The Effect of Online Shopping Orientation on Perceived Behavioural Control and Attitude Toward Online Purchasing GMC 2012 Revised (2)

THE EFFECT OF ONLINE SHOPPING ORIENTATION ON PERCEIVED BEHAVIOURAL CONTROL AND ATTITUDE TOWARD ONLINE PURCHASING OF CLOTHES Aida Pereira Santos, IPAM Aveiro, Portugal Sandra Maria Correia Loureiro, University of Aveiro, Portugal ABSTRACT This study seeks to investigate the effect of nine constructs of online shopping orientation on perceived behavioural control and attitude toward purchase. The results suggest that in-home shopping tendency, convenience consciousness, and impulsive purchase are the most significant constructs in forming online shopping orientation. Keywords: online purchasing orientations, online intention, TPB INTRODUCTION The emergence of the Internet with Web shopping and its rapid development has allowed companies to offer their customers a variety in terms of goods and services and so expand their business opportunities. Online commerce is growing very rapidly all over the world; in March 2011, Internet penetration was found to be 30,2% of world population, and its growth between 2000 and 2011 (up to March) was 480.4% (Internet word Stats 2011). Most young Internet users (16-24 years) in the European Union (80% in EU and 90% in Portugal) use the Internet to search and buy, and post messages to chat sites, blogs or social networking sites (Seybert & Lööf, 2010).Web-shopping behaviour does not necessarily follow traditional consumer behaviour offline (Hughes, 2011) and thus, online store marketers and managers are interested in understanding the determinants of customers’ online purchasing intention. However, most studies to date have explored the direct relationship between several components of online shopping orientation and purchase intentions (e.g., Seock & Bailey, 2008; Sung & Jeon, 2009; Liebermann & stashevsky, 2009; Ling, Chai, & Piew, 2010). Therefore, in order to go further towards understanding this phenomenon, this study intends to extend the Theory of Planned Behaviour (TPB) model, regarding the components of online shopping orientation as external variables. This article is structured as follows. First, we present a review of TPB, types (or constructs, as Seock and Bailey (2008) suggested) of online shopping orientation, attitude, perceived behavioural control, and online purchase intentions. Secondly, the research model and hypotheses are introduced, followed by the method and results. Finally, we discuss the results, present conclusions and point out future research avenues.

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THEORETICAL BACKGROUND Internet Subscription and Web-Shopping The online market has shown rapid growth all over the world (Sung & Yangjin, 2010). According to a study published by Internet Word Status, (2010) the Internet has a penetration of around 53% in Europe, with growth of 305.1% between 2000 and 2009, representing approximately 24% in terms of users globally. The same study estimates a 19.6% growth of online sales in Europe in 2010, representing 5.5% of the total retail market. In Portugal, almost 30% of Internet users are e-shoppers, most of them being 1624 years old followed by the 25-54 age group (Seybert & Lööf, 2010). The 18-35 age group, also called the Y generation (1979-1994), the first generation of digital users, developed new behavioural needs and habits, as a result of the information era in which they were born and live (Paina & Luca, 2010). In fact, from childhood they have been under the constant influence of the Internet and the new technologies which are part of their daily life (e.g., laptops, digital photography and video cameras, mp3 players, mobile phones, iphones and others). According to Acepi (2010), these Portuguese consumers prefer to shop online for books, travel tickets and holidays. Clothes appear in 6th place in the list of preferences. Thus, this study becomes pertinent in helping to increase the search for and purchase of clothes online, all the more so since consumers tend to consider online clothes retailing as the sale of differentiated products with great variations in perceived quality, and consequently, online purchases are seen as more risky than offline ones (Hansen & Jensen, 2009). According to Dionísio et al. (2009), around 79 % of internauts visiting sites, do so in order to find out and gather information, with purchases usually being made in physical shops; 18% admit to buying mostly on the Internet; this study reveals how important it is for a company to have more than one sales channel, since one can activate the other. Theory of Planned Behaviour The Theory of Planned Behaviour (TPB) has been applied in several contexts in order to explain consumer behaviour (e.g., George, 2002; Athiyaman, 2002; Laohapensang, 2009; Suntornpithug & Khamalah, 2010). The TPB extends theTheory of Reasoned Action (TRA), which states that human beliefs influence attitudes and shape behavioural intentions (Davis, 1989). At the core of TPB is attitude, subjective norms and perceived behavioural control. These three variables are direct determinants of behavioural intentions, which in turn affect behaviour. According to Ajzen (1985, 1991), attitude measures the feelings of favourableness or unfavourableness towards performing a behaviour; perceived behavioural control means perceptions of internal and external constraints on behaviour; and subjective norms reflect perceptions that significant references cause the individual to perform or not to perform a behaviour. Subjective norms can also be regarded as beliefs about other people’s normative expectations and motivation to comply with these expectations

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(Suntornpithug & Khamalah, 2010). However, this third component is regarded by several authors as the weakest component. For instance, Sheppard, Hartwick and Warshaw (1988), Van den Putte’s (1991), Godin and Kok (1996), or even Sparks et al. (1995) found that the subjective norm component was the weakest predictor of intentions. Even Armitage and Conner (2001), in their meta-analysis study are not absolutely conclusive about the significant influence of subjective norms on intentions. Therefore, in line with these previous researchers, this study does not contemplate subjective norm in the proposed model. Instead, we consider the attitude toward online purchasing intention as the degree to which a person has favourable or unfavourable evaluations/appraisals of online purchasing (Jarvenpaa, Tractinsky & Vitale, 2000). Concerning perceived behavioural control, this variable represents an intrinsically motivating aspect of the interaction between human and computer (Suntornpithug & Khamalah, 2010), which reflects the level of confidence a consumer has in their ability to control the online shopping process; perceived control over the access to information about goods and services, control over access to interpersonal communications, over online stores during the online navigation process by taking past experiences and expected obstacles into account, and over the acquisition and purchasing processes (Hoffman, Novak & Peralta, 1999). Online Shopping Orientations Shopping orientations have been regarded as the general predisposition towards shopping actions and may be demonstrated in different forms, such as information search, product selection and alternative evaluation (e.g., Li, Kuo and Russell, 1999; Brown, Pope and Vosges, 2003; Ling, Chai & Piew, 2010). Based on previous studies on online shopping orientation, there is no consensus about the number of types of online shopping orientations (e.g., Donthu & Garcia, 1999; Vijayasarathy & Jones, 2000; Hong et al., 2004; Choi & Park, 2004; Gehrt, et. al., 2007, Seock & Bailey, 2008; Hansen & Jensen, 2009; Ling, Chai & Piew, 2010). However, generally we can identify nine different types (constructs), which are used in the current study: shopping enjoyment, fashion consciousness, price consciousness, shopping confidence, convenience consciousness, inhome shopping, brand or store loyalty, quick shopping, and impulse purchase. In order to identify the types of online shopping orientation, a systematic literature review was implemented by using an automated search in electronic databases (EBSCO, Science Direct, Springer, Google Scholar, ISI web of knowledge) for the last five years. The selection process for the identification and inclusion of the relevant research articles included a broad screen of the titles and abstracts and a strict screen of the remaining articles and selection of the most relevant. Purchasing activity related to clothing is often viewed as an hedonic experience, meaning that consumers tend to like buying clothes for fun (Babin et al., 1994; Bloch et al., 1986). Choi and Park (2004) found that online shoppers are more economically and recreationally oriented than offline shoppers. Thereby, shopping enjoyment, fashion consciousness and price consciousness are the important types of online purchase.

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Shopping enjoyment is regarded here as pleasure achieved during the shopping process, including the time spent browsing for items of apparel. Fashion consciousness reflects the interest in fashion and trends. The greater efficiency of e-commerce allows the purchaser to reduce the cost of searching. This results in lower prices for various online articles and services, compared to their offline equivalent, with the promise of good quality for thrifty shoppers (Hannah & Lybecker, 2010). Thus, online shoppers show price consciousness and perceive greater benefits of price and alternative comparisons online (Elliot & Fowell, 2000; Walsh & Godfrey, 2000). Shopping confidence reflects consumers’ confidence in their ability to shop for clothing and select the right products for themselves. Brand or store loyalty means that consumers keep the brand and/or online store that they like, which also suggests a certain kind of self-confidence on the part of the consumer. In this study, convenience consciousness considers the convenience of Internet use, with regard to the ease of searching in several web stores to find up-to-date information about goods and services. In-home shopping reflects the tendency to enjoy shopping from home (Ling, Chai & Piew, 2010) which can also be regarded as a specific convenience because consumers do not need to visit to shopping centres. Quick shopping is related to time-saving oriented consumers. These consumers tend to agree that clothing purchases can be more quickly accomplished on the internet (Kim & Kim, 2004). Piron (1991) defines impulsive purchase as an unplanned action resulting from a specific stimulus. Rook (1987) argues that an impulsive purchase occurs when consumers feel a sudden impulse to buy something immediately, without substantial additional assessment and acting based on impulse. Ling, Chai and Piew (2010) state that impulsive purchase behaviour is unplanned behaviour which is reasonable when related to objective assessment and emotional preferences in shopping. Weinberg and Gottwald (1982) claim that impulsive purchase generally arises in purchasing scenarios with higher emotional activation, less cognitive control and behaviour which is above all reactive. Such purchasers are usually more emotional than non-purchasers, and so Donthu and Garcia (1999) state that online purchasers are more likely to be guided by impulse. Online Purchase Intentions Customers' behavioural intentions can be viewed as signals of repurchase intentions, increased spending, willingness to recommend to others, and even willingness to pay premium prices (ZeithamI, Berry, & Parasuraman, 1996). Customer online purchase intention in the web-shopping context determines the strength of a consumer’s intention to purchase via the Internet (Vijayasarathy & Jones, 2000; Salisbury et al., 2001). In this study we examine online purchase intentions as willingness to continue to shop online and willingness to recommend the online shop to others (word-of-mouth).

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CONCEPTUAL MODEL AND RESEARCH HYPOTHESES Ajzen (1985, 1991) suggested that consumer behaviour can be predicted from intentions. He also points out that favourable attitude and perceived behavioural control will increase consumers’ intentions. In the online context, attitude and perceived behavioural control are regarded as playing a significant role in the intention to purchase online (e.g., Tan & Teo, 2000; Lynch, Kent, & Srinivasan, 2001; Lu & Lin, 2002). Several previous studies show the direct and positive influence of online purchase orientation, in its different types (constructs), on online purchase intention, online loyalty or online purchase (e.g., Donthu & Garcia, 1999; Vijayasarathy & Jones, 2000; Brown, Pope & Vosges, 2003; Gehrt et al., 2007; Seock & Bailey, 2008; Ling, Chai & Piew, 2010; Kwek, Tan & Lau., 2010). Other studies found that a customer’s online purchase experience tends to have a significant effect on his/her online purchase intention (e.g., Shim et al., 2001; So, Wong & Sculli, 2005; Brown, Pope & Vosges, 2003; Lynch & Ariely, 2000). According to social cognitive theory, perceived behavioural control is a form of selfevaluation which can be influenced by an individual’s own experience (Bandura, 1997; Bolt, Killough & Koh, 2001). Experience in online shopping allows customers to develop confidence in searching for relevant information, controlling the navigation process safely and at convenient times. Experience can make consumers feel somewhat in control of the interaction (Weiss &Jessel, 1998). A favourable attitude toward online purchasing can be derived from the experience of machine interactivity and perceived control (Stern, 1994; Dongyoung et al. 2007). Therefore, we can postulate that the more experienced a consumer is in online shopping and online shopping orientation, the greater will be his/her perception of control and positive attitude toward online purchasing. The above considerations lead us to formulate the following hypotheses (see Figure 1): H1: Online purchase orientation (in its nine dimensions) impacts positively on perceived behavioural control. H2: Online purchase orientation (in its nine dimensions) impacts positively on a favourable attitude toward online purchasing. H3: Perceived behaviour control will influence positively a favourable attitude toward online purchasing. H4: Perceived behavioural control impacts positively on online purchase intention. H5: Positive attitude toward online purchase impacts positively on online purchase intention. METHODOLOGY According to the European Union, most Internet users are young (16-24 years old) (Seybert & Lööf, 2010). In Portugal, almost 30% of Internet users are e-shoppers, most of them being 16-24 years old, followed by the 25-54 age group (Seybert & Lööf, 2010). Based on such considerations, our target population is the so-called Generation Y (Solomon et al., 2007), who use, and buy through, the Internet.Therefore, university

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students could be regarded as a good proxy for the target population. Students with actual online purchase experience were targeted. The questionnaire, which captured both latent and demographic variables, was pre-tested using 14 consumers who were personally interviewed (six master and six undergraduate students, and two marketing professors who had already purchased online), which resulted in minor changes to wording in some questions. An invitation to participate in our online survey questionnaire was subsequently sent to the undergraduate and master students of two major universities in the North and South of Portugal (through the mailing lists of student associations), corresponding to almost 15000 students. A total of 1044 were returned and from those a sample of 378 corresponded to respondents who had already bought clothes online at least once in the previous 12 months. The sample (378) is split almost equally by gender. Almost 95% of respondents were under 31 years old and almost 70% were under 25 years old, as expected, corresponding to our target population. The average number of times respondents bought clothes online within the last 12 months is four.The constructs under study were measured by means of multi-item scales adapted from the existing literature following Anderson and Gerbing (1984). The items in the questionnaire were first written in English, translated into Portuguese, and then back translated to English. Back translation was used to ensure that the items in Portuguese communicated similar information to those in English (Brislin, 1970; Sekaran, 1983) meaning that conceptual equivalence was assured. Concerning the items for measuring shopping orientation, its nine types (constructs) were adapted from previous research (Korgaonkar, 1984; Shim & Kotsiopulos, 1992; Swaminathan et al., 1999; Vijayasarathy & Jones, 2000; Moye & Kincade, 2002; Hansen & Jensen, 2009; Ling, Chai, & Piew, 2010). Attitude and perceived behavioural control were measured with four items, each based on Novak Hoffman and Yung (2000). Finally, the two items for online purchase intention were adapted from Chen and Barnes (2007) and Loureiro and Silvina (2010). For each construct, respondents were asked to rate their degree of agreement and disagreement with its measuring items on a 5-point Likert-type scale. A structural equation model approach using PLS (Ringle et al., 2005) was employed to test the hypotheses of this study. PLS is based on an iterative combination of principal component analysis and regression; it aims to explain the variance of the constructs in the model (Chin, 1998). In terms of analysis advantages, PLS simultaneously estimates path coefficients and individual item loadings in the context of a specified model. As a result, it enables researchers to avoid biased and inconsistent parameter estimates. Based on recent developments (Chin et al., 2003), PLS has been found to be an effective analytical tool to test interactions by reducing Type II errors. By creating a latent construct that represents an interaction term, a PLS approach significantly reduces this problem by accounting for error related to the measures (Echambadi et al., 2006). Tenenhaus et al. (2005) propose the geometric mean of the average communality (outer mode) and the average R2 (inner model) as overall goodness of fit (GoF) measures for the PLS (Cross validated PLS GoF), which range from 0 to 1. Wetzels et al. (2009) point out that three different effect sizes for R2 have different acceptable GoF values. The effect size for R2 (f2) defined by Cohen and Cohen (1983, p. 155) is determined by f2 = R2/(1 - R2). Thus,

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the effect sizes for R2 include the limits: small = 0.02, medium = 0.13, and large = 0.26. Following GoF criteria for each effect size has been proposed: GoFsmall ≥ 0.1, GoFmedium ≥ 0.25 and GoFlarge ≥ 0.36 (Wetzels et al., 2009). The model proposed in the current study is complex (12 constructs) and has formative constructs. Thus, in order to test the model we used the repeated indicators method (Chin et al., 2003; Kleijnen et al., 2007). Moreover, as Wold (1985, p. 590) writes, ‘in large, complex models with latent variables, PLS is virtually without competition.’ Therefore, we choose PLS to accommodate the presence of a large number of variables and formative factors. RESULTS The PLS model is analysed and interpreted in two stages. First, suitability of the measurements is assessed by evaluating the reliability of the individual measures and the discriminant validity of the constructs (Hulland, 1999). Then, the structural model is appraised. Item reliability is assessed by examining the loading of the measures on their corresponding construct. Items with loadings of 0.707 or more should be accepted, which indicates that more than 50% of the variance in the observed variable is explained by the construct (Carmines and Zeller, 1979). In this study, only three items (see Table 1) have item loading lower than 0.707 and they were thus eliminated from the structural analysis. Composite reliability was used to analyse the reliability of the constructs since it has been considered a more accurate measurement than Cronbach’s alpha (Fornell & Larcker, 1981; Sánchez-Franco & Roldán, 2005). Table 1 indicates that all constructs are reliable, since the composite reliability values are over 0.7 (Nunnally, 1978). –––––––––––––––––––– Insert table 1 about here –––––––––––––––––––– The measures demonstrated that convergent validity as the average variance of manifest variables extracted by constructs (AVE) was at least 0.5, indicating that more variance was explained than unexplained in the variables associated with a given construct. The criterion used to assess discriminant validity was proposed by Fornell and Larcker (1981), and suggests that the square root of AVE should be higher than the correlation between the two constructs in the model. In this study all latent variables have discriminant validity because the above criterion has been met.The structural results are presented in Figure 1. In this study a nonparametric approach, known as Bootstrap, was used to estimate the precision of the PLS estimates and supports the hypotheses (Chin, 1998; Fornell & Larcker, 1981). All path coefficients are found to be significant at the 0.001, 0.01 or 0.05 levels, and so all hypotheses are supported. However, as models yielding significant bootstrap statistics can still be invalid in a predictive sense (Chin, 1995), measures of predictive validity (such as (R2 and Q2) for focal endogenous constructs should be employed. All values of Q2 (chi-squared of the Stone-Geisser Criterion) are positive, so

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the relations in the model have predictive relevance (Fornell & Cha, 1994). The model also demonstrated a good level of predictive power (R2) as the modelled constructs explained 60.3% of the variance in attitude, 33.3% in perceived behavioural control and 51.2% in online purchase intention. In fact, the good value of GoF, regarding the large effect size and the good level of predictive power (R2) reveals a good overall fit of the structural model (see Figure 1). –––––––––––––––––––– Insert figure 1 about here ––––––––––––––––––––

CONCLUSIONS, MANAGERIAL IMPLICATIONS AND FURTHER RESEARCH This study proposes a model, as far as we know for the first time, that regards online shopping orientation (its nine types or constructs) as an antecedent of perceived behavioural control and attitude toward online shopping. The findings seems to show that online shopping orientation can lead to positive perceived behavioural control and a positive attitude toward online shopping. We also tested the effect of perceived behavioural control on attitude toward purchasing. The results confirm what we expected, i.e., an online consumer who feels he/she has confidence in his/her ability to seach in online stores, thinks it is easy to access customer service, knows clearly what to do in online stores, and feels comfortable with the level of security in the payment process is more likely to have a good attitude toward online shopping, and feel content and satisfied. In accordance with TPB, we also found that perceived behavioural control and a positive attitude toward shopping have a positive effect on the intention to speak favourably about online purchase to family and friends and to purchase clothes online again. Our study also suggests that online shopping orientation could be even stronger in the perception of control than in the formation of a favourable attitude. All nine types of online shopping orientation are found to be significant in the formation of online shopping orientation itself. However, online consumers who like to buy clothes from home, feel comfortable and put a high value on convenience in buying clothes, and those who tend to feel more fulfilled shopping spontaneously online tend to be more oriented towards online shopping. Fashion consciousness is also an important dimension of online shopping orientation. By fashion consciousness we mean online consumers interested in fashion and new trends. Thus, these findings are in accordance with those of Kwek Tan, and Lau (2010), Ling, Chai, and Piew (2010), and Seock and Bailey (2008). In this vein, our study contributes theoretically to this field of research because it regards online shopping orientation as an antecedent of perceived control and attitude, but it also reveals some important managerial implications. For example, online stores should be more concerned with and committed to online consumers who like to shop at home, in a more convenient store, and desire to buy new fashions on impulse. Consumers with these

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Table 1. Measurement results Items Shopping enjoyment I enjoy shopping for clothes. Shopping for clothes puts me in a good mood. I enjoy spending time browsing for clothes. Fashion consciousness I try to keep my wardrobe up to date with fashion trends. I’m interested in fashion. Price consciousness I shop a lot for special deals on clothing. I watch advertisements for sales on clothing. Shopping confidence I feel confident in my ability to shop for clothes. I think I’m a good clothing shopper. Convenience consciousness I usually buy my clothes at the most convenient place. I put a high value on convenience when shopping for clothes. In-home shopping tendency I like to shop for clothes online. I like to shop from home. Brand/store loyalty Once I find a brand I like, I stick with it. I try to stick to certain brands and stores when I buy clothes. Quick shopping It is important for me that shopping for my clothes (for my partner’s clothes) is done as quickly as possible I usually buy my clothes (clothes for my partner) where I can get it over with as expediently as

Mean(SD)

Item Loading

3.8(1.18) 3.7(0.99)

0.873 0.849

2.4(1.27)

0.778

2.9(1.16)

0.892

3.3(1.13)

0.900

4.4(0.82)

0.823

3.6(1.22)

0.878

4.1(0.80)

0.807

3.5(0.90)

0.928

3.1(1.06)

0.861

3.6(0.92)

0.708

3.1(1.04) 3.1(1.12)

0.971 0.966

3.2(1.14)

0.925

2.9(1.22)

0.709

3.1(1.04)

0.707

3.8(0.93)

0.932

Composite AVE Reliability 0.873 0.696

0.891

0.803

0.840

0.725

0.861

0.756

0.735

0.584

0.968

0.938

0.798

0.668

0.786

0.654

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possible Impulse Purchase I am impulsive when purchasing clothes through online stores. When I purchase clothes spontaneously from the online store, I feel fulfilled. Attitude Shopping in online stores is pleasant. I am content shopping in online stores. Shopping in online store satisfies my needs. In general, I have a good attitude toward online shopping. Perceived Control I feel that I have confidence over my product search in online stores. I find it is easy to access customer services at online stores. I clearly know the right things to do (not confused) in the transaction process (e.g.. paying process) at online stores. I feel comfortable with the level of security online stores provide in the payment process. Online Purchase Intention I will speak favourably about online purchase to my family and friends I will purchase clothes online again in the future.

1.7(0.89)

0.747

2.8(1.08)

0.862

2.8(1.08)

0.793

3.2(1.16)

0.843

2.9(1.12)

0.777

3.5(0.97)

0.893

3.2(1.00)

0.817

3.3(1.00)

0.746

3.9(1.07)

0.728

3.4(1.01)

0.790

3.7(0.99)

0.906

3.6(1.09)

0.866

SD- Standard Deviation; AVE-Average Variance Extracted.

0.782

0.642

0.897

0.685

0.854

0.595

0.879

0.784

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Figure 1. Structural Results

Price C.

Convenience C.

S. Enjoyment

0.204*** 0.199* Fashion C.

0.157* 0.284***

Attitude R 2 =60.3% Q2 = 0.69 0.403***

0.202** S. Confidence

Online Shopping Orientation R 2 = 100%

0.143*

O. P. Intention 2 R =51.2% Q2 = 0.78

0.577***

0.430***

0.364***

0.577*** In-home 0.136*

Brand L.

Impulse P.

0.220***

0.162*

Quick S.

P. Control R 2 =33.3% Q2 = 0.59

*p< 0.05; ** p< 0.01;*** p< 0.001 f 2 = 0.93 GoF = 0.59