect how others viewed them.X27-X34Purchase Intention

(5 item)n.a.The propensity of consumers to buy a certain product.X35-X39

Note: item contents is shown in the Appendix

Confirmatory factor analysis (CFA) was used in this study to test the hypotheses and answering the research questions. This CFA is a multivariate statistical method that aims to deductively test the existence of certain structures or intercorrelated patterns between variables in a set of data, based on certain hypotheses set prior to the testing. The hypothesis tested might be obtained from existing models and theories. ‘LISREL 8:51 for WINDOWS Full Version’ (Jőreskog and Sőrbom, 2001) software was used to run the confirmatory factor analysis.

The first step of validation analysis is to see whether the measurement model is acceptable. This is proven when there are significant differences between the correlations matrix obtained from the data and the correlations matrix based on the model specification. If there is no significant difference, then it can be concluded that the measurement model is acceptable or the model is fit. The difference is tested using the statistical significance of chi-square with alpha 5%. If the p-value of the chi-square statistics is above 00:05, then it can be concluded that the measurement model is acceptable.

The second step is to see whether there are items that are not valid in measuring the construct being measured. An item can be considered valid only if it has factor loading with t-values ​​greater than +1.96 or lesser than -1.96. However, since all items used in the questionnaire are favorable items, the range of t-values ​​accepted are limited only to t-value above 1.96. Items that are found to be not valid will be excluded from the measurement model to obtain the measurement set that is completely valid. Researchers also see and compare the quality of items from each construct by calculating the reliability and extent of crossloading for each item.

The third step is testing the concurrent and convergent validity of the instrument by looking at the correlations between constructs and between instruments of the same construct. The instrument is considered to have good convergent validity if it has significant correlation to the measurements of other instrument that measures the same construct. While the instrument is considered to have good concurrent validity if it has significant correlation to the measurements of other instruments that measure constructs that theoretically are correlated to the constructs measured by instruments like the first.

Each step of the validity analysis will also compare the validity of items between Smartphone and Netbook purchase context. The purpose of this comparison is to determine whether the validity of each item is consistent across both product contexts. Items will only be considered valid for general use in the context of technological goods purchase if it consistently qualifies in both purchase contexts.

RESULT AND DISCUSSION

The initial test for the Unidimensional Perceived Purchase Risk construct did not yield measurement models with a good fit. Model testing for Netbook purchase yields chi-square value of 64.12, while Smartphone purchase yields chi-square value of 126.96 with degree of freedom (df) for both contexts is 14. Testing the significance of chi-square value yields p-value of under 0.05, which means that the model was rejected because significant differences exists between the correlation matrix obtained from the data with the correlation matrix specified from the model.

The initial testing also yields t-values for each item factor loading as well as modification index, a set of recommendations for additional specification for error covariances between item errors. Modifications were performed by removing non-valid items and adding several error covariances according to the modification index. Since the objective is to obtain items valid in both purchase context, two items –PRD and PRE– were eliminated from both contexts because they were considered non valid in the Netbook purchase context. Item loadings and t-values from the initial model testing are shown in Table 2, while item contents are shown in the Appendix.

Testing the modified measurement model yields chi-square value of 5.80 with degree of freedom of 4 for Netbook purchase and chi-square value of 14.57 with degree of freedom of 10 for Smartphone purchase. Thus, the model yields p-values of 0.214 and 0.148 for the context of Netbook and Smartphone purchases respectively. Therefore, the modified measurement model for both contexts was accepted.

Table 2: Item Validity for Unidimensional Perceived Purchase Risk Construct

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

PRA

0.68

0.53

9.01

Significant

0.68

0.54

8.24

Significant
PRB

0.60

0.64

 7.64

Significant

0.72

0.48

8.97

Significant
PRC

0.61

0.62

 7.90

Significant

0.78

0.39

9.99

Significant
PRD

0.59

0.65

 7.49

Significant

0.28

0.92

1.60

Not Significant
PRE

0.61

0.63

 7.77

Significant

0.22

0.95

1.01

Not Significant
PRF

0.64

0.59

 8.33

Significant

0.56

0.69

6.59

Significant
PRG

0.85

0.32

 12.00

Significant

0.78

0.39

9.87

Significant

Source: Data Processing

The second measurement instrument tested was the construct validity of the Multidimensional Perceived Purchase Risk, which divided Perceived Purchase Risk into four risk dimensions. The test was conducted by using 2nd order Confirmatory Factor Analysis in order to test the construct’s multidimensionality as well as the construct validity of the measurement.

The initial test for the Multidimensional Perceived Purchase Risk construct did not yield measurement models with a good fit. Model testing generated chi-square value of 612.63 for Netbook purchase and 733.19 for Smartphone purchase, with degree of freedom for both contexts are 320. Testing the significance of chi-square value yields p-value of under 0.05, which means that the model was rejected.

Modifications were performed by removing non-valid items and adding several error covariances according to the modification index. Testing both modified measurement models yields chi-square value of 113.78 with degree of freedom of 94 for Netbook purchase and chi-square value of 180.21 with degree of freedom of 157 for Smartphone purchase. Thus, the test obtained p-values of 0.08 and 0.09 for the context of Netbook and Smartphone purchases, respectively. Since both measurement model yields p-values greater than 0.05, therefore the modified measurement model for both contexts was accepted.

The 2nd order Confirmatory Factor Analysis for the Multidimensional Perceived Purchase Risk construct yields validity index for dimensions as well as indicators. The secondary hypotheses tested from the construct of Multidimensional Perceived Purchase Risk is whether the validity of the four dimensions proposed in the model and the validity of each item of measurement is consistent in both product context. Based on this analysis, all four dimensions in the Multidimensional Perceived Purchase Risks are considered valid for both purchase contexts. Loading factors and t-values for each dimension are shown below in Table 3.

Table 3: Dimension Validity for Multidimensional Perceived Purchase Risk Construct

Dimension

SMARTPHONE

NETBOOK

SLF

T-VAL

SIG

SLF

T-VAL

SIG

Performance Risk

0.61

5.47

Significant

0.62

4.66

Significant
Financial Risk

0.76

4.42

Significant

0.70

4.30

Significant
Convenience Risk

0.71

5.66

Significant

0.87

2.87

Significant
Psychosocial Risk

0.62

4.77

Significant

0.63

4.90

Significant

Source: Data Processing

One indicator for Performance Risk dimension, PR4, was found to be not significant for both purchase contexts while three other items, PR5, PR6 and PR7, were invalidated in the Netbook purchase context. Thus, all four items must be excluded from the final measurement model. The standardized loading factor, standard error and t-values for Performance Risk measurements are shown in Table 4 while the content for each item are shown in the Appendix.

Table 4: Item Validity for Performance Risk Dimension

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

PR1

0.71

0.49

 8.76

Significant

0.78

0.39

 7.42

Significant
PR2

0.78

0.39

 9.67

Significant

0.70

0.51

 7.06

Significant
PR3

0.78

0.39

 9.59

Significant

0.56

0.69

 5.86

Significant
PR4

0.22

0.95

1.19

Not Significant

0.25

0.94

1.36

Not Significant
PR5

0.53

0.72

 6.39

Significant

0.29

0.91

1.89

Not Significant
PR6

0.59

0.65

 7.12

Significant

0.28

0.92

1.73

Not Significant
PR7

0.59

0.65

 7.18

Significant

0.19

0.96

1.01

Not Significant

Source: Data Processing

Three indicators for Financial Risk dimension, FR1, FR2 and FR6, were invalidated in both purchase contexts. Thus, all three items must be excluded from the final measurement model. The standardized loading factor, standard error and t-values for Financial Risk measurements are shown in Table 5 while the content for each item are shown in the Appendix.

Table 5: Item Validity for Financial Risk Dimension

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

FR1

0.25

0.94

1.75

Not Significant

0.14

0.98

1.01

Not Significant

FR2

0.12

0.99

0.98

Not Significant

0.20

0.96

1.60

Not Significant

FR3

0.69

0.52

6.42

Significant

 0.72

0.48

 6.54

Significant

FR4

0.95

0.10

 6.51

Significant

 0.64

0.59

 6.04

Significant

FR5

0.58

0.66

 5.74

Significant

 0.78

0.39

 6.75

Significant

FR6

-0.15

0.98

-1.02

Not Significant

-0.18

0.97

-1.06

Not Significant

Source: Data Processing

One indicator for Convenience Risk dimension, CR6, was found to be not significant for both purchase contexts while one other item, CR5, was invalidated in the Netbook purchase context. Thus, both items must be excluded from the final measurement model. The standardized loading factor, standard error and t-values for Convenience Risk measurements are shown in Table 6 while the content for each item are shown in the Appendix.

Table 6: Item Validity for Convenience Risk Dimension

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

CR1

 0.86

0.26

 8.65

Significant

 0.78

0.39

 3.57

Significant

CR2

 0.82

0.33

 9.77

Significant

 0.82

0.33

 3.76

Significant

CR3

 0.78

0.39

 9.22

Significant

 0.88

0.23

 3.81

Significant

CR4

 0.90

0.19

 10.09

Significant

 0.95

0.10

 3.69

Significant

CR5

 0.92

0.15

 10.14

Significant

0.15

0.98

0.48

Not Significant

CR6

-0.35

0.88

-4.03

Not Significant

-0.21

0.96

-1.43

Not Significant

Source: Data Processing

Two indicators for Psychosocial Risk dimension, SR1 and SR2, were found to be not significant for both purchase contexts. Thus, both items must be excluded from the final measurement model. The standardized loading factor, standard error and t-values for Psychosocial Risk measurements are shown in Table 7 while the content for each item are shown in the Appendix.

Table 7: Item Validity for Psychosocial Risk Dimension

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

SR1

-0.24

0.94

-2.65

Not Significant

-0.35

0.88

-2.95

Not Significant

SR2

0.11

0.99

1.82

Not Significant

0.16

0.97

1.91

Not Significant

SR3

 0.88

0.23

 10.15

Significant

 0.89

0.21

 10.20

Significant

SR4

 0.86

0.26

 10.17

Significant

 0.91

0.17

 10.48

Significant

SR5

 0.86

0.26

 10.16

Significant

 0.90

0.19

 10.43

Significant

SR6

 0.90

0.19

 10.31

Significant

 0.75

0.44

 8.64

Significant

SR7

 0.80

0.36

 9.44

Significant

 0.77

0.41

 8.87

Significant

SR8

0.69

0.52

 8.27

Significant

 0.64

0.59

 7.38

Significant

Source: Data Processing

The third measurement model tested was the Purchase Intention construct. The initial model test did not produce good fit. Model testing generated chi-square value of 33.46 for Netbook purchase and 19.84 for Smartphone purchase, while degree of freedom obtained for both contexts is 5. Testing the significance of chi-square value yields p-value of under 0.05, which means that the model was rejected.

Modifications were conducted by removing non-valid items and adding several error covariances according to the modification index. Testing both modified measurement models yields chi-square value of 6.32 with degree of freedom of 3 for Netbook purchase and chi-square value of 5.04 with degree of freedom of 4 for Smartphone purchase. Thus, the test obtained p-values of 0.096 and 0.283 for the context of Netbook and Smartphone purchases, respectively. Since both measurement model yields p-values greater than 0.05, therefore the modified measurement model for both contexts was accepted. None of the indicators for Purchase Intention have t-values lower than 1.96 in either contexts, thus all indicators for Purchase Intention were confirmed to be valid. The standardized loading factor, standard error and t-values for Purchase Intention measurements are shown in Table 8 while the content for each item are shown in the Appendix.

Table 8: Item Validity for Purchase Intention

ITEM

SMARTPHONE

NETBOOK

SLF

SE

T-VAL

SIG

SLF

SE

T-VAL

SIG

PI1

0.82

0.28

12.11

Significant

0.83

0.3

11.76

Significant

PI2

0.81

0.34

11.85

Significant

0.88

0.23

12.26

Significant

PI3

0.83

0.3

12.44

Significant

0.84

0.29

11.46

Significant

PI4

0.74

0.45

10.47

Significant

0.67

0.55

8.70

Significant

PI5

0.85

0.28

12.71

Significant

0.77

0.41

10.49

Significant

Source: Data Processing

The last two measurement validity examined is the convergent and concurrent validity of the instrument. Convergent validity was measured by examining the correlation between scores from the newly developed instrument with scores from existing instrument that measures the same construct, while concurrent validity was measured by looking at the correlation between scores from the newly developed instrument with scores from existing instrument that measured a theoretically-related construct.

The correlations between the measurements scores was obtained from the standardized path coefficient between two constructs when processed in pairs while correlation significance was obtained from the t-value of the path between each pair (Hair et al., 2009). Result from correlating both Unidimensional and Multidimensional Perceived Purchase Risk measurement scores indicated that both measurement have significant positive correlation for both purchase contexts (r=0.75, t=5.50 for Smartphone; r=0.74, t=5.63 for Netbook). This means that the newly developed measurement instrument has good convergent validity.

Result from correlating Multidimensional Perceived Purchase Risk with Purchase Intention measurement scores indicate that both measurement have significant negative correlation for both purchase contexts (r=-0.28, t=-2.83 for Smartphone; r=-0.19, t=-2.02 for Netbook). In contrast, result from correlating Unidimensional Perceived Purchase Risk with Purchase Intention measurement scores indicate that both measurement have stronger significant negative correlation for both purchase contexts (r=-0.33, t=-3.53 for Smartphone; r=-0.40, t=-4.12 for Netbook).

Both construct have significant negative correlations, which is consistent with existing theories that greater perceived risk increases the likelihood of a prospective buyer to postpone or cancel the purchase (Simonson, 1992; Anderson, 2003). This means that the newly developed measurement instrument also has good concurrent validity. However, it seems that the existing Unidimensional measurement still has greater concurrent validity than the newly developed measurement. Then again, this shortcoming is offset by more detailed information provided by the newly developed instrument.  The complete score correlations results for both purchase contexts are shown in Table 9 below.

Table 9: Standardized Correlation Coefficient

Correlations

SMARTPHONE

NETBOOK

UPPR

MPPR

PI

UPPR

MPPR

PI

UPPR rt-value

1.00

n.a

1.00

n.a

MPPR rt-value

0.75

5.50

1.00

n.a

0.74

5.63

1.00

n.a

PI rt-value

-0.33

-3.53

-0.28

-2.83

1.00

n.a

-0.40

-4.12

-0.19

-2.02

1.00

n.a

Source: Data Processing

Notes:

UPPR: Unidimensional Perceived Purchase Risk

MPPR: Multidimensional Perceived Purchase Risk

PI: Purchase Intention

INVEST IN YOURSELF: Aplikasi Konsep Human Capital dari Sudut Pandang Karyawan

Written by imams on Jan 20 2011

Please Cite: Salehudin, Imam (2010) INVEST IN YOURSELF: Aplikasi Konsep Human Capital dari Sudut Pandang Karyawan. Manajemen Usahawan Indonesia. No. 06/TH. XXXIX 2010. ISSN: 0302‐9859

INVEST IN YOURSELF: Aplikasi Konsep Human Capital dari Sudut Pandang Karyawan

IMAM SALEHUDIN, SE, MSi1
gsimam@gmail.com

Abstract
Davenport (1999) provides a different point of view at the concept of Human Capital by Gary Becker (1993). He focused the application of human capital from the employee’s point of view and coined the term Employee‐Investor where employees are seen as both owners and investors of their own human capitals. This shift of paradigm involves the change of interaction pattern between employees and employers, especially interactions related to human development activities.

This paper discusses the application of Human Capital from the employee’s point of view, especially regarding how each employee must manage and develop their own human capital investment in order to maximize their return on investment. On the other hand, management of human capital by employers should focus on attracting and retaining human capital investors by providing working environment that is conducive to personal development and self‐investment of human capital.

Keywords: Human Capital, Employee‐Investor, Competences.

Abstrak
Davenport (1999) memberikan sudut pandang yang berbeda untuk aplikasi konsep Human Capital yang diangkat oleh Becker (1993). Ia mengulas aplikasi human capital dari sudut pandang karyawan, sehingga tercipta istilah Karyawan‐Investor dimana karyawan sebagai pemilik modal manusia dipandang sebagai investor. Perubahan sudut pandang ini melibatkan perubahan pola interaksi antara karyawan dan perusahaan, khususnya yang terkait dengan kegiatan pengembangan manusia.

Paper ini membahas aplikasi konsep Human Capital dari sudut pandang karyawan, dimana masing‐masing individu dituntut untuk mengembangkan dan mengelola modal manusia mereka sendiri untuk memaksimalkan pengembalian yang dapat mereka peroleh. Sebaliknya, dari sisi perusahaan pengelolaan modal manusia harus lebih ditekankan pada upaya menyediakan lingkungan kerja yang dapat menarik dan mempertahankan karyawan‐investor untuk menginvestasikan modal manusia mereka, khususnya dengan menyediakan peluang bagi mereka untuk mengembangkan modal manusia yang mereka miliki.
Kata Kunci: Human Capital, Karyawan‐Investor, Kompetensi.

1) Staf Pengajar pada Departemen Manajemen Fakultas Ekonomi Universitas Indonesia, Peneliti independen pada bidang Interaksi Karyawan‐Pelanggan.

Konsep Human Capital merupakan konsep yang lebih mudah untuk disampaikan daripada diterapkan pada tataran korporasi. Premis utama dari konsep Human Capital adalah bahwa manusia bukan sekedar sumber daya namun merupakan modal (capital) yang menghasilkan pengembalian (return) dan setiap pengeluaran yang dilakukan dalam rangka mengembangkan kualitas dan kuantitas modal tersebut merupakan kegiatan investasi (Becker, 1993). Permasalahan muncul ketika perusahaan yang sudah menginvestasikan dana yang cukup besar untuk mengembangkan modal manusia yang mereka miliki ternyata tidak memperoleh tingkat pengembalian yang mereka harapkan.

Salah satu penyebab utama sulitnya implementasi konsep ini adalah belum
adanya sistim akunting yang dapat mengakomodasi pencatatan “investasi”
perusahaaan terhadap karyawan yang menjadi modal manusia. Penyebab utama yang lain adalah pusingnya perusahaan menghadapi modal manusia mereka yang dengan mudah pindah ke perusahaan pesaing dengan iming‐iming posisi dan gaji yang lebih tinggi. Dilain pihak, konsep ini sangat mudah untuk diterapkan dari sudut pandang individu karyawan. Bahkan sebagian karyawan anda mungkin sudah menerapkan konsep ini pada diri mereka sendiri, meskipun mereka tidak menyadari.

Davenport (1999) mengusulkan pendekatan yang berbeda terhadap hubungan modal‐investor dalam konsep Human Capital. Ia mengajukan premis bahwa karyawan merupakan investor yang menanamkan modal manusia yang mereka miliki ke dalam perusahaan dengan tujuan untuk memperoleh tingkat pengembalian yang memuaskan. Tentu saja tingkat pengembalian yang diperoleh karyawan‐investor akan sebanding dengan nilai modal manusia yang ia tanamkan kedalam perusahaan.

Oleh karena itu, karyawan‐investor memiliki tanggung jawab untuk
mengembangkan sendiri modal manusia yang ia miliki dan akan memilih tempat investasi yang tidak hanya memberikan tingkat pengembalian yang menarik tetapi juga memberikan peluang untuk mengembangkan modal manusia yang ia miliki. Tidak hanya itu, seorang karyawan‐investor akan menarik kembali modal manusia yang telah ia setor, jika ia merasa bahwa investasi yang ia tanamkan tidak berkembang dan masih ada peluang untuk mengembangkan modal manusia yang ia miliki di tempat lain. Dari sudut pandang ini, bisa jadi konsep human capital justru dapat menjelaskan tingkah laku karyawan anda yang sudah membuat anda pusing.

Mengembangkan modal manusia anda sendiri

Sebagaimana yang telah dijelaskan diatas, seorang karyawan‐investor akan
berusaha memaksimalkan pengembalian dari modal manusia yang ia setor dengan mencari tempat investasi yang tidak hanya memberikan “deviden” paling tinggi tetapi juga memberikan kesempatan untuk mengembangkan modal manusia yang ia miliki sehingga dapat menghasilkan pengembalian yang lebih tinggi lagi. Berikut ini adalah beberapa hal yang diyakini dapat mengembangkan modal manusia seorang karyawan-investor:

1. Pengalaman

Modal manusia yang secara alami berkembang seiring dengan investasi seorang karyawan‐investor adalah pengalaman. Karyawan‐Investor yang sudah memiliki pengalaman pada suatu bidang akan beradaptasi lebih cepat dan berkontribusi lebih banyak sehingga dapat meminta gaji yang lebih tinggi daripada Karyawan‐Investor yang belum memiliki pengalaman sama sekali. Zarutskie (2008) menemukan bahwa manajer investasi yang memiliki pengalaman sebagai manajer bisnis atau sebagai venture capitalist memiliki performa yang lebih baik dari pada manajer investasi yang hanya memiliki latar belakang pendidikan MBA. Pengalaman kerja adalah hasil dari waktu yang telah anda investasikan bekerja pada suatu profesi atau posisi.

Meskipun demikian, perlu diperhatikan bahwa pengalaman kerja tidak sama
dengan lama kerja. Tambahan pengalaman yang diperoleh individu tiap tahun akan mengalami diminishing return. Seseorang yang sudah bekerja selama 20 tahun pada suatu pekerjaan belum tentu mendapatkan pengalaman kerja yang jauh lebih banyak daripada orang yang baru bekerja selama 2‐3 tahun saja pada pekerjaan yang sama.

Selain itu, sebagian besar modal manusia yang diperoleh dari pengalaman adalah modal manusia spesifik yang belum tentu dapat ditransfer pada situasi kerja yang lain.  Perubahan situasi pekerjaan dapat mengakibatkan pengalaman yang sudah dikumpulkan bertahun‐tahun menjadi tidak bermanfaat. Oleh karena itu, seorang Karyawan‐Investor harus sangat berhati hati dalam menginvestasikan waktu yang ia miliki. Terlebih lagi karena jumlah waktu yang dapat ia investasikan terbatas dan ini adalah investasi yang tidak dapat ditarik kembali. Fresh graduate akan memiliki golden years selama 5‐10 tahun pertama karir mereka, sebelum karir mereka mengalami maturity atau bahkan plateau dimana perkembangan karir mereka melandai atau bahkan datar sama sekali. Oleh karena itu, seorang fresh graduate harus sangat berhati‐hati memilih dimana dan bagaimana ia menginvestasikan waktunya.

2. Pendidikan

Pendidikan merupakan salah satu sumber modal manusia yang menjadi
perhatian sejak awal (Becker, 1993). Pendidikan adalah investasi modal manusia dalam bentuk waktu dan biaya. Pendidikan juga dipandang sebagai salah satu bentuk investasi modal manusia yang paling penting, khususnya untuk meningkatkan tingkat pendapatan seorang karyawan. Secara teori, rerata pendapatan seorang lulusan S3 akan lebih tinggi dari lulusan S2, rerata pendapatan seorang lulusan S2 akan lebih tinggi dari lulusan S1, rerata pendapatan seorang lulusan S1 akan yang lebih tinggi dari lulusan
D3, dan seterusnya.

Meskipun demikian, pada kenyataannya, terjadi trend devaluasi pendidikan
tinggi dimana jumlah penawaran lulusan pendidikan tinggi terus bertambah meskipun jumlah permintaannya tidak banyak berubah. Oleh karena itu, banyak lulusan S2 yang bersedia mengisi lowongan kerja untuk level S1 dengan standar gaji S1, lulusan S1 bersedia mengisi lowongan kerja untuk level D3 dengan standar gaji D3, dan seterusnya.

Devaluasi ini semakin parah akibat kurangnya link and match antara institusi
perguruan tinggi dan pengguna lulusan sehingga hanya menghasilkan tenaga kerja yang overeducated namun underskilled. Hal ini menyebabkan lowongan‐lowongan kerja yang membutuhkan kompetensi spesifik terbuka lebar kekurangan pelamar, sementara lowongan‐lowongan kerja yang tidak membutuhkan kompetensi spesifik justru kebanjiran pelamar‐pelamar yang berpendidikan tinggi namun tidak memiliki kompetensi khusus.

Salah satu isu yang penting dicermati dalam masalah pendidikan dan modal
manusia adalah masuknya pelamar yang lulus dari institusi pendidikan di luar negeri dalam pasar tenaga kerja. Untuk saat ini, memang lulusan institusi pendidikan luar negeri memiliki keunggulan untuk diterima bekerja dan/atau mendapat gaji awal yang lebih tinggi dibanding lulusan institusi pendidikan dalam negeri.

Namun perlu diketahui bahwa tidak semua institusi pendidikan tinggi di luar
negeri memiliki kualitas yang setara. Sebagai contoh, insitusi pendidikan di Amerika Serikat terbagi menjadi beberapa tingkatan (tiers) sesuai akreditasi dimana universitasuniversitas ternama seperti Harvard, Princeton, Stanford, atau Yale menempati tingkatan pertama, sementara universitas‐universitas yang bukan termasuk 100 universitas terbaik menempati tingkatan ketiga atau keempat yang bisa jadi tidak memiliki kualitas yang lebih tinggi dari kualitas institusi pendidikan ternama dalam negeri.

Disamping hal diatas, terkadang untuk bidang‐bidang ilmu sosial tertentu
seperti ilmu hukum, ilmu bisnis atau ilmu psikologi, ilmu yang diperoleh dari institusi pendidikan dalam negeri lebih relevan dan lebih dapat diterapkan pada pekerjaan sehingga memiliki nilai modal manusia yang lebih tinggi dibanding ilmu yang diperoleh dari institusi pendidikan asing. Ilmu sosial yang diajarkan di luar negeri akan sarat dengan konteks sosial dan budaya tempat institusi pendidikan tersebut sehingga belum tentu ilmu yang diperoleh pelamar yang lulus dari sana dapat digunakan pada
konteks sosial dan budaya kita.

Oleh karena itu, dalam memilih investasi pada pendidikan, seorang Karyawan‐Investor harus bijak dalam memilih bidang ilmu dan institusi pendidikan yang mampu memberikan kompetensi yang dibutuhkan oleh pasar tenaga kerja. Kesalahan dalam memilih bidang ilmu atau institusi pendidikan yang tepat akan menghabiskan waktu dan biaya yang banyak tanpa memberikan peningkatan nilai modal manusia yang diharapkan.

3. Pelatihan

Becker (1993) menyatakan pelatihan sebagai kegiatan investasi modal manusia yang terpenting kedua setelah pendidikan dan salah satu alat utama perusahaan untuk mengembangkan modal manusia yang dimiliki oleh karyawan mereka. Modal manusia ini dikembangkan dalam wujud kompetensi berupa keahlian (skill), pengetahuan (knowledge) dan sikap (attitude) yang dibutuhkan untuk dapat menyelesaikan pekerjaan dengan baik. Kompetensi yang dikembangkan ini dapat meningkatkan nilai Karyawan‐Investor di mata perusahaan sehingga dapat meminta gaji yang lebih tinggi, serta mengakses pekerjaan dan karir yang lebih baik.

Kompetensi yang dibangun oleh suatu pelatihan dapat dikelompokkan menjadi dua, yaitu kompetensi umum dan kompetensi spesifik. Kompetensi umum meningkatkan modal manusia yang dapat dengan mudah diadaptasikan dan ditransfer pada situasi dan tempat kerja yang lain, sementara kompetensi spesifik lebih terikat dengan situasi dan tempat kerja yang ada sehingga lebih sulit diadaptasi dan ditransfer pada situasi dan tempat kerja yang lain.

Secara umum, Karyawan‐Investor akan lebih menyukai investasi dalam bentuk pelatihan kompetensi umum yang dapat meningkatkan nilai jual mereka di pasar tenaga kerja daripada investasi dalam bentuk pelatihan kompetensi spesifik yang tidak memiliki nilai tambah diluar pekerjaan yang sekarang. Oleh karena itu, setiap Karyawan‐Investor akan menangkap semua peluang untuk mengembangkan kompetensi umum yang ia miliki dan bahkan bersedia mengorbankan sumber daya pribadi yang dia miliki untuk mengembangkan sebuah kompetensi umum jika ia merasa kompetensi umum tersebut dapat memberikan nilai tambah yang lebih besar pada modal manusia yang ia miliki.

Sebaliknya, pemberi kerja akan lebih enggan dan berhati‐hati dalam membiayai investasi modal manusia dalam bentuk kompetensi umum karena kekhawatiran investasi tersebut akan meningkatkan resiko mereka kehilangan investasi mereka akibat dibajak oleh perusahaan lain. Pemberi kerja akan lebih suka mendanai pengembangan kompetensi khusus yang hanya memberikan manfaat bagi perusahaan yang memberikan pelatihan dan bahkan dapat meningkatkan switching cost yang dipersepsikan seorang Karyawan‐Investor untuk berpindah pekerjaan.

Meskipun demikian, Galunic & Andersen (2000) justru menemukan bahwa
investasi perusahaan pada kompetensi umum pada kondisi tertentu justru dapat meningkatkan komitmen Karyawan‐Investor pada perusahaan. Hal ini terkait dengan bagaimana iklim kerja yang kondusif terhadap pengembangan diri serta kontrak psikologis antara pekerja dan pemberi kerja dapat meningkatkan komitmen seorang Karyawan‐Investor terhadap tempat kerjanya.

Selain itu, seringkali pengembangan kompetensi khusus tidak dapat terlepas
dari pengembangan kompetensi umum dimana pengembangan kompetensi khusus tertentu terlebih dahulu membutuhkan pengembangan kompetensi umum tertentu sebagai persyaratan yang dibutuhkan. Dengan demikian, investasi perusahaan dalam pengembangan kompetensi umum masih diperlukan walaupun perlu dilakukan dengan lebih berhati‐hati.

4. Modal Sosial

Meskipun belum mendapat banyak perhatian, modal sosial (social capital) yang dimiliki Karyawan‐Investor memiliki keterkaitan yang kuat dengan modal manusia yang ia miliki. Salah satu penelitian menemukan adanya efek moderasi dari jejaring sosial yang dapat meningkatkan pengaruh modal manusia untuk mengakses pekerjaan dan karir yang lebih baik (James, 2000).

Dengan demikian, jika terdapat dua karyawan-investor yang memiliki modal manusia yang setara, maka Karyawan‐Investor yang memiliki modal sosial lebih besar akan memiliki probabilitas yang lebih besar untuk mendapatkan promosi dibanding Karyawan‐Investor yang memiliki lebih sedikit modal sosial.

Modal sosial juga dapat di konversi menjadi Social/Network Power, yaitu
kekuasaan yang bersumber dari kemampuan seseorang mengakses basis‐basis kekuasaan yang lain melalui hubungan sosial yang ia miliki, terlepas dari apa sumber daya yang menjadi basis kekuasaan, baik kekuasaan formal maupun personal (Salehudin, 2009).

Konsep Social/Network Power menekankan bahwa sebagaimana seseorang yang memiliki modal sosial dapat mengakses sumber daya melalui hubungan mereka dengan orang lain, modal sosial dapat dirubah menjadi kekuasaan dengan mengakses basis kekuasaan orang lain ‐termasuk modal manusia orang lain.

Oleh karena itu, seorang Investor‐Karyawan yang memiliki modal sosial yang besar tidak hanya mampu mengakses modal manusia yang ia miliki sendiri namun juga mampu mengakses modal manusia orang lain melalui hubungan interpersonal yang dimungkinkan oleh modal sosial tersebut.

Gambar 1: Empat Media Investasi Modal Manusia

Menarik dan mempertahankan Karyawan‐Investor

Sebagai penutup, perlu disadari oleh pihak manajemen perusahaan bahwa
aplikasi konsep Human Capital dari sudut pandang Karyawan‐Investor membutuhkan perubahan paradigma dalam pengelolaan modal manusia perusahaan. Perubahan paradigma yang paling mendasar adalah bahwa modal manusia yang dikelola perusahaan bukanlah aset / hak milik perusahaan, tetapi adalah investasi dari modal manusia dimiliki oleh masing‐masing individu Karyawan.

Sebagaimana yang telah dijelaskan diawal, seorang Karyawan‐Investor akan
menanamkan modal manusia yang ia miliki di tempat yang ia yakini akan memberikan tingkat pengembalian yang memuaskan dan dapat menarik atau memindahkan modal manusia yang sudah ia tanamkan jika ia merasa perusahaan tidak memberikan pengembalian yang memuaskan. Sedangkan salah satu bentuk pengembalian yang dibutuhkan oleh Karyawan‐Investor adalah dalam bentuk investasi terhadap modal manusia, seperti pengalaman, pendidikan, pelatihan, atau modal sosial yang telah dijelaskan diatas. Oleh karena itu, pengeluaran perusahaan dalam pelatihan dan pengembangan adalah bagian dari upaya perusahaan untuk menarik dan mempertahankan investasi modal manusia dari Karyawan‐Investor yang dibutuhkan.

Selain itu, masing‐masing Karyawan‐Investor memiliki ekspektasi tingkat
pengembalian yang berbeda‐beda. Oleh karena itu, rekrutmen dan seleksi juga memainkan peranan penting dalam pengelolaan modal manusia perusahaan. Secara umum, perusahaan akan menginginkan Karyawan‐Investor yang memiliki modal manusia sebesar mungkin dengan ekspektasi pengembalian sekecil mungkin. Meskipun demikian, perlu dipahami bahwa umumnya Karyawan‐Investor akan memiliki ekspektasi tingkat pengembalian yang berbanding lurus dengan nilai modal manusia yang dimiliki, karena seorang Karyawan‐Investor dengan modal manusia yang besar akan cenderung menyadari nilai modal manusia yang ia miliki sehingga meminta tingkat pengembalian yang besar dan sebaliknya.

Referensi

Becker, G.S. (1993) HUMAN CAPITAL: A Theoretical and Empirical Analysis, with Special Reference to Education 3rd Edition. The University Of Chicago Press: Chicago

Davenport, T.O. (1999) HUMAN CAPITAL: What it is and why people invest it. Jossey‐Bass: San Francisco

Galunic D.C. & Anderson, E. (2000) From Security to Mobility: Generalized Investments in Human Capital and Agent Commitment. Organization Science, 11(1), 1‐20. http://www.jstor.org/stable/2640402

James, E.H. (2000) Race‐Related Differences in Promotions and Support: Underlying Effects of Human and Social Capital. Organization Science. 11 (5), pp. 493‐508. http://www.jstor.org/stable/2640341

Salehudin, I. (2009) Social/Network Power: Applying Social Capital Concept to Explain the Behavioral Tendency of Individuals in Granting Favors within the Organizational Context. Proceeding 4th International Conference on Business and Management Research (ICBMR), Dipresentasikan
tanggal 22 November 2009, Bali‐Indonesia. http://ssrn.com/abstract=1682343

Zarutskie, R. (2008). The role of top management team human capital in venture capital markets: evidence from first‐time funds. SSRN Accepted Paper Series. http://ssrn.com/abstract=870501

[Artikel] Pemasaran Halal: Definisi, Konsep dan Implikasi

Written by imams on Sep 05 2010

Please Cite: Salehudin, I. and Mukhlish, B.M. (2010) Pemasaran Halal: Definisi, Konsep dan Implikasi.

PEMASARAN HALAL

Imam Salehudin, SE, MSi [1]

Basuki Muhammad Mukhlish, SE [2]

Definisi

Islam sebagai agama, juga menjadi jalan hidup yang mengatur segala sendi kehidupan pemeluknya. Syariat islam tidak hanya mengatur aspek ibadah (hubungan antara manusia dengan Alloh) tetapi juga mengatur aspek muamalah (hubungan antara manusia dengan sesamanya). Meskipun saat ini isu syariah lebih banyak diperhatikan dalam konteks ilmu keuangan, pemahaman atas syariat Islam tidak hanya penting bagi bidang ilmu keuangan saja tetapi juga bagi bidang ilmu pemasaran.

Allah subhanahu wa ta’ala berfirman:  “Hai sekalian manusia, makanlah yang halal lagi baik dari apa yang terdapat di bumi, dan janganlah kamu mengikuti langkah-langkah syaitan; karena sesungguhnya syaitan itu adalah musuh yang nyata bagimu. Sesungguhnya syaitan itu hanya menyuruh kamu berbuat jahat dan keji, dan mengatakan terhadap Allah apa yang tidak kamu ketahui.”(QS. Al-Baqarah: 168-169). Sementara Rasululloh shollallohu ‘alayhi wa sallam bersabda: “Perkara yang halal itu jelas dan yang haram itu jelas, sedangkan diantara keduanya terdapat perkara-perkara yang tersamar (meragukan) dan banyak orang tidak mengetahuinya. Maka siapa yang menghindari perkara-perkara yang meragukan, iapun telah membersihkan kehormatan dan agamanya. Dan siapa yang terjerumus dalam perkara-perkara yang meragukan, iapun bisa terjerumus dalam perkara yang haram. Seperti penggembala yang menggembala di sekitar tempat terlarang dan nyaris terjerumus di dalamnya” (HR Bukhari dan Muslim, Hadist ke 6 pada Arba’in Imam Nawawi).

Kedua dalil diatas merupakan dasar hukum perintah bagi setiap muslim untuk hanya mengkonsumsi barang dan jasa yang halal saja dan menghindari semua barang dan jasa yang haram dan meragukan. Sebagai aplikasi dari perintah tersebut, Pemasaran Halal atau Halal marketing merupakan pengembangan dari konsep marketing konvensional dengan menambahkan aspek kepatuhan terhadap syariat Islam (Syariah Compliance) dalam proses pembentukan nilai bagi konsumen.

Jika Philip Kotler mendefinisikan Pemasaran sebagai proses sosial dimana individu dan kelompok mendapatkan apa yang mereka butuhkan dan inginkan melalui penciptaan, penawaran dan pertukaran barang dan jasa yang memiliki nilai tertentu dengan individu atau kelompok lain dengan bebas; maka dengan demikian Pemasaran Halal dapat didefinisikan sebagai proses sosial dimana individu dan kelompok mendapatkan apa yang mereka butuhkan dan inginkan melalui penciptaan, penawaran dan pertukaran barang dan jasa yang memiliki nilai tertentu dengan individu atau kelompok lain sesuai kaidah dan tuntunan yang ditetapkan oleh Syari’at Islam.

Konsep

Syari’at Islam mengarahkan para pemasar untuk melakukan usaha-usaha pemasaran yang mengedepankan nilai-nilai akhlak yang mulia. Dengan demikian, cakupan dari Pemasaran Halal tidak hanya pada aspek product (misalnya: tidak mengandung unsur atau bahan baku yang diharamkan) tetapi juga pricing (misalnya: penetapan harga yang tidak mengandung judi, gharar dan riba), promotion (misalnya: tidak menggunakan penipuan atau sumpah palsu, tidak menggunakan sex appeal dalam tayangan iklan), dan juga place (misalnya: tidak berjualan di tempat yang dilarang seperti masjid atau pada waktu yang dilarang seperti waktu sholat berjamaah).

Sebagian orang menyangka bahwa syari’at Islam mengekang kreativitas karena banyak hal-hal yang dilarang. Sebenarnya apa yang dihalalkan oleh Allah jauh lebih banyak daripada apa yang dilarang. Berdasarkan dalil-dalil dari Al-Qur`an dan As-Sunnah, para ‘ulama telah merumuskan kaidah bahwa hukum asal dari mu’amalah adalah boleh kecuali bila ada dalil yang melarangnya (Imam As-Suyuthi, Al-Asybah wa An-Nadzha`ir, I/60). Para ‘ulama menyatakan bahwa tidaklah Allah melarang sesuatu melainkan karena hal tersebut mengandung mudharat atau sesuatu yang merugikan dan membahayakan. Oleh karena itu, aturan syari’at seharusnya dapat menjadi sumber inspirasi bagi para pemasar untuk lebih mengasah kreativitasnya sehingga menghasilkan usaha-usaha marketing yang kreatif dan tidak melanggar syari’at.

Implikasi

Islam merupakan agama yang dianut lebih dari 20% penduduk dunia, dan pasar konsumen Muslim dunia mencapai nilai 2.7 Trilliun USD (JWT, 2007). Sementara itu, Indonesia dengan populasi penduduk Muslim terbesar di dunia merupakan pasar potensial yang besar bagi berbagai produsen barang dan jasa. Meskipun masing-masing konsumen Muslim memiliki kadar kepatuhan terhadap syariah yang berbeda-beda tergantung tingkat religiusitas mereka, secara umum konsumen Muslim akan memiliki sikap yang positif terhadap produk-produk yang menggunakan pendekatan Halal dalam proses pemasaran mereka (Aliman dan Othman, 2007). Produk-produk semacam ini, menggunakan Halal appeal sebagai salah satu daya tarik dan identitas pembeda dari produk-produk sejenis yang menjadi pesaingnya.

The Halal Journal” pada tahun 2008 mengestimasi total nilai industri barang dan jasa yang menggunakan halal appeal ini melebihi 1 Trilliun USD di seluruh dunia. Beberapa contoh produk-produk yang menggunakan Halal appeal ini, seperti: Turisme dan Hospitality (Hotel Syar’i dan Restoran Halal), Jasa Keuangan (Perbankan Syariah), Kesehatan (Thibbun Nabawi), Kecantikan (Kosmetik dan Salon Muslimah), Pendidikan Umum (Sekolah Islam Terpadu), Real Estate (Perumahan Islami), dan Toiletries (Shampo Muslimah). Tentunya produk-produk yang menggunakan Halal appeal tersebut harus mempertahankan konsistensi mereka dalam menggunakan pendekatan Halal untuk menghindari disonansi dan kehilangan kepercayaan konsumen mereka (Salehudin dan Luthfi, 2010).

Terlebih lagi, segmen konsumen Muslim di Indonesia yang memiliki kepedulian tinggi terhadap terhadap kehalalan barang dan jasa yang mereka konsumsi saat ini berkembang dengan pesat (Sucipto, 2009). Tidak berbeda dengan segmen konsumen umum, segmen konsumen ini sama-sama menginginkan produk yang berkualitas, namun mereka juga menuntut produk yang mereka konsumsi untuk mematuhi aturan-aturan yang ditetapkan oleh syariat Islam. Segmen ini menjadi peluang pasar yang menarik karena memiliki kecenderungan yang lebih besar untuk merekomendasikan produk yang mereka persepsikan halal dan bahkan membayar dengan harga yang lebih mahal jika tidak terdapat alternatif produk sejenis yang mereka persepsikan halal.

Selain itu, meskipun memiliki proporsi yang lebih kecil daripada mayoritas konsumen muslim, segmen konsumen yang memiliki kesadaran dan keterlibatan yang tinggi terhadap kehalalan produk yang mereka konsumsi secara umum lebih vokal dalam menghadapi produk yang mereka persepsikan tidak halal dan pada taraf tertentu mampu mempengaruhi segmen konsumen muslim yang lebih besar terutama jika mereka mempersepsikan produk tertentu secara nyata melanggar syariat Islam dalam salah satu aspek pemasarannya (Soesilowati, 2010). Terlebih lagi dengan semakin mudahnya media komunikasi massa seperti sms dan situs sosial untuk menyebarkan pesan-pesan tertentu terhadap jangkauan konsumen yang luas, maka anjuran untuk melakukan boikot terhadap produk tertentu yang dinilai melanggar hak konsumen muslim untuk mengkonsumsi produk halal akan semakin mudah pula tersebar.

Secara umum, pendekatan halal dalam proses pemasaran suatu produk juga dapat menetralisir image negatif yang diasosiasikan konsumen muslim terhadap suatu produk. Sebagai contoh, sebuah penelitian pada tahun 2006 menemukan bahwa McDonald di Singapura mengalami peningkatan jumlah kunjungan sebesar 8 juta kunjungan setelah memperoleh sertifikasi halal. Sementara KFC, Burger King dan Taco Bell juga mengalami peningkatan penjualan sebesar 20% setelah mereka memperoleh sertifikasi halal (Lada, Tanakinjal dan Amin, 2009).

Oleh karena itu, dapat disimpulkan bahwa Pemasaran Halal menjadi penting bagi pemasar yang ingin berbisnis di negara dengan mayoritas penduduk Muslim seperti Indonesia. Sementara resiko bagi pemasar yang gagal untuk menghormati hak dan kebutuhan konsumen Muslim untuk memperoleh barang dan jasa sesuai dengan apa yang diatur oleh Syariat Islam, adalah hilangnya penjualan, pangsa pasar, brand equity dan loyalitas konsumen.

Wallohu’alam bisshowaab.

Referensi

Al-Qur’anul Karim.

Imam An-Nawawi, Al-Arba’in An-Nawawiyah.

Imam As-Suyuthi, Al-Asybah wa An-Nadzha`ir, I/60.

Aliman, N.K. dan Othman, M.N. (2007) Purchasing Local and Foreign Brands: What Product Attributes Matter? Proceedings of the 13th Asia Pacific Management Conference, Melbourne, Australia, pp 400-411

Halal Journal (2008) Eyes on trillion dollars Halal market.

JWT (2007) The Life and Times of the Modern Muslims: Understanding the Islamic Consumer.

Kotler, P. (2008) Marketing Management 13th Edition. Prentice Hall.

Lada, S., Tanakinjal, G.H., dan Amin, H. (2009) Predicting intention to choose halal products using theory of reasoned action. International Journal of Islamic and Middle Eastern Finance and Management. 2(1) pp. 66-76. www.emeraldinsight.com/1753-8394.htm

Salehudin, I. and Luthfi, B.A. (2010) Marketing Impact of Halal Labeling toward Indonesian Muslim Consumer’s Behavioral Intention Based on Ajzen’s Planned Behavior Theory: A Policy Capturing Study in Five Different Product Categories. Proceedings of 5th International Conference on Business and Management Research (ICBMR), Depok, Indonesia.

Soesilowati, E.S. (2010) Behavior of Muslims in Consuming Halal Foods: Case of Bantenese Muslim. Materi Presentasi “Sharia Economics Research Day” Seminar, Widya Graha LIPI, 6 Juli 2010

Sucipto (2009) Label Halal Dan Daya Saing Waralaba. Harian Pikiran Rakyat, Jumat 3 April 2009.


[1] Staf Pengajar pada Departemen Manajemen Fakultas Ekonomi Universitas Indonesia, Peneliti independen pada bidang Pemasaran Halal. Email: imams@ui.ac.id atau gsimam@gmail.com

[2] Staf Pengajar pada Departemen Manajemen Fakultas Ekonomi Universitas Indonesia.


5th ICBMR: Marketing Impact of Halal Labelling toward Indonesian Muslim Consumer’s Behavioral Intention Based on Ajzen’s Planned Behavior Theory: Policy Capturing Studies on Five Different Product Categories

Written by imams on Sep 05 2010

Please Cite: Salehudin, I. and Luthfi, B.A. (2010) Marketing Impact of Halal Labeling toward Indonesian Muslim Consumer’s Behavioral Intention Based on Ajzen’s Planned Behavior Theory: A Policy Capturing Study in Five Different Product Categories. Proceeding of 5th International Conference on Business and Management Research (ICBMR), Presented 4th August 2010, Depok-Indonesia.

Title:
Marketing Impact of Halal Labelling toward Indonesian Muslim Consumer’s Behavioral Intention Based on Ajzen’s Planned Behavior Theory: Policy Capturing Studies on Five Different Product Categories

Author:
Imam Salehudin, SE                     Bagus Adi Luthfi, SE
University of Indonesia             University of Indonesia

gsimam@gmail.com ; imams@ui.ac.id
Research Abstract:
Purpose – Indonesia is the biggest Muslim country in the world. Attention on the importance of Halal labeling in Indonesia is now growing. Halal-conscious consumer segment is getting bigger and the Halal Product Protection Act is being drafted. Understanding purchase behavior of Muslim consumer regarding Halal Labeling is imperative for marketer doing business in a Muslim country. The purpose of this paper is to test the applicability of the theory of planned behavior (TPB) in explaining the intention to switch from products without certified Halal labels within a wide array of purchase context, especially in the purchase of food and medicine products.

Design/methodology/approach – A policy capturing questionnaire was used to elicit responses from consumers using a convenience sampling technique. A total of 7500 responses were obtained from 150 participating respondent in 50 different scenario cases. Data is analyzed using Multi-Group Structural Equation Modeling.

Findings – The findings is that Theory of Planned Behavior (TPB) is not completely valid to explain both the behavioral intention of Muslim consumers in Indonesia to seek information about the Halal certification of a product and to cancel their purchase if the product did not have Halal certification.  Differences in magnitude and significance of causal relationships exist between different product categories.

Research limitations/implications – The study employs a limited population, thus this research has weak external validity. However, because this research is using quasi-experimental method, this research has strong internal validity in return. Thus, relationships among variables can be explained, even though a generalization to field conditions still needs further research.

Practical implications – The results will be primarily beneficial to marketers of food and medicine product sold in Muslim countries by offering an insight into the intentions of consumers to cancel purchases of products without Halal labeling.

Originality/value – The paper extends the understanding of the behavior of Muslim consumer toward products without Halal labeling within a variety of purchase context.

Keywords:  Purchase Behavior, Halal Label, Muslim Consumer

A. Background and Literature Review

Indonesia, country with the largest number of Muslims in the world, is also a large potential market for consumables such as foods, drinks and OTC medicine products. Foreign marketer of these products, however, must have good understanding of the local consumers and operate carefully in order to avoid offending the locals and obtain good foothold in the market.

Islam is not only a religion, but also a way of life. Muslims have strict commandment regarding what they consume. Allah Subhanahu Wa Ta’ala commands Muslims to consume only things that are good and Halal (Al Qur’anul Karim, 16:114; 23:51). Halal, which is the opposite of haram, is a term to say that something is not forbidden to be consumed by the scriptures of Qur’an, by the saying of the prophet or by the ijma’ (consensus) of the ulama’. His Prophet, Muhammad Shollallohu Alayhi Wa Sallam, also forbids his ummat to avoid consuming things that are ambiguous whether it is Halal or haram (HR Bukhari and Muslim, Al-Arba’in An-Nawawiyyah).  These commandments regulate the lives of Muslims worldwide and its compliance is mandatory. Of course, the actual compliance to this commandment differs between individuals depending on their own religiosity (Susilowati, 2010).

In order to protect the rights of Muslim consumers to obey their commandment in consuming only Halal products, certification institutions emerged in several countries around the world to provide certifications to different food, drinks and medicine products that it is free of haram components. One such institution emerged in Indonesia, under the MUI (Indonesian Ulama’ Assembly), called LPPOM-MUI. Halal certification from LPPOM-MUI is also recognized internationally (Republika Online, 2009).

The desire to comply to the commandment in consuming only Halal products could create consumer involvement and influence consumer’s purchase decision in choosing what product they consume. The Halal certification provided by LPPOM-MUI can provide these Muslim consumers with the assurance they can rely on. Thus, attention on the importance of Halal labeling in Indonesia is now growing. Halal-conscious consumer segment is getting bigger and the Halal Product Protection Act is being drafted (Sucipto, 2009).

Not only in Indonesia, the awareness for Halal certification among Muslim consumers in neighboring Muslim country of Malaysia is also growing and Muslim consumers are getting more sensitive to those issues (Sadek, 2001). Muslims in Malaysia are beginning to question and avoid products with no Halal certification, especially foreign products (Aliman and Othman, 2007). Understanding purchase behavior of Muslim consumer regarding Halal Labeling is therefore imperative for marketer doing business in a Muslim country.

In order to understand how the Halal certification label influence the behavior of Muslim consumers, a theoretical framework is necessary. Lada, Tanakinjal dan Amin (2009) discovered that the theory of reasoned action (TRA) is applicable to explain the intention of Muslim consumers in Malaysia to choose products with the Halal label. TRA was developed by Fishbein and Ajzen to explain the psychological process in regard of how under the assumption that every conscious behavior starts from a behavioral intention, an individual’s beliefs about the outcome and the social pressures of a certain behavior would influence their intention to perform the said behavior thus influencing the behavior itself. TRA was further developed into the theory of planned behavior (TPB) by Icek Ajzen by adding a third belief to increase its domain of explanation (Ajzen, 2004). The third belief added was called Perceived Behavioral Control, which in essence is the self efficacy of the individual regarding a certain behavior.

Thus, the purpose of this paper is to test the applicability of the theory of planned behavior (TPB) in explaining how the Halal certification label influence the behavioral intention of Muslim consumers within a wide array of purchase context, especially in the purchase of food, drink and over the counter medicine products in Indonesia. There are two behavioral intention of the Muslim consumers in which the model will be tested, one is the intention of Muslim consumer to seek information regarding the Halal certification of a certain product (i.e. looking for it in the packaging; asking the proprietor, etc.) and the other is the intention to cancel the purchase of certain products without Halal certification labels.

B. Methodology

Research Design

This research is designed as a quasi experimental research using the policy capturing method. Kline and Sulsky (1995) elaborate the main research question in policy capturing studies: “What decision would individuals take with the available information?” Policy capturing is performed by exposing the respondent to a series of stimulus in the form of situational scenarios and measuring their response for each scenario. Researchers would then use regression analysis to measure the effect of each stimuli to the response measured (Aiman-Smith, Scullen & Barr, 2002).

This method is more commonly used in the field of human resources, such as researches in how personal and organizational characteristics influences recruitment and selection process (Graves, & Karren, 1992), performance appraisals and reward allocation decisions and satisfactions (Hobson & Gibson, 1983; Beatty, McCune, & Beatty, 1988; Deshpande, & Schoderbek, 1993; Zhou & Martocchio, 2001; Hu, Hsu, Lee & Chu, 2007; Law & Wong, 1998; Barclay & York, 2003) or how job-seeker chooses the company they intend to work in (Aiman-Smith, Bauer & Cable, 2001; Williamson, Cope, Thompson & Wuensch, 2002; Slaughter, Richard & Martin, 2006).

Policy capturing is also used in marketing research, even though much less often, such as in consumer product selection (Brinberg, Bumgardner & Daniloski, 2007). In this research, policy capturing is used to capture the decision of consumers to seek information regarding Halal certification of a certain product and to cancel the purchase if no Halal certification is found.

Population and Sampling of Respondent

This research uses quasi-experimental design in which internal validity is more paramount than external validity, thus probabilistic sampling design is less essential to the methodology. Subjects were recruited using non probabilistic cluster sampling from the Muslim undergraduate students currently studying in the University of Indonesia. During the data collection period, 150 subjects were recruited to participate in the data collection. All subjects recruited were participating voluntarily in this research.

Data Collection

The data used in this research was gathered in a period of five days, between 5th and 9th July 2010. Data collection was conducted by two assistant supervised by a researcher. Data collection was performed by giving each subject a set of questionnaire consisting of 4 questions about subject profiles, 20 items measuring individual beliefs, 17 items measuring actual Halal literacy, 10 scenarios measuring Halal information seek intention, and 40 scenarios measuring purchase cancel intention. Verified questionnaires were then inputted to Microsoft Excel for further analysis using advanced statistical tools. The list of variables used in the questionnaire is displayed below.

Table 1: List of Variables and Their Operationalization

Independent Variables Operationalization
X1= Attitude toward Halal Compliance : Individual belief about the personal evaluation regarding the good compliance to the commandment about Halal consumption
X2= Subjective Norms regarding Halal Compliance : Individual belief about the social expectations regarding the good compliance to the commandment about Halal consumption
X3= Perceived Behavioral Control : Individual belief about the sufficiency of resources required to perform good compliance to the commandment about Halal consumption
X4= Actual Behavioral Control : Actual sufficiency of resources required to perform good compliance to the commandment about Halal consumption
Situational Variables Operationalization
X5A= Origin of the Product Imported or local product
X5B= Halal Labels Non-MUI* Halal label or no Halal label
X5C= Availability of Alternatives The availability of alternative product with the MUI Halal certification
Dependent Variables Operationalization
Y1= Halal Info-Seek Behavioral Intention Behavioral intention to seek information regarding the existence of Halal certification of a product
Y2= Halal Switching Behavioral Intention Behavioral intention to cancel purchase if no Halal label is found

*) Non-MUI Halal label is every Halal labels that came from the producer or other institution without the certification of MUI or accredited Halal certification institution.

C. Data Analysis

The method of analysis employed to test the hypotheses in this research is Multi-group Structural Equation Modeling (MG-SEM) using LISREL for WINDOWS 8.51 Full Version (Jőreskog dan Sőrbom, 2001). Structural Equation Modeling is an analysis method employed to test structural models that depicts structural relationships between latent constructs. Multi-group analysis is employed to compare model fitness and path coefficients of the structural model between groups of observation. In this case, the model will be compares across different product context.

Measurement Model

Testing the construct validity of the measurement used in this research is the first step of analysis required before the structural model can be tested. Good construct validity of the instrument must be established before any conclusion about the causal relationship among constructs can be determined.

The initial measurement model yields a chi-square value of 740.36 with degree of freedom as much as 588, thus a p-value of 0.00002 was obtained. This result showed a non-valid model and was modified in order to improve the chi-square. One item from Subjective Norms and nine items from Actual Behavioral Control were found not valid and excluded from the instrument. Modifications include adding error covariance between several items. There were two pairs of error covariance added between three items in the Attitude construct, while two pairs of error covariance were added between four items in the Perceived Behavioral Control construct.

The improved measurement model obtained from the modification yields a chi-square value of 246.08 with degree of freedom as much as 220, thus a p-value of 0.10961 was obtained. This result showed a valid measurement model and further analysis on the structural model can be resumed. The final valid measurement model is shown below.

Figure 1: Path Diagram of the Valid Measurement Model

Multi-Group Structural Model

The first hypothesis is that TPB can be applied to the behavioral intention to seek information (Y1) regarding the existence of Halal certification of a product for five different product contexts. The structural model testing yields a chi-square value of 2436 with degree of freedom as much as 1400, thus a p-value of 0.0000 was obtained. This result showed a non-valid model. This result is not usable, however, because the number of observation used in this testing is large (n=1500). Chi-square was found to be overly sensitive bias toward large number of n, thus even a very small number of chi-square could be rejected (Meuleman and Billiet, 2009). Thus, for model fit testing with large number of observation, RMSEA would be more reliable as measurement of fit. The structural model yields RMSEA of 0.050, thus because the RMSEA value is lower than 0.8 the structural model is considered to have good model fit. Summarized path coefficients and the path diagram of the structural model are shown below.

Table 2: Summarized Path Coefficient of Structural Model Y1

PROD1 PROD2 PROD3 PROD4 PROD5
X1 à Y1 SLF 0.50 0.47 0.65 0.71 0.45
T-Val 2.53 2.38 3.30 3.60 2.31
X2 à Y1 SLF 0.02 0.09 -0.02 -0.07 0.03
T-Val 0.11 0.46 -0.12 -0.34 0.14
X3 à Y1 SLF -0.23 -0.28 -0.27 -0.44 -0.27
T-Val -2.47 -3.00 -2.78 -4.54 -2.89
X4 à Y1 SLF 0.14 0.23 0.14 0.06 0.15
T-Val 1.64 2.75 1.70 0.68 1.77
X5A à Y1 SLF 0.01 0.00 -0.02 -0.07 0.18
T-Val 0.17 0.03 -0.31 -0.82 2.26
X1 à X3 SLF 0.32 0.32 0.32 0.32 0.32
T-Val 4.14 4.15 4.12 4.16 4.13
X4 à X3 SLF -0.07 -0.07 -0.07 -0.07 -0.07
T-Val -1.14 -1.14 -1.15 -1.15 -1.15

Figure 2: Path Diagram of Structural Model Y1

The second hypothesis is that TPB can be applied to the behavioral intention to cancel purchase if no Halal label is found (Y2) for five different product contexts. The structural model testing yields a chi-square value of 9158.72 with degree of freedom as much as 1619, thus a p-value of 0.062 was obtained. This result also showed a non-valid model. However, because the number of observation used in this testing is also large (n=6000) the chi-square result can also be ignored and substituted with RMSEA as explained in the above. Thus, the structural model yields RMSEA of 0.062, thus because the RMSEA value is lower than 0.8 the structural model is considered to have good model fit. Summarized path coefficients and the path diagram of the structural model are shown below.

Table 3: Summarized Path Coefficient of Structural Model Y2

PROD1 PROD2 PROD3 PROD4 PROD5
X1 à Y2 SLF 0.21 0.19 2.15 0.19 0.25
T-Val 2.51 2.22 8.77 2.21 2.88
X2 à Y2 SLF 0.18 0.25 -1.31 0.25 0.21
T-Val 1.89 2.58 -14.03 2.67 2.20
X3 à Y2 SLF -0.15 -0.20 -0.25 -0.18 -0.22
T-Val -3.79 -4.95 -5.01 -4.30 -5.28
X4 à Y2 SLF 0.12 0.15 0.12 0.10 0.16
T-Val 3.56 4.46 2.85 3.00 4.73
X5A à Y2 SLF -0.01 -0.02 -0.06 0.00 0.08
T-Val -0.43 -0.47 -1.32 0.05 2.16
X5B à Y2 SLF -0.45 -0.40 -0.49 -0.37 -0.43
T-Val -13.08 -11.44 -11.73 -10.57 -12.26
X5C à Y2 SLF 0.30 0.26 0.25 0.31 0.25
T-Val 8.55 7.56 5.89 9.03 7.10
X1 à X2 SLF 0.65 0.65 1.24 0.65 0.65
T-Val 11.37 11.36 8.24 11.37 11.38
X2 à X3 SLF 0.31 0.31 0.30 0.31 0.31
T-Val 8.08 8.08 7.92 8.09 8.05
X4 à X3 SLF -0.06 -0.07 -0.06 -0.06 -0.06
T-Val -2.07 -2.09 -1.95 -2.07 -2.07

Figure 3: Path Diagram of Structural Model Y2
D. Discussion and Conclusion

Based on the result of data analysis above, each structural model are valid in explaining their respective behavioral intentions. However, the analysis shows that different path coefficients exist between product categories and some path coefficients are even consistently insignificant across product categories.

The structural model for Y1 shows that Attitude (X1) is consistently significant in explaining behavioral intention (Y1) across product categories. Subjective Norms (X2), however, is consistently not significant in explaining behavioral intention (Y1) across product categories. Thus, the hypothesis that X2 influences Y1 is rejected outright. On the other hand, even though Perceived Behavioral Control (X3) is consistently significant in explaining behavioral intention (Y1) across product categories, the coefficient is negative. This negative sign is contrary to the theoretical framework thus this hypothesis is also rejected.

One possible explanation is that Perceived Behavioral Control, as the individual belief about the sufficiency of resources required to perform good compliance to the commandment about Halal consumption, is subjective. Thus it may be possible for individuals to overrate or underrate their own behavioral control. This explanation is also supported by the structural model. The structural model for Y1 shows that Attitude (X1) is consistently significant in explaining Perceived Behavioral Control (X3), while the Actual Behavioral Control (X4) is consistently not significant in explaining Perceived Behavioral Control (X3). This shows that individual perceived their behavioral control based on their attitude and not their actual capabilities. This bias would create overconfidence in individuals that increase their tendency to underestimate the importance of Halal label certification.

The structural model comparison for Y1 shows that differences of path coefficients exist between product categories. The path of Actual Behavioral Control (X4) in explaining behavioral intention (Y1) is only significant for Vegetable based Foods products, while the path of Product Origin (X5A) in explaining behavioral intention (Y1) is only significant for Fast Food Franchises.

The structural model for Y2 shows that Attitude (X1) is also consistently significant in explaining behavioral intention (Y2) across product categories. Subjective Norms (X2), however, is inconsistent in explaining behavioral intention (Y2) across product categories. X2 is only significant in explaining behavioral intention (Y2) for Vegetable based Foods, Over the Counter Medicines and Fast Foods Franchises while not significant for Animal/Meat based Foods and Packaged Beverages. Thus, the hypothesis that X2 influences Y1 is rejected because it can not be generalized over different product context.

Similar to the previous model, Perceived Behavioral Control (X3) also have consistently significant negative coefficient in explaining behavioral intention (Y2) across product categories. The previous explanation that individual tends to overrate or underrate their actual behavioral control is even further supported by the structural model.

The structural model for Y2 shows that Attitude (X1) is also consistently significant in explaining Perceived Behavioral Control (X3), while the Actual Behavioral Control (X4) have consistently significant negative coefficient in explaining Perceived Behavioral Control (X3). This shows that people with higher behavioral control may tend to underrate their own Halal literacy or people with low behavioral control may tend to overrate their own Halal literacy. Actual Behavioral Control (X4), however, have consistently significant positive coefficient in explaining behavioral intention (Y2). This finding further support the postulation that people with low Halal literacy tend to be overconfidence about their behavioral control and tend to underestimate the importance of Halal labels. This would negatively influence their behavioral intention to seek information regarding Halal labels and to cancel purchase if no Halal labels are found.

Similar to the Y1 model, the structural model for Y2 also shows that Product Origin (X5A) have positive significant coefficient in explaining behavioral intention (Y1) for Fast Food Franchises only. This shows that the impact of Halal label toward information seek and switching intention is greater for Fast food franchises, thus foreign franchises have greater importance in registering their product for halal certification than local franchises.

The interesting conclusion from structural model for Y2 is that Non MUI Labels (X5B) have consistently significant negative coefficient in explaining behavioral intention (Y2). This shows that even Halal labels without certification from the legitimate institution still have significant influence in reducing the switching intention of Muslim consumers. This could be dangerous if irresponsible marketer put Halal labels on product that contain haram substances.

On the other hand, the existence of alternative product with Halal label (X5C) would significantly increase the intention of Muslim consumer to cancel purchases in no halal label is found (Y2). This could be an important opportunity for competing products that wants to attract new customers and capture the market share of existing products that have no halal labels. The existing products that have no halal labeling would also need to cover this threat by certifying their own product to prevent the loss of market share because of this halal issue.

It can be concluded from the discussion above that Ajzen’s Theory of Planned Behavior is not fully applicable to explain the behavioral intention of Muslim consumers to seek information regarding Halal label (Y1) and to cancel purchase if no Halal label is found (Y2). Even though the structural models have good fit, differences in magnitude and significance of causal relationships exist between different product categories.

This shows that regarding the impact of halal labels, the same person might have different behaviors across different product categories. Thus further testing would be required to inquire whether the model can be generalized to wider context of products. Modifications to the model would also be of use, by adding multiple attitudes and behavioral control variables to explain behavioral intention. Further research on the negative effect of perceived behavioral control should also be of academic value.
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Appendix 1: Sample Questions for Independent Variables

Independent Variables Sample Questions Scale
X1= Attitude toward Halal Compliance Flesh that grows from foods and drinks that are haram (forbidden) will be touched by the fires of Hell 5 point Likert Scale
X2= Subjective Norms regarding Halal Compliance It is mandatory for every Muslim to check the halal certification  before they consume something 5 point Likert Scale
X3= Perceived Behavioral Control I am capable in identifying which product is halal and which product is haram 5 point Likert Scale
X4= Actual Behavioral Control To dine in restaurants that also serve alcohol is… True-False

(Halal-Haram-Don’t Know)

Appendix 2: Sample Policy Capturing Questions

Y1 Y2
Halal Info-Seek Behavioral Intention Halal Switching Behavioral Intention
If you are going to purchase a certain imported meat-based food product, how likely are you to check whether the product you are going to purchase have halal certification? If it so happens that the product that you are going to purchase have no halal certification, while alternative product with halal certification is available, how likely are you to cancel your intended purchase?
* *

Appendix 3: Descriptive Result of Responses Grouped By Categories

Y1: Halal Info-Seek Behavioral Intention

Product Meat-based Veggie-based Beverages OTC Meds Fast Foods
Imported? Yes No Yes No Yes No Yes No Yes No
Average 5.78 5.82 5.52 5.57 5.65 5.82 5.48 5.47 5.92 5.70
Std-Dev 2.00 1.87 2.00 1.99 1.97 2.85 2.05 2.06 1.90 1.91
n 150 150 150 150 150 150 150 150 150 150

Y2: Halal Switching Behavioral Intention

Product Meat-based Veggie-based Beverages OTC Meds Fast Foods
Imported? Yes No Yes No Yes No Yes No Yes No
Average 6.17 6.13 5.77 5.76 5.74 5.80 5.47 5.63 6.27 5.84
Std-Dev 1.57 1.65 1.84 1.85 1.79 1.78 1.94 1.93 1.63 1.71
n 600 600 600 600 600 600 600 600 600 600

4th ICBMR: Social/Network Power: Applying Social Capital Concept to Explain the Behavioral Tendency of Individuals in Granting Favors within the Organizational Context

Written by imams on Sep 05 2010

Please Cite: Salehudin, Imam (2009) Social/Network Power: Applying Social Capital Concept to Explain the Behavioral Tendency of Individuals in Granting Favors within the Organizational Context. Proceeding of 4th International Conference on Business and Management Research (ICBMR), Presented in 22nd November 2009, Bali-Indonesia.

Social/Network Power: Applying Social Capital Concept to Explain the Behavioral Tendency of Individuals in Granting Favors within the Organizational Context

Imam Salehudin, SE.

Department of Management

Faculty of Economics University of Indonesia

gsimam@gmail.com

imams@ui.edu

Abstract:

The concept of Social Capital started from the domain of sociology and was transferred to broader application in other social sciences, such as economics and politics. It has also migrated from the inter-individual to the inter-societal level of society. This study returns to the original context of Social Capital by applying it to explain the behavioral tendency of individuals within the organizational context. The concept of Social/Network Power borrows the concept of Social Capital to explain how someone can access the power of other person, both formal and personal-based, by accessing its power base through social network. The independent variables used in this study are length of relationship, valence of relationship, existence of past favors, existence of potential favors, source of power and gender. This study uses the quasi-experimental method of policy capturing to determine whether social networks enables individuals to access the power base of other person, both formal and personal. This study uses 33 volunteers that were given 48 different scenarios, which yields 1583 unique cases for analysis. The result shows that all independent variable, except gender, has significant influence toward the behavioral tendency of individuals in granting favors by lending their power base, both formal and personal. However, using log linear model, the analysis shows that the effect of past favors toward the tendency to grant favors are moderated by the source of power. Owed favors have greater effect toward influencing the tendency to lend personal power base than formal power base to pay for those favors.

Keywords: Power, Social Capital, Individual Behavior

Summary:

Background

Power and politics plays major and interesting roles in organizational dynamics. Politics, as defined as the acquisition and use of power, determines the shifts of resources and influence decision that affects the entire organization, either for better or for worse. Meanwhile the scope of literature about sources of power in organizational context is severely limited. Majority of research in sources of power has depended on the classification of power by French and Raven (1959). It is necessary to explore possible sources of power in organizational context that have not been explored in the classical classification of power by French and Raven.

Research Statement:

Social Capital is a source of organizational power, in which people with social capital can elicit holders of power as defined by French and Raven to lend their source of power.

Objectives:

The objective of this research is to explore the concept of social capital in the organizational context as a source of power in order to broaden the classification of power by French and Raven.

Methodology:

The methodology used in this research is quasi experimentation, using Policy Capturing method to collect data. The primary data analysis is done with logistic regression, to analyze the effect of each variable, with log linear modeling as secondary data analysis, to analyze the moderation effect of the source of power to the favors owed.

Result:

In situation where the all of the situational variable is absent (constant), there are only 20.13% probability that a request to access a powerbase be granted. The maximum probability of the request to be granted -when all positive variables are included- is 97.97%, while the minimum probability –when only the negative variable is included- is 2.48%.People with long standing relationship have 2.53 times more probability to be granted a favor than people with relatively new relationship. People with positive relationship have 3.20 times more probability to be granted a favor than people with no positive relationship. People with negative relationship have 0.10 times lesser probability to be granted a favor than people with no negative relationship. People with past favors due have 2.22 times more probability to be granted a favor than people with no past favors. People with the potential to repay favors have 1.65 times more probability to be granted a favor than people without potentials. When the favor asked is concerning personal power, it has 4.90 times more probability to be granted than favors regarding formal power. Even though gender is found to be not significant, the result showed that males have a slight tendency to grant favors more, with 1.331 times or 6.73% more probability to grant favors than females do.

The probability to grant requests from individuals with past favors due, when the request asked is concerning the use of personal power, is 1.87 times greater than individuals without past favors. The probability to grant requests from individuals with past favors due, when the request asked is concerning the use of formal power, is only 1.58 times greater than individuals without past favors due. The probability to grant request concerning personal power, when the person who asked have past favors due, is 3.10 times greater than request concerning formal power.  The probability to grant request concerning personal power, when the person who asked did not have past favors due, is only 2.62 times greater than request concerning formal power.

A. Background and Literature Review

Power in Organization

Power and politics plays some major and interesting roles in organizational dynamics. Politics, as defined as the acquisition and use of power, determines the shifts of resources and influence decision that affects the entire organization, either for better or for worse.

Understanding the dynamics of power would benefit organizations by giving insight on how to harness it as well as how to control the players involved in the pursuit of it. Thus, quite a number of researchers have tried to develop theories that explain various dynamics of power and politics. The concept of power has always been inseparable from the behavior of individuals, especially within the organizational context. Effective leaders must understand the sources of power and the proper tactics required in using it to his benefit. It can be said that power is inseparable from leadership.

Max Weber, in his book that was translated to English in 1962, “Basic Concepts in Sociology” defined power as the opportunity within a social relationship that enables one to obtain anything he desires even if there is resistance. Meanwhile, the most commonly used definition of power in the field of political science is as the capacity to influence the behavior of other people, both with and without any resistance.

In accordance with definitions above, Stephen Robbins (2007) in his book “Organizational Behavior” gave the definition of power as the capacity possessed by someone to influence the behavior of others to act according to his desire. Robbins (2007) also described the classification of power by French and Raven (1959) that classifies power according to its source, which is formal and personal power. Formal power is power that is derived from the formal position within an organization.  The sources of formal power are the capacity to coerce by threats of punishments (coercive power), the capacity to promise rewards (reward power), and the legitimate formal authority of the structural position (legitimate power) held by individuals.

Personal power is power that is derived from the personal characteristic of an individual. The sources of personal power are the expertise (expert power) and the desirable traits that induce identification (reference power) owned by individuals. Robbins explains that those types of power (coercive, reward, legitimate, expert, and referent) all come from the dependency of a client to the resources held by a patron. Greater dependency of a client to a patron creates greater power of the patron to the client. The extent of the dependency is based on the extent of importance, scarcity and non-substitutability of the resources owned by the patron.

Social Capital

Social capital is a trendy phrase nowadays in the circle of social scientists and practitioners. This phrase is first used in the field of sociology in the individual scope of view, but then spreads to other field of science with wider scope of view. Portes (2000) explored the usage of this phrase and stated that Bourdieu (1985) is the first one who used this phrase in his paper to explain his opinion that one purpose of individuals in building relationships with other individuals is to obtain future benefits. Meanwhile, Putnam (1993) expanded the concept of individually-owned social capital as defined by Bordieu into community-owned social capital used with bigger scope of applications.

This expansion of application from the individual scope into bigger societal role often caused disambiguation among researcher. In order to avoid disambiguation, this paper limits the definition of social capital to its individual scope of application only.

Cornwell and Cornwell (2008) summarized previous researches concerning Social Capital (Burt 1992; Coleman 1988; Granovetter 1973; Lin 1999; Portes 1998) and conclude that the core of Social Capital Theory proposes that individuals can access resources owned by others through social connections or relationships with the owners.

Social capital is the social structure and relationship that enables individuals to access certain resources owned by other people. It is different than personal resources that is possessed and used by solely individuals, in which the usage of social capital incorporates interpersonal relationship and social dynamics of its user. Cornwell and Cornwell (2008) summarize at least three benefits of social capital at the individual level identified in previous researches, which is: (1) access to information, (2) social control, and (3) social support and solidarity (Coleman 1988; Sandefur and Laumann 1998).

Social/Network Power

Based on the discussions above, because the source of power is the dependency to a certain resources and resources can be accessed with certain social structure and relationships with the owner of the resources, thus it can be concluded by basic logic that someone can access the power base of other people through social relationship. The concept of Social/Network Power can be defined as the power that comes from the capacity to access the powerbase of others, aside of the power base -either formal or personal, through social relationships.

The concept of Social/Network Power emphasizes that as someone with social capital can access the resources of others through social relationship, social capital can be converted into power through the access to the power base of other people, either formal or personal. Thus, in order to observe how social/network power affects the behavior of others, we have to see it from the point of view of the owner of the powerbase. People with Social/Network Power would have better chance of success in asking a person with power to lend his or her powerbase. This is why observing the factor influencing the decision of individuals whether to grant a favor or not is relevant in measuring the effect of social/network power.

Social/network power comes from the social capital owned by individuals. Therefore, factors that build social capital would also build individual social/network power. The first factor used is the length of relationship. This factor is relevant in determining the trust upon a relationship is built. McAllister (1995) quotes the finding of Zucker (1986), Cook & Wall (1980) and Granovetter (1985) that one factor influencing inter-individual trust is the frequency and consistency of past successful interaction between individuals. This is because the personal nature of interaction that makes it possible for people to keep track record of past behaviors of each other.

The second factor is the valence of the relationship or whether the individual sees the relationship with the other individual as a positive, negative or neutral relationship. Positive valence would signal a relationship with strong trust, while negative valence would signal a relationship with strong distrust. Neutral valence would mean the relationship has neither strong trust nor distrust. Valence of the relationship would influence the social capital since people could keep track of their behavior to each other and would behave consistently based on the principle of fairness and reciprocity (Lindskold, 1978; Stack, 1988 in McAllister, 1995).

The third and fourth variable concerns the principle of reciprocity generally prevalent within eastern culture. Abdulkadiro˘glu & Bagwell (2005) discovered that individuals would only exhibit trust and facilitates cooperation if such behavior is seen as a favor that must be reciprocated, both instantly or deferred.

The fifth variable is the source of power (powerbase) that is being accessed. Powerbase can be classified into two groups, either formal or personal source of power, as explained previously. In general, the social access to formal-based power is more limited and regulated by organizational norms and rules compared to the social access to personal-based power. Thus this variable acts as moderating variable between the previous variables and the dependent variable.

B. Methodology

Research Design

This research is designed as a quasi experimental research using the policy capturing method. Kline and Sulsky (1995) elaborated that the main question in researches using policy capturing is “What decision would individuals take with the available information?”

This method is executed by exhibiting a series of scenarios to each research subject, in which each scenario is based on a combination of information cues derived from the independent variables used in the research, and then measuring their response to each scenario.  Waller and Novack (1995) describes their decision to use policy capturing by quoting several previous researches that explains that in individual decision making, there is usually not enough time to consider all the detailed information and implications regarding the decision. Meanwhile, psychological studies shows that 70% of variations in human judgement can be explained by a linear model called judgmental structure. This judgmental structure is built by observing the available information cues. Policy capturing is used to capture the judgmental structure of individuals.

Therefore, it can be concluded that the purpose of this approach is to understand the individual judgmental structure in making decisions, by observing the relationships between various information cues used with the final decision made in each scenario.

Selection of Subjects

This research involved 40 subjects participating in the data collection. However, only data from 33 subjects are used in the data analysis because 7 subjects did not pass the manipulation check.

Since the research design for this research is quasi experimental, thus there is no requirement for the sampling process to be probabilistic. All subjects recruited for this research is participating voluntarily.

Subjects are recruited using non-probabilistic sampling from a single homogenous group. The group selected as the subject for this research is college students that have experience (past or present) as officers in an organization.

Data Collection

Data collection is conducted for one week between 19th and 25th of April 2009. Data collection is conducted by giving respondents a set of questionnaire consisting of 48 different scenarios, 6 respondent identification items, and 4 manipulation check items. Displayed below is the list of variables and the dimensions used in this research.

Table B.1 Dimension of Variables Used

Variables Coding Dimension
Code Name
X1 Length of Relationship 1 Old
0 Recent
X2a Positive Valence 1 Existing
0 Non Existing
X2b Negative Valence 1 Existing
0 Non Existing
X3 Favors Owed 1 Existing
0 Non Existing
X4 Potential Favors 1 Existing
0 Non Existing
X5 Source  of Power 1 Personal
0 Formal

C. Data Analysis

Descriptive Data Analysis

In order to understand the data obtained before running the inferential statistics, we use descriptive statistics to capture the relationship between variables. The descriptive statistics used is cross tabulation. The results of the cross tabulations between each independent situational variable to the participant’s final decision, are shown in Appendix B.

Logistic Regression Analysis

This research uses two different data analysis method. The first method is the logistic regression method that is used to see the general decision structure by measuring the effect of each manipulated independent variable to the response probability of individual respondent for each scenario. Here is the result of the data analysis with Logistic Regression method using SPSS 15.0

Case Processing Summary

Unweighted Cases(a) N Percent
Selected Cases Included in Analysis 1583 100.0
Missing Cases 0 .0
Total 1583 100.0

a  If weight is in effect, see classification table for the total number of cases.

Step 0: Model only consist of constant:

Classification Table (a,b)

Observed Predicted
Comply Percentage Correct
No Yes
Step 0 Comply No 0 707 .0
Yes 0 876 100.0
Overall Percentage 55.3

a  Constant is included in the model.

b  The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)
Step 0 Constant .214 .051 17.973 1 .000 1.239

Thus the model’s predictive capability, with just the constant, is only 55.3% with a value that significantly does not equal zero.

Step 1: Enter Method, All Independent Variables Included:

Classification Table(a)

Observed

Predicted
COMPLY Percentage Correct
No Yes
Step 1 COMPLY No 508 199 71.9
Yes 151 725 82.8
Overall Percentage 77.9

a. The cut value is .500

Overall, the proposed model predicted 77.9% of the decisions correctly. Even though this value is not the same as R2; this value can be used as some measure for the model’s prediction power. Besides that, in comparison with the “step 0” model, this model has greater prediction power.

Omnibus Tests of Model Coefficients

Chi-square df Sig.
Step 1 Step 691.289 7 .000
Block 691.289 7 .000
Model 691.289 7 .000

Model Summary

Step Cox & Snell R Square Nagelkerke R Square
1 .354 .474

The R square of the proposed model is 0.354 (Cox&Snell) and 0.474 (Nagelkerke). This means that the predictors used in the model can predict between 35.4% to 47.4% of the variability of response to a request.

Variables in the Equation

B S.E. Wald df Sig. Exp(B)
Step 1(a) Length of Relationship .928 .132 49.452 1 .000 2.528
Positive Valence 1.165 .159 53.345 1 .000 3.205
Negative Valence -2.294 .163 198.892 1 .000 .101
Favors Owed .798 .131 37.126 1 .000 2.222
Potential Favors .500 .130 14.879 1 .000 1.648
Source of Power 1.590 .139 130.692 1 .000 4.903
Gender .271 .129 4.406 1 .036 1.311
Constant -1.377 .179 58.901 1 .000 .252

a  Variable(s) entered on step 1: lama, pandpos, pandneg, favorowe, potentfav, source, gender.

The analysis above shows that all independent situational variables possess significant effect in explaining the individual decision in granting a favor. Therefore, we obtain the following equation:

Ln(Y)=-1.377+0.928X1+1.165X2a-2.294X2b+0.798X3+0.5X4+1.59X5+0.271X6

Sig. 0.000     0.000         0.000           0.000       0.000     0.000    0.000     0.036

We would also obtain from the information regarding the Exp(B) that in situation where the all of the situational variable is absent (constant), people have more tendency to refuse the request since there are only 20.13% probability that the request be granted. The maximum probability of a request to be granted -when all positive variables are included- is 97.97%, while the minimum probability –when only the negative variable is included- is 2.48%.

If each variable is calculated individually, people with long standing relationship have 2.528 times more probability to be granted a favor than people with relatively new relationship and the probability of the favor approved increased by 18.83% if asked by people with longstanding relationship.

Meanwhile, people with positive relationship have 3.205 times more probability to be granted a favor than people with no positive relationship. On the contrary, people with negative relationship have 0.101 times less probability to be granted a favor than people with no negative relationship.

Regarding the reciprocity of favors, people with past favors due have 2.222 times more probability to be granted a favor than people with no past favors. In addition, people with the potential to repay favors have 1.648 times more probability to be granted a favor than people without potentials.

When the favor asked is concerning personal power, it has 4.903 times more probability to be granted than favors regarding formal power. Regarding gender, even though this variable is considered not significant, the result showed that males have a slight tendency to grant favors more, with 1.331 times or 6.73% more probability to grant favors than females do.

Log linear Model Analysis

The second method of analysis is the log linear model used in order to see the interaction effect between gender and the source of power to the other situational variable used in this research. Displayed below is the result of the log linear model analysis using Microsoft Excel. The interaction effect analyzed in this case is only the interaction between Favors Owed and Source of Power in affecting the Decision to Grant Favor. The Contingency and Marginal table for the Log linear model analysis is shown in appendix C.


Fitted Value
Model
Source Agree Favor (S,A,F) (SA,F) (AF,S) (AF,AS) (SAF)
Personal Yes Yes 219.00 268.17 243.35 297.98 295 295.5
No 218.72 267.83 194.38 238.02 241 241.5
No Yes 176.75 127.58 152.40 110.01 101 101.5
No 176.53 127.42 200.87 144.99 154 154.5
Formal Yes Yes 219.28 170.11 243.65 189.02 192 192.5
No 219.00 169.89 194.62 150.98 148 148.5
No Yes 176.97 226.14 152.60 194.99 204 204.5
No 176.75 225.86 201.13 257.01 248 248.5
G2 126.4 26.5 102.1 2.2092 0
df 4 3 2 1 0
p-value 0.0000 0.0000 0.0000 0.1372

Based on the fitted value result in the above, only the (AF,AS) model is calculated because it is the only significant model while the other models has p-value of less than 0.05. Since the (AF,AS)  model is significant, this means that there are interaction effect between favors owed and source of power in influencing the decision for granting a favor request.

Estimated Odds Ratio Conditional Association Marginal Association
Model SA FA SA FA
(S,A,F) 1.00 1.00 1.00 1.00
(SA,F) 2.79 1.00 2.79
(AF,S) 1.00 1.65 1.65
(AF, AS) 2.79 1.65 2.79 1.65
(SAF) Level 1 3.10 1.87 2.79 1.65
(SAF) Level 2 2.62 1.58

The analysis will only concern the (AF,AS) model since it is the only model significant. Thus, based on the estimated odds ratio table for the (AF,AS) model above, it can be concluded that:

1. The probability to grant requests from individuals with past favors due, when the request asked is concerning the use of personal power, is 1.87 times greater than individuals without past favors.

2. The probability to grant requests from individuals with past favors due, when the request asked is concerning the use of formal power, is only 1.58 times greater than individuals without past favors due.

3. The probability to grant request concerning personal power, when the person who asked have past favors due, is 3.10 times greater than request concerning formal power.

4. The probability to grant request concerning personal power, when the person who asked did not have past favors due, is only 2.62 times greater than request concerning formal power.

5. In total, the interaction effect between personal and favors due caused request from individuals with favors due and concerning personal power have 1.18 times greater probability to be granted.

Discussion and Conclusion

Regarding the findings, there are several important implications to note. The first one is that social/network power has greater effect on personal-based power has than formal-based. The use of formal power has more strict rules and regulation as well as social and ethical norms that might limit the actions of individuals with formal power. Personal-based power is less regulated and individuals can use theirs freely with less limitation from social and ethical norms.

The second implication is that negative relationship has greater impact than positive relationship. This finding is generally consistent with the tendency of humans to give greater reaction to negative actions than positive actions.

The third implication is that the reciprocation of favors, both past and future, plays important roles in influencing the decision. Even though this interplay of favors is still moderated by the source of power concerned, it is still significant even in favor requests concerning formal power that is limited by rules and regulations.

The last implication is that gender differences have only slight influence in regarding the effect of social/network power. Of course, the gender of the requester is not supplied in the scenario, thus the effect gender interaction in this case is still open for further investigation.

From the results above, it is most likely that social/network power can significantly explain the tendency of individuals to grant favors regarding their powerbase. However, it is imperative to note the limitation of this research in which policy capturing is a quasi experimental approach. Experimental and quasi experimental approaches tend to focus on high internal validity in sacrifice of external validity. With so many external factors controlled, it is possible that there are other important factors that is not analyzed in this research. One important factors not included in this research is the effect of culture and values. This study is conducted within oriental culture environment that scored high on collectivism. A replication in low collectivism culture environment might yield different result, thus it is interesting to contrast the difference between these cultures.



List of Reference

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Daniel J. McAllister (1995)Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations. The Academy of Management Journal, Vol. 38, No. 1 (Feb., 1995), pp. 24-59

Kline, T.J. & Sulsky, L.M. (1995) A policy-capturing approach to individual decision-making: a demonstration using professors’ judgments of the acceptability of psychology graduate school applicants. Canadian Journal of Behavioural Science. Ottawa: Oct 1995. Vol. 27, Iss.4; pg. 393

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Appendix A: Sample Question

n
If someone you are acquainted (from way back /recently), with (positive/no particular/negative) view of the person, while you (owed/did not owed) favors and he (have/does not have) potential in repaying favors in the future, asks you of something outside your job description that uses your (personal capacity/organizational authority), will you grant his request?
Yes No

Appendix B: Descriptive Cross tabulation

Table C.1 Cross tabulation between Length of Relationship and Decision to Comply

Comply Total

Response

NO YES
Length of Relationship Recently 410 381 791
Old 297 495 792 Chi Square Sig.
Total 707 876 1583 32.8958 0.0000

Table C.2 Cross tabulation between Positive Valence and Decision to Comply

Comply Total

Response

NO YES
Positive Valence NO 612 443 1055
YES 95 433 528 Chi Square Sig.
Total 707 876 1583 227.9996 0.0000

Table C.3 Cross tabulation between Negative Valence and Decision to Comply

Comply Total

Response

NO YES
Negative Valence NO 289 766 1055
YES 418 110 528 Chi Square Sig.
Total 707 876 1583 381.6414 0.0000

Table C.4 Cross tabulation between Favors Owed and Decision to Comply

Comply Total

Response

NO YES
Favors Owed NO 402 389 791
YES 305 487 792 Chi Square Sig.
Total 707 876 1583 24.2712 0.0000

Table C.5 Cross tabulation between Potential Favors and Decision to Comply

Comply Total

Response

NO YES
Potential Favors NO 384 408 792
YES 323 468 791 Chi Square Sig.
Total 707 876 1583 9.3720 0.0022

Table C.6 Cross tabulation between Source of Power and Decision to Comply

Comply Total

Response

NO YES
Source  of Power Formal 452 340 792
Personal 255 536 791 Chi Square Sig.
Total 707 876 1583 98.7458 0.0000

Table C.7 Cross tabulation between Gender and Decision to Comply

Comply Total

Response

NO YES
Gender Female 359 408 767
Male 348 468 816 Chi Square Sig.
Total 707 876 1583 2.7666 0.0962

Appendix C: Log linear Model Analysis

Table C.8 Contingency Table for 3 Variables (Source-Agree-Favor)

Favor
Source Agree Yes No Total
Personal Yes 295 241 536
No 101 154 255
Formal Yes 192 148 340
No 204 248 452
Total 792 791 1583

Table C.9 Marginal Table (Source-Favor)

Favor
Source Yes No Total
Personal 396 395 791
Formal 396 396 792
Total 792 791 1583

Table C.10 Marginal Table (Agree-Favor)

Favor
Agree Yes No Total
Yes 487 389 876
No 305 402 707
Total 792 791 1583

Table C.11 Marginal Table (Agree-Source)

Source
Agree Personal Formal Total
Yes 536 340 876
No 255 452 707
Total 791 792 1583

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

Written by imams on Sep 05 2010

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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My Professorship Journey

Written by imams on Apr 16 2008

Welcome to my blog.

Every journey began with the first step. I have started my first step in becoming a full-fledged academic Professor, by working as teaching assistant in the Department of Management Faculty of Economics University of Indonesia. I have just completed my second step by graduating from the Psychometrics Applied Graduate Program, Faculty of Psychology University of Indonesia, and soon to be promoted as teaching staff. The journey is still far from over. Attaining Professorship would require further dedication to science in expanding the boundary of science, such as doing academic researches, presenting papers, writing and publishing articles in scientific journals. This blog will document my journey toward Professorship. I will post any steps and milestones here, every articles and papers, presented and published, of my journey toward Professorship.

Imam Salehudin.