Dr. Cheol Park, Assistant Professor of E-Marketing, Department of Management Information Systems, Korea University, Jochiwon, Chungnam, 339-700, South Korea, Email: cpark@korea.ac.kr
The cyberspace through the Internet forms an enormous world marketspace. The B2C (Business to Consumer) electronic commerce has grown rapidly and its market size was $ 1.98 billion in 2001. This study attempt to develop a model of online buying intention and test the model empirically. Through an online survey, 500 samples of Internet users were completed. Regression was used to estimate the unique effect of product interest, product type, shopping orientations (entertainment, experiential, and convenience), experience of online buying, and Web-site trust on consumer¡¯s online buying intention level. The regression coefficients for product interest, product type, entertainment shopping orientation, experience of online buying, and Web-site trust are statistically significant. However, the coefficients for experiential and convenience shopping orientation are statistically insignificant. Limitations and further research issues are suggested.
The cyberspace through the Internet forms an enormous world marketspace. The number of the online consumers and amount being spent by online buyers has been on the rise. Online retailing became big business in late 1998, as millions of people placed orders for holiday gifts online and retailers scrambled to upgrade their distribution networks to cope with the growth (Lohse 2000). Forrester Research expects by the year 2003, the electronic commerce (E-commerce) conducted globally will reach $ 3.2 trillion, representing 5% of global transaction. The Jupiter communications expects E-commerce to exceed $6.3 trillion by year 2005 while OECD has a figure of $1 trillion by 2003-05.
The size of E-commerce in Korea has increased rapidly. The total number of Internet users is estimated at over 20 million in Korea. In 2001, the total volume of E-commerce was 3,347 billion Korean Won (approximately $ 2.57 billion) and the B2C was 77% (2,580 billion Korean Won, about $ 1.98 billion) of it. . There are 2,166 Internet shopping mall, and Samsung-Mall, Hansol CS, Interpark, and LG e-shop are the largest Internet shopping mall in Korea.
Understanding consumer behaviors relating to online shopping is necessary for the effective Internet marketing. Recently, many studies in e-business have tried to explore factors influencing on online shopping behavior (c.f. Bhatnagar et. al 2000; Citrin et. al. 2000; Javenpaa et. al. 1999; Koufaris et. al. 2002; Lee and Turban 2001; Li et. al. 1999; Liao and Cheung 2001; Lohse 2000; Swaminathan et. al. 1999; Szymanski and Hise 2000; Tan 1999; Phau and Poon 200; Poel and Leunis 1999). The purpose of the research is develop a model of online buying intention and test the model empirically. Factors influencing on online buying intention are suggested and their effects are identified in the study.
This would normally introduce the content of the paper. Note that papers should be formatted using standard html tags (H1-H6, UL, OL, etc.). In particular, do not use FONT or SIZE tags - the Cascading Style Sheet that AusWeb uses (and which is referred to by this template) will take care of formatting the text for you. Format main section headings in your paper as H2 (H1 is reserved for the title of the paper).
Shopping orientations are related to general predisposition toward acts of shopping. They are conceptualized as a specific dimension of lifestyle and operationalized on the basis of activities, interests and opinion statements pertaining to acts of shopping (Li et. al 1999). Sopping is not only economic activity but also psychological and social. According to consumers, shopping orientations are various; economic, convenience, experiential, leisure, and self-fulfillment, etc. Tauber (1972) noted that consumers often shop out of personal motives (diversion from the routine of daily life, self-satisfaction, and sensory stimulation) and social motives (social experience outside the home, peer group attraction, and pleasure of bargaining).
Shoppers are very often of the ¡®touch-and-feel¡¯ type, who prefer to handle and compare goods before deciding to buy (Liao and Cheung 2001). Since Internet shopping is a high technology form of non-store shopping and consumer can not see, touch, or smell the goods, consumers will tend to perceive a higher level of risk when buying products through Internet than by in-store means (Tan 1999). In general, Internet shopping has a long way to go in developing the shopping experience. Despite multimedia presentations, shopping on the Internet is no substitute for the leisure experience associated with conventional shopping (Rowley 1996). ). Liao and Cheung (2001) found that consumer perceptions of the relative life content of Internet shopping had significant and negative effect on willingness to Internet shopping in their regression model. Hence, it is reasonable to expect that the higher experiential orientation, the lower the online buying intention. Also consumers who make online purchases are lower in entertainment orientation than those who do not purchase online
Online shopping is preferred over in-store shopping by some Internet users because of its convenience and time savings. Bhatnagar et. al (2000) suggested that the likelihood of purchasing on the Internet increase as consumer¡¯s perception of Internet shopping convenience increases. Consumers who make online purchases are more convenience-oriented than who do not buy online. So, consumers who value convenience are more likely to buy on the Web, while those who prefer experiencing products are less likely to buy online (Li et.al. 1999).
Product interest is a general term that can be defined as the degree of personal concern of a product or service to a consumer. If a person is very concerned about a car, his/her car interest could be high. Product interest affects buying intention. If a consumer have high degree of product interest, both online and off-line channels are considered as sources of purchasing or searching products. On the Internet, the higher product interest one has the higher the online buying intention.
Nelson (1970) differentiated between search and experience goods. Search goods are defined as those dominated by product attributes for which full information can be acquired prior to purchase. Experience goods are dominated by attributes that cannot be known until purchase and use of the product or for which information search is more costly and/or difficult than direct product experience. Search goods include book, music CD, and branded products, etc. Clothes, musical instruments, fruits, and vegetables are kinds of experience goods.
If a good is a search good and its features can be objectively assessed using readily available information, the Internet could serve significant transaction and communication functions and hence affect transaction channel and communication channel intermediaries involved with the good. (Peterson et. al. 1997). If a good is an experience good, information about the good¡¯s features mat not be sufficient for a consumer to engage in an Internet-based transaction (Poon and Joseph 2000). Klein (1998) suggested that search goods were proper to buy on Internet for consumers because the incremental value of the Internet will be the provision of information in a more accessible, less costly, and more customizable format. Service fit for Internet selling because physical distribution is not a problem to sell service on the net (e.g. stock exchange or banking). Connecting the Internet means connecting the channel of service. The Internet is a good and effective marketing channel of service itself. In case of search goods, online buying intention might be higher than services or experience goods and online buying intention of services might be higher than search goods.
Vijayasarathy and Jones (2000) found Internet shopping experience was associated with both attitudes towards and intentions to shop using Internet catalogue. Since, the experience is the best teacher, the experience of online shopping reduces the perceived risks of online shopping. So, the likelihood of online buying increases as the consumer¡¯s experience on the Internet increases.
Consumer¡¯s trust toward Web-site is a determinant factor for the electronic commerce (Friedman et. al. 2000; Hoffman et. al. 1999; Javenpaa et. al. 1999; Lee and Turban 2001; Ratnasingham 1998; Tan and Thoen 2001; Swaminathan et. al.1999). Trust is defined as ¡°the willingness of to rely on an exchange partner in whom one has confidence,.¡± And as confidence that the other party is reliable, honest, consistent, competent, fair, responsible, helpful and altruistic (Swaminathan et. al. 1999). Friedman et. al.(2000) said that trust can be cultivated to enhance our personal and social lives and increase our social capital.
E-commerce lacks security and reliability arising from the issues of a ¡°complete trustworthy relationship¡± among the trading partners (Ratnasingham 1998). Trust is an especially important factor under conditions of uncertainty and risk. As a new form of commercial activity, Internet shopping involves more uncertainty and risk than traditional shopping. Moreover, a consumer cannot physically check the quality of a product before making a purchase, or monitor the safety and security of sending sensitive personal and financial information through the Internet to a party whose behaviors and motives may be hard to predict (Lee and Turban 2001). B2C e-commerce success is determined in part by whether consumers trust sellers and products they cannot see or touch, and electronic systems with which they have no previous experience. Hence, the greater the perceived trust of seller¡¯s Web-site, the greater the likelihood of online buying intention.
An Online survey was performed for getting data. The subjects consisted of a panel from an online survey company in Korea (www.survey.co.kr). An html-formed questionnaire was on the Web-site and the panel members visited and responded to it. They were given mileage points as rewards.
Five hundred completed questionnaires were collected. The sample consisted of 57.4% (n=287) males and 42.6% (n=213) females. The mean age was 28.7; 31.4% of them were between the ages of 26 and 30, 29.8% were between 31-39, 25.6% were between 20-25, 4.4% were younger than 19, and 8.8% were older than 40. With respect to job distribution, 27.8% were students, 43.8% were workers, 12.6% were professionals or entrepreneurs and 12.8% were others. Monthly income of the sample indicated 29.6% earned less than half a million Korean Won (about $ 385), 18.1% between a half and 1 million Won (about $ 770), 35.4% between 1 and 2 million Won (about $ 1,540), 12.4% between 2 and 3 million Won (about $ 2,310), 3.6% between 3 and 4 million Won (about $ 3,080), and 1.0% earned more than 4 million Won. Eighty seven percent of the sample has purchased online. Respondents divided into 3 groups by allocating product types; searching goods (music CD), service (tourism), and experience goods (casual clothes). Respondents for music CD were one hundred seventy six, tourism service one hundred sixty, and casual clothes one hundred sixty four. Quantitative analyses of survey data were conducted using factor and regression analysis with SPSS Win 8.0.
A questionnaire was designed to measure shopping orientation, product interests, Experience of online buying, Web-site, product types and demographic variables. It was based on findings from previous studies. A measurement of shopping orientation included a seven-point Likert-scale from "strongly agree (5) to strongly disagree (1)" (c.f. Li et. al 1999). After testing reliability and validity, the final nine items were chosen (see Table 1). A seven-point Likert-type scale from ¡°very intend (7) to ¡°never intend¡± (1) measures online shopping intention. A measurement of product interest included a seven-point Likert-type scale from ¡°very interested¡± (7) to ¡°never interested¡± (1). A measurement of Web-site trust, a degree of reliability toward Web-site for online shopping, included a seven-point Likert-type scale from ¡°very trust¡± (7) to ¡°very distrust¡± (1). Product type was coded 1 of experience goods (casual clothes), 2 of tourism service, and 3 of searching goods (music CD). A measurement of experience of online buying product interest included binomial 1 (yes) and 0 (no experience).
Factor analysis, using the principal component method with varimax rotation of factors, was performed to identify characteristics of consumer values of eating-out. The factor structure is summarized in Table 1. The factor structure of consumer shopping orientation consisted of three aspects, entertainment, experiential, and convenience. The three-factor solution explained 68.3% of the variance in the correlation matrix. The eigenvalue of the entertainment factor was 3.48, for the experiential factor was 1.56 and for the convenience factor was 1.10.
Regression was used to estimate the unique effect of product interest, product type, shopping orientation (entertainment, experiential, and convenience), experience of online buying, and Web-site trust on consumer¡¯s online buying intention level. The regression coefficients are presented in Table 2. The regression coefficients for product interest, product type, entertainment shopping orientation, experience of online buying, and Web-site trust are statistically significant. However, the coefficients for experiential and convenience shopping orientation are statistically insignificant. Their signs are also in the direction expected. Moreover, the result shows that product interest has the greatest impact on online buying intention (Beta = .407). The data also demonstrate that experience of online buying (Beta = .361), product type (Beta = .290), Web-site trust (Beta = .244), and entertainment shopping orientation (Beta = -.123) are important to online buying intention.
The results suggest that the higher the product interest and Web-site trust one has, the higher online buying intention and the higher the entertainment shopping orientation one has, the lower online buying intention. Also if one has purchased online, his/her intention of online buying is higher and the online buying intention of searching goods (music CD) is higher than tourism service and experience goods (casual clothes).
This research attempted to develop a model of online buying intention and test the model empirically. Regression was used to estimate the unique effect of product interest, product type, shopping orientations (entertainment, experiential, and convenience), experience of online buying, and Web-site trust on consumer¡¯s online buying intention level. The regression coefficients for product interest, product type, entertainment shopping orientation, experience of online buying, and Web-site trust are statistically significant. However, the coefficients for experiential and convenience shopping orientation are statistically insignificant.
The result that product interest has the greatest impact on online buying intention suggests Internet shopping malls should target product mania first. Those who have high involvement of a product are likely to purchase the product online. It is helpful for e-retailer to offer specific products information and virtual communities of products on the Web-site. Experience of online purchasing also affects the intention of online buying. This means that CRM (Customer Relationship Management) is important to make a trial-buyer be a repeated-buyer.
The result shows that the search goods more fit for online selling. In some context, Internet shopping can be inherently attractive when compared with normal shopping. For example, in retailing CDs, where in a normal store the user may need to pour over large catalogues and browse through racks of discs, in the Internet CD store, good database management can provide easy access to this information (Rowley 1996). For experience goods, the Internet may provide the greatest value through ¡®virtual experience¡¯ (Klein 1998). That is by allowing the consumer to experience product performance prior to purchase, the marketer can ¡®virtually¡¯ turn an experience good into a search good.
As a result, the more a consumer trusts the Web-site, the higher the intention of online buying. For increasing consumer trust online, online retailers should make efforts to establish reliability and security of the technology, to protect consumer¡¯s privacy, to keep a promise with customer, and to get the third party certification of trust (Friedman et. al. 2000; Hoffman et. al. 1999;.Lee and Turban 2001; Ratnasingham 1998; Tan and Thoen 2001). The result shows that an Entertainment orientation of shopping has negative relationship with online buying intention. Initiatives to increase the life content of product and render the virtual marketplace more attractive and enjoyable in terms of shopping entertainment would be required for further development (Liao and Cheung).
Present research is in the exploratory stage, and this study presents several challenges with respect to theory building and method. Some measurement scales (e.g. product interest and Web-site trust) were based on a single item and developing measurement scales will be needed. Also more variables (e.g. perceived risks, the characteristics of the shopping site, and situational variables of online shopping, etc.) relating to the model of online buying should be examined. The contribution of the present work will be made clearer through that effo
Bhatnagar, Amit, Sanjog Misra, and H. Raghav Rao (2000), ¡°On Risk, Convenience, and Internet Shopping Behavior,¡± Communications of the ACM, 43 (11), 98-105.
Citrin, Alka Varma, David E. Sprott, Steven N. Silverman (2000), "Adoption of Internet Shopping: The Role of Consumer Innovativeness," Industrial Management & Data Systems, 100 (7), 294-300.
Friedman, Batya, Peter Kahn, and Daniel Howe (2000), "Trust Online," Communications of the ACM, 43 (12), 34-40.
Hoffman, Donna L. and Thomas P. Novak, and Marcos Perlta (1999), "Building Consumer Trust in Online,¡± Communications of the ACM, 42 (4), 50-56.
Javenpaa, Srikka L. and Noam Tractinsky (1999), "Consumer Trust in an Internet Store: A Cross-Cultural Validation," Journal of Computer Mediated Communication, 5 (2), Available online [HREF1].
Klein, Lisa R. (1998), "Evaluating the Potential of Interactive Media through a New Lens: Search versus Experience Goods," Journal of Business Research, 41 (3), 195-203.
Koufaris, Marios, Ajit Kambil, and Priscilla Ann Labarbera (2002), ¡°Consumer Behavior in Web-Based Commerce: An Empirical Study,¡± International Journal of Electronic Commerce, 6 (2), 115-138.
Lee, Matthew K. O. and Efraim Turban (2001), "A Trust Model for Consumer Internet Shopping," International Journal of Electronic Commerce, 6 (1), 75-91.
Li, Kairong, Cheng Kuo, and Martha G. Russel (1999), "The Impact of Perceived Channel Utilities, Shopping Orientations, and Demographics on the Consumer's Online Buying Behavior," Journal of Computer Mediated Communication, 5 (2), Available online [HREF2]
Liao, Ziqi, and Michael Tow Cheung (2001), "Internet-based e-shopping and Consumer Attitudes: An Empirical Study," Information & Management, 38, 299-306.
Lohse, Gerald, Steven Bellman, and Eric J. Johnson (2000), "Consumer Buying Behavior on the Internet: Findings from Panel Data," Journal of Interactive Marketing, 14(1), 15-29.
Nelson, Pillip J. (1970), ¡°Information and Consumer Behavior,¡± Journal of Political Economy, 78 (2), 311-329.
Tan, Soo Jiuan (1999), ¡°Strategies for Reducing Consumers¡¯ Risk Aversion in Internet Shopping,¡± Journal of Consumer Marketing, 16 (2), 163-180.
Tan, Yao-Hua and Walter Thoen (2001), "Toward a Generic Model of Trust for Electronic Commerce," International Journal of Electronic Commerce, 5 (2), 61-74.
Peterson, Robert A., Sridhar Balasubramanian, and Bart J. Bronnenberg (1997), "Exploring the Implications of the Internet for Consumer Marketing," Journal of the Academy of Marketing Science, 25 (4), 329-346.
Phau, Ian and Sui Meng Poon (2000), ¡°Factors Influencing the Typs of Products and Services Purchased over the Internet,¡± Internet Research, 10 (2), 102-113.
Poel, Dirk Van den and Joseph Leunis (1999), ¡°Consumer Acceptance of the Internet as a Channel of Distribution,¡± Journal of Business Research, 45, 249-256.
Poon, Simpson and Matthew Joseph (2000), ¡°Product Characteristics and Internet Commerce Benefit among Small Business,¡± Journal of Product & Brand Management, 9 (1), 21-34.
Ratnasingham, Pauline (1998), "The Importance of Trust in Electronic Commerce," Internet Research, 8 (4), 313-321.
Rowley, Jennifer (1996), ¡°Retailing and Shopping on the Internet,¡± Internet Research, 6 (1), 81-91.
Swaminathan, Vanitha, Elzbieta Lepkowska-White, and Bharat P. Rao (1999), "Browsers or Buyers in Cyberspace? An Investigation of Factors Influencing Electronic Exchange," Journal of Computer Mediated Communication, 5 (2), Available online [HREF3].
Szymanski, David M. and Richard T. Hise (2000), ¡°e-Satisfaction: An Initial Examination,¡± Journal of Retailing, 76 (3), 309-322.
Vijayasarathy, Leo R. and Joseph M. Jones (2000), ¡°Print and Internet Catlalog Shopping: Assessing Attitudes and Intentions, Internet Research, 10 (3), 191-202.