Survey on factors affecting customers’ intention to use ...

Survey on factors affecting customers’ intention to use RFID in fresh seafood

Survery on Factors Affecting Customers’ Intention

to Use RFID in Fresh Seafood

BY

Cheng Pak Cheong

05018617

Information Systems Management Option

An Honors Degree Project Submitted to the

School of Business in Partial Fulfillment

Of the Graduation Requirement for the Degree of

Bachelor of Business Administration (Honors)

Hong Kong Baptist University

Hong Kong

April 2008

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Acknowledgement

I would like to give my deepest gratitude to my honor’s project supervisor, Dr. Shi

X.P.., for his useful guidance and support throughout my whole research project.

Moreover, I would like to say thank you to all respondents and Susan Poon who have

helped me to deliver many questionnaires in Stanley Caritas. Without their support, I

might not be able to finish my project.

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Abstract

The major purpose of this study is to give insights on factors affecting customers’

intention to use RFID in fresh seafood. Both empirical and theoretical study has been

investigated. In particular, this project further provides evidence for perceived

usefulness and perceived ease of use have largest influence to customers’ intention to

use RFID in fresh seafood, followed by AQNIP (Acquire product related novel

information). A model was developed based on Davis’ TAM (1989) and perceived risk

and AQNIP from Tanawat and Audhesh (2006).

The result of path analysis revealed that attitude has significant direct effect to

behavioral intention. Perceived usefulness, perceived ease of use and AQNIP has

significant indirect effects to customers’ intention. In short, the result indicated that

perceived usefulness has larger predictive power than perceived ease of use, followed

by AQNIP.

The findings are important to provide useful suggestions to RFID fresh seafood

providers. It is recommended that they can improve their business performance through

offering more useful, easy to use and attracting information to customers.

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Table of Contents

1.1 Introduction .............................................................................................................. 5

1.2 Objectives of This Study .......................................................................................... 6

2. Literature Review ....................................................................................................... 7

3. Research Model ........................................................................................................ 11

4. Research Methodology ............................................................................................. 15

4.1. Questionnaire Design .................................................................................... 15

4.2. Sample and Data Collection Procedures ..................................................... 16

4.3. Data Analysis Method ................................................................................... 16

5. Analysis and Result .................................................................................................. 18

5.1 Primary Data analysis and Descriptive Statistics........................................ 18

5.2 Internal Consistency Reliability .................................................................... 20

5.3 Path Analysis.................................................................................................. 21

5.3.1 Direct Effects........................................................................................ 22

5.3.2 Indirect Effects..................................................................................... 25

5.3.3 Total Effects ......................................................................................... 26

6. Discussion and Implications .................................................................................... 27

6.1 Effects on Behavioral intention ..................................................................... 27

6.2 Effects on Attitude .......................................................................................... 29

6.3 Effects on perceived usefulness and perceived ease of use ......................... 31

6.4 Effects on AQNIP ........................................................................................... 31

7. Limitations: ............................................................................................................... 32

8. Conclusion:................................................................................................................ 34

References: .................................................................................................................... 35

Appendix A - Reliability Test Tables.......................................................................... 40

Appendix B - Regression Test Tables ......................................................................... 43

Appendix C - Questionnaire Development................................................................. 54

Appendix D – Questionnaire (Chinese version)......................................................... 55

Appendix E - Descriptive Statistics............................................................................. 56

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1.1 Introduction

Until now, customers have had to rely on a fish's appearance, as well as any manually

recorded information about its origins, age, weight and health. But customers are often

suspicious of the animal's health when there is little traceability as to its origins.

With transportation advancements, seafood products today commonly originate from

many parts of the world. Those products, often produced in a single central location, are

distributed to an increasing number of consumers worldwide. Although these trends

benefit both producers and consumers in many ways, they also hasten the spread of

health threats and economic disruptions caused by food-borne incidents. (Petersen and

Green, 2005) Therefore, ensuring the safety and defense of our seafood supply chain is

more critical than ever before.

A study published in the November 2006 issue of Science has raised the alarm about the

declining number of eatable fish in the world. It projects the collapse of all fish stock

by 2048 because of contamination and over-fishing (Eilperin, 2006). In Hong Kong, the

number of disease cases caused by intake of seafood has raised by 40 % over the past 10

years (Department of Health, 2006). Local consumers are probably more concerned

with the unrecognized (or illegal), unauthorized and unidentified seafood. Especially for

the seafood from China.

“A new study found samples from China markets that contained concentrations of

contaminants high enough to pose threats to human health.” (Cassandra, 2007)

The study is published in the latest issue of Environmental Toxicology and Chemistry.

Moreover, according to the Agriculture, Fisheries and Conservation Department, over

60% of Hong Kong’s seafood are imported from China. Therefore, it the issue of

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http://www.sciencemag.org/cgi/content/summary/314/5800/745

http://projects.washingtonpost.com/staff/email/juliet+eilperin/

http://community.kmtr.com/members/Cassandra.aspx


tracking and recording information about the seafood’s origin has become critical. In

particular, those seafood which are unrecognized (or illegal), unauthorized and

unidentified are more difficult to track their origin.

Currently, some packaged seafood has been using bar code as a mean to label seafood’s

origin. But for many unpackaged seafood, it is difficult to “label” using barcode because

of barcode’s weakness in water, dirty and freezing environment. (White, Gardiner G.,

Prabhakar, and Razak, 2007)

Different from barcode, RFID (Radio Frequency Identification) can accommodate with

extreme temperature, humidity and even watery and dirty conditions such as foods, like

fresh and frozen meat, seafood and identifying genuine food products facilitate food

tracking, food safety and quality.

The use of RFID has become prevalent. Many domestic and international businesses are

starting to apply this technology in the supply chain businesses. (Chen, 2007)

Comparing to the positive impacts RFID has made on electronic businesses and the

supply chains management, the use of RFID on ensuring food quality in Hong Kong

still seems to be undeveloped. From the managerial standpoint, it’s necessary to discuss

the application of RFID technology centered on the topic of ensuring seafood quality.

This thesis is aimed to analyze the customer perceptions of applying RFID into the

seafood management from the customers’ point of view (i.e. intention).

1.2 Objectives of This Study RFID application is a hot research topic at the moment, however, many researchers

were either focus on supply chain perspective (Gaukler 2005; Wong 2004; Wu, Xiao

and Ye 2005; Kelley 2006), or technological perspective (Xie &You 2005; Riggins

2007). Only a few demonstrating customers respects (Ma & Zhou 2005). There is yet a

systematic research to provide insight particularly on factors affecting user acceptance

of RFID on seafood.

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http://service.ilib.cn/Search/Search.aspx?Query=MA+Xin-lu


This research aims at giving directions to seafood providers on the application of RFID

on seafood, by explaining the most important factors that affecting user intention to use.

Besides, the relative prediction power of every factor will be tested.

2. Literature Review In this chapter, we will focus on literature about: 2.1) previous studies in RFID

applications 2.2) Technology Acceptance Model 2.3) perceived risk 2.4) AQNIP

2.1 Previous studies in RFID applications

RFID has provided new products, services and solutions. For instance, it is used to

improve anti-counterfeiting issues (Staake, Thiesse and Fleisch, 2005), asset or product

tracking, security and safety, industrial warehousing, condition monitoring, product

handshaking, positioning/locating, and theft or tampering detection (Wilding and

Delgado, 2004). In logistics field, logistic enterprises often transport sensitive goods

under specific conditions (e.g. frozen food or vaccines). RFID tags with sensors enable

inspecting and controlling if required conditions were met throughout the entire

transport. Thus, it increases product security and providing both logistician and client

with accurate information. (Knebel, Leimeister and Krcmar, 2006)

In fact, Taiwan has already started using RFID tag in expensive fresh seafood (such as

Ide (石斑、海鱺魚 ) ) in 2006. Some Taiwan local fresh seafood suppliers have tried

to put the RFID tag on the fish fins. It can identify the fish supplier, environment

changes, allow seafood to be traceable and ensure health certificates are tagged to the

healthy fish. (Department of industrial technology, Taiwan, 2006).

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2.2 Technology Acceptance Model (TAM)

Based on the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), the original

TAM provide a basis for tracing the effect of external factors on internal beliefs,

attitudes, and intentions. (Davis, 1989). It aims at identifying factors affecting

behavioral intention to use. Perceived usefulness and perceived ease of use are major

factors affecting intention to use a technology. Moreover, attitude acts as a mediator

between external factors and behavioral intention. According to Davis, actual usage

could be predicted base on the behavioral intention. As defined by Davis, perceived

usefulness, refers to “the degree to which a person believes that using a particular

system would enhance his or her job performance”, and he defined perceived ease of

use as “the degree to which a person believes that using a particular system would be

free of effort”.

Despite of a number of theoretical frameworks for researchers such as TRA and Theory

of Planned Behavior (TPB), "TAM shows significant relationships between variables in

the model. These data results confirm that TAM is a valuable tool for predicting attitude,

satisfaction, and usage from beliefs and external variables.” (Algahtani and King, 1999)

With the proven statistics records, both perceived usefulness and perceived ease of use

were proven to be significant determinants of behavioral intention. Further results of the

studies shown that perceived usefulness was a significantly stronger determinant than

perceived ease of use (Davis, 1989).

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2.3 Perceived Risk

Cox and Rich (1964) defined perceived risk as “the nature and amount of risk perceived

by a consumer in contemplating a particular purchase decision”. It represents consumer

uncertainty about loss or gain in a particular transaction (Murray, 1991). In fact, there

are a number of researches trying to study the way perceived risk will affect consumers’

buying behavior. (Wang, Wang, Lin and Tang, 2003). As suggested by Mitchell (1999),

perceived risk is a powerful tool in explaining consumers' behavior because consumers

strive more to avoid mistakes than to maximize utility in buying.

Perceived risk can be divided into six different types: financial, performance, social,

psychological, physical, and time/convenience loss (Mitchell, 1999). Below are

definition of different types of perceived risks adapted from (Tanawat and Audhesh,

2006):

Table 1: Definitions of different types of perceived risks

Risk type Definition

Psychological Nervousness arising from the anticipated post-purchase emotions such as frustration, disappointment, worry, and regret

Physical Perception that product will be harmful to adopters

Time Perception that the adoption and the use of the product will take too much time

Financial Negative financial outcomes for consumers after they adopt products

Performance Concerns that products will not perform as expected

Social Negative responses from consumer’s social network.

According to So and Sculli (2002), customers may not buy a product even though they

perceive a high value in product or service because of their high perceived risk in

purchasing the product. Therefore, it is necessary to take into consideration the effect of

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perceived risk to consumer’s behavioral intention.

2.4 Acquire product related novel information (AQNIP)

AQNIP is defined as the extent to which consumers acquire novel products’ information

associated with new high-tech products (Hirschman, 1980). According to Hirschman,

“the desire to seek out the new and different (i.e. inherent novelty seeking) is

conceptually indistinguishable from the willingness to adopt new products (i.e. inherent

innovativeness). Especially when one defines products in their broad sense, it becomes

apparent that new products may constitute new information in the form of ideas (eg.

from magazines), services (e.g. education courses), and tangible goods (eg. apparel,

automobiles). Thus a consumer who express a willingness to adopt a new product is

necessarily also expressing a desire for novel information.” In another study of high-

tech electronic product adoptions, Tanawat and Audhesh (2006) define AQNIP as “the

extent to which consumers acquire (or seeks) new products novel information with or

without actual adoption.”

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3. Research Model

The purpose of this project is to test examine the customers’ intention to use RFID in

fresh seafood. In the past, there are no well-established models specially designed for

RFID application. To test the customer’s intention to use of RFID in fresh seafood,

TAM, perceived risks, and AQNIP will be used in this research model.

The following will describe the relationship between the above variables:

Firstly, Davis hypothesizes that the behavioral intention is immediately determined by a

consumer's attitude towards the system. Therefore, the first hypothesis is:

H1: Attitude will be positively related to the behavioral intention.

Perceived usefulness (PU) - This was defined by Davis, F.D. (1989) as "the degree to

which a person believes that using a particular system would enhance his or her job

performance". Perceived usefulness means to what extent the consumers find the RFID

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is useful to them (such as anti-counterfeit, know about the seafood’s origins and product

information).

H2: Perceived usefulness will be positively related to attitude.

Perceived ease of use (PEOU) - Davis defined it as "the degree to which a person

believes that using a particular system would be free from effort" (Davis, F.D. 1989). In

this project, perceived ease of use refers to the level that customers will find the RFID

in fresh seafood is easy to use.

H3: Perceived ease of use will be positively related to attitude.

As mentioned in the literature review, high perceived risk will discourage consumers

from adopting a new product even though a consumer perceived a high value. Therefore,

perceived risk will directly bring negative effect to intention. While the perceived risk is

multidimensional in nature, not all dimensions will affect all products purchase

decisions. It appears only some of the risks are important in affecting overall risk

(Campbell and Goodstein, 2001). Therefore, in this project, only financial, performance

and psychological risks are chosen to represent the perceived risk.

Perceived psychological risk refers to “the experience of anxiety or psychological

discomfort arising from anticipated post behavioral affective reactions such as worry

and regret from purchasing and using the product.” (Utpal, 2001). It is important to

reduce stress, mistrust, worries and regret of customers from purchasing RFID tagged

fresh seafood.

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Perceived performance risk refers to “Concerns that products will not perform as

expected” in this project refers to the situation when a RFID tag does not perform

correctly as it expected (eg. mal-function) (Tanawat and Audhesh, 2006)

Perceived financial risk refers to “negative financial outcomes for consumers after they adopt

products“ (Tanawat and Audhesh, 2006). Since price of fresh seafood is possible to

increase due to the use of RFID technology, consumers may need to bear extra cost/risk

on using this new product.

Therefore, perceived risks will include perceived financial risk, perceived performance

risk and perceived financial risk. They will impose negative effect on the behavioral

intention.

H4: Perceived risk will be negatively related to behavioral intention.

In addition, AQNIP refers to “the extent to which consumers acquire (or seeks) new

products novel information” (Tanawat and Audhesh, 2006). If consumers have higher

AQNIP, they will learn more information about the new products, and know more about

the its usefulness and ease of use. Therefore, the following hypothesis are suggested:

H5: AQNIP will be positively related to the perceived ease of use.

H6: AQNIP will be positively related to the perceived usefulness.

According to Tanawat and Audhesh (2006), high financial risk may discourage

consumers from acquiring further information about new products. If the losses from

the adoption become important, consumers are less likely to engage in search for

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information about new products to reduce risk (Conchar et al, 2004). Therefore, higher

perceived financial, psychological and performance risk will discourage AQNIP:

H7: Perceived risk will be negatively related to AQNIP.

Also, “a consumer who express a willingness to adopt a new product is necessarily also

expressing a desire for novel information “(Hirschman, 1980). If consumers have higher

extent of AQNIP, they may have higher intention to use as well.

H8: AQNIP will be positively related to behavioral intention.

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4. Research Methodology

Research methodology is presented in this section. The English and Chinese version of

questionnaires are attached in Appendix C and D respectively. This section consisted of

3 parts: 1) Questionnaire Design, 2) Sample and Data Collection Procedures, and 3)

Data Analysis Method.

4.1. Questionnaire Design

In this project, five-point Likert scales are used ranging from “strongly disagree” to

“strongly agree”. To make sure the content validity, items used in the questionnaire

were all adapted from literature of Chen, Gillenson and Sherrell’s (2004), DelVecchio

and Smith (2005), Tanawat and Audhesh (2006), Goldsmith, Flynn and Goldsmith

(2003) and Wang (2005). The original version of questionnaire and amended version

can be referenced in Appendix C. It includes 3 parts.

In part one, demographic questions are raised including gender, age, marital status,

monthly income, education level, frequency of purchasing fresh seafood and occupation.

Part two includes questions about factors affecting customers’ intention to use RFID tag

in fresh seafood. It includes behavioral intention (Q1-3), attitude (Q4-6), perceived

usefulness (Q7-9), perceived ease of use (Q10-12), AQNIP (Q13-18), domain-specific

innovativeness (Q19-22), perceived financial risk (Q23-27), perceived performance risk

(Q28-32) and perceived psychological risk (Q33-35). Part three is other questions.

Question number 36 is about customers’ expectation of RFID application in fresh

seafood. Question 37 is about whether they will recommend this product to their friends.

Since housewives and fresh seafood buyers may not have good English level, my

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questionnaire is translated into Chinese to fit their needs. In questionnaire delivery, only

Chinese version is delivered because it is believed that respondents will prefer reading

Chinese than English.

4.2. Sample and Data Collection Procedures

The data was collected from housewives, students and working population in Hong

Kong who are customers of fresh seafood. The reason of choosing this sample is that

they are likely to reflect the customer’s intention to use RFID tag in fresh seafood.

Especially, housewives have greater chance to buy fresh seafood. Students may

sometimes go to buy seafood foods with their families, and working people may also be

frequent buyers of fresh seafood.

In order to increase the number of respondents, 3 modes of questionnaire deliveries are

used, namely hard-copy questionnaire, soft-copy questionnaire and online questionnaire.

Moreover, convenience sample was used in this project. The questionnaires were sent to

my friends, university students and their family members during 15th March 2008 and

5th April 2008. A total of 225 people were invited to answer the questionnaire, 180

responses were received and 171 were usable questionnaires. The 9 unusable

questionnaires were either have missing information or giving more than one answers

for same question.

4.3. Data Analysis Method

This chapter describes the statistical analysis techniques applied in this project to test

the research model and its hypothesis. Internal consistency reliability test, primary data

analysis and descriptive statistics, and path analysis will be included in this project.

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SPSS v 16.0 was used for the statistical calculation.

Internal consistency reliability measures the reliability of respondents’ answer for data

analysis. Cronbach’s alpha is used for measurement. The higher the Cronbach’s alpha,

the more reliability of respondents answers for data analysis. Usually, more than 0.7 is

acceptable (Nunnally, 1978).

Path analysis will be used to find out the relationship among variables. Multiple

regression analysis is used to know the indirect effects and direct effects caused by

independent to dependent variable. Dependent variable is affected by independent

variable, but independent variable is unaffected by other variables. In order to confirm a

relationship between independent variable and dependent variables, P value need to be

less than 0.05.

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5. Analysis and Result

5.1 Primary Data analysis and Descriptive Statistics

Total number of usable questionnaire is 171. 126 respondents belong to female because

56 respondents belong to housewives.

Occupation: 1/3 housewife,1/3 student,1/3 working

Because 1/3 respondents are students, the age group 19-25 is the most frequent.

Purchase frequency: About 80% of the respondents buy fresh seafood at least once each

month. 60% of the respondents buy fresh seafood 1-10 times every month. More than

15% of the respondents buy fresh seafood more than 10 times each month. About 6% of

the respondents buy fresh seafood nearly everyday. Only 20% do not buy fresh seafood.

This project is focus on the fresh seafood customers. So most of the respondents are

fresh seafood buyers.

Q1-3 asking about the behavioral intention to buy RFID tagged fresh seafood. The mean

of answer is about 3.2. It seems most people slightly intend to buy RFID tagged fresh

seafood.

Q2 is asking for whether people will buy fresh seafood more frequently if RFID tagged

fresh seafood product is available. Interestingly, most of them say neutral. It means the

availability of RFID tagged fresh seafood will not increase people’s frequency to buy

RFID tagged fresh seafood.

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Q4-6 is about attitude. Most people have positive attitude in using RFID tagged fresh

seafood service. In Q4, 60% are interested in and positively evaluate this service. In Q6,

40% say they like to use this service.

Q7-9 is related to perceived usefulness. 60% of them agree/strongly agree this service is

useful for them.

Q10-12 is related to perceived ease of use. Almost 50% of them agree/strongly agree

that RFID tag is easy to use.

Q13-18 is about the customer’s knowledge about RFID tagged fresh seafood. Most of

them say they are not familiar with this kind of product. Surprisingly, according to Q1-3,

most of them will still buy it.

Q20-22 is about domain-specific innovativeness. It refers to the tendency to learn about

and adopt new products. More people say they are less often to buy new products. Most

of they say that they are not the last one who buy or know the latest products.

Q23-27 refers to the financial risk. 70% of the people agree that they are worried that

the price will increase and it is too expensive if the tag is added to seafood. Most of the

people think if the RFID tag is mal-function, they will feel like losing money.

Q31-32 refers to performance risk. A high number of people agree that if the RFID tag

fails to perform its function correctly, the consequence will be significant.

Q33-35 is about psychological risk. People tends to disagree that RFID tagged fresh

seafood will bring them negative psychological impact.

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Finally, for the last two questions, most people expect RFID tagged seafood will be

available very soon.

For Q37, most people will recommend RFID tagged seafood to their friends.

Surprisingly, there are no significant difference in customers’ intention are observed,

even though their occupation (students, housewives and working population) are

different.

5.2 Internal Consistency Reliability

In general, the higher the Alpha, the more reliable the test is. There is no commonly

agreed cut-off point. Usually more than 0.7 is acceptable (Nunnally, 1978).

Cronbach’s Alpha Test results Construct Cronbach alphas

Behavioral Intention 0.845 Attitude 0.782 Perceived Ease of use 0.929 Perceived Usefulness 0.835 AQNIP 0.900 Perceived Risk* 0.75

* Original test result is 0.465. So the question number 28-30 are deleted to raise the reliability to acceptable leve

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5.3 Path Analysis

Path analysis is to measure the relationship of constructs. The table 1 demonstrates the

regression analysis result. The direct effect, indirect effect and total effect from those

variables are analyzed as follows:

Table 1 Direct Effects Direct Effect (β)

Dependent Independent

Risk AQNIP PEOU PU Attitude BI

Risk

-------- -0.053 (H7)

-------- -------- -------- -0.012 (H4)

AQNIP -------- -------- 0.486* (H5)

0.305* (H6)

-------- 0.071 (H8)

PEOU

-------- -------- -------- -------- 0.310* (H3)

--------

PU

-------- -------- -------- -------- 0.509* (H2)

--------

Attitude

-------- -------- -------- -------- -------- 0.691* (H1)

BI

-------- -------- -------- -------- -------- --------

* p <= 0.01 ** p <= 0.05 Risk: Perceived Risk; AQNIP: Acquire product related novel information; PEOU: Perceived Ease of Use; PU: Perceived Usefulness; BI: Behavioral Intention

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5.3.1 Direct Effects

1.1 Direct Effect on Behavioral Intention

Hypothesis 1, 4, 8 are trying to examine the direct impact to behavioral intention in

terms of attitude, perceived risk and AQNIP.

Attitude has a significant positive effect on behavioral intention at (β=0.719 p<0.01).

(H1 is accepted) as shown:

Unstandardized Coefficients

Standardized Coefficients Correlations

Model B Std. Error Beta t Sig.

Zero-order Partial Part

(Constant) .339 .449 .754 .452 AQNIP .067 .054 .071 1.233 .219 .320 .095 .066 risk -.024 .115 -.012 -.211 .833 -.158 -.016 -.011

1

Attitude .789 .067 .691 11.808 .000 .719 .675 .632 Dependent Variable: Behavioral_Intention

However, from the above table, perceived risk and AQNIP have insignificant influence

to behavioral intention as their P-value is higher than 0.05. (H4 and H8 are rejected)

Interestingly, when only the regression of perceived risk is tested against behavioral

intention, ignoring the effect of attitude and AQNIP, the result indicates a significant

negative relationship with behavioral intention to use:

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Standardized Coefficients

Model B Std. Error Beta t

(Constant) 4.020 .411 9.777 Sig. .000 1

risk -.250 .125 -.153 -2.007 .046 Dependent Variable: Behavioral_Intention

Similarly, when attitude is not considered in regression, it is found that AQNIP has

significant positive relationship with behavioral intention.



Model B Std. Error Beta t

(Constant) 2.486 .174 14.295 Sig. .000 1

AQNIP .303 .069 .320 4.397 .000 Dependent Variable: Behavioral_Intention

1.2 Direct Effect on Attitude

Perceived usefulness has a positive direct effect on attitude at (β=0.509 p<0.01). (H2 is

accepted)

Perceived ease of use has a positive direct effect on attitude at (β=0.310 p<0.01). (H3 is

accepted)


Standardized Coefficients t Sig. Correlations

Model B Std. Error Beta

Zero-order Partial Part

(Constant) .908 .209 4.340 .000 Perceived_ Usefulness .483 .058 .509 8.353 .000 .643 .542 .460

1

Perceived_ Ease_of_Use .257 .050 .310 5.093 .000 .529 .366 .280

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1.3 Direct Effect on Perceived Ease of Use

AQNIP has a significant positive direct effect on perceived ease of use at (β=0.486,

p<0.01). (H5 is accepted)




(Constant) 2.233 .170 13.136 .000 1 AQNIP .487 .067 .486 7.22

0 .000

Dependent Variable: Perceived_Ease_of_Use

1.4 Direct Effect on Perceived Usefulness

AQNIP has a significant positive direct effect on perceived usefulness at (β=0.305,

p<0.01). (H6 is accepted)

Unstandardized CoefficientsStandardized Coefficients


(Constant) 2.987 .162 18.472 .000 1 AQNIP .267 .064 .305 4.167 .000

Dependent Variable: Perceived_Usefulness

1.5 Direct Effect on AQNIP

As shown below, since the p-value is higher than 0.05, perceived risk has an

insignificant direct effect on AQNIP. (H7 is rejected)

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Unstandardized CoefficientsStandardized Coefficients


(Constant) 2.724 .516 5.281 .000 1 risk -.119 .171 -.053 -.695 .488

Dependent Variable: AQNIP

5.3.2 Indirect Effects

Table 2 Indirect Effect

Dependent Path

BI

1) PU-A-BI (0.509*0.691)= 0.352

2) PEOU-A-BI

(0.310*0.691)= 0.214

3) AQNIP-PU-A-BI (0.305*0.509*0.691)= 0.107

4) AQNIP-PEOU-A-BI (0.486*0.310*0.691)= 0.104

* p <= 0.01 ** p <= 0.05

Risk: Perceived Risk; AQNIP: Acquire product related novel information; PEOU: Perceived Ease of Use; PU: Perceived Usefulness; BI: Behavioral Intention

Table 3 Total Indirect Effects of AQNIP Total Indirect Effects of AQNIP = 0.107 + 0.104 = 0.211

As seen from table 2, the most significant indirect effect are perceived usefulness

(β=0.352), perceived ease of use (β=0.214). Then followed by AQNIP (β=0.211) as

shown in table 3 above.

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5.3.3 Total Effects

Table 4 Total Effects

Direct

Indirect

Total (β)

Dependent Independent

BI BI BI

Attitude 0.691 - 0.691 PU - 0.352 0.352 PEOU - 0.214 0.214

AQNIP - 0.211 0.211 Risk - - - * p <= 0.01 ** p <= 0.05 Risk: Perceived Risk; AQNIP: Acquire product related novel information; PEOU: Perceived Ease of Use; PU: Perceived Usefulness; BI: Behavioral Intention

5.4. Hypothesis Testing Results

The hypothesis testing results are concluded as follows:

Table 5

Hypothesis Relationship P Results H1 H2 H3 H4 H5 H6 H7 H8

Attitude BI PU Attitude

PEOU Attitude Risk BI

AQNIP PEOU AQNIP PU

Risk AQNIP AQNIP BI

0.691 0.509 0.310 -0.012 0.486 0.305 -0.053 0.071

Accepted Accepted

Accepted Rejected Accepted Accepted Rejected Rejected

* Risk: Perceived Risk; AQNIP: Acquire product related novel information; PEOU: Perceived Ease of Use; PU: Perceived Usefulness; BI: Behavioral Intention

26


6. Discussion and Implications

The aim of this study is to examine factors affecting people’s behavioral intention of

using RFID in Fresh Seafood in Hong Kong. The relationship between attitude,

perceived ease of use, perceived usefulness, perceived risk and AQNIP will be

discussed.

6.1 Effects on Behavioral intention

Consistent with previous researches (Davis, 1989; Venkatesh, 1999; Van der Heijden,

2004), attitude has significant relationship with behavioral intention. According to

Brown (2002), attitudes can influence perceptions of user’s satisfaction with the system.

Generally speaking, if people are interested in using RFID tagged fresh seafood service,

they will have higher intention to use this service. As a result, when people are

completely free to choose, their attitude becomes important to determine whether to use

RFID tagged fresh seafood service. Attitude is an important factor which may also

include in the future’s research.

As for perceived risk and AQNIP, they both have insignificant relationship with

behavioral intention. However, when either perceived risk or AQNIP is considered, it

will have significant effect to behavioral intention (as shown in the section Direct Effect

on Behavioral Intention). This result implies that 1) attitude is very significant to affect

people’s intention to use RFID in fresh seafood. 2) perceived risk actually has

insignificant relationship with behavioral intention, but once “attitude” and “AQNIP”

are removed, perceived risk become significant (at β= -0.153, P=0.046) to affect

behavioral intention. 3) Similarly, in case “attitude” and “perceived risk” are removed,

27


AQNIP has positive significant effect to behavioral intention (at β= 0.32, P<0.001).

Perceived risk does not have critical relationship with people’s intention to use RFID in

fresh seafood. Firstly, as from with previous researches, the more intangible the product

or service is, the higher the perceived risk, vice versa (Laroche, Bergeron and Goutaland,

2003). Maybe from customers’ perspective, RFID tag is not so closed to intangible good.

As a result, perceived risk has insignificant influence to people’s intention to use RFID

in fresh seafood. Secondly, resistant to innovation adoption holds that novel attributes of

new products features (eg. technological complexity, newness, high price) may produce

unexpected side-effects (i.e. higher risks) (Waddell and Cowan, 2003). To customers,

RFID is not that technologically complex. It is also not a new thing to them because

they are using RFID such as Octopus card and Smart ID card everyday. So their

perceived risk is not significant to affect their intention. Thirdly, findings from Tanawat

and Audhesh (2006) has also shown perceived financial risk, perceived psychological

risk and perceived performance risk has insignificant relationship with innovative

behavior in high technology and innovative goods.

Implications

Firstly, it is about “perceived performance risk”. In this survey, it can be seen that over

70% of people have confidence in RFID tag performance. More than 30% believe RFID

tagged seafood service will provide satisfactory customer service while 50% say neutral.

That means, perceived performance risk is quite low to buyers. It is probably because

Hong Kong has been using RFID technology for years (eg. Octopus card and Hong

Kong ID card). Many people have been trusted with using RFID technology in their

daily lives. They expect a good performance in RFID tag. In this paper, perceived

28


performance risk is found insignificant to affect behavioral intention.

Secondly, it is the financial risks. 70% of the people agree that they are worried that the

price will increase and it will be too expensive. That means, people tends to have high

financial risk about RFID tags to use in fresh seafood. In contrast, it is surprising that

40% of the people intend to buy RFID tagged fresh seafood. Is it contradictory? In fact,

RFID tagged fresh seafood are going to apply in more expensive seafood products. That

means, buyers of these products should be less price sensitive. Even though the rise in

cost if RFID tag is used, it is not so significant to affect people’s intention to buy RFID

tagged fresh seafood.

Thirdly, when it comes to the psychological risk, over 40% of the people disagree to

feel worry and stressful when they think of buying RFID tagged fresh seafood. 40% say

neutral. That means, people’s psychological risk is not that high. Comparing to financial

risk, less people agree they have psychological risk. In this project, perceived

psychological risk is found insignificant to affect behavioral intention.

Lastly, AQNIP (i.e. consumers’ extent to acquire information regarding a product) has

insignificant relationship with behavioral intention. In fact, no previous study has

showed that AQNIP has a direct impact to behavioral intention. This study further

revealed that AQNIP affect behavioral intention through indirect effects (to PU and

PEOU) rather than direct effects. This point will be clarified in section 4.3.

Perceived usefulness and perceived ease of use may be more important to affect

people’s attitude and intention. They are studied as follows:

6.2 Effects on Attitude

29


Both perceived usefulness and perceived ease of use have shown significant relationship

with attitude. These findings were consistent to previous study on perceived usefulness

and perceived ease towards attitude (Davis, 1989; Venkatesh, 1999; Van der Heijden,

2004). In this paper, PU and PEOU are the two most important factors affecting the

consumers’ attitude to use RFID tagged fresh seafood. They also provide significant

indirect effect to people’s intention as shown in table 4. It may provide grounds to

retain PU and PEOU in future study in the same topic.

Implications

Since the perceived usefulness has higher influence to attitude, it implies attitude is

more sensitive to the usefulness than the ease of use. In the future model development

for RFID using in fresh seafood, external variables of TAM which can affect perceived

ease of use, especially affect perceived usefulness, can be added.

For the firms who develop such RFID tags should be more focus on the way to provide

useful features to customers. For example, 1) they can have an RFID receiver to let

customers to try using the tags and show the product information on the screen. 2) tell

customers what other foods are recommended to eat together with his seafood. 3) the

best way to cook or eat his product etc. A real life example is available in Taiwan.

company offers online service to give response to customers’ enquiries. Customers can

know the fresh seafood’s inspection, supplier and other product information

(Department of industrial technology, Taiwan, 2006). It may help to raise the perceived

usefulness of applying RFID in fresh seafood.

30


For the perceived ease of use, it has significant indirect effect to impact intention. Some

ways to increase the ease of use can be considered: such as the RFID receiver should be

sensitive (at a distance), detect many tags quickly at a short time etc.

6.3 Effects on perceived usefulness and perceived ease of use

Another important factor is AQNIP. It directly affects PEOU and PU with β=0.486 and

β=0.305 respectively. In the survey, over 50% people disagree that they have good

knowledge on the RFID tagged fresh seafood. It means, if more knowledge is delivered

to customers (eg. promotions) and attract them to know about the usefulness and ease of

use of the RFID tagged fresh seafood, it will help to raise people’s perceived usefulness

and ease of use towards the RFID tagged fresh seafood. As a result, AQNIP will help to

raise the customers’ intention to use RFID in fresh seafood through indirect effects.

Implication for future research is that, other than PU and PEOU, AQNIP will also

indirectly affect attitude and intention. In order to attract customers to know about this

new product, enjoyment to customers may be made (eg. interesting advertisement). Also,

through improving customer communications, (such as better customer relationship

management or CRM), customers may acquire more novel information and have less

rejection on this new product. The implication for future’s research may include ways to

enhance after-sales services which may keep customers updated about a company’s

products. So that customers can have higher level of AQNIP, and it may increase their

intention to buy RFID tagged fresh seafood.

6.4 Effects on AQNIP

31


In this project, perceived risk (including perceived performance risk, perceived

psychological risk and perceived financial risk) have shown insignificant relationship

with AQNIP. Previous study by Tanawat and Audhesh (2006) revealed that perceived

performance risk, perceived psychological risk and perceived financial risk have

insignificant relationship with AQNIP. In this study, perceived risk has insignificant

effect on AQNIP probably because customers are usually passive. They usually do not

actively search for product information about a fresh seafood. A higher or lower

perceived risk will have no effect to their search behavior, and so their level of

information will be unaffected as well.

As mentioned before, people disagreed that they have good knowledge on the RFID and

the way it will be applied on fresh seafood.

7. Limitations:

1. Perceived risk in this research only include 3 types of risks: performance risk,

financial risk and psychological risk. It may not be enough to reflect all risk factors that

may be possible to affect people’s behavioral intention to use RFID tag in seafood. For

example, social risk, time risk, physical risk and network externality risk (Tanawat and

Audhesh, 2006) are not included. We cannot eliminate the possibility that other risks

may have important impact to people’s behavioral intention to use RFID in fresh

seafood.

2. Other external variables of TAM are not included into this study, and they may have

significant effects to intention such as subjective norms.

32


3. Respondents do not have much knowledge about RFID, and how it will be applied to

the fresh seafood. Although there is explanation about RIFD at the beginning of the

questionnaire, it may not be long enough for customers to know all details. Some people

may just skip reading the introduction at the beginning, causing bias in filling the

questionnaire which is difficult to estimate. It may partly explained by a number of

people answering “neutral” in the questionnaire.

4. This study limited to customers’ intention of RFID tag applied in fresh seafood. The

supplier’s intention is not studied here. In fact, while some people might wonder the

accuracy of the information if it just put the RFID on the boxes or other containers,

and, installing those equipment might be costly and the ways are limited, it might

ultimately only benefit those big companies and further weakens those small companies.

5. There are only 171 respondents for this project related to 3 major occupations:

students, housewives and working class. On one hand, students and working class may

not be the major buyers of fresh seafood in the population. On the other hand, this

sample size may not be large enough to truly reflect the whole population. Besides,

convenience sample was taken. The respondents were my friends and schoolmates.

Since they were not randomly chosen, it forms a bias in collecting those samples.

33


8. Conclusion:

The proposed model was based on Davis’ TAM (1989) and perceived risk and AQNIP

from Tanawat and Audhesh (2006). The major purpose of this study is to examine

factors affecting customers’ intention to use RFID in fresh seafood.

The result indicates that attitude has significant direct effect to behavioral intention,

whereras perceived usefulness, perceived ease of use and AQNIP have indirect effects

to behavioral intention. However, perceived risk has insignificant effect to behavioral

intention.

It is suggested that firms can provide useful features to customers. For example, 1)

online enquiries service available 2) can have an RFID receiver to let customers to try

using the tags and show the product information on the screen. 3) tell customers what

other foods are recommended to eat together with his seafood. 4) the best way to cook

or eat his product etc. Since real-life example is available in Taiwan, their company

offering online service may give useful hints to Hong Kong’s application.

Furthermore, the perceived ease of use has significant indirect effect to impact intention.

It is suggested that ways to increase the ease of use can be considered: such as the RFID

receiver should be sensitive (at a distance), detect many tags quickly at a short time etc.

Since AQNIP will also indirectly affect attitude and intention. In order to attract

customers to know about this new product, enjoyment to customers may be made (eg.

interesting advertisement). Also, through improving customer communications, (such as

better customer relationship management or CRM), customers may acquire more novel

information and have less rejection on this new product.

34


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38

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4&IdxID=14&top_cid

39


Appendix A - Reliability Test Tables

Reliability Test Results Table 1 – Reliability – BI

Reliability Statistics

Cronbach's Alpha N of Items

.845 3

Item-Total Statistics

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Q1 6.24 2.913 .745 .753

Q2 6.56 2.825 .684 .811

Q3 6.43 2.906 .706 .788

Reliability - Attitude Reliability Statistics


.782 3


Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Q4 7.05 2.038 .640 .687

Q5 6.88 2.567 .607 .726

Q6 7.22 2.229 .627 .697

40


Table 2 –Reliability - Risk /VARIABLES=Q23 Q24 Q26 Q31 Q32 Q33 Q34 Q35



.750 8


Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha if

Item Deleted

Q23 22.35 13.357 .357 .739

Q24 22.35 12.606 .465 .720

Q26 23.07 12.148 .448 .724

Q31 22.57 13.059 .323 .748

Q32 22.26 12.992 .352 .741

Q33 23.08 11.906 .592 .696

Q34 23.40 12.183 .564 .702

Q35 23.50 12.616 .489 .716

Reliability - AQNIP Cronbach's

Alpha N of Items

.900 4


Scale Mean if

Item Deleted

Scale Variance

if Item Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if Item

Deleted

Q15 7.23 7.204 .771 .873

Q16 7.06 6.714 .821 .854

Q17 7.05 6.744 .798 .862

Q18 7.08 7.059 .717 .892

41


Table 3 –Reliability - PEOU



.929 3


Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Q10 6.76 3.136 .862 .892

Q11 6.84 2.961 .882 .875

Q12 6.72 3.215 .822 .923

Reliability - PU



.835 3


Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Q7 7.29 2.279 .691 .781

Q8 7.29 2.479 .691 .775

Q9 7.13 2.670 .717 .758

42


Appendix B - Regression Test Tables

Regression Results Table 4 – Regression – Risk -> BI

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .153a .023 .017 .81033

a. Predictors: (Constant), risk

ANOVAb

Model Sum of Squares df Mean Square F Sig.

Regression 2.644 1 2.644 4.026 .046a

Residual 110.970 169 .657

1

Total 113.614 170


b. Dependent Variable: Behavioral_Intention

Coefficientsa


Standardized

Coefficients Correlations

Model B Std. Error Beta t Sig. Zero-order Partial Part

(Constant) 4.020 .411 9.777 .000 1

risk -.250 .125 -.153 -2.007 .046 -.153 -.153 -.153

a. Dependent Variable: Behavioral_Intention

Coefficient Correlationsa

Model risk

Correlations risk 1.000 1

Covariances risk .016


43


Table 5–Regression – Attitude -> BI

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .719(a) .517 .515 .56956

a Predictors: (Constant), Attitude

ANOVA(b)

Model

Sum of

Squares df Mean Square F Sig.

Regression 58.791 1 58.791 181.230 .000(a)

Residual 54.823 169 .324

1

Total 113.614 170

a Predictors: (Constant), Attitude

b Dependent Variable: Behavioral_Intention

Coefficients(a)

Unstandardized

Coefficients

Standardized



(Constant) .313 .219 1.427 .155 1

Attitude .821 .061 .719 13.462 .000 .719 .719 .719

a Dependent Variable: Behavioral_Intention

Coefficient Correlations(a)

Model Attitude

Correlations Attitude 1.000 1

Covariances Attitude .004

a Dependent Variable: Behavioral_Intention

44


Table 6 –

Regression – AQNIP -> BI Model Summary


Std. Error of the

Estimate

1 .320a .103 .097 .77669

a. Predictors: (Constant), AQNIP

ANOVAb


Regression 11.665 1 11.665 19.337 .000a

Residual 101.949 169 .603

1

Total 113.614 170



Coefficientsa


Standardized



(Constant) 2.486 .174 14.295 .000 1

AQNIP .303 .069 .320 4.397 .000 .320 .320 .320



Model AQNIP

Correlations AQNIP 1.000 1

Covariances AQNIP .005


45


Table 7

–Regression – AQNIP + Attitude + Risk -> BI

Model Summary


Std. Error of the

Estimate

1 .722a .522 .513 .57031

a. Predictors: (Constant), Attitude, risk, AQNIP

ANOVAb


Regression 59.296 3 19.765 60.768 .000a

Residual 54.318 167 .325

1

Total 113.614 170

a. Predictors: (Constant), Attitude, risk, AQNIP


Coefficientsa


Standardized



(Constant) .339 .449 .754 .452

AQNIP .067 .054 .071 1.233 .219 .320 .095 .066

risk -.024 .115 -.012 -.211 .833 -.158 -.016 -.011

1

Attitude .789 .067 .691 11.808 .000 .719 .675 .632


46



Model Attitude risk AQNIP

Attitude 1.000 .202 -.357

risk .202 1.000 -.023

Correlations

AQNIP -.357 -.023 1.000

Attitude .004 .002 -.001

risk .002 .013 .000

1

Covariances

AQNIP -.001 .000 .003


47


Table 8

–Regression – PEOU + PU -> Attitude

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .701(a) .491 .485 .51411

a Predictors: (Constant), Perceived_Ease_of_Use, Perceived_Usefulness

ANOVA(b)

Model

Sum of


Regression 42.912 2 21.456 81.179 .000(a)

Residual 44.403 168 .264

1

Total 87.315 170

a Predictors: (Constant), Perceived_Ease_of_Use, Perceived_Usefulness

b Dependent Variable: Attitude

Coefficients(a)

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig. Correlations

B

Std.

Error Beta

Zero-

order Partial Part

1 (Constant) .908 .209 4.340 .000

Perceived_Usefulness .483 .058 .509 8.353 .000 .643 .542 .460

Perceived_Ease_of_U

se .257 .050 .310 5.093 .000 .529 .366 .280

a Dependent Variable: Attitude

Coefficient Correlations(a)

Model

Perceived_Ease_

of_Use

Perceived_Usefu

lness

Perceived_Ease_of_Use 1.000 -.430 Correlations

Perceived_Usefulness -.430 1.000

Perceived_Ease_of_Use .003 -.001

1

Covariances

Perceived_Usefulness -.001 .003

a Dependent Variable: Attitude

48


Table 9 –

Regression – AQNIP -> PU

Model Summary


Std. Error of the

Estimate

1 .305a .093 .088 .72213


ANOVAb


Regression 9.054 1 9.054 17.362 .000a

Residual 88.128 169 .521

1

Total 97.181 170


b. Dependent Variable: Perceived_Usefulness

Coefficientsa


Standardized



(Constant) 2.987 .162 18.472 .000 1

AQNIP .267 .064 .305 4.167 .000 .305 .305 .305

a. Dependent Variable: Perceived_Usefulness


Model AQNIP



a. Dependent Variable: Perceived_Usefulness

49


Table 10 –

Regression – AQNIP -> PEOU

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .486a .236 .231 .75907


ANOVAb

Model

Sum of


Regression 30.040 1 30.040 52.135 .000a

Residual 97.376 169 .576

1

Total 127.415 170


b. Dependent Variable: Perceived_Ease_of_Use

Coefficientsa

Unstandardized

Coefficients

Standardized


Model B

Std.

Error Beta t Sig. Zero-

order Partial Part

(Constant) 2.233 .170 13.136 .000 1

AQNIP .487 .067 .486 7.220 .000 .486 .486 .486

a. Dependent Variable: Perceived_Ease_of_Use

50



Model AQNIP



a. Dependent Variable:

Perceived_Ease_of_Use

51


Table 11 –

Regression – Risk -> AQNIP

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

1 .053a .003 -.003 .86450


ANOVAb

Model

Sum of


Regression .361 1 .361 .483 .488a

Residual 126.304 169 .747

1

Total 126.664 170


b. Dependent Variable: AQNIP

Coefficientsa

Unstandardized

Coefficients

Standardized


Model B

Std.

Error Beta t Sig. Zero-

order Partial Part

(Constant) 2.724 .516 5.281 .000 1

risk -.119 .171 -.053 -.695 .488 -.053 -.053 -.053

a. Dependent Variable: AQNIP

52



Model risk

Correlations risk 1.000 1

Covariances risk .029

a. Dependent Variable: AQNIP

53


Appendix C - Questionnaire Development

Questionnaire Development

[This is a blank page]

54


Appendix D – Questionnaire (Chinese version)

Questionnaire (Chinese version)

[This is a blank page]

55


Appendix E - Descriptive Statistics

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Sex 171 1 2 1.74 .442

Age 171 1 5 3.07 1.244

Marriage 171 1 2 1.51 .501

Salary (monthly) 171 1 5 2.06 1.096

Education 171 1 4 2.50 .723

Purchase frequency 171 1 4 2.08 .793

Occupation 171 1 7 3.76 2.508

Q1 171 1 5 3.37 .901

Q2 171 1 5 3.06 .974

Q3 171 1 5 3.18 .931

Q4 171 1 5 3.52 .935

Q5 171 2 5 3.70 .760

Q6 171 1 5 3.36 .872

Q7 171 1 5 3.57 .946

Q8 171 1 5 3.57 .874

Q9 171 1 5 3.73 .790

Q10 171 1 5 3.40 .911

Q11 171 1 5 3.32 .950

Q12 171 1 5 3.44 .914

Q13 171 1 5 2.57 .939

Q14 171 1 5 3.47 .870

Q15 171 1 5 2.24 .930

Q16 171 1 5 2.42 .993

Q17 171 1 5 2.42 1.005

Q18 171 1 5 2.40 1.009

Q19 171 1 5 3.26 .865

Q20 171 1 5 3.10 .956

Q21 171 1 5 2.83 .901

Q22 171 1 5 2.67 .913

Q23 171 1 5 3.74 .756

Q24 171 2 5 3.73 .803

Q25 171 1 5 3.10 .859

56


Q26 171 1 5 3.01 .927

Q27 171 1 5 3.43 .939

Q28 171 1 5 3.23 .746

Q29 171 1 5 3.06 .745

Q30 171 2 5 3.07 .716

Q31 171 1 5 3.44 .921

Q32 171 1 5 3.80 .879

Q33 171 1 5 3.01 .815

Q34 171 1 4 2.68 .787

Q35 171 1 5 2.58 .773

Q36 171 1 5 3.34 .806

Q37 171 1 5 3.32 .859

Valid N (listwise) 171

57


Appendix F - Frequency Tables

Frequency Tables

Sex

Frequency Percent Valid Percent Cumulative Percent

1 45 26.3 26.3 26.3

2 126 73.7 73.7 100.0

Valid

Total 171 100.0 100.0

Age


1 3 1.8 1.8 1.8

2 80 46.8 46.8 48.5

3 27 15.8 15.8 64.3

4 24 14.0 14.0 78.4

5 37 21.6 21.6 100.0

Valid

Total 171 100.0 100.0

Marriage


1 83 48.5 48.5 48.5

2 88 51.5 51.5 100.0

Valid

Total 171 100.0 100.0

58


Salary (monthly)


1 69 40.4 40.4 40.4

2 44 25.7 25.7 66.1

3 42 24.6 24.6 90.6

4 10 5.8 5.8 96.5

5 6 3.5 3.5 100.0

Valid

Total 171 100.0 100.0

Education


1 16 9.4 9.4 9.4

2 60 35.1 35.1 44.4

3 88 51.5 51.5 95.9

4 7 4.1 4.1 100.0

Valid

Total 171 100.0 100.0

Purchase frequency


1 36 21.1 21.1 21.1

2 96 56.1 56.1 77.2

3 28 16.4 16.4 93.6

4 11 6.4 6.4 100.0

Valid

Total 171 100.0 100.0

59


Occupation


1 56 32.7 32.7 32.7

2 30 17.5 17.5 50.3

3 2 1.2 1.2 51.5

5 3 1.8 1.8 53.2

6 54 31.6 31.6 84.8

7 26 15.2 15.2 100.0

Valid

Total 171 100.0 100.0

Q1


1 6 3.5 3.5 3.5

2 17 9.9 9.9 13.5

3 69 40.4 40.4 53.8

4 65 38.0 38.0 91.8

5 14 8.2 8.2 100.0

Valid

Total 171 100.0 100.0

Q2


1 9 5.3 5.3 5.3

2 40 23.4 23.4 28.7

3 63 36.8 36.8 65.5

4 50 29.2 29.2 94.7

5 9 5.3 5.3 100.0

Valid

Total 171 100.0 100.0

60


Q3


1 6 3.5 3.5 3.5

2 33 19.3 19.3 22.8

3 66 38.6 38.6 61.4

4 56 32.7 32.7 94.2

5 10 5.8 5.8 100.0

Valid

Total 171 100.0 100.0

Q4


1 6 3.5 3.5 3.5

2 18 10.5 10.5 14.0

3 45 26.3 26.3 40.4

4 85 49.7 49.7 90.1

5 17 9.9 9.9 100.0

Valid

Total 171 100.0 100.0

Q5


2 9 5.3 5.3 5.3

3 56 32.7 32.7 38.0

4 84 49.1 49.1 87.1

5 22 12.9 12.9 100.0

Valid

Total 171 100.0 100.0

61


Q6


1 5 2.9 2.9 2.9

2 15 8.8 8.8 11.7

3 80 46.8 46.8 58.5

4 56 32.7 32.7 91.2

5 15 8.8 8.8 100.0

Valid

Total 171 100.0 100.0

Q7


1 4 2.3 2.3 2.3

2 21 12.3 12.3 14.6

3 42 24.6 24.6 39.2

4 82 48.0 48.0 87.1

5 22 12.9 12.9 100.0

Valid

Total 171 100.0 100.0

Q8


1 3 1.8 1.8 1.8

2 16 9.4 9.4 11.1

3 52 30.4 30.4 41.5

4 81 47.4 47.4 88.9

5 19 11.1 11.1 100.0

Valid

Total 171 100.0 100.0

62


Q9


1 1 .6 .6 .6

2 13 7.6 7.6 8.2

3 38 22.2 22.2 30.4

4 99 57.9 57.9 88.3

5 20 11.7 11.7 100.0

Valid

Total 171 100.0 100.0

Q10


1 4 2.3 2.3 2.3

2 20 11.7 11.7 14.0

3 69 40.4 40.4 54.4

4 60 35.1 35.1 89.5

5 18 10.5 10.5 100.0

Valid

Total 171 100.0 100.0

Q11


1 6 3.5 3.5 3.5

2 25 14.6 14.6 18.1

3 63 36.8 36.8 55.0

4 62 36.3 36.3 91.2

5 15 8.8 8.8 100.0

Valid

Total 171 100.0 100.0

63


Q12


1 4 2.3 2.3 2.3

2 19 11.1 11.1 13.5

3 65 38.0 38.0 51.5

4 64 37.4 37.4 88.9

5 19 11.1 11.1 100.0

Valid

Total 171 100.0 100.0

Q13


1 17 9.9 9.9 9.9

2 68 39.8 39.8 49.7

3 65 38.0 38.0 87.7

4 13 7.6 7.6 95.3

5 8 4.7 4.7 100.0

Valid

Total 171 100.0 100.0

Q14


1 4 2.3 2.3 2.3

2 18 10.5 10.5 12.9

3 56 32.7 32.7 45.6

4 80 46.8 46.8 92.4

5 13 7.6 7.6 100.0

Valid

Total 171 100.0 100.0

64


Q15


1 35 20.5 20.5 20.5

2 79 46.2 46.2 66.7

3 42 24.6 24.6 91.2

4 11 6.4 6.4 97.7

5 4 2.3 2.3 100.0

Valid

Total 171 100.0 100.0

Q16


1 29 17.0 17.0 17.0

2 70 40.9 40.9 57.9

3 50 29.2 29.2 87.1

4 16 9.4 9.4 96.5

5 6 3.5 3.5 100.0

Valid

Total 171 100.0 100.0

Q17


1 31 18.1 18.1 18.1

2 68 39.8 39.8 57.9

3 44 25.7 25.7 83.6

4 25 14.6 14.6 98.2

5 3 1.8 1.8 100.0

Valid

Total 171 100.0 100.0

65


Q18


1 34 19.9 19.9 19.9

2 65 38.0 38.0 57.9

3 44 25.7 25.7 83.6

4 26 15.2 15.2 98.8

5 2 1.2 1.2 100.0

Valid

Total 171 100.0 100.0

Q19


1 3 1.8 1.8 1.8

2 32 18.7 18.7 20.5

3 59 34.5 34.5 55.0

4 71 41.5 41.5 96.5

5 6 3.5 3.5 100.0

Valid

Total 171 100.0 100.0

Q20


1 8 4.7 4.7 4.7

2 37 21.6 21.6 26.3

3 65 38.0 38.0 64.3

4 52 30.4 30.4 94.7

5 9 5.3 5.3 100.0

Valid

Total 171 100.0 100.0

66


Q21


1 7 4.1 4.1 4.1

2 59 34.5 34.5 38.6

3 67 39.2 39.2 77.8

4 32 18.7 18.7 96.5

5 6 3.5 3.5 100.0

Valid

Total 171 100.0 100.0

Q22


1 12 7.0 7.0 7.0

2 68 39.8 39.8 46.8

3 59 34.5 34.5 81.3

4 28 16.4 16.4 97.7

5 4 2.3 2.3 100.0

Valid

Total 171 100.0 100.0

Q23


1 1 .6 .6 .6

2 10 5.8 5.8 6.4

3 41 24.0 24.0 30.4

4 100 58.5 58.5 88.9

5 19 11.1 11.1 100.0

Valid

Total 171 100.0 100.0

67


Q24


2 14 8.2 8.2 8.2

3 42 24.6 24.6 32.7

4 91 53.2 53.2 86.0

5 24 14.0 14.0 100.0

Valid

Total 171 100.0 100.0

Q25


1 3 1.8 1.8 1.8

2 41 24.0 24.0 25.7

3 68 39.8 39.8 65.5

4 54 31.6 31.6 97.1

5 5 2.9 2.9 100.0

Valid

Total 171 100.0 100.0

Q26


1 2 1.2 1.2 1.2

2 54 31.6 31.6 32.7

3 67 39.2 39.2 71.9

4 36 21.1 21.1 93.0

5 12 7.0 7.0 100.0

Valid

Total 171 100.0 100.0

68


Q27


1 1 .6 .6 .6

2 31 18.1 18.1 18.7

3 52 30.4 30.4 49.1

4 67 39.2 39.2 88.3

5 20 11.7 11.7 100.0

Valid

Total 171 100.0 100.0

Q28


1 3 1.8 1.8 1.8

2 18 10.5 10.5 12.3

3 91 53.2 53.2 65.5

4 54 31.6 31.6 97.1

5 5 2.9 2.9 100.0

Valid

Total 171 100.0 100.0

Q29


1 2 1.2 1.2 1.2

2 33 19.3 19.3 20.5

3 91 53.2 53.2 73.7

4 42 24.6 24.6 98.2

5 3 1.8 1.8 100.0

Valid

Total 171 100.0 100.0

69


Q30


2 35 20.5 20.5 20.5

3 92 53.8 53.8 74.3

4 41 24.0 24.0 98.2

5 3 1.8 1.8 100.0

Valid

Total 171 100.0 100.0

Q31


1 3 1.8 1.8 1.8

2 23 13.5 13.5 15.2

3 59 34.5 34.5 49.7

4 67 39.2 39.2 88.9

5 19 11.1 11.1 100.0

Valid

Total 171 100.0 100.0

Q32


1 1 .6 .6 .6

2 14 8.2 8.2 8.8

3 38 22.2 22.2 31.0

4 83 48.5 48.5 79.5

5 35 20.5 20.5 100.0

Valid

Total 171 100.0 100.0

70


Q33


1 4 2.3 2.3 2.3

2 41 24.0 24.0 26.3

3 79 46.2 46.2 72.5

4 44 25.7 25.7 98.2

5 3 1.8 1.8 100.0

Valid

Total 171 100.0 100.0

Q34


1 6 3.5 3.5 3.5

2 71 41.5 41.5 45.0

3 66 38.6 38.6 83.6

4 28 16.4 16.4 100.0

Valid

Total 171 100.0 100.0

Q35


1 8 4.7 4.7 4.7

2 77 45.0 45.0 49.7

3 66 38.6 38.6 88.3

4 19 11.1 11.1 99.4

5 1 .6 .6 100.0

Valid

Total 171 100.0 100.0

71


Q36


1 1 .6 .6 .6

2 25 14.6 14.6 15.2

3 68 39.8 39.8 55.0

4 69 40.4 40.4 95.3

5 8 4.7 4.7 100.0

Valid

Total 171 100.0 100.0

Q37


1 4 2.3 2.3 2.3

2 20 11.7 11.7 14.0

3 76 44.4 44.4 58.5

4 59 34.5 34.5 93.0

5 12 7.0 7.0 100.0

Valid

Total 171 100.0 100.0

72

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