survey on factors affecting customers’ intention to use
TRANSCRIPT
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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Survey on factors affecting customers’ intention to use RFID in fresh seafood
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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Survey on factors affecting customers’ intention to use RFID in fresh seafood
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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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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Unstandardized Coefficients
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.
Unstandardized Coefficients
Standardized Coefficients
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)
Unstandardized Coefficients
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)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(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
Model B Std. Error Beta t Sig.
(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
Model B Std. Error Beta t Sig.
(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
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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,
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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
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Survey on factors affecting customers’ intention to use RFID in fresh seafood
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
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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.
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Survey on factors affecting customers’ intention to use RFID in fresh seafood
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
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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.
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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.
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Survey on factors affecting customers’ intention to use RFID in fresh seafood
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.
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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
Cronbach's Alpha N of Items
.782 3
Item-Total Statistics
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
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Table 2 –Reliability - Risk /VARIABLES=Q23 Q24 Q26 Q31 Q32 Q33 Q34 Q35
Reliability Statistics
Cronbach's Alpha N of Items
.750 8
Item-Total Statistics
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
Item-Total Statistics
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
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Table 3 –Reliability - PEOU
Reliability Statistics
Cronbach's Alpha N of Items
.929 3
Item-Total Statistics
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
Reliability Statistics
Cronbach's Alpha N of Items
.835 3
Item-Total Statistics
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
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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
a. Predictors: (Constant), risk
b. Dependent Variable: Behavioral_Intention
Coefficientsa
Unstandardized Coefficients
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
a. Dependent Variable: Behavioral_Intention
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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
Coefficients Correlations
Model B Std. Error Beta t Sig. Zero-order Partial Part
(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
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Table 6 –
Regression – AQNIP -> BI Model Summary
Model R R Square Adjusted R Square
Std. Error of the
Estimate
1 .320a .103 .097 .77669
a. Predictors: (Constant), AQNIP
ANOVAb
Model Sum of Squares df Mean Square F Sig.
Regression 11.665 1 11.665 19.337 .000a
Residual 101.949 169 .603
1
Total 113.614 170
a. Predictors: (Constant), AQNIP
b. Dependent Variable: Behavioral_Intention
Coefficientsa
Unstandardized Coefficients
Standardized
Coefficients Correlations
Model B Std. Error Beta t Sig. Zero-order Partial Part
(Constant) 2.486 .174 14.295 .000 1
AQNIP .303 .069 .320 4.397 .000 .320 .320 .320
a. Dependent Variable: Behavioral_Intention
Coefficient Correlationsa
Model AQNIP
Correlations AQNIP 1.000 1
Covariances AQNIP .005
a. Dependent Variable: Behavioral_Intention
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Table 7
–Regression – AQNIP + Attitude + Risk -> BI
Model Summary
Model R R Square Adjusted R Square
Std. Error of the
Estimate
1 .722a .522 .513 .57031
a. Predictors: (Constant), Attitude, risk, AQNIP
ANOVAb
Model Sum of Squares df Mean Square F Sig.
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
b. Dependent Variable: Behavioral_Intention
Coefficientsa
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
a. Dependent Variable: Behavioral_Intention
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Coefficient Correlationsa
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
a. Dependent Variable: Behavioral_Intention
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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
Squares df Mean Square F Sig.
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
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Table 9 –
Regression – AQNIP -> PU
Model Summary
Model R R Square Adjusted R Square
Std. Error of the
Estimate
1 .305a .093 .088 .72213
a. Predictors: (Constant), AQNIP
ANOVAb
Model Sum of Squares df Mean Square F Sig.
Regression 9.054 1 9.054 17.362 .000a
Residual 88.128 169 .521
1
Total 97.181 170
a. Predictors: (Constant), AQNIP
b. Dependent Variable: Perceived_Usefulness
Coefficientsa
Unstandardized Coefficients
Standardized
Coefficients Correlations
Model B Std. Error Beta t Sig. Zero-order Partial Part
(Constant) 2.987 .162 18.472 .000 1
AQNIP .267 .064 .305 4.167 .000 .305 .305 .305
a. Dependent Variable: Perceived_Usefulness
Coefficient Correlationsa
Model AQNIP
Correlations AQNIP 1.000 1
Covariances AQNIP .004
a. Dependent Variable: Perceived_Usefulness
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Table 10 –
Regression – AQNIP -> PEOU
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .486a .236 .231 .75907
a. Predictors: (Constant), AQNIP
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
Regression 30.040 1 30.040 52.135 .000a
Residual 97.376 169 .576
1
Total 127.415 170
a. Predictors: (Constant), AQNIP
b. Dependent Variable: Perceived_Ease_of_Use
Coefficientsa
Unstandardized
Coefficients
Standardized
Coefficients Correlations
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
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Coefficient Correlationsa
Model AQNIP
Correlations AQNIP 1.000 1
Covariances AQNIP .005
a. Dependent Variable:
Perceived_Ease_of_Use
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Table 11 –
Regression – Risk -> AQNIP
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .053a .003 -.003 .86450
a. Predictors: (Constant), risk
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
Regression .361 1 .361 .483 .488a
Residual 126.304 169 .747
1
Total 126.664 170
a. Predictors: (Constant), risk
b. Dependent Variable: AQNIP
Coefficientsa
Unstandardized
Coefficients
Standardized
Coefficients Correlations
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
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Coefficient Correlationsa
Model risk
Correlations risk 1.000 1
Covariances risk .029
a. Dependent Variable: AQNIP
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Appendix C - Questionnaire Development
Questionnaire Development
[This is a blank page]
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Appendix D – Questionnaire (Chinese version)
Questionnaire (Chinese version)
[This is a blank page]
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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
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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
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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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
1 83 48.5 48.5 48.5
2 88 51.5 51.5 100.0
Valid
Total 171 100.0 100.0
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Salary (monthly)
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Occupation
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q3
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q6
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q9
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q12
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q15
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q18
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q21
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q24
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q27
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q30
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q33
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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Q36
Frequency Percent Valid Percent Cumulative Percent
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
Frequency Percent Valid Percent Cumulative Percent
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
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