Al-Barkaat Journal of Finance & Management/Print ISSN: 0974-7281 50 Al-Barkaat Journal of Finance & Management 10.5958/2229-4503.2016 .00005.9 Online ISSN: 2229-4503 A Bi-annual Refereed Journal Volume 8, Number 1, January 2016, pp. 50-67
Evaluation of Customer Preferences on Showrooming and Webrooming: An Empirical Study Sana Nesar* and Lamay Bin Sabir** Abstract In the age of globalization electronic marketing is a great revolution. Over the last decade majority of business organizations are running with technological change. Online shopping or marketing is the use of technology for better marketing performance. And retailers are devising strategies to meet the demand of online shoppers; they are busy in studying consumer behavior and attitudes in the field of online shopping. In our study, we also tried to study consumer’s attitudes towards online as well as offline shopping and specifically studying the factors influencing consumers. This paper tries to understand the new behaviour which customers are now showing, showrooming and webrooming. The reasons for this new phenomenon and the reasons behind are analyzed and conclusions are made. 1. Introduction: Shopping habits are changing, so we need to have an idea of what a modern shopper is and some of their behaviour in-store. Regarding this, there are some buzzwords that everyone may have heard about; “Showrooming” and “Webrooming.” Showrooming is when you’re standing in a store, and you pull out your Smartphone to see if you can get a better price online. On the contrary, Webrooming is when you’re searching online, check what item you like and go to the store to pick it up. Every consumer is unique and behaves differently from others. Each person buys a product for his/her own reason or reasons. People differ widely in their purchasing behaviour. Showrooming — Consumer behaviour of viewing a physical product in-store but deciding to purchase it online, possibly due to the ease of price comparison. This could result in consumers leaving the store empty handed and placing an order online (eMarketer. 2012; Smith, 2013; Butler, 2013). Webrooming — Consumer behaviour where the research is conducted online on a stationary or mobile device, but the product is purchased in-store (Philips, 2013). Technology in the hands of the common man has empowered him beyond the level that is comfortable to the business. Information technology is one such force that has catapulted his power over the market and has influenced his buying behaviour. Internet has, over a couple of decades, developed into market place – market space – for the *Student, Al-Barkaat Institute of Management Studies, Aligarh **Assistant Professor, Al-Barkaat Institute of Management Studies, Aligarh E-mail for correspondence:
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exchange of goods and services. E-business or E-commerce has now come to stay and is slowly occupying an important place in business transactions. Even the online stores are gaining prominence. To be successful, it will require business to be patient and understanding in dealing with the behaviour patterns of the customer behaviour. This understanding should help sales promotion activities to aid business growth. Customers are using mobile to become more informed during the shopping experience, whether it is showrooming or webrooming. Next section (section 2) deals with the review of literature followed by objectives of the study in section 3. In section 4, research methodology was discussed in detail followed by data analysis in section 5. Conclusions and future research directions are chalked out in section 6 of this paper. 2. Review of Literature Nowadays, internet is not only for stage in networking and it is also as a medium to bond together for approximately every business with its clientele (Delafrooz et al., 2009). “E-commerce is also called online shopping.” It means running the entire procedure of business electronically by means of the internet (Chaffey et al., 2006). Online Shopping is a method where consumers decide to purchase via the internet. For online shopping retailer, in order to make sure the accomplishment of e-commerce, it is significant to ascertain consumer needs and wants (Chaffey et al., 2006). Furthermore, online shopping is a fresh business strategy in Asian country (MasterCard, 2008). According to research done by ACNielsen, total of internet user is increasing time by time, 627 million people in the world has used internet as a medium to shopping (ACNielsen, 2007). Research done by Joines et al. (2003) and Houque et al. (2006) had come out with the same judgment which the internet user has constantly increasing and give impact to the online purchase on the internet. This result shows an opportunity arrived from the technology factor and can be as a benefit to company if they know how to use these chances. Li and Zhang (2002) examined the representative existing literature on consumer online shopping attitudes and behaviour based on an analytical literature review. As per Li and Zhang (2002), three out of the five dependent variables (purchasing behaviour, consumer attitudes and intentions) and three out of the five independent variables (service/vendor/ /product characteristics, personal characteristics, website quality) receive the most attention. The direct inference of these findings is that targeting more suitable customer groups, improving item for consumption and or service quality, and improving website feature can definitely persuade consumer attitudes and behaviour, leading to increased rate of recurrence of early purchase. Iyer and Eastmen (2014) found that the population of seniors who are more educated, well-informed and who are aware of the technology and those who have an optimistic behaviour towards online shopping and internet are more interested in online shopping. However their understanding and the use of internet by them have no association with their age and their satisfaction level whilst purchasing online. Chaing and Dholakia (2003) carried out a study in which they examined the rationale of the customer to purchase goods online during their shopping. There are three variables in their study; accessibility features of the shopping sites, the type of the products and Volume 8, Number 1, January 2016
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their characteristic, and the actual price of the product. The study revealed that the accessibility and the convenience of the shopping sites create the intention in the customer to purchase. When there are difficulties faced by a consumer in purchasing online then the customer switch to the offline shopping for the purchase behaviour and the consumer face difficulty in offline purchasing then they go to the online purchasing. Tabatabaei (2009) has explored the opinion of the consumers who are purchasing online and the consumers who are purchasing from offline market. The objective is to know why the traditional customer chooses to shop online and what are the factors influencing them to purchase online and what are the factor for them to not use the sites for shopping. The author has done a survey of 264 respondents in a small mall and then that data was analyzed. All the customers of this study are literate and have knowledge of computer and internet. The survey consists some of the questions like demographic profile, computer knowledge and the knowledge over the internet. The outcome of the study was that the consumers of online shopping use to shop online more than one time in a month and the consumer of offline shopping shop one to five times in a year from shopping sites. Soopramanien and Robertson (2007) conducted a study in UK on acceptance and practice of online shopping. Their exploration shows that the online consumers choose different course of action based on the apparent beliefs. They found that, how socio demographic variables, attitude and beliefs towards internet shopping effect on the both decision to practice and use of online shopping channels. They categorized online buying behaviour as the one who purchase from online sites and the one who only browse online sites and purchase from the store, and third those who do not buy online. The study do not cover the buyers who choose products in stores and buy online. Shergill and Chen (2005) conducted study in New Zealand regarding the consumer’s attitude towards online shopping. Hausman and Siekpe (2009) analyzed a practical study in US regarding the effect of web interface features on consumer online purchase intention. E-commerce system is different from traditional information system. It has both features of information system and marketing channels. It contains machine and human element. An empirical finding shows that to know the motivation factors for online shopper, cognitive and psychological factors do have meanings. The study finds both human and computer factors are necessary for antecedent for online shopping. Johnson et.al (1999) discussed to identify the factors influencing online shopping. This paper seeks to identify web consumer’s demographic attitude towards shopping and reasons of online buying behaviour. This survey asked members of WVTM (Wharton virtual test market) whether they have purchased anything online. This study concludes that the consumer shop online or use online facilities to save time. The result of these study suggest several suggestions for the design of online shopping environment such as shopping site should make it more suitable to buy standard to repeat purchase items they should provide the information needed to make a purchase decision and purchasing process should be easy for the consumer. This paper conclude that the consumer appears to value the web time saving over its cost saving. The consumers attitude may change over time, accessibility rather than cost saving. The results show that the people who spend more money on lifestyle are on the net more and receive more emails compared to the other email users and internet users. Online shoppers who have a weirder lifestyle are on the net more and receives Volume 8, Number 1, January 2016
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more emails compared to the other email users and internet users. Bellman et al. (1999) examine the relationship among demographics, personal characteristics, and attitudes towards online shopping. Scarborough and Lindquist (2006) studied an empirical study on E-shopping in a multiple channel environment in which a segmentation schema is suggested based on patterns of epurchasing and e-browsing including browsing on the internet with planned purchasing in an offline channel. Devaraj et.al (2006) critically analyzed an empirical study in USA regarding examination of online channel preference. They examined the behavioural and economic features that add to online consumer’s satisfaction and further head to their preference of online channel. The results indicate that asset specificity and uncertainty structure variables the electronic marketplace are related with the conduct constructs such as, personalization, website design, time responsiveness, security and reliability of the online channel. Further, it was found that, personalization, time responsiveness, security, and reliability are also significantly linked to the consumer satisfaction outcome with the channel. Website design has no significant effect on online consumer’s satisfaction. Finally, it was indicated that satisfaction resulting from the above conduct variables was strongly related to the consumer’s online channel preference. Consumers navigate channels in a way that suits them on any particular shopping occasion, and they expect retailers to be accessible through every touch point. In order to understand how showrooming and webrooming shape the customers journey, the interaction of customers across multiple channels needs to be examined. An empirical study by Frambach et al. (2007) has demonstrated that ‘the buying stage has an important influence on channel usage intention’. Consumers seek different benefits at the pre-purchase stage than during or after purchase. This can lead to dynamic channel preference during the whole buying decision process. While Frambach et al. (2007) focused on the dichotomy of online–offline, such dualism is now largely outdated, and the utilization of the growing number of channels by consumers is yet to be examined in the light of consumer decision making. To understand fully consumer shopping behaviour and engagement with different touch points, the terms customer journey and consumer decision-making process must be clarified. A review of consumer decision-making models led to the identification of the general stages consumers are said to go through to reach (or reject) a purchase decision (Evans et al. 2006). Quint et al. (2013) in their paper identified five types of shopping behaviors which are related to showrooming and webrooming. These are; Traditionalists- “Prefer the in-store shopping experience”; Experience-Seekers- “Value the best experience, not just price”; Savvys - “Calculating, but persuadable”; Price-Sensitive- “Don’t plan, but always opt for deals” and Exploiters - “Premeditated about lower prices” The review clarifies and simplifies the dominant dimension consumers consider when they make any online purchase decision. Following this, the major theoretical gap related to understanding what and why consumers do, and do not purchase using the Internet is explored with respect to the theories of retail change and consumer behaviour theory with particular reference to the buying decision process. More specifically, the study examined the interrelationships among quality, value, satisfaction, and loyalty when consumers choose to shop online. Volume 8, Number 1, January 2016
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3. Objectives of the Study After looking at the importance of showrooming and webrooming, and customer satisfaction, following objectives were formulated. To examine the factors influencing the consumer to switch from the offline shopping to online shopping and online to offline shopping. To analyze the significant difference between the online and offline consumer groups in terms of demographic, purchasing habits and ease of shopping. To find any relation between age, gender and monthly income vis-à-vis ease of online shopping and overall satisfaction levels. To understand the perspective thinking of a customer while shopping. 4. Research Methodology The credibility of findings and conclusions extensively depend on the quality of the research design, data collection, data management, and data analysis. This section is dedicated to the description of the methods and procedures done in order to obtain the data, how they will be analysed, interpreted, and how the conclusions will be done. This study conducted in order to know the customer preferences towards showrooming and webrooming. The type of study is descriptive using both primary and secondary data. The primary data is collected through self administered close ended questionnaire. The primary data were derived from the answers respondents gave in the selfadministered questionnaire. In addition, the information obtained from the interview also provided primary research data that supported the study. The secondary data on the other hand, were derived from the findings stated in published documents and literatures related to the research problem. A pilot survey was done with a set of 50 questions. After the pilot study and inputs from experts, some questions were added and some are deleted. Also ambiguity was removed in order to make the questionnaire easy to understand for the respondent. The sample frame is selected from different age group, different sex and from different location of Aligarh with people including students, employed and unemployed, etc who are shopping online and offline. Target sample size was 300 and convenience sampling technique is employed to collect the data. Questionnaire was filled by 378 respondents from which 48 responses were incomplete, therefore the overall sample size is 330 with a response rate of 87%. As a general rule of thumb data from at least 300 respondents is deemed comfortable, 500 is considered as very good and 1000 as excellent as per Tabachnick and Fidell (2001) and Garson (2007). Due care was taken to ensure that the respondents understood all the questions and responded to the best of their ability. Malhotra and Grover, (1998) have suggested a response rate of 20% for the positive assessment of the surveys. 4.1 Following hypotheses are developed H01: There is no association of Gender and the frequency of purchase. H03: There is no relation between monthly income and time spent online H04: There is no relation between age and ease of online shopping. H05: There is no relation between gender and ease of online shopping. Volume 8, Number 1, January 2016
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H06: There is no relation between monthly income and overall satisfaction levels of online shopping 4.2 Following tools and techniques are used in the study to analyze and interpret the data 1. For demographics- descriptive statistics (percentage analysis) is used. 2. For ranking – weighted average of means are considered. 3. For hypothesis testing - t-test, ANOVA and Chi-Square test are used. 5. Data Analysis and Interpretation 5.1 Demographics Table 1 shows the age category of population. The major of customer who use online/ offline shopping ranges mostly between 18 to 25 year age comprising of 62% of the total consumer. Table 1: Age Profile
Frequency Percent
18-25 Years 204 61.8
Age 25-30 Years 51 15.5
30-35 Years 57 17.3
> 35 Years 18 5.5
From Table 2, we can conclude that, 298 respondents were taken into consideration for the study. The graph shows the percentage of male and female who are doing online and offline shopping. It shows 54% male go for the shopping and 46% female do the shopping online and offline. Table 2: Gender profile
Frequency Percent
Gender Male 177 53.6
Female 153 46.4
The above Table 3 shows the income of the respondents. It shows that the 22% of respondents who having income 25000, 35% respondents having income between 25000 to 50000, 24% respondents having income between 50000-75000 and last comes the 19% 0f respondents who having income 75000 or above. Table 3: Monthly Income Income < INR 25000 INR25000- INR 5000050000 75000 Frequency 72 117 78 Percent 21.8 35.5 23.6
> INR 75000 63 19.1
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Table 4 shows that 35% respondents purchase once a month, 30% purchase once every 2-3 weeks, 21% purchase twice a week and 14% purchase once in a week. Table 4: Frequency of Purchase Once a week Frequency Percent
Purchase frequency twice a week once every 2-3 weeks
48 14.5
69 20.9
once a month
99 30.0
114 34.5
Table 5 shows that 28% respondents spend time on online shopping 1-2 hours per day, 27% respondents spend 0-1 hours, 24% respondents spend 2-3 hours and 21% respondents spend more than 3 hours per day. Table 5: Time Spent online Time spent online 0 -1 hours per day
1-2 hours per day
2-3 hours per day
more than 3 hours per day
Frequency
90
93
78
69
Percent
27.3
28.2
23.6
20.9
5.2 Type of Customers a. Traditionalists: I prefer the in store shopping Experience We ask the respondents that they prefer the in-store shopping experience rather than offline, above table 6 shows the results. We take respondents who agree and strongly agree as Traditionalists. Therefore we can say that out of 330 respondents, 189 (57.3%) respondents are Traditionalists (171 agreed and 18 strongly agree). Table 6: Traditionalists Consumers
Freq
SD 3
%age
1%
Traditionalists D NAND 63 75 19%
23%
A 171
SA 18
52%
5%
b. Exploiters: I check lower prices before purchasing the product online or offline Next we ask the question to the respondents, if they check the prices before purchasing the product online and offline (table 7). We took respondents who agree and strongly agree as exploiters. Therefore out of 330 respondents, 210 (63%) respondents are exploiters (147 agreed and 63 strongly agree).
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Table 7: Exploiters Exploiters NAND
D
A
SA
Freq
69
51
147
63
%age
21%
15%
45%
19%
c. Savvys: Online shopping is a good pass time Table 8 shows that out of 330 respondents 225 (68%) respondents are savvys. (165 agreed and 60 strongly agree). Table 8: Savvy’s Savvys NAND
SD
D
A
SA
Freq
9
33
63
165
60
%age
3%
10 %
19%
50%
18%
d. Price-Sensitive: I always look for discounts and offers to purchase online/offline & Discounts and rewards influence my purchase. We asked the question, if they are looking for offers and discounts while purchasing online and offline? (Table 9). Researchers take respondents who agree and strongly agree as a price-sensitive. Therefore out of 330 respondents 168 (51%) respondents are price sensitive (138 agreed and 30 strongly agree). Table 9: Price-Sensitive Price Sensitive NAND A
D
SA
Freq
39
123
138
30
%age
12%
37%
42%
9%
e. Experience-Seekers: I value the best experience not just price Table 10 shows the result of Experience Seekers. Out of 330 respondents, 216 (65.5%) respondents are experience seekers (153 agreed and 63 strongly agree). Table 10: Experience Seekers SD
Experience Seekers D NAND
A
SA
Freq
15
36
63
153
63
%age
5%
11%
19%
46%
19%
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Fig 1 shows the composite results and therefore we can conclude from Fig 1 below that majority of customers are savvys and experience seekers, followed by exploiters and traditionalists.
Fig. 1 5.3 Ranking Parameters a. Preferred Online Store In response to next question pertaining to the most visited internet site respondent’s visit to purchase online. In this question the researchers asked the respondents to mark the rank of these five internet sites (Q. 4) with 1 as most frequent and 5 as least frequent. To know the overall importance of its use, the researcher has calculated weighted score for each factor using the formula given below. Since highly preferred factor is ranked 1 and the least preferred as 5, the practice with the highest weighted score has got the highest rank, and so on. WSi = Where, WSi = the weighted score of ith web site Pj = rank (1 to n) N = number of respondents and Rj = Weight assigned to the respective ranks. From the above table we can see that, the most preferred site for online shopping is flipkart covered (93%) respondents preferred it, next comes the Amazon which covered 70% of the respondents, next online site preferred is Snapdeal covered 64% respondents and next online sites preferred are Myntra and Jabong ant the percentage of the respondents are 57% and 43% respectively. Volume 8, Number 1, January 2016
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Table 11: Ranking of Website for Shopping
b. Reasons to Choose Offline Shopping Researchers asked the respondents reason to choose offline shopping. Table 12 below shows the results. The first reason is to feel and touch the product, second is better return policy and the third reason of offline shopping is to take advantage of in store discount. Volume 8, Number 1, January 2016
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Table 12: Ranking of Reasons to Choose Offline Shopping
c. Reasons to Choose Online Shopping Researchers asked the respondents reason to choose online shopping. Above table shows the results. The first reason is its convenience, second is availability of online store 24*7 and the third reason of online is saving on petrol and valuable time. Volume 8, Number 1, January 2016
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Table 13: Ranking of Reasons to Choose Online Shopping
5.4 Hypotheses Testing The researchers have used Chi-Square test to determine the relationship between Gender and frequency of purchase. As we can conclude from table 14 below, the chi square value is 11.47 and p is 0.009, which is less than 0.05. Hence null hypothesis H01: There is no association of Gender and the frequency of purchase is rejected. Volume 8, Number 1, January 2016
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Gender
Male
Once a Week 18
Female Total
Table 14.1 How often do you buy Twice a Once Every Once a Week 2-3 Weeks Month 24 75 60
Total 177
30
45
24
54
153
48
69
99
114
330
Therefore gender and frequency of purchase has a negative and weak relationship with each other, as is evident from table 14.2 below. Table 14.2
Chi-Square Tests Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
a
11.472
3
0.009
Likelihood Ratio
11.891
3
0.008
Linear-by-Linear
2.707
1
0.100
Association N of Valid Cases
330
To test the next null hypothesis, H02: There is no relation between monthly income and time spends online, we again used Chi-Square test. As is evident from table 15, Chiaquare value is 3.028 with p value 0.963 which is greater than 0.05. Hence null hypothesis is accepted. Therefore it can be concluded that there is no relation between monthly income and time spent online. Table 15
Time Spent Per Day 0-1 hours 2-3 hours 2-3 hours per day per day per day Income < INR 25000 18 18 18 INR2500039 27 30 50000 INR 5000018 30 15 75000 > INR 75000 15 18 15 Total 90 93 78 Volume 8, Number 1, January 2016
> 3 hours per day 18
Total
21
117
15
78
15
63
69
330
72
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Table 15.2 Chi-Square Tests
Value
df
Pearson Chi-Square
3.028a
9
Likelihood Ratio
2.941
9
Linear-by-Linear Association
0
1
N of Valid Cases
330
Asymp. Sig. (2-sided) 0.963 0.967 0.987
To test the next hypothesis H03: There is no relation between age and ease of online shopping; ANOVA is used since one variable is metric and other is non-metric. Nonmetric variable has more than two categories.
Between Groups Within Groups Total
Table 16 ANOVA Sum of Mean Squares df Square 1.008 3 .336 8.553 306 .081 9.560 329
F 4.162
Sig. .008
As is evident from Table 16, F-value is 4.162 and p value is 0.008 which is less than 0.05, therefore null hypothesis is rejected. Therefore it can be concluded that there exists a relation between age and ease of online shopping. This may be the case because young people may find it easy to adjust with new technology as compared to old customers. To test the next hypothesis H04: There is no relation between gender and ease of online shopping, t-test was run. As we can conclude from table 17, t-value is 0.546 and Table 17 L e v en e 's T e s t for E qu ali ty o f V ar ian ce s
F
S ig .
t-tes t f o r E qu ali ty o f M ea ns
t
df
EA S E E q u al O N L I N E v ari a n ce s 1 . 0 4 6 . 30 9 .5 43 1 0 8 a s s u me d E q u al v ari a n ce s .5 46 1 0 7.4 not a s s u me d
9 5% Co nf id e n ce S ig. (2M e an Std . E rr o r In ter v al of ta ile d ) D if fe ren c e D iff ere n ce the D if fer en ce L ow e r U p p e r .5 88
.031
.0568
- .0 8 1
.1 4 3
.5 86
.031
.0564
- .0 8 1
.1 4 2
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p-value is 0.586 which is greater than 0.05, hence null hypothesis is accepted in this case. Therefore we can conclude that gender has no impact on ease of online shopping. To test the next null hypothesis H05: There is no relation between monthly income and overall satisfaction levels of online shopping, ANOVA is used. As is evident from table 18, F-value for ANOVA is 2.017 and p-value is 0.116, which is greater than 0.05, therefore null hypothesis will be accepted. It means there is a no relation monthly income and overall satisfaction. Table 18
Between Groups Within Groups Total
Sum of Squares .516
ANOVA Mean df Square 3 .172
9.044
326
9.560
329
F 2.017
Sig. .116
.085
Table-19 below shows the summary of hypotheses testing. Table-19 Hypotheses
Statement
H01
There is no association of Gender and the frequency of purchase. There is no relation between monthly income and time spend online There is no relation between age and ease of online shopping. There is no relation between gender and ease of online shopping There is no relation between monthly income and overall satisfaction levels of online shopping
H03 H04 H05 H06
Result Rejected Accepted Rejected Accepted Accepted
6. Conclusion As we can conclude from the analysis, majority of customers are between the age group 18-25 years who are interested in online as well as offline shopping. The phenomenon of showrooming and webrooming is also becoming more prevalent on this age group only. Majority of customers are doing online shopping because of experience and some of them are exploiters who are actually comparing the prices to get the best deal. As far as ranking is concerned, flipkart is the most preferred website when it comes to online shopping followed by amazon and snapdeal. Feel of the product, better return policy and in-store discounts are the three major reasons that customer purchase offline. Volume 8, Number 1, January 2016
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When it comes to online shopping convenience, 24 hour availability and savings on time are three most important reasons for customers. As is evident from table 19, gender plays an important role in frequency of purchase whereas monthly income has no relation over the time spent online. Also age is an important criterion when it comes to ease of online shopping. Retailers should focus on search engine optimization practices & web mobile site technologies that ensure that big search engines find the products on their sites that consumers are looking for. Retailers should create inventory & ordering systems that eliminate the line between the online & in-store experiences by offering in store pickup of item ordered online & home delivery of items ordered in the store. Reduce the frustration of shopping on mobile devices while creating cross device connections so customers don’t feel like strangers every time they use different device to approach stores online sites. In future, researchers may extend this study in understanding the policies and practices that brick and click companies need to employ in order to fight with the dynamic environment of online shopping.Can brick & mortar stores remain nimble enough to survive their customers ever changing life styles & shopping strategies? Studies in future should focus how to bridge the gap between online and offline shopping. How could the gap between online & offline shopping be reduced so that the phenomenon of showrooming and webrooming may not impact the retailer’s profit.
References ACNielsen (2007), Seek and You Shall Buy. Entertainment and Travel [online]. Available at:http://www.acnielsen.com/news/20051019.html [Accessed 3 November 2011] Bellman, S., Lohse, G., and Johnson, E. .Predictors of online buying behavior,. Communications of the ACM (42:12), 1999, pp. 32-38. Butler, M. (2013) Showrooming: Are retailers ready to embrace it? The Guardian report, 9 May, available at http://www.theguardian.com/media-network/media-network-blog/ 2013/may/09/ showrooming-retail-solution-e-commerce, accessed 10 February 2014. Chaffey, D. Ellis-Chadwick, F. Mayer, R. and Johnston, K. (2006), Internet Marketing, Strategy, Implementation and Practice, 3rd Pearson Education Limited, England. Delafrooz ,N. Pain, H.L. Haron, S.A. Sidin, S.N. And Khatibi, A. (2009), “Factors affectings’ attitude toward online shopping”, African Journal of Business Management, Vol. 3, No. 5, pp. 200-209. eMarketer. (2012) ‘Showrooming’ is a valid concern for retailers. eMarketer report, 19 March, available at http://www.emarketer.com/Article/Showrooming-Valid-ConcernRetailers/1008910, accessed 10 February 2016. Evans, M. J., Ahmad, J. and Gordon, R. F. (2006) Consumer Behaviour, John Wiley & Sons, Chichester, UK. Volume 8, Number 1, January 2016
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Frambach, R. T., Henk, C. R. and Trichy, V. K. (2007) ‘The impact of consumer internet experience on channel preference and usage intentions across the different stages of the buying process’, Journal of Interactive Marketing, Vol. 21, No. 2, pp. 26–41. Garson, D. (2007). Structural Equation Modeling. Retrieved December 10, 2014 from http://www2.chass.ncsu.edu/garson/pa765/structur.htm\Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research,62(1), 5-13. Houque, A. Sadeghzadeh, J, Khatibi, A (2006). “Identifying Potential Online Sales In Malaysia: A Study On Customer Relationships Online Shopping. Journal Application Business.Vol. 22.No. 4, pp. 119-130, 2006. Joines, L.J. Scherer, W.C. And Scheufele, A.D. (2003), “Exploring motivations for consumer Web use and their implications for e-commerce”, Journal of Consumer MarketingVol. 20, No. 2, pp. 90-108, 2003. Kaufman-Scarborough, C., & Lindquist, J. D. (2002). E-shopping in a multiple channel environment. Journal of consumer marketing, 19(4), 333-350. Kuan –Pin Chaing & Ruby Roy Dholakia. (2003). Factor Driving Consumer Intention to Shop Online: An Empirical Investigation:Journal of Consumer Psychology, 13 (1&2), 177-183. Li, N., & Zhang, P. (2002). Consumer online shopping attitudes and behavior: An assessment of research. AMCIS 2002 Proceedings, 74. Little, R. D., Folz, C., Manning, S. P., Swain, P. M., Zhao, S. C., Eustace, B., ... & Benchekroun, Y. (2002). A mutation in the LDL receptor–related protein 5 gene results in the autosomal dominant high–bone-mass trait. The American Journal of Human Genetics, 70(1), 11-19. Liu, P., Chary, S., Devaraj, R., Jing, Y., Darlington, C. L., Smith, P. F. & Zhang, H. (2008). Effects of aging on agmatine levels in memory associated brain tructures. Hippocampus, 18(9), 853-856. Malhotra MK, Grover V (1998) An assessment of survey research in POM: from constructs to theory. J of Oper Manage 16(4):407–425 Manouchehr Tabatabaei. (2009). Online Shopping Perception of Offline Shoppers. Issue in Information System, Vol.X, No.2. MasterCard (2008), Online Shopping in Asia/Pacific-Patterns, Trends and Future Growth [online], Available at: http://www.mastercard.com/us/companyy/en/insights/pdfs/2008/ Asia_Pacific_Online_Shop.pdf [Accessed 15 February 2016] Volume 8, Number 1, January 2016
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Philips, C. (2013) ‘Webrooming’ — New trend holds promise for in-store sales. Power Retail report, 23 April, available at http://www.powerretail.com.au/multi- channel/ accenture-seamlessretail- study/, accessed 10 February 2016. Quint, M., Rogers, D., & Ferguson, R. (2013). Showrooming and the rise of the mobile assisted shopper. Columbia Business School. Center on Global Brand Leadership. Rajesh Iyer & Jacqueline, Eastman (2014). The Elderly and Their Attitude Toward s the Internet : The Impact of Internet use, Purchases, and Comparison Shopping . Journal of Marketing Theory and Practice, Vol.14, No.1. Shergill, G. S., & Chen, Z. (2005). Web-Based Shopping: Consumers’attitudes Towards Online Shopping In New Zealand. Journal Of Electronic Commerce Research, 6(2), 78. Smith, K. (2013) Overcoming showrooming, EditD report, 25 June, available at http:// editd.com/ blog/2013/06/overcoming-showrooming/, accessed 10 February 2014. Soopramanien, D. G., & Robertson, A. (2007). Adoption and usage of online shopping: An empirical analysis of the characteristics of “buyers””browsers” and “non-internet shoppers”. Journal of Retailing and Consumer Services,14(1), 73-82. Tabachnick, B. & Fidell, L. (2001). Using multivariate statistics. (pp. 57 – 85 Data samples); (pp. 653 – 771 Structural Equation Modeling). Boston: Allyn and Bacon.
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