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P r o c e d i a - S o c i a l a n d B e h a v i o r a l S c i e n c e s 8 8 ( 2 0 1 3 ) 1 0 8 – 1 1 7
1877-0428 © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of Department of Planning and Development, Kasem Bundit University - Bangkok
doi: 10.1016/j.sbspro.2013.08.486
ScienceDirect
Social and Behavioral Sciences Symposium, 4
th
International Science, Social Science,
Engineering and Energy Conference 2012 (I-SEEC 2012)
Modeling e-CRM for Community Tourism in Upper Northeastern
Thailand
Panithan Mekkamol
a,
*
, Sarawut Piewdang
a
, Asst.Prof.Dr. Subchat Untachai
a
a
Faculty of Management Science, Udonthani Rajabhat University, Udonthani, 41000, Thailand
Abstract
The paper is designed to provide a quantitative measure of e-CRM for community tourism in Upper Northeastern Thailand.
The purpose of this study is to develop the e-CRM model in the upper Northeast of Thailand. Specifically, the objective of
this study is to examine the validity and reliability of the four-factor model in e-CRM. The research mainly involves a survey
design. It includes a pilot test using undergraduate business students at Udon Thani Rajabhat University for pretesting
questionnaire items. In addition, this investigate into website character, website contact interactivity, shopping convenience,
and care and service attributes necessitates uncovering variables of interest and this involves a large-scale field study. The
data are collected via personal questionnaires from 447 samples. They include the customers of hospitality and tourism
industries in 3 provinces such as Udon Thani, Nongkhai, Loei. Respondents are asked to rate, on a five-point Likert scale,
their agreement or disagreement on the e-CRM attributes. LISREL program is used for data analysis since the proposed model
is a simultaneous system of equations having latent constructs and multiple indicators. Quantitative data are analyzed by the
statistical techniques, namely exploratory factor analysis and structural equation modeling. It is found from the study that the
effect of website character and shopping convenience, on care and service through website contact interactivity. The
managerial implications are discussed.
© 2013 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of Faculty of Science and Technology, Kasem Bundit University, Bangkok.
Keywords: e-CRM; Community Tourism; Exploratory Factor Analysis; Structural Equation Modeling
1. Introduction
Over the next few years, the World Wide Web (the Web) is expected to increase by a factor of , growing to
million sites by 8 The number of actual Web pages will increase even more, with existing Web sites
continuing to add pages[1]. Web sites provide the key interface for consumer use of the Internet. Online travel
website strategies dimensions were 4PsC such as product, promotion, place, and customer relationship[2-3]
The customer relationship management(CRM) referred to the managerial process that most of organizations
-
* Corresponding author. E-mail address: naikung@hotmail.com
Available online at www.sciencedirect.com
© 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of Department of Planning and Development, Kasem Bundit University - Bangkok
109
Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
term relationship with the customers. The process of the CRM consists of three steps such as selling services and
marketing through information technology (i.e., internet). In northeastern Thailand, the internet be enable tourism
enterprises to establish the customer relationship by adding more distribution channels[3].
Electronic customer relationship management (e-CRM) rooted from the CRM techniques that added direct
information technology to market goods and services to small market segments[4]. Based on business strategy
that requires development of a set of integrated software application to deal with all aspects of customer
interaction like sales, marketing field support and customer service, e-CRM demonstration would mainly focus
upon acquiring new customer enhancing profitability of existing customer segment high value customer and
maximize life true value of profitable customer[5-6].
The purpose of this paper is to develop and empirically test the e-CRM model for community tourism in
Upper North eastern Thailand.
The paper was organized as follows, starting with the review literature on the exploration variables of the
proposed model. The following section presented the conceptual model and defines the sets of research
hypotheses. The study proceeds with a description of methods which are applied, including information about the
data and statistical procedures. Results were presented and some of their implications and limitations were
discussed in the final section.
2. Literature Review
The e-CRM business process integrated activities of designing an interaction based on relevant information,
personalizing every interaction, reaching the customer at the appropriate place and time, facilitating the
interaction and closing the ensuing transaction[7]. As increasingly electronic channels,
buyers and sellers by lowering the transaction costs of participating in the market[8-9].
Sigala[10] examine the effect of cultural dimensions on e- -CRM
comprises of six factors such as website contact interactivity, shopping experience, personalization of
information, cultivation, community, and website character[11].
Feinberg and Romano[12] investigated the effects of e-CRM attributes on customer satisfaction in retail
section.
They have identified the 15 features out of 41 e-CRM features. These were as followings:
Contact and information, general e-CRM features including site customization, web-based CRM design,
alternative channels, local search engine, membership, mailing list, site tour, site map, chat, electronic bulletin
board.
E-commerce features including online purchasing, product information online, customization possibilities,
purchase conditions, preview product, and links.
Post-sales support features including FAQs, problem solving, complaining ability, spare parts.
To supplement these factors we reviewed the professional literature and identified additional e-CRM
features that might be present on a retail web site:
( ) Affinity program - affiliations with philanthropic agencies or organizations;
(2) Product highlights - benefits of particular products/services highlighted;
(3) Request for catalog;
(4)Quick order ability - three click order;
(5) Ease of check out - subjective rating of ability to check out on an ease" scale of easy)- difficult);
(6)Ability to track order status;
(7) Gift certificate purchase;
(8) Store locator;
(9) On-sale area - highlighted place on opening web page highlighting sale items;
(10) Member benefits - description of benefits of shopping or belonging to site;
110 Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
(11) Order - ability to place an order within three clicks;
(12) Speed of download page - less than seconds was considered fast;
(13) Account information;
(14) Customer service page;
(15) Company history/profile;
(16) Posted privacy policy.
Wolfinbarger and Gilly[13] specified 4 factors of electronic retail including website design,
fulfilment/reliability, privacy/security, and customer service. They found that fulfilment/reliability and customer
service are related to satisfaction, loyalty, and attitude toward website. They also pointed out internet based CRM
has three general areas namely presales information, e-commerce services and post-sales support.
Palmer [14] suggested that website success is significantly associated with website download delay (speed of
access and display rate within the website), navigation (organization, arrangement, layout, and sequencing),
content (amount and variety of product information), interactivity (customization and interactivity), and
responsiveness (feedback options and FAQs).
Galletta, et al. [15] found that delay, familiarity, and breadth factors of website have strong direct impacts on
performance and user attitudes, in turn affecting behavioral intentions to return to the site, as might be expected.
A significant three-way interaction was found between all three factors indicating that these factors not only
individually impact a user's experiences with a website, but also act in combination to either increase or decrease
the costs a user incurs.
3.Objective and Hypothesis
The purpose of this study is to develop and test model of e-CRM in the community tourism in Upper
Northeastern Thailand. The objectives of this study are, (1) to examine the relationship and reliability of the
measure of the modeling e-CRM (2) to examine the relationship between Website character, website contact
interactivity, shopping convenience, Care and service dimensions of modeling in the Community Tourism in
Upper Northeastern Thailand
On the basis of the literature discussed above [16-17-18], the following hypotheses are developed
H
1
there is a positive relationship between shopping convenience and website contact interactivity dimensions
of e-CRM in the community tourism in upper northeastern, Thailand.
H
2
there is a positive relationship between Website character and Website contact interactivity dimensions of
e-CRM in the community tourism in upper northeastern, Thailand.
H
3
there is a positive relationship between Website contact interactivity and Care and service dimensions of
e-CRM in the community tourism in upper northeastern, Thailand.
4. Methodology
4.1 The Sample and Data Collection
The research mainly involves a survey design. It includes a pilot test using undergraduate student at Udon
Thani Rajabhat University, for pretesting questionnaire items, In addition, this investigation into Website
character, Website contact interactivity, shopping convenience and Care and service attributes necessitates
uncovering variables of interest and this involves a large-scale field study. The sample was drawn from a list of
all undergraduate computer business students at Udon Thani Rajabhat University, From the initial list of 300
students, a sample of 30 was purposively selected. The data were collected via personal questionnaires.
111
Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
Respondents were asked to rate, on a five-point Likert scale of their agreement or disagreement on the e-CRM
dimension. In April 2012, 30 questionnaires were distributed to the students of Udon Thani Rajabhat University.
4.2 Developing a better measure
The authors have developed measurement items followed the process that recommended by Churchill [19],
and Gerbing and Anderson [20]. First, to generate items, the authors translated the 52 item e-CRM items [21] into
Thai language. Second, the questionnaire items were submitted to the review of three academic experts in the
fields of Community Tourism and Information technology. They were asked to review the survey for domain
representativeness, item specificity, clarity of construct, and readability (i.e. content and face validity). Drawn
upon their inputs, some measurement items were eliminated or reworded, and others were added. Third, the
resultant survey instrument is pretested with 30 undergraduate students in Thailand. They were asked to complete
a survey and indicate any ambiguity or other difficulties they experienced in responding to the items. Their
feedback and suggestions were used to modify the questionnaire. These completed responses were also analyzed
with SPSS. An exploratory factor analysis using Principal Component Extraction indicated that all items load on
expected factors (loadings range from 0.669 to 0.920). Construct reliability tests with Cronbach's Alpha also
yielded satisfactory results (range from 0.71 to 0.77). Finally, 23 items were verified with confirmatory factor
analysis using LISREL 8.30. After the iterative process of item refinement and purification, a battery of items
was reduced to the final set of 23 items to measure the four integration-related constructs such as website
character, website contact interactivity, shopping convenience, and care and service attributes. Besides the
eighteen structured items (measured on a five-point scale) were anchored strongly. This study has utilised parts of
the instruments to develop e-CRM model in community tourism, Thailand.
4.3 Validity
This study adopted the Gerbing and Anderson [22] methodology to determine the construct, and discriminant
validity of the e-CRM measures. To determine the convergent and discriminant validity of the e-CRM, measures
were also included in the questionnaire. These cover website contact interactivity, care and service, website
character, shopping convenience with marketing philosophy expected to be more closely associated with the
major market orientation measures than other business philosophies. Discriminant validity is required when
evaluating measures[23], especially when the measures are interrelated, as in the case of reliability, response,
empathy, assurance, and tangible dimensions.
4.4 Analytical techniques
Before the data were analysed, the questionnaires were reviewed to ensure that appropriate information was
being collected and defective questionnaires were discarded. The complete questionnaires were coded and the
data keyed into the computer. At this time the LISREL was applied to the analysing process and a data analyst
was employed to supervise. It was the most important part of the survey. This paper mainly employed three
statistical techniques to analyze the data. They were confirmatory factor analysis and structural equation
modeling (Bollen, 1989; Hulland et al., 1996)[24].
112 Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
Table 1: Mean, standard deviation, covariance matrices
Y1
Y2
Y3
Y4
Y5
Y6
Y7
Y8
Y9
Y10
Y11
Y12
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
Y1
0.84
Y2
0.16
0.57
Y3
0.22
0.18
0.65
Y4
0.27
0.20
0.26
0.69
Y5
0.25
0.20
0.17
0.22
0.66
Y6
0.25
0.23
0.20
0.25
0.23
0.71
Y7
0.27
0.17
0.17
0.20
0.16
0.27
0.66
Y8
0.21
0.20
0.20
0.21
0.20
0.31
0.27
0.72
Y9
0.19
0.14
0.11
0.18
0.15
0.17
0.27
0.26
0.66
Y10
0.22
0.12
0.13
0.21
0.20
0.25
0.18
0.26
0.21
0.69
Y11
0.19
0.13
0.15
0.16
0.13
0.23
0.26
0.23
0.25
0.27
0.71
Y12
0.23
0.15
0.17
0.20
0.16
0.21
0.15
0.21
0.16
0.20
0.25
0.75
X1
0.31
0.17
0.20
0.18
0.22
0.20
0.23
0.22
0.19
0.20
0.16
0.12
0.51
X2
0.19
0.10
0.12
0.16
0.15
0.15
0.12
0.19
0.11
0.11
0.11
0.12
0.22
0.66
X3
0.24
0.02
0.11
0.12
0.15
0.19
0.17
0.14
0.09
0.16
0.19
0.13
0.17
0.28
0.70
X4
0.24
0.08
0.16
0.18
0.20
0.17
0.19
0.14
0.16
0.16
0.18
0.17
0.23
0.22
0.24
0.71
X5
0.28
0.09
0.16
0.15
0.13
0.15
0.18
0.18
0.16
0.16
0.15
0.16
0.20
0.15
0.26
0.28
0.72
X6
0.23
0.14
0.14
0.16
0.11
0.17
0.17
0.11
0.09
0.11
0.17
0.17
0.16
0.25
0.16
0.21
0.20
0.64
X7
0.22
0.19
0.18
0.17
0.17
0.23
0.20
0.26
0.17
0.18
0.19
0.21
0.21
0.13
0.13
0.17
0.19
0.17
0.62
X8
0.21
0.18
0.16
0.19
0.18
0.20
0.13
0.23
0.14
0.16
0.13
0.13
0.14
0.12
0.12
0.13
0.12
0.12
0.22
0.62
X9
0.23
0.18
0.22
0.20
0.19
0.20
0.19
0.23
0.21
0.20
0.20
0.20
0.20
0.16
0.11
0.21
0.16
0.15
0.27
0.23
0.72
X10
0.23
0.17
0.16
0.26
0.13
0.22
0.20
0.19
0.18
0.16
0.12
0.13
0.20
0.11
0.09
0.14
0.13
0.12
0.18
0.20
0.29
0.60
X11
0.25
0.17
0.18
0.20
0.16
0.26
0.22
0.29
0.16
0.20
0.20
0.18
0.20
0.12
0.13
0.13
0.13
0.11
0.25
0.14
0.24
0.24
0.67
5. Results
5.1 Hypothesis Testing
5.1.1 Assessing fit between model and data
The analysis started with the calculation of the mean and standard deviation for each unweighted, interval
scale. We also report covariance between each scale in Table 1. The overall adequacy of the proposed theoretical
framework was examined using LISREL 8.30 causal modeling procedures[25], and the maximum likelihood
method of estimation and the two-stage testing process were adopted. A substantial portion of the variance of the
Modeling in ecotourism has been explained by the model. The results are shown in Table 2. The model is a close
fit to the data at
2
(218) value of 453.08 (P<0.00002). However, the ratio of chi-square and degree of freedom is
2.07 (453.08/218), GFI of 0.92, AGFI of 0.90, CFI of 0.97 and RMSEA of 0.043. Therefore, the e-CRM model
can be acceptable [26-27].
113
Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
Table 2 : Properties of the CFA for e-CRM
Construct
indicators
Standardized
loadings
t-value
Composite
reliability
Variance
extracted
estimate: AVE
Website contact interactivity
0.43
.29
.71
Y1
0.55
1.355
14.13*
14.08*
13.74*
14.04*
Y2
0.38
Y3
0.42
Y4
0.48
Y5
0.43
Care and service
0.46
0.34
.77
Y6
0.56
11.99*
13.31*
12.56*
13.29*
13.90*
-
-
Y7
0.46
Y8
0.56
Y9
0.42
Y10
0.40
Y11
-
Y12
-
Website character
0.44
0.30
.74
X1
0.53
9.83*
13.70*
14.11*
13.16*
13.61*
12.80*
X2
0.40
X3
0.36
X4
0.48
X5
0.44
X6
0.41
Shopping convenience
0.48
0.36
.72
X7
0.48
13.05*
13.24*
12.93*
13.13*
12.77*
X8
0.44
X9
0.53
X10
0.47
X11
0.50
*indicates significance at p<0.01 level
The results of the hypothesis testing are provided in Table 3, along with parameter estimates, their
corresponding t- values, and the fit statistics. As shown in Table 5, the H
1
, H
2
, H
3
are supported.
H
1
suggested that there is a positive relationship between shopping convenience and website contact
interactivity dimensions of the modeling e-CRM for community tourism in upper northeastern Thailand was
supported (
1
= 0.61, p<0.01).
H
2
predicted a positive relationship between website character and website contact interactivity dimensions
of the modeling e-CRM for community tourism in upper northeastern Thailand was supported (
2
= 0.41, p<0.01).
114 Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
H
3
predicted a positive relationship between Website contact interactivity and care and service dimensions of
the modeling e-CRM for community tourism in upper northeastern Thailand was supported (
1
= 0.87, p<0.01).
On the basis of these findings, we conclude that although empathy and response play significant moderating roles
in e-CRM of community tourism in Thailand, the role of the tangible in this context remains certain.
Table 3 Hypotheses testing for e-CRM
Hypothesized Paths
Expected
Sign
Standardized
Coefficients
t
A/R
H1 Shopping convenience Website
contact interactivity
+
0.61
7.5***
H2 Website character Website contact
interactivity
+
0.41
5.37***
H3 Website contact interactivity Care
and service
+
0.87
10.14***
Notes:
2
=453.08; significance 0.0; df=218; NFI=0.98; NNFI=0.94; CFI=0.97; GFI=0.92; AGFI=0.90; RMSEA=0.049.
A/R, acceptance or rejection of hypothesis.
a
Hypothetical sign of the relation, **p<0.05 and t>1.96; ***p<0.01 and t>2.58
Fig 1. the standardized solution of the e-CRM model
Shopping
convenience
Website contact
interactivity
Website
character
Care and
service
Y1
Y2
Y3
Y4
Y5
Y6
Y7
Y8
Y9
Y10
Y11
Y12
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
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Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
5. Conclusions and Discussions
The purpose of this study is to develop the e-CRM model of the community tourism in the upper Northeast of
Thailand. It is found from the study that the effect of website character and shopping convenience on care and
service through website contact interactivity.
According to H
1
that there is a positive relationship between shopping convenience and website contact
interactivity dimensions of e-CRM for community tourism in Upper Northeastern Thailand was supported. This
findings would be consistency with the research of Sigala [28].
According to H
2
that there is a positive relationship between website character and website contact
interactivity dimensions of the modeling e-CRM for community tourism in upper northeastern Thailand was
supported. This findings would be consistency with the research of Feinberg and Romano [29].
According to H
3
that there is a positive relationship between website contact interactivity and care and service
dimensions of the Modeling e-CRM for community tourism in upper northeastern Thailand was supported. This
findings would be consistency with the research of Wolfinbarger and Gilly [30]. One explanation for the findings
may be that, customer relationship may be based on two main sources: information quality and system quality [31].
6. Research and Managerial Implications
For the researcher, this study has implications on the examination of the validity of e-CRM model. This
article has provided a comprehensive evaluation for understanding the structure of e-CRM in Thai tourism.
However, several limitations are acknowledged, leading to suggested directions for future research. First, this
research was limited to validating the e-CRM based on path analysis. Whereas, many researchers have used the
TAM view to examine the associations between e-CRM and consumer behaviour and firm performance, future
research could apply this view to ascertain antecedent and consequent relationships among consumer and
business markets, resources, capability, competitive advantage, and firm performance. Also, the analysis used in
this study was static, which evaluation of re
Longitudinal research has to investigate how key e-CRM components might change over time.
For a managerial perspective, an entrepreneur who implements strategies in different environment settings
cannot have an ethnocentric view about management imperatives. This study provides some guidelines for
tourism entrepreneurs handling e-CRM model across the country. For example, the result of the study
demonstrates that website contact interactivity has important attributes for the e-CRM. The entrepreneur in the
Thai community tourism should have a web-master or customer relationship manager for continuously
-CRM strategies in a timely
manner in the market. Thus, community tourism should increase communication channels, or develop
information systems for integrating tourism activities. It might be collaborated among Thailand officials, such as
Department of Agricultural Extension, The Thai Chamber of Commerce, Community Development Department
and Commission on Higher Education, Tourism Authority of Thailand, and Ministry of Information and
Communication Technology of Thailand.
7. Limitations and Future Research
Although this paper has provided relevant and interesting insights into the understanding of the components
of e-CRM structure in Thai community tourism, it should be clearly recognized the limitations associated with
this study. First, cross-sectional data were used in the paper. Subsequently, the time sequence of the e-CRM
model cannot be determined unambiguously. Therefore, the results might not be interpreted as proof of a causal
relationship, but rather as lending support for a prior causal scheme. The development of a time-series database
116 Panithan Mekkamol et al. / Procedia - Social and Behavioral Sciences 88 ( 2013 ) 108 – 117
and testing of the e-CRM structure relationship with performance in a longitudinal framework would provide
more insight into probable causation.
Second, the conceptualization of e-CRM structure may be somewhat limited and it is arguable that e-CRM
structure may consist of more than market information gathering, and the development and implementation of a
market-oriented strategy.
Third, the LISREL methodology may be construed as a limitation because the results presented here are based
on the analysis of a causal non-experiment design.
8. Acknowledgement
The author sincerely thanks anonymous reviewers for their very helpful comments and input; Banchob
Chiemwichit for his very thorough readings of the paper and his welcomes suggestions for explanations
regarding particular topics. Financial support from Research and Development Institute of UdonThani Rajabhat
University is gratefully acknowledged.
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