© 2019 IJSRST | Volume 6 | Issue 3 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology DOI :https://doi.org/10.32628/IJSRST196329 A Conceptual Model to Measure the Impact of Consumer Behaviour on ERetailing in India Raja Sarkar1, Dr. Sabyasachi Das2 1 2 Ph.D. Scholar, Department of Business Administration, Utkal University, Bhubaneswar, Odisha Lecturer, IMBA, Department of Business Administration, Utkal University, Bhubaneswar, Odisha ABSTRACT 21st century is the era of information technology. Be it social networking, banking, ticket booking or e-retailing, the presence of information technology is ubiquitous in our day-to-day affairs. IT has transcended the physical distance between the service providers and the service receivers. It has also provided the consumers the much needed convenience and offered them competitive price for various products and services. In this context, eretailing has become a major shopping medium for customers specially the younger generations. The tech savvy young generation has taken to e-retailing like a fish takes to water. Even the older generations are becoming comfortable with the use of information technology for shopping purpose. India despite being a late starter, has become a major force in e-retailing and managed to achieve the tag of the fastest growing market in this category within a very short period. Apart from the home grown Flipkart, Snapdeal, Paytm, Shopclues, the largest e-tailer in the world Amazon has also made it into the country. Top retailers like Walmart and Alibaba have picked up major stakes in various e-tailers. The competition has become intense with large discounts and large assortment of products the order of the day. In this context, it has become essential for e-tailers to gauge the consumer behaviour to effectively target them. The present study is an effort to find out the various essential factors impacting e-retail purchase in India and develop a conceptual model for the same. Keywords :E-retail, Information technology, Factor Analysis, Constructs, Conceptual model I. INTRODUCTION and data processing, e-retailing has brought in new business opportunities for companies. They are Online shopping is on a high growth trajectory due spending billions to find the perfect business model to the massive response of people. The forecast to attract customers and bring in revenues. Apart doesn’t indicate towards any change in this trend in from the foreseeable future and is expected to grow at an additional benefits like larger choice of products, even faster pace. Customers are enjoying the ability to interact and exchange ideas using online convenience of shopping from home and with the communities so on. Both companies and customers easy availability of devices capable of accessing have recognized these benefits and gradually, internet and plunging data tariffs, more and more shopping using e-retailing sites is becoming an number of people will be encouraged to adopt this medium of shopping in near future. With the integral part of people’s daily life. The revolution in the field of internet has dramatically changed the advancement in the field of networking, multimedia way customers search and use information. Internet, convenience, IJSRST196329 | Received: 10 May2019 | Accepted : 30 May 2019 | May-June-2019 [ 6 (3) : 141-165] e-retailing offers customers 141 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 which was conceptualized as a medium to gather Kim &Eom (2002) did a study to find out the information has become a vital place to conduct significant factors impacting the intention towards e- business. retailing. They found convenience, dependable The literature review has been carried out related to the area of e-retailing consumer behaviour. There have been very few studies carried out related to shopping, reliability of retailers, additional information and product perception as the important factors impacting e-retailing intention. consumer behaviour in e-retailing in the Indian Hirst & Omar (2007) carried out a study to assess the context. Detailed search of literature didn’t reveal too apparel shopping behaviour of women on the many research works in this area in the Indian context. As a result, the researcher was forced to internet where they found convenience, usefulness, ease of use and efficiency to have positive impact on review literatures related to foreign countries in this e-retailing. area. Under the circumstances, it is well understood that there is a need for an extensive and detailed study analyzing various behavioural aspects impacting e-retailing consumer behaviour in the Indian context.Through this research work, the researcher expects to understand the various behavioural aspects of online shoppers impacting the adoption of shopping using e-retailing sites. Objectives: Vijayasarathy (2001) carried out a study to find out the factors impacting attitude and intention to use eretailing. The study revealed that integrated factors related to web or e-retailing aids could explain consumer e-retailing behaviour better. i) Perceived Usefulness Chu & Lu (2007) carried out a study to understand the factors impacting online music purchase intention in Taiwan. They found that perceived usefulness had a significant impact on online music i) To develop constructs to measure the impact purchase intentionMangin et al. (2011) used the of consumer behaviour on e-retailing in Technology acceptance model to understand the Indian context impact of perceived usefulness on adoption of online ii) To test the reliability and validity of the banking services in Canadian banking environment. iii) constructs To develop a conceptual model for further They found perceived usefulness to be an important factor of adoption of online banking. According to an study empirical research carried out by Liao & Shi (2009), Review of literature: Karayanni (2003) carried out a comparative study to perceived usefulness had a positive influence on consumer attitude towards online shopping adoption. find out the factors differentiating online shoppers from non shoppers. The major factors found to be the ii) Perceived Ease of Use Ramayah& Ignatius (2005) found evidence to suggest key were web-shopping motives related to time that perceived ease of use had a positive impact on efficiency, availability of 24 hour shopping and behavioural intention of e-retailing. Jisoo (2015) avoidance of queues during shopping. The study also carried out a study on improving user interaction in found lack of trust on e-retailing to be a negative interactive TV based on ethnographic insights from factor of e-retailing behaviour. real life problems where perceived ease of use was found to have a significant impact on perceived International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 142 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 usefulness. Davis et al. (1989) in their study on aesthetically to the eyes, had a favourable impact on comparison of two theoretical models regarding user the intention to use a website. acceptance of computer technologyfound perceived ease of use to have a significant impact on perceived vi) Attitude and Intention usefulness and attitude. Laroche (2002) carried out a study on consumer iii) Perceived Enjoyment intention to buy a particular brand not only was Sun & Zhang (2006) tested two alternative models to impacted by the attitude towards that particular find out the relationship between perceived ease of brand, but also by the attitude towards other use and perceived enjoyment where the impact of perceived enjoyment on perceived ease of use was considered brands. According to Chen (2007), attitude of consumers to purchase organic food has a found to be stronger than the other way round. Davis positive impact on the intention to purchase organic et al. (1992) found intrinsic enjoyment to be a direct food. Thogersen&Ölander (2006) did a panel study on determinant of user acceptance online. Jarvenpaa& the dynamic interaction of personal norms and Todd (1997) found perceived enjoyment to have a environment-friendly buying behavior where they significant impact on attitude and intention to shop found attitude to be a vital predictor of intention to online. consume organic food in case of vegetables and fruits. iv) Perceived Risk vii) Impact of Demographic variables on Attitude and Clemes et al. (2014) found perceived risk to be an Intention important factor determining e-retailing adoption. a) Gender According to Zhao & Li (2012), perception of higher risk towards e-retailing among customers results in Sebastianelli et al. (2008) in their study on perceived significant negative impact on attitude and intention categories- search, experience and credence. They to shop online. Alreck& Settle (2002) found that due found that males tend to purchase search products to limited access to product and sales people, more whereas female affinity is higher towards perceived risk in e-retailing was far higher which was purchasing adversely impacting purchase via internet. segregated attitude into three types- cognitive, affective and behavioral. According to him, in case of brand choice where it was found that consumers’ v) Web Aesthetics An empirical study carried out by Huang (2003) found that perceived complexity of a website had a negative influence on pleasure. Lindguard et al. (2003) in their study on user satisfactionfound that highly appealing websites had an impact on satisfaction of online users. Van der Heijden (2003) carried out a study to find out factors impacting usage of websiteswhich showed that online visual attractiveness which was the degree to which someone believed that a website was appealing quality of e-retailing, categorized products into three experience products. Hasan (2010) cognitive attitude, males found e-retailing to be more useful as a shopping platform than their female counterparts. As a result, females also displayed less positive affective attitude towards e-retailing.A study conducted by Flavian et al. (2011) showed that quality images had the ability to increase female satisfaction which resulted in improved purchase intention whereas in case of males despite of increased satisfaction, it didn’t result in improved purchase intention. International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 143 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 b) Age like internet and catalogues to search for products, Goldsmith & Goldsmith (2002) carried out a study to gather product information and make purchases. understand the factors impacting online apparel buying behaviour of consumers where they found that age didn’t have any impact on buying apparels online. According to a study on attitude and age differences conducted by Sorce et al. (2005), older online shoppers managed to find far lesser number of useful products online compared to the younger shoppers. They also found differences in attitude based on age. Dholakia &Uusitalo (2002) carried out a study on consumer characteristics of electronic stores where theyfound that perception of hedonic benefit was higher among older individuals for offline shopping compared to younger people. c) Income Lohse et al. (2000) commented that income didn’t have any impact on if someone purchases online or not. But they also added that income had an impact on online spending. The more one’s household income, the more they spend online. Iyer& Eastman (2004) in their study on attitude towards the internet found significant influence of income on e-retailing. According to them, people with higher income tend to be users of internet and online shoppers. Sim & Koi (2002) carried out a study on Singapore’s internet shoppers and their impact on shopping patterns where they found that people with higher income shopped online more often. d) Educational Qualification Fram& Grandy (1997) carried out a study on internet shoppers where education was frequently found to be impacting e-retailing purchase. Sultan &Henrichs (2000) in their study on consumer preferences for e) Internet Experience Shim et al. (2001) conducted a study where they found direct and indirect relationships between previous internet purchase experience and online purchase intention. According to Koyunchu& Lien (2003), prior internet experience reduced the time needed to navigate websites and search for information resulting in higher possibility of online purchase. Verplanken et al. (1998) saidthat prior experience enhances the affective component of one’s attitude. Research Methodology: i) Research Method The present study is a quantitative research in nature. Since this study was carried out to develop a conceptual model for further study, the inductive approach was used for the same. ii) Research Design The present research work is exploratory in nature in the initial part. Once the insights of the problem were gained, it was verified and conclusive research was used to quantify it. Descriptive study was carried out subsequently to meet the conclusive research requirements. Expert opinion was used to frame the research instrument. iii) Sampling Design Purposive sampling has been used to collect data in this study. Keeping in mind that majority of online shoppers are younger, more representation was given to them during selection of samples. internet services reported that education had a iv) Data Collection Method significant influence on e-retailing. According to Primary data has been used in the study. For Burke (2002), consumers with better education were investigation and data collection purpose, survey more comfortable using non store shopping channels method has been used which is quite common in behavioural and marketing researches. Data was International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 144 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 collected using structured questionnaire as an v) Sample Size instrument, which was created using the Google According to Gorsuch (1983) and Kline (1994), Forms programme. All in the minimum 100 subjects are required for sampling questionnaire were strictly answerable and hence purpose to carry out an exploratory factor analysis. there were no receipt of incomplete questionnaire. Hence, for the present study, data from total 108 The questionnaires were distributed using strictly respondents were collected. Samples were collected online method via e-mail and Facebook. Basic from Bhubaneswar and Kolkata, which are the capital demographic data was collected using nominal scale cities of the states of Odisha and West Bengal and behavioural aspects were recorded using likert scale. The collected data was quantitative in nature. respectively. the questions Table-1: Original items adapted for the study and their sources Factors Items Sources Using CHART-MASTER in my job would enable me to accomplish tasks more quickly Perceived Using CHART-MASTER would improve my job performance Usefulness Using CHART-MASTER in my job would increase my productivity Davis (1989) Using CHART-MASTER would enhance my effectiveness on the job Using CHART-MASTER would make it easier to do my job I would find CHART-MASTER useful in my job Learning to operate CHART-MASTER would be easy for me I would find it easy to get CHART-MASTER to do what I want it to do Perceived Ease My interaction with CHART-MASTER would be clear and of Use understandable Davis (1989) I would find CHART-MASTER to be flexible to interact with It would be easy for me to become skillful at using CHARTMASTER I would find CHART-MASTER easy to use Enjoyable-disgusting Perceived Exciting-dull Van der Enjoyment Pleasant-unpleasant Heijden (2004) Interesting-boring Financial risk Perceived Risk Performance risk Peter Social risk &Tarpey(1975); Time risk Cheng et al. International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 145 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Privacy risk (2013) The Web site is pleasing to look at I like the look and feel of the web site Visual Appeal* Cai et al. (2008) The Web site is visually appealing The visual design of the Web site is attractive The design of Web site is harmonious Organization* Cai et al. (2008) The layout of the Web site is intuitive The Web site has logically organized elements The layout of the Web site was designed in a manner I am accustomed to All things considered, my using spreadsheets in accomplishing various tasks in industry was good All things considered, my using spreadsheets in accomplishing Attitude various tasks in industry was wise Al-Gahtani& All things considered, my using spreadsheets in accomplishing various tasks in industry was favourable King (1999) All things considered, my using spreadsheets in accomplishing various tasks in industry was beneficial All things considered, my using spreadsheets in accomplishing various tasks in industry was positive I will do most of my future purchase for [product] with this Web site Intention Cai et al. (2008) I will recommend this Web site to friends, neighbors, and relatives I will use this Web site the very next time I need to shop * Cai et al. (2008) proposed a two dimensional structure of Web site Aesthetics by taking Visual Appeal and Organization as two of its components Table-2 :Demographic Characteristics of the sample Variables Gender Category Male Female Total Less than 25 Frequency Percent (%) 69 63.9 39 36.1 108 100.0 28 25.9 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 146 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Age Between 25 and 35 Between 35 and 50 More than 50 Total Educational Qualification Class 12 Diploma Graduation Post graduation or higher Total Less than Rs.10000 Monthly Income Between Rs.10000 and Rs.30000 Between Rs.30000 and Rs.50000 More than Rs.50000 Total Internet Experience Less than 1 year Between 1-3 years Between 3-5 years More than 5 years Total 57 52.8 16 14.8 7 6.5 108 100.0 6 5.6 19 17.6 50 46.3 33 30.5 108 100.0 36 33.3 33 30.6 22 20.4 17 15.7 108 100.0 4 3.7 8 7.4 16 14.8 80 74.1 108 100.0 Content validity: some subject experts for their opinions regarding the The items considered for the study are adapted suitability, wordings and completeness of the same. versions of various established and validated scales. After making necessary modifications within the According to their suggestions, further modifications were carried out within the items to achieve the final items to suit the present study, they were shown to set of items. International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 147 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Final items for the study: The following 37 adapted items were used in the study: Item-1: Using e-retailing as a shopping medium enables me to accomplish my shopping tasks more Item-16: E-retailing sites are interesting Item-17: Using e-retailing sites could lead to financial loss for me Item-18: Products purchased using e-retailing sites quickly might not perform as expected Item-2: Using e-retailing as a shopping medium Item-19: Society might not approve my purchases improves my shopping performance from e-retailing sites Item-3: Using e-retailing sites for shopping increases my productivity Item-20: Using e-retailing sites could lead to waste of Item-4: Using e-retailing sites for shopping enhances my shopping effectiveness Item-5: Using e-retailing sites makes it easierfor meto time due to time required for product exchanges and delivery of products Item-21: Using e-retailing sites could lead to loss of privacy shop Item-22: E-retailing sites are pleasing to look at Item-6: I find e-retailing sites useful for shopping Item-23: I like the look and feel of e-retailing sites Item-7: Learning to operate e-retailing sites was easy Item-24: E-retailing sites are visually appealing for me Item-8: I find it easy to get the e-retailing sites to do what I want it to do Item-9: My interaction with e-retailing sites is clear and understandable Item-10: I find the e-retailing sites flexible to interact with Item-11: It is easy for me to become skilful at using the e-retailing sites Item-12: I find e-retailing sites easy to use Item-25: The visual designs of e-retailing sites are attractive Item-26: The designs of e-retailing sites are harmonious Item-27: The layouts of e-retailing sites are intuitive Item-28: The e-retailing sites have logically organized elements Item-29: The layout of e-retailing sites are designed in a manner I am accustomed to Item-30: All things considered,using e-retailing sites Item-13: E-retailing sites are enjoyable for shopping is good Item-14: E-retailing sites are exciting Item-31: All things considered, myusing e-retailing sites for shopping is wise Item-15: E-retailing sites are pleasant International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 148 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Item-32: All things considered,my using e-retailing method would be oblique rotation. Promax raises the sites for shopping is favourable loadings to a power of four which ultimately offers Item-33: All things considered,my using e-retailing better correlations among the factors and develops a simple structure. (Gorsuch, 1983) sites for shopping is beneficial Item-34: All things considered,my using e-retailing sites for shopping is positive There are three objectives of carrying out factor analysis; (i) to unravel the factors underlying the data, (ii) to test the validity (unidimensionality, convergent Item-35: I will do most of my future shopping from and discriminant validity) of the factors, and (iii) to e-retailing sites calculate the factor scores through subsequent analyses. Item-36: I will recommend e-retailing sites to friends, neighbours, and relatives Upon factor analysis, two of the items namely Item Item-37: I will use e-retailing sites the very next time two factors each since both the items had loaded I need to shop higher than 0.32 on both those factors (Costello & 29 and Item 31 were found to have cross loadings on Osborne, 2005) and the difference being less than 0.2 Exploratory factor analysis: between the loadings in each case and thus To understand the various factors influencing customer’s attitude towards e-retailing, the responses of respondents were examined with the help of factor analysis using principal component analysis method with Promax rotation. According to McDonald (1999), in case of actual application, rarely factors underlying tests can be truly uncorrelated. Hence it was suggested that the most appropriate rotation insignificant (Cudeck& O’Dell, 1994). Hence, these two items were dropped from further analysis and the exploratory factor analysis was repeated in the absence of the two above mentioned items. According to Lai et al. (2004), we need to repeat EFA until we manage to drop all the deletable items. Hence, EFA was repeated with the remaining 35 items. The various results obtained from the second exploratory factor analysis are given below: Table-2 :KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity .846 Approx. Chi-Square 1999.839 df 595 Sig. .000 The Kaiser-Meyer-Olkin (KMO) measures the sampling adequacy, which determines if the responses received using the sample is adequate, or not. The above table shows the value of KMO measure of sampling adequacy statistics to be 0.846 which is above the stipulated lower limit of 0.7 and hence acceptable. (Kaiser, 1974). Apart from that Bartlett’s test of Sphericity value was found to be 1999.839 which is also significant (p< 0.05). Hence, the sample is amenable to factor analysis. International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 149 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Table-3 :Communalities Item1 Item2 Item3 Item4 Item5 Item6 Item7 Item8 Item9 Item10 Item11 Item12 Item13 Item14 Item15 Item16 Item17 Item18 Item19 Item20 Item21 Item22 Item23 Item24 Item25 Item26 Item27 Item28 Item30 Item32 Item33 Item34 Item35 Item36 Item37 Initial 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Extraction .693 .596 .740 .598 .735 .671 .559 .861 .751 .568 .729 .857 .853 .694 .603 .852 .805 .599 .641 .526 .547 .522 .573 .429 .567 .628 .562 .592 .658 .588 .521 .737 .756 .591 .633 Extraction Method: Principal Component Analysis. International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 150 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Table-4: Total Variance Explained Component Initial Eigenvalues Total % of Variance 20.751 Cumulativ e% 20.751 Extraction Sums of Squared Loadings Total % of Variance 20.751 Cumulat ive % 20.751 Rotation Sums of Squared Loadingsa Total 4.398 1 7.263 2 3.868 11.051 31.803 3.868 11.051 31.803 3.986 3 3.659 10.454 42.257 3.659 10.454 42.257 3.761 4 3.012 8.606 50.863 3.012 8.606 50.863 3.164 5 2.637 7.534 58.397 2.637 7.534 58.397 2.934 6 1.811 5.174 63.571 1.811 5.174 63.571 2.834 7 1.435 4.100 67.671 1.435 4.100 67.671 2.608 8 .908 2.594 70.266 9 .835 2.386 72.651 10 .802 2.291 74.943 11 .768 2.194 77.137 12 .712 2.034 79.171 13 .653 1.866 81.037 14 .612 1.749 82.786 15 .584 1.669 84.454 16 .533 1.523 85.977 17 .512 1.463 87.440 18 .491 1.403 88.843 19 .453 1.294 90.137 20 .412 1.177 91.314 21 .384 1.097 92.411 22 .359 1.026 93.437 23 .312 .891 94.329 24 .293 .837 95.166 25 .264 .754 95.920 26 .236 .674 96.594 27 .213 .609 97.203 28 .192 .549 97.751 29 .171 .489 98.240 30 .146 .417 98.657 31 .128 .366 99.023 32 .103 .294 99.317 33 .099 .283 99.600 7.263 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 151 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 34 .087 .249 99.849 35 .053 .151 100.000 Extraction Method: Principal Component Analysis. Communalities for each factor are presented in Table-3. Communalities show how much of the variance in the variables have been accounted for by the extracted factors. The above table shows that the extracted values for all the items are more than 0.4 which is the minimum acceptable score for the same (Fabrigar et al., 1999). Hence, all the items were retained for subsequent steps of factor analysis. The Total Variance explained is presented in Table-4. Eigen value reflects the number of extracted factors whose sum should be equal to number of items which are subjected to factor analysis. The final decision to arrive at the number of factors to be retained was taken on the basis of latent root criterion which is variables depicting Eigen Values greater than 1 (Kaiser, 1960). Keeping this criteria in mind, seven rotated factors were extracted which together explains 67.671% of the total variance which is higher than the acceptable variance limit of 60 per cent (Zikmund et al., 2010). Eigen values for factors F1 to F7 are 7.263, 3.868, 3.659, 3.012, 2.637, 1.811, and 1.435 respectively. Subsequently, appropriate names were assigned to all the seven dimensions extracted based on the various items representing each one of them. Table-5:Pattern Matrixa Item35 Item37 Item36 Item26 Item23 Item22 Item25 Item24 Item28 Item27 Item3 Item5 Item1 Item6 Item4 1 .844 .770 .743 2 3 Component 4 5 6 7 .845 .821 .785 .749 .710 .695 .661 .827 .812 .802 .799 .692 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 152 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Item2 Item34 Item30 Item33 Item32 Item17 Item19 Item18 Item20 Item21 Item16 Item13 Item14 Item15 Item12 Item8 Item9 Item11 Item7 Item10 .581 .841 .768 .686 .593 .879 .788 .751 .674 .556 .876 .873 .796 .667 .900 .878 .823 .804 .643 .636 Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization. a. Rotation converged in 7 iterations. The objective of rotation is to reduce the number of factors on which the variables under investigation have high loadings. It only makes the interpretation of analysis easier without changing anything (Osborne & Costello, 2009). The lower limit for item loadings was set at .30 since according to several researchers, anything less than that should not be considered (Armor, 1974). International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 153 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Table-6:Component Correlation Matrix Component 1 2 3 4 5 6 7 1 1.000 .160 .112 .160 -.135 .205 .415 2 .160 1.000 .103 .246 -.236 .378 .117 3 4 .112 .160 .103 .246 1.000 .154 .154 1.000 -.151 -.277 .128 .276 .118 .132 5 -.135 -.236 -.151 -.277 1.000 -.209 -.197 6 .205 .378 .128 .276 -.209 1.000 .169 7 .415 .117 .118 .132 -.197 .169 1.000 Extraction Method: Principal Component Analysis Rotation Method: Promax with Kaiser Normalization Factors extracted: The 7 factors extracted are given below Factor-1: Intention to purchase Online This factor contains three items- Item 35, Item 37, Item 36 explaining 20.751% of variance in the data, with the Eigen value of 7.263. The items associated with this factor are given below: INT1: I will do most of my future shopping from e-retailing sites INT2: I will use e-retailing sites the very next time I need to shop INT3: I will recommend e-retailing sites to friends, neighbours, and relatives Factor-2: Web Aesthetics This factor contains eight items- Item 26, Item 23, Item 22, Item 25, Item 24, Item 29, Item 28, Item 27 explaining 11.051% of variance in the data, with the Eigen value of 3.868. The items associated with this factor are given below: WA1: The designs of e-retailing sites are harmonious WA2: I like the look and feel of e-retailing sites WA3: E-retailing sites are pleasing to look at WA4: The visual designs of e-retailing sites are attractive WA5: E-retailing sites are visually appealing WA6: The e-retailing sites have logically organized elements WA7: The layouts of e-retailing sites are intuitive Factor-3: Perceived Usefulness This factor contains six items- Item 3, Item 5, Item 6, and Item 1, Item 4, Item 2 explaining 10.454% variance in the data, with the Eigen value of 3.659. The items associated with this factor are given below: PU1: Using e-retailing sites for shopping increases my productivity PU2: Using e-retailing sites makes it easierfor meto shop PU3: Using e-retailing as a shopping medium enables me to accomplish my shopping tasks more quickly PU4: I find e-retailing sites useful for shopping International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 154 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 PU5: Using e-retailing sites for shopping enhances my shopping effectiveness PU6: Using e-retailing as a shopping medium improves my shopping performance Factor-4: Attitude towards E-retailing This factor contains five items- Item 31, Item 34, Item 30, Item 33, Item 32 explaining 8.606% of variance in the data, with the Eigen value of 3.012. The items associated with this factor are given below: ATT1: All things considered,my using e-retailing sites for shopping is positive ATT2: All things considered,using e-retailing sites for shopping is good ATT3: All things considered,my using e-retailing sites for shopping is beneficial ATT4: All things considered,my using e-retailing sites for shopping is favourable Factor-5: Perceived Risk This factor contains five items- Item 17, Item 19, Item 18, Item 20, and Item 21 explaining 7.534% of variance in the data, with the Eigen value of 2.637. The items associated with this factor are given below: PR1: Using e-retailing sites could lead to financial loss for me PR2: Society might not approve my purchases from e-retailing sites PR3: Products purchased using e-retailing sites might not perform as expected PR4: Using e-retailing sites could lead to waste of time due to time required for product exchanges and delivery of products PR5: Using e-retailing sites could lead to loss of privacy Factor-6: Perceived Enjoyment This factor contains four items- Item 16, Item 13, and Item 14, Item 15 explaining 5.174% variance in the data, with the Eigen value of 1.811. The items associated with this factor are given below: PEN1: E-retailing sites are interesting PEN2: E-retailing sites are enjoyable PEN3: E-retailing sites are exciting PEN4: E-retailing sites are pleasant Factor-7: Perceived Ease of Use This factor contains three statements- Item 12, Item 8, and Item 9, Item 11, Item 7, Item 10 explaining 4.100% variance in the data, with the Eigen value of 1.435. The items associated with this factor are given below: PEU1: I find e-retailing sites easy to use PEU2: I find it easy to get the e-retailing sites to do what I want it to do PEU3: My interaction with e-retailing sites is clear and understandable PEU4: It is easy for me to become skilful at using the e-retailing sites PEU5: Learning to operate e-retailing sites was easy for me PEU6: I find the e-retailing sites flexible to interact with International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 155 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Table-7:Test for Item total correlation and Internal reliability Factors Intention to Purchase Online Web Aesthetics Perceived Usefulness Attitude towards E-retailing Perceived Risk Items Item total correlation INT1 0.70 INT2 0.60 INT3 0.59 WA1 0.65 WA2 0.64 WA3 0.59 WA4 0.56 WA5 0.53 WA6 0.52 WA7 0.47 PU1 0.76 PU2 0.76 PU3 0.71 PU4 0.72 PU5 0.57 PU6 0.47 ATT1 0.71 ATT2 0.69 ATT3 0.57 ATT4 0.49 PR1 0.78 PR2 0.63 PR3 0.61 PR4 0.55 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) Cronbach’s alpha 0.79 0.83 0.86 0.84 0.81 156 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Perceived Enjoyment Perceived Ease of Use PR5 0.42 PEN1 0.85 PEN2 0.86 PEN3 0.70 PEN4 0.54 PEU1 0.69 PEU2 0.68 PEU3 0.64 PEU4 0.64 PEU5 0.62 PEU6 0.65 0.87 0.86 From Table-7 it can be seen that, all the items are having item total correlation in excess of 0.40 which is the minimum acceptable limit for the same (Loiacono et al., 2002). Also, internal reliability (Cronbach’s alpha) for all the factors are found to be in excess of 0.70 which is the minimum acceptable limit for the same (Nunnally, 1978). Hence, all the factors meet the conditions of item total correlation and internal reliability. Table-8: Test for Composite Reliability Factors Items Factor Error variance ϵ Composite reliability loadings (λ) λ = (1 - λ ) 2 2 Intention to INT1 0.844 0.712336 0.287664 Purchase INT2 0.770 0.5929 0.4071 Online INT3 0.743 0.552049 0.447951 WA1 0.845 0.714025 0.285975 WA2 0.821 0.674041 0.325959 Web WA3 0.785 0.616225 0.383775 Aesthetics WA4 0.749 0.561001 0.438999 WA5 0.710 0.5041 0.4959 WA6 0.695 0.483025 0.516975 WA7 0.661 0.436921 0.563079 PU1 0.827 0.683929 0.316071 PU2 0.812 0.659344 0.340656 PU3 0.802 0.643204 0.356796 Perceived International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 0.83 0.90 157 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Usefulness PU4 0.799 0.638401 0.361599 PU5 0.692 0.478864 0.521136 PU6 0.581 0.337561 0.662439 ATT1 0.841 0.707281 0.292719 Attitude ATT2 0.768 0.589824 0.410176 Towards E- ATT3 0.686 0.470596 0.529404 retailing ATT4 0.593 0.0.351649 0.648351 PR1 0.879 0.772641 0.227359 PR2 0.788 0.620944 0.379056 PR3 0.751 0.564001 0.435999 PR4 0.674 0.454276 0.545724 PR5 0.556 0.309136 0.690864 PEN1 0.876 0.767376 0.232624 Perceived PEN2 0.873 0.762129 0.237871 Enjoyment PEN3 0.796 0.633616 0.366384 PEN4 0.667 0.444889 0.555111 PEU1 0.900 0.81 0.19 PEU2 0.878 0.770884 0.229116 Perceived Ease PEU3 0.823 0.677329 0.322671 of Use PEU4 0.804 0.646416 0.353584 PEU5 0.643 0.413449 0.586551 PEU6 0.636 0.404496 0.595504 Perceived Risk 0.89 0.82 0.85 0.88 0.91 From Table-8 it can be seen that, all the factors are having composite reliability in excess of 0.70 which is the minimum acceptable limit for the same (Hair et al., 2014). Hence all the 7 factors meet the composite reliability condition. Table-9: Test for Convergent Validity Factors Items Error variance ϵ Factor loadings (λ) λ = (1 - λ ) 2 AVE* 2 Intention to INT1 0.844 0.712336 0.287664 Purchase INT2 0.770 0.5929 0.4071 Online INT3 0.743 0.552049 0.447951 WA1 0.845 0.714025 0.285975 WA2 0.821 0.674041 0.325959 WA3 0.785 0.616225 0.383775 WA4 0.749 0.561001 0.438999 Web WA5 0.710 0.5041 0.4959 Aesthetics WA6 0.695 0.483025 0.516975 WA7 0.661 0.436921 0.563079 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 0.619 0.570 158 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 PU1 0.827 0.683929 0.316071 PU2 0.812 0.659344 0.340656 Perceived PU3 0.802 0.643204 0.356796 Usefulness PU4 0.799 0.638401 0.361599 PU5 0.692 0.478864 0.521136 PU6 0.581 0.337561 0.662439 ATT1 0.841 0.707281 0.292719 Attitude ATT2 0.768 0.589824 0.410176 Towards E- ATT3 0.686 0.470596 0.529404 retailing ATT4 0.593 0.351649 0.648351 PR1 0.879 0.772641 0.227359 PR2 0.788 0.620944 0.379056 Perceived PR3 0.751 0.564001 0.435999 Risk PR4 0.674 0.454276 0.545724 PR5 0.556 0.309136 0.690864 PEN1 0.876 0.767376 0.232624 Perceived PEN2 0.873 0.762129 0.237871 Enjoyment PEN3 0.796 0.633616 0.366384 PEN4 0.667 0.444889 0.555111 PEU1 0.900 0.81 0.19 PEU2 0.878 0.770884 0.229116 Perceived PEU3 0.823 0.677329 0.322671 Ease of Use PEU4 0.804 0.646416 0.353584 PEU5 0.643 0.413449 0.586551 PEU6 0.636 0.404496 0.595504 0.574 0.530 0.544 0.652 0.620 * AVE- Average Variance Extracted From Table-9 it can be seen that, Average Variance Extracted for each of the 7 factors mentioned above are greater than the minimum acceptable 0.5 for the same. Hence, all the 7 factors meet the convergent validity condition (Fornell&Larcker, 1981). Table-10:Test for Discriminant validity Square of the Factors Correlation with Correlation highest other Factors coefficient correlation AVE* Remarks coefficient (MSV**) PU < -- > PEU 0.118 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 159 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 PU < -- > PEN 0.128 Perceived PU < -- > PR -0.151 Usefulness PU < -- > WA 0.103 PU < -- > ATT 0.154 PU < -- > INT 0.112 PEU < -- > PU 0.118 PEU < -- > PEN 0.169 Perceived PEU < -- > PR -0.197 Ease of Use PEU < -- > WA 0.117 PEU < -- > ATT 0.132 PEU < -- > INT 0.415 PEN < -- > PU 0.128 PEN < -- > PEU 0.169 Perceived PEN < -- > PR -0.209 Enjoyment PEN < -- > WA 0.378 PEN < -- > ATT 0.276 PEN < -- > INT 0.205 PR < -- > PU -0.151 PR < -- > PEU -0.197 PR < -- > PEN -0.209 Perceived PR < -- > WA -0.236 Risk PR < -- > ATT -0.277 PR < -- > INT -0.135 WA < -- > PU 0.103 WA < -- > PEU 0.117 WA < -- > PEN 0.378 WA < -- > PR -0.236 WA < -- > ATT 0.246 WA < -- > INT 0.160 ATT < -- > PU 0.154 ATT < -- > PEU 0.132 Attitude ATT < -- > PEN 0.276 towards E- ATT < -- > PR -0.277 retailing ATT < -- > WA 0.246 ATT < -- > INT 0.160 INT < -- > PU 0.112 INT < -- > PEU 0.415 Intention to INT < -- > PEN 0.205 Purchase INT < -- > PR -0.135 Web Aesthetics 0.024 0.574 MSV < AVE 0.172 0.620 MSV < AVE 0.143 0.652 MSV < AVE 0.018 0.544 MSV < AVE 0.143 0.570 MSV < AVE 0.076 0.530 MSV < AVE 0.172 0.619 MSV < AVE International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 160 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 Online INT < -- > WA 0.160 INT < -- > ATT 0.160 * AVE- Average Variance Extracted ** MSV- Maximum Shared Variance From Table-10 it can be seen that, Average variance extracted of each of the factors is greater than the corresponding Maximum Shared Variance. Hence, all the 7 factors meet the divergent validity condition (Fornell&Larcker, 1981). Findings: The 7 factors extracted using exploratory factor analysis, which include total 35 items, were tested for reliability and validity and all of them were found to have met the criteria. Hence, the constructs along with the items can be adopted for further study. Based on the literature reviews and various tests, a conceptual model has been developed which is given below: Conceptual model International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 161 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 II. Limitations of the study The study was limited to the cities of Bhubaneswar and Kolkata in the eastern part of the country due to financial and time constraints. The vast and diverse culture of India which has a significant impact in shaping the behaviour of the citizens, makes it almost impossible to extrapolate the results to the entire country. Along with that, the relatively small number of samples can also pose a risk in implementing the conceptual model in the entire country. Despite of an elaborate literature review, all the possible factors couldn’t be included in the conceptual model due to the fear of making the study excessively complex. Despite of all these limitations, this study is an honest attempt by the researchers to get some picture of various behavioural aspects of online shoppers. III.DISCUSSION AND CONCLUSION Consumer behaviour remains the single biggest riddle for the marketers. Every organization wants to accurately map consumer behaviour to maximize gains from their products and services. In that context, it is not only important for companies to produce quality products and services, but analysing the various behavioural aspects of consumers also become vital to effectively target them. Shopping using e-retailing websites take place in the virtual world where sellers and buyers never come face to face and hence analysing online consumer behaviour is a far bigger challenge than analyzing customers in a physical setup for sellers. This research work attempts to provide an online consumer behaviour model which sellers can effectively utilize to tap maximum gain from their customers. As already mentioned, a detailed study on online consumer behaviour in the Indian context is a rarity and hence can be of immense help for those millions of sellers who want to sell their commodities using the online medium. IV. REFERENCES [4]. Burke, R. R. (2002). Technology and the Customer Interface: What Consumers Want in [1]. Al-Gahtani, S. S., & King, M. (1999). Attitudes, the Physical and Virtual Store. Journal of the Satisfaction and Usage: Factors Contributing to Academy of Marketing Science, 30(4), 411-432 Each in the Technology. [2]. Acceptance Behaviour of and Information Information Cai, S., Xu, Y., Yu, J., & De Souza, R. (2008, July 4-7). Understanding Aesthetics Design for E- Technology, 18(4), 277-297 commerce Web sites: A Cognitive-Affective Alreck, P., & Settle, R. B. (2002). Gender effects on internet, catalogue and store shopping. Framework. Paper presented at the Pacific Asia Conference on Information Systems, PACIS Journal of Database Marketing & Customer 2008, Suzhou, China Strategy Management, 9(2), 150-162 [3]. [5]. [6]. Chen, M. F. (2007). Consumer attitudes and Armor, D. J. (1974). Theta Reliability and purchase intentions in relation to organic foods Factor Scaling. Sociological Methodology, 5, in Taiwan: Moderating effects of food-related 17-50 personality traits. Food Quality and Preference, 18(7), 1008-1021 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 162 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 [7]. [8]. [9]. Chu, C. W., & Lu, H. P. (2007). Factors Product’s Online Visual Representation: Impact Influencing Online Music Purchase Intention on satisfaction and Purchase Intention. ESIC in Taiwan. Internet Research, 17(2), 139-155 Market Economic and Business Journal, 38(1), Clemes, M. D., Gan, C., & Zhang, J. (2014). An 145-170 empirical analysis of e-retailing adoption in [17]. Fornell, C. G., & Larcker, D. F. (1981). Beijing, China. Journal of Retailing and Evaluating Structural Equation Models with Customer Services, 21(3), 364-375 Unobservable Costello, A. B., & Osborne, J. W. (2005). Best Error. Journal of Marketing Research, 18(1), 39- Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most from 50 [18]. Fram, E. H., & Grandy, D. B. (1997). Internet Your Analysis. Practical Assessment Research Shoppers: Is there a Surfer Gender Gap? Direct and Evaluation, 10(7), 1-9 Marketing, 59(1), 46-50 Variables and Measurement [10]. Cudeck, R., & O’Dell, L. L. (1994). Applications [19]. Goldsmith, R. E., & Goldsmith, E. B. (2002). of Standard Error Estimates in Unrestricted Buying Apparel Over the Internet. Journal of Factor Analysis: Significance Tests for Factor Product and Brand Management, 11(2), 89-102 Loadings and Correlations. Psychological Bulletin, 115(3), 475-487 [20]. Gorsuch, R. L. (1983). Factor Analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates [11]. Davis, F. D. (1989, September). Perceived [21]. Hasan, B. (2010). Exploring Gender Differences Usefulness, Perceived Ease of Use, and User in E-retailing Attitude. Computers in Human Acceptance of Information Technology. MIS Behavior, 26(1), 597-601 Quarterly, 13(3), 319-340 [22]. Hirst, A., & Omar, O. (2007). Assessing [12]. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A comparison of Two Theoretical Models. Management Science, 35(8), 982-1003 [13]. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132 Women’s Apparel Shopping Behaviour on The Internet. Journal of Retail Marketing Management Research, 1(1), 32-40 [23]. Huang, M. (2003). Modeling virtual exploratory and shopping dynamics: An environmental psychology approach. Information and Management, 41(1), 39-47 [24]. Iyer, R., & Eastman, J. S. (2004). The Elderly’s [14]. Dholakia, R. R., & Uusitalo, O. (2002). Uses and Attitudes Towards the Internet. Switching to Electronic Stores: Consumer Characteristics and the Perception of Shopping Journal of Consumer Marketing, 21(3), 208-220 [25]. Jarvenpaa, S. L., & Todd, P. A. (1997). Is there a Benefits. International Journal of Retail and future for retailing on the internet? In R. A. Distribution Management, 30(10), 459-469 Peterson (Ed.), Electronic marketing and the [15]. Fabrigar, L. R., Wegener, D. T., MacCallum, R. consumer (pp. 139-154). Thousand Oaks, CA: C., & Strahan, E. J. (1999). Evaluating the Use SAGE publications Inc. of Exploratory Factor Analysis in Psychological [26]. Jisoo, P. (2015). Improving User Interaction in Research. Psychological Methods, 4(3), 272-299 Interactive TV Based on Ethnographic Insights [16]. Flavian, B. C., Gurrea, R. S., & Orus, S. C. from Real-life Problems. International Journal (2011). Gender Differences Regarding the of Human-Computer Interaction, 31(1), 17-32 International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 163 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 [27]. Kaiser, H. F. (1960). The Application of the [38]. Mangin, J. P., Bourgault, N., Guerrero, M. M., Electronic Computers to Factor Analysis. & Egea J. M. (2011). Modeling Perceived Educational and Psychological Measurement, Usefulness 20(1), 141-151 Through the TAM Model in a Canadian [28]. Kaiser, H. F. (1974). An Index of Factorial [29]. Karayanni, D. A. (2003). Web-Shoppers and Compatibility, Advantage and Demographics. Business Review, 15(3), 141-152 Adopting Online Banking Banking Environment. Journal of Internet Banking and Commerce, 16(1), 1-23 Simplicity. Psychometrika, 39(1), 31-36 Non-Shoppers: on [39]. McDonald, R. P. (1999). Test Theory: A Unified Relative Treatment (1st ed.). Mahwah, NJ: Lawrence European Erlbaum Associates Publishers [40]. Nunnally, J. C. (1978). Psychometric Theory. [30]. Kim, E. B., & Eom, S. B. (2002). Designing New York, NY: McGraw-Hill Effective Cyber Score Interface. Industrial [41]. Osborne, J. W., & Costello, A. B. (2009). Best Management and Data Systems, 102(5), 241- Practices in Exploratory Factor Analysis: Four 251 recommendations for Getting the Most from [31]. Kline, P. (1994). An Easy Guide to Factor Your Analysis. Pan-Pacific Management Review, 12(2), 131-146 Analysis. New York, NY: Routledge [32]. Koyunchu, C. & Lien, D. (2003). E-Commerce [42]. Peter, J. P., & Tarpey, L. X. (1975). A and Consumer’s Purchasing Behaviour. Applied Comparative Analysis of Three Consumer Economics, 35(6), 721-726 Decision Strategies. Journal of Consumer [33]. Laroche, M. (2002). Selected Issues in Modeling Research, 2, 215-224 Extended [43]. Ramayah, T., & Ignatius, J. (2005). Impact of Competitive Vulnerability (Vol. 11, pp. 69- perceived usefulness, perceived ease of use and 114). Emerald Group Publishing Limited perceived enjoyment on intention to shop Consumer [34]. Liao, Z., Brand & Choice: Shi, X. The (2009). Consumer perceptions of internet-based e-retailing: An online. The ICFAI Journal of Management Research, 3(3), 36-51 empirical research in Hong Kong. Journal of Services Marketing, 23(1), 24-30 [44]. Sebastianelli, R., Tamimi, N., & Rajan, M. (2008). Perceived Quality of E-retailing: Does [35]. Lindguard, G., & Dudek, C. (2003). What is the Gender Make a Difference? Journal of Internet Evasive Beast We Call User Satisfaction? Commerce, 7(4), 455-469 [45]. Shim, S., Eastlick, M. A., Lotz, S. L., & Interacting with Computers, 15(3), 429-452 [36]. Lohse, G. L., Bellman, S., & Johnson, E. J. (2000). Consumer Buying Behaviour on the Internet: Findings from Panel Data. Journal of Warrington, P. (2001). An Online Pre Purchase Intentions Model: The Role of Intention to Search. Journal of Retailing, 77(3), 397-416 [46]. Sim, L. L., & Koi, S. M. (2002). Singapore’s Interactive Marketing, 14(1), 15-29 [37]. Loiacono, E. T., Watson, R. T., & Goodhue, D. Internet Shoppers and their Impact on L. (2002). Webqual: A measure of Website Traditional Shopping Patterns. Journal of Quality, Presented at American Marketing Retailing and Consumer Services, 9(1), 115-124 Association: Winter Conference, Chicago, Marketing 2002. Educators’ Chicago, IL: [47]. Sorce, P., Perotti, V., & Widrick, S. (2005). Attitude and Age ifferences in Online Buying. American Marketing Association International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 164 Raja Sarkar, Dr. Sabyasachi DasInt J Sci Res Sci Technol.May-June-2019; 6 (3) :141-165 International Journal of Retail and Distribution [56]. Zikmund, W. G., Bobin, B. J., Carr, J. C., & Griffin, M. (2010). Business Research Methods Management, 33(2), 122-132 [48]. Sultan, C., & Henrichs, P. (2000). Community (8th ed.). Mason, HO: Cengage Learning Level Socio-Economic Communication. Journal of Computer-Mediated Communication, 5(1), 13-22 [49]. Sun, H., & Zhang, P. (2006). Causal Relationships between Perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7(9), 618-645 [50]. Thogersen, J., & Ölander, F. (2006). The Dynamic Interaction of Personal Norms and Environment-Friendly Buying Behaviour: A panel Study. Journal of Applied Social Cite this article as : Raja Sarkar, Dr. Sabyasachi Das, "A Conceptual Model to Measure the Impact of Consumer Behaviour on E-Retailing in India", International Journal of Scientific Research in Science and Technology (IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 6 Issue 3, pp. 141-165, May-June 2019. Available at doi https://doi.org/10.32628/IJSRST196329 Journal URL : http://ijsrst.com/IJSRST196329 Psychology, 36(7), 1758-1780 [51]. Van der Heijden, H. (2003). Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands. Information and Management, 40(6), 541-549 [52]. Van der Heijden, H. (2004). User Acceptance of Hedonic Information Systems. MIS Quarterly, 28(4), 695-704 [53]. Verplanken, B., Aarts, H., van Krippenberg, A., & Moonen, A. (1998). Habit, Information Acquisition, and the Process of Making Travel Mode Choices. British Journal of Social Psychology, 37, 111-128 [54]. Vijayasarathy, L. R. (2001). The Impact of Shopping Orientations, Product Types, and Shopping Aids on Attitude and Intention to Use E-retailing. Quarterly Journal of Electronic Commerce, 2(2), 99-113 [55]. Zhao, H., & Li, Y. (2012). Research on the influence of perceived risk in consumer online purchasing decision. Physics Procedia, 24, Proceeding of International Conference on Applied Physics and Industrial Engineering (pp. 1304-1310). International Journal of Scientific Research in Scienceand Technology (www.ijsrst.com) 165 :