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Anatolia – An International Journal of Tourism and Hospitality Research
Vol. 22, No. 1, April 2011, 120–124
RESEARCH NOTE
The impact of channel knowledge on shopping orientations
in consumer buying behavior
Bomi Kang*
Department of Management, Marketing, and Resort Tourism Management, E. Craig Wall College of
Business Administration, Coastal Carolina University, PO Box 261954, Conway, SC 29528-6054,
USA
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(Received 27 October 2010; final version received 3 January 2011)
Introduction
Tourism websites are plentiful and sales are increasing. The growth rate of the online
travel market is exceeding that of the total travel market. PhoCusWright (2006) projects
that internet booking will account for 54% of all US travel bookings in 2007. The internet,
indeed, is the fastest growing distribution market.
Responding to such demands, tourism suppliers (e.g. hotels and airlines) have added
their direct online channels in fear of losing their business to a new, yet increasingly
refined, online business model (web-based travel agents), such as merchant and opaque
(Kang, 2005). Channel friction is shaping up to be a huge problem in industry in general
(McCune, 1999; Schoenbachler & Gordon, 2002) but particularly in the tourism industry,
where traditional tour operators and travel agents are concentrated and control a large
percentage of sales (Kang, 2005). Internet-based travel agents have become a substantial
threat to traditional intermediaries and also to tourism suppliers, who actually own
inventory – rooms, seats, cars, and ships.
Although tourism literature and experts unanimously agree that today’s customers are
proficient at using the internet and possess unprecedented power and knowledge about
products, inventories, and competitive offerings (Schoenbachler & Gordon, 2002), they
ask the same unanswered questions; what are the reasons why internet users buy travel
products online. To answer this question, the author investigated factors of internet
purchase with a special reference to tourism-related products from both theoretical and
practical perspectives. Utilizing well-established shopping orientation theory, the current
study examined the convenience and recreational shopping orientation, self-deregulation,
and channel knowledge on the purchase of travel products online.
Literature review
Shopping orientations are related to a general predisposition toward acts of shopping, are
conceptualized as a specific dimension of lifestyle and are operationalized on the basis of
*Email: [email protected]
ISSN 1303-2917 print/ISSN 2156-6909 online
q 2011 Taylor & Francis
DOI: 10.1080/13032917.2011.556226
http://www.informaworld.com
Anatolia – An International Journal of Tourism and Hospitality Research
121
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attitudes toward activities, interests, and opinion statements pertaining to acts of shopping
(Holbrook, 1986; Kim & LaRose, 2004). This study examined the two most well-studied
shopping orientations in the marketing literature; convenience (Bellenger, Robertson, &
Greenberg, 1977; Girad, Silverblatt, & Korgaonkar, 2002; Jarvenpaa & Todd, 1997;
Li, Kuo, & Russel, 1999) and recreational (Bellenger & Korgaonkar, 1980; Donthu &
Garcia, 1999) orientation as well as self-deregulation, which was found to be an important
determinant of online shopping behavior (Kim & LaRose, 2004).
Convenience orientation
The convenience maximization orientation (Girad, Silverblatt, & Korgaonkar, 2002;
Jarvenpaa & Todd, 1997; Li, Kuo, & Russel, 1999) refers to shoppers’ attitudes
(Holbrook, 1986) toward shopping as a procedure to maximize their individual economic
efficiencies; specifically, to minimize their search and transaction costs. Convenience
orientation stresses the utilitarian value of shopping, as a task-related, rational, deliberate,
and efficient activity (Babin, Darden, & Griffin, 1994). Therefore, shoppers with
convenience orientations try to minimize their search cost as much as possible to save time
or energy for activities other than shopping (Anderson, 1971). These previous studies were
tested in a retail-based setting, yet the convenience orientations may perfectly explain the
increase of online shopping as it saves the time and effort needed for visits for product or
price comparisons (Darian, 1987; Girard, et al., 2002; Jarvenpaa & Todd, 1997; Li et al.,
1999).
Recreational orientation
Shoppers with a recreational orientation view shopping as a form of recreation and
often make impulse buys (Bellenger & Korgaonkar, 1980; Donthu & Garcia, 1999).
The hedonic value of recreational orientation results from enjoyment and playfulness
rather than from task completion (Holbrook & Hirschman, 1982). Hedonic value is
indicated by increased arousal (e.g. excitement caused by bargains), perceived freedom,
fantasy fulfillment, and escapism (Hirschman, 1983). Thus, shoppers who pursue hedonic
or recreational outcomes from shopping tend to spend more time on shopping, go shopping
without plans or product lists, and continue shopping even after purchasing products they
planned to buy. In these situations, purchases may be driven by ‘need to purchase’ rather
than ‘need for a product’ (Rook, 1987). Therefore, shopping experiences driven by a
recreational orientation lead shoppers to make more unregulated buys (Bellenger &
Korgaonkar, 1980).
Self-deregualtion
In Babin et al.’s (1994) study, a website shopper with a specific gift purchase in mind was
attracted by an on-site shopping recommendation to buy a fun gift for himself on impulse.
More recent marketing studies (Kim & LaRose, 2004) found that online shoppers may
exhibit unregulated purchase behavior regardless of shopping orientations under the
circumstances of exposed stimuli (solicitation, the interactive features of website). Online
shoppers were found to possess multiple shopping orientations (Brown, Pope, & Voges,
2003), combining the pursuit of convenience and recreational outcomes when they were
attracted by a nature of impulse buying. This suggests that shopping with a convenience
orientation may be accompanied by pleasure or arousal, and does not need to exclude
122
B. Kang
hedonic outcomes. LaRose and Eastin (2002) found that deficient self-regulation was
related to the amount of online shopping activity.
Channel knowledge
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While there are a plethora of debates on whether customers know who they are paying or
who provides products and services, it is interesting that a very limited amount of
empirical research has been done on examining the impact of customers’ channel
knowledge on the purchasing of travel products online. Because empirical research on
channel knowledge, especially online channels, is quite limited, the author had to rely on a
restricted number of previous studies of e-commerce. Li, Kuo and Russell (1999) found
channel knowledge is the strongest predictor of online buying behavior, suggesting that
knowledgeable customers tend to have more positive perceptions of the online channel’s
utility and thus are more frequent web buyers.
Methodology
Data were collected at two large state universities in the southeastern region of the
United States. Participants were screened if they had made an online reservation (e.g., air
travel, lodging, cruise, and rental car) in the past six months, and then asked about their
online booking experience. After removing influential points, 87 responses were retained
for further analysis.
A battery of 28 items was generated based on the literature, encompassing four
domains (convenience, recreational, self-regulation, channel knowledge). Respondents
were asked to rate their level of agreement on 28 items in order to measure the perceptions
about their online purchase experience. A seven-point Likert scale was used, ranging from
strongly disagree (1) to strongly agree (7). The frequency of purchase was measure by a
single item on a seven-point scale, ranging from never (1) to very often (7).
A principal component analysis (PCA) with varimax rotation was conducted.
The Kalser-Meyer-Olkin (KMO) measure of sampling adequacy of the final model was
0.778, which verifies that factor analysis is appropriate for the data. The PCA generated
four factors with an eigenvalue of above 1.0. Four factors accounted for about 66.4% of
the total variances. The factors were labeled according to the items with higher loadings
and common characteristics in each dimension, namely convenience, recreational,
deficient self-regulation, and channel knowledge.
A multiple regression was used to examine the extent to which a factor contributed to the
actual purchase of a tourism product. Retained factor scores for four domains were regressed
on the frequency of online reservations. From Table 1, three independent variables report
statistically significant results on the frequency of online reservation, which include
convenience (b ¼ 0.237, p ¼ 0.014), recreational (b ¼ 0.304, p ¼ 0.002), and deficient
self-regulation (b ¼ 0.324, p ¼ 0.001). Channel knowledge, however, showed a
marginally significant influence on the dependent variable (b ¼ 0.180, p ¼ 0.061).
Conclusion and implications
The results of this study offer very interesting explanations of online booking behavior.
First, the two dominant shopping orientation theories hold true in electronic shopping of
tourism products. Secondly, the result implies a possible relationship between the
customer’s channel knowledge and booking preference on the various booking websites.
Anatolia – An International Journal of Tourism and Hospitality Research
123
Table 1. Results of multiple regression of factor scores on online reservation.
Standardized beta
p-value
DV ¼ ‘how often did you make online reservation in the past six months?’
Convenience
Recreational
Deficient Self-regulation
Channel Knowledge
0.237
0.304
0.324
0.180
0.014**
0.002**
0.001**
0.061*
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R 2 ¼ 29%. * p , 0.10; ** p , 0.05; *** p , 0.001.
Future study should examine a direct linkage between recreational and convenience
orientation with deficient self-regulation (Kim & LaRose, 2004), as well as a linkage
between customers’ knowledge and perceived utility (conventional orientation).
In addition, the result calls for a holistic path model for shopping orientation, selfregulation, channel knowledge, and its outcome of actual purchasing behavior, with
consideration of demographic variables, such as income and age (Li et al., 1999).
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