CHAPTER 3 – METHODOLOGY
3.2 Hypotheses Development
Data collection took place through a paper and pencil survey and web-based survey. In following section, the relationships among the variables are discussed respectively, and ten hypotheses are developed in line with prior literatures.
3.2.1 Online store attributes and e-attitude
Jin et al. (2010) according to Madlberger (2004) suggestion and then chose six online store attributes: website design, security/privacy, fulfillment, merchandising, communication and promotion. Cheung and Lee (2005) and Zhang et al. (1999) view website design, order fulfillment and security aspects of online retailing as basic attributes as they are necessities for online transaction.
Montoya-Weiss et al. (2003) discover that website design factors such as navigation structure and graphic style affect customers’ overall satisfaction. Graphic design represents a synthesis of Koo and Ju (2010) online categories graphics (e.g., visually comforting) and colors (e.g., use of distinctive colors). Castaneda et al. (2007) also show that attitudes toward a website are a strong predictor of intentions to revisit the website.
The perceived benefit of using privacy controls is define as the overall predicted favorable consequence to a user for using privacy controls; also, users will only take advantage of the mechanisms if they feel there is a real benefit from using them (Taneja et al. 2014). Therefore, an individual forms a positive attitude toward behaviors leading to desirable
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consequences (Bulgurcu et al. 2010).
Recently, the operations management literature has explored order fulfillment in an e-commerce context (Davis-Sramek et al. 2008). Heim and Sinha (2001) found ease of return, product availability and timeliness of delivery significantly impact customers’ future buying behavior. A complementary stream of operations management e-commerce research focused on the impact of perceptions of order fulfillment service on repurchase intention(Davis-Sramek et al. 2008).
H1a: Basic attributes positively influence consumer e-attitude level.
Cheung and Lee (2005) and Zhang et al. (1999) view merchandising, communication and promotion aspects of online retailing as marketing attributes as they are necessities for online transaction.The positive characteristics of communication messages canenhance the value of the store and thus increase acceptanceamong consumers. The communication aspect has been found to be important in enhancing customer online customer attitude. Moreover, quality communication can create a more positive attitude toward the store, which affects consumer willingness to purchase products (Chen, Ching, Tsai, & Kuo, 2008; Fiore, Jin, & Kim, 2005).
Pegler (2001)defined visual merchandising as product presentation that communicates product concepts with customers in order to optimize products sales and profits. The behavioral process model from display to consumption developed by Kerfoot et al. (2003)explained the effect of visualmerchandising within the retail store environment on customer’s
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psychological and behavioral outcomes. Effective merchandise display guides and coordinates merchandise selection for shoppers (Pegler, 2001).
According to Khakimdjanova and Park (2005), attitude toward visual product presentation influenced browsing and purchasing behavior in the store. A positive attitude led to more browsing and purchasing, whereas a negative attitude toward the visual product presentation resulted in an immediate exit from the store (Kerfoot et al. 2003).
Greater positive attitudes toward bonus promotions are expected to lead to stronger subscription intentions and recommendation intentions (Ajzen, 1985). Garretson et al. (2002) find that value consciousness and smart shopper self-perception affect both store brand attitudes and attitudes toward promotions. Chandon et al. (2000) argued that sales promotions providehedonic and utilitarian benefits to consumers, and that a rationaleconomicfocus on monetary savings cannot fully explain howand why consumers respond to promotional offers.Researchers have consistently demonstrated that attitude toward the online store, i.e., an individual’s favorable or unfavorable evaluations of the store, is positively related to purchase intentions, very few studies have incorporated the premise that attitude in the context of online shopping includes both utilitarian and hedonic dimensions (Bridges and Florsheim, 2008; Childers et al. 2001).However, smart shopper self-perception has a greater effect on promotions attitudes (Garretson et al. 2002).
H1b: Marketing-related attributes positively influence e-attitude
level.
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3.2.2 The effect of firm reputation on online and offline attitude
Firm reputation sets the initial expectation for a consumer, serving as a halo effect (Jin et al. 2010). That is, regardless of retailers’
performance, well-known stores are evaluated more positively than relatively unknown stores (Dodds et al. 1991, Grewal et al. 1998, Estelami et al. 2003). The issue of company reputation and its impact on consumers’ attitudes and perceptions has been explored by a number of marketing researchers.Yoon et al. (1993) provided for a summary of the roles of company reputation in product/ service markets and in channel relations. For example, tested the proposition that acompany’s reputation and its service offering information collectively determine a buyer’s expectations; also, they found evidence to support the view that a buyer’s response to a service is consistent with his/her attitude toward the vendor’s reputation (Yoon et al. 1993) .
H2: Firm reputation positively influences consumer offline attitude.
H3: Firm reputation positively influences consumer e-attitude.
3.2.3 The effect of perceived risk on online and offline attitude
Heijden, Verhagen, and Creemers (2001) indicated that perceived risk stemming from trust has an indirect effect on shopping intention through attitudes. Especially with regard to innovative technologies such as the e-customization process, perceived risk has influence on consumers' decision making processes such as shaping attitudes toward adopting technologies or purchasing behavior (Im et al. 2008). When the level of perceived risk is high, described by fear, skepticism, cynicism, wariness and watchfulness, and vigilance (McKnight, Kacmar, & Choudhury,
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2004), even though consumers may have positive attitudes toward e-customized products, they are unlikely to maintain the purchase intention long enough to purchase the products.
H4: Perceived risknegatively influences consumer offline attitude.
H5: Perceived risk negatively influences consumer e-attitude.
3.2.4 The effect of attitude on purchase intention
Attitudes are defined as the individual’s positive or negative feelings about performing a behavior; also, they evaluated the effect of six behavioral beliefs including perceptions of content, made for the medium, ease of use, emotion, promotion, and esthetics (Pallud and Straub, 2014).
Moreover, attitude- toward- an- advertisement has been defined as ‘a predisposition to respond in a favorable or unfavorable manner to a particular advertising stimulus during a particular exposure occasion’
(Lutz, 1985). It is important to understand customer attitudes because attitudes can generally predict customer purchasing intentions and behavior (Oliver, 1980; Shih, 2004). A considerable number of studies found positive link between attitude and purchase intention across different products and services (Pavlou and Fygenson, 2006). Studies found that consumer attitudes towards retailers affect purchase intention from those retailers (Jarvenpaa and Todd, 1997).The theory of reasoned action (TRA) posits that a person’s beliefs about the nature of anticipated outcomes influence the formation of attitudes toward behavior, which in turn, influence behavioral intentions (Ajzen and Fishbein,1980). Several studies have employed TRA in the online shopping context and have examined the inter relationships between three focal constructs: trust,
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attitude, and purchase intentions (Badrinarayananetal. 2012; HaandStoel, 2009). The TRA also shows how several attitudes converge together to formulate behavioral intentions (Sheppard et al. 1988). Recent studies of online advertising have also shown a positive relationship between attitude and purchase intentions and between attitude and behavior, such as the likelihood of buying, online visitations, and online shopping frequency (Bruner andKumar, 2005; Karson and Fisher, 2005;
Korgaonkar and Wolin, 2002; Stevenson et al. 2000; Wang et al. 2009;
Wolin et al. 2002). The conceptualization of consumer attitudes toward the online store as a second-order construct made up of hedonic and utilitarian dimensions; therefore, could enable a better understanding of the complexities of the attitude formation process as well as the influence of each unique dimension on purchase intentions (Voss et al. 2003).
While it is straightforward that attitude relates positively to purchase intentions (Hwang et al. 2011).
H6: Offline attitude positively influences offline purchase intention.
H7: E-attitude positively influences E-purchase intention.
3.2.5 Inter-channel relationships of attitude and purchase intention
The summative model of attitude (Ajzen and Fishbein, 1980) has provided theoretical support for many studiesthat examined the relationship between consumers’ beliefsand attitudes about offline retail stores (James et al. 1976; Morschett et al. 2005). Many studies have examined the intra-channel effect of online beliefs or perceptions about a Web site’s performance (e.g., perceived ease of use and usefulness) on consumers’ online attitude or behavioral intention (Lee et al. 2006; Reza27
et al. 2008).Likewise, in the multichannel retailing context, consumers’
online and offline attitudes about a retailer may be influenced by their beliefs about the retailer from all channels, not only from the respective channel (Kwon and Lennon, 2009).
H8: Offline attitude positively influences e-attitude.
Rohm and Swaminathan (2004) investigate online grocery purchases;
they also findings suggest that, of the four types of online shoppers, only the physical shopper is interested in also gathering information from offline sources such as brick and mortar grocery stores.According to Day (1969), intentional measure can be more effective than behavioral measure in capturing the consumers’ mind because customers may make purchases due to constraints instead of real preferences. Jeong et al. (2003) found that customers’ informationsatisfactionturns out to be an important factor of online behavioral intentions, and that website quality is essential for information satisfaction.To examine consumers’ behavioral patterns, purchase intention has been used to predict actual behavior (Ajzen and Fishbein, 1980). While examining the service failures of online retailers, Collier and Bienstock (2006) found that customer dissatisfaction with recovery measures affects their future behavioral intentions, such as switching and negative word-of-mouth. However, customers having experienced satisfactory recovery from a service failure not only engage in positive word-of-mouth communications (Holloway et al. 2005) but also have persistent and more faith in online shipping (MontoyaWeiss, Glenn& Grewal, 2003) and higher repurchase intentions (Holloway et al.
2005). Consumers with a higher level of satisfaction tend to have a
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stronger intention to repurchase and recommend the purchased product (Zeithaml et al. 1996). In other words, when customer satisfactionis enhanced, repurchase can be more frequent (Kuo et al. 2009).
H9: Offline purchase intention positively influences e- purchase intention.
Table 3 Overall Hypotheses
Hypotheses Content
H1a Basic attributes positively influence consumer e-attitude level.
H1b Marketing-related attributes positively influence consumer e-attitude level.
H2 Firm reputation positively influences customer offline attitude.
H3 Firm reputation positively influences consumer e-attitude.
H4 Perceived risk positively influences consumer offline attitude.
H5 Perceived risk positively influences consumer e-attitude.
H6 Offline attitude positively influences offline purchase intention.
H7 E-attitude positively influences e-purchase intention.
H8 Offline attitude positively influence e-attitude.
H9 Offline purchase intention positively influences e-purchase intention.