Analysis on the Educational Qualification and access to Mobile Internet
 
Neeru Amarya1*, Dr. Meenakshi Bindal2
1 Research Scholar, Lords University, Alwar, Rajasthan, India
2 Professor, Lords University, Alwar, Rajasthan, India
Email: bindalmeenakshi95@gmail.com
Abstract - One of the most rapidly rising segments of e-commerce is retail sales, which are growing at a rate of up to 50% per year, thanks in part to mobile commerce, often known as m-commerce. The main aim of the study is Analysis on the Educational Qualification and Access to Mobile Internet. Data are collected by theoretical basis and the practical means such that the characteristics of a population can be inferred with known estimates of error. With the tools available in the time window offered, this study was able to provide a complete evaluation of the effect that mobile commerce had on Kanpur's customers.
Keywords: Educational, Qualification, Retail, Evaluation, Error,
INTRODUCTION
One of the most rapidly rising segments of e-commerce is retail sales, which are growing at a rate of up to 50% per year, thanks in part to mobile commerce, often known as m-commerce. M-commerce refers to the proliferation of mobile apps and services that may be accessed via the internet. Compared to traditional e-commerce, these makes use of cutting-edge tools, services, and business strategies. You no longer must carry a laptop or desktop computer about with you when you have a mobile phone that allows you to access the internet at any time and from anywhere. Personal devices are becoming increasingly commonplace for internet use as a result. For services that are time-sensitive, mobile commerce applications have taken off, appealing to individuals on the go by allowing chores to be completed more quickly.
More and more items and services are now available for purchase and sale through mobile commerce (m-commerce), which encompasses a wide range of activities such as online financial management, bill payment, and travel booking and information delivery. On the other hand, clients can use both Apple Pay and Android Pay to make purchases in stores or on mobile commerce apps without any issues whatsoever (Investopedia n.d.). Consumers typically begin their online purchasing searches using Google, email and other social media, which often leads them to mobile browsers on smartphones and other mobile devices social media sites have also seen an increase in mobile commerce, allowing users to make purchases of goods and services. Bottoms were introduced to mobile platforms by Face book, Integra, and Pinterest in 2015 as "purchase" bottoms (Investopedia n.d.).
M-Commerce in India
E-commerce is the practice of conducting business over the internet utilizing a personal computer or laptop. E-commerce has many advantages, including round-the-clock availability, quickness of access, a broader assortment of goods and services, worldwide reach, and accessibility. All of this can be done in a matter of seconds on any of the many websites that are available to the general public. It is a form of E-commerce that is carried out on mobile devices over the internet. A mobile phone, tablet, or other wireless handheld device is used to purchase or sell goods and services, referred to as M Commerce (mobile commerce). The internet can be accessed without the requirement for a plug by using m-commerce. On-line transactions are carried out via mobile phones, mobile apps, and the internet.
Mobile phones can be carried and accessed at all times while preserving privacy. As a result, M Commerce's accessibility, mobility, flexibility, and reach ability allow customers to shop, search for various products, transfer funds, book tickets, and pay utility bills whenever and wherever they choose. There are a variety of M Commerce services available to mobile subscribers via various mobile applications and the internet, including mobile money transfer, mobile tickets, mobile vouchers, discounts, and loyalty cards, as well as location-based services and information.
LITERATURE REVIEW
Aida Licina (2018) Electronic commerce has grown significantly in recent years and now plays a larger role in daily life than it ever has before. Mobile commerce refers to the practise of purchasing and selling goods and services via the internet on a mobile device, which is referred to as electronic commerce. A device in the user's hand can now perform all the functions of a physical store, freeing them to shop online instead. As a research objective, this study sought to identify the elements that drive mobile commerce, and how essential each of these factors is to the client when doing mobile commerce. Using a quantitative method, this thesis sought to acquire insight into consumer behavior while shopping on a mobile phone, as well as to identify the most important usability characteristics from the perspective of the end user. The result was a self-assigned survey that drew in 200 people. Users' decisions on whether to engage in mobile commerce can be affected by a wide range of factors, according to the self-completion questionnaire. Results from a study show that most consumers still prefer to shop in physical locations, followed by online purchases using a computer. The small screen and input mechanisms on mobile phones where the primary reasons other ways were chosen before using the Smartphone to make a transaction. Most participants said they would cancel their order if usability issues like faults and slow site performance were not taken into account in the application, which supports previous research. Usability is a critical consideration in the creation of mobile applications and websites. Mobile commerce applications that are well-liked by customers can be developed with the help of the knowledge in this essay. Researching m-commerce is crucial since it will have a significant impact on business soon. It would be fascinating to investigate more elements that are important when creating m-commerce mobile applications in the future. M-commerce customers place a high value on security, and our research shows that this is a problem that warrants more investigation.
Maria Cristina Enache (2016) Mobile commerce, often known as m-commerce, is the practice of conducting online transactions through mobile devices. Mobile commerce uses wireless networks to connect cell phones, portable devices like Blackberries, and personal computers to the Internet. In addition to stock trading, pricing comparisons, banking, and travel reservations, mobile users are able to undertake other transactions once they are connected. This paper focuses on m-commerce—the use of the Internet and the World Wide Web to conduct business via mobile devices. In a more formal sense, we're interested in commercial transactions that are made possible through the use of digital technology. Our working definition of m-commerce includes all of these components. All transactions that are made possible through the use of digital technology are referred to as digitally enabled transactions. In most cases, this refers to transactions that take place online. Value (e.g., cash) is exchanged across organizational or individual boundaries in exchange for goods and services in the commercial sector. To comprehend the limits of ecommerce, it is necessary to understand the exchange of value. There can be no commerce if there is no exchange of value.
Madhurima Khosla (2017) The Indian economy's e-commerce sector is one of the fastest-growing. Despite its rapid expansion, India's e-commerce sector has lagged those of many other developed and growing nations, partly because of the country's small population of internet users. AT Kearney, a multinational management consultancy firm conducted a survey in 2015 that found only 39 million internet shoppers in India, a miniscule proportion of the country's 1.2 billion residents. However, as the internet and mobile devices become more widespread in India, a favorable environment is emerging for the growth of e-commerce in the country. An unprecedented digital transformation is underway in the United States. Reduced data plan and data card/USB dongle rates, along with the introduction of 4G services, have all helped to lower the cost of owning a reliable internet connection. Increases in the availability of low-cost smart phones and internet access in remote areas will help bridge the gap between potential online shoppers and actual purchasers. The country's demographic dividend appears to have a positive effect on the growth of ecommerce as well. It is difficult for e-commerce companies to survive in a rapidly changing environment, especially with the fierce rivalry that exists in the field. The onus is then on the businesses to constantly adapt and develop while providing a rich and seamless experience rich in information to ensure consumer loyalty. Researchers in this study set out to learn more about how e-commerce has evolved in India and how they expect it to continue to grow in the future. As defined by the MIT Sloan Management Review, "e-commerce is the use of digital information processing technology for business transactions to create, modify, and redefine relationships for the generation of value between or among businesses"
Dr. Sunil Batra (2013) this paper expands the scope of mobile commerce research in India. In this section, the Indian M-commerce industry's challenges are laid forth. As time and technology progress, so do businesses and their strategies. Businesses used to base their growth plans on a small geographic area. This is changing due to the rapid development of Internet and communications technology. It is relatively new to India's m-commerce scene. 9 percent of Indians use smart phones to quickly consume content such as games, videos, songs and entertainment on their smart devices and this leads to constant growth in the mobile advertising and app industries. The m Commerce market in India, on the other hand, has not yet evolved enough to be compared to the industrialized countries' m Commerce markets. Some political, social, economic, and cultural differences explain some of the discrepancy, but given the current rate of expansion, more discrepancy is likely in the future. Beginning in the early 1990s, E-commerce (electronic commerce) has added greater value to a variety of enterprises and academic institutions; as a result, customers are shifting their purchasing habits from offline to online. The latter method is the most straightforward, most convenient, and least expensive. Advances in wireless technology since the year 2000 have transformed the way businesses operate and provided new benefits and conveniences for everyone who uses them. M-commerce, or Mobile Commerce, is the name given to this cutting-edge technology. The term "m-commerce" refers to the practice of conducting business using mobile devices. All e-commerce transactions are included in the advanced version of e-commerce, which is mobile commerce, but subscribers are given more flexibility and convenience. M-commerce is becoming increasingly important in the eyes of the telecommunications industry as well as the business community.
Dr. Sachin Gupta (2014) the goal of this study is to discover the elements that influence the adoption of M-commerce in the United Kingdom. In India, M-commerce has grown at a phenomenal rate. A growing number of consumers are making the switch to mobile commerce in order to benefit from the convenience and speed of transactions in the market. M-commerce is a multifaceted and ever-evolving industry. In India, m-commerce is still at a relatively early stage of development. With each passing day, India's mobile penetration, mobile technology, and networking continue to advance at an incredible rate. Mobile phones may now be used for a wide range of activities, including browsing the internet, communicating with friends, and more. This study examines the factors that influence the use of mobile commerce. The theoretical contribution of this work is to explain the "Hows M-commerce is emerging in India and to find obvious contexts and assistive mechanisms. The term "e-commerce" is commonly used to refer to online transactions. E-retailing is a subset of E-commerce, which encompasses a wide range of online business activities. The use of the most recent web technologies in accordance with the policies of the company is discussed when dealing with digitally / Internet-enabled business transactions between organizations and individuals. Business-to-business trading and internal processes used by organizations to support purchasing, selling, hiring, planning, and other operations are also included in electronic commerce. E-commerce is simply defined as the buying, selling, and renting of a certain product or service via the internet. It's termed M-commerce since e-commerce activities are carried out using a mobile device, such as a cell phone, which is why it's named M-commerce in the first place. When a transaction is carried out through a Wi-Fi or mobile network, it is referred to as "m-commerce."
METHODOLOGY

Sampling Design

Data are collected by theoretical basis and the practical means such that the characteristics of a population can be inferred with known estimates of error. The succeeding paragraphs highlight about the sampling design adopted by this research.
Selection of Sampling Area
Kanpur is India’s fourth-largest city by population. Kanpur has one of the highest rates of Mobile Commerce acceptance and utilization everywhere, as indicated by the constant construction of new warehouses and delivery facilities. Residents have come from all around Uttar Pradesh and the rest of India, making it a true cross section of the country’s population. The city of Kanpur was chosen for this study because it has a diverse population and may therefore be utilized to draw relevant conclusions about consumer perceptions of the impact of mobile retail.
RESULTS
Descriptive Analysis
In this study the descriptive analysis is used in exploring the following domains:
Table No. 4.1 Demographic Profile of the Respondents
 
 
Occupation
Student
246
34.10
Housewife
28
3.90
Self employed
210
29.20
Employed
232
32.20
Retired
4
0.60
 
Monthly Income
Less than Rs.15000
309
42.90
15001-30000
158
21.90
30001-40000
103
14.40
Above 40000
150
20.80
Family Size
2-4 members
543
75.40
5-6 members
143
19.90
More than 6 members
34
4.70
Family Type
Joint Family
328
45.60
Nuclear Family
392
54.40
 
Inferential Analysis
It is based on the independent variable and the dependent variables. This analysis helps to identify the various degrees of relationships among the variables in the study. The outcome of the inferential analysis can be generalized to the whole population to test the hypothesis. The following tools are used:
Hypothesis 1: Type of connection depends on the income of the respondents
Null Hypothesis (H0): There is no significant association between income of the respondents and the type of connection
Alternate Hypothesis (H1): There is significant association between income of the respondents and the type of connection.
Table 4.2 Cross-Tabulation for type of Connection and Income of the Respondents
 
Type of connection
Total
Post paid
Prepaid
 
Less than Rs.15000
113
36.6%
33.7%
196
63.4%
50.9%
309
100%
42.9%
Monthly Income
Rs.15001 – 30000
58
36.7%
17.3%
100
63.3%
26%
158
100%
21.9%
 
Rs,30001 – 40000
51
49.5%
15.2%
52
50.5%
13.5%
103
100%
14.3%
 
Above Rs.40000
113
75.3%
33.7%
37
24.7%
9.6%
150
100%
20.8%
 
335
385
720
Total
46.5%
53.5%
100%
 
100%
100%
100%
 
Table No. 4.3 applies Chi-square test to find out whether there is a significant association between the income of the respondents and type of connection.
Table 4.3 Chi-square for Type of Connection and Income of the Respondents
 
Value
df
P-value
Pearson Chi-Square
68.835
3
0.000**
 
** denotes 1% level of significance
Chi-square test helps to understand the association between two variables. It also shows how association changes from one variable to another.
i. Chi-square analysis is used to see whether the respondents' income correlates significantly with the kind of link. Null hypothesis rejected, and alternative hypothesis accepted, with a P value less than 0.01, which is significant at 1% significance level. As a result, there is a strong correlation between respondents' income and the sort of relationship they have. The respondents' choice of connection is depending on their income, according to this study. Respondents who earn more prefer post-paid service.
There are more people with pre-paid connections, which means their income is lower than those with post-paid connections, which is what we found out.
To test the hypothesis that "there is no correlation between the income of respondents and the kind of connection," the study was conducted. No significant correlation was found between the respondents' income and the sort of relationship they had.
Hypothesis 2: Income of the respondents has a bearing on the money spent on mobile usage
Null Hypothesis (H0): There is no association between income of the respondents and the money spent on mobile usage
Alternate Hypothesis (H1): There is association between income of the respondents and the money spent on mobile usage
Table 4.4 Cross-Tabulation for Income and Money Spent on Mobile Usage
 
Money spent on mobile usage
Total
Less than
300
301 –
500
501 –
700
701 –
1000
Above
1000
 
Less than 15000
173
56%
55.8%
94
30.4%
37.9%
19
6.1%
26.8%
13
4.2%
27.7%
10
3.2%
22.7%
309
100%
42.9%
 
 
Monthly
15001 –
30000
56
35.4%
18.1%
66
41.8%
26.6%
16
10.1%
22.5%
10
6.3%
21.3%
10
6.3%
22.7%
158
100%
21.9%
Income
30001 –
40000
25
24.3%
8.1%
49
47.6%
19.8%
12
11.7%
16.9%
11
10.7%
23.4%
6
5.8%
13.6%
103
100%
14.3%
 
Above 40000
56
37.3%
18.1%
39
26%
15.7%
24
16%
33.8%
13
8.7%
27.7%
18
12%
40.9%
150
100%
20.8%
 
310
248
71
47
44
720
Total
43.1%
34.4%
9.9%
6.5%
6.1%
100%
 
100%
100%
100%
100%
100%
100%
 
Table 4.5 Chi-square for Income and Money Spent on Mobile Usage
 
Value
df
P-Value
Pearson Chi-Square
65.328
12
0.000**
** denotes 1% level of significance
  1. Chi-square analysis is used to investigate the correlation between respondents' income and their mobile use expenditures. A p-value less than 0.01 suggests that respondents' mobile-usage costs are strongly linked to their household incomes. H1: There is a link between respondents' incomes and their mobile use expenditures is recognized
  2. Almost half of the respondents (56 percent) spent less on mobile usage since their incomes were lower.
Hypothesis 3: Reliability of network is associated with the network provider
Null Hypothesis (H0): There is no significant association between network provider and the reliability of network
Alternate Hypothesis (H1): There is significant association between network provider and the reliability of network
Table 4.6 Cross-Tabulation for Network Provider and Reliability of Network
 
Reliability of Network
 
Total
Never
Rarely
Sometimes
Often
Always
 
 
 
 
 
 
 
 
 
Network Provider
Airtel
51
13.7
53
14.3%
64
17.3%
95
25.6%
108
29.1%
371
100%
BSNL
36
22%
29
17.7%
31
18.9%
32
19.5%
36
22%
164
100%
Idea
1
2.4%
6
14.6%
16
39%
12
29.3%
6
14.6%
41
100%
Reliance
0
0%
2
15.4%
1
7.7%
8
61.5%
2
15.4%
13
100%
Tata
2
6.9%
5
17.2%
8
27.6%
7
24.1%
7
24.1%
29
100%
Vodafone
4
5.3%
15
20%
13
17.3%
27
36%
16
21.3%
75
100%
Others
0
0%
2
7.4%
6
22.2%
9
33.3%
10
37%
27
100%
Total
94
13.1
112
15.6%
139
19.3%
190
26.4%
185
25.7%
720
100%
 
Table 4.7 Chi-Square for Network Provider and Reliability of Network
 
Value
df
P-value
Pearson Chi-Square
56.858
24
0.000**
** denotes 1% level of significance
Chi-square analysis is used to see whether the network provider has any influence on the network's dependability. Because the p-value was less than 0.01, the null hypothesis was rejected. As a result, the quality of the network and the quality of the network provider are linked. There is a clear majority of respondents who utilize Airtel based on the results of this cross-tabulation.
As a result, the alternative hypothesis (H1) that the network provider has a considerable impact on network dependability is accepted.
For the second study purpose, "to determine the degree of awareness of mobile commerce service consumers," the following hypothesis 4 is proposed.
Hypothesis 4: Educational qualification of the respondents influences the access to internet using mobile.
Null Hypothesis (H0): There is no association between educational qualification of the respondents and the access to internet using mobile.
Alternate Hypothesis (H1): There is an association between educational qualification of the respondents and the access to the internet using mobile.
Table 4.8 Cross-Tabulation for Educational Qualification and Access to Mobile Internet
 
Educational Qualification
 
Total
School
Diploma
Graduate
Post graduate
Others
 
 
Never
11
14.1%
15.9%
18
23.1%
10.1%
40
51.3%
10.8%
7
9%
7.1%
2
2.6%
40%
78
100%
10.8%
 
 
Access
 
Rarely
17
19.3%
24.6%
23
26.1%
12.9%
37
42%
10%
11
12.5%
11.2%
0
0%
0%
88
100%
12.2%
to Internet Using Mobile
 
Sometimes
11
9.7%
15.9%
31
27.4%
17.4%
53
46.9%
14.3%
17
15%
17.3%
1
0.9%
20%
113
100%
15.7%
 
Often
12
7.3%
17.4%
39
23.8%
21.9%
85
51.8%
23%
26
15.9%
26.5%
2
1.2%
40%
164
100%
22.8%
 
 
Always
18
6.5%
26.1%
67
24.2%
37.6%
155
56%
41.9%
37
13.4%
37.8%
0
0%
0%
277
100%
38.5%
 
Total
69
9.6%
100%
178
24.7%
100%
370
51.4%
100%
98
13.6%
100%
5
0.7%
100%
720
100%
100%
 
Table 4.9 Chi-Square for Educational Qualification and Access to Mobile Internet
 
Value
df
p-Value
Pearson Chi-Square
26.922
16
0.042*
* denotes 5% level of significance
In this study, we are interested in whether the respondents' educational level has an impact on their ability to access the internet through their mobile devices. The p-value was determined to be less than 0.05, hence the alternative hypothesis that there is a correlation between educational level and mobile internet access is accepted.
ii. Graduates are more likely than the general population to use their mobile devices to access the internet at all times (56 percent). Only 6.5 percent of those with a high school education access the internet through their mobile devices, according to a new survey. Higher-educated people are more comfortable utilizing their mobile devices to access the internet, according to one study.
As a result, the alternative hypothesis (H1) that the respondents' educational level is significantly associated with their use of mobile internet is accepted.
Hypothesis 5: Occupation of the respondents influences the utilization of mobile commerce services.
Null Hypothesis (H0): There is no significant difference in mean awareness of utilization of Mobile commerce services based on the occupational groups.
Alternate Hypothesis (H1): There is significant difference in mean awareness of utilization of Mobile commerce services based on the occupational groups.
Table 4.10 Occupation and Utilisation of Mobile Commerce Services
Utilisation
Occupation
N
Mean
Std.
Deviation
 
 
Internet
Student
246
4.03
1.323
Housewife
28
2.64
1.367
Self
210
3.40
1.338
Employed
232
3.69
1.390
Retired
4
2.50
1.000
Total
720
3.67
1.388
 
 
Entertainment
Student
246
4.09
1.218
Housewife
28
3.07
1.245
Self
210
3.71
1.307
Employed
232
3.57
1.409
Retired
4
2.00
.816
Total
720
3.76
1.336
 
 
Message (Whats App / Hike)
Student
246
3.90
1.487
Housewife
28
2.93
1.331
Self
210
3.27
1.344
Employed
232
3.40
1.543
Retired
4
2.50
1.915
Total
720
3.51
1.489
 
 
Video Calling (Skype
/Viber/IMO)
Student
246
3.02
1.530
Housewife
28
2.93
1.274
Self
210
3.27
1.371
Employed
232
2.96
1.519
Retired
4
2.00
.816
Total
720
3.07
1.474
 
 
Shopping (eBay/Amazon/Myntra/Snapdeal)
Student
246
3.04
1.505
Housewife
28
3.04
1.138
Self
210
2.74
1.455
Employed
232
2.91
1.446
Retired
4
3.00
1.826
Total
720
2.91
1.462
 
Utilisation
 
Occupation
 
N
 
Mean
Std.
Deviation
 
 
Ola/Uber
Student
246
2.33
1.438
Housewife
28
3.00
1.089
Self
210
2.96
1.434
Employed
232
2.84
1.490
Retired
4
3.00
1.826
Total
720
2.71
1.467
 
 
Navigation (GPS)
Student
246
2.48
1.399
Housewife
28
2.43
.997
Self
210
2.53
1.471
Employed
232
2.72
1.538
Retired
4
2.50
1.915
Total
720
2.57
1.456
 
 
Sodexo
Student
246
1.91
1.229
Housewife
28
2.43
.997
Self
210
2.62
1.489
Employed
232
2.28
1.505
Retired
4
1.75
.500
Total
720
2.26
1.417
 
 
Virtual Money (Paytm Wallet/mPesa)
Student
246
2.93
1.502
Housewife
28
2.96
1.170
Self
210
3.22
1.534
Employed
232
3.16
1.566
Retired
4
3.25
1.500
Total
720
3.09
1.522
 
The observed variance in a particular variable is partitioned into components attributable to different sources of variation in ANOVA. It provides a statistical test of whether the means of several groups are equal.
CONCLUSION
With the tools available in the time window offered, this study was able to provide a complete evaluation of the effect that mobile commerce had on Kanpur's customers. There is still a lot of room for growth in mobile apps and mobile commerce. Almost one billion people use smartphones every day to access the internet, and mobile commerce is booming like never before. Focusing on customer-centric experiences, restricting the emphasis to the most value initiatives, and adopting the correct technological approach will be critical to achieving success in the long run.
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