|
Dr. Vivek Singh Tomar (Corresponding Author) Assistant Professor Amity Business School, Amity University Uttar Pradesh F3 Block, 3rd Floor, Amity University Campus Sector-125, Noida, Uttar Pradesh 201313 |
Dr. Varsha Khattri Associate Professor FORE School of Management, New Delhi, India B-18, Qutub Institutional Area, New Delhi 110016 |
The infrastructural support provided by Amity Business School, Noida and FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.:
This study investigates the internet usage variables like internet usage experience, internet usage frequency, internet usage duration, online shopping experience, online shopping frequency and the device used for online shopping among Indian online shoppers. Two established and pre-validated scales were used to measure perceived benefits and perceived risks of online shopping and to assess the overall perception of internet users towards online shopping. The study further analyzes and ascertains the impact of internet usage variables on perception towards online shopping. A survey of 650 respondents based on structured questionnaire was conducted in National Capital Region of Delhi. The questionnaire involved six categorical variables for internet usage, 39 scale items for perceived benefits of online shopping and 32 scale items for perceived risks of online shopping. Perception towards online shopping was determined by taking into account the benefits as well as risks of online shopping. Kruskal Wallis H test was applied to ascertain the impact of internet usage variables on the perception towards online shopping. Test results revealed significant impact of internet usage on perception towards online shopping. This study provides a contribution to the concept of perceived benefits and perceived risks and aids online retailers in developing strategies for increasing sale and traffic on their online portal. The study findings are broad in implications and empirically validate that higher internet usage leads to corresponding higher and stronger perception towards online shopping. Marketing practitioners can use the findings of this study when they perform their strategy development and implementation.
Keywords:Online Shopper Behaviour, Perceived Benefits, Perceived Risks, Internet Usage, Online Shopping
With expanding internet penetration, everyday new users of internet are joining this technology oriented virtual platform which include almost all human activities like communication, entertainment, banking, commerce/shopping, information gathering, travel booking, socializing and much more. In 2018 there were close to 500 million internet users in India which is higher than the present population of USA (Ayyar, 2018). Indian Internet penetration was only 2% (40 million) in year 2006, which increased to 4% (80 million) in 2009, which further reached to 27% (405 million) in 2016 and in 2018 it reached to almost 35% including both rural-urban users(IMRB, 2018). Internet usage refers to the behavioral aspects of consumer involvement with internet. It refers to the familiarity, experience, immersion, stickiness and comfort level that the user of internet feel while working on internet platform and availing internet services. Consumer perception is marketing concept that encompasses a customer’s impression, awareness and/or consciousness about a company or its offerings (Ireo, 2019). It is an outcome of sensory interpretation applied in the area of marketing and advertising to have better understanding of consumer behavior. Sensory interpretation involves assigning meaning to the sum total of olfactory, gustatory, tactile, visual and auditory senses which leads to development of opinion and evaluation of businesses and the brands or products it offers in market. Businesses take help of consumer perception theory to understand consumer’s perception towards them. Consumer perception theory further helps businesses to formulate marketing and advertising strategies to retain existing customers and to attract new ones. Online shopping also referred as electronic retail, e-tailing, internet shopping or e-shopping is a kind of electronic commerce which facilitates consumers to buy goods or services directly from a seller using a web browser over the Internet. Other similar names referring to e-tailing are e-web-store, Internet shop, web-shop, web-store, online store, online storefront, e-shop, e-store, and virtual store. Online retailers offer a web based virtual shopping environment to shoppers through websites which could be accessed through desktops, laptops or tabs, as well as, apps and mobile adaptable versions of websites which could be accessed and operated through mobile phones. Perception towards online shopping can be best understood through considering benefits as well as risks of online shopping and their consolidated evaluation. This study investigates and generalizes the consumer’s overall perception towards online shopping through factoring benefits as well as risk perception of online shopping. It is generally understood that higher internal usage may translate into higher perception towards online shopping. This paper aims at exploring the below-mentioned hypotheses to explore the impact of internet usage on perception towards online shopping.
H1: There is no significant impact of internet usage experience on perception towards online shopping. H2: There is no significant impact of frequency of internet usage on perception towards online shopping H3: There is no significant impact of duration of internet usage on perception towards online shopping H4: There is no significant impact of online shopping experience on perception towards online shopping H5: There is no significant impact of frequency of online shopping on perception towards online shopping H6: There is no significant impact of mode of online shopping on perception towards online shopping
Internet has played a significant role in all walks of life. One of the key applications of the Internet that combines the virtual market is online shopping. The online platform facilitates ‘anywhere, anytime’ shopping and online sellers have attracted local and global buyers across the world with competitive offers. Demographics (age, gender, occupation, education, annual household income, marital status) help online marketers to segment their market thereby leading to design marketing communications products and services and loyalty programs as per their target audience (Parikh, 2006). Infact, these variables especially during the initial embryonic days of Internet were regarded as the most accurate indicators of online shoppers (Vijayasarathy, 2003).Research findings state that age and income are significantly correlated to online shopping (Donthu and Garcia, 1999). Joines et all (2003) established that demographics have a role in predicting shopping preferences and younger people are more likely to resort to online shopping. Many studies found out that the highly educated and high income group men are more likely to buy online as compared to the less educated and low income group women (Forsythe and Shi, 2003; Kau et al., 2003; Swinyard and Smith, 2003). Consumers who have dealt in prior online purchase have a positive relationship with the future online purchases (Brown, 2003). This implies that those who have some experience with online purchase, their likelihood of indulging in online purchase again or intention to buy is more as compared to those consumers who have no experience of buying online. The search activity involved in purchasing online includes number of websites visited by consumers before making an online purchase, the types of websites searched, the frequency of browsing online, the number of searches, and the use of keywords to search the required item (product or service) (Ahuja, 2003). Some studies also suggest that spending more time on the internet and having more online experience leads to more research (search information on product or service) and eventually the consumer ends up buying more (Koyuncu, 2003; Leonard, 2003). The amount of time a consumer has in hand is a basis to decide whether to make an online purchase (Bhatnagar et al., 2000). It can be inferred that the time spent by the consumers online in terms of months, weeks, hours in order to search the information for the desired product or service predicts online purchases (buying online products/services or not buying online products/services) (Bellman et al., 1999). Forsythe, et al. (2006), suggest that perceived benefit is what customers gain from online shopping. Leung (2013) opines that perceived benefit is the perception of the positive consequences that are caused by a specific action. Sheth (1983) states that factors affecting shopping in traditional formats are influenced by functional and nonfunctional motives. Functional or utilitarian motives refer to shopping convenience, quality of the item, assortment of product selection or variety and price of the item. Nonfunctional or hedonic motives are related to social and emotional needs (Bhatnagar &Ghose, 2004a, 2004b). Hedonic shoppers are found in the online shopping environment generally for gathering information for products or services, positive sociality and surprise and bargain offers (Wolfinbarger and Gilly, 2001). Previous studies indicate that utilitarian or functional motives that include convenience (Bhatnagar &Ghose, 2004a, 2004b, Korgaonkar&Wolin, 2002), wide selection opportunity (Rowley, 2000) unique products (Januz, 1983) and lower prices (Korgaonkar, 1984) are the major reasons for shopping in non-store formats. Forsythe et al. (2006) highlighted shopping convenience, product selection, ease/comfort of shopping; and hedonic/enjoyment as the key perceived benefits of online shopping. In line with such studies, Li et al. (1999) enumerated price, convenience and recreational benefits and likewise, Delafrooz et. al (2009) established wide selection choices and good selection as important benefits associated with online shopping. Online environment consumers while shopping often run the risk of lack of face to face interaction, the tangible indicators, the touch and feel of the product and the purchase also has security and privacy concerns (Laroche et al., 2005). Major impediment of online shopping is its uncertainty (Liang and Huang, 1998). This can also be termed as perceived risk associated with online purchasing. Perceived risk is the degree to which a shopper expresses uncertainty regarding the purchase of a product or service and the post purchase consequence. Barnes et al. (2007) suggests that consumers’ intent to shop online may reduce because of this perceived risk.Lee and Tan (2003) state that the level of risk that consumers perceive while shopping online is higher relative to the traditional shopping format. Previous studies elucidate six types of perceived risks in shopping via internet, namely, physical, social, product, convenience, financial, and psychological risks. Following is an explanation of each type of perceived risk: - Physical Risk – Arslan et al. (2013) state that the physical risk refers to the fear of consumers that the desired product may injure or harm the consumer’s health. In other words, it encompasses a potential danger to an individual’s physical health and safety (Lu et al., 2005). Social Risk –Pandit and Karpen (2008) established that consumers pay attention to the advice given by their dear ones in their social network while making a purchase. In case if the consumer ends up buying a poor product or making a poor choice for availing a service, the consumer may suffer from disapproval by the family, friends and other peer group (Uelstschy et al., 2004). Product Risk – This risk involves the risk of quality and suitability of the product due to physical distance (Forsythe et al., 2006). Since the consumer cannot examine the desired item physically, the perception of the consumer that the desired item may not be as per expectations is the ‘product risk’ (Kim et al., 2008). Convenience Risk – This concerns the perception of online shoppers that the time and effort involved to get the purchased item repaired or if need be replaced (Chang and Chan, 2008). Another concern is the potential loss of delivery which may happen on account of the product being delivered elsewhere and not to the consumer who ordered the product or the received product is damaged or the product is lost and hence not delivered (Dan et al., 2007). Financial Risk – One of the most common financial risk while online shopping is the fear of fraud via payment card information (Saprikis et al., 2010). Peter and Olson (2010) suggest that financial risk involves monetary loss and unexpected costs (for e.g. alteration cost in case of an ill-fitted apparel or an expensive outfit which causes discomfort when worn). Psychological Risk – Consumers feel mental stress if the purchases are not successful. A loss of self-esteem, frustration and disappointment due to making a poor product choice or not being able to achieve a successful buying goal is a psychological risk. (Peter and Ryan, 1976; Stone and Gronhaug, 1993), This risk acts as a mediating function for all the other stipulated perceived risks as the psyche translates any type of perceived risk into anything that the consumer does not approve of or experiences discomfort (Eggert, 2006). The overall review of past studies in the area of internet usage, benefit and risk perception towards online shopping phenomenon, expounds upon the need to study the association between internet usage and its impact on perception towards online shopping.
The study is a continuum of extensive literature review followed by survey of 650 respondents in National Capital Region (NCR) of Delhi in India with the help of a structured questionnaire. After filtering of data 40 outliers were removed and analysis was conducted on 610 valid responses. The data for analysis involved six demographic variables, six internet usage variables, 39 variables for perceived benefits of online shopping (PBOS) and 32 variables for perceived risks of online shopping (PROS) (Refer Appendix A and B). The choice of variables for PBOS and PROS were taken from the scales developed and refined in previous studies (Tomar, Sharma, & Pandey, 2018; Tomar, Tomar, & Tomar, 2018). Respondents with some past online shopping experience were intercepted for participation in the survey (during March 2017- July 2019) and responses were collected using an online survey created with the help of google forms. The collected data was further coded and analyzed using Statistical Package for Social Science (SPSS) version 23.0. The major categorical internet usage variables considered for this study were internet usage experience, internet usage frequency, duration of internet usage, online shopping experience, online shopping frequency and mode of internet usage. PBOS value was calculated by finding out the average of 39 PBOS items (Appendix A) on five point Likert Scale, similarly PROS value was calculated by finding out the average of 32 PROS items (Appendix B), where scale value 1 means “strongly disagree” and 5 means “strongly agree”. Perception towards online shopping was key scale variable used in the study which was created by finding out the difference between values of PBOS and of PROS. The analysis of data involved descriptive analysis of demographic variables and internet usage variables. The normality of the perception towards online shopping was checked and finally non parametric Kruskal-Wallis H Test was used for one-way analysis of variance. The findings and interpretations based on analysis of data is presented in the next section.
Descriptive analysis summary of primary data on demographic and internet usage variables is presented in table 1 below:
|
|
Count |
Column N % |
|
|
|
|
|
|
|
Age |
18-25 Years |
229 |
37.5% |
|
25-35 Years |
134 |
22.0% |
|
|
35-45 Years |
71 |
11.6% |
|
|
Above 45 Years |
176 |
28.9% |
|
|
Gender |
Male |
305 |
50.0% |
|
Female |
305 |
50.0% |
|
|
Education |
Upto Intermediate |
59 |
9.7% |
|
Graduate |
231 |
37.9% |
|
|
Post Graduate & Above |
320 |
52.5% |
|
|
Occupation |
Self employed |
86 |
14.1% |
|
Salaried (Private) |
153 |
25.1% |
|
|
Salaried (Government) |
61 |
10.0% |
|
|
Student |
180 |
29.5% |
|
|
Housewife |
130 |
21.3% |
|
|
Annual Household Income |
Upto 5 Lac |
184 |
30.2% |
|
5-10 Lac |
142 |
23.3% |
|
|
10-15 Lac |
113 |
18.5% |
|
|
Above 15 Lac |
171 |
28.0% |
|
|
Marital Status |
Single |
276 |
45.2% |
|
Married (without kids) |
53 |
8.7% |
|
|
Married (with kids) |
281 |
46.1% |
|
The demographic details of respondents included in the sample study as specified in table 1 above represents fair depiction of different sections of respondents which adequately covers various age groups, gender, education levels, occupations, Income levels and marital status. Responses on six internet usage profile variables were collected during the survey which is summarized and presented in table 2 given below:
|
|
Count |
Column N % |
|
|
Internet Usage Experience |
Less than 1 Years |
20 |
3.3% |
|
1-3 Years |
80 |
13.1% |
|
|
3-5 Years |
97 |
15.9% |
|
|
5-7 Years |
130 |
21.3% |
|
|
More than 7 Years |
283 |
46.4% |
|
|
Frequency of active internet usage per week |
Once a week |
17 |
2.8% |
|
1-3 days |
51 |
8.4% |
|
|
3-5 days |
64 |
10.5% |
|
|
Daily |
478 |
78.4% |
|
|
Duration of Internet usage per use |
Less than 30 Minutes |
69 |
11.3% |
|
30 Minutes to 1 Hour |
143 |
23.4% |
|
|
1-3 Hours |
183 |
30.0% |
|
|
3-5 Hours |
101 |
16.6% |
|
|
More than 5 Hours |
114 |
18.7% |
|
|
Online Shopping Experience |
Less than 1 Year |
102 |
16.7% |
|
1-3 Years |
290 |
47.5% |
|
|
3-5 Years |
155 |
25.4% |
|
|
More than 5 Years |
63 |
10.3% |
|
|
Frequency of Online Shopping Per Year |
Once in a Year |
32 |
5.2% |
|
2-5 Times |
187 |
30.7% |
|
|
5-10 Times |
154 |
25.2% |
|
|
10-15 Times |
106 |
17.4% |
|
|
More than 15 Times |
131 |
21.5% |
|
|
Medium for Online Shopping |
Website |
116 |
19.0% |
|
App |
111 |
18.2% |
|
|
Both |
383 |
62.8% |
|
The analysis of the frequencies and percentage for sample respondents falling under various groups under different internet usage variables listed in table 2 revealed a lot of observations. The growth curve of internet usage experience of the respondents was found to be very high with 46.4% of respondents having more than 7 years of internet usage experience. Internet usage frequency of respondents also follows a similar growth pattern with 78.4% of respondents as daily internet users.The duration of internet use per usage was found to be little bit positively skewed with maximum 30% of respondents, who use internet for 1-3 hours.The online shopping experience of respondents was found to be positively skewed with maximum 47.5% of respondents, who have 1-3 years of online shopping experience, followed by 25.5% of shoppers who have 3-5 years of experience.A maximum of 30.7% of respondent shop online 2-5 times in a year, followed by 25.2% of respondents who shop online 5-10 times in a year.A vast majority of 62.8 % respondents use both website as well as mobile app for shopping online with almost close 19% website shoppers and 18.2% mobile app shoppers. The key scale variable representing perception towards online shopping was calculated by subtracting mean PROS from mean PBOS. As per the descriptive details of perception towards online shopping specified in table 3 below the perception was found to be in the mean range of -1.16 to 2.54 out of the possibility for -5 to 5 as extreme possible values. In the present study overall perception was calculated by taking into consideration the benefits as well as risks of online shopping. The mean score of .5688 represents a marginally positive perception after considering possible risks of online shopping. The positive value of skewness coefficient .356 indicates positive skewness in frequency distribution of perception towards online shopping.
|
Range |
Minimum |
Maximum |
Mean |
Std. Deviation |
Skewness |
Kurtosis |
|
3.71 |
-1.16 |
2.54 |
.5688 |
.74382 |
.356 |
-.218 |
|
Kolmogorov-Smirnova |
Shapiro-Wilk |
|||||
|
Statistic |
df |
Sig. |
Statistic |
df |
Sig. |
|
|
Perception Towards Online Shopping |
0.048 |
610 |
0.002 |
0.986 |
610 |
0 |
Since positive skewness was indicated in the descriptive analysis of perception towards online shopping, therefore the test for checking deviation from the normality was conducted with the help of Kolmogorov-Smirnov and Shapiro-Wilk. As indicated in table 4 above both the tests indicate deviation from normality as the significance p value was found to be ≤0.05.
|
Independent Grouping Variable |
Perception Towards Online Shopping (Dependent Variable) |
||
|
Chi-Square |
df |
Asymp. Sig. |
|
|
Internet usage experience |
48.721 |
4 |
.000 |
|
Frequency of internet usage per week |
21.628 |
3 |
.000 |
|
Duration of internet usage per use |
14.919 |
4 |
.005 |
|
Online Shopping Experience |
25.945 |
3 |
.000 |
|
Frequency of online shopping per year |
52.152 |
4 |
.000 |
|
Medium of online shopping |
11.216 |
2 |
.004 |
Since the distribution of perception towards online shopping was found not to be normally distributed, therefore the non-parametric Kruskalwallis H-test was used to study the impact of six internet usage variable on the dependent variable perception towards online shopping. The results specified in table 5 above indicate significant impact (p≤0.05) for all six independent categorical variables. Therefore, it was found that all the test hypothesis H1-H6 were rejected and internet usage variables were found to significantly influence the perception towards online shopping.
The specific influence of internet usage variables is further illustrated with the help of figure 1, which graphically portrays the specific influences as elaborated below: Internet usage experience was found to significantly impact the perception towards online shopping (H1 Rejected). With increase in internet usage experience, perception towards online shopping also increase proportionately. Only exception was found between 3-5 years and 5-7 years where the growth of perception score is proportionately little low as compared to general trend. Internet usage frequency was found to significantly impact the perception towards online shopping (H2 Rejected). The relationship between internet usage frequency and perception towards online shopping was observed to follow exponential growth pattern. Initially its slow growth followed by rapid growth in perception towards online shopping with increase in internet usage frequency. Duration of internet usage per use was found to significantly impact the perception towards online shopping (H3 Rejected). Increase in duration of internet usage per usage leads to corresponding logarithmic growth in perception towards online shopping. The growth in perception is steep till 1-3 hours of internet usage per usage, which further slows down till 3-5 hours and then further slightly decline for more than 5 hours of internet usage per use. Online shopping experience was found to significantly impact the perception towards online shopping (H4 Rejected). With increasing experience of online shopping the perception towards online shopping grows and follows a logarithmic growth curve pattern. Frequency of online shopping was found to significantly impact the perception towards online shopping (H5 Rejected). Higher frequency of online shopping leads to increase in perception towards online shopping. The growth curve shape is logarithmic. Medium of online shopping was found to significantly impact the perception towards online shopping (H6 Rejected). Exponential growth in perception towards online shopping was observed when medium of online shopping changes from website to app and further from app to both.
DISCUSSION AND CONCLUSION
This study aims at deepening the understanding of the influences of internet usage on perception towards online shopping. The significance of this study lies in empirically validating the fact, that as consumer develops more familiarity with internet usage and online shopping through experience, frequency and time of usage his perception eventually turns more favorable towards shopping online. This study also signifies the profiling of internet users on the basis of internet usage behavior as it is evident that a vast majority of internet users has more than 7 years of online experience on internet and a major proportion of them use internet on daily basis with 1-3 hours of internet usage per day. In the context of online shopping experience, the vast majority has 1-3 years of shopping experience and they mostly prefer to shop online 2-5 times in a year. It was also discovered that the app is preferred more for shopping online than the website, and the majority of online shoppers prefers both website as well as app to shop online. The perception towards online shopping used in this study discounted the risk of online shopping from benefits of online shopping and the resultant overall perception was found positive, which indicate and empirically validate the acceptance of online shopping by internet users.
An understanding of the dimensions of internet usage and its impact can be used by online retailers in developing strategies for increasing sale and traffic on their online portal. Based on the research findings, the organizations can focus its marketing efforts towards more profitable customers and strategic channel structures. For the internet usage variables that follow a logarithmic growth curve pattern, the marketers can break the strategies into smaller tasks that can be mastered more quickly. Smaller tasks have steeper growth curves because they are easier to master. This strategy works especially well for accelerating the progress that experience logarithmic growth. Moreover, in logarithmic domains, in the beginning, high-growth phase, the emphasis needs to be on maintaining long-term habits. Since growth is fast initially, care needs to be taken by the marketers that it shouldn’t slide back down once effort is removed. This can be practiced with variables- ‘online shopping experience’ and ‘frequency of online shopping’. Perceived risks and perceived benefits have been the indispensable force that lead the consumers’ intent to shop online. If online shopping would not offer substantial value and benefits to consumers, they would have negative attitude towards the same. To instill more confidence and trust for novice users of the internet, online businesses should endorse more factors that have significant perceived benefits and adopt adequate risk-reduction strategies enabling the consumers’ with reasons’ to buy their product offerings. E-retailers preparing to develop and expand their operations can use the determinants of the perceptions of online shopping. This will aid them in marketing strategy development and implementation.
Since the profiling of internet users on the basis of internet usage behavior suggests that users have more than 7 years of online experience on internet. Future research could examine a sample of new internet users to study their apprehensions and perceptions thereby enabling the marketers to devise strategies to increase their customer database (inclusive of internet users with less than 7 years of online experience). Research on a specific sector or a product/service category can be conducted to validate the factors influencing internet usage and online shopping. Future studies could remain committed to help organizations by promptly altering the variables of perceived benefits and perceived risk to suit the evolving consumers and changing marketing environment. Variation of these perceptions over time (Appendix A and B) can also be examined to test the scale stability over time. Further, examining the results of the present study using samples with different demographics also offers a direction for future research.
The infrastructural support provided by Amity Business School, Noida and FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.
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|
Variable Code |
Variable Question |
Variable Label |
|
B1 |
I can buy from any place with internet access |
Anyplace Access |
|
B2 |
I can buy anytime as per my convenience |
Anytime Access |
|
B3 |
I can buy with least shopping efforts |
Least Shopping Efforts |
|
B4 |
I don’t have to wait in queues for shopping |
No Waiting in Queues - Shopping |
|
B5 |
I don’t have to wait in queues for billing/checkout |
No Waiting in Queues -Billing |
|
B6 |
I don’t feel need for any shopping assistance |
No Shopping Assistance |
|
B7 |
I can pay by any convenient mode of payment |
Any Payment Mode |
|
B8 |
I can easily get my big purchases financed into EMI |
Big Purchases Financed |
|
B9 |
I can take my time and don’t need to hurry my shopping |
No hurried Shopping |
|
B10 |
I don't have to waste time in travelling to buy |
No time wasted in travelling |
|
B11 |
I can save myself from struggling through the crowd |
No Crowd |
|
B12 |
I get better price through online shopping |
Better Price |
|
B13 |
I get better discounts and rebates through online shopping |
Better Discount |
|
B14 |
I get better price as no middleman commission is involved |
No Middleman Commission |
|
B15 |
I get better loyalty points benefits |
Loyalty benefit |
|
B16 |
I get better information on loyalty points earned |
Loyalty Information |
|
B17 |
I get several brands and products from different sellers |
Several Brands |
|
B18 |
I get best global brands without International travel |
Global Brands |
|
B19 |
I can buy products of other parts of the country easily |
Products from whole country |
|
B20 |
I get better selection of colors, style and size |
Better choices of color, style and size |
|
B21 |
I find no stock out problem |
No Stock Out |
|
B22 |
I can avoid additional cost like transportation, parking |
No additional Cost |
|
B23 |
I can avoid additional money on eating out while shopping |
Avoid Eating Out |
|
B24 |
I can compare prices easily and can take more informed decision |
Easy Price Comparison |
|
B25 |
I can easily research on my product before purchase |
Easy Product Research |
|
B26 |
I can read other consumer reviews to reach my decision |
Other Consumer's Reviews |
|
B27 |
I can write reviews and share my feedback with other buyers |
Write Reviews and Feedback |
|
B28 |
I can easily connect and write feedback to retailer |
Easy Connect with Retailer |
|
B29 |
I can have personalized interaction with online seller |
Personalize interaction with Seller |
|
B30 |
I can easily raise queries and clarify my doubts |
Raise Queries and Clarify Doubts |
|
B31 |
I can custom design my product online |
Custom Design Product |
|
B32 |
I don’t feel any social pressure while buying |
No Social Pressure |
|
B33 |
I don’t have to buy on impulses because of attractive display |
No Impulse Purchases |
|
B34 |
I don’t have to buy because of sales tactics of salesman |
No Salesman Tactics |
|
B35 |
I don’t buy the product which I don’t need |
No Unwanted Shopping |
|
B36 |
I can ensure the privacy of my purchases |
Purchase Privacy |
|
B37 |
I don’t have to worry about other people watching what I buy |
No worries of others |
|
B38 |
I can comfortably buy without embarrassment |
No Embarrassment |
|
B39 |
I find online shopping fun |
Fun Shopping |
|
Variable Code |
Variable Question |
Variable Label |
|
|
R1 |
I find placing an order as complicated and cumbersome |
Complicated Process |
|
|
R2 |
I find it difficult to find the appropriate site to shop online |
Difficulty in finding suitable website |
|
|
R3 |
It takes too long for reaching to the desired product |
Time wasted in searching |
|
|
R4 |
I worry that I may not get the product on time |
Late delivery |
|
|
R5 |
I worry that I may not get the desired product as ordered |
Product attribute mismatch |
|
|
R6 |
I doubt on the quality of product delivered |
Product quality |
|
|
R7 |
I worry that the product I get may be used/ second hand |
Second hans/used product |
|
|
R8 |
I worry that I may not get the product delivered at all |
No product delivery |
|
|
R9 |
I doubt on the originality of the product |
Originality Issue |
|
|
R10 |
I worry that the product delivered may be from old/outdated stock |
Outdated Product |
|
|
R11 |
I think that my personal information may be misused |
personal information misuse |
|
|
R12 |
I can’t try the product before placing order |
Can't try/sample product |
|
|
R13 |
I can’t touch and feel the product before buying |
No touch and feel experience |
|
|
R14 |
I may have to pay extra for shipping and handling |
Extra shipping charges |
|
|
R15 |
I must have to wait for receiving the product |
Waiting to receive the product |
|
|
R16 |
I worry about the risk posed by the delivery boy |
Risk posed by delivery boy |
|
|
R17 |
I think that I may be missing the human involvement/feel |
No human involvement/feel |
|
|
R18 |
I feel that I may be missing the fun of going out to buy |
fun of going out to buy |
|
|
R19 |
I feel that online purchases makes me anxious |
Online Purchase Anxiety |
|
|
R20 |
I fear stress of follow ups for delivery/ refund/ replacement |
follow ups for delivery/ refund/ replacement |
|
|
R21 |
I doubt on the very existence of unfamiliar shopping sites |
Doubt on unfamiliar sites |
|
|
R22 |
I fear loss of my money |
Fear of money loss |
|
|
R23 |
I feel that my family/friends may not approve my online purchase |
Non approval of family/friends |
|
|
R24 |
I fear that hidden cost may show up just before payment |
Hidden Costs |
|
|
R25 |
I may buy some product accidently which I may not want |
accidental purchases |
|
|
R26 |
I feel that I may be overcharged for the convenience |
overcharge for convenience |
|
|
R27 |
I feel that the grantee/ warranty may not be honored |
Guarantee/ warranty may not be honored |
|
|
R28 |
I feel that it would be difficult to replace the product |
Difficulty in product replacement |
|
|
R29 |
It will be difficult to get the refund, if I don’t want to replace |
Difficulty in refund |
|
|
R30 |
I may make impulse purchases |
Fear of Impulse Purchase |
|
|
R31 |
I can’t bargain on price before placing order |
No scope of bargaining |
|
|
R32 |
I feel uncomfortable with shopping sites mostly in English language |
Language issue |
|
