INTRODUCTION:
The future Mobile commerce is the next logical step for Indian merchants. With the growth of mobile phones and increased issuing and use of debit and credit cards, mobile commerce will deliver strong growth over the coming years. Mobile technology gives us the edge over our competitors. First Data’s mobile commerce solutions can help businesses meet the growing demands of the mobile and social media revolution. Social media networks such as Facebook are likely to increasingly become channels for sales and consumer engagement.
First Data already offers a loyalty solution for the Facebook social media network as well as mobile payments opportunities using our Trusted Service Manager (TSM) service, which powers part of the Google Wallet which has made headlines recently. With Google Wallet, millions of consumers will no longer need to carry their leather wallets. This mobile application securely stores credit cards, offers, gift cards and more on their mobile phone.
This virtual wallet is changing the face of commerce by enabling customers to simply make “tap and go” payments with their mobile devices, while increasing loyalty at merchant locations. Although m-commerce market in India is still in its initial phase, India is getting richer and phones are getting cheaper. It is also estimated that, by 2020, 80% of the mobile devices used in the country will be smartphones. Many e-tailers have realized the potential of mobile commerce in multiplying their sales and are rapidly adopting the necessary tools, with encouraging results. Snapdeal.com, for example, gets over half of its sales from customers using mobile devices, while even one year ago the share of mobile purchases was a mere 5%. Flipkart, the largest online shopping platform in India, receives 20% of its e-commerce orders from customers using mobile devices and plans to increase this number to as much as 50% in future. India has more than 100 million users in India, of which nearly 84 million access the site through mobile phones. This suggests the rise of the “mobile first” generation of internet users.
REVIEW OF LITERATURE:
Xiongfei Cao et al (2018), The purpose of this paper is to investigate the mechanism of building trust during the transition from online payment to mobile payment, as well as to examine the effect of trust on the satisfaction and continuance intention of mobile payment users. Based on trust transfer theory, this study proposes that trust in online payment (i.e. trust in source) and two source-target relationship factors, namely, perceived similarity and entitativity, affect trust in mobile payment (i.e. trust in target). In turn, the resulting trust influences user satisfaction and continuance intention toward mobile payment in an online-mobile payment context. The model was empirically tested on data collected from 219 mobile payment users of a famous payment enterprise in China. The results indicated that the trust transfer process positively influences the continuance intention of mobile payment through satisfaction. Satisfaction is an important factor affecting continuance intention. Moreover, trust in online payment, perceived similarity, and perceived entitativity between online and mobile payments can positively influence trust in mobile payment. This study investigates the post-adoption usage of mobile payment from the trust transfer perspective. It focuses on the trust-building process and emphasizes the importance of trust on the continuance intention toward mobile payment in an online-mobile payment context.
Prasanta Kr. Chopdar et al (2018), The purpose of this research is to investigate the influence of psychological contract violation (PCV) on service quality and perceived value, and consequently on users positive word of mouth intention towards mobile shopping applications. The role of personalization as a moderator is further investigated. A descriptive research approach was adopted, and responses were gathered from 252 mobile shopping application users in India, using an online survey. The variance-based partial least square structural equation modelling approach was opted for analysing the research model. The results showed the deleterious effects of PCV on service quality and perceived value. The findings further confirm the significant positive impact of service quality and perceived value on the positive word of mouth intention of users. The role of personalization in mitigating the adverse effects of PCV on perceived value among users of mobile shopping application is highlighted in the study; however, its role in safeguarding service quality is found to be insignificant. A study with larger sample of respondents from varied nationalities will aid in generalizing the findings of this research. This is the first time that PCV and its consequences have been studied in the context of mobile shopping applications.
OBJECTIVES OF THE STUDY:
· To study the level of awareness of mobile commerce among consumers.
· To analyse the usage of mobile commerce service and its applications.
· To find out the problems facing by consumer in mobile commerce activities.
RESEARCH METHODOLOGY:
The methodology is empirical in nature. It is primarily based on survey method. Technique like, interview, discussion and observations are employed in this study. Primary data were collected from mobile consumers by the researcher with the help of structured questionnaire. About 100 samples were taken for the study. This study has done based on both primary data and secondary data. Primary data were collected by a questionnaire containing questions which was administrated directly by the researcher on the sample. The secondary data were collected from the text books, journals. The collected data has been processed with the help of appropriate statistical tools. The analysed for data at percentage analysis and independence t-test.
Hypothesis:
· Ho- There is no relationship between gender and level of usage of different mobile apps in mobile phone
Data Interpretation:
Gender wise classification of the respondents
Table - 1
|
S. No |
Gender |
No. of respondents |
% of respondents |
|
1. |
Male |
60 |
60 |
|
2. |
Female |
40 |
40 |
|
|
Total |
100 |
100% |
Source: Primary Data
Table 1 depicts that in the gender category, among total (100) respondents, 60 of the respondents are male and 40 of the respondents are female. In other words, 60% of the respondents are male and the rest of the respondents (40%) are female. Therefore it can be concluded that the majority of the respondents (60%) are male.
Age wise classification of the respondents
Table - 2
|
S. No |
Age group |
No. of respondents |
% of respondents |
|
1. |
18-28 |
40 |
40 |
|
2. |
29-39 |
24 |
24 |
|
3. |
40-50 |
11 |
11 |
|
4. |
51-60 |
15 |
15 |
|
5. |
Above 60 |
10 |
10 |
|
|
Total |
100 |
100% |
Source: Primary Data
Table 2 inferred that 40 of the respondents belong to the age group of ‘18-28, 24 of the respondents belong to 29-39, 11of the respondents belong to 40-50, 15 of the respondents belong to 51-60 and 10 of the respondents belong to above 60 years of age. In other words, 40% of the respondents belong to the age group of ’18-28, 24% belong to 29-39, 11% belong to 40-50, 15% belong to 51-60 and 10% belong to above 60 years of age. Thus the majority of the respondents 40% are aged between18-28.
Occupation of the respondents
Table -3
|
S. No |
Occupation |
No. of respondents |
% of respondents |
|
1. |
Student |
24 |
24 |
|
2. |
Businessman |
16 |
16 |
|
3. |
Government employee |
12 |
12 |
|
4. |
Professional |
11 |
11 |
|
5. |
private employee |
29 |
29 |
|
6. |
Others |
8 |
8 |
|
|
Total |
100 |
100% |
Source: Primary Data
Table 3 evident that 24 of the respondents are student, 16 of the respondents are businessman, 12of the respondents are government employees, 11 of the respondents are professionals, 29 of the respondents private employees, 8 of the respondents are others. In other words, 24% of the respondents are student, 16% are businessman, 12% are government employees, 11% are professionals, 29% private employees, 8% are others. Therefore the majority of the respondents 29% are private employee.
Awareness of the various purpose of mobile phone
Table - 4
|
S. No |
Purpose of mobile phone |
No. of respondents |
% of respondents |
|
1. |
Banking |
15 |
15 |
|
2. |
Entertainment |
7 |
7 |
|
3. |
Information services |
2 |
2 |
|
4. |
Shopping |
2 |
2 |
|
5. |
All of them |
36 |
36 |
|
6. |
Above few |
38 |
38 |
|
|
Total |
100 |
100 |
Source: Primary Data
From Table 4 it is found that 15 of the respondents are aware of the purpose of banking, 7 of the respondents are aware of entertainment, 2 of the respondents are aware of information services, 2 of the respondents are aware of shopping, 36 of the respondents are aware of all of them and 38 of the respondents are aware of above few. In other words, 15% of the respondents are aware of the purpose of banking, 7% of the respondents are aware of entertainment, 2% of the respondents are aware of information services, 2% of the respondents are aware of shopping, 36% of the respondents are aware of all of them and 38% of the respondents are aware of above few. Therefore it can be concluded that most of the respondents 38% are aware of the above few purpose of the mobile phones.
Since p value is 0.000 less than 0.05, then null hypothesis is rejected at 5 % significance level. Hence there is significant relationship between male and female in respect to level of usage of different mobile apps in mobile phone.
Since p value is 0.099 more than 0.05, then null hypothesis is accepted at 5 % significance level. Hence there is no significant relationship between male and female in respect to level of usage of different mobile apps in mobile phone.
Since p value is 0.337 more than 0.05, then null hypothesis is accepted at 5 % significance level. Hence there is no significant relationship between male and female in respect to level of usage of different mobile apps in mobile phone.