|
Gautam Deka Research Scholar School of Management Sir PadampatSinghania University Udaipur 313601, Rajasthan, India Email: dekagautam@gmail.com |
Dr Sumangla Rathore Assistant Professor School of Management Sir PadampatSinghania University Udaipur 313601, Rajasthan, India Contact No.- +91-9828260098 Email: sumanglarathore@gmail.com |
Dr Avinash Panwar Assistant Professor, School of Engineering, Sir PadampatSinghania University Udaipur 313601, Rajasthan, India Contact No.- +91-9414164608 Email: avinashpanwar@gmail.com |
Online shopping has grown exponentially in India over the last few years. Several eCommerce companies have been established to cater to the need of the online customers. To understand consumer behaviour towards online shopping, traditionally survey, experiment or interview based researchhas been mostly used. However with the introduction of Netnography, the prospect of studying consumer behaviour by observing and capturing the real sentiments of customers has increased several folds. In the present study an attempt has been made to characterize the customers with respect to their gender, age and preference of time for posting comments oneCommerce companies through Netnography.
The findings of the study indicate that the frequency of posting comments on eCommerce companies by male participants is significantly higher than the female participants and participation of the younger generation (between 20 to 30 years) is significantly higher than the older generation (45 years and above). The most active age group of participants involved in posting comments on eCommerce companies is between 26 to 30 years, for both male and female participants. The most preferred timing for posting comments was between 12 pm to 4 pm for the female participants, and between 4 pm to 8 pm for male participants. The study has implications for marketers in terms of providing a better and more accurate profile of customers who are actively engaged in spreading online word of mouth.
Keywords: Online comments, netnography, online buyers, customer profile
Social networking sites have generated to a network where increasing number of people are coming in contact with one another and share a common interest. Consumers tend to seek advice before they buy anything and social media has become a medium where they can not only seek advice but also gather information, opinions from people about the product. The emergence of web is one of the most influential developments in the business world that has exposed the change in the relationship between the companies and the consumers. The initiation of social media has given a platform to the marketers how to conduct their marketing strategies and reach out to the consumers. The core of any business is to have profitable customer relationship and these social networking sites are helping them to not only understand the need of the customers and maintain relationship but also build goodwill (Sharma and Gupta, 2015).Social networking sites are defined as the websites which link millions of users from all over the world with same interest, views and hobbies(Sin et al., 2012). Some of the examples of popular social media among the consumers are Blogs, video sharing sites such as Youtube, social networking sites such as Facebook, Twitter, LinkedIn and websites based on user-generated content and reviews such as Mouthshut etc. Social media has also influenced consumer behaviour from information acquisition to post-purchase behaviour such as dissatisfaction statements or behaviours about a product or a company (Mangold and Faulda, 2009).
Social media is a source for collection of the viewpoints of the people which may be favourable or unfavourable. Therefore, one aspect of the social networking sites is that it is helping the people to easily get information about the product and the company, which helps the consumers to form their own opinion about the product and the company before they actually buy. At the same time there is another aspect of social networking sites that is especially damaging the marketing campaigns, which is the negative posts (comments) made by the people. Unhappy customers and industry competitors are able to post disparaging and offensive pictures, posts or videos and there is not much a marketer can do to prevent such activities. Further, marketers cannot effort to ignore these negative or non-constructive feedbacks. Therefore it is important to know about the people involved in posting both negative and positive comments about the product and the company.
Although studies of online shopping attitude are widespread in the literature, studies of gender differences in online shopping attitude are scarce and reported findings are inconsistent (Dittmar et al., 2004; Cry and Bonanni, 2005). An extensive review of online shopping literature indicate that more men than women are buying online in some studies, and that no significant gender differences exist in online shopping behaviour between genders in other studies (Chang et al., 2005; Zhou et al. 2007). Thus gender differences in online shopping attitude deserve more attention and better understanding, particularly under Indian conditions.
Similarly, there are contradictions in the findings of research on age and online purchasing with some indicating that younger generation tend to shop more online (Dholakia and Uusitalo, 2002; Joines et al., 2003) while others have found that older consumers were more likely to shop online (Donthu and Garcia, 1999; Korgaonkar and Wolin, 1999). Sorce et al. (2005) have concluded that demographic factors versus shopping motivations and attitudes in predicting online shopping remain as open question.
Hoelzl (2015) reported that peak online spending coincides with prime TV time (8 pm to 11 pm) compared to rest of the day. They came to this conclusion after examining 1.2 million online purchases from eleven sectors (including fashion, electronica and travel). However, there exists no report on the preference of time for posting comments on online shopping fromeCommerce companies.
Studies on online consumer behaviour and opinion have relied largely on a range of qualitative and quantitative market research techniques conducted such as focus groups, surveys and interviews. Such methods were the only means for gathering this kind of data in the past, whereas today internet has provided another approach to obtain the desired information. With the rapid emergence of blogs, forums, social networks and plethora of information posted on the internet, huge amount of information (data) is now available, which needs to be collected and analyzed. This has opened a new approach to understanding consumer behavior, by collection and analysis of data from the internet. This approach is called “netnography” (Kozinets, 2002). Netnography is the process of accessing and analyzing sentiments and opinions expressed by consumers chatting in blogs, forums and online discussion groups. This method is much quicker, cheaper and results are arguably more authentic expressions of opinion and need(Deka, Rathore and Panwar, 2015).With the introduction of netnography the scope for such studies has increased several folds. Therefore, keeping the above facts in view, a netnographic study was undertaken to characterize the participants involved in posting comments (both negative and positive) online (on social media site) on shopping from few important eCommerce companies in India, with the following objectives:
Considering the various definitions and descriptions of research methods, the study falls in the category of non-verbal, non-interactive observation method, where the observer does not interact to a great degree with those he or she is observing. The researcher’s role is mainly confined to observe and record, and not to actively participate. The study aimed at understanding the characteristics of online customers who are involved in posting comments online regarding their online buying experiences at the leading e-commerce companies in India. For this purpose, four eCommerce companies namely, Flipkart, Snapdeal, Amazon India and eBay India were selected for the study on the basis of their customer base, market share and popularity.
Kozinets(2002) has given following methodological steps for netnography:
The social media site Mouthshut.com was identified as the online community for data collection. MouthShut.com was launched in year 2000 in Mumbai and was India’s first user-generated content and review based website. It a highly popular and trusted consumer feedback network and is one among the leaders in the user-generated content space in India. Mouthshut.com hosts millions of customer reviews written by ordinary consumers on products and services and allows them to not only read reviews but also post their opinions and ratings. (www.mouthshut.com, 2016).
For this purpose, all the comments that were posted by customers of the selected e-commerce companies for a period of one year (July 2014 to June 2015) were collected.
Finally data was cleansed and prepared for further analysis by tabulating the comments using a spreadsheet application. The participants were classified on the basis of sex (male and female) and also on the basis of their age. The participants represented the age groups from 16 to 70 years. For convenience 11 age groups of the participants were formed with five years of interval as follows: 16-20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, and 66-70.The comments were also classified on the basis of the time of the day at which it was posted. For this, the six time groups with the interval of four hours were formed as follows: 12 am - 4 am, 4 am - 8am, 8 am – 12 pm, 12 pm – 4 pm, 4 pm – 8 pm, and 8 pm to 12am.
The collected data were statistically analysed with the help of Chi-square test and ANOVA - F-test.
1. Characterization of participants on the basis of gender and age
The total number of comments posted on four eCommerce companies during the selected period of data collection are as follows:
Flipkart – 793, Snapdeal - 416, Amazon – 380 and eBay – 226 comments. The highest number of comments were posted about Flipkart followed by Snapdeal, Amazon and eBay.
Distribution of male, female and all (male + female) participants of different age groups involved in posting comments on four eCommerce companies, namely Flipkart, Snapdeal, Amazon and eBay are presented in Table 1, 2, 3, and 4 respectively.
Table 1.Distribution of male, female and all participants (male + female) of different age groups involved in posting comments on Flipkart.
| Age Group (Years) | Male (M) | Female (F) | Total (M + F) | |||
| No. | % | No. | % | No. | % | |
| 15-20 | 40 | 6.46 | 14 | 8.05 | 54 | 6.81 |
| 21-25 | 160 | 25.85 | 45 | 25.90 | 205 | 25.85 |
| 26-30 | 169 | 27.30 | 59 | 33.90 | 228 | 28.75 |
| 31-35 | 112 | 18.09 | 37 | 21.25 | 149 | 18.79 |
| 36-40 | 65 | 10.50 | 8 | 4.60 | 73 | 9.21 |
| 41-45 | 40 | 6.46 | 5 | 2.85 | 45 | 5.67 |
| 46-50 | 18 | 2.91 | 2 | 1.15 | 20 | 2.52 |
| 51-55 | 8 | 1.29 | 4 | 2.30 | 12 | 1.52 |
| 56-60 | 2 | 0.32 | -- | -- | 2 | 0.25 |
| 61-65 | 4 | 0.65 | -- | -- | 4 | 0.50 |
| 66-70 | 1 | 0.16 | -- | -- | 1 | 0.13 |
| Total | 619 | 78.06 | 174 | 21.94 | 793 | 100.00 |
| Mean | 56.21 | 21.75 | 72.09 | |||
| SD | 63.01 | 22.02 | 83.59 | |||
| Chi-Square | 705.603 | 156.115 | 969.289 | |||
| Df | 10 | 7 | 10 | |||
| Sig. | 0.000 | 0.000 | 0.000 | |||
Table 2.Distribution of male, female and all participants (male + female) of different age groups involved in posting comments on Snapdeal.
| Age Group (Years) | Male (M) | Female (F) | Total (M + F) | |||
| No. | % | No. | % | No. | % | |
| 15-20 | 18 | 5.31 | 1 | 1.29 | 19 | 4.57 |
| 21-25 | 78 | 23.00 | 19 | 24.67 | 97 | 23.32 |
| 26-30 | 89 | 26.25 | 27 | 35.06 | 116 | 27.88 |
| 31-35 | 72 | 21.24 | 16 | 20.78 | 88 | 21.15 |
| 36-40 | 37 | 10.91 | 6 | 7.79 | 43 | 10.37 |
| 41-45 | 29 | 8.55 | 7 | 9.09 | 36 | 8.65 |
| 46-50 | 9 | 2.65 | -- | -- | 9 | 2.16 |
| 51-55 | 1 | 0.29 | -- | -- | 1 | 0.24 |
| 56-60 | 3 | 0.88 | 1 | 1.29 | 4 | 0.96 |
| 61-65 | 1 | 0.29 | -- | -- | 1 | 0.24 |
| 66-70 | 2 | 0.59 | -- | -- | 2 | 0.48 |
| Total | 339 | 81.49 | 77 | 18.51 | 416 | 100.00 |
| Mean | 30.81 | 11.00 | 37.81 | |||
| SD | 33.72 | 9.88 | 42.99 | |||
| Chi-Square | 368.991 | 53.273 | 488.967 | |||
| Df | 10 | 6 | 10 | |||
| Sig. | 0.000 | 0.000 | 0.000 | |||
Table 3.Distribution of male, female and all participants (male + female) of different age groups involved in posting comments on Amazon.
| Age group (Years) | Male (M) | Female (F) | Total (M + F) | |||
| No. | % | No. | % | No. | % | |
| 15-20 | 18 | 6.08 | 5 | 5.95 | 23 | 6.05 |
| 21-25 | 70 | 23.65 | 23 | 27.38 | 93 | 24.47 |
| 26-30 | 72 | 24.32 | 26 | 30.95 | 98 | 25.79 |
| 31-35 | 60 | 20.27 | 19 | 2.61 | 79 | 20.79 |
| 36-40 | 37 | 12.50 | 6 | 7.14 | 43 | 11.32 |
| 41-45 | 19 | 6.41 | 5 | 5.95 | 24 | 6.32 |
| 46-50 | 10 | 3.38 | -- | -- | 10 | 2.63 |
| 51-55 | 3 | 1.01 | -- | -- | 3 | 0.79 |
| 56-60 | 1 | 0.34 | -- | -- | 1 | 0.26 |
| 61-65 | 4 | 1.35 | -- | -- | 4 | 1.05 |
| 66-70 | 2 | 0.68 | -- | -- | 2 | 0.53 |
| Total | 296 | 77.89 | 84 | 22.11 | 380 | 100.00 |
| Mean | 26.91 | 14 | 34.54 | |||
| SD | 28.11 | 9.75 | 38.01 | |||
| Chi-Square | 293.689 | 34.000 | 418.311 | |||
| Df | 10 | 5 | 10 | |||
| Sig. | 0.000 | 0.000 | 0.000 | |||
Table 4.Distribution of male, female and all participants (male + female) of different age groups involved in posting comments on eBay.
| Age Group (Years) | Male (M) | Female (F) | Total (M + F) | |||
| No. | % | No. | % | No. | % | |
| 15-20 | 4 | 2.19 | 3 | 6.98 | 7 | 3.10 |
| 21-25 | 36 | 19.67 | 7 | 16.28 | 43 | 19.03 |
| 26-30 | 44 | 24.04 | 13 | 30.23 | 57 | 25.22 |
| 31-35 | 41 | 22.40 | 10 | 23.26 | 51 | 22.57 |
| 36-40 | 27 | 14.75 | 5 | 11.63 | 32 | 14.16 |
| 41-45 | 16 | 8.74 | 4 | 9.30 | 20 | 8.85 |
| 46-50 | 5 | 2.73 | 1 | 2.33 | 6 | 2.65 |
| 51-55 | 3 | 1.64 | -- | -- | 3 | 1.33 |
| 56-60 | 3 | 1.64 | -- | -- | 3 | 1.33 |
| 61-65 | 1 | 0.55 | -- | -- | 1 | 0.44 |
| 66-70 | 3 | 1.64 | -- | -- | 3 | 1.33 |
| Total | 183 | 80.97 | 43 | 19.03 | 226 | 100.00 |
| Mean | 16.63 | 6.14 | 20.54 | |||
| SD | 17.09 | 4.18 | 21.43 | |||
| Chi-Square | 175.672 | 9.326 | 223.735 | |||
| Df | 10 | 5 | 10 | |||
| Sig. | 0.000 | 0.000 | 0.000 | |||
It is evident from the tables that most active group of participants belong to the age group from 21 to 35 for both males and females in the case of all the four eCommerce companies. However, highest percentage of participants (both male, female) belong to the age group of 26 – 30 years for all the four eCommerce companies. In the case of males, the active age of participants has been found to be up to 40 years. Thereafter there is a sharp decline in the male percentage of participants and becomes almost negligible or nil after the age 50. In the case of female, the active age of the participants has been found to be up to 35 and thereafter it declines drastically becomes negligible or nil after the age 45 years. This trend was found to be similar in all the four eCommerce companies.
It is evident from the Tables 1, 2,3,and 4 that the percentage of male participants (78.06%, 81.49%, 77.89%, and 80.97% for Flipkart, Snapdeal, Amazon and eBay, respectively) is significantly higher than the female participants (21.94%, 18.51%, 22.11%, and 19.03% for Flipkart, Snapdeal, Amazon and eBay, respectively) in all the four eCommerce companies. It is also evident that there was a significant relation between both age and gender of the participants and the involvement in posting comments on eCommerce companies as revealed through Chi-square test (Table 1, 2, 3, and 4).
Table 5 and Figure 1 show the distribution of male, female and all (male + female) participants involved in posting comments on all the four eCommerce companies.
Table 5. Distribution of male, female and all (male + female) participants involved in posting comments on four e-Commerce companies.
| eCommerce companies | Male (M) | Female (F) | Total (M + F) | |||
| No. | % | No. | % | No. | % | |
| Flipkart | 619 | 78.06 | 174 | 21.94 | 793 | 43.69 |
| Snapdeal | 339 | 81.49 | 77 | 18.51 | 416 | 22.92 |
| Amazon | 296 | 77.89 | 84 | 22.11 | 380 | 20.93 |
| eBay | 183 | 80.97 | 43 | 19.03 | 226 | 12.45 |
| Total | 1437 | 79.17 | 378 | 20.83 | 1815 | 100.00 |
| Mean | 359.25 | 79.55 | 94.50 | 20.39 | 453.75 | 24.99 |
| SD | 185.24 | 55.94 | 240.71 | |||
| SE | 92.62143 | 27.97171 | 67.15107 | |||
| Chi-Square | 286.555 | 99.354 | 383.085 | |||
| Df | 3 | 3 | 3 | |||
| Sig. | 0.000 | 0.000 | 0.000 | |||
Fig. 1. Distribution of the male, female and all (male + female) participants involved in posting comments on four eCommerce companies. FK=Flipkart, SD=Snapdeal, AM=Amazon, EB=eBay.
The percentage distribution of the male participants ranged from 77.89% (Amazon) to 81.49% (Snapdeal), and for females from 18.51% (Snapdeal) to 22.11% (Amazon). The mean value being 79.55% for the males and 20.83% for the females. The percentage distribution within the males andwithin females participants involved in posting comments has not been found to be significant. It was evident that there is a significant relation between the gender and in the participation for posting comments on the four eCommerce companies as revealed through Chi-square test (Table 5).
In the present study, participation of males was found to be significantly higher than females in posting comments on eCommerce companies. Earlier it has also been reported that males are more likely to shop online than females (Tweney, 1999; Leonardo, 2003). Moreover attitude towards Internet is also shown to be more positive for males than females (Durndell and Haag, 2002; Liaw, 2002). Jayawardhena et al., (2007) attested for a significant relationship between gender and online purchasing intension.
Another study revealed that if number of Internet users is equally divided among the gender, more men than women engage in online shopping and make online purchase (Rodgers and Harris, 2003). In the Western culture, studies relating to constructs like perceived risk of online buying (Garbarino and Strahilevitze, 2004) and technology adoption (Sanchez-Franco, 2006) have been performed. But there is a dearth of literature regarding investigative studies on gender differences in online buying attitude in emerging economies like India. Although, Ahmed and Khan (2015) suggested that there is positive inclination of Indian consumer towards eCommerce, other reports are inconsistent (Cyr and Bonanni, 2005; Fatahuddin and Khan, 2006).
In general, men demonstrated higher behavioural intention to shop online than women. Attitude theory presents the behavioural component of attitude as a function of the cognition and affect components. Since females show lower cognitive and affective attitudes than males, their behavioural intention to shop online is lower (Sanchez-Franco, 2006, Hasan 2010). Since cognitive attitude pertains to understanding pros and cons of an object (Zhou et al. 2007), it has been suggested that females are still unconvinced or sceptical about the benefits of online shopping. Similarly, it may suggest that the females are still concerned and apprehensive about the risks and threats associated with online shopping (Garbarino and Strahilevitze, 2004) Accordingly, greater understanding the value of online shopping (cognition) or improvement in social and emotional experiences in online shopping (affection) are likely to boost online shopping behaviour among female consumers (Hasan 2010). Although gender has been largely studied in relation to online shopping behaviour, studies related to the feedbacks or comments posted by the online shoppers are non-existent.
The second finding of the study indicates that age of the participants is an important factor in determining the involvement in posting comments about online buying experiences. Participation of younger people (both male and female) is significantly higher than the older people. The Internet has typically been described as a youth’s medium. Young men and women have also been regarded as the typical profile of the early adopters of online shopping. Cassis (2007) reported that that college going students spend hours using the Internet every day and more keen in buying online. However, as the Internet has become more ubiquitous, the profile of the online shopper has come to resemble that of the general population (Stores, 2001). In the USA consumers aged 50 and above comprise of 16% of new online shoppers and the number is expected to increase in the following years (Tedeschi, 2002). In the present study participation of older person above the age of 45 years, in posting comments on eCommerce companies, was found to be negligible. Thus the finding in the USA is not yet applicable in India. This means that younger people are still the dominating age group involved in online activities.
Joines et al., (2003) found that age did not impact search behaviour but did impact purchase behaviour, and younger consumers purchased more than older consumers. Sorce et al. (2005) reported that while older online shoppers search for significantly fewer products than their younger counterparts, they actually purchase as much as younger consumers. They further reported that attitudinal factors explained more variance in online searching behaviour. Age explains more variance in purchasing behaviour if the consumer had first searched for the product online (Sorce et al., 2005). Donthu and Garcia (1999) also found that those who had ever purchased from the Internet were older and had higher income.
2. Characterization of participants on the basis of the time of posting comments
Table 6 and Figure 2 show the distribution of male participants involved in posting comments at different time of the day on four eCommerce companies.
Table 6. Distribution of male participants involved in posting comments at Different Time of the Day on four eCommerce Companies.
| eCommerce Companies | Male participants | |||||||||||
| Timing of posting the comments | ||||||||||||
| 12am-4am | 4am-8am | 8am-12pm | 12pm-4pm | 4pm-8pm | 8pm-12am | |||||||
| No. | % | No. | % | No. | % | No. | % | No. | % | No. | % | |
| Flipkart | 56 | 9.04 | 1 | 0.16 | 97 | 15.67 | 150 | 24.23 | 161 | 26.00 | 153 | 24.71 |
| Snapdeal | 14 | 4.12 | 5 | 1.47 | 50 | 14.74 | 106 | 31.26 | 109 | 32.15 | 79 | 23.30 |
| Amazon | 19 | 6.41 | 7 | 2.36 | 50 | 16.89 | 96 | 32.43 | 63 | 21.28 | 61 | 20.60 |
| eBay | 9 | 4.91 | 7 | 3.82 | 27 | 14.75 | 54 | 29.50 | 55 | 30.05 | 31 | 16.93 |
| Total | 98 | 6.72 | 20 | 1.37 | 224 | 15.36 | 404 | 27.70 | 388 | 26.61 | 324 | 22.22 |
| Mean | 24.50 | 5.00 | 56.00 | 101.50 | 97.00 | 81.00 | ||||||
| SD | 21.39 | 2.82 | 29.40 | 39.40 | 48.85 | 51.92 | ||||||
| SE | 10.69 | 1.41 | 14.70 | 19.70 | 24.42 | 25.96 | ||||||
| F | 4.718 | |||||||||||
| Sig. | 0.066 (>0.05, significant) | |||||||||||
Fig.2. Distribution of male participants involved in posting comments on four eCommerce companies at different time of the day.
Period of the time of the day
It is evident that the highest percentage of comments posted by male participants on three eCommerce companies (Flipkart: 26%, Snapdeal: 32.15%, and eBay: 30.05%) was between 4 pm to 8 pm. However, in the case of Amazon highest percentage (32.43%) being between 12 pm to 4 pm and the second highest (21.28%) being between 4 pm and 8 pm.The highest overall percentage (27.70%) was between 12 pm – 4 pm. On the other hand lowest percentage (1.37%) was between 4 am to 8 am.It is evident from the F-test that the particular period time of the day has significant bearing on the male participants in their participation for posting comments on eCommerce companies (Table 6). Table 7and Figure 3show the distribution pattern of female participants involved in posting comments at different time of the day on four eCommerce companies.
Table 7. Distribution of female participants involved in posting comments at Different Time of the Day on four eCommerce Companies.
| eCommerce Companies | Female participants | |||||||||||
| Timing of posting the comments | ||||||||||||
| 12pm-4am | 4am-8am | 8am-12pm | 12pm-4pm | 4pm-8pm | 8pm-12am | |||||||
| No. | % | No. | % | No. | % | No. | % | No. | % | No. | % | |
| Flipkart | 16 | 9.19 | -- | -- | 30 | 17.24 | 53 | 30.45 | 49 | 28.16 | 35 | 20.11 |
| Snapdeal | 8 | 10.78 | -- | -- | 12 | 15.58 | 23 | 29.87 | 11 | 14.28 | 23 | 29.87 |
| Amazon | 7 | 8.33 | 2 | 2.38 | 12 | 14.28 | 20 | 23.80 | 18 | 21.42 | 25 | 29.76 |
| eBay | 2 | 4.65 | 3 | 6.97 | 8 | 18.60 | 12 | 27.90 | 12 | 27.90 | 6 | 13.95 |
| Total | 33 | 8.52 | 5 | 1.29 | 62 | 16.02 | 108 | 27.90 | 90 | 23.25 | 89 | 22.99 |
| Mean | 8.25 | 2.50 | 15.50 | 27.00 | 22.50 | 22.25 | ||||||
| SD | 5.79 | 0.70 | 9.84 | 17.94 | 17.93 | 12.03 | ||||||
| SE | 2.897 | 0.750 | 4.924 | 8.972 | 8.967 | 6.019 | ||||||
| F | 2.501 | |||||||||||
| Sig. | 0.69 (>0.05, significant) | |||||||||||
Fig. 3. Distribution of female participants involved in posting comments on four eCommerce companies at different time of the day.
Period of the time of the day
It is evident that the highest percentage (30.45%) of comments posted by female participants on Flipkart was between 12 pm to 4 pm, whereas in the case of Snapdeal the highest percentage was equal (29.87%) between 12 pm - 4 pm, and 8 pm -12 am. In the case of Amazon the highest percentage (29.76%) was between 8 pm and 12 am, whereas in the case of eBay the highest percentage was equal (27.90%)between 12 pm – 4 pm, and 4 pm – 8 pm. The highest overall percentage was between 12pm – 4 pm (28%).On the other hand lowest percentage (1.29%) was between 4 am to 8 am. It is evident from the F-test that the particular period time of the day has significant bearing on the female participants in their participation for posting comments on eCommerce companies (Table 7).
Table 8and Figure 4show the distribution pattern of all participants (male + female) involved in posting comments at different time of the day on four eCommerce companies.
Table 8. Distribution of all participants (male + female) involved in posting comments at Different Time of the Day on four eCommerce Companies.
| eCommerce Companies | All (Male+ Female) participants | |||||||||||
| Timing of posting the comments | ||||||||||||
| 12am-4am | 4am-8am | 8am-12pm | 12pm-4pm | 4pm-8pm | 8pm-12am | |||||||
| No. | % | No. | % | No. | % | No. | % | No. | % | No. | % | |
| Flipkart | 72 | 9.07 | 1 | 0.12 | 128 | 16.14 | 203 | 25.59 | 190 | 23.95 | 187 | 23.58 |
| Snapdeal | 25 | 6.00 | 5 | 1.20 | 62 | 14.90 | 129 | 31.00 | 96 | 23.07 | 102 | 24.51 |
| Amazon | 26 | 6.84 | 9 | 2.36 | 62 | 16.31 | 117 | 30.78 | 81 | 21.31 | 86 | 22.63 |
| eBay | 11 | 0.44 | 10 | 4.42 | 35 | 15.58 | 66 | 29.20 | 67 | 29.64 | 38 | 16.81 |
| Total | 134 | 7.41 | 25 | 1.38 | 287 | 15.87 | 515 | 28.48 | 434 | 24.00 | 413 | 22.84 |
| Mean | 33.50 | 6.25 | 71.75 | 128.75 | 108.50 | 103.25 | ||||||
| SD | 26.56 | 4.11 | 39.60 | 56.53 | 55.60 | 62.10 | ||||||
| SE | 13.282 | 2.056 | 19.80 | 28.267 | 27.804 | 31.052 | ||||||
| F | 4.349 | |||||||||||
| Sig. | 0.099 (>0.05, significant) | |||||||||||
Fig.4. Distribution of all (M + F) participants involved in posting comments on four eCommerce companies at different time of the day.
Period of the time of the day
It is evident that the highest percentage of comments posted on all the eCommerce companies was between 12 pm to 4 pm (25.59%, 31%, 30.78%, 29.20% for Flipkart, Snapdeal, Amazon and eBay, respectively), and the lowest percentage (0.12%, 1.20%, 2.36%, and 4.42% for Flipkart, Snapdeal, Amazon and eBay, respectively) was between 4 am – 8 am in the case of all the four eCommerce companies. Although this is a small fraction, but indicate that small fraction of the consumers remain active at these late and odd hours also. It is evident from the F-test that the particular period time of the day has significant bearing on the all participants in their participation for posting comments on eCommerce companies (Table 8).
So far we have not came across any study of similar nature. However, studies on identification of the prime time for online shopping have been carried out (Patel, 2005; Hoelzl, 2015). Patel (2005) observed that visitors and followers of online social media prefer using social media sites during specific hours. Therefore, if someone starts sharing their contents when users are on the social sites, than they will not only gain more shares, but also will also notice an increase in traffic. Unfortunately there is no perfect answer as to when be the best time to post content to social media, as different customers may find different days and times suitable for them. Kolowich (2016), however, observed that there exist ample data on optimal times to post on different social media such as Facebook, Twitter, Linkedin, Pinterest and Instagram. The pulled data available from the sources like QuickSprout, SurePayroo, The Huffington Post, Buffer, TrackMavan, Fast Company and KISSmetrics revealed that in Facebook the best time to post is between 12.00 pm to 1.00 pm on Saturdays and Sundays, 3.00 pm to 4.00 pm on Wednesdays and 1.00 pm to 4.00 pm on Thursdays and Fridays. Similarly, in Twitter the best time to post is between 12.00 pm to 3.00 pm on Mondays through Fridays and from 5.00 pm to 6.00 pm on Wednesdays (Kolowich, 2016). He further observed that timing often depends on the platform, how the target audience interacts with the platform, regions being targeted, content of the post and the goals.
In the early days of the Internet, the overwhelming majority of online shoppers logged on from work place, as they had access to high-speed connections at work, but not at home. Over time things have changed and more homes are now connected with broadband. However a survey conducted in USA by CyberSource (2006)indicated that most eCommerce shopping happens during work hours. CyberSource(2006) found that the peak shopping hours was between 1.00 pm to 4.00 pm. On the other hand, online transactions hit lowest between 11.00 pm and 4.00 am. The survey also found that highest volume online shopping days were Mondays and Tuesdays, while Saturdays and Sundays had the lowest volume. According to another survey conducted in USA by NetElixir (Sullivan, 2011) online shopping purchases peak during the midday hours between 2.00 pm and 7.00 pm.
In the present study the peak hours for posting comments have been found to be between 12 pm and 8 pm. This also indicates towards a possibility that the consumers still prefer to utilize Internet and social media from their work place rather than from home.
In recent years, online shopping has grown exponentially in India. According to a recent eCommerce survey, around 52% Indian shoppers prefer online store purchase and 89% respondents said they bought more online in 2015 in comparison to 2014.Behaviour towards a phenomenon is a result of combination of factors, however, attitude towards online shopping is considered to be a significant predictor of online shopping behaviour (Ahn et al., 2007; Lin, 2007).
The findings of the study shall be useful for the marketers to draw strategies for managing online word of mouth generated by their customers. Key findings of the present study and their implications for online businesses are as follows:
Ahn, T.,Ryu, S.& Han, I. (2007).The Impact of Web Quality and Playfulness on User Acceptance of Online Retailing.Information Management, 44, 263-275.
Ahmed, A.& Khan, M.N. (2015).Mapping Online Buyer Behaviour: A Critical Review of Empirical Studies.Pacific Business Review Inernational, 8, 37-48.
Cassis, C. (2007).College Students Help Fuel Ever Growing Internet Sales.www.http://media.www.dailyfreepress.com/media/storage/paper87/news/2007/01/22/News
CyberSource. (2006). http://www.cybersource.com
Cyr, D.&Bonanni, C. (2005).Gender and Website Design in E-Business.International Journal of Electronic Business, 3, 565-582.
Deka, G., Rathore, S., &Panwar, A. (2016).Developing a Research Framework to Assess Online Consumer Behaviour Using Netnography in India: A Review of Related Research. In S. Rathore, & A. Panwar (Eds.) Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace (pp. 154-167). Hershey, PA: Business Science Reference. doi:10.4018/978-1-4666-9449-1.ch009
Dholakia, R. R.&Uusltalo, O. (2002).Switching to Electronic Stores: Consumer Characteristics and the Perception of Shopping Benefits.International Journal of Retail Distribution Management, 30, 459-469.
Donthu, N.&Gracia, A. (1999).The Internet Shopper.Journal of Advertising Research, 39, 52-59.
Durndell, A.& Haag, Z. (2002). Computer Self Efficiency, Computer Anxiety, Attitude Towardsthe Internet and Reported Experience with the Internet, by Gender, in an East European Sample. Computers Human Behaviour, 18, 521-535.
Fatahuddin, S.M.& Khan, M.N. (2016). Online Shopping Attitudes of Management Students in India: An Empirical Exploration of Genders.Pacific Business Review International, 8, 40-45.
Garbarino, E.&Strahilevitze, M. (2004).Gender Differences in the Perceived Risk of Buying Online and the Effects of Receiving a Site Recommendation.Journal of Business Research, 57, 768-775.
Hasan, B. (2010). Exploring Gender Differences in Online Shoping Attitude.Computers Human Behaviour. 26, 597-601.
Hoelzl C. (2015). IntelliAd Customer Journey Analysis- When Does Online Shopping Peak? http://www.intelliad.com/blog/intelliad-customer-journey-analysis-when-does-online-shopping-peak/
Jayawardhena, C, Wright, L.T.& Dennis, C. (2007). Consumer Online: Intensions, Orientations and Segmentation.International Journal of Retail Distribution Management, 35, 515-526.
Joines, J., Scherer, C.&Scheufele, D. (2003).Exploring Motivations for Consumer Web Use and their Implications for E-Commerce.Journal of Consumer Marketing, 20, 90-108.
Kolowich, L. (2016). The Best Times to Post on Facebook, Twitter, Linkedinand other Social Media Sites (Infographic). http://www.bloghub.com/marketing/est-times-post-pin-tweet-social-media-infographic#sm.000gimkns1cg5ep9xv02finpmauuv6
Korgaonkar, P.K.&Wolin, L.D. (1999).A Multivariate Analysis of Web Usage.Journal of Advertising Research, 39, 53-68.
Kozinets, R.V. (2002). The Field Behind the Screen: Using Netnographyfor Marketing Research in Online Communities. Journal of Marketing Research. 39, 61-72
Leonard, L.N.K. (2007). Acquiring Goods and Services via the Internet: Consumer Shopping Perceptions. http://www.firstmonday.org/issues/issue8-11/Leonard.
Liaw, S.S. (2002). An Internet Survey for Perceptions of Computers and World Wide Web: Relationship, Prediction and Differences. Computer Human Behaviour, 18, 17-35.
Lin,H.F.(2007). The Role of Online and Offline Features in Sustaining Virtual Communities: an Empirical Study.Internet Research, 17, 119-138.
Mangold, G.&Faulds, D. (2009).Social Media: The New Hybrid Element of the Promotion Mix. Usiness Horizons, 52, 357-365.
Patel, N. (2015). What are the best times to post on social media?http://www.quicksprout.com/2015/01/02/what-are-the-est-time-to-post-on-social-media/
Rodgers, S.& Harris, M.A. (2003).Gender and Ecommerce: An Exploratory Study. Journal of Advertising Research, 43, 322-329.
Sanchez-Franco, M.J. (2006). Exploring the Influence of Gender on the Web Usage Via Partial Least Squares.Behaviour Information Technology, 25, 19-36.
Sharma, L.& Gupta, M. (2015).Impact of Social Networking Sites on Consumer’ Purchase Intentions.KAIM Journal of Management Research, 7, 68-73.
Sin, S., Nor, K.M.& Al-Agaga, A.M. (2012). Factors Affecting Malaysian Young Consumers’ Online Purchase Intention in Social Media Websites.Social and Behavioural Science, 40, 326-333.
Sullivan, L. (2011). Searches: What Are the Peak Times for Clicks and Conversations? Search Marketing Daily. 29 March. pp. 1-5.
Tedeschi, B. (2002). Though There are Fewer New Internet Users, Experienced Ones, Particularly the Middle Aged, are Increasingly Shoping Online.New York Times. 4th March. C.7.
Tweney, D. (1999). Men and Women: Online, We Should be More than Markets. InfoWorld, 21, 76.
Sorce, P., Perotti, V.&Widrick, S. (2005). Attitude and Age Differences in Online Buying.International Journal of Retail Distribution Management, 33, 122-132.
Stores, K. (2001).Consumer Trends in Online Shopping.International Journal of Retail Distribution Management, 83, 5-9.
Zhou, L., Dai, L.& Zhang, D. (2007). Online Shopping Acceptance Model- A Critical Survey of Consumer Factors in Online Shopping. Journal of Electronic Commerce Research, 8, 41-62.
www.mouthshut.com
