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Online Buying Behaviour- A Brief Review and Update
Strategic Management (MG3407)
Teerthanker Mahaveer University
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AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
Article No. 16
ONLINE BUYING BEHAVIOUR: A BRIEF REVIEW
AND UPDATE
Mamta Chawla
Research Scholar, Department of Business Administration, AMU- Aligarh.
Dr. Mohammad Naved Khan
Associate Professor, Department of Business Administration, AMU- Aligarh.
Dr. Anuja Pandey
Associate Professor, AIMA, New Delhi.
Abstract : Internet has gained status of as a dynamic commercial platform, more than a rich source of communication. It has intensified the complexities of the simple act of buying. “Google” has become the generic term for “searching information”. Traditional buying by individuals has taken the complex mixture of store, mall, television, internet, mobile- based shopping. Not only developed western-countries but even Asian countries, with poor infrastructure and low internet penetration rates, are equally adopting online buying. Indeed, a simple search combining the terms “online” and “buying” or “ shopping” results into more than 150 00 results on any academic database source. A review of selected published work in the area of “online buying” reveals that a wide range of topics have been explored and a rich theoretical framework in the form of different models is inexistent. This paper aims to present a comprehensive framework of the relevant literature available in the field of online buying behavior, in the form of different theories, models and constructs; and research results based on them. Tradition 5- staged model of consumer behavior has different stages- need identification, information search, evaluation of alternatives, buying and post purchase evaluation. Additionally, for online buying behavior the stages involved in online buying can be divided into: attitude formation, intention, adoption and continuation with online buying. Most important factors that influence online buying: attitude, motivation, trust, risk, demographics, website etc. are widely researched and reported. “Internet adoption” is widely used as foundation framework to study “adoption of online buying”. Post adoption or continuation with online buying is the area which still needs substantiate research work. Current state of this emerging field offers the potential to identify areas that need attention for future researchers. Through review of online buying literature available, this paper offers theoretical basis to the academicians, practitioners and web-marketers. In addition, the clear understanding of the online buying behavior can provide the opportunities for designing new capabilities and strategies that would quench online buyers’ thrust on value.
Key Words: Internet, online buying, attitude, adoption, continuation, literature review
Introduction With the development of IT and its application in different spheres of business even the traditional buying is challenged by online marketers. The development and intensification of competition and expanding list of products available online is indicative of gaining patronage of online buying. As a result of acceptability of Internet, dynamism in market and consumers attraction towards online buying, researchers are keen to unearth the currents driving and identify leading indicators of future success of online buying. Current article provides a summary review of relevant published work and issues that play an important role in online buying. This article,
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
by synthesizing online buying literature, helps to understand online buying behavior and offers future research priorities in the field.
The rational for holding this secondary research work is to explore and integrate the available literature on online buying behavior to have a holistic view about this discipline. Further to built a strong foundation for extending and relating it in Indian context by identifying the research gaps. So that empirical research can be undertaken for the doctoral research work. The scope is limited only to the overt behaviors displayed by individual customers while buying online for personal use. Related branch-fields of study are excluded from current exploration e. “online group buying behavior”, “online impulse buying behavior” etc. Focus of current research is on theories and; research outcomes based on those theories. Methodological reviews are done in a limited manner. Purposive sample of pivotal published research work has been selected from three academic databases available online during last two years: EbescoHost, ProQuest and Google scholar. All of the foundational theoretical models have been reviewed but only last ten years pivotal output form empirical research work have been included in this review. Key words like “online buying”, “internet buying”, “online purchasing behavior”, “online buying behavior” “internet/e- shopping behavior” and “online/ e- shopping” have been used to retrieve relevant research articles majorly from different Journals and conference Proceedings. Articles from trade magazines and consultancy reports have been excluded from this review. “Mendeley 1.13” has been utilized to manage and review the research data for citation and bibliography. Present review paper has been methodological structured for the academicians and marketing practitioners. Following section discuss concept of online buying behavior, followed by discussion of major theories and their constructs, then the next section highlight major research outcome with relevant constructs reported in previous researches. The last section discusses current state and possible future direction in the field.
Online Buying Behavior One of the most research oriented area of marketing discipline is consumer behavior. There are plethora of quantitative and qualitative studies resulting into a robust set of different theories available on Buying Behavior(Solomon, Russell-Bennett, & Previte, 2012). Most of the theories have been adopted from different field of studies e. psychology, economics, anthropology to name a few. Engel, Kottat and Blackwell known as EKB model of consumer decision making is widely recognized and accepted by scholars.
Online buying or shopping refers to the process of researching and purchasing products or services over the Internet (Varma & Agarwal, 2014). No. of online buying researchers utilized the five stages EKB model: Need/problem recognition, Information search, Evaluation of alternatives, Purchase decision, Post-purchase behavior (Wen Gong & Maddox, 2011). Still, there is no consensus on the applicability of consumer behaviors models to online buying scenario. An online transaction can involve three steps: process information retrieval, information transfer, and product purchase (P. A. Pavlou & Chai, 2002; P. A. Pavlou, 2003; P. Pavlou & Fygenson, 2006). Whereas, the entire online buying has even been divided into two stages: first consisting of searching, comparing and selecting, placing an order termed as ordering stage and second stage is order tracking and keeping or returning termed as order fulfillment stage (C. Liao, Palvia, & Lin, 2010). Online consumer behavior research articles
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
which a person believes that using a particular system would be free of effort; “attitude towards use” is the user’s evaluation of the desirability of employing a particular information system application. Behavioral “intention to use” is a measure of the likelihood a person will employ the application Davis (1989) asserted that PU and PEU represent the beliefs that lead to such acceptance. Empirical tests suggest that TAM predicts intention and use. He found that TAM successfully predicted use of a word processing package and reported PEU and PU were significantly correlated with use of an office automation package, a text editor, and two graphics packages. A limitation of TAM mentioned, that it assumes usage is volitional, that is, there are no barriers that would prevent an individual from using an IS if he or she chose to do so. Although, there are many factors preventing a person from using an application such as perceived user resources (Kieran et al., 2001) and perceived behavior control (Ajzen 2002).
Kim (2012) integrated model TAM with initial trust belief. Other studies examined relative strengths of the associations between the individual independent variables and online buying intention clearly indicated that Customer Service, Trust and Reliability can explain much of the variation in online buying intention (Johar & Awalluddin, 2011). Attempts have been made to utilize TAM with TPB (Sentosa & Mat, 2012) or by adding more constructs to it.
- Innovation Diffusion Theory (IDT) Along with above three, this theory proposed by Roger (1962, 1995), has also been widely cited and adopted to understand adoption of an innovation. Technology adoption speed, amount and degree depends upon five characteristics of the innovation namely: relative advantage, compatibility, complexity, divisibility or trialibility, and communicability or observability (T Hansen, 2005; Turan, 2012). Researchers have utilized this model along with other constructs to understand online buying intentions (Wen Gong, Maddox, & Stump, 2012; Wen Gong & Maddox, 2011). Online buying has been considered as “discontinuous innovation” as it includes technological and buying changes as well (T Hansen, 2005; Torben Hansen, Jensen, & Solgaard,
Adopted in combination to other theories, to explain intention and adoption of online buying in different setting e. internet banking (Lallmahamood, 2007), online travel purchase, online grocery buying(Torben Hansen et al., 2004) (AMARO, 2014; N Delafrooz, Paim, & Khatibi, 2011)(Amaro & Duarte, 2015)(H. Y. Lee, Qu, & Kim, 2007)(N Delafrooz et al., 2011)(Eri, Islam, Daud, & Amir, 2011)(Sinha, 2010)(Ganguly, Dash, & Cyr, 2011)(Narges Delafrooz, Paim, Haron, & Sidin, 2009)(Ostrowski, 2009)(Choi & Geistfeld, 2004). Some of the refined models explained even 64% of actions (Sentosa & Mat, 2012).
In a comparative study, TPB model reported to be better fit in a developing country as compare to extended TAM model (Turan, 2012). Other extensions and revisions based on these four models have been compared and proposed to predict online buying.
- Motivational Model Motivation with other psychological factors like perception, learning and attitude is always been cited as major factors influencing consumer to buy even by Kotler (2000) and Schiffman (2000).
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
Different studies explored consumer motives to buy online. A detailed typology (Shrivastava, 2011) classified motives into: Pragmatic motivations (e. Convenience, Learning about new Trends, Ease of use, Comparison), Product motivations (e. Availability, variety, quality), Service excellence motivations (Accessibility, Timely delivery, Reliability, Responsiveness), Economic motivations (discounts and deals, competitive prices) Hassel reduction motivations (e. transportation, timing, driving and parking), Social motivations (e. social influence, peer pressure, social learning, status and authority), Hedonic motivations (Self gratification, fun-of- buying , Going through search pages, Sensory stimulations, Impulsive shopping ). Rest named as exogenous motivations (Prevision online experience, life style, trust). Understanding of online buying motivation is insufficient to explain the complexities on online buying behavior.
Social Cognitive Theory According to SCT, environment, cognition and human behavior are three interactive factors operating as a triadic reciprocal causation (Bandura, 1986; Wood & Bandura, 1989) cited in (Chen, Huang, & Hsu, 2010). Concept of self-efficacy has been added to existing models to form construct of Internet- self-efficacy, proposed to directly influence performing online buy. In combination to other technology adoption models this theory has been utilized to explore online buying intention and continuation- intention (Suharno, Astut, Raharjo, & Kertahadi, 2014). But, mixed findings have been reported (Sarigiannidis & Kesidou, 2009).
UTAUT Model Unified theory of acceptance and use of technology (UTAUT) model explains user intentions to use IS and subsequent behavior. Performance expectancy (PE), effort expectancy (EE), Social Influence (SI) and facilitating conditions (FC) are 4 direct determinants of usage intention and behavior which can be moderated by Demographic variables (gender, age), experience and voluntariness of use of IS. The constructs are very similar to the previous models but have been named differently. As this theory is based upon earlier eight models to explain usage of IS- TRA(Theory of Reasoned Action), TAM (Technology Acceptance Model), Motivational Model, Theory of Planned Behavior, a combined theory of Planned Behavior and TAM, Model of Personal computer use, DOI(Diffusion of Innovation) and Social cognitive theory (Venkatesh et al., 2003). Number of researchers applied this model (e. Koivumäki et al., 2008; Eckhardt et al., 2009; Curtis et al., 2010; Verhoeven et al., 2010) to different setting of adoption of technology, but not all have adopted the full model. Modified UTAUT model is also proposed to better understand adoption of online buying in developing country (Chiemeke & Evwiekpaefe, 2011). Like with other models this model is partially utilized or only cited as part of available theoretical framework (Williams, Rana, Dwivedi, & Lal, 2011) with little work support to its robustness for understanding adoption of online buying.
ECM-IT Model Researchers have utilized expectation-confirmation model (Expectation Confirmation Model by Oliver, 1980) to IT framework in order to explain post-adoption online buying behavior (e. Liao et al., 2010; Chen et al., 2010; Kim et al., 2003; Lee, 2010). As the initial ECM-IT framework suggest, satisfaction and perceived usefulness are main determinants of consumers’ intention to continue buying online. Claudia, (2012) reported that Expectations Disconfirmation Theory for IT Use is an adaption of Oliver’s expectations disconfirmation paradigm which
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explained by attitude uniformly in the previous studies (Turan, 2012), but all constructs have not found universally applicable across all environments. In a Chinese study PEU “Perceived ease of use” has not been found influencing, whereas PU- “Perceived usefulness” influence online buying intentions (Wen Gong & Maddox, 2011)Wen; Gong et al., 2013). Subjective norms have been statistically significant and have reported to have positive influence
. - Adoption of online buying Investigations, on the segments buying online have been reported extensively. Studying buying of different products online (Sarigiannidis & Kesidou, 2009) e. books, travel, grocery (T Hansen, 2005), electronics (Bashir, 2013; J. Kim & Forsythe, 2010; Liu, Forsythe, & Black, 2011), e-ticketing (Sulaiman, Ng, & Mohezar, 2008). Characteristics of adopters in terms of age, gender and other socio-demographical along product category have been examined. Some reported online buyer to be typically characterized as high income level (T Hansen, 2005).
- Continuation with online Buying Behavior Available literature of online buying behavior can be clearly divided into two major sets; first set of studies concentrating acceptance or adoption and second set of studies concerning continuation-intention, which is still in its infancy stage. Earliest study of online banking employed Expectation- confirmation theory (Bhattacherjee, 2001). Bhattacherjee (2001) highlighted application of ECM better than adaptation of SERVQUAL model to the online buying behavior. ECM is the only framework available which constitute of three constructs namely: expectation, perceived performance and resulting level of satisfaction (Luo, Ba, & Zhang, 2012). Against attitude, satisfaction temporally and causally precedes post-purchase attitude and influence continue-intentions. In contrast to traditional buying, delivery of product is part of post purchase stage. Delivery time, the delivery of the right product regarding its attributes and performance is highly associated with post-purchase satisfaction (Jiang and Rosenbloom, 2005) cited in (Claudia, 2012). Hence return policy has been reported an important factors in considering transaction quality. Another research, combining ECM and TAM in two stages of online buying-ordering and fulfillment, reported customers’ satisfaction with the ordering process and the fulfillment process, and the perceived usefulness of the website contribute significantly (C. Liao et al., 2010)
By combining TAM and ECM and other construct - trust, utilitarian and hedonic motivation the constructed model explained as high as 64% of variance in US (Wen, Prybutok, & Xu, 2011).Contrary to it one more extension of ECM by adopting online buying perspective incorporated both constraint-based and dedication-based relationships in a model (Chang & Chou, 2011). Dedication-based influences included two constructs, “satisfaction” and “perceived usefulness”. Constraint-based influences included two constructs, “trust” and “perceived switching costs”. Also “website effectiveness” and “perception of relationship closeness” proposed as antecedent to trust. This study reported stronger influence of Constraint-based influences. Along with satisfaction, trust, perceived-usefulness and “perceived switching costs” combined to predict continuation of online buying, but only 61% of variance were explained by the model in China. TAM and ECM combined with SCT also utilized to express continuation intentions (Chen et al., 2010).
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Other Variables and Major Constructs Following section covers major predictor and exogenous variables reported in different studies.
Demographics characteristics Online buyers have different characteristics with varying motives to buy online, consequently have been extensively studied, in the context of attitude, behavioral intention and adoption of online buying with respect to different categories of products and different cultural settings. The factors what characterize the consumer demographic profile: age, sex, occupation, education, family status, income, living conditions and life expectancy (Andersone & Gaile-Sarkane, 2009). Age, education and profession have been reported to have significant impact against other variables- income, gender and ethnicity. Regarding gender there is no consensus e. Chinese male and female consumers hold similar online shopping intentions (Wen Gong & Maddox, 2011). Same is found even in developed countries. Yet, few reported male more likely to shop online(Cha, 2011). Interestingly, different online buying motives have been reported for both the gender. In the same Chinese study age and Perceived risk were not found significantly different, but income and marital status were found to have influence on online buying intentions. Contrary to other findings married with children are more likely to buy online as compare to singles or married with no children. Which is consistently found in other studies as well (Brown, Pope, & Voges, 2003). Students as online buyers have been studied (Al-Swidi, Behjati, & Shahzad, 2012).
Trust, Risk and Security
To overcome the inherent limitation of employing different IS-adoption models which have their foundations in TRA other related psychological theories, construct of trust, risk and security concerns have been strongly established in the online buying literature. “Online trust” has been reported to be an integral component of customer purchase intention in the context of both developed and developing countries (Thamizhvanan & Xavier, 2013). Perceived trust has been reported as positively influencing intention, adoption and continuation behavior. Other equally important, extensively studied and found as predictor variables are- risk (having inverse relation) and privacy & security concerns. Online security concern varies over the product category bought online(Cha, 2011).
Social Influences Subjective norm is defined as ―the perceived social pressure that most people who are important to him/her think he/she should or should not perform the behavior in question (Ajzen, 1991; Cameron, Ginsburg, Westhoff, & Mendez, 2012; Fishbein & Ajzen, 2011). SN have been found to be strongly influencing intention to buy online (Turan, 2012) (Cha, 2011).
Product characteristics Three major types of product: search, experience, and credence goods (Luo et al., 2012). Search products are those that can be evaluated from externally provided information. Experience products, on the other hand, require not only information, but also need to be personally inspected or tried. Credence products are those that are difficult to assess, even after purchase
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representative sample selected on convenience, response bias, specific cultural environment, inherent limitation of psychological theories employing replica of intention as behavior. Detailed discussion on methodology is beyond the scope of this paper.
Conclusion Online buying behavior researchers, majorly explores demographics influence on the buying intentions and adoption stages. However, there is no systematic interpretation about how the first time buyer is likely to continue with buying online or would like to intensify or pull more of existent products available offline. Deductive theory approach has been utilized to identify main factors influencing different stages of online buying. Psychological theories are utilized to understand behavior of an individual which is extensively employed to predict “information system” or “technology” adoption behavior. Further, extending and applying the same framework to understand “online buying behavior” in business to consumer (B2C) setting of E-Commerce. The relation between internet as an invention and its broadening application in business activities can be labeled both as a driver and result of consumer’s online behavior, which needs exploration. Interestingly, time-saving and convenience are long been associated with adoption of online mode is contradictory to the strengthening mall-culture and retail-chains, emergence in even developing countries like India.
The researcher of online buying behavior mainly focuses on the quantitative analysis of constructing model based on survey, limiting only to intention and adoption stages. Interestingly, majority of the study utilize students either university or college as representative of online buyers (Cha, 2011; Wen Gong & Maddox, 2011; Suharno et al., 2014; Turan, 2012). Its contrasting to the findings that married with children are more likely to buy. What makes an information-seeker over internet to become buyer over internet has been explored in detail, yet the supporting factors that encourage online consumer to remain active online needs to be established. Questions like will online mode of buying is going to dominate (given the rapid rate of smart phones as a driver-current) other modes of buying like traditional store, mall etc. remains still un-attempted. Other possibility of disappearance of this mode due to high reliance on internet services and security threats cannot be completely ruled out. In essence, it is high time to focus more on continuation and intensification of online buying. Moreover forces that can intensify buyers spending in absolute amount and over different categories remain unanswered.
References: Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50 (2), 179–211. doi:10/0749-5978(91)90020-T Alkailani, M. (2009). PERCIEVED RISK, LOCUS OF CONTROL, PRODUCT TACTILITY AND CULTURAL IMPACT: A STUDY OF ONLINE BUYING BEHAVIOR IN USA, INDIA, AND JORDAN. Alliant International University. Al-Swidi, A. K., Behjati, S., & Shahzad, A. (2012). Antecedents of Online Purchasing Intention among MBA Students: The Case of University Utara Malaysia Using the Partial Least Squares Approach. International Journal of Business ... , 7 (15), 35–49. Retrieved from ccsenet/journal/index.php/ijbm/article/download/17490/
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Amaro, S., & Duarte, P. (2015). An integrative model of consumers’ intentions to purchase travel online. Tourism Management , 46 , 64–79. doi:10.1016/j.tourman.2014. AMARO, S. F. (2014). DETERMINANTS OF INTENTIONS TO PURCHASE TRAVEL ONLINE : A HOLISTIC APPROACH. Universidade de Aveiro. Andersone, I., & Gaile-Sarkane, E. (2009). BEHAVIORAL DIFFERENCES IN CONSUMER PURCHASING BEHAVIOR BETWEEN ONLINE AND TRADITIONAL SHOPPING: CASE OF LATVIA. ECONOMICS & MANAGEMENT: , 14 , 345–352. Badrinarayanan, V., Becerra, E. P., Kim, C.-H., & Madhavaram, S. (2012). Transference and congruence effects on purchase intentions in online stores of multi-channel retailers: initial evidence from the U. and South Korea. Journal of Academy of Mareting Science , 40 , 539 – 557. Bashir, A. (2013). Consumer Behavior towards online shopping of electronics in. Seinäjoki University of Applied sciences. BECERRA, E. P. (2006). INFLUENCE OF CONSUMERS’ TRUST BELIEFS ON INTENTIONS TO TRANSACT IN THE ONLINE ENVIRONMENT: AN E-TAIL STUDY. Florida Atlantic University Fort Lauderdale, Florida. Bhattacherjee, A. (2001). UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION- CONFIRMATION MODEL1. MIS Quarterly , 25 (3), 351–370. Bonifield, C., Cole, C., & Schultz, R. (2010). Product returns on the Internet: A case of mixed signals? Journal of Business Research. Retrieved from sciencedirect/science/article/pii/S Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing ? An exploration of shopping orientations and online purchase intention. European Journal of Marketing , 37 (11/12), 1666 – 1684. doi:10/ Cai, Y., Zhao, J., & Chi, M. (2012). Research on e-business value creation: an integration of e- business process view and task technology fit. International Journal of Networking and Virtual Organisations. doi:10.1504/IJNVO. Cameron, R., Ginsburg, H., Westhoff, M., & Mendez, R. V. (2012). Ajzen’s Theory of Planned Behavior and Social Media Use by College Students. AMERICAN JOURNAL OF PSYCHOLOGICAL RESEARCH , 8 (1), 1–20. Cha, J. (2011). Exploring the Internet as a unique shopping channel to sell both real and virtual items: a comparison of factors affecting purchase intention and consumer Characteristics. Journal of Electronic Commerce Research , 12 (2), 115 – 132. Retrieved from csulb/web/journals/jecr/issues/20112/Paper1.pdf Chami, B. (2013). Cultural Analysis of Factors affecting customers’ online shopping behavior in Finland and Morocco. University of OULU. Chan, G., Cheung, C., Kwong, T., Limayem, M., & Zhu, L. (2003). Online consumer behavior: a review and agenda for future research. In 16th Bled eCommerce Conference eTransformation Bled, Slovenia, June 9 - 11, 2003 (pp. 194–218). Bled, Slovenia,. Retrieved from aisel.aisnet/cgi/viewcontent.cgi?article=1112&context=bled Chang, S., & Chou, C. (2011). Factors affecting users online shopping behavior: Integrating the constraint-based and dedication-based relationship perspectives. African Journal of Business Management , 5 (2), 370–382. doi:10/AJBM10.
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
Gong, W., Stump, R. L., & Maddox, L. M. (2013). Factors influencing consumers’ online shopping in China. Journal of Asia Business Studies , 7 (3), 214–230. doi:10/JABS-02- 2013- Hansen, T. (2005). Consumer adoption of online grocery buying: a discriminant analysis. International Journal of Retail & DistributioN Management , 33 (2/3), 101–121. Retrieved from emeraldinsight/journals.htm?articleid=1464115&show=abstract Hansen, T., Jensen, J. M., & Solgaard, H. S. (2004). Predicting online grocery buying intention: A comparison of the theory of reasoned action and the theory of planned behavior. In International Journal of Information Management (Vol. 24, pp. 539–550). Hong, W., Thong, J. Y. L., & Tam, K. Y. (2005). The Effects of Information Format and Shopping Task on Consumers’ Online Shopping Behavior: A Cognitive Fit Perspective.. Journal of Management Information Systems , 21 (3), 149–184. doi:Article Johar, M. G. M., & Awalluddin, J. A. A. (2011). THE ROLE OF TECHNOLOGY ACCEPTANCE MODEL IN EXPLAINING EFFECT ON E - COMMERCE. International Journal of Managing Information Technology , 3 (3), 1–14. Kamarulzaman, Y. (2011). A focus group study of consumer motivations for e-shopping: UK versus Malaysia. African Journal of Business Management , 5 (16), 6778–6784. Retrieved from academicjournals/journal/AJBM/article-abstract/1F41D7F Kim, J., & Forsythe, S. (2010). Factors affecting adoption of product virtualization technology for online consumer electronics shopping. International Journal of Retail & Distribution .... Retrieved from emeraldinsight/journals.htm?articleid=1846102&show=abstract Kim, J. J. (2004). UNDERSTANDING CONSUMERS’ ONLINE SHOPPING AND PURCHASING BEHAVIORS. Kwon, S. J., & Chung, N. (2010). The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map. Electronic Commerce Research and Applications , 9 (6), 522 – 536. doi:10.1016/j.elerap.2010. Lallmahamood, M. (2007). An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of this on their Intention to Use E-commerce: Using. Journal of Internet Banking and Commerce , 12 (3), 1 – 26. Retrieved from arraydev/commerce/JIBC/2007-12/Muniruddeen_Final.pdf Lee, H., Kim, J., & Fiore, A. (2010). Affective and Cognitive Online Shopping Experience Effects of Image Interactivity Technology and Experimenting With Appearance. Clothing and Textiles Research Journal. Retrieved from ctr.sagepub/content/28/2/140.short Lee, H. Y., Qu, H., & Kim, Y. S. (2007). A study of the impact of personal innovativeness on online travel shopping behavior—A case study of Korean travelers. Tourism Management , 28 (3), 886–897. doi:10.1016/j.tourman.2006. Li, N., & Zhang, P. (2002). Consumer online shopping attitudes and behavior: An assessment of research. In AMCIS 2002 Proceedings (pp. 508 – 517). Retrieved from aisel.aisnet/cgi/viewcontent.cgi?article=1433&context=amcis Liao, C., Palvia, P., & Lin, H. H.-N. (2010). Stage antecedents of consumer online buying behavior. Electron Markets , 20 , 53–65. doi:10/s12525-010-0030-
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
Liao, S., & Chung, Y. (2011). The Effects of Psychological Factors on Online Consumer Behavior. IEEE , 1380–1383. Ling, K., Daud, D. bin, Piew, T., Choon, K., & Corresponding, L. (2011). Perceived Risk , Perceived Technology , Online Trust for the Online Purchase Intention in Malaysia. International Journal of Business and Management , 6 (6), 167 – 182. doi:10.5539/ijbm Liu, C., Forsythe, S., & Black, W. (2011). Beyond adoption: sustaining online shopping.. The International Review of Retail, .... Retrieved from tandfonline/doi/abs/10.1080/09593969.2011. Luo, J., Ba, S., & Zhang, H. (2012). The Effectiveness of Online Shopping Characteristics and Well-Designed Websites on Satisfaction. MIS Quarterly , 36 (4), 1131–1144. Retrieved from buy4now/images/mis-quarterly.pdf Mishra, S., & Priya Mary Mathew. (2013). Analyzing Perceived Risks and Website attributes in E-Retailing: A Study from India. Journal of Internet Banking and Commerce , 18 (2), 1 – 21. Muthalif, R. A. (2014). ONLINE SHOPPING BEHAVIOUR AMONG COLLEGE STUDENTS IN TAMILNADU. Scholars WORLD-IRMJCR , II (IV), 162–166. Ostrowski, P. J.. (2009). A DETERMINATION OF NON-FUNCTIONAL SHOPPING MOTIVATIONS OF Y-GENERATION ONLINE SHOPPERS. Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce : Integrating Trust and Risk with the Technology Acceptance Model. INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE , 7 (3), 69–103. Pavlou, P. A., & Chai, L. (2002). WHAT DRIVES ELECTRONIC COMMERCE ACROSS CULTURES ? A CROSS-CULTURAL EMPIRICAL INVESTIGATION OF THE THEORY OF PLANNED BEHAVIOR. Journal of Electronic Commerce Research , 3 (4), 240 – 253. Pavlou, P., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: an extension of the theory of planned behavior. MIS Quarterly. Retrieved from jstor/stable/ Rapp, A., Rapp, T., & Schillewaert, N. (2008). An empirical analysis of e-service implementation: antecedents and the resulting value creation. Journal of Services Marketing. doi:10/ Sarigiannidis, L., & Kesidou, E. (2009). CONSUMER CHARACTERISTICS AND THEIR EFFECT ON ACCEPTING ONLINE SHOPPING , IN THE CONTEXT OF. In 5th HSSS Conference, Democritus University of Thrace (pp. 1–24). Sentosa, I., & Mat, N. K. N. (2012). EXAMINING A THEORY OF PLANNED BEHAVIOR ( TPB ) AND TECHNOLOGY ACCEPTANCE MODEL ( TAM ) IN INTERNETPURCHASING USING STRUCTURAL EQUATION MODELING. Journal of Arts, Science & Commerce , 2 (2), 62–77. Shim, S., Eastlick, M., Lotz, S., & Warrington, P. (2001). An online prepurchase intentions model: The role of intention to search: Best Overall Paper Award—The Sixth Triennial AMS/ACRA Retailing Conference, 2000☆ 11. Journal of Retailing. Retrieved from sciencedirect/science/article/pii/S Shrivastava, A. (2011). A Business Intelligence Model for Indian Consumers ’ Behaviour with respect to Motivation. International Journal of Computing and ICT Research , 5 (2), 11–31. Retrieved from ijcir/volume5-number2 /article2
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Williams, M. D., Rana, N. P., Dwivedi, Y. K., & Lal, B. (2011). IS UTAUT REALLY USED OR JUST CITED FOR THE SAKE OF IT ? A SYSTEMATIC REVIEW OF CITATIONS OF UTAUT ’ s. In ECIS’11. Retrieved from arnetminer/publication/is-utaut- really-used-or-just-cited-for-the-sake-of-it-a-systematic-review-of-citations-of-utaut-s- originating-article-3224963;jsessionid=C1E25960E66B645F26D83BE4043A5458 Yu, T.-K. T., & Wu, G.-S. G. (2007). Determinants of Internet Shopping Behavior : An Application of Reasoned Behaviour Theory. International Journal of Management , 24 (4), 744 – 823. Retrieved from eshare.stust.edu/EshareFile/2009_10_94e0af9f.pdf Zhang, H., Tian, C., & Xiao, T. (2014). Study on Consumers’ Channel Choice Behavior Based on Fishbein's Model of Reasoned Action. In Proceedings of the 5th International Asia Conference on Industrial Engineering and Management Innovation (pp. 39–44). Paris, France: Atlantis Press. doi:10.2991/iemi-14. Zhang, K. K. Z. K., Cheung, C. C. M. K., & Lee, M. K. O. M. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers’ online shopping decision. International Journal of Information Management , 34 (2), 89–98. Retrieved from sciencedirect/science/article/pii/S
APPENDIX 1- Different models of OCBB
MODEL AUTHOR USAGE CONSTRUCT
Innovation Diffusion Theory (IDT)
Rogers, (1962)
Adapted to information systems innovations by Moore and Benbasat (1991). Five attributes from Rogers’ model and two additional constructs are identified.
Relative Advantage, Compatibility, Complexity, Observability and Trialability.
Theory of Reasoned Action (TRA)
Fishbein and Ajzen, (1975)
To predict behavior by understanding attitude, intention and behavior.
Attitude, Subjective norm, Behavioral intention
Theory of Planned Behavior (TPB)
Ajzen, (1991) Extension of TRA. Includes one more variable to determine intention and behavior.
Attitude, Subjective norm, Perceived Behavioral Control
Expectation- Confirmation Theory (ECT) or Expectation disconfirmation theory (EDT)
Oliver (1977, 1980)
Understanding post purchase satisfaction determined by confirmation of Expectation and Experience
Expectations, Perceived Performance and Confirmation, Satisfaction
Technology Acceptance Model (TAM)
Davis et al., (1989)
Understanding attitude towards IS- information system adaptation and predicts Intentions & adoption reject computers
Perceived Usefulness, Perceived Ease of Use, Attitude, Intention to Use, Actual use, Subjective Norm* Experience*,Voluntariness*, Image* Job-Relevance*, Output Quality*, Result Demonstrability*
Technology Acceptance Model 2 (TAM2)
Venkatesh and Davis, (2000)
adapted from TAM and includes more variables (marked with *)
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 – 497 Copy right© 2015
Conceptual Model-- Adoption of Internet Shopping
Citrin et al., (2000)
Understanding the shift from general internet usage to a product purchase via the internet
Open- processing (more general innovativeness) and domain- specific innovativeness explaining move from general Internet usage to a product purchase via the Internet. Model of Intention, Adoption and Continuance (MIAC)
Cheung et al., (2003)
Framework of all three Online Consumer Behaviour stages- Intention to Purchase to Repurchase
Intention, Purchase behavior, Repurchase,Consumer Characteristics, Product Characteristics, Merchant-&- Intermediaries Characteristics, Medium Characteristics and Environment Influence
Unified Theory of Acceptance and Use of Technology Model (UTAUT)
Venkatesh et al. (2003)
integrates different theories and models to measure user intention and usage on technology
Performance Expectancy, Effort Expectancy, Attitude toward Using Technology, Social Influence, Facilitating Conditions, Self-Efficacy Anxiety Consumer Personal Characteristics Extended TAM (CPCETAM)
Bigné- Alcaniz et al., (2008)
Understanding innovators and pre- purchase information as a trigger for future online shopping intention through applying TAM
consumer innovativeness and online shopping information dependency , future online shopping intention
7Cs Model Rayport and Jaworski (2001)
Understanding quality of electronic commerce Website design from the online consumers’ perspective.
contents, choice, context, comfort, convenience, support of clients and communications
Online Buying Behaviour- A Brief Review and Update
Course: Strategic Management (MG3407)
University: Teerthanker Mahaveer University
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