Theory of Planned Behaviour

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Rodgers and Sheldon (2002) observed the motivational factors behind internet shopping and suggested four underlying motives which are information, communication, exploration and acquisition. In conducting marketing research for e-commerce companies it is particularly important to study the consumer internet usage pattern. The need of the consumer which may be termed as the ultimate function or utility of the consumer instigates the requirement of conducting a particular behaviour. To understand the motives of each segment and to discover naturally occurring consumer groups drive towards knowing the characteristics and needs of every segment. Strategic and competitive advantage can be gained through identifying the unique needs and attitudes of the diverge consumer segments. Theory of planned behaviour The human decision making process could be explained by the theory of planned behaviour which is an extension of the theory of reasoned action. The variables in the theory are attitude, behaviour, subjective norm, perceived behaviour control and intention. As an extension of theory of reasoned action, the Theory of Planned Behavior (TPB) explains the human decision making process (Azjen 1985, 1991; Azjen and Fishbein 1980). TPB assists the researcher in explaining behaviors over which individuals have incomplete voluntary control (See Figure 1). Variables included in the theory include a) attitude, b) behavior, c) subjective norm, d) perceived behavior control, and e) intention. Attitude toward a behavior and subjective norm about engaging in a behavior are supposed to control intention. Attitude depicts an individual‘s feelings, inclination or disinclination towards performing a behavior. Subjective norms reveal the individual‘s perceptions of the influence of significant others (e.g., family, friends, peers, etc.) TPB additionally includes perceived behavior control over engaging in behaviors as an influential form of intention. Perceived behavior control (PBC) relates to the sense of uncertainty that influences behavior directly or indirectly. As shown in the Figure 1, the Theory of Planned Behavior Model suggests that human behavioral decision- making is partially controlled by the consumer‘s actions. This part is defined as the ‘perceived behavioral control’ variable. The perceived behavioral control variable may influence behavior through its impact on intentions as well as directly. For example, a consumer might refrain from shopping online if he/she perceives the purchasing process is complicated or if does not possess a credit card. Such considerations are incorporated into the Theory of Planned Behavior (Ajzen 1985, 1991). TAM (Technology Acceptance Model) Adapted from the Theory of Reasoned Action (Azjen 1980; Fishbein and Ajzen 1975), the Technological Acceptance Model suggests that a prospective technology user‘s overall attitudes toward using a given technology-based system or procedure represents major determinants as to whether or not he/she will ultimately use the system (Davis 1993). TAM has been utilized in numerous settings involving varying forms of Theory of Planned Behavior (Figure-1) Attitude towards the behavior Intention Perceived behavioral Control Subjective Norm Behavior 11 technological adoption (Venkatesh and Davis 2000). It has also been successfully applied to help understand and explain the adoption of information systems/technology in marketing contexts; including Internet-based, retail consumer behaviors (O‘Cass and Fenech 2003). In marketing contexts, external variables examined include shopping motives (Eastlick and Feinberg 1999), consumer skill/expertise, demographics (Mattilia et al. 2003), personality characteristics, and computer anxiety (Harrison and Rainer 1992).   Diffusion of Innovation The concept of innovation has received a great deal of attention within the diffusion of innovation framework particularly in relation to the information technology and marketing research (Agarwal and Prasad 1998; Midgley and Dowling 1978; Rogers, 1995). According to the Diffusion of Innovation a A¢â‚¬•personal innovativenessA¢â‚¬- construct is conceptualized as the degree and pace of adoption of innovation by an individual. The innovativeness concept represents an innate phenomenon and is widely used in psychology to identify innovative characteristics of individuals (Kirton 1976). Innovativeness is considered a generalized personality trait (also called A¢â‚¬•global innovativenessA¢â‚¬-) (Goldsmith and Hofacker 1991; Goldsmith et al. 1995). The conceptualization of innovativeness has also been examined in marketing literature (e.g., Midgley and Dowling 1978; Flynn and Goldsmith 1993). Consumers who are innovative are representative as being highly abstract and possess a generalized personality trait (Im, Bayus, and Mason 2003). Examples as to the levels of abstraction inherent across the various literatures utilizing this perspective include A¢â‚¬•a willingness to changeA¢â‚¬- (Hurt et al. 1977) and the receptivity to new experiences and novel stimuli (Goldsmith 1984; Leavitt and Walton 1975). In general however, consumers are not necessarily receptive to change. The Internet is a fairly new and discontinuous innovation. That is, a form of innovation that requires individuals to learn new skills in order to use the technology. Research reveals that diffusion of innovation theory is applicable to the study of online shopping. Specifically, consumers who have personal preferences of shopping in a brick-and-mortar store or patronize one specific retailer will typically have difficulty in changing purchasing patterns (Kaufman-Scarborough and Lindquist, 2002). Consumers who prefer traditional retail channels or conversations with customer service personnel during the purchasing process will typically avoid the online shopping channel regardless of the benefits offered (e.g., convenience).   Perceived Risks Online transaction involves a temporal separation of payment and product delivery. A consumer must provide financial information (e.g., credit card details) and personal information (e.g., name, address and phone number) for delivery in order to complete the purchasing process. Risks perceived or real, exist due to technology failure (e.g., breaches in the system) or human error (e.g., data entry mistakes). The most frequently cited risks associated with online shopping include financial risk (e.g., is my credit card information safe?), product risk (e.g., is the product the same quality as viewed on the screen?), convenience (e.g., Will I understand how to order and return the merchandise?), and non-delivery risk (e.g., What if the merchandise is not delivered?) The level of uncertainty surrounding the online purchasing process influences consumers‘ perceptions regarding the perceived risks (Bhatnagar et al. 2000). Financial Risk: – Financial risk is a possibility with shopping online due to the fact that consumers disclose sensitive information (e.g., credit card information) to a vendor. While the majority of online retailers are legitimate and have a secure website, there may be some illegal persons posing as online retailing for the purpose of credit card fraud (Bhatnagar et al. 2000). Security of the financial information and resources is the number one consumer of US online shoppers (Ranganathan and Ganapathy 2002). The perceived level of risk is often the deciding factor regarding which retail channel to patronize (i.e., brick-and-mortar vs. online retailer). Although research regarding Indian‘s perceptions of financial risk could not be found, it can be assumed that consumers in India and indeed worldwide have similar concerns regarding the perceived financial risk associated with online shopping. Product risk: – Product risk consists of the risk associated with making an unwise or inappropriate purchase decision. The decision is typically made based on insufficient information that was provided on the company‘s website. Product risk may also be the risk of purchasing an expensive product because of the inability to compare prices, being unable to easily return a product, or not receiving delivery on a purchased product (Jarvenpaa, Todd and Bradd 1997; Vijayasarathy and Jones 2000). Poor product performance is another type of product risk. As customers‘ expectations of product performance increase, the potential for product performance problem also increases (Jarvenpaa, Todd and Bradd 1997; Vijayasarathy and Jones 2000, Bhatnagar et al. 2000). Convenience Risk: – Convenience risk addresses the risk associated with the ease (or lack thereof) with ordering products and services through an online retailer. In a brick-and mortar store, consumers can readily solve their purchasing problems by going into the store as most retailers have a designated Return Department. The process is handledA¢â‚¬-face-to-face.‘ The transaction is convenient in part because consumers are accustomed to the process. Online retailing poses a convenience risk. To ask questions, return a product, find a different size, or complain, consumers are unable to walk into the store. Instead, each online retailer has a different protocol. Some online retailers have a phone line, while others require the consumers to e-mail the question or comment. The consumers often perceive an increased level of convenience risk because they have a loss of connection with the retailer (Poal and Leunis 1999). After-sales service and timely responses to enquiries are of great importance to consumers (Spence et al. 1970; Festervand et al. 1986). Non-delivery Risk: – Once a consumer purchases a product online, delivery is the final step of the process. The risk of delayed and non-delivery of product. Since there is a physical separation between customers and products in web interface, it creates order assembly and transportation costs that are not incurred in traditional retailing (Rosen & Howard 2000). These costs are so high that firms have no choice but to charge the shipping cost to the customers. Survey results have indicated that shipping fees are the main complaint of more than 50 percent of online shoppers and that more than 60 percent of shoppers have abandoned an order when shipping fees are added (Ernst and Young 1999). Lewis (2006) found that higher shipping fees are associated with reduced ordering rates, and policies that charge more shipping fees to larger orders lead to reduced order size. Time taken in receipt of order is also an important element in online shopping. Demographics Previous research has revealed that online buying behavior is affected by demographics, channel knowledge, perceived channel utility and shopping orientations (e.g., Li, Cheng, and Russell 1999; Weiss 2001). Results indicate that compared with brick-and-mortar shoppers, online consumers tend to be are older (Bellman et al. 1999; Donthu and Garcia 1999; Weiss 2001), better educated (Bellman et al. 1999; Li et al. 1999; Swinwyard and Smith 2003), have higher income (Bellman et al. 1999; Li et al. 1999; Donthu and Garcia 1999; Swinwyard and Smith 2003), and more technologically savvy (Li et al. 1999; Swinwyard and Smith 2003). Men are more likely to purchase products and/or services from the Internet than women (Garbarino & Strahilevitz 2004; Korgaonkar and Wolin 1999; Slyke et al. 2002). Reasons for shopping online have been cited for time efficiency, avoidance of crowds, and 24 hour shopping availability (Karayanni 2003).

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