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ISSN : 2234-3040(Print)
ISSN : 2234-3059(Online)
The East Asian Journal of Business Management Vol.4 No.3 pp.5-12
DOI : https://doi.org/10.13106/eajbm.2014.vol4.no3.5.

Online Shopping Motivations, Information Search, and Shopping Intentions in an Emerging Economy.

Devinder Pal Singh*

* Corresponding Author, Assistant Professor, Punjabi University Regional
Centre for IT & Management, Mohali, Punjab, India.
E-mail: sidhudps@gmail.com
Received: February 07, 2014. Revised: April 14, 2014. Accepted: July 16, 2014.

Abstract

Purpose – This study is aimed at examining Indian consumers’online shopping motivations, information search, and shoppingintentions. The study intends to reveal the relationship betweenonline shopping motivations, information search, andshopping intentions.
Design, methodology, and approach – The study employsfactor analysis to verify correct loading of items on correspondingfactors, and to confirm the applicability of constructs inthe Indian context. The model was verified using stepwise regressionanalysis.
Results – The findings show that hedonic and utilitarian motivationssignificantly affect online information search and shoppingintentions. The information search is a significant predictor ofonline purchase intention.
Conclusions – Hedonic and utilitarian motivations are the salientfactors affecting online information search and purchaseintentions. Marketers are required to design websites that fosteran enjoyable online experience. This will attract customers whowill browse the website for a longer duration. More time devotedto information search will ensure brand building and loyalty.


JEL Classifications: M31, M39, D21.

0063-02-0004-0003-1.pdf199.8KB

1. Introduction

 India offers significant opportunities for online retail business due to consumers’ growing buying power, rising Internet penetration and metamorphosed lifestyles. It is currently becoming a significant retail channel for almost all the business firms in the country (Dash & Saji, 2008; Khare & Rakesh, 2011). With the rise in consumer demand, the B2C sales are expected to grow 57% annually through 2012-2016 (Forrester, 2012). It is predicted that India’s Internet users will grow fivefold by 2015 (McKinsey, 2011). The country is going to be the biggest e-commerce market in the Asia Pacific as it is expected to reach $ 8.8 billion by 2016 (Forrester, 2012). The increased Internet diffusion has augmented its acceptability but it has failed to be accepted as an alternative to other retail channels (Khare, Singh & Khare, 2010). The online sales are restricted to Indian youth who purchase small-value products for their personal use (Gupta, Handa & Gupta, 2008). Even though there is substantial research but still there is a gap in knowledge of Indian online purchase behavior (Beldona, Racherla & Mundhra, 2011). This requires understanding the reasons or motivations that stimulate the consumers to shop online.

 Motivations have been considerably explored within the realms of consumer behavior research. Shopping motivations in customer research have been chiefly listed as utilitarian and hedonic (Babin, Darden & Griffin, 1994; Batra & Ahtola, 1990). In an online environment, consumers pursue ‘information search’ along with hedonic and utilitarian pursuits. Information search has been acknowledged as salient motive for online shopping (Bigne´-Alcan~iz et al., 2008; Noble, Griffith, & Adjie, 2006; Liu & Forsythe, 2010; Rose & Samouel, 2009; To, Liao & Line, 2007; Yulihasri, Islam & Daud, 2011). Internet serves as a source of information on product feature, prices, retailer information and store comparison (Khare, Singh & Khare, 2010). Consumers increasingly shop online to buy goods and services, assemble product information or even surf for enjoyment (Demangeot & Broderick, 2007). This merits understanding the relationship between online shopping motivations, information search and shopping intentions.

 The shopping motivations differ in different cultures, as it is evident that Chinese consumers are more hedonically oriented (Zhang, Sirion & Howard, 2011), while the Hungarian consumers place more value on utilitarian aspects (Millan & Howard, 2007). The existent literature is bereft of adequate research on the Indian online shopping motives and behavior. It seeks to understand the association between online shopping motivations, information search and shopping intentions. The research proposes investigating the influence of hedonic and utilitarian motivations on online information search and shopping intention. Further, it studies the relationship between online information search and shopping intention. The study differs from earlier studies as this relationship has not been studied much in the Indian context. It is expected that this study will contribute to the theory of shopping motivations and provide valuable inputs for online marketing strategies in the Indian market.

2. Literature Review

 Motivation is the basic driving force behind all consumer actions (Chen, 2012). It is an important construct of shopping behavioral research and is highly relevant for retail marketing (Wagner, 2007). Shopping motivations are the reasons that inspire people to shop (Arnold & Reynolds, 2003). Tauber (1972) postulated eleven motives for shopping and clubbed them as personal and social motivations. Westbrook and Black (1985) suggested seven motivations namely anticipated utility, role enactment, negotiation, choice optimization, affiliation, power and the authority, and stimulation that stimulated shopping. According to Kim, Kim and Kang (2003) shopping motivations consisted of five dimensions: service motivation, economic motivation, diversion motivation, eating-out motivation, and social motivation. Wagner (2007) recognized the societal, experiential and utilitarian aspects of shopping.

 Range of motivational typologies has been proffered in the field of online shopping. Davis, Bagozzi and Warshaw (1989) have identified aspects like ease of use, usefulness, and enjoyment as antecedents of technological adoption like Internet shopping. Noble, Griffith, and Adjie (2006) acknowledge information search, price information, uniqueness, product assortment, convenience and socialization as Internet shopping motives. Liu and Forsythe (2010) recognize the ease of use, usefulness, enjoyment, convenience, and information search as the benefits sought by online consumers. Kim and Eastin (2011) identified price of goods and prior computer knowledge as the factors that affect online shopping. Naseri (2011) recognized that compatibility, usefulness, ease of use and security positively affect online buying. Similarly, Al-Maghrabi and Dennis (2011) in a study on Saudi Arabian respondents identified perceived usefulness, enjoyment and social pressure as factors that impact online behavior. Convenience, flexibility, and temporal factors are the causes that consumers shop online (Khare, Singh & Khare, 2010). In their study on US respondents Ha and Stoel, (2009) find that usefulness is the most potent predictor of attitude towards online shopping while ease of use, trust, and enjoyment are the secondary determinants. Huang and Oppewal (2006) find that cost, convenience, time factor, enjoyment and risk as factors that affect the channel choice. Despite the advancement of diverse taxonomies of shopping motives, the motives can be primarily classified as utilitarian and hedonic. Hedonic and utilitarian aspects take into account the shopping motivations (Babin, Darden & Griffin, 1994; Batra & Ahtola, 1990) and are the antecedents of shopping behaviors (Guido, 2006). The consumer’s assessment of a store providing good hedonic and utilitarian value leads to superior customer satisfaction (Ha & Im, 2012). The importance of hedonic along with utilitarian motivations has been widely acknowledged in the extant literature (Childers, Carr, Peck & Carsons, 2001; To, Liao & Lin, 2007). 

2.1. Utilitarian and Hedonic Motivations

 Utilitarian motivations stimulate shopping for the accomplishment of utility, functionality and pecuniary value of a purchase. It is the consumers’ evaluation of a product’s utilitarian value and its functional attributes (Batra & Ahtola, 1990). The utilitarian shopping behavior is guided by rationality and goals (Farrag, Sayed & Belk, 2010). The utilitarian aspects in online shopping are linked to value, information, ease of use (Khare & Rakesh, 2011) and convenience (Bhatnagar, Misra & Rao, 2000; Chen 2012; Khare & Rakesh, 2011).

 Utilitarian motivations have been traditionally considered to be the main determinants of purchasing behavior. Levy (1959) was one of the earliest researchers who recognized shopping an enjoyable activity. This led to the acknowledgment that not just the utilitarian aspects motivate the consumer buying but also the hedonic considerations (Arnolds & Reynolds, 2003; Ahtola, 1985; Babin, Darden & Griffin, 1994; Batra & Ahtola, 1991; Hirschman & Holbrook, 1982). Hedonic pursuit for consumer behavior "relate to the multi-sensory, fantasy and emotive aspects of one’s experience with products" (Hirschman & Holbrook, 1982). It also indicates enjoyment and entertainment (Babin, Darden & Griffin, 1994; Hirschman & Holbrook, 1982). The hedonic consumption may assist people to escape to pleasant environment (Arnolds & Reynolds, 2012) as derive satisfaction by enjoying ambience, browsing, and social experiences outside the home (Kim, Kang & Kim, 2005; Overby & Lee, 2006).

 Fiore, Kee, and Kunz (2003) posit that online shopping appeals to consumers because of the fun, enjoyment, and engagement (Khare, Singh & Khare, 2010). Consumers search for enjoyment, socialization while shopping (Dennis et al. 2010). Al-Maghrabi and Dennis (2011) in a study on Saudi respondents postulated that usefulness; enjoyment and social pressure prompted consumers to online shopping. Childers et al. (2001) find that ease of use, usefulness, and enjoyment as strong predictors of attitude towards online shopping. However, they conclude that usefulness is a stronger predictor of Internet shopping relative to ease of use and enjoyment. Chen (2012) identifies profit, value, emotion and achievements as motivations for online buying. Even though online shoppers seek hedonic and utilitarian benefits (Koo, Kim & Lee, 2008) but there is a lack of consensus on prominence of either of the motivational aspects. Liu and Forsythe (2010) find that online shoppers perceive utilitarian not hedonic benefit. Utilitarian motives have been recognized to have stronger impact on online shopping than hedonic motives (Bridges & Florsheim, 2006; Ha & Stoel, 2009; Overby & Lee 2006; To, Liao & Line, 2007). Yulihasri, Islam and Daud (2011) conclude that hedonic shopping motivation is a strong predictor of online shopping. Bridges and Florsheim (2006) in their study on US students suggest that websites should be convenient, informative and goal focused thereby serving utilitarian goals.

 Consumers from different ethnic backgrounds differ in their motivations for consumption (Hirschman & Holbrook, 1982). Consumers in different cultures shop for different reasons. Chilean consumers seek utilitarian benefits (Nicholls, Mandokovic, Roslow & Kranendonk, 2000); Americans look for entertainment (Iksuk, Tim, Richard & Hyunjip, 2005), Hungarian consumers pursue utilitarian goals (Millan & Howard, 2007) while Chinese consumers value hedonic aspects (Zhang, Sirion & Howard, 2011). There exists an association between the country’s economic development and valuation of hedonic aspects of shopping (Millan & Howard, 2007). Shopping motives in India are in a state of transition (Khare, 2011). This necessitates understanding the consumer motives for online shopping in the transitional market.

2.2. Information Search

 Information search is an important aspect of consumer decision process. Consumers’ quest for information, motivates them to search Internet. They expend money, time, and other resources in information consumption that may consequently lead to purchasing (Maity, Hsu & Pelton, 2012). Consumers vary in terms of the pleasure they receive from information surfing (Rose & Samouel, 2009). Information search is a significant motivation for online consumers (Rose & Samouel, 2009; Verhoef, Neslin & Vroomen, 2007). The motivation variables have an important causal association with information search (Vazquez & Xu, 2009).

 Extant literature lacks consensus as to the motivations that affect information search. Yulihasri, Islam and Daud (2011) posit hedonic motivations as the strong predictors of information search. Though both utilitarian and hedonic motivations drive the information search and shopping intentions but utilitarian motivations have more effect than the hedonic motivations(To, Liao & Line, 2007).

 Information search has a positive relationship with on line buying (Yulihasri, Islam & Daud, 2011). Noble, Griffith, and Adjie (2006) have identified information search, and browsing as Internet shopping motives. Khare and Rakesh (2011) posit that hedonic motives directly impact customers’ information search intention whereas indirectly affect online shopping intentions. Information search directly affects online shopping intentions (Bigne´-Alcan~iz et al. 2008; So et al., 2005). Product information search is the strongest predictor of consumer’s purchase intention (Kim and Park, 2005).

2.3. Shopping Intention

 Intention is supposed to capture the motivational factors that influence the behavior (Ajzen, 1991). Intentions are the antecedents of behavior. Stable intentions play a significant role in guiding human action and relatively are better predictors of subsequent behavior (Ajzen, 2001). Since the intention indicates how hard the people are willing to try to perform a behavior so the stronger the intention to engage in a behavior the more likely it will be performed (Ajzen, 1991). Since numerous situational variables influence actual shopping behavior (Goldsmith, Flynn & Clark, 2011), the current study examines the relationship of various motives with online shopping intention).

 The examination of literature results in the following hypotheses and the research model (Figure 1):

Figure 1. Research Model

 H1. There is a positive association between consumers’ hedonic motivations and information search.

 H2. There is a positive association between consumers’ utilitarian motivations and information search.

 H3. There is a positive association between consumers’ information search and their intention to shop online.

 H4. There is a positive association between consumers’ hedonic motivations and their intention to shop online.

 H5. There is a positive association between consumers’ utilitarian motivations and their intention to shop online.

3. Methodology

 The study adopted the consumer intercept method of survey. The intercept interview method has proved to be one of the most popular methods in marketing and consumer research (Hornik & Ellis, 1989) because of convenience, and cost advantage (Burns & Bush, 2003). The intercepted respondents being frequent shoppers can provide better quality shopping information (Bush & Hair, 1985). Four hundred respondents were intercepted across the various shopping centers and malls of the Chandigarh City, a tier II city in northern India. As per the Government of India census report (2011) the city ranks the highest on the Internet density in the country.

 Data was collected using a self-completion questionnaire administered to the respondents. The questionnaire was part of a larger questionnaire and contained multiple items derived from extant literature. Items related to motivations were measured on a 5-point Likert scale, ranging from ‘strongly disagree’ to ‘strongly agree. Variables under study were operationalized using published scales. Scales related to motivations, information search and intentions had been adopted from previous studies. The section related demographic characteristics like age, gender, occupation, marital status and educational qualifications were incorporated in the questionnaire.

 Four hundred respondents were intercepted across the numerous shopping places of Chandigarh city and asked to complete the questionnaire. The effective sample size constituted of 326 respondents. The sample comprised of 72.4 per cent males and 27.6 per cent females. However, the sample varied in age from below 18 years to 70 years of age but 90 per cent of the sample comprised of respondents below the age of 30 years.

4. Results

 The data were analyzed employing 326 complete questionnaires. The factor analysis was conducted to verify whether the items loaded correctly on corresponding factors as in prior research and to confirm the applicability of the scales to the Indian consumers. The items related to all the constructs were subjected to exploratory factor analysis employing varimax rotation. The KMO measure of sampling adequacy was .892, thus confirming the appropriateness of the factor analysis. The Barlett’s test of sphericity confirmed that the items were related (=3182.69, df=153, p< 0.001). The factor analysis extracted four factors (having Eigen values above 1) accounting for 66.44 percentage of the total variance (see Table 1).

<Table 1> Factor Analysis

 The first factor extracted had a high loading (above.5) of items related to hedonic motivations (α= .713). This factor was accountable for 17.052 percentages of variance. The second factor included items of utilitarian motivations (α= .882). The third factor had loadings related to online search loaded on it (α = .829) and the items related to ‘online shopping intention’had a higher loading on the fourth factor (α= .846). The factor analysis results confirm the convergent and discriminant validities of the constructs. The Cronbach’s value for all the constructs was higher than 0.7 and satisfied Nunnally’s (1978) reliability scale criterion value.

 To include the effect of hedonic and utilitarian motivations on online information search multiple regression analysis was performed using the hierarchical method of analysis. Similarly to study the relationship of information search, hedonic and utilitarian motivations with online purchase intention multiple regression employing the hierarchical method was performed. The results show support for hypotheses H1 and H2 as both hedonic and utilitarian motivations have proved to be significant predictors of online information search. Similarly, H3, H4 and H5 were confirmed as online information search, hedonic and utilitarian motivations substantially predicted online purchase intention.

 Stepwise regression analysis for H1 and H2 generated two models (refer to Table 2). In the first model, hedonic motivations emerged as the predictor variable for online search (R²= .351, p< .001). The R² for the first model shows that the hedonic motivations independently account for 35% of variance in the online information search.

<Table 2> Regression Results-Online Information Search N=326 ** Significance at 0.001 level

 The ß value (.594) shows hedonic motives as salient contributors for online information search. The ANOVA test shows that  the predicted regression by hedonic motives is significant in the model (F=175.86, p< .001). It indicates that hedonic motivations are important stimulators for online information search.

 In the model 2, utilitarian motivations were seized along with hedonic motivations. Both the hedonic and utilitarian motivations emerge as predictors of online formation search (R²= .412, p< .001). Both above variables are responsible for 41.2% of the variance in the online information search. The increase in percentage of variance with the addition of the utilitarian motivations of the model implies that as both hedonic and utilitarian motivations increase the intention to information search. The ßvalues of .491 and .264 for hedonic and utilitarian motivations respectively show their contribution of each of these motivations to the Model.

 To study the effect of information search, hedonic and utilitarian motivations on online purchase intention another regression analysis was carried out for the second time. The regression results for H3, H4 and H5 generated three models (Table 3).

<Table 3> Regression Results-Online Shopping Intention N=326 ** Significance at 0.001 level

 In the first model hedonic motive as an independent variable was entered. The variable accounted for 41.8% of the variance, (R²= .418, p< .001) and proved to be a significant predictor of online purchase intention. The ß value (.647) of hedonic motivations displays its contribution to this model. Hence, hedonic motivations significantly predict online purchase intention.

 The second model shows the predictive efficacy of hedonic motives along with online information search for online purchase intention (R²= .453, p< .001). The online information search (ß = .232) along with hedonic motivations (ß = .509) show the predictive regression of this model (F= 35.072, p< .01).

 The R² for the third model shows that the addition of utilitarian motivation as independent variable along with the hedonic motivation and online information search. The model is accountable for 46.8% of the variance in the online shopping intention. The ßvalue of .136 shows the contribution of utilitarian motivations for the model. The ANOVA test shows that the predicted regression by utilitarian motive is significant in the model (F=93.99, p< .001).

 The results of the research prove the hypotheses H1, H2, H3, H4 and H5 to be true. The study showed that hedonic and utilitarian motivations significantly predict the online information search. The online information search, hedonic and utilitarian motivations positively influence online purchase intention.

5. Discussion and Conclusions

 The results of this research reveal that hedonic and utilitarian motivations substantially affect online information search and online shopping intention. The factor analysis demonstrates that hedonic aspects are held accountable for more variance than the utilitarian motivations. Even though both utilitarian and hedonic shopping motivations drive ‘online information search’and purchase intentions but among these hedonic motivations have emerged as strong predictors. The study establishes the prominence of hedonic motives over the utilitarian motives in information search. The findings show that Indian consumers are more hedonically oriented. This is significant for marketers as hedonist consumers can be targeted by offers delivering fun, enjoyment, and mood alleviating consumption experiences. The browsing and shopping on the Internet may be enjoyable activity and result in hedonic consumption experience (Menon &Kahn, 2002). The marketers should design the websites to satiate the adventurous and gratification goals of the consumers. This websites should be pleasing and incorporate enjoyable stimuli so that the consumers are induced to browse and spend more time at the website (Menon & Kahn, 2002). The web pages should be stimulating, appealing and colorful.

 The feelings of enjoyment and contentment positively link with purchase intentions (Bridges & Florsheim, 2008). The involvement in a playful and exploratory behavior is self-motivating as it fosters feelings of pleasure by stimulating consumers to online shopping (Smith & Sivakumar, 2004). Assimilation of hedonic aspects by websites would attract traffic to websites. It would make customers spend more time and repeat visits to the websites (Bridges & Florsheim, 2008). The online search experience results in positive consumer attitudes (Mathwick & Rigdon, 2004). The hedonic experiences results in augmentation of flow and is carried forward effects on subsequent shopping intention (Menon & Kahn, 2002).

 The study also shows that utilitarian aspects also stimulate information search. This mandates that marketers ensure that the websites provide adequate product information and easy access to consumers. Online retailers should ensure convenience, information quality and detailed instructions (Khare & Rakesh, 2011). The web pages should have satisfactory uploading speed for the flow experience. The consumers who can easily navigate and access information related to price, product features and delivery options would have a more positive attitude towards the brand (Rose & Samouel, 2009).

 Utilitarian aspects serve as crucial factors for online shopping. The website should take into consideration the entire product range of product mix to fulfill the utilitarian shopping goals. The hassle free one stop-shopping environment along with convenient layout would facilitate quick shopping thus providing utilitarian value. The websites integrating sufficient product and price information would involve the customers to spend more time on the website and gain more knowledge about brands (Smith & Sivakumar, 2004). This will foster brand loyalty as consumer will draw upon the existing information for purchase decisions and avoid information of rival brands (Rose & Samouel, 2009). The findings support research by Khare and Rakesh (2011) that information search assists in purchase intentions. The more duration spent on the website would help offset other factors like perceived risk, self-confidence and an unwillingness to purchase (Smith & Sivakumar, 2004). The more time devoted to browsing will lead to more information gain leading to more online sales. Online information search is a salient mediating variable between the online purchase intention and predictor variables (Kim & Park, 2005).

 The utilitarian value-shopping motive could be stimulator if there are creative bargain schemes like ‘loyalty programs’and the customers are adequately informed through the media. The online retailers need to promote reduced prices targeting these value-seeking consumers. 

 The ‘online information search’ experience positively influences consumer attitudes and subsequently influences the purchase intentions. The online retailers should satiate the hedonic and utilitarian motives of online customers to engage them in more information search and consequently online purchase.

Reference

1.Ajzen, I. (2001). Nature and Operation of Attitudes. Annual Review of Psychology, 52, 27-58.
2.Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
3.Al-Maghrabi, T., & Dennis, C. (2011). What Drives Consumers' Continuance Intention to E-shopping?: Conceptual Framework and Managerial Implications in the Case of Saudi Arabia. International Journal of Retail & Distribution Management, 39(12), 899-926.
4.Arnold, M. J., & Reynolds, K. E. (2003). Hedonic Shopping Motivations. Journal of Retailing, 108(2), 1-20.
5.Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or Fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20, 644-656.
6.Batra, R., & Ahtola, O.T. (1991). Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes. Marketing Letters, 2(2), 159-170.
7.Beldona, S., Racherla, P., & Mundhra, G.D. (2011). To Buy or Not to Buy: Indian Consumers' Choice of Online Versus Offline Channels for Air Travel Purchase. Journal of Hospitality Marketing & Management, 20(8), 831-854.
8.Bhatnagar, A., Misra, S., & Rao, H. R. (2000). On Risk, Convenience and Internet Shopping Behavior. Communications of the ACM, 43(11), 98-105.
9.Bigne´-Alcan~iz, E., C. Ruiz-Mafe´, Alda´s-Manzano, J., & Sanz-Blas, S. (2008). Influence of Online Shopping Information Dependency and Innovativeness on Internet Shopping Adoption. Online Information Review, 32(5), 648-667.
10.Bridges, E., & Florsheim, R. (2008). Hedonic and Utilitarian Shopping Goals: The Online Experience. Journal of Business Research, 61, 309-314.
11.Burns, A.C., & Bush, R.F. (2003). Marketing Research: Online Research Applications. New Jersey: Prentice Hall.
12.Bush, A. J., & Hair, J. F. (1985). An Assessment of the Mall Intercept as a Data Collection Method. Journal of Marketing Research, 22, 158-167.
13.Chen, Y-T. (2012). Exploring the Continuance Intentions of Consumers for B2C Online Shopping: Perspectives of Fairness and Trust, Online Information Review, 36(1), 104-125.
14.Childers, T. L., Carr, C.L., Peck, J., & Carson, S. (2001). Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior. Journal of Retailing, 77, 511-535.
15.Dash, S., & Saji, K. B. (2008). The Role of Consumer Self-Efficacyand Website Social-Presence in Customers' Adoption of B2C Online Shopping. Journal of International Consumer Marketing, 20(2), 33-48.
16.Davis, F., Bagozzi, R., & Warshaw, R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003.
17.Demangeot, C., & Broderick, A.J. (2007). Conceptualising Consumer Behaviour in Online Shopping Environments. International Journal of Retail & Distribution Management, 35(11), 878-894.
18.Dennis, C., Morgan, A., Wright, L.T., & Jayawardhena, C. (2010). The Influences of Social E-shopping in Enhancing Young Women's Online Shopping Behavior. Journal of Customer Behaviour, 9(2),151-174.
19.Farrag, D.A., Sayed, I.M.E., & Belk, R.W. (2010). Mall Shopping Motives and Activities: A Multi Method Approach. Journal of International Consumer Marketing, 22, 95-115.
20.Forrester. (2012). Research Online Retail Forecast, 2011 To 2016 (Asia Pacific). Retrieved December, 2013, from http://www.forrester.com/.
21.Goldsmith, R.E., Flynn, L. R., & Clark, R.A. (2011). Materialism and Brand Engagement as Shopping Motivations. Journal of Retailing and Consumer Services, 18, 278-284.
22.Guido, G. (2006). Shopping Motives, Big FiveFactors, and the Hedonic/Utilitarian Shopping Value: An Integration and Factorial Study. InnovativeMarketing, 2(2), 57-67.
23.Gupta, N., Handa, M., & Gupta, B. (2008). Young Adults of India-Online Surfers or Online Shoppers. Journal of Internet Commerce, 7(4), 425-444.
24.Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62, 565-557.
25.Hirschman, E.C., & Holbrook, M.B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101.
26.Hornikk, J., & Ellis, S. (1988). Strategies to Secure Compliance for a Mall Intercept Interview, Public Opinion Quarterly, 52, 539-551.
27.Huang, Y., & Oppewal, H. (2006). Why Consumers Hesitate to Shop Online: An Experimental Choice Analysis of Grocery Shopping and the Role of Delivery Fees. International Journal of Retail & Distribution Management, 34(4), 334-353.
28.Iksuk, K., Tim, C., Richard, F., & Hyunjip, C. (2005). Mall Entertainment and Shopping Behavior: a Graphical Modeling Approach. Advances in Consumer Research, 32, 487-492.
29.Khare, A., & Rakesh, S. (2011). Retailing in Indian Malls: Antecedents to Retailers' Preferences for Mall-store Space. The International Review of Retail, Distribution and Consumer Research, 21(2), 187-200.
30.Khare, A. (2011). Influence of Hedonic and Utilitarian Values in Determining Attitude towards Malls: A Case of Indian Small City consumers. Journal of Retail & Leisure Property, 9(5), 429-442.
31.Khare, A., Singh, S., & Khare, A. (2010). Innovativeness/Novelty-Seeking Behavior as Determinants of Online Shopping Behavior among Indian Youth. Journal of Internet Commerce, 9(3), 164-185.
32.Kim, S., & Eastin, M.S. (2011). Hedonic Tendencies and the Online Consumer: An Investigation of the Online Shopping Process. Journal of Internet Commerce, (10), 68-90.
33.Kim, J., & Park, J. (2005). A Consumer Shopping Channel Extension Model: Attitude Shift toward Online Store. Journal of Fashion Marketing and Management, 9(1), 106-121.
34.Kim, Y-K, Kim, E., & Kang, J. (2003). Teens' Mall Shopping Motivations: Functions of Loneliness and Media Usage. Family and Consumer Sciences Research Journal, 32(2), 140-167.
35.Koo, D-M., Kim, J-J., & Lee, S-H. (2008). Personal Values as Underlying Motives of Shopping Online. Asia Pacific Journal of Marketing and Logistics, 20(2), 156-173.
36.Levy J. (1959). Symbols for Sales. Harvard Business Review, 37(4), 117-124.
37.Liu, C., & Forsythe, S. (2010). Sustaining Online Shopping: Moderating Role of Online Shopping Motives. Journal of Internet Commerce, 9, 83-103.
38.Maity, M., Hsu, M.K., & Pelton, L.E. (2012). Consumers' Online Information Search: Gen Yers' Finding Needles in the Internet Haystack, Journal of Marketing Channels, 19(1), 49-76.
39.Mathwick, C., and Rigdon, E. (2004). Play, Flow and Online Search Experience, Journal of Consumer Search, 31(2), 324-332.
40.Menon S., & Kahn, B. (2002). Cross-category Effects of Induced Arousal and Pleasure on the Internet Shopping Experience, Journal of Retailing, 78, 31-40.
41.Millan, E.S., & Howard, E. (2007). Shopping for Pleasure? Shopping Experiences of Hungarian Consumers. International Journal of Retail & Distribution Management , 35(6), 474-487.
42.Naseri, M.B. (2011). Role of Demographics, Social Connectedness and Prior Internet Experience in Adoption of Online Shopping: Applications for Direct Marketing. Journal of Targeting, Measurement and Analysis for Marketing, 19(2), 69-84.
43.Nicholls, J.A.F., Li, F., Mandokovic, T., Roslow, S., & Kranendonk, C. (2000). US-Chilean Mirrors: Shoppers in Two Countries. Journal of Consumer Marketing, 17, 106-119.
44.Noble, S.M. Griffith, D. A, & Weinberger, M. G. (2005). Consumer Derived Utilitarian Value and Channel Utilization in a Multi-channel Retail Context, Journal of Business Research, 58, 1643-1651.
45.Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill
46.Overby, J.W., & Lee, Eun-Ju. (2006). The Effects of Utilitarian and Hedonic Online Shopping Value on Consumer Preference and Intentions. Journal of Business Research, 59, 1160-1166.
47.Rose, S., & Samouel, P. (2009). Internal Psychological VersusExternal Market-driven Determinants of the Amount of Consumer Information Search amongst Online Shoppers. Journal of Marketing Management, 25(1), 171-190,
48.Smith, D.N., & Sivakumar, K. (2004). Flow and Internet Shopping Behavior: A Conceptual Model and Research Propositios, Journal of Business Research, 57, 1199-1208.
49.So, W. C. M., Wong, T. N. D., & Sculli, D. (2005). Factors Affecting Intentions to Purchase via the Internet. Industrial Management & Data Systems, 105(9), 1225-1244.
50.Tauber, E.M. (1972). Why Do People Shop? Journal of Marketing, 36(October), 46-49.
51.To, P.-L., Liao, C. & Lin, T-H. (2007). Shopping Motivations on Internet: A StudyBased on Utilitarian and Hedonic Value. Technovation, 27, 774-787.
52.Vazquez, D., & Xu, X. (2009). Investigating Linkages BetweenOnline Purchase BehaviorVariables. International Journal of Retail & Distribution Management, 37(5), 408-419.
53.Verhoef, P.C., Nesline, S.A., & Vroomen, B. (2007). Multichannel Customer Management: Understanding the Research-shopper Phenomenon, International Journal of Research in Marketing, 24, 129-148.
54.Wagner, T. (2007). Shopping Motivation Revised: AMeans-end Chain Analytical Perspective. International Journal of Retail & Distribution Management, 35(7), 569-582.
55.Westbrook, R.A., & Black, W. (1985). A Motivation-based Shopper Typology. Journal of Retailing, 61(Spring), 78-103.
56.Yulihasri, Islam, M. A., & Daud, K. A. K. (2011). Factors that Influence Customers' Buying Intention on Shopping Online. International Journal of Marketing Studies, 3(1), 128-139.
57.Zhang, Y., Sirion, C. & Howard, C. (2011). The Influence of the Mall Environment on Shoppers' Values and Consumer Behavior in China, Proceedings of ASBBS annual conference(pp. 214-224). Las Vegas, US: American Society of Business and Behavioral Sciences.