THE DECISION MAKING IN SELECTING ONLINE TRAVEL AGENCIES: AN APPLICATION OF ANALYTIC HIERARCHY PROCESS

Abstract

The emergence of Internet‐based distribution channels has created both opportunities for and challenges to the travel agencies. Based on the customer‐value perspective, this study focused on exploring the relative weights of the nine proposed fundamental travel products from an Internet perspective.

The study cohort comprised customers who had purchased room products from travel agencies websites, with data collected using a questionnaire survey. Customers were surveyed during a 3‐month period at Taipei International Airport. Excluding useless questionnaires resulted in 131 respondents, corresponding to a response rate of 18.71%.

We found that privacy, safety, and product quality were the three most important factors influencing customer purchases of room products on the Internet. The results of this study have some implications for travel agencies—companies will need to make a significant investment in website design of safety mechanism, and the greater the effort managers make to alleviate these concerns, the more likely it is that consumers will visit and buy products on their website.

Keywords:

• Analytic hierarchy process
• online shopping
• customer value
• electronic commerce

INTRODUCTION

Since the beginning of Internet commerce, the impact of the Internet on pricing and competition has been actively debated by both practitioners and academics. The potential to reduce distribution costs using Internet channels has made hotel managers more conscious of the need to maximize contributions to gross profit rather than just the revenue obtained from the occupation of a given room (Choi & Kimes, 2002). A Forrester Research study found that while the proportion of online travelers who use the Internet to both research and buy travel fell 9% between 2005 and 2007, online leisure‐travel spending increased 41% over the same period (Travel Industry Association of America, 2007). In Taiwan, travel products constitute the largest proportion of the online customer market (Market Intelligence Center, 2005), and it is almost certain that the Internet will continue to grow as a travel information source, with online bookings increasing as more people have access to high‐speed Internet connectivity.

Attracting consumers to buy products online is the main goal of Internet marketers. In order to attract customers, many online travel sites sell multiple products (e.g., airplane tickets, hotel rooms, rental cars) from multiple vendors, as their key attraction is that they are “full service” and thus offer to consumers the ability to research and purchase an entire trip online from a single site. That is, marketing managers must understand the value of online shopping those consumers are looking for. Marketing managers and researchers begin to adopt the customer value concept and believe the consumers will pursue value maximization under limited search costs, knowledge, mobility, and incomes (Gale, 1994; Smith & Rupp, 2003). Kotler, Swee, Siew, and Chin (1996) argued that customers' perceived value is the difference between total customer value and total customer cost. Total customer value is the bundle of benefits customers expect from a given product or service.

Online travel agencies provide a point of contact via Internet to enable customers to search for appropriate flights and fares and make a selection. If traditional travel agencies intend to sell products on the Internet, management must strive for providing higher or new value to Internet consumers (Wolfe, Hsu, & Kang, 2004; McMillan, Hwang, & Lee, 2003; Xue & Harker, 2002). Smith and Colgate (2007) referred to this process as value innovation. The concept of customer value to Internet commercial activities has begun to receive researchers' attention.

Previous researchers have argued that an individual will prefer Internet shopping to conventional channels only when he or she perceives that all or some of the attributes are higher for the Internet (Keeney, 1992, 1994; Torkzadeh & Dhillon, 2002). Also, the Internet has attributes that are distinct from those of conventional channels, such as maximizing time flexibility in purchasing and privacy. However, should management pay more attention to a particular factor? In other words, are the fundamental factors perceived equally by consumers? If not, what are the most valued fundamental factors? Nine fundamental values of Internet shopping are described in the literature (Keeney, 1999; Wang, Yeh, & Jiang 2006): product quality, cost, time to receive product, convenience, time spent, privacy, shopping enjoyment, safety, and environmental impact.

Based on the customer‐value perspective, this study focused on exploring the relative weights of the nine proposed fundamental purchasing travel products from an Internet perspective. The analytic hierarchy process (AHP) method proposed by Saaty (1990) was adopted to analyze the relative weights assigned by consumers. This study surveyed 131 respondents with the aim of providing data of strategic importance to management.

THEORETICAL PERSPECTIVES

Customer Value

Customer value is the foremost driver of competitive advantage in attracting and retaining customers in the Internet shopping environment (Chen & Dubinsky, 2003; Srinivasan, Anderson, & Ponnavoulu, 2002; Han & Han, 2001); various studies related to customer value have been conducted in traditional offline business contexts (e.g., Eggert & Ulaga, 2002; Lapierre, 2000; Ulaga & Chacour, 2001; Vandenbosch & Dawar, 2002). However, a few studies have been conducted in the e‐business context (e.g., Chen & Dubinsky; Keeney, 1999; Mathwick, Malhotra, & Rigdon, 2001; Wang et al., 2005). In general, customer value is perceived by customers as a trade‐off between what customers receive and what they sacrifice (Joo, 2007; Khalifa, 2004; Oorni, 2004; Zhuang, 2005). In this study, we define customer value as benefits perceived by customers of e‐business.

With the Internet as a commercial medium, new ways of doing business have developed in almost every industry sector. Ghosh (1998) pointed out that the Internet presents four different types of opportunities: (a) Through the Internet companies can establish a direct link to customers to complete transactions or complete transactions more easily; (b) the technology lets companies bypass others in the value chain; (c) companies can use the Internet to develop and deliver new products and services for new customers; and (d) a company could conceivably use the Internet to become the dominant player in the electronic channel of a specific industry or segment, controlling access to customers and setting up new business rules. Marcus and Anderson (2006) argued that the increasing availability of pricing information on the Internet affords consumers the opportunity to be more strategic in their purchasing behavior. The ease of price information potentially makes these guarantees very costly to the service or good provider. Keeney (1999) develops a list of the values that can be used to maximize customer satisfaction in Internet commerce, logically categorizing them into means and fundamental objectives, and indicates their relationships. Huizingh (2000) develops a framework for analyzing and categorizing the capabilities of websites, which distinguishes content from design.

From the Internet perspective, Keeney's (1999) research in understanding the value of Internet commerce to the customer was based on the concept of “value propositions.” He characterizes the value proposition as benefits and costs of what the Internet offers to the customer in terms of products and services and how this offering can be better than what is currently available through conventional means. To operationalize the value proposition and develop the framework, Keeney (1999) uses a value‐focused thinking approach. The intent behind value‐focused thinking is to focus on activities that occur prior to a decision problem being solved. As a result, value‐focused thinking helps in uncovering hidden objectives.

Keeney's work provides a useful list of constructs that can be used as a basis for measuring factors that influence Internet shoppers. He encourages the development of specific measures for the recommended means and fundamental objectives. Consistent with Keeney's description of this construct, we define Internet commerce as the sale and purchase of products and services over the Internet. The factors associated with Internet commerce relate to the net value of both the benefits and costs of a product, and the processes of finding, ordering, and receiving it. This broad definition seems to be consistent with the current practice of Internet commerce and appropriate for this evolving construct. Hsu (2006) discussed the relationship between website quality, customer value, and customer satisfaction; the results identified that customer value positively relates to customer satisfaction, and suggested that firms need to continue to improve and pay close attention to the information presented on their websites such as accuracy, comprehensiveness, reliability, and relevance; Wu, Mahajan, and Balasubramanian (2003) suggested enhancing customer satisfaction and developing stronger relationships.

Online Travel Agencies

In Taiwan, travel products constitute the largest proportion of the online consumer market (Market Intelligence Center, 2005), and according to TravelCLICK's (http://www.travelclick.net/) 2005 full‐year eTRAK results, Internet reservations received at major hotel brands grew nearly 27% in 2005 over the prior year, with brand websites gaining share compared to third‐party merchant and opaque websites. Brand websites were the source of 75% of the brands' centrally booked Internet reservations, compared to 71% in 2004. Reservations booked through brand websites climbed 33%, while bookings through merchant websites, Jupiter (Dabas & Manaktola, 2007) predicts that travel products will eventually outsell other online products by nearly twofold. O'Connor (2003) found that almost 6 out of 10 leisure travelers actively seek the lowest possible prices for travel services, and a study by the Joint Hospitality Industry Congress (2000) found that consumers expected Internet prices to be lower than those available in bricks‐and‐mortar channels.

The electronic distribution of travel information, prices, and availability has opened new channels by which people can reserve travel products. The most notable development is that reservations—which used to come through travel agents—is now being generated online by individual customers and corporate travel planners who are as likely to use online intermediaries as they are to contact travel agencies directly (Carroll & Siguaw, 2003; Miller, 2004). Online travel agencies face increasing levels of competition and, thus, experience an ever greater need to evaluate the effectiveness of their websites (Park, Gretze, & Sirakaya‐Turk, 2007).

Paradoxically, in addition to there being more competition, there is also more cooperation. Many online travel sites sell multiple products (e.g., airplane tickets, hotel rooms, and rental cars) from multiple vendors, as their key attraction is that they provide details of all of the services available, and thus offer consumers the ability to research and purchase an entire trip online using a single site. Such sites need detailed content and reservation facilities, which they can only obtain by cooperating with other vendors. As a result, non‐exclusive virtual alliances are being formed. A good example of both trends is the newly formed Global Distribution Systems (O'Connor and Frew, 2002; Lee, Sung, DeFranco, & Arnold, 2004), which in addition to distributing travel products directly to the consumer through the Travelweb product (http://www.travelweb.com), also provide information and a ticket booking engine for a large number of other web‐based travel services, such as Microsoft Expedia. The companies using such systems could be regarded as competitors. There is a need to establish a clear picture of the various channels available. Furthermore, travel agencies need to determine which channels are currently the most effective at driving business to travel agencies, how they compare with the traditional hotel electronic‐distribution channels, and which are likely to dominate in the future.

In particular, the advent of electronic commerce has forced companies to face new types of competition and customer relationship management to survive in markets. Online travel agents provide a point of contact via the Internet to enable customers to search for appropriate flights and fares and make a selection, which is then booked and ticketed by the Internet (O'Connor & Murphy, 2003). Kim and Lee (2004) found that online travel agencies and online travel suppliers share similar commonalities with regard to information content, reputation and security, structure and ease of use, and usefulness—commonly derived dimensions of web service quality. Information content was found to be the most important dimension of online travel agencies in explaining the overall level of customer satisfaction while structure and ease of use was thought of as the most important dimension of online travel suppliers.

The OTAs environment has several unique competitive characteristics. First, OTAs can only select tickets from the available set offered from the airlines: They do not have the ability to alter prices or other product features. OTAs compete for consumers by striving to select the best available tickets according to their preferences, attempting on the consumers' behalf to offset non‐competitive pricing by airlines (Clemons, Hann, & Hitt, 2002; O'Connor & Murphy, 2003). Second, by offering multiple choices for a given request, Internet commerce can attempt to serve multiple customer groups simultaneously; undoubtedly, electronic markets on the Internet have made it far easier for consumers to search for services from various electronic markets (O'Connor & Frew, 2002). Card, Chen, & Shu (2003) argued shoppers and nonshoppers were similar in how they viewed differences between Internet shopping and shopping at traditional stores (i.e., store characteristics). That is, the management of travel agencies should pay more attention to one or some objectives of Internet commerce.

The Analytic Hierarchy Process

The AHP is a mathematically‐based, multi‐objective decision‐making tool which was introduced by Saaty (1990). It uses the pair wise comparison method to rank order alternatives of a problem that are formulated and solved in hierarchical structure. The technique has the advantage of being simple and thorough in handling difficult real‐life problems.

The AHP requires a problem be decomposed into levels, each of which is comprised of elements or factors. The elements of a given level are mutually independent, but comparable to the elements of the same level. The structure presupposes that elements of any given level are influenced by elements at the level immediately above them. The process of AHP comprises the following steps:

1. Define the problem and determine its goal.

2. Structure the hierarchy from the top (the objectives from a decision‐maker's viewpoint) through the intermediate levels (criteria on which subsequent levels depend) to the lowest level which usually contains the list of alternatives.

3. Construct a set of pairwise comparison matrices (size n × n) for each of the lower levels with one matrix for each element in the level immediately above by using the relative scale measurement. The pairwise comparisons are done in terms of which element dominates the other.

4. There are n (n−1)/2 judgments required to develop the set of matrices in step 3. Reciprocals are automatically assigned in each pairwise comparison.

5. Hierarchical synthesis is now used to weight the eigenvectors by the weights of the criteria and the sum is taken over all weighted eigenvector entries corresponding to those in the next lower level of the hierarchy.

6. Having made all the pairwise comparisons, the consistency is determined by using the eigenvalue, λ _max, to calculate the consistency index, CI as follows: CI  =  (λ _max − n)/(n−1), where n is the matrix size. Judgment consistency can be checked by taking the consistency ratio (CR) of CI with the appropriate value in Appendix A. The CR is acceptable, if it does not exceed 0.10. If it is more, the judgment matrix is inconsistent. To obtain a consistent matrix, judgments should be reviewed and improved.

7. Steps 3–6 are performed for all levels in the hierarchy.

Expert Choice 2000 software (Expert Choice, Arlington, VA, USA) provides two methods of developing a decision model: direct construction using the Evaluation and Choice module, and assisted construction using the Structuring module. The Structuring module provides an interface mechanism for deriving criteria and subcriteria. It is a framework for collecting ideas and transforming these into an AHP model, a facilitating mechanism for constructing models. The Evaluation and Choice module provides the facilities for model creation, pairwise comparisons, solution synthesis, and sensitivity analysis and report generation.

The Structuring module was used to create an AHP model of the decision problem, and the Evaluation and Choice module—which is the principal component of Expert Choice—was used to create a model, elicit expert comparison assessments, solve the model, perform sensitivity analysis, and generate reports. Expert Choice decision models follow the standard AHP format, employing a functional hierarchy that contains a broad overall goal or objective at the highest level.

The calculations for these items will be explained next for illustration purposes. Synthesizing the pairwise comparison matrix is performed by dividing each element of the matrix by its column total. Numerical values used in the AHP process are 1–9, In AHP, each pairwise comparison represents an estimate of the ratio of priorities or weights of compared elements.

The pairwise comparisons can be denoted as a matrix as follows:

Having recorded the quantified judgments on the pair, in the matrix A, a set of numerical weights w ₁, w ₂, ……, w_n reflects the recorded value weights on the objectives. Assume that the quantified vector W is:

where w _i means the quantified weight of item i. Assume that the judged weights are merely the result of precise physical measurement:

where n is the number of judgments. By simplifying the above equation, one can obtain AW  =  λW, where λ is an eigenvalue of A and W is an eigenvector of A, respectively (Saaty, 2003).

We then compute the average of these values to obtain λ _max:

when λ _max and n are not equal, the deviation represents the degree of inconsistency, which is evaluated with consistency index (CI): (λ _max − n)/(n−1). Thus a higher ratio of CI shows a higher degree of inconsistency. Furthermore, Saaty (1990) developed another consistency index for AHP, called random consistency index (RI). The ratio of CI to the average RI for a matrix of the same order is called the consistency ratio (CR): CI/RI. A consistency ratio of 0.1 or less is considered acceptable.

METHODOLOGY

Sample

This study explored the relative weights that customers assign to the factors influencing fundamental Internet travel agency shopping objectives. The population of the research is OTAs shopping customers. A questionnaire survey was used to gather data from customers who purchased travel products from OTAs. Customers were surveyed during a 3‐month period, from April 16, 2007 to July 15, 2007, at Taipei International Airport. The reason we survey in the Taipei International Airport not only it is the main and largest airport in Taiwan, but also we will make sure the travelers' buying behavior which travel products from Internet.

The filtering questions in the questionnaire excluded customers those who contract with travels directly, fellow travelers; Out of a non‐random sample of 700 people who were interviewed in the restricted areas of the departure lounge at the Taipei International Airport, excluding useless questionnaires resulted in 131 respondents (Table 1), corresponding to a response rate of 18.71%.

Table 1. Sample

Samples	N	%
Male	73	55.73%
Female	58	44.27%
Ages (years)	N	%
< 20	17	12.97%
21–25	28	21.37%
26–30	41	31.29%
31–35	23	17.57%
36–40	12	9.16%
41–45	8	6.11%
46–50	2	1.53%

Measures

A total of nine most fundamental values for Internet shopping were found in the literature: product quality, cost, time to receive product, convenience, time spent, privacy, shopping enjoyment, safety, and environmental impact (Keeney, 1999; Wang et al., 2006). A standard AHP questionnaire was designed to collect the relative weights of the fundamental objective (see Appendix B). Each respondent must make the decision between each pair of fundamental objectives. First, each respondent compares two distinct objectives and decides which one is more important than the other. Then, each respondent assigns an importance rating (see Table 2), from 1–9, where 9 indicates the objective has the highest possible order of affirmation; 7 means strongly favored and its dominance demonstrated in practice; 5 indicates experience and judgment strongly favor one objective over the other; 3 means experience and judgment slightly favored one objective over the other; and 1 means two objectives contribute equally to the consumer.

Table 2. Pairwise of Question

	9	7	5	3	1	3	5	7	9
Product quality	□	□	□	□	□	□	□	□	□	Cost

ANALYSIS

To explore the relative weights put by customers on the fundamental Internet travel agencies' shopping objectives, the AHP method was adopted to analyze the relative weights given by the responds. First, the Expert Choice software assists the study in all phases of the problem‐solving process, from model formulation to final report output. The structuring module feature assists the study in creating an AHP model of the decision problem. Second, the evaluation and choice module is the principal component of Expert Choice software and is used for creating a model, eliciting expert comparison assessments, solving a model, performing sensitivity analysis, and generating reports. Expert Choice decision models follow the standard AHP format, a functional hierarchy with the broad overall goal or objective at the highest level.

First, the study evaluated factors in selecting online travel agencies and comprised several levels, including the goal hierarchy, criteria hierarchy, sub‐criteria hierarchy. Second, based on the ability of experts to assign weight values, the geometric mean value is used to calculate comprehensive decision‐making scores from respondents. In doing so, the standard weight values can be established to select online travel agency (Table 3).

Table 3. Standard Weight Values for Shopping OTAs' Product (N  =  131)

	Product Quality	Cost	Time to Receive Product	Convenience	Time Spent	Privacy	Shopping Enjoyment	Safety	Environmental Impact
Product quality	1	2.33408	2.85962	2.27767	2.38453	0.39820	0.96919	0.33517	0.97144
Cost	0.42843	1	1.06774	0.69588	0.91099	0.25114	0.90805	0.25626	0.66324
Time to receive product	0.34970	0.93656	1	0.48555	0.55219	0.23537	0.53673	0.22025	0.56331
Convenience	0.43905	1.43703	2.05952	1	1.77425	0.22964	0.89235	0.24682	0.66668
Time spent	0.41937	1.09771	1.81097	0.56362	1	0.21692	0.91639	0.22380	0.58271
Privacy	2.51133	3.98189	4.24869	4.35464	4.60996	1	4.29394	1.18305	2.13639
Shopping enjoyment	1.03179	1.10126	1.86314	1.12064	1.09124	0.23289	1	0.31530	0.62867
Safety	2.98353	3.90224	4.54020	4.05159	4.46825	0.84527	3.17155	1	2.41539
Environmental impact	1.02940	1.50776	1.77522	1.49998	1.71611	0.46808	1.59067	0.41401	1
	10.19260	17.29853	21.22510	16.04957	18.50752	3.87750	14.27886	4.19467	9.62782

Third, the pairwise comparison matrix of the criteria and subcriteria is used to obtain each hierarchical factor weight. Table 4 summarizes those results. Fourth, if the results of the six experts in terms of consistency ratio and consensus of CR are smaller than “0.1,” they conform to principles of consistency. In this study, we calculated the whole level weight to select the online travel agencies. In this study, the weights of nine fundamental objectives are shown in Table 4. In the results of the study, we find the consistency index, CI, as follows: CI  =  0.01982; and selecting appropriate value of random consistency ratio, RI, for a matrix size of nine, we found RI  =  1.45 (see Appendix 2). We then calculate the consistency ratio, CR  =  CI/RI  =  0.01367. As the value of CR is less than 0.1, the judgments are acceptable. Similarly, the pairwise comparison matrices and priority vectors for the remaining criteria can be found as shown in Table 4.

Table 4. Customer Value Factor Weights for Shopping OTAs' Product (N  =  131)

	Product Quality	Cost	Time to Receive Product	Convenience	Time Spent	Privacy	Shopping Enjoyment	Safety	Environmental Impact	W′
Product quality	0.09811	0.13493	0.13473	0.14191	0.12884	0.10269	0.06788	0.07990	0.10090	0.10999
Cost	0.04203	0.05781	0.05031	0.04336	0.04922	0.06477	0.06359	0.06109	0.06889	0.05567
Time to receive product	0.03431	0.05414	0.04711	0.03025	0.02984	0.06070	0.03759	0.05251	0.05851	0.04499
Convenience	0.04307	0.08307	0.09703	0.06231	0.09587	0.05922	0.06249	0.05884	0.06924	0.07013
Time spent	0.04114	0.06346	0.08532	0.03512	0.05403	0.05594	0.06418	0.05335	0.06052	0.05701
Privacy	0.24639	0.23019	0.20017	0.27132	0.24909	0.25790	0.30072	0.28204	0.22190	0.25108
Shopping enjoyment	0.10123	0.06366	0.08778	0.06982	0.05896	0.06006	0.07003	0.07517	0.06530	0.07245
Safety	0.29272	0.22558	0.21391	0.25244	0.24143	0.21799	0.22212	0.23840	0.25088	0.23950
Environmental impact	0.10099	0.08716	0.08364	0.09346	0.09273	0.12072	0.11140	0.09870	0.10387	0.09918
Note. λ max: 9.0012; CI: 0.00013; CR: 0001.

In our results, privacy (W′: 0.25108), safety (W′: 0.23950), and product quality (W′: 0.10999) are the three most important objectives of customer shopping OTAs. In the OTAs' environment, travel agents provide customers with information about the tourist products and services that they distribute; at the same time, OTAs will develop privacy and safety exchange mechanism, under the Internet environment, OTAs provide excellent quality to customer.

CONCLUSION AND IMPLICATIONS

Travel‐product intermediaries must change to meet consumer demands as the prevalence of Internet shopping increases, given that online shopping represents a viable alternative to conventional shopping. Consumers will be attracted to Internet commerce if they feel that they are getting a better deal. In fact, Internet commerce should provide new methods of value creation and provision. Nine fundamental factors have been proposed in the literature, and we assessed whether these factors should receive equal attention from company managers by analyzing data from 131 consumers. The nine factors were assigned different weights by customers purchasing room products on the Internet. We found that privacy, safety, and product quality were the three main factors considered by customers purchasing travel products on the Internet, which contradicts the conventional wisdom that cost (Clemons et al., 2002) and convenience (Harris & Duckworth, 2005) are the key factors underlying the success of a website. Our results suggest that the prices of products offered on the Internet could be the same as those offered in traditional channels or by competing websites, and that convenience is also not a main factor attracting consumers to purchase particular travel products on the Internet.

Another critical concern is safety, which relates to the risk associated with purchasing products on the Internet. The perceived risk is, therefore, likely to be an important factor in online shopping, which is supported by 95% of U.S. consumers polled in a Gartner Group Survey reporting concerns about privacy and security when making online purchases (Saban, McGivern, & Saykiewicz, 2002). Different firms provide websites with similar information content, reputation, security, structure, ease of use, and usefulness, which are commonly quoted dimensions of the quality of a web service. Websites developed to ensure the safety of consumer privacy and information will facilitate for a carefree shopping experience. Furthermore, privacy protection should prevent nonpermitted, illegal, and unethical use of private information.

With regard to product quality, the Internet market for a product may reduce or even disappear when buyers are unable to differentiate high‐quality products from those that appear identical but are actually of inferior quality. Thus, to achieve success with electronic commerce, managers must address the problem of product quality. Card et al. (2003) argued that shoppers and nonshoppers have similar views of the differences between Internet shopping and shopping at traditional stores. Their results have implications for managers, in that travelers tend to want to make complicated bookings using websites that provide extra assistance in the booking procedure, including concierge‐like services prior to departure. That is, hotels and travel agencies must provide personal service strategies over the Internet.

The results of this study also have implications for online marketers. Our elucidation of the relative weights of the nine fundamental factors assigned by customers indicates that the designers of websites should establish marketing policies that focus on only three factors: privacy, safety, and product quality. Travel agencies need to make significant investments in the safety of designed websites, with a greater effort made to alleviate the concerns of consumers. Our results provide evidence that travel agencies should employ a combination of Internet commerce and specialization to provide customers with added value.

LIMITATIONS

The first limitation of this study was that we did not compare the different operation scopes of travel agents—wholesalers and retailers have different optimal strategies. Future research directions for researchers can also be suggested from our study. First, only travel goods were examined in this study, and hence future studies could examine other product categories. Second, since the AHP has been widely verified to be an effective technique for studying relative weights among product attributes in a variety of applications, future studies could examine the fundamental factors underlying Internet commerce using the AHP technique and procedures adopted in this study.

APPENDIX A

Questionnaire

	9	7	5	3	1	3	5	7	9
Quality	□	□	□	□	□	□	□	□	□	Cost
Quality	□	□	□	□	□	□	□	□	□	Time to receive product
Quality	□	□	□	□	□	□	□	□	□	Convenience
Quality	□	□	□	□	□	□	□	□	□	Time spent
Quality	□	□	□	□	□	□	□	□	□	Privacy
Quality	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Quality	□	□	□	□	□	□	□	□	□	Safety
Quality	□	□	□	□	□	□	□	□	□	Environmental impact
Cost	□	□	□	□	□	□	□	□	□	Time to receive product
Cost	□	□	□	□	□	□	□	□	□	Convenience
Cost	□	□	□	□	□	□	□	□	□	Time spent
Cost	□	□	□	□	□	□	□	□	□	Privacy
Cost	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Cost	□	□	□	□	□	□	□	□	□	Safety
Cost	□	□	□	□	□	□	□	□	□	Environmental impact
Time to receive product	□	□	□	□	□	□	□	□	□	Convenience
Time to receive product	□	□	□	□	□	□	□	□	□	Time spent
Time to receive product	□	□	□	□	□	□	□	□	□	Privacy
Time to receive product	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Time to receive product	□	□	□	□	□	□	□	□	□	Safety
Time to receive product	□	□	□	□	□	□	□	□	□	Environmental impact
Convenience	□	□	□	□	□	□	□	□	□	Time spent
Convenience	□	□	□	□	□	□	□	□	□	Privacy
Convenience	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Convenience	□	□	□	□	□	□	□	□	□	Safety
Convenience	□	□	□	□	□	□	□	□	□	Environmental impact
Time spent	□	□	□	□	□	□	□	□	□	Privacy
Time spent	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Time spent	□	□	□	□	□	□	□	□	□	Safety
Time spent	□	□	□	□	□	□	□	□	□	Environmental impact
Privacy	□	□	□	□	□	□	□	□	□	Shopping enjoyment
Privacy	□	□	□	□	□	□	□	□	□	Safety
Privacy	□	□	□	□	□	□	□	□	□	Environmental impact
Shopping enjoyment	□	□	□	□	□	□	□	□	□	Safety
Shopping enjoyment	□	□	□	□	□	□	□	□	□	Environmental impact
Safety	□	□	□	□	□	□	□	□	□	Environmental impact

APPENDIX B

Random Consistency Index

Order	1	2	3	4	5	6	7	8	9	10
R.I.	0.00	0.00	0.52	0.89	1.11	1.25	1.35	1.40	1.45	1.49