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Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types
Computers in Human Behavior Computers in Human Behavior 24 (2008) 48–65 www.elsevier.com/locate/comphumbeh
Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types Jiunn-Woei Lian a
a,*
, Tzu-Ming Lin
b
Nanhua University, Department of Information Management, No. 32, Chung Keng Li, Dalin, Chiayi 622, Taiwan, ROC b National Central University, Department of Information Management, No.300, Jhongda Road, Jhongli City, Taoyuan County 32001, Taiwan, ROC Available online 12 March 2007
Abstract Previous electronic commerce (EC) studies have found that consumer characteristics are important when considering issues related to the acceptance of online shopping. However, most studies have focused on a single product or similar products. The effects of different product types have been relatively neglected. Previous studies have limited the generalizability of their results to a few products at best. To overcome this limitation, the purpose of this study was to explore the effects of different product types. The Internet product and service classification grid proposed by Peterson, Balasubramanian and Bronnenberg (Peterson, R. A., Balasubramanian, S., & Bronnenberg, B. J. (1997). Exploring the implications of the Internet for consumer marketing. Journal of Academy of Marketing Science, 25(4), 329–346) was employed to examine the effects of consumer characteristic differences on online shopping acceptance in the context of different products and services. A surveybased approach was employed to investigate the research questions. Regression analysis demonstrated that the determinants of online shopping acceptance differ among product or service types. Additionally, personal innovativeness of information technology (PIIT), perceived Web security, personal privacy concerns, and product involvement can influence consumer acceptance of online shopping, but their influence varies according to product types. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Online shopping; Consumer characteristics; Product types *
Corresponding author. Tel.: +886 9 22309530. E-mail addresses: [email protected], [email protected] (J.-W. Lian), [email protected], [email protected] (T.-M. Lin). 0747-5632/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2007.01.002
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1. Introduction The development of the Internet has increased the popularity of online shopping. However, many Internet users avoid shopping online due to security and privacy concerns. Despite this though, online sales continue to grow as Internet-based businesses become more sophisticated; indeed many users remain interested in online shopping. Understanding potential markets is thus important for businesses investing in electronic commerce (EC). Amichai-Hamburger (2002) indicated that the personality of Internet users plays an important role in their online behavior. Moreover, Hills and Argyle (2003) reached similar viewpoints. They found that individual Internet use correlates with individual personality differences. Kotler (2003) asserted that personal factors are the main influence on buyer behavior. Thus, understanding the personality differences between these two groups (Internet shoppers and non-Internet shoppers) is extremely important to businesses. Understanding the characteristics of potential online customers can help businesses accurately target potential markets. Peterson, Balasubramanian, and Bronnenberg (1997) indicated that owing to the special characteristics of the Internet, its suitability for marketing depends on the characteristics of the products and services being marketed. Thus, considering the differences among product types is essential to fully understanding the influence of online shopping. Liang and Huang (1998) expressed a similar opinion, noting that when dealing with electronic markets, increased attention must be paid to understanding which products are suitable for marketing online. Notably, they indicated that different product types influence consumer online shopping acceptance. Phau and Poon (2000) also obtained similar findings and found that product type affects consumer decisions when choosing between traditional or online channels. Previous studies found that consumer characteristics are important when considering online shopping acceptance-related issues. However, most studies neglect the effects of different product types. The generalizability of these studies thus was limited to only a few products. To overcome this limitation, the purpose of this study explores the effects of different product types. 2. Literature review This section summarizes the relevant literature regarding the determinants of user acceptance of online shopping and product categories. 2.1. Determinants of user acceptance of online shopping Previous research has identified four determinants of consumer acceptance of online shopping, namely consumer characteristics, personal perceived values, website design and the product itself. The first factor is consumer characteristics (Swaminathan, Lepkowska-White, & Rao, 1999). Variables belonging to this factor include personality traits (O’Cass & Fenech, 2003), self-efficacy (Eastin, 2002), demographic profiles (Li, Kuo, & Russell, 1999; Sim & Koi, 2002; Vrechopoulos, Siomkos, & Doukidis, 2001) and acceptance of new IT applications (Childers, Carr, Peck, & Carson, 2001; Citrin, Sprott, Silverman, & Stem, 2000; O’Cass & Fenech, 2003).
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The second factor is personal perceived values (Li et al., 1999). Variables in this dimension include perceived risk (Bhatnager, Misra, & Rao, 2000; Eastin, 2002), perceived convenience (Eastin, 2002), perceived website quality (O’Cass & Fenech, 2003) and perceived benefits (Eastin, 2002). The third factor is website design (Dahlen & Lange, 2002; Liang & Lai, 2002; Ranganathan & Grandon, 2002). Variables included in this factor are security (Belanger, Hiller, & Smith, 2002; Liao & Cheung, 2001; Ranganathan & Grandon, 2002; Swaminathan et al., 1999) and privacy (Belanger et al., 2002; Ranganathan & Grandon, 2002; Swaminathan et al., 1999). The fourth factor is the product itself. Successful Internet marketing depends on the product and service types being marketed (Peterson et al., 1997). Product type affects consumer attitude to shopping online (Bhatnager et al., 2000; Liao & Cheung, 2001; Peterson et al., 1997). Furthermore, Liao and Cheung (2001) described the effects of product life content (the degree to which the product is essential to the daily lives of its users) on consumers initially prone to shop online. An integrated model involving the four factors is proposed and tested in this study. This work presents the results of surveys conducted in Taiwan to clarify the effects of product difference when understanding the relationships between consumer characteristics and consumer acceptance of online shopping. 2.2. Online products and services In conventional marketing research, researchers have used level of information asymmetry to divide products into three types: search goods, experience goods and credence goods (Nelson, 1970, 1974). Besides, Kotler (2003) used product characteristics as a basis for classifying products into three categories: durability, tangibility and use goods. Numerous researches have employed these models (Hsieh, Chiu, & Chiang, 2005), but they are
Table 1 Product and service classification grid Dimension 1
Dimension 2
Dimension 3
Low outlay, frequently purchased goods
Value proposition tangible or physical
Differentiation high Differentiation low Differentiation high Differentiation low Differentiation high Differentiation low Differentiation high Differentiation low
Value proposition intangible or informational
High outlay, infrequently purchased goods
Value proposition tangible or physical
Value proposition intangible or informational
potential potential potential potential potential potential potential potential
Note. From ‘‘Exploring the Implications of the Internet for Consumer Marketing’’, by Peterson et al. (1997).
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not designed for online marketing. Peterson et al. (1997) insisted that in the context of the Internet, a more relevant classification system is necessary for classifying products online. Based on the special characteristics of the Internet, they proposed a specially designed classification system for online products and services. The system is based on the following three dimensions: cost and purchase frequency, value proposition and degree of differentiation. The first dimension ranges from inexpensive, frequently purchased goods to expensive, infrequently purchased goods. Individuals avoid purchasing inexpensive and frequently purchased goods online (Peterson et al., 1997). The second dimension follows the product value proposition, and classifies the products as either tangible and physical products or intangible services. The third dimension refers to the degree of product differentiation and enables the businesses to create competitive advantage. The three dimensions are illustrated as Table 1. 3. Research model and hypotheses Based on the above discussion, an integrated model involving the four determinate factors of user acceptance of online shopping is proposed and tested in this study. Five critical variables from factor 1 to factor 3 that related to consumer characteristics were included to understand their effects on customer acceptance of online shopping. Additionally, the above relationships were discriminated in the context of different product types (factor 4). Five critical consumer characteristic variables included personal innovativeness of information technology (PIIT), Internet self-efficacy, perceived Web security, privacy concerns and product involvement. The definitions of these variables are listed below: (1) Agarwal and Prasad (1998) defined PIIT as ‘‘The willingness of an individual to try out a new Information Technology’’ (p. 206). (2) Internet self-efficacy is derived from Eastin’s Social Cognitive Theory (2002). Eastin (2002), and O’Cass and Fenech (2003) applied this concept to Internet use and termed it Internet self-efficacy. This concept is defined as the belief of individuals in their capability to organize and successfully execute Internet use. (3) Perceived Web security describes individual awareness of Web security when providing and sending personal or financial information (O’Cass & Fenech, 2003). (4) Privacy concerns can be divided into four sub-areas: data collection, errors, unauthorized secondary use and improper access (Smith, Milberg, & Burke, 1996). (5) Product involvement is derived from the concept of Personal Involvement Inventory (PII) developed by Zaichkowsky (1985). Zaichkowsky defined this construct as ‘‘product relevance to consumer needs and values (p. 342)’’. Koufaris (2002) developed a generally accepted definition, as follows: ‘‘(a) individual motivation state toward an object where (b) that motivational state is activated by the relevance or importance of the object in question (p. 211)’’. The above five variables are inferred to be the key influences on personal acceptance of online shopping, with the relationship varying according to different products and services. From the above discussions, the following research model was developed and six hypotheses were proposed (Fig. 1).
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PIIT
H1 (+)
Internet self-efficacy
H2 (+)
H3 (+)
Perceived Web security H4 (-)
Privacy concerns
H6 Attitudes toward online shopping (in the context of different product types) 1. Books 2. TV Gaming Systems 3. Online News or Magazines 4. Computer Games
H5 (+)
Product involvement
Fig. 1. Research model.
3.1. PIIT This study, conducted in Taiwan, assumes that online shopping is an innovative and new IT application for customers, because the development of electronic commerce in Pacific Asian countries is less mature than in the United States and other industrialized countries. Particularly in Taiwan, due to geographical features (the relatively small size and high population density), it is convenient for people to purchase whatever they need without using online shops, which are not a major channel. In Taiwan, undergraduate students are the main users of online shopping. Based on Rogers (1995) diffusion of innovation, most of these online shoppers are innovators (the first 2.5% of adopters) and early adopters (the next 13.5% of adopters) of online shopping. Online shopping is a new IT application for Taiwanese consumers. Personal innovativeness affects consumer acceptance of new innovation (Agarwal, 2000; Lu, Yao, & Yu, 2005; Rogers, 1995; Wang, Pallister, & Foxall, 2006). Rogers (1995) defined personal innovativeness as ‘‘the degree to which an individual adopts new ideas earlier than other members of a system (p. 22)’’. Following this concept, Agarwal and Prasad (1998) applied personal innovativeness in the domain of information technology named PIIT and defined it as ‘‘the willingness of an individual to try out a new Information Technology (p. 206)’’. They noted that PIIT is an important construct in understanding new information technology diffusion and usage intentions. Furthermore, PIIT moderates the antecedents and consequences of individual perceptions of new information technologies (Agarwal & Prasad, 1998). PIIT significantly influences attitudes toward online shopping. Individuals with high PIIT are more likely to accept online shopping. The following hypotheses can be inferred based on the above: H1: High levels of PIIT positively affect user attitudes toward online shopping.
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3.2. Internet self-efficacy Internet self-efficacy is derived from the Social Cognitive Theory proposed by Bandura (1997). Eastin (2002) and O’Cass and Fenech (2003) applied this concept in the context of Internet use named it Internet self-efficacy and defined it as a belief in one’s ability to organize and successfully execute the use of the Internet. They also indicated that personal Internet self-efficacy positively affects individual acceptance of online activities. Since the Internet is a platform for online shopping, users’ Internet self-efficacy influences their attitudes toward online shopping. High Internet self-efficacy is associated with higher acceptance of various Internet applications. Thus, the following hypothesis is inferred: H2: High levels of Internet self-efficacy positively affect user attitudes toward online shopping. 3.3. Perceived web security Since buyers and sellers do not interact face-to-face and the virtual environment allows high anonymity, online shopping involves greater security concerns than conventional trading. Liao and Cheung (2001) found that transaction security concerns significantly affect shopping behavior among Singaporeans. Moreover, Ranganathan and Ganapathy (2002) identified security as one of the key dimensions of business-to-consumer (B2C) website design. Additionally, Wolfinbarger and Gilly (2003) identified security as one of the four factors for measuring online retailer service quality. Meanwhile, O’Cass and Fenech (2003) expressed the similar opinion that user perceptions of Web security influence their adoption of Web retailing. From the above discussions, the following hypothesis is inferred: H3: High levels of personal perceived Web security positively affect user attitudes toward online shopping. 3.4. Privacy concerns Pan and Zinkhan (2006) found that privacy disclosures on websites will affect an online shopper’s trust of e-tailers. Wolfinbarger and Gilly (2003) also indicated that privacy concerns strongly influence customers’ perception of online retailer service quality. Smith et al. (1996) found that personal privacy concerns can be divided into four categories: data collection, errors, unauthorized secondary use and improper access. When making purchases online, consumers must provide private information to complete their transactions, deterring many consumers from purchasing online owing to privacy concerns. Privacy concerns vary due to personal differences, including educational background, culture, demographic background and so on (Hsu, 2006; Peslak, 2006). Ranganathan and Ganapathy (2002) also identified privacy as a key dimension in designing B2C websites. We hypothesize that individuals with high privacy concerns will avoid purchasing online. From the above discussions, the following hypothesis is derived: H4: High privacy concerns negatively affect user attitudes toward online shopping.
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3.5. Product involvement The concept of personal involvement was first proposed by Zaichkowsky (1985, 1994) as ‘‘individual perceptions of the relevance of an object based on inherent needs, values, and interests (p. 342)’’. Koufaris (2002) indicated that numerous definitions of this concept exist. He used the term ‘‘product involvement’’ as a substitute for personal involvement and proposed the following as its generally accepted definition: ‘‘individual motivation regarding an object where that motivational state is activated by the relevance or importance of the object in question (p. 211)’’. The same method was employed by Chaudhuri (2000) and Wang et al. (2006), both using the term ‘‘product involvement’’ to indicate the same concept. Chaudhuri (2000) identified a correlation between information search and product involvement. That is, consumers with high product involvement also have high levels of intention to collect related information online. Moreover, Shim, Eastlick, Lotz, and Warrington (2001) found that individuals who frequently search for information online have high acceptance of online shopping. Similarly, Koufaris (2002) indicated that individuals with higher product involvement have more positive shopping experiences and greater interest in specific products. Finally, Wang et al. (2006) identified consumer product involvement as one of the determinants of online financial service purchases. Two brief summaries are derived from the above discussions. First, since the term ‘‘product involvement’’ is used more frequently than ‘‘personal involvement’’ in studies focused on the Internet, it was employed in this study. Second, this study expects that high levels of product involvement positively influence user attitudes toward online shopping. H5: High levels of product involvement positively affect user attitudes toward online shopping. 3.6. Product and service types Many researchers have insisted on the importance of product differences in online marketing. (Bhatnager et al., 2000; Liao & Cheung, 2001; Peterson et al., 1997). However few empirical studies have reported on this issue. Most of previous studies focused on a single product or a group of similar products. For instance, Liang and Lai (2002) focused on book-buying activities. Dahlen and Lange (2002) concentrated on grocery retailing. Shim et al. (2001) focused on search goods, and Ruyter, Wetzels, and Kleijnen (2001) concentrated on travel services. This narrow focus limited the generalizability of their results to a few products at best. Although Eastin (2002) used four common B2C activities (e-shopping, online banking, online investing and electronic payment) to understand the critical influences on user acceptance, the four product types are homogeneous. The product effect thus is eliminated, and additional effort is required to systematically examine the effects of product types. This work maintains that product and service type influence the relationships between consumer characteristics and attitudes toward online shopping. H6: Product and service type affect the relationships between consumer characteristics and attitudes toward online shopping.
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4. Research methodology 4.1. Sample The target respondents were undergraduate students with online shopping experience in Taiwan. This study attempts to clarify the relationships between consumer characteristics and attitudes toward online shopping in the context of different products. In the Internet product and service classification grid (Peterson et al., 1997), personal positions regarding specific products vary; for example, some individuals view computer games as a low-cost and frequent purchase, while others hold different opinions. This diversity influences the study findings. The fact that the respondents come from a relatively homogeneous group (students) and share a similar background (including education, consumption habits, economic situation, and so on) helps avoid the above question. Undergraduate students were selected as subjects to ensure that respondents have similar opinions regarding the four selected products. A total of 220 student subjects were selected to complete a research questionnaire. 4.2. Measurement development Five consumer characteristics variables are included in this study. Well developed measurements were employed. PIIT is measured using the four items developed by Agarwal and Prasad (1998). Internet self-efficacy and perceived Web security are measured using the scales of O’Cass and Fenech (2003). Meanwhile, the instrument developed by Smith et al. (1996) is employed to measure privacy concerns. Moreover, product involvement is determined by the personal involvement inventory (PII) developed by Zaichkowsky (1994). Finally, the scale of Taylor and Todd (1995) is used to measure consumer attitudes toward online shopping. The main section of this questionnaire contains 41 items. All of the constructs and variables employed in this study are multidimensional and have validated measurement scales (Table 2).
Table 2 Variable definitions and measurements Constructs/ Variables
Definitions
Sources
PIIT
The willingness of an individual to try out a new Information Technology The beliefs in one’s capability to organize and execute successfully to use Internet One’s awareness of Web security when providing and sending personal or financial information Personal privacy concerns could be divided into four sub-areas: data collection, errors, unauthorized secondary use, and improper access Product relevance to the needs and values of the consumer If customers like to buy online. If online shopping is interesting and attractive for someone and they will increased buying online
Agarwal and Prasad (1998) O’Cass and Fenech (2003) O’Cass and Fenech (2003) Smith et al. (1996)
15
Zaichkowsky (1994)
10
Modify from Taylor and Todd (1995)
5
Internet selfefficacy Perceived Web security Privacy concerns
Product involvement Attitude toward online shopping
Items 4 4 3
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Table 3 Products and services assessed in this study
Tangible or physical Intangible or informational
Low outlay, frequently purchased goods
High outlay, infrequently purchased goods
Books Online news and magazines
TV gaming systems Computer games
The questionnaire comprised three parts, namely introduction, personal information and variables measurement sections. The introduction section outlined the purpose and background of the study. Personal information requested in this study included sex, age, Internet experience, WWW experience, online shopping experience and email address. Finally, the main part of the questionnaire included variable measurements illustrated in Appendix. This research was conducted in Taiwan and thus the measurement scale was translated into Chinese. To ensure the content validity, a doctoral student and an expert in management information systems reviewed the research instruments. The survey questionnaire was then pilot tested using 100 student samples to identify any areas requiring modification. The pilot test was designed to determine whether the questionnaire contained ambiguous sentences. Following the pilot test, researchers interviewed 10 students to determine whether the wording and sentences were misunderstood or too vague. The questionnaire was then modified based on their suggestions. Another purpose of the pilot study was to clarify whether the selected products were appropriate for the study subjects. 4.3. Online product and service selection Due to the characteristics of the Internet, this study employed the product classification grid model proposed by Peterson et al. (1997). This model includes three relevant dimensions: cost and purchase frequency, value proposition and degree of differentiation. For simplicity, this study focused on the first two dimensions, while the third dimension fixed on high differentiation potential products or services (Table 3). Study subjects were mainly undergraduate students – a demographic that has had few opportunities to consume products and services with low differentiation potential. For example, few students have had experience in stock investments or insurance purchasing. Students thus might have difficulty in making effective decisions when purchasing such products and services. Four products were chosen based on the two selected dimensions. Books were used as an example of tangible, low outlay and frequently purchased goods. Online news and magazines were used to represent intangible, low outlay and frequently purchased goods. TV gaming systems were used to represent tangible, high outlay and infrequently purchased goods. Finally, computer games were adopted as an example of intangible, high outlay and infrequently purchased goods. Products and services assessed in this study are illustrated in Table 3. 5. Data analysis and results Questionnaires were sent to 220 undergraduate students who were randomly selected from a list of 400 students who took data processing courses at a Taiwanese university.
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Questionnaires were distributed in class and participants were given sufficient time (30 min) to complete the questionnaire. To motivate subjects, participants received a gift and some course credits in return for completing the questionnaire. A total of 216 usable questionnaires were returned. Among these usable questionnaires, only 123 respondents had online shopping experience. Data from 123 online shoppers were used to analyze the research model. The subjects were 43% male and 57% female, and their average age was 22 years old. The majority of the subjects had 3 to 5 years of Internet experience. Table 4 shows respondent product involvement in four selected online products or services. 5.1. Instrument validity Factor analysis with VARIMAX rotation was used to assess the discriminant and convergent validity. The threshold of factor loading is 0.5. Based on the above criteria, one of the 15 items used to measure personal privacy concerns was eliminated. That is, 14 items were used to obtain an overall score for personal privacy concerns. Tables 5 and 6 illustrate the results and show that most of the constructs have acceptable instrument validity. 5.2. Instrument reliability Cronbach’s a is employed to test instrument reliability. Hair, Tatham, Anderson, and Black (1998, p. 88) indicated that ‘‘Cronbach’s alpha is used to measure reliability that ranges from 0 to 1, with values of .60–.70 deemed the lower limit of acceptability’’. Table 7 lists the results, which indicate that all the values are reasonably acceptable (>0.7). 5.3. Results Table 8 illustrates the descriptive statistics used in this study. Means and standard deviations are computed for each variable. This study used multiple regression analysis, with user attitudes toward online shopping as dependent variables and variables about individuals as independent variables. The study considered four products or services, and thus four regression models were used to quantify the effects of consumer characteristics on their acceptance of online shopping for different products. The four models are shown as follows:
Table 4 The level of respondent product involvement
High involvement Low involvement
Books
TV gaming systems
Online news and magazines
Computer games
97 (78.86%) 26 (21.14%)
52 (42.28%) 71 (57.72%)
51 (41.46%) 72 (58.54%)
60 (48.78%) 63 (51.22%)
Note: Product involvement is scaled form 1 to 7, 10 is the anchor for low involvement, 70 is the anchor for high involvement and 40 is the midpoint of the scale (Zaichkowsky, 1994). In this study scale mean is 42.00 (average score of subjects’ product involvement), subjects whose product involvement is scored above scale mean are categorized as high involvement others are categorized as low involvement. Results are illustrated in Table 4.
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Table 5 Consumer characteristics factor loading FactorsnComponents
1
PIIT PIIT1 PIIT2 PIIT3 PIIT4
0.61 0.86 0.61 0.85
Internet self-efficacy Internet self-efficacy1 Internet self-efficacy2 Internet self-efficacy3 Internet self-efficacy 4
2
4
5
0.74 0.80 0.80 0.67
Perceived Web security Perceived Web security1 Perceived Web security2 Perceived Web security3 Privacy concerns Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy
3
0.81 0.74 0.83
concern1 concern2 concern3 concern4 concern5 concern6 concern7 concern8 concern9 concern10 concern11 concern12 concern13 concern14
0.66 0.58 0.69 0.72 0.78 0.79 0.80 0.67 0.88 0.56 0.82 0.58 0.84 0.66
Table 6 Attitude factor loading
Attitude Attitude Attitude Attitude Attitude
1 2 3 4 5
Books
TV gaming systems
Online news and magazines
Computer games
0.81 0.80 0.67 0.66 0.75
0.72 0.80 0.72 0.76 0.68
0.79 0.71 0.71 0.76 0.70
0.82 0.84 0.67 0.74 0.77
y 1 ¼ b01 þ b11 x1 þ b21 x2 þ b31 x3 þ b41 x4 þ b51 x5 y 2 ¼ b02 þ b12 x1 þ b22 x2 þ b32 x3 þ b42 x4 þ b52 x5 y 3 ¼ b03 þ b13 x1 þ b23 x2 þ b33 x3 þ b43 x4 þ b53 x5 y 4 ¼ b04 þ b14 x1 þ b24 x2 þ b34 x3 þ b44 x4 þ b54 x5
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Table 7 Instrument reliability Variables
Item number Cronbach’s a
PIIT
Internet selfefficacy
Perceived Web security
Individual privacy concerns
Attitude Books
TV gaming systems
Online news and magazines
Computer games
4
4
3
14
5
5
5
5
0.81
0.86
0.79
0.79
0.79
0.79
0.83
0.94
Table 8 Descriptive statistic (N = 123) Variables
Mean
Standard Deviation
PIIT Internet self-efficacy Perceived Web security Privacy concerns Product involvement (books) Product involvement (TV gaming systems) Product involvement (online news and magazines) Product involvement (computer games) Attitude (books) Attitude (TV gaming systems) Attitude (online news and magazines) Attitude (computer games)
3.64 3.96 2.53 4.26 50.65 38.09 40.05 39.20 3.02 2.77 3.00 2.82
0.69 0.67 0.82 0.63 10.69 14.17 10.91 15.24 0.65 0.66 0.64 0.69
Note: Variables including PIIT, Internet self-efficacy, perceived Web security, privacy concerns, and Attitude are scaled from 1 to 5. Mean scores and standard deviation are computed in this table. Additionally, product involvement is scale from 1 to 7, therefore it ranges from 10 to 70.
The dependent variables measure consumer attitudes toward online shopping in the context of the selected product (y1: books; y2: TV gaming systems; y3: online news and magazines; y4: computer games). The independent variables are x1: PIIT; x2: Internet self-efficacy; x3: Perceived Web security; x4: Privacy concerns; x5: Product involvement. Regression results are shown in Tables 9 and 10. Table 9 computed F-values and R2 to understand the overall significance of each equation. All of the models yield significant p-values (p < .01) and R2 around 20% of the variance in attitudes toward online shopping Table 9 Summary of regression analysis Regression Statistics
Books
TV gaming systems
Online news and magazines
Computer games
F-Value p-Value R2 Durbin–Watson test
3.88 .00** 0.14 1.81
6.14 .00** 0.21 1.77
5.85 .00** 0.20 2.18
6.59 .00** 0.22 1.94
* **
p < .05. p < .01.
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Table 10 Analyses of the four products Variables/product types
Regression coefficient
Standard error of coefficient
Standardized regression coefficient (beta) (p-value)
Buying books online PIIT Internet self-efficacy Perceived Web security Privacy concerns Product involvement
0.12 5.845E02 0.17 0.24 0.29
0.10 0.11 0.09 0.11 0.09
0.12 0.06 0.17 0.24 0.29
(.25)-b11 (.60)-b21 (.08)-b31 (.04)*-b41 (.00)**b51
Buying TV gaming systems online PIIT 0.19 Internet self-efficacy 6.064E02 Perceived Web security 0.27 Privacy concerns 0.26 Product involvement 0.18
0.10 0.11 0.09 0.11 0.09
0.19 0.06 0.27 0.26 0.18
(.06)-b12 (.56)-b22 (.00)**b32 (.02)*-b42 (.04)*-b52
Buying online news or magazines online PIIT 0.14 Internet self-efficacy 1.219E02 Perceived Web security 0.12 Privacy concerns 0.20 Product involvement 0.37
0.10 0.11 0.09 0.11 0.08
0.14 0.01 0.12 0.17 0.37
(.15)-b13 (.91)-b23 (.19)-b33 (.13)-b43 (.00)**b53
Buying computer games online PIIT 0.20 Internet self-efficacy 0.10 Perceived Web security 0.34 Privacy concerns 0.17 Product involvement 0.17
0.10 0.10 0.09 0.11 0.09
0.20 0.10 0.34 0.17 0.17
(.05)*-b14 (.33)-b24 (.00)**b34 (.12)-b44 (.05)*-b54
* **
p < .05. p < .01.
was explained. Table 9 lists detailed data on the statistical coefficient in each of the regression models. Table 10 lists the results of significance testing of the study variables. The regression results suggest the following: In the context of book buying, privacy concerns (p = .040) and product involvement (p = .002) yield coefficients with significant p-value. In the context of TV gaming systems purchases, perceived Web security (p = .003), privacy concerns (p = .018) and product involvement (p = .044) yield significant p-values for their coefficients. Furthermore, in the context of online news and magazine purchases, p-values are only significant for product involvement (p = .000). Finally, in the context of computer game purchase, three variables yield significant p-values including PIIT (p = .050), perceived Web security (p = .000) and product involvement (p = .046). Details are illustrated in Table 10. 6. Discussions This study developed a model for determining online shopping attitudes and tested it in the context of different products or services. Analysis results demonstrated that four
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regression functions were all significant in the context of different products or services. Furthermore, this study found that the significant variables differed in the context of different products or services. The analytical results are discussed below. 6.1. Low outlay, frequently purchased and tangible products or services This study employed books as an example of a low outlay, frequently purchased and tangible product or service. The research findings indicate that individual privacy concerns negatively affect consumer attitudes toward buying books online. Consumer product involvement with books positively affects those attitudes. The other three consumer characteristics variables are insignificant in the context of book purchasing. For respondents, books are a low-cost product, and since the amounts of money involved in individual transactions are small. Consumers are less concerned with Web security than they are for more expensive products or services (for example TV gaming systems, which are discussed in the next section). Additionally, the Internet is very popular on campus and most respondents have taken computer-related courses. Therefore, Internet self-efficacy is not a critical factor in the context of online book purchasing. Moreover, books are a common and popular product, meaning that users tend to have positive attitudes toward purchasing books online regardless of their level of personal innovativeness in relation to information technology. 6.2. High outlay, infrequently purchased and tangible products or services This study used TV gaming systems as an example of a high outlay, infrequently purchased and tangible product or service. The analytical results indicate that user perceptions of Web security positively affect attitudes toward online purchases of TV gaming systems. Although both TV gaming systems and books are tangible products, unlike books, TV gaming systems are relatively expensive, and only consumers who perceive the Web as a secure environment are likely to purchase gaming systems via the Internet. The analytical results indicate that individual privacy concerns negatively affect attitudes toward online purchases of TV gaming systems. Moreover, product involvement also positively affects attitudes toward online purchases of TV gaming systems. Additionally, another two consumer characteristics variables (PIIT and Internet self-efficacy) are insignificant in the context of TV gaming system purchasing. These results resemble those low outlay, frequently purchased, tangible products or services. 6.3. Low outlay, frequently purchased and intangible products or services Online news and magazines were adopted as an example of a low cost, frequently purchased and intangible product or service. Based on the research findings, this study finds that only product involvement significantly and positively affects attitudes toward purchasing online news and magazines. Meanwhile, the other four variables proposed in this study are insignificant. This phenomenon can be explained by the fact that online news or magazines (particularly news) are extremely popular and cheap, meaning consumers are primarily concerned with newspaper or magazine content. This fact may be the reason why other variables besides level of product involvement do not exert a significant influence in the context of low cost, frequently purchased and intangible products or services.
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6.4. High outlay, infrequently purchased and intangible products or services This study adopted computer games as an example of high cost, infrequently purchased and intangible products or services. PIIT, perceived Web security and product involvement were found to positively affect attitudes toward online purchases of computer games, while no significant effect was found for the other two variables (Internet self-efficacy and individual privacy concerns). Although the relationship between individual privacy concerns and attitudes toward buying computer games online is negative, it is not significant. This study suggests conducting further investigations in the future to clarify the issue further. 7. Conclusions The study examined how individual differences affect online shopping acceptance. Importantly, the determinants of user acceptance of online shopping differ according to product or service type. Based on studying a limited set of products or services, the following findings are obtained: (1) Increased PIIT positively affects user attitudes toward purchasing high cost, infrequently purchased, and intangible products or services online. (2) Increased personal perceptions of Web security positively affect user attitudes toward purchasing expensive, infrequently purchased products or services. (3) Increased personal privacy concerns negatively affect user attitudes toward purchasing tangible or physical products or services. (4) High product involvement positively affects user attitudes toward online shopping in the context of all employed products or services. (5) Products and service types influence the relationships between consumer characteristics and attitudes toward online shopping. Consistent with previous studies, consumer characteristics are found to influence online shopping acceptance. However, this study found that these relationships were affected by different product types. When designing a marketing plan, online retailers must consider two key questions. The first question involves the identity of potential buyers. This study provided the consumer characteristics of online shoppers. Based on these characteristics, online business can identify their target market easily. The second question involves the type of products that are suitable for online marketing. Present research results can help businesses to focus on their potential market and increase their marketing edge. Acknowledgement The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC922416-H-008-013-.
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Appendix. Scales and items PIIT (Agarwal & Prasad, 1998) 1. If I heard about a new information technology, I would look for ways to experiment with it. 2. Among my peers, I am usually the first to try out new information technologies. 3. In general, I am hesitant to try out new information technologies.* 4. I like to experiment with new information technologies. Note: * Denotes items that were reverse scored. Internet self-efficacy (O’Cass & Fenech, 2003) 1. I could easily use the Web to find product information on a product/service. 2. I can get to a specific Web site with a browser. 3. I feel comfortable searching with World Wide Web on my own. 4. I would be able to use Web on my own to locate retail sites. Perceived Web security (O’Cass & Fenech, 2003) 1. I feel secure sending personal information across the Web. 2. I feel safe providing personal information about me to Web retailers. 3. Web is safe environment to provide personal information. Privacy concerns (Smith et al., 1996) 1. It usually bothers me when companies ask me for personal information. 2. All the personal information in the computer database should be double-checked for accuracy – no matter how much this costs. 3. Companies should not use personal information for any purpose unless it has been authorized by the individuals who provided the information. 4. Companies should devote more time and effort to preventing unauthorized access to personal information. 5. When companies ask me for personal information, I sometimes think twice before providing it. 6. Companies should take more steps to make sure that the personal information in their files is accurate. 7. When people give personal information to a company for some reason, the company should never use the information for any other reason. 8. Companies should have better procedures to correct errors in personal information. 9. Computer databases that contain personal information should be protected from unauthorized access – no matter how much it costs. 10. It bothers me to give personal information to so many companies. 11. Companies should never sell the personal information in their computer databases to other companies. 12. Companies should devote more time and effort to verifying the accuracy of the personal information in their databases. 13. Companies should never share personal information with other companies unless it has been authorized by the individuals who provided the information. 14. Companies should take more steps to make sure that unauthorized people cannot access personal information in their computers. 15. I’m concerned that companies are collecting too much personal information about me.
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Product involvement (Zaichkowsky, 1994) For me [selected product] is: 1. important – unimportant* 2. boring – interesting 3. relevant – irrelevant* 4. exciting – unexciting* 5. means nothing – means a lot to me 6. appealing – unappealing* 7. fascinating – mundane* 8. worthless – valuable 9. involving – uninvolving* 10. not needed – needed Note: *Denotes items that were reverse scored. Attitudes toward online shopping. Modify from Taylor and Todd (1995) 1. I like buying [selected product] online. 2. Buying [selected product] online is interesting. 3. Buying [selected product] online makes my life more attractive. 4. I intend finishing [selected product] buying processes totally online. 5. I will increase buying [selected product] online in the future. References Agarwal, R. (2000). Individual acceptance of information technologies. In R. W. Zmud (Ed.), Framing the domains of IT management projecting the future. . .through the past (pp. 85–104). Pinnaflex Educational Resources Inc. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. Amichai-Hamburger, Y. (2002). Internet and personality. Computers in Human Behavior, 18(1), 1–10. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company. Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: The role of privacy, security, and site attributes. Journal of Strategic Information Systems, 11(3–4), 245– 270. Bhatnager, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and internet shopping behavior. Communications of the ACM, 43(11), 98–105. Chaudhuri, A. A. (2000). Macro analysis of the relationship of product involvement and information search: The role of risk. Journal of Marketing Theory and Practice, 8(1), 1–15. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. Citrin, A. V., Sprott, D. E., Silverman, S. N., & Stem, D. E. (2000). Adoption of Internet shopping: The role of consumer innovativeness. Industrial Management and Data System, 100(7), 294–300. Dahlen, M., & Lange, F. (2002). Real consumers in the virtual store. Scandinavian Journal of Management, 18(3), 341–363. Eastin, M. S. (2002). Diffusion of E-commerce: An analysis of the adoption of four E-commerce activities. Telematics and Informatics, 19(3), 251–267. Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (1998). Multivariate data analysis (fifth ed.). Prentice Hall. Hills, P., & Argyle, M. (2003). Uses of the internet and their relationships with individual differences in personality. Computers in Human Behavior, 19(1), 59–70. Hsieh, Y. C., Chiu, H. C., & Chiang, M. Y. (2005). Maintaining a committed online customer: A study across search-experience-credence products. Journal of Retailing, 81(1), 75–82. Hsu, C. W. (2006). Privacy concerns, privacy practices and web site categories toward a situational paradigm. Online Information Review, 30(5), 569–586.
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Kotler, P. (2003). Marketing management (Eleventh ed.). Prentice Hall. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online customer behavior. Information Systems Research, 13(2), 205–223. Liang, T. P., & Huang, J. S. (1998). An empirical study on consumer acceptance on products in electronic markets: A transaction cost model. Decision Support Systems, 24, 29–43. Liang, T. P., & Lai, H. J. (2002). Effect of store design on consumer purchases: an empirical study of on-line bookstores. Information and Management, 39(6), 431–444. Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes: An empirical study. Information and Management, 38(5), 299–306. Li, H., Kuo, C., & Russell, M. G. (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)http://jcmc.indiana.edu/vol5/issue2/hairong.html. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. Journal of Strategic Information Systems, 14, 245–268. Nelson, Phillip (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311– 329. Nelson, Phillip (1974). Advertising as information. Journal of Political Economy, 82(4), 729–754. O’Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the future of Internet users web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81–94. Pan, Y., & Zinkhan, G. M. (2006). Exploring the impact of online privacy disclosures on consumer trust. Journal of Retailing, 82(4), 331–338. Peslak, A. R. (2006). PAPA revisited: A current empirical study of the mason framework. The Journal of Computer Information Systems, 46(3), 117–123. Peterson, R. A., Balasubramanian, S., & Bronnenberg, B. J. (1997). Exploring the implications of the Internet for consumer marketing. Journal of Academy of Marketing Science, 25(4), 329–346. Phau, I., & Poon, S. M. (2000). Factors influencing the types of products and services purchased over the Internet. Internet Research, 10(2), 102–113. Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information and Management, 39(6), 457–465. Ranganathan, C., & Grandon, E. (2002). An exploratory examination of factors affecting online sales. Journal of Computer Information Systems, Spring, 87–93. Rogers, E. M. (1995). Diffusion of innovations. The Free Press, A Division of Simon & Schuster Inc. Ruyter, K. D., Wetzels, M., & Kleijnen, M. (2001). Customer adoption of e-service: An experimental study. International Journal of Service Industry Management, 12(2), 184–207. Shim, S., Eastlick, M. A., Lotz, S. L., & Warrington, P. (2001). An on-line prepurchase intentions model: The role of intention to search. Journal of Retailing, 77(3), 397–416. Sim, L. L., & Koi, S. M. (2002). Singapore’s Internet shoppers and their impact on traditional shopping patterns. Journal of Retailing and Consumer Services, 9(2), 115–124. Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals’ concerns about organizational practices. MIS Quarterly, 20(2), 167–196. Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999). Browsers of buyers in cyberspace? An investigation of factors influencing electronic exchange. Journal of Computer-Mediated Communication, 5(2)http:// jcmc.indiana.edu/vol5/issue2/swaminathan.htm. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. Vrechopoulos, A. P., Siomkos, G. J., & Doukidis, G. I. (2001). Internet shopping adoption by Greek consumers. European Journal of Innovation Management, 4(3), 142–153. Wang, H. C., Pallister, J. G., & Foxall, G. R. (2006). Innovativeness and involvement as determinants of website loyalty: III. Theoretical and managerial contributions. Technovation, 26, 1374–1383. Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: Dimensionalzing, measuring and predicting etail quality. Journal of Retailing, 79, 183–198. Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341– 352. Zaichkowsky, J. L. (1994). The personal involvement inventory: Reduction, revision, and application to advertising. Journal of Advertising, 23(4), 59–70.
Effects of consumer characteristics on their acceptance of online shopping: Comparisons among different product types Jiunn-Woei Lian a
a,*
, Tzu-Ming Lin
b
Nanhua University, Department of Information Management, No. 32, Chung Keng Li, Dalin, Chiayi 622, Taiwan, ROC b National Central University, Department of Information Management, No.300, Jhongda Road, Jhongli City, Taoyuan County 32001, Taiwan, ROC Available online 12 March 2007
Abstract Previous electronic commerce (EC) studies have found that consumer characteristics are important when considering issues related to the acceptance of online shopping. However, most studies have focused on a single product or similar products. The effects of different product types have been relatively neglected. Previous studies have limited the generalizability of their results to a few products at best. To overcome this limitation, the purpose of this study was to explore the effects of different product types. The Internet product and service classification grid proposed by Peterson, Balasubramanian and Bronnenberg (Peterson, R. A., Balasubramanian, S., & Bronnenberg, B. J. (1997). Exploring the implications of the Internet for consumer marketing. Journal of Academy of Marketing Science, 25(4), 329–346) was employed to examine the effects of consumer characteristic differences on online shopping acceptance in the context of different products and services. A surveybased approach was employed to investigate the research questions. Regression analysis demonstrated that the determinants of online shopping acceptance differ among product or service types. Additionally, personal innovativeness of information technology (PIIT), perceived Web security, personal privacy concerns, and product involvement can influence consumer acceptance of online shopping, but their influence varies according to product types. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Online shopping; Consumer characteristics; Product types *
Corresponding author. Tel.: +886 9 22309530. E-mail addresses: [email protected], [email protected] (J.-W. Lian), [email protected], [email protected] (T.-M. Lin). 0747-5632/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2007.01.002
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1. Introduction The development of the Internet has increased the popularity of online shopping. However, many Internet users avoid shopping online due to security and privacy concerns. Despite this though, online sales continue to grow as Internet-based businesses become more sophisticated; indeed many users remain interested in online shopping. Understanding potential markets is thus important for businesses investing in electronic commerce (EC). Amichai-Hamburger (2002) indicated that the personality of Internet users plays an important role in their online behavior. Moreover, Hills and Argyle (2003) reached similar viewpoints. They found that individual Internet use correlates with individual personality differences. Kotler (2003) asserted that personal factors are the main influence on buyer behavior. Thus, understanding the personality differences between these two groups (Internet shoppers and non-Internet shoppers) is extremely important to businesses. Understanding the characteristics of potential online customers can help businesses accurately target potential markets. Peterson, Balasubramanian, and Bronnenberg (1997) indicated that owing to the special characteristics of the Internet, its suitability for marketing depends on the characteristics of the products and services being marketed. Thus, considering the differences among product types is essential to fully understanding the influence of online shopping. Liang and Huang (1998) expressed a similar opinion, noting that when dealing with electronic markets, increased attention must be paid to understanding which products are suitable for marketing online. Notably, they indicated that different product types influence consumer online shopping acceptance. Phau and Poon (2000) also obtained similar findings and found that product type affects consumer decisions when choosing between traditional or online channels. Previous studies found that consumer characteristics are important when considering online shopping acceptance-related issues. However, most studies neglect the effects of different product types. The generalizability of these studies thus was limited to only a few products. To overcome this limitation, the purpose of this study explores the effects of different product types. 2. Literature review This section summarizes the relevant literature regarding the determinants of user acceptance of online shopping and product categories. 2.1. Determinants of user acceptance of online shopping Previous research has identified four determinants of consumer acceptance of online shopping, namely consumer characteristics, personal perceived values, website design and the product itself. The first factor is consumer characteristics (Swaminathan, Lepkowska-White, & Rao, 1999). Variables belonging to this factor include personality traits (O’Cass & Fenech, 2003), self-efficacy (Eastin, 2002), demographic profiles (Li, Kuo, & Russell, 1999; Sim & Koi, 2002; Vrechopoulos, Siomkos, & Doukidis, 2001) and acceptance of new IT applications (Childers, Carr, Peck, & Carson, 2001; Citrin, Sprott, Silverman, & Stem, 2000; O’Cass & Fenech, 2003).
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The second factor is personal perceived values (Li et al., 1999). Variables in this dimension include perceived risk (Bhatnager, Misra, & Rao, 2000; Eastin, 2002), perceived convenience (Eastin, 2002), perceived website quality (O’Cass & Fenech, 2003) and perceived benefits (Eastin, 2002). The third factor is website design (Dahlen & Lange, 2002; Liang & Lai, 2002; Ranganathan & Grandon, 2002). Variables included in this factor are security (Belanger, Hiller, & Smith, 2002; Liao & Cheung, 2001; Ranganathan & Grandon, 2002; Swaminathan et al., 1999) and privacy (Belanger et al., 2002; Ranganathan & Grandon, 2002; Swaminathan et al., 1999). The fourth factor is the product itself. Successful Internet marketing depends on the product and service types being marketed (Peterson et al., 1997). Product type affects consumer attitude to shopping online (Bhatnager et al., 2000; Liao & Cheung, 2001; Peterson et al., 1997). Furthermore, Liao and Cheung (2001) described the effects of product life content (the degree to which the product is essential to the daily lives of its users) on consumers initially prone to shop online. An integrated model involving the four factors is proposed and tested in this study. This work presents the results of surveys conducted in Taiwan to clarify the effects of product difference when understanding the relationships between consumer characteristics and consumer acceptance of online shopping. 2.2. Online products and services In conventional marketing research, researchers have used level of information asymmetry to divide products into three types: search goods, experience goods and credence goods (Nelson, 1970, 1974). Besides, Kotler (2003) used product characteristics as a basis for classifying products into three categories: durability, tangibility and use goods. Numerous researches have employed these models (Hsieh, Chiu, & Chiang, 2005), but they are
Table 1 Product and service classification grid Dimension 1
Dimension 2
Dimension 3
Low outlay, frequently purchased goods
Value proposition tangible or physical
Differentiation high Differentiation low Differentiation high Differentiation low Differentiation high Differentiation low Differentiation high Differentiation low
Value proposition intangible or informational
High outlay, infrequently purchased goods
Value proposition tangible or physical
Value proposition intangible or informational
potential potential potential potential potential potential potential potential
Note. From ‘‘Exploring the Implications of the Internet for Consumer Marketing’’, by Peterson et al. (1997).
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not designed for online marketing. Peterson et al. (1997) insisted that in the context of the Internet, a more relevant classification system is necessary for classifying products online. Based on the special characteristics of the Internet, they proposed a specially designed classification system for online products and services. The system is based on the following three dimensions: cost and purchase frequency, value proposition and degree of differentiation. The first dimension ranges from inexpensive, frequently purchased goods to expensive, infrequently purchased goods. Individuals avoid purchasing inexpensive and frequently purchased goods online (Peterson et al., 1997). The second dimension follows the product value proposition, and classifies the products as either tangible and physical products or intangible services. The third dimension refers to the degree of product differentiation and enables the businesses to create competitive advantage. The three dimensions are illustrated as Table 1. 3. Research model and hypotheses Based on the above discussion, an integrated model involving the four determinate factors of user acceptance of online shopping is proposed and tested in this study. Five critical variables from factor 1 to factor 3 that related to consumer characteristics were included to understand their effects on customer acceptance of online shopping. Additionally, the above relationships were discriminated in the context of different product types (factor 4). Five critical consumer characteristic variables included personal innovativeness of information technology (PIIT), Internet self-efficacy, perceived Web security, privacy concerns and product involvement. The definitions of these variables are listed below: (1) Agarwal and Prasad (1998) defined PIIT as ‘‘The willingness of an individual to try out a new Information Technology’’ (p. 206). (2) Internet self-efficacy is derived from Eastin’s Social Cognitive Theory (2002). Eastin (2002), and O’Cass and Fenech (2003) applied this concept to Internet use and termed it Internet self-efficacy. This concept is defined as the belief of individuals in their capability to organize and successfully execute Internet use. (3) Perceived Web security describes individual awareness of Web security when providing and sending personal or financial information (O’Cass & Fenech, 2003). (4) Privacy concerns can be divided into four sub-areas: data collection, errors, unauthorized secondary use and improper access (Smith, Milberg, & Burke, 1996). (5) Product involvement is derived from the concept of Personal Involvement Inventory (PII) developed by Zaichkowsky (1985). Zaichkowsky defined this construct as ‘‘product relevance to consumer needs and values (p. 342)’’. Koufaris (2002) developed a generally accepted definition, as follows: ‘‘(a) individual motivation state toward an object where (b) that motivational state is activated by the relevance or importance of the object in question (p. 211)’’. The above five variables are inferred to be the key influences on personal acceptance of online shopping, with the relationship varying according to different products and services. From the above discussions, the following research model was developed and six hypotheses were proposed (Fig. 1).
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PIIT
H1 (+)
Internet self-efficacy
H2 (+)
H3 (+)
Perceived Web security H4 (-)
Privacy concerns
H6 Attitudes toward online shopping (in the context of different product types) 1. Books 2. TV Gaming Systems 3. Online News or Magazines 4. Computer Games
H5 (+)
Product involvement
Fig. 1. Research model.
3.1. PIIT This study, conducted in Taiwan, assumes that online shopping is an innovative and new IT application for customers, because the development of electronic commerce in Pacific Asian countries is less mature than in the United States and other industrialized countries. Particularly in Taiwan, due to geographical features (the relatively small size and high population density), it is convenient for people to purchase whatever they need without using online shops, which are not a major channel. In Taiwan, undergraduate students are the main users of online shopping. Based on Rogers (1995) diffusion of innovation, most of these online shoppers are innovators (the first 2.5% of adopters) and early adopters (the next 13.5% of adopters) of online shopping. Online shopping is a new IT application for Taiwanese consumers. Personal innovativeness affects consumer acceptance of new innovation (Agarwal, 2000; Lu, Yao, & Yu, 2005; Rogers, 1995; Wang, Pallister, & Foxall, 2006). Rogers (1995) defined personal innovativeness as ‘‘the degree to which an individual adopts new ideas earlier than other members of a system (p. 22)’’. Following this concept, Agarwal and Prasad (1998) applied personal innovativeness in the domain of information technology named PIIT and defined it as ‘‘the willingness of an individual to try out a new Information Technology (p. 206)’’. They noted that PIIT is an important construct in understanding new information technology diffusion and usage intentions. Furthermore, PIIT moderates the antecedents and consequences of individual perceptions of new information technologies (Agarwal & Prasad, 1998). PIIT significantly influences attitudes toward online shopping. Individuals with high PIIT are more likely to accept online shopping. The following hypotheses can be inferred based on the above: H1: High levels of PIIT positively affect user attitudes toward online shopping.
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3.2. Internet self-efficacy Internet self-efficacy is derived from the Social Cognitive Theory proposed by Bandura (1997). Eastin (2002) and O’Cass and Fenech (2003) applied this concept in the context of Internet use named it Internet self-efficacy and defined it as a belief in one’s ability to organize and successfully execute the use of the Internet. They also indicated that personal Internet self-efficacy positively affects individual acceptance of online activities. Since the Internet is a platform for online shopping, users’ Internet self-efficacy influences their attitudes toward online shopping. High Internet self-efficacy is associated with higher acceptance of various Internet applications. Thus, the following hypothesis is inferred: H2: High levels of Internet self-efficacy positively affect user attitudes toward online shopping. 3.3. Perceived web security Since buyers and sellers do not interact face-to-face and the virtual environment allows high anonymity, online shopping involves greater security concerns than conventional trading. Liao and Cheung (2001) found that transaction security concerns significantly affect shopping behavior among Singaporeans. Moreover, Ranganathan and Ganapathy (2002) identified security as one of the key dimensions of business-to-consumer (B2C) website design. Additionally, Wolfinbarger and Gilly (2003) identified security as one of the four factors for measuring online retailer service quality. Meanwhile, O’Cass and Fenech (2003) expressed the similar opinion that user perceptions of Web security influence their adoption of Web retailing. From the above discussions, the following hypothesis is inferred: H3: High levels of personal perceived Web security positively affect user attitudes toward online shopping. 3.4. Privacy concerns Pan and Zinkhan (2006) found that privacy disclosures on websites will affect an online shopper’s trust of e-tailers. Wolfinbarger and Gilly (2003) also indicated that privacy concerns strongly influence customers’ perception of online retailer service quality. Smith et al. (1996) found that personal privacy concerns can be divided into four categories: data collection, errors, unauthorized secondary use and improper access. When making purchases online, consumers must provide private information to complete their transactions, deterring many consumers from purchasing online owing to privacy concerns. Privacy concerns vary due to personal differences, including educational background, culture, demographic background and so on (Hsu, 2006; Peslak, 2006). Ranganathan and Ganapathy (2002) also identified privacy as a key dimension in designing B2C websites. We hypothesize that individuals with high privacy concerns will avoid purchasing online. From the above discussions, the following hypothesis is derived: H4: High privacy concerns negatively affect user attitudes toward online shopping.
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3.5. Product involvement The concept of personal involvement was first proposed by Zaichkowsky (1985, 1994) as ‘‘individual perceptions of the relevance of an object based on inherent needs, values, and interests (p. 342)’’. Koufaris (2002) indicated that numerous definitions of this concept exist. He used the term ‘‘product involvement’’ as a substitute for personal involvement and proposed the following as its generally accepted definition: ‘‘individual motivation regarding an object where that motivational state is activated by the relevance or importance of the object in question (p. 211)’’. The same method was employed by Chaudhuri (2000) and Wang et al. (2006), both using the term ‘‘product involvement’’ to indicate the same concept. Chaudhuri (2000) identified a correlation between information search and product involvement. That is, consumers with high product involvement also have high levels of intention to collect related information online. Moreover, Shim, Eastlick, Lotz, and Warrington (2001) found that individuals who frequently search for information online have high acceptance of online shopping. Similarly, Koufaris (2002) indicated that individuals with higher product involvement have more positive shopping experiences and greater interest in specific products. Finally, Wang et al. (2006) identified consumer product involvement as one of the determinants of online financial service purchases. Two brief summaries are derived from the above discussions. First, since the term ‘‘product involvement’’ is used more frequently than ‘‘personal involvement’’ in studies focused on the Internet, it was employed in this study. Second, this study expects that high levels of product involvement positively influence user attitudes toward online shopping. H5: High levels of product involvement positively affect user attitudes toward online shopping. 3.6. Product and service types Many researchers have insisted on the importance of product differences in online marketing. (Bhatnager et al., 2000; Liao & Cheung, 2001; Peterson et al., 1997). However few empirical studies have reported on this issue. Most of previous studies focused on a single product or a group of similar products. For instance, Liang and Lai (2002) focused on book-buying activities. Dahlen and Lange (2002) concentrated on grocery retailing. Shim et al. (2001) focused on search goods, and Ruyter, Wetzels, and Kleijnen (2001) concentrated on travel services. This narrow focus limited the generalizability of their results to a few products at best. Although Eastin (2002) used four common B2C activities (e-shopping, online banking, online investing and electronic payment) to understand the critical influences on user acceptance, the four product types are homogeneous. The product effect thus is eliminated, and additional effort is required to systematically examine the effects of product types. This work maintains that product and service type influence the relationships between consumer characteristics and attitudes toward online shopping. H6: Product and service type affect the relationships between consumer characteristics and attitudes toward online shopping.
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4. Research methodology 4.1. Sample The target respondents were undergraduate students with online shopping experience in Taiwan. This study attempts to clarify the relationships between consumer characteristics and attitudes toward online shopping in the context of different products. In the Internet product and service classification grid (Peterson et al., 1997), personal positions regarding specific products vary; for example, some individuals view computer games as a low-cost and frequent purchase, while others hold different opinions. This diversity influences the study findings. The fact that the respondents come from a relatively homogeneous group (students) and share a similar background (including education, consumption habits, economic situation, and so on) helps avoid the above question. Undergraduate students were selected as subjects to ensure that respondents have similar opinions regarding the four selected products. A total of 220 student subjects were selected to complete a research questionnaire. 4.2. Measurement development Five consumer characteristics variables are included in this study. Well developed measurements were employed. PIIT is measured using the four items developed by Agarwal and Prasad (1998). Internet self-efficacy and perceived Web security are measured using the scales of O’Cass and Fenech (2003). Meanwhile, the instrument developed by Smith et al. (1996) is employed to measure privacy concerns. Moreover, product involvement is determined by the personal involvement inventory (PII) developed by Zaichkowsky (1994). Finally, the scale of Taylor and Todd (1995) is used to measure consumer attitudes toward online shopping. The main section of this questionnaire contains 41 items. All of the constructs and variables employed in this study are multidimensional and have validated measurement scales (Table 2).
Table 2 Variable definitions and measurements Constructs/ Variables
Definitions
Sources
PIIT
The willingness of an individual to try out a new Information Technology The beliefs in one’s capability to organize and execute successfully to use Internet One’s awareness of Web security when providing and sending personal or financial information Personal privacy concerns could be divided into four sub-areas: data collection, errors, unauthorized secondary use, and improper access Product relevance to the needs and values of the consumer If customers like to buy online. If online shopping is interesting and attractive for someone and they will increased buying online
Agarwal and Prasad (1998) O’Cass and Fenech (2003) O’Cass and Fenech (2003) Smith et al. (1996)
15
Zaichkowsky (1994)
10
Modify from Taylor and Todd (1995)
5
Internet selfefficacy Perceived Web security Privacy concerns
Product involvement Attitude toward online shopping
Items 4 4 3
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Table 3 Products and services assessed in this study
Tangible or physical Intangible or informational
Low outlay, frequently purchased goods
High outlay, infrequently purchased goods
Books Online news and magazines
TV gaming systems Computer games
The questionnaire comprised three parts, namely introduction, personal information and variables measurement sections. The introduction section outlined the purpose and background of the study. Personal information requested in this study included sex, age, Internet experience, WWW experience, online shopping experience and email address. Finally, the main part of the questionnaire included variable measurements illustrated in Appendix. This research was conducted in Taiwan and thus the measurement scale was translated into Chinese. To ensure the content validity, a doctoral student and an expert in management information systems reviewed the research instruments. The survey questionnaire was then pilot tested using 100 student samples to identify any areas requiring modification. The pilot test was designed to determine whether the questionnaire contained ambiguous sentences. Following the pilot test, researchers interviewed 10 students to determine whether the wording and sentences were misunderstood or too vague. The questionnaire was then modified based on their suggestions. Another purpose of the pilot study was to clarify whether the selected products were appropriate for the study subjects. 4.3. Online product and service selection Due to the characteristics of the Internet, this study employed the product classification grid model proposed by Peterson et al. (1997). This model includes three relevant dimensions: cost and purchase frequency, value proposition and degree of differentiation. For simplicity, this study focused on the first two dimensions, while the third dimension fixed on high differentiation potential products or services (Table 3). Study subjects were mainly undergraduate students – a demographic that has had few opportunities to consume products and services with low differentiation potential. For example, few students have had experience in stock investments or insurance purchasing. Students thus might have difficulty in making effective decisions when purchasing such products and services. Four products were chosen based on the two selected dimensions. Books were used as an example of tangible, low outlay and frequently purchased goods. Online news and magazines were used to represent intangible, low outlay and frequently purchased goods. TV gaming systems were used to represent tangible, high outlay and infrequently purchased goods. Finally, computer games were adopted as an example of intangible, high outlay and infrequently purchased goods. Products and services assessed in this study are illustrated in Table 3. 5. Data analysis and results Questionnaires were sent to 220 undergraduate students who were randomly selected from a list of 400 students who took data processing courses at a Taiwanese university.
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Questionnaires were distributed in class and participants were given sufficient time (30 min) to complete the questionnaire. To motivate subjects, participants received a gift and some course credits in return for completing the questionnaire. A total of 216 usable questionnaires were returned. Among these usable questionnaires, only 123 respondents had online shopping experience. Data from 123 online shoppers were used to analyze the research model. The subjects were 43% male and 57% female, and their average age was 22 years old. The majority of the subjects had 3 to 5 years of Internet experience. Table 4 shows respondent product involvement in four selected online products or services. 5.1. Instrument validity Factor analysis with VARIMAX rotation was used to assess the discriminant and convergent validity. The threshold of factor loading is 0.5. Based on the above criteria, one of the 15 items used to measure personal privacy concerns was eliminated. That is, 14 items were used to obtain an overall score for personal privacy concerns. Tables 5 and 6 illustrate the results and show that most of the constructs have acceptable instrument validity. 5.2. Instrument reliability Cronbach’s a is employed to test instrument reliability. Hair, Tatham, Anderson, and Black (1998, p. 88) indicated that ‘‘Cronbach’s alpha is used to measure reliability that ranges from 0 to 1, with values of .60–.70 deemed the lower limit of acceptability’’. Table 7 lists the results, which indicate that all the values are reasonably acceptable (>0.7). 5.3. Results Table 8 illustrates the descriptive statistics used in this study. Means and standard deviations are computed for each variable. This study used multiple regression analysis, with user attitudes toward online shopping as dependent variables and variables about individuals as independent variables. The study considered four products or services, and thus four regression models were used to quantify the effects of consumer characteristics on their acceptance of online shopping for different products. The four models are shown as follows:
Table 4 The level of respondent product involvement
High involvement Low involvement
Books
TV gaming systems
Online news and magazines
Computer games
97 (78.86%) 26 (21.14%)
52 (42.28%) 71 (57.72%)
51 (41.46%) 72 (58.54%)
60 (48.78%) 63 (51.22%)
Note: Product involvement is scaled form 1 to 7, 10 is the anchor for low involvement, 70 is the anchor for high involvement and 40 is the midpoint of the scale (Zaichkowsky, 1994). In this study scale mean is 42.00 (average score of subjects’ product involvement), subjects whose product involvement is scored above scale mean are categorized as high involvement others are categorized as low involvement. Results are illustrated in Table 4.
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Table 5 Consumer characteristics factor loading FactorsnComponents
1
PIIT PIIT1 PIIT2 PIIT3 PIIT4
0.61 0.86 0.61 0.85
Internet self-efficacy Internet self-efficacy1 Internet self-efficacy2 Internet self-efficacy3 Internet self-efficacy 4
2
4
5
0.74 0.80 0.80 0.67
Perceived Web security Perceived Web security1 Perceived Web security2 Perceived Web security3 Privacy concerns Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy Individual privacy
3
0.81 0.74 0.83
concern1 concern2 concern3 concern4 concern5 concern6 concern7 concern8 concern9 concern10 concern11 concern12 concern13 concern14
0.66 0.58 0.69 0.72 0.78 0.79 0.80 0.67 0.88 0.56 0.82 0.58 0.84 0.66
Table 6 Attitude factor loading
Attitude Attitude Attitude Attitude Attitude
1 2 3 4 5
Books
TV gaming systems
Online news and magazines
Computer games
0.81 0.80 0.67 0.66 0.75
0.72 0.80 0.72 0.76 0.68
0.79 0.71 0.71 0.76 0.70
0.82 0.84 0.67 0.74 0.77
y 1 ¼ b01 þ b11 x1 þ b21 x2 þ b31 x3 þ b41 x4 þ b51 x5 y 2 ¼ b02 þ b12 x1 þ b22 x2 þ b32 x3 þ b42 x4 þ b52 x5 y 3 ¼ b03 þ b13 x1 þ b23 x2 þ b33 x3 þ b43 x4 þ b53 x5 y 4 ¼ b04 þ b14 x1 þ b24 x2 þ b34 x3 þ b44 x4 þ b54 x5
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Table 7 Instrument reliability Variables
Item number Cronbach’s a
PIIT
Internet selfefficacy
Perceived Web security
Individual privacy concerns
Attitude Books
TV gaming systems
Online news and magazines
Computer games
4
4
3
14
5
5
5
5
0.81
0.86
0.79
0.79
0.79
0.79
0.83
0.94
Table 8 Descriptive statistic (N = 123) Variables
Mean
Standard Deviation
PIIT Internet self-efficacy Perceived Web security Privacy concerns Product involvement (books) Product involvement (TV gaming systems) Product involvement (online news and magazines) Product involvement (computer games) Attitude (books) Attitude (TV gaming systems) Attitude (online news and magazines) Attitude (computer games)
3.64 3.96 2.53 4.26 50.65 38.09 40.05 39.20 3.02 2.77 3.00 2.82
0.69 0.67 0.82 0.63 10.69 14.17 10.91 15.24 0.65 0.66 0.64 0.69
Note: Variables including PIIT, Internet self-efficacy, perceived Web security, privacy concerns, and Attitude are scaled from 1 to 5. Mean scores and standard deviation are computed in this table. Additionally, product involvement is scale from 1 to 7, therefore it ranges from 10 to 70.
The dependent variables measure consumer attitudes toward online shopping in the context of the selected product (y1: books; y2: TV gaming systems; y3: online news and magazines; y4: computer games). The independent variables are x1: PIIT; x2: Internet self-efficacy; x3: Perceived Web security; x4: Privacy concerns; x5: Product involvement. Regression results are shown in Tables 9 and 10. Table 9 computed F-values and R2 to understand the overall significance of each equation. All of the models yield significant p-values (p < .01) and R2 around 20% of the variance in attitudes toward online shopping Table 9 Summary of regression analysis Regression Statistics
Books
TV gaming systems
Online news and magazines
Computer games
F-Value p-Value R2 Durbin–Watson test
3.88 .00** 0.14 1.81
6.14 .00** 0.21 1.77
5.85 .00** 0.20 2.18
6.59 .00** 0.22 1.94
* **
p < .05. p < .01.
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Table 10 Analyses of the four products Variables/product types
Regression coefficient
Standard error of coefficient
Standardized regression coefficient (beta) (p-value)
Buying books online PIIT Internet self-efficacy Perceived Web security Privacy concerns Product involvement
0.12 5.845E02 0.17 0.24 0.29
0.10 0.11 0.09 0.11 0.09
0.12 0.06 0.17 0.24 0.29
(.25)-b11 (.60)-b21 (.08)-b31 (.04)*-b41 (.00)**b51
Buying TV gaming systems online PIIT 0.19 Internet self-efficacy 6.064E02 Perceived Web security 0.27 Privacy concerns 0.26 Product involvement 0.18
0.10 0.11 0.09 0.11 0.09
0.19 0.06 0.27 0.26 0.18
(.06)-b12 (.56)-b22 (.00)**b32 (.02)*-b42 (.04)*-b52
Buying online news or magazines online PIIT 0.14 Internet self-efficacy 1.219E02 Perceived Web security 0.12 Privacy concerns 0.20 Product involvement 0.37
0.10 0.11 0.09 0.11 0.08
0.14 0.01 0.12 0.17 0.37
(.15)-b13 (.91)-b23 (.19)-b33 (.13)-b43 (.00)**b53
Buying computer games online PIIT 0.20 Internet self-efficacy 0.10 Perceived Web security 0.34 Privacy concerns 0.17 Product involvement 0.17
0.10 0.10 0.09 0.11 0.09
0.20 0.10 0.34 0.17 0.17
(.05)*-b14 (.33)-b24 (.00)**b34 (.12)-b44 (.05)*-b54
* **
p < .05. p < .01.
was explained. Table 9 lists detailed data on the statistical coefficient in each of the regression models. Table 10 lists the results of significance testing of the study variables. The regression results suggest the following: In the context of book buying, privacy concerns (p = .040) and product involvement (p = .002) yield coefficients with significant p-value. In the context of TV gaming systems purchases, perceived Web security (p = .003), privacy concerns (p = .018) and product involvement (p = .044) yield significant p-values for their coefficients. Furthermore, in the context of online news and magazine purchases, p-values are only significant for product involvement (p = .000). Finally, in the context of computer game purchase, three variables yield significant p-values including PIIT (p = .050), perceived Web security (p = .000) and product involvement (p = .046). Details are illustrated in Table 10. 6. Discussions This study developed a model for determining online shopping attitudes and tested it in the context of different products or services. Analysis results demonstrated that four
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regression functions were all significant in the context of different products or services. Furthermore, this study found that the significant variables differed in the context of different products or services. The analytical results are discussed below. 6.1. Low outlay, frequently purchased and tangible products or services This study employed books as an example of a low outlay, frequently purchased and tangible product or service. The research findings indicate that individual privacy concerns negatively affect consumer attitudes toward buying books online. Consumer product involvement with books positively affects those attitudes. The other three consumer characteristics variables are insignificant in the context of book purchasing. For respondents, books are a low-cost product, and since the amounts of money involved in individual transactions are small. Consumers are less concerned with Web security than they are for more expensive products or services (for example TV gaming systems, which are discussed in the next section). Additionally, the Internet is very popular on campus and most respondents have taken computer-related courses. Therefore, Internet self-efficacy is not a critical factor in the context of online book purchasing. Moreover, books are a common and popular product, meaning that users tend to have positive attitudes toward purchasing books online regardless of their level of personal innovativeness in relation to information technology. 6.2. High outlay, infrequently purchased and tangible products or services This study used TV gaming systems as an example of a high outlay, infrequently purchased and tangible product or service. The analytical results indicate that user perceptions of Web security positively affect attitudes toward online purchases of TV gaming systems. Although both TV gaming systems and books are tangible products, unlike books, TV gaming systems are relatively expensive, and only consumers who perceive the Web as a secure environment are likely to purchase gaming systems via the Internet. The analytical results indicate that individual privacy concerns negatively affect attitudes toward online purchases of TV gaming systems. Moreover, product involvement also positively affects attitudes toward online purchases of TV gaming systems. Additionally, another two consumer characteristics variables (PIIT and Internet self-efficacy) are insignificant in the context of TV gaming system purchasing. These results resemble those low outlay, frequently purchased, tangible products or services. 6.3. Low outlay, frequently purchased and intangible products or services Online news and magazines were adopted as an example of a low cost, frequently purchased and intangible product or service. Based on the research findings, this study finds that only product involvement significantly and positively affects attitudes toward purchasing online news and magazines. Meanwhile, the other four variables proposed in this study are insignificant. This phenomenon can be explained by the fact that online news or magazines (particularly news) are extremely popular and cheap, meaning consumers are primarily concerned with newspaper or magazine content. This fact may be the reason why other variables besides level of product involvement do not exert a significant influence in the context of low cost, frequently purchased and intangible products or services.
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6.4. High outlay, infrequently purchased and intangible products or services This study adopted computer games as an example of high cost, infrequently purchased and intangible products or services. PIIT, perceived Web security and product involvement were found to positively affect attitudes toward online purchases of computer games, while no significant effect was found for the other two variables (Internet self-efficacy and individual privacy concerns). Although the relationship between individual privacy concerns and attitudes toward buying computer games online is negative, it is not significant. This study suggests conducting further investigations in the future to clarify the issue further. 7. Conclusions The study examined how individual differences affect online shopping acceptance. Importantly, the determinants of user acceptance of online shopping differ according to product or service type. Based on studying a limited set of products or services, the following findings are obtained: (1) Increased PIIT positively affects user attitudes toward purchasing high cost, infrequently purchased, and intangible products or services online. (2) Increased personal perceptions of Web security positively affect user attitudes toward purchasing expensive, infrequently purchased products or services. (3) Increased personal privacy concerns negatively affect user attitudes toward purchasing tangible or physical products or services. (4) High product involvement positively affects user attitudes toward online shopping in the context of all employed products or services. (5) Products and service types influence the relationships between consumer characteristics and attitudes toward online shopping. Consistent with previous studies, consumer characteristics are found to influence online shopping acceptance. However, this study found that these relationships were affected by different product types. When designing a marketing plan, online retailers must consider two key questions. The first question involves the identity of potential buyers. This study provided the consumer characteristics of online shoppers. Based on these characteristics, online business can identify their target market easily. The second question involves the type of products that are suitable for online marketing. Present research results can help businesses to focus on their potential market and increase their marketing edge. Acknowledgement The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC922416-H-008-013-.
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Appendix. Scales and items PIIT (Agarwal & Prasad, 1998) 1. If I heard about a new information technology, I would look for ways to experiment with it. 2. Among my peers, I am usually the first to try out new information technologies. 3. In general, I am hesitant to try out new information technologies.* 4. I like to experiment with new information technologies. Note: * Denotes items that were reverse scored. Internet self-efficacy (O’Cass & Fenech, 2003) 1. I could easily use the Web to find product information on a product/service. 2. I can get to a specific Web site with a browser. 3. I feel comfortable searching with World Wide Web on my own. 4. I would be able to use Web on my own to locate retail sites. Perceived Web security (O’Cass & Fenech, 2003) 1. I feel secure sending personal information across the Web. 2. I feel safe providing personal information about me to Web retailers. 3. Web is safe environment to provide personal information. Privacy concerns (Smith et al., 1996) 1. It usually bothers me when companies ask me for personal information. 2. All the personal information in the computer database should be double-checked for accuracy – no matter how much this costs. 3. Companies should not use personal information for any purpose unless it has been authorized by the individuals who provided the information. 4. Companies should devote more time and effort to preventing unauthorized access to personal information. 5. When companies ask me for personal information, I sometimes think twice before providing it. 6. Companies should take more steps to make sure that the personal information in their files is accurate. 7. When people give personal information to a company for some reason, the company should never use the information for any other reason. 8. Companies should have better procedures to correct errors in personal information. 9. Computer databases that contain personal information should be protected from unauthorized access – no matter how much it costs. 10. It bothers me to give personal information to so many companies. 11. Companies should never sell the personal information in their computer databases to other companies. 12. Companies should devote more time and effort to verifying the accuracy of the personal information in their databases. 13. Companies should never share personal information with other companies unless it has been authorized by the individuals who provided the information. 14. Companies should take more steps to make sure that unauthorized people cannot access personal information in their computers. 15. I’m concerned that companies are collecting too much personal information about me.
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Product involvement (Zaichkowsky, 1994) For me [selected product] is: 1. important – unimportant* 2. boring – interesting 3. relevant – irrelevant* 4. exciting – unexciting* 5. means nothing – means a lot to me 6. appealing – unappealing* 7. fascinating – mundane* 8. worthless – valuable 9. involving – uninvolving* 10. not needed – needed Note: *Denotes items that were reverse scored. Attitudes toward online shopping. Modify from Taylor and Todd (1995) 1. I like buying [selected product] online. 2. Buying [selected product] online is interesting. 3. Buying [selected product] online makes my life more attractive. 4. I intend finishing [selected product] buying processes totally online. 5. I will increase buying [selected product] online in the future. References Agarwal, R. (2000). Individual acceptance of information technologies. In R. W. Zmud (Ed.), Framing the domains of IT management projecting the future. . .through the past (pp. 85–104). Pinnaflex Educational Resources Inc. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. Amichai-Hamburger, Y. (2002). Internet and personality. Computers in Human Behavior, 18(1), 1–10. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company. Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: The role of privacy, security, and site attributes. Journal of Strategic Information Systems, 11(3–4), 245– 270. Bhatnager, A., Misra, S., & Rao, H. R. (2000). On risk, convenience, and internet shopping behavior. Communications of the ACM, 43(11), 98–105. Chaudhuri, A. A. (2000). Macro analysis of the relationship of product involvement and information search: The role of risk. Journal of Marketing Theory and Practice, 8(1), 1–15. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511–535. Citrin, A. V., Sprott, D. E., Silverman, S. N., & Stem, D. E. (2000). Adoption of Internet shopping: The role of consumer innovativeness. Industrial Management and Data System, 100(7), 294–300. Dahlen, M., & Lange, F. (2002). Real consumers in the virtual store. Scandinavian Journal of Management, 18(3), 341–363. Eastin, M. S. (2002). Diffusion of E-commerce: An analysis of the adoption of four E-commerce activities. Telematics and Informatics, 19(3), 251–267. Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (1998). Multivariate data analysis (fifth ed.). Prentice Hall. Hills, P., & Argyle, M. (2003). Uses of the internet and their relationships with individual differences in personality. Computers in Human Behavior, 19(1), 59–70. Hsieh, Y. C., Chiu, H. C., & Chiang, M. Y. (2005). Maintaining a committed online customer: A study across search-experience-credence products. Journal of Retailing, 81(1), 75–82. Hsu, C. W. (2006). Privacy concerns, privacy practices and web site categories toward a situational paradigm. Online Information Review, 30(5), 569–586.
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