September 11, 2022

My 2022 Publications

Filed under: Artikel,Marketing — Tags: , , — imams @ 9:30 am

I’d like to promote a couple of my papers, published in 2022. I aim for a full professorship at my current institution. Currently, I am an assistant professor, and the tenure track journey requires me to write and publish routinely. Not only that, the papers I publish need to have a high impact measured by the number of citations. So, post-publication promotion of the papers is necessary, and I plan to summarize my papers in my blog annually to promote them.

My first paper in 2022

My first paper in 2022, titled “A deeper understanding of student preferences for in-class video use: a segmentation analyses of needs, group differences and preference clusters”, was co-authored with my supervisor and published in Education+Training [Q1]. DOI: 10.1108/et-02-2021-0045. You can check it here also: https://link.growkudos.com/1s83s0w4qo0

My second paper in 2022

My second paper in 2022 was co-authored with my student, Ajeng. The paper is titled “Quick Response Indonesian Standard (QRIS): Does Government Support Contribute to Cashless Payment System Long-term Adoption?” and published in the Journal of Marketing Innovation (JMI). DOI: 10.35313/jmi.v2i1.29. You can check the paper here also: https://link.growkudos.com/1edvfvftiww

My third paper in 2022

My third paper in 2022 was developed from the quantitative part of my doctoral thesis. It was co-authored with my supervisor, Prof Frank Alpert, titled “Perceived aggressive monetization: why some mobile gamers won’t spend any money on in-app purchases” and published in Electronic Commerce Research [Q1]. DOI: 10.1007/s10660-022-09603-2. You can check the free read-only access here: https://rdcu.be/cUAfO.

My fourth paper in 2022 was co-authored with my student, Adrian. The paper is titled “Hey Google: Does Environmental Beliefs and Perceived Privacy Risk Influence Potential User’s Intention to Use a Smart Home System in Indonesia?” published in Smart City. You can check the paper here: https://scholarhub.ui.ac.id/smartcity/vol2/iss1/5.

That is all for now. I will update this blog post later if I have more papers published in 2022.

January 15, 2022

My 2021 Publications

I write this blog post, my first after getting my Ph.D. from UQ, to summarize my publication history of 2021. Publications are an integral part of my Professorship journey, and this blog is made to document the said journey. So I plan to summarize my publication achievements annually.

Paper 1: To Pay or Not to Pay: Understanding Mobile Game App User’s Unwillingness to Pay for In-app Purchases

Abstract of Paper 1

First, one of the three papers of my Ph.D. titled “To pay or not to pay: understanding mobile game app users’ unwillingness to pay for in-app purchases” got accepted and is forthcoming in The Journal of Research in Interactive Marketing. This paper is my second qualitative paper. The paper analyzed qualitative data and developed the construct of Perceived Aggressive Monetization to build a novel framework that explains why most mobile gamers are unwilling to pay for in-app purchases. You can access the journal articles here: https://doi.org/10.1108/JRIM-02-2021-0053. The full citation is as follows: Salehudin, I., & Alpert, F. (Forthcoming 2022). To pay or not to pay: understanding mobile game app users’ unwillingness to pay for in-app purchases. Journal of Research in Interactive Marketing.

Paper 2: No Such Thing as A Free App: A Taxonomy of Freemium Business Models and User Archetypes in the Mobile Games Market

Abstract of Paper 2

Next, I published the second paper of my Ph.D. titled “No Such Thing As A Free App: A Taxonomy of Freemium Business Models and User Archetypes in the Mobile Games Market” in ASEAN Marketing Journal. This paper is a qualitative taxonomy paper that classifies various business models in the Freemium mobile games market. You can access the journal articles here: https://doi.org/10.21002/amj.v13i2.13540. The full citation is as follows: Salehudin, I., & Alpert, F. (2021). No Such Thing As A Free App: A Taxonomy of Freemium Business Models and User Archetypes. ASEAN Marketing Journal, 13(2), 118-137.

Paper 3: The Effect of Perceived Product Quality, Brand Personality, and Loyalty on Brand Switching Intention of Technological Products

Abstract of Paper 3

Third, I co-authored a paper with one of my Master’s students titled “The Effect of Perceived Product Quality, Brand Personality, and Loyalty on Brand Switching Intention of Technological Products” in the South East Asian Journal of Management. This paper is a quantitative study on factors that shaped Brand Loyalty and Brand Switching Intentions of Laptops. You can access the journal articles here: http://www.ijil.ui.ac.id/index.php/tseajm/article/viewArticle/13336. The full citation is as follows: Hanifati, L.N., & Salehudin, I. (2021). The Effect of Perceived Product Quality, Brand Personality, and Loyalty on Brand Switching Intention of Technological Products. The South East Asian Journal of Management, 15 (2), 169-187.

In conclusion, I am grateful for a productive 2021 and hopeful for more in 2022. Thank you for reading this update. See you in my next blog post, hopefully sometime soon.

May 23, 2012

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

Filed under: Artikel,Marketing — Tags: , — imams @ 6:20 pm

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

Filed under: Artikel,Marketing — Tags: , — imams @ 6:19 pm

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

Filed under: Artikel,Marketing — Tags: , — imams @ 6:00 pm

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

Filed under: Artikel,Marketing — Tags: , — imams @ 5:49 pm

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

September 5, 2010

[Artikel] Pemasaran Halal: Definisi, Konsep dan Implikasi

Filed under: Artikel,Marketing — Tags: — imams @ 10:24 pm

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

Filed under: Marketing,Proceeding — Tags: — imams @ 10:15 pm

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

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