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(Editor in Chief)
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\,Characterization of the Customers Involved in Posting Online Comments on Ecommerce Companies through Netnography

GautamDeka,

Research Scholar,

School of Management,

Sir PadampatSinghania University

Udaipur 313601, Rajasthan, India

dekagautam@gmail.com

 

Dr SumanglaRathore

Assistant Professor,

School of Management,

Sir PadampatSinghania University

Udaipur 313601, Rajasthan, India

sumanglarathore@gmail.com, 9828260098

 

 

Dr AvinashPanwar

Assistant Professor,

School of Engineering,

Sir PadampatSinghania University

Udaipur 313601, Rajasthan, India

avinashpanwar@gmail.com, 9414164608

 

 


 

Abstract

 

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

 

 

  1. Introduction

 

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:

  • To determine whether there are differences in posting online comments on eCommerce companies based on gender.
  • To determine whether there are differences in posting online comments on eCommerce companies based on age.
  • To determine whether there are specific preferences in the timing of posting online comments on eCommerce companies by different age groups of different gender.


 

  1. Methodology

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:

  1. Identification and Selection of Online Communities:

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).

  1. Community Observation and Data Collection:

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.

  1. Data Analysis and Aggregation of Consumer Insights- Iterative Interpretation of Findings.

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. Results and Discussion

3.1 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.

 

3.2 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.

  1. Conclusion

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:

  • The frequency of posting comments on eCommerce companies by male customers is significantly higher than the female customers. This finding is also consistent with the fact that there are more male online buyers than female buyers. Considering a relatively lower percentage of active female customers who are engaged in online conversation about online buying, e-commerce companies should develop specific schemes or tactics for encouraging them to participate more. At the same time, it is important for the companies to ensure that their male customers are provided with best of services and online buying experience so that they can spread a positive word of mouth in the online space. Companies can also develop gender specific tools that can enhance people's intentions to publish online.
  • The frequency of posting comments on eCommerce companies by younger generation (between 20 to 30 years) is significantly higher than the older generation (40 years and above), for both male and female participants. 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. This implies that the younger generation which is also considered to be impulsive buyer group can be easily influenced by the online reviews posted by others and thus, companies need to focus more on this age group.
  • The most preferred timing for posting comments on eCommerce companies is between 12 pm to 4 pm for the female participants, and between 4 pm to 8 pm for male participants. This information can be useful for the advertisers who can publish online ads and other promotional information during this time of the day when there are higher chances of customers staying online for shopping related activities.

 

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