The purpose of this study is to identify the main factors influencing a traveler’s Destination Decision-Making Process (DDMP) by applying a hybrid Multi-Criteria Decision-Making (MCDM) model. To examine the relationship among the three dimensions such as tourist motivation, information searching process, decision making of the DDMP, the MCDM model, combining decision-making trial and evaluation laboratory (DEMATEL) and Analytic Network Process (ANP), was adopted. Based on a literature review, six main perspectives and fifteen criteria were extracted and subsequently validated by six tourism experts. A questionnaire was then constructed and answered by both tourism experts and tourists. The results show that the external search is the most important perspective, and it also influences the remaining perspectives. Furthermore, this work presents the criteria for each perspective. By proposing strategies to academics and practitioners, this study can serve as a valuable guide and reference for travel destinations in order to attract more tourists.
International destinations have increased their competitiveness by making their places more attractive to tourists. Hence, destination and tourism related topics have attracted a growing amount of attention in recent years because of their role as the predominant sector in the global economy. As such, discussions on topics related to tourism have proliferated, not only in academia but also in business practices as well. The rapid growth of the Internet has created two-way communications [
Wong and Yeh [
This study, then, is expected to give directions and suggestions that can help tourism businesses better understand tourist-SNS users. This, in turn, can help experts make decisions when creating marketing strategies to promote destinations. Furthermore, this study can contribute to the recently emerged theoretical effort of using the role of SNSs as external factors, or push factors, when obtaining information for motivation theories.
Many researchers have done research on tourist motivation [
The most widely applied psychological motivation theory in tourism is Maslow’s hierarchy of need [
Information searching is potentially the greatest factor involved in a tourist’s pre-travel experience. Since the Internet possesses the capability for a high level of interactivity, it has grown to be one of the most effective means for a tourist to seek information and to purchase tourism-related products [
Many decision-making theories have been introduced, and most of them have proposed consumer behavior, and product, which the consumer knows, trust, and then purchase. According to the Kim and Srivastava [
The above theories of consumer behavior have been transformed and adopted by researchers in terms of travel behavior, e.g., a useful approach to understand travel behavior. Woodside and Jacobs [
Tourist motivation is a dynamic process: combining the internal, or push, factors with the external, or pull, factors. Devesa, Laguna, and Palacios [
Tourist Characteristics
The tourist is first motivated by given “push factors” to choose a place for vacation. Wolfe and Hsu [
Destination Characteristics
Tourism is closely linked to environment characteristics. People travel because everyday life and familiar surroundings make them feel bored hence, a new environment at a long-distance from home may help travelers to find their everyday life more enjoyable. According to the definition of Fridgen [
Tourism is an industry based on the imagery of the destination. Tourist motivation relates to a destination’s “pull factor,” which attracts tourists to that destination. In addition, according to Beerli and Martín [
Destination branding is one of the research areas of academic interest in tourism [
Many researchers have proposed that information plays a central role in the context of tourism. Both the destination selection process and behavior at a destination were prominent elements in tourism decision-making. Fodness and Murray [
Whenever travelers realize that they need to decide upon a destination, an information search is likely to take place. Initially, it takes place internally. Previous experience and knowledge are used as the basic information for making a decision [
As decision-making is a highly contingent form of information processing, prior knowledge is considered to be a rudimentary determinant of an individual’s information search behavior. Consumer behavior research has consistently examined the effect of variables related to earlier knowledge, such as familiarity or product experience. Furthermore, other research has asserted that subjective knowledge provides a better understanding of a decision maker’s systematic biases. While investigating the effect of prior knowledge on information search behavior, Dipietro, Wang, Rompf and Severt [
Hyde [
Tourism in the past used many different intermediaries in the delivery of a tourism product starting with the travel agent [
Many definitions of an SNS from various researchers and authors can be stated as “a platform that enables users to publicize personal information and to share with others with common interests and activities” [
According to studies about the potential usefulness of online SNS for tourism, researchers have found that information searches and information sharing through e-WOM communications have been prominent for travel and tourism marketers in developing marketing programs for destinations. They are equally important to tourists when making their decision to choose a destination [
Travel Planning with DDMP
Planning Characteristic
The purpose of the trip is perhaps the greatest influence on the DMP. Dwyer, Forsyth, and Rao [
Decrop and Snelders [
Decision Choice Set
Many researchers’ study models of pre-vacation decision making initially examined a single issue: the tourist’s choice of a vacation destination [
After having evaluated all the alternatives identified in the search of information stages, the individual is ready to make the final decision. The tourist has to make a decision from the best alternatives. Logic would dictate that purchase or bookings would proceed from the information search and vacation planning. Depending on the complexity of the decision, the consumer will spend more time on this stage, possibly re-evaluating all the alternatives. Because tourists tend to reduce the risk of purchasing an unsatisfactory product or service, sometimes they start to avoid the decision-making process and gather even more information from their friends and relatives, and they tend to prefer already reputable brand names and guarantees.
The decision-making trial and evaluation laboratory (DEMATEL) method was created and developed by the Science and Human Affairs Program of the Battelle Association of Geneva Research Center between 1972 and 1976 [
The DEMATEL method finds the interrelations between entwined criteria. According to factor analysis results, some experts were invited to discuss the relationships and influence level of criteria under the same factor, and to score the relationships among criteria based on the DEMATEL method. Factors were divided into different types, so the experts could answer the questionnaire in areas they were familiar with. In order to limit information loss from DEMATEL method results, threshold values were decided upon after discussion with these experts, and an acceptable impact-digraph-map was found.
As ANP and DEMATEL have these advantages, this paper proposes an effective solution based on a combined ANP and DEMATEL approach to help the tourism industry (See Apendices for the calculation part). An integrated MCDM technique combining the DEMATEL and ANP methods can overcome the problem of interdependence and feedback between criteria and alternatives [
In this research, our survey took place from February until July 2013 in Vietnam, and was divided into two stages. A list of criteria, which affect DDMP, was collected from a comprehensive literature review. The questionnaire survey was designed in stage one to help narrow down the list and to find important criteria by asking 6 experts their opinions on the relative importance of the given criteria. After narrowing down the questionnaire, in the second stage sixteen experts were invited to evaluate the influence among criteria in the DEMATEL survey.
Goal | Aspects | Objective | Criteria |
---|---|---|---|
Destination Decision Making | Motivation | Tourist Characteristics (A1) | Social-demographic (C1) |
Personality (C2) | |||
Destination Characteristics (A2) | Environment (C3) | ||
Image (C4) | |||
Brand (C5) | |||
Information Searching Process | Internal Search (A3) | Memory (C6) | |
Knowledge (C7) | |||
External Search (A4) | WOM (C8) | ||
Social Network Sites (C9) | |||
Tourism Intermediate (C10) | |||
Travel Planning | Planning Characteristics (A5) | Travel Purpose (C11) | |
Travel Distance (C12) | |||
Length of Stay (C13) | |||
Choice Set (A6) | Evaluation (C14) | ||
Purchase (C15) |
Both of the expert who attended in this study was have at least 5 years of working directly on tourism industry or at least 14 years indirectly working with tourism field (detail in
The aim of this research not only determines the most important factors of DDMP, but also measures the relationships among dimensions and criteria. The averaged initial direct relation 15 × 15 matrix obtained comparisons in terms of influences and direction between criteria (
However, in order to reduce the complexity of the elements in the matrix, threshold value α was carefully calculated [
Finally, α is chosen with the value of α = 0.86625 and influence matrix after threshold T is driven by above equation. Then, the results for the criteria, based on values of d + r and d − r, are presented in
Number of experts | Title | Expertise | Years (At least) |
---|---|---|---|
2 | Operation Manager | Tourism company | 5 |
3 | Director | Tourism company | 5 |
1 | Director | Vietnam Tourism Association (Website) | 7 |
1 | General Manager | Hanoi Tourism Association | 15 |
1 | Chief of Travel department | Hanoi Tourism Organization | 12 |
2 | General Manager | Tourism Magazine | 17 |
3 | Back Packer Tourist | They also are operation manager of biggest website about backpacker tourist in Vietnam | 8 |
3 | Professor | Tourism department, Tourism College | 14 |
T | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | 0.7747 | 0.8438 | 0.8522 | 0.8504 | 0.8507 | 0.8245 | 0.8488 | 0.8775 | 0.9154 | 0.8619 | 0.8558 | 0.8306 | 0.8529 | 0.8393 | 0.8329 |
C2 | 0.8516 | 0.8007 | 0.8669 | 0.8739 | 0.8731 | 0.843 | 0.8719 | 0.8929 | 0.9448 | 0.8706 | 0.8709 | 0.866 | 0.8702 | 0.8649 | 0.8469 |
C3 | 0.8461 | 0.8652 | 0.8045 | 0.8855 | 0.8662 | 0.8455 | 0.8678 | 0.8698 | 0.927 | 0.8795 | 0.8735 | 0.8578 | 0.862 | 0.853 | 0.8548 |
C4 | 0.8288 | 0.8454 | 0.8548 | 0.7968 | 0.862 | 0.8241 | 0.8535 | 0.8687 | 0.9128 | 0.8617 | 0.8534 | 0.8359 | 0.8469 | 0.829 | 0.8179 |
C5 | 0.7863 | 0.7939 | 0.7983 | 0.8118 | 0.7411 | 0.762 | 0.7857 | 0.828 | 0.8513 | 0.8046 | 0.7997 | 0.792 | 0.7978 | 0.7774 | 0.7669 |
C6 | 0.8721 | 0.8763 | 0.874 | 0.8954 | 0.8813 | 0.7949 | 0.894 | 0.9066 | 0.9435 | 0.8939 | 0.8973 | 0.8688 | 0.8844 | 0.883 | 0.8759 |
C7 | 0.8866 | 0.8963 | 0.9088 | 0.9172 | 0.9137 | 0.8656 | 0.8411 | 0.9256 | 0.9757 | 0.9104 | 0.9162 | 0.8914 | 0.9067 | 0.9 | 0.8878 |
C8 | 0.8915 | 0.8987 | 0.9197 | 0.9174 | 0.9112 | 0.8805 | 0.9114 | 0.8677 | 0.9736 | 0.9252 | 0.9163 | 0.8997 | 0.9091 | 0.9026 | 0.8979 |
C9 | 0.8915 | 0.9 | 0.9041 | 0.9194 | 0.9161 | 0.8584 | 0.9026 | 0.932 | 0.9059 | 0.9153 | 0.9283 | 0.9023 | 0.9054 | 0.911 | 0.8912 |
C10 | 0.8874 | 0.8885 | 0.8945 | 0.9138 | 0.8973 | 0.8678 | 0.8984 | 0.9149 | 0.967 | 0.842 | 0.9022 | 0.8857 | 0.8756 | 0.896 | 0.8852 |
C11 | 0.8974 | 0.9048 | 0.9184 | 0.9233 | 0.9075 | 0.8863 | 0.9199 | 0.9342 | 0.9823 | 0.9214 | 0.8561 | 0.9094 | 0.9092 | 0.9125 | 0.8978 |
C12 | 0.8312 | 0.854 | 0.8599 | 0.8715 | 0.8597 | 0.8234 | 0.8502 | 0.8779 | 0.9124 | 0.861 | 0.8632 | 0.7841 | 0.8604 | 0.8513 | 0.8349 |
C13 | 0.8902 | 0.89 | 0.9061 | 0.9147 | 0.9048 | 0.8775 | 0.9074 | 0.9375 | 0.9777 | 0.9176 | 0.9102 | 0.8924 | 0.8376 | 0.8975 | 0.877 |
C14 | 0.7931 | 0.8003 | 0.8008 | 0.8009 | 0.8066 | 0.7805 | 0.8035 | 0.8361 | 0.8658 | 0.8199 | 0.8128 | 0.7885 | 0.7893 | 0.7415 | 0.7839 |
C15 | 0.8 | 0.7996 | 0.8099 | 0.8116 | 0.8197 | 0.8044 | 0.8203 | 0.84 | 0.8759 | 0.8271 | 0.815 | 0.7937 | 0.817 | 0.8198 | 0.7413 |
d | r | d + r | d − r | |
---|---|---|---|---|
A1 | 5.1236 | 5.0972 | 10.2208 | 0.0263 |
C1 | 1.6185 | 1.6263 | 3.2448 | −0.0078 |
C2 | 1.6524 | 1.6445 | 3.2969 | 0.0078 |
A2 | 4.9943 | 5.1990 | 10.1933 | −0.2047 |
C3 | 2.5562 | 2.4575 | 5.0137 | 0.0987 |
C4 | 2.5135 | 2.4941 | 5.0076 | 0.0194 |
C5 | 2.3512 | 2.4693 | 4.8206 | −0.1181 |
A3 | 5.3368 | 5.0852 | 10.4220 | 0.2516 |
C6 | 1.6889 | 1.6605 | 3.3494 | 0.0284 |
C7 | 1.7067 | 1.7351 | 3.4418 | −0.0284 |
A4 | 5.4058 | 5.3716 | 10.7774 | 0.0342 |
C8 | 2.7664 | 2.7146 | 5.4810 | 0.0519 |
C9 | 2.7531 | 2.8465 | 5.5996 | −0.0933 |
C10 | 2.7239 | 2.6825 | 5.4064 | 0.0415 |
A5 | 5.3239 | 5.1619 | 10.4858 | 0.1620 |
C11 | 2.6747 | 2.6296 | 5.3042 | 0.0451 |
C12 | 2.5077 | 2.6296 | 5.1372 | −0.1219 |
C13 | 2.6402 | 2.5858 | 5.2259 | 0.0544 |
A6 | 4.8271 | 5.0966 | 9.9238 | −0.2695 |
C14 | 1.5254 | 2.6072 | 4.1326 | −1.0818 |
C15 | 1.5611 | 1.5614 | 3.1225 | −0.0003 |
In order to compare the differences among experts and tourists, questionnaires were also distributed through the tourism companies by tour guides and interviewed tourism experts. 300 questionnaires were distributed to international tourist who travels to Vietnam from February to July 2013, from 224 return only 185 respondents qualified for this research since in our study we chose the tourists who also used SNSs (FB, MySpace, Twitter) as our respondents. DEMATEL survey is difficult to understand and time consuming to complete, it is why 17 percent of respondents was eliminate from total respondents. Finally, the result of the DEMATEL survey was calculated using 185 respondents.
The results of level of influence between criteria, based on d + r and d ? r, of tourist are present in
By combining the DEMATEL and ANP methods, we found external search was the most important factor in the DDMP of tourists. External search plays an important role and has a direct influence on destination image and brand. According to the expert DEMATEL results, knowledge was the highest value variable for tourists before making a decision. In fact, they sometimes consider their knowledge to be the best choice. According to the degree of influential impact, d + r provides an index of strength of influences given and received, and so the more positive the d + r is, the greater is the degree of influence on other factors. On other hand, if d ? r is positive, the factor affects other factors; if d ? r is negative, then that factor is being influenced by other factors.
Accordingly, the NRM of DDMP can reduce risk and enhance the motivation of tourists to choose a destination by improving the information about a destination. Since the results of DEMATEL showed that the external search is a powerful factor that can have the greatest effect on other factors, the destination decision maker should provide more information about destination image to build up destination brand, which creates more opportunities for tourists to come.
Moreover, according to the results of this study, we can determine that information searching is most important step of DDMP. Therefore, we should improve it first by using the Internet, which is cheap and the fastest
T | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | 0.5402 | 0.6348 | 0.651 | 0.6611 | 0.6623 | 0.6521 | 0.6577 | 0.6807 | 0.7257 | 0.6537 | 0.6615 | 0.6631 | 0.6591 | 0.6668 | 0.6715 |
C2 | 0.5909 | 0.5718 | 0.6527 | 0.6675 | 0.6585 | 0.6516 | 0.6566 | 0.6798 | 0.7226 | 0.6505 | 0.6568 | 0.6638 | 0.657 | 0.6627 | 0.6664 |
C3 | 0.589 | 0.6277 | 0.5875 | 0.6616 | 0.6555 | 0.6535 | 0.6566 | 0.674 | 0.7168 | 0.6514 | 0.6565 | 0.6581 | 0.6532 | 0.661 | 0.6679 |
C4 | 0.5875 | 0.6223 | 0.6422 | 0.593 | 0.6578 | 0.6477 | 0.6494 | 0.6717 | 0.715 | 0.6486 | 0.6514 | 0.6536 | 0.6485 | 0.655 | 0.6566 |
C5 | 0.5884 | 0.6226 | 0.644 | 0.6552 | 0.5903 | 0.6458 | 0.6497 | 0.6735 | 0.7109 | 0.6507 | 0.6494 | 0.6549 | 0.652 | 0.6558 | 0.6614 |
C6 | 0.5916 | 0.6297 | 0.6482 | 0.6585 | 0.6563 | 0.5898 | 0.6607 | 0.6796 | 0.7187 | 0.6537 | 0.658 | 0.6634 | 0.6548 | 0.6656 | 0.6719 |
C7 | 0.5874 | 0.6261 | 0.6443 | 0.6533 | 0.6495 | 0.6445 | 0.5914 | 0.6766 | 0.7174 | 0.646 | 0.6515 | 0.6605 | 0.6544 | 0.6638 | 0.6638 |
C8 | 0.5897 | 0.6223 | 0.645 | 0.6558 | 0.6464 | 0.6419 | 0.6526 | 0.6107 | 0.7116 | 0.6506 | 0.6563 | 0.6575 | 0.6533 | 0.6597 | 0.665 |
C9 | 0.617 | 0.6551 | 0.673 | 0.6837 | 0.6827 | 0.6726 | 0.6793 | 0.7052 | 0.6775 | 0.6788 | 0.6861 | 0.6901 | 0.6825 | 0.6895 | 0.6959 |
C10 | 0.5986 | 0.6319 | 0.6553 | 0.6682 | 0.6622 | 0.6523 | 0.6622 | 0.6871 | 0.7288 | 0.5982 | 0.6653 | 0.6677 | 0.6615 | 0.673 | 0.674 |
C11 | 0.5955 | 0.6339 | 0.6538 | 0.661 | 0.6595 | 0.6501 | 0.6565 | 0.6765 | 0.7218 | 0.6558 | 0.5986 | 0.6694 | 0.6572 | 0.6684 | 0.6691 |
C12 | 0.5753 | 0.6128 | 0.6307 | 0.641 | 0.6357 | 0.6295 | 0.6378 | 0.6604 | 0.6972 | 0.6377 | 0.6413 | 0.5841 | 0.6402 | 0.6427 | 0.6494 |
C13 | 0.5916 | 0.6295 | 0.6494 | 0.6579 | 0.656 | 0.6496 | 0.6554 | 0.6769 | 0.7178 | 0.6554 | 0.6553 | 0.6648 | 0.5955 | 0.6654 | 0.6672 |
C14 | 0.5801 | 0.617 | 0.6349 | 0.6458 | 0.6434 | 0.636 | 0.6426 | 0.6656 | 0.7019 | 0.6391 | 0.641 | 0.6464 | 0.6428 | 0.5893 | 0.6519 |
C15 | 0.58 | 0.6118 | 0.6331 | 0.6409 | 0.6397 | 0.6358 | 0.6395 | 0.6608 | 0.703 | 0.6335 | 0.6409 | 0.6465 | 0.6451 | 0.6488 | 0.5909 |
T | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | d |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C1 | 0 | 0 | 0.651 | 0.6611 | 0.6623 | 0.6521 | 0.6577 | 0.6807 | 0.7257 | 0.6537 | 0.6615 | 0.6631 | 0.6591 | 0.6668 | 0.6715 | 8.666 |
C2 | 0 | 0 | 0.6527 | 0.6675 | 0.6585 | 0.6516 | 0.6566 | 0.6798 | 0.7226 | 0 | 0.6568 | 0.6638 | 0.657 | 0.6627 | 0.6664 | 7.996 |
C3 | 0 | 0 | 0 | 0.6616 | 0.6555 | 0.6535 | 0.6566 | 0.674 | 0.7168 | 0.6514 | 0.6565 | 0.6581 | 0.6532 | 0.661 | 0.6679 | 7.966 |
C4 | 0 | 0 | 0 | 0 | 0.6578 | 0 | 0 | 0.6717 | 0.715 | 0 | 0.6514 | 0.6536 | 0 | 0.655 | 0.6566 | 4.661 |
C5 | 0 | 0 | 0 | 0.6552 | 0 | 0 | 0 | 0.6735 | 0.7109 | 0.651 | 0 | 0.6549 | 0.652 | 0.6558 | 0.6614 | 5.315 |
C6 | 0 | 0 | 0 | 0.6585 | 0.6563 | 0 | 0.6607 | 0.6796 | 0.7187 | 0.6537 | 0.658 | 0.6634 | 0.6548 | 0.6656 | 0.6719 | 7.341 |
C7 | 0 | 0 | 0 | 0.6533 | 0 | 0 | 0 | 0.6766 | 0.7174 | 0 | 0.6515 | 0.6605 | 0.6544 | 0.6638 | 0.6638 | 5.341 |
C8 | 0 | 0 | 0 | 0.6558 | 0 | 0 | 0.6526 | 0 | 0.7116 | 0 | 0.6563 | 0.6575 | 0.6533 | 0.6597 | 0.665 | 5.312 |
C9 | 0 | 0.6551 | 0.673 | 0.6837 | 0.6827 | 0.6726 | 0.6793 | 0.7052 | 0.6775 | 0.6788 | 0.6861 | 0.6901 | 0.6825 | 0.6895 | 0.6959 | 9.552 |
C10 | 0 | 0 | 0.6553 | 0.6682 | 0.6622 | 0.6523 | 0.6622 | 0.6871 | 0.7288 | 0 | 0.6653 | 0.6677 | 0.6615 | 0.673 | 0.674 | 8.057 |
C11 | 0 | 0 | 0.6538 | 0.661 | 0.6595 | 0.651 | 0.6565 | 0.6765 | 0.7218 | 0.6558 | 0 | 0.6694 | 0.6572 | 0.6684 | 0.6691 | 8 |
C12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.6604 | 0.6972 | 0 | 0 | 0 | 0 | 0 | 0 | 1.358 |
C13 | 0 | 0 | 0 | 0.6579 | 0.656 | 0 | 0.6554 | 0.6769 | 0.7178 | 0.6554 | 0.6553 | 0.6648 | 0 | 0.6654 | 0.6672 | 6.672 |
C14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.6656 | 0.7019 | 0 | 0 | 0 | 0 | 0 | 0.6519 | 2.019 |
C15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.6608 | 0.703 | 0 | 0 | 0 | 0 | 0 | 0 | 1.364 |
r | 0 | 0.6551 | 3.2858 | 7.2837 | 5.9508 | 3.9332 | 5.9377 | 9.4684 | 10.687 | 4.5998 | 6.5986 | 7.967 | 6.585 | 7.9867 | 8.6825 |
way to transfer information to tourists, and then to build up a strong destination image and brand for all destinations. Strategy makers should direct their attention to not only the decision-making process of the expert, but also to that of the tourist. Experts and tourist all recognize that searching information through the Internet must come first and will affect the remaining dimensions. Therefore, tourism organizations and strategy managers should concentrate resources on the Internet by using SNSs (two-way communication) to attract more tourists.
d | r | d + r | d − r | |
---|---|---|---|---|
A1 | 3.9103 | 3.6221 | 7.5324 | 0.2882 |
C1 | 1.1750 | 1.1311 | 2.3060 | 0.0439 |
C2 | 1.1627 | 1.2066 | 2.3692 | −0.0439 |
A2 | 3.8805 | 3.8951 | 7.7756 | −0.0146 |
C3 | 1.9047 | 1.8738 | 3.7785 | 0.0309 |
C4 | 1.8930 | 1.9098 | 3.8028 | −0.0169 |
C5 | 1.8895 | 1.9036 | 3.7931 | −0.0141 |
A3 | 3.8874 | 3.8717 | 7.7591 | 0.0157 |
C6 | 1.2505 | 1.2343 | 2.4848 | 0.0162 |
C7 | 1.2360 | 1.2521 | 2.4881 | −0.0162 |
A4 | 3.9599 | 4.0638 | 8.0237 | −0.1038 |
C8 | 1.9728 | 2.0030 | 3.9758 | −0.0301 |
C9 | 2.0615 | 2.1179 | 4.1794 | −0.0564 |
C10 | 2.0140 | 1.9275 | 3.9415 | 0.0865 |
A5 | 3.8745 | 3.9171 | 7.7916 | −0.0427 |
C11 | 1.9252 | 1.8952 | 3.8205 | 0.0300 |
C12 | 1.8656 | 1.9183 | 3.7839 | −0.0527 |
C13 | 1.9156 | 1.8929 | 3.8085 | 0.0227 |
A6 | 3.8067 | 3.9495 | 7.7561 | −0.1428 |
C14 | 1.2411 | 1.2380 | 2.4792 | 0.0031 |
C15 | 1.2397 | 1.2428 | 2.4825 | −0.0031 |
This study aimed to determine the causal relationship among DDMP criteria by adopting a MCDM approach. In order to identify the key factors’ impact on DDMP, this study has found that there is a causal relationship among the six dimension of DDMP, and that they are all interrelated and ultimately lead to tourist satisfaction. According to the results of both the tourist and expert survey, the external search dimension plays the most important role in the tourist’s DDMP (
Furthermore, by determining the weight of all criteria through the combined ANP and DEMATEL method, the results show the difference between the expert survey and tourist survey, especially when considering which factor is the most important factor. For example, the experts consider destination characteristic as the second most important factor, whereas the tourists think choice set is the second, although choice set is the fifth on the expert list.
According to the priority/weight list, the research showed the bias between the tourists and experts when they considered the rate of influence factors.
Based on the result of ANP and the impact relationship map acquired from DEMATEL, a strategy map was obtained based on the network relationship map to provide suggestions for the tourism industry to develop their
DANP Results | Experts Ranking | Tourists Ranking | Global Weight | Experts Global Ranking | Tourists Global Ranking | Experts Local Ranking | Tourists Local Ranking |
---|---|---|---|---|---|---|---|
A1 | 4 | 6 | C1 | 4 | 6 | 2 | 2 |
C2 | 3 | 5 | 1 | 1 | |||
A2 | 2 | 4 | C3 | 13 | 15 | 3 | 3 |
C4 | 10 | 10 | 1 | 1 | |||
C5 | 12 | 13 | 2 | 2 | |||
A3 | 6 | 5 | C6 | 6 | 4 | 2 | 2 |
C7 | 1 | 3 | 1 | 1 | |||
A4 | 1 | 1 | C8 | 8 | 8 | 2 | 2 |
C9 | 7 | 7 | 1 | 1 | |||
C10 | 9 | 14 | 3 | 3 | |||
A5 | 3 | 3 | C11 | 11 | 11 | 1 | 2 |
C12 | 15 | 9 | 3 | 1 | |||
C13 | 14 | 12 | 2 | 3 | |||
A6 | 5 | 2 | C14 | 2 | 2 | 1 | 2 |
C15 | 5 | 1 | 2 | 1 |
strategies and attract more tourists to their destinations. This research has highlighted the key criteria and their interrelationships, offering a more comprehensive DDMP model which can serve as a reference for national tourism organizations that build up and promote their country’s destination brand. Moreover, the interrelationship of important criteria identified in this study will also provide assistance for further researchers to study the key factors impacting DDMP from both expert and tourist perspectives.
Thi Hai Ninh Do,Wurong Shih, (2016) Destination Decision-Making Process Based on a Hybrid MCDM Model Combining DEMATEL and ANP: The Case of Vietnam as a Destination. Modern Economy,07,966-983. doi: 10.4236/me.2016.79099
The DEMATEL method in this study following below steps:
Step 1: Calculate the original average matrix
We assume Z experts in this study, who were asked to indicate the impact that they estimated through a scale ranging of the matrix and to evaluate the relationships among elements. A scale, ranging from 0 to 4, representing no influence to very high influence, was used (
Step 2: Establishing the structure model and calculating the direct-influence matrix.
The next matrix, N, is obtained by normalizing the average matrix X. The matrix is normalized by calculating the sum of rows and columns separately. The maximum values are obtained by using the equation (2), then dividing X by N, providing the normalized initial direct-relation matrix A (3). Each value in matrix A is between 0 and 1:
Step 3: Calculate the full relation matrix.
The normalized direct-influence matrix A by the summation of i or j is obtained. The full relation matrix T is calculated by equation (4) and equation (5) with the “I” denoted as the n × n identity matrix:
The requirements are:
Step 4: Calculating value and analyzing the result.
Let tij (i, j = 1, 2, ・・・, n) be the elements of the total-relation matrix T, then the sum of rows and the sum of columns, denoted as vector di and rj, using equation (6) and (7).
where di represents the sum by taking element i as the cause to influence other elements, rj represents the sum by taking element j as the result being influence by other elements. The horizontal axis vector (d + r) is made by adding vector d to vector r, called prominence, and importantly indicates the element’s degree of influence and being influence. Similarly, the vertical axis vector (d − r) is made by deducting vector d from vector r, called relation, and separates criteria into a cause group and an affect group. Conceptually, when the value (d − r) is positive, the criterion belongs to the cause group. When the (d − r) is negative, the criterion belongs to the effect group. Hence, causal diagrams can visualize the complicated causal relationships between criteria into a visible structural model, providing valuable insight for problem solving. Furthermore, with the help of a causal diagram, we may make proper decisions by recognizing the difference between cause and effect criteria.
Integrated method
In this study, tourist DDMP is presented to illustrate the application of the DEMATEL and ANP for proposing the most important criteria, which affect other criteria and the weight of each criterion. A hybrid MCDM model, combining the DEMATEL technique with the ANP method (DANP), has been widely applied in different decision-making problems with great success. It also can be used to solve dependence and feedback problems.
Step 5
Then, we raise the weighted super matrix to limiting power suntil we get the super matrix converged to get the global priority vectors or weights.
Finally, we limit the weighted super matrix by raising it to a sufficiently large power until the super matrix has converged and has become a long term stable super matrix to get the global priority vectors called ANP weights, such as
Submit or recommend next manuscript to SCIRP and we will provide best service for you:
Accepting pre-submission inquiries through Email, Facebook, LinkedIn, Twitter, etc.
A wide selection of journals (inclusive of 9 subjects, more than 200 journals)
Providing 24-hour high-quality service
User-friendly online submission system
Fair and swift peer-review system
Efficient typesetting and proofreading procedure
Display of the result of downloads and visits, as well as the number of cited articles
Maximum dissemination of your research work
Submit your manuscript at: http://papersubmission.scirp.org/