Utilisation, Awareness, and opinion of Mobile Commerce Services
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 significant trends in retail in the last several years has been the growth of mobile devices. The main aim of the study is Utilisation, Awareness and Opinion of Mobile Commerce Services. In Study Design, the entire strategy for doing this research to find out the answers to the research questions and the hypotheses generated, during the early phases, is discussed. Both practitioners and academics are interested in mobile commerce. After the year 2000, research into mobile commerce saw a substantial uptick.
Keywords: Academics, Mobile, Commerce, Utilisation, Awareness
INTRODUCTION
One of the most significant trends in retail in the last several years has been the growth of mobile devices. Mobile phones, also referred to as cell phones or just cell phones, are our major source of all media consumption, our digital diaries, our electronic wallets, and our portal to real-time information tailored to our individual needs. Technology in the wireless sector has grown enormously during the last several years. People's habits have altered as a result of this rise in mobile commerce. Failing to create a mobile app may put the firm at risk because of the growing popularity of mobile commerce, which is becoming increasingly commonplace. After the internet revolution, India is now experiencing a mobile revolution. With today's latest mobile technologies, even a person who is illiterate may effortlessly conduct mobile transactions.
India's mobile service charges are among the lowest in the world, making it possible for even low-income families to get a phone. In the words of the co-founder of an online men's retailer, "Ecommerce and Mobile Commerce have radically revolutionized the way brands approach customers, making it faster and easier for consumers to make purchases on the fly while eliminating the trouble of going to the store". Increasing numbers of consumers are using mobile devices to get into the internet world, therefore it's critical for businesses to take advantage of this trend.
Mobile Commerce
There are several ways to define mobile commerce. ICT, mobile technology, and internet all come together to make Mobile Commerce. The following part explains what mobile commerce is all about in a simple and straightforward manner. As defined by Ovum, mobile commerce "is the use of a terminal (telephone, PDA, PC device or custom terminal) and the public mobile network (required but not sufficient) to access information and conduct transactions that result in the transfer of value." "Business-to-consumer transactions made through a mobile device," is how J.P. Morgan describes Mobile Commerce. According to Robinson-Humphreys, mobile commerce is "M-commerce using mobile devices." As defined by Forrester, Mobile Commerce is the use of mobile portable devices to communicate, interact, and engage in commerce over an always-on high-speed Internet connection.
Electronic products and services can be purchased and sold using wireless handheld devices such as mobile phones and personal digital assistants, known as m-commerce (PDAs).
A mobile consumer can use M-Commerce to access banking and other commercial services via his phone. The transaction itself is not M Commerce. It offers services and information that could lead to a future transaction. The scope of M-Commerce expands beyond the original one-time business transaction, as well. SMS, financial and banking services, logistics, goods/services purchase and sale as well as wireless customer relationship management are some of the most common uses of M Commerce.
Mobile commerce, according to Mobil city, is "the use of cellular technologies to provide convenient, tailored, and location-based services to your consumers, employees, and partners. Any electronic transaction or information contact undertaken via a mobile device and mobile networks that results in real or perceived value being exchanged for information, services or goods are referred to as Mobile Commerce by mobileinfo.com." Hotel booking and reservation, airline ticket purchase, movie ticket purchase and restaurant booking and reservation, online shopping through Amazon Flipchart, Big Basket and the list goes on. In India, using Ola or Umber to hail a cab is common and well-liked.
Opportunities And Challenges Of M-Commerce In India
Most countries' progress has been facilitated by developments in telecommunications, as seen by the widespread use of mobile phones. Today, almost everyone, including youngsters, always carries a cell phone with them. As a result, mobile phone designers strive to get the most out of this technology. In the past, mobile phones were only used to make and receive phone calls and text messages. Designers, on the other hand, have been increasingly successful in luring new users to this device with each passing day. Mobile phones are no longer merely tools for making and receiving phone calls or text messages; they are also capable of offering a wide range of additional services and capabilities to their owners. Mobile commerce includes these services (M-commerce).
One definition of mobile commerce is 'The buying and selling of products and services through wireless hand-held devices such as cellular telephones and personal digital assistants.' Mobile Internet gadgets are offering an unprecedented opportunity for M-commerce, according to these experts. A typical definition of M-commerce is that it involves buying and selling goods and services via wireless handheld devices such as mobile phones and personal data assistants (PDAs).
LITERATURE REVIEW
Hina Gull (2022) In addition to COVID-19's increase in online buying, digital transformation of businesses has grown tremendously recently. Online buying should be safe and enjoyable, but for businesses, this is critical. This study examines how Saudi Arabian customers view the security of a number of popular mobile commerce apps. Specifically, we asked about consumer rating, trustworthiness, and credit card security in our poll questions. Consumers in Saudi Arabia believe that the security of mobile commerce applications needs to be improved. In this paper, a paradigm for enhancing Saudi Arabian customers' perceptions of security is offered. It is possible to apply this approach to various regions of the world. Security infrastructure, authentication procedures, and trustworthiness are all outlined in the model, which can be used by practitioners and policymakers. Apps for mobile devices have become an essential part of our daily lives and have taken over nearly every area from health care to entertainment to marketing. Businesses could take advantage of mobile computing low-cost and wide features. Since it has become a valuable facilitator for businesses, it can be called a disruptive technology that has become a cost-effective and efficient method for enterprises to reach out to worldwide clients. M-commerce (also known as mobile commerce) has become an integral part of the supply chain in recent years. The digital transformation of firms and customers is influenced by social, legal, economic, political, and technological factors. Disparities in technical accessibility, skill levels, security concerns, shopping habits and purchasing power are only few of the factors that affect the acceptance of e-commerce. There has been a lack of uniformity in the global adoption of digital transformation by corporations. The global pandemic of COVID-19 has fueled an increase in online buying. As clients all throughout the world faced lockdown, many firms developed online channels to ensure business continuance.
Mohammad awni ahmad mahmoud (2019) The rise of m-commerce can be attributed to the proliferation of mobile devices and the increased use of the internet around the world. There is a lack of trust in the platform despite numerous technological breakthroughs, according to studies. For this reason, the current investigation is focused on identifying concerns affecting mobile commerce consumers of Amazon, Alibaba, and eBay. It's our goal here to come up with solutions that can help alleviate these issues. It is anticipated that the study's findings would help regulators and online merchants gain a better understanding of how consumers perceive trust in m-commerce. The proliferation of mobile devices and the widespread acceptance of the internet has led to an increase in the use of mobile computing. As a result, tasks formerly only possible on a desktop computer can now are carried out on a mobile phone or tablet. Mobile computing efficiency, productivity, and connection is driving change in how businesses communicate and conduct day-to-day operations. However, despite the advantages of mobile computing, such as portability and flexibility, it is constrained by issues such as periodic network disconnection, low reliability, and a lack of security. Mobile computing is further restricted by limitations in hardware, software, and communication that already exist in mobile devices. Furthermore, mobile computing environments present a number of obstacles to consumers, including limited compute power, reduced storage capacity, and limited battery life.
ViswanathVenkatesh (2021) Customers' privacy worries regarding online transactions persist despite the rise of internet selling. Online shopping sites are looking for ways to relieve these worries by employing various interventions. Suggestions that help buyers choose the proper product, either based on historical purchase correlations or merchant recommendations, and discounts that raise the value of products are two frequent treatments used to drive online sales. Key recommendations and discounts can alter the effects of inhibitors or facilitators on online purchasing intention, according to privacy calculus. For this study, we used recommendations from a variety of sources (retailers and other customers' preferences), product relatedness (related and unrelated products with previous purchases associated to the main product), and two distinct kinds of discounts (regular and bundle). n = 496 people participated in a survey and a browsing task in a controlled online shopping environment Results on the influence of inhibitors and facilitators on purchase intention were varied when it came to moderating effects of recommendations and product relatedness. Enablers such as suggestions boosted online purchasing intent more so than inhibitors, despite the fact that recommendations did not strengthen their impacts. Privacy facilitators' impact on online purchasing intentions was not boosted by product similarity, however. The findings also demonstrated that discounts can counterbalance the moderating effect of recommendations on the connection between inhibitors and purchase intention when combined with enablers. There are theoretical and practical considerations to be discussed.
Anil Gurung, (2016) Consumers' worries about their personal information and safety have been cited as a barrier to the expansion of e-commerce in recent years. Consumers' perception of danger is heightened by these considerations. There may be a way out if we can figure out how risk perceptions relate to privacy and security issues. The notion of planned behavior is used to examine the link between privacy and security. Trust, privacy and security concerns will be examined as a factor in the adoption of e-commerce. The model's predictions are corroborated by survey data. Surveys of business students at the undergraduate level were used to get the data. People were asked to select a product they intend to buy in the next six months while responding to the survey. When a product was selected, the respondents were asked to provide the name of an online retailer that carries the product. The poll participants were asked to answer questions about their preferred online service provider. The allotted time was roughly 20 minutes. The findings suggest that people's perceptions of danger were influenced by their concerns about privacy and security, as well as their faith in authorities. Trust was the most important factor, followed by privacy and security issues. Furthermore, attitudes were influenced by risk perception and trust beliefs. A person's mindset was more strongly influenced by their trustworthiness than their risk aversion. A favorable and direct impact on intention to participate in e-commerce was also found to be associated with subjective norm, perceived behavioral control, and attitude. In this investigation, students were used as the subjects. Because this research was conducted in a classroom, it cannot be applied to the broader public at large. The study's second flaw is its reliance on a single method. In terms of risk perception, privacy concerns had a greater impact than those related to security. Consumers' views of danger aren't swayed by their first security concerns as they become more adept at accessing the Internet. They are likely to have taken their own efforts to preserve their privacy online. Faking personal data when requested by online businesses is one such tactic that may be employed. An integrative framework for e-commerce adoption at the person level was developed and validated in this study. Concerns about privacy and security are part of the model, as are ideas about risk and trust. Moreover, this study demonstrated the importance of distinguishing between privacy and security issues, as well as their various effects on other related research models.
Chiang-nan Chao (2019) m commerce, short for mobile commerce, has become a significant market for retailers in the United States since smart phones were adopted by about 80% of the population. It has become the first time in history that the typical adult's daily use of a smart phone has surpassed the use of a personal computer. eMarketer predicts that by 2020, mobile commerce in the United States will account for half of all ecommerce. Marketing budgets have been diverted to mobile ads as a result. Using programmatic advertising, marketers discover that they can better target Smartphone users, particularly when the phone users are interested in certain things they browse. The effectiveness of mobile marketing is the subject of this study, which utilized an empirical survey to gather data. As a marketing trend is confirmed by the study results, it provides useful information for marketers to use in their future efforts.
METHODOLOGY
Research Design
The focus of this investigation is on providing context rather than providing new information. Conditions, practices, structures; differences or correlations; opinions held; processes; and trends are all aspects of descriptive research. In Study Design, the entire strategy for doing this research to find out the answers to the research questions and the hypotheses generated, during the early phases, is discussed. There is also a sample strategy, data gathering devices, statistical tools for data processing and interpretation.
Sources of Data
Customers in Kanpur's perceptions of the effect of mobile commerce are being gauged using a survey technique used in this study. For this study, statistical tools and a computer were employed to investigate the correlations between and among these factors. A well-structured questionnaire is used to obtain primary data throughout the research phase. Academic journals, magazines, newspapers, and websites were all used in the study's secondary data collection.
RESULTS
Descriptive Analysis
In this study the descriptive analysis is used in exploring the following domains:
The following Table No. 4.1 describes various demographic profiles of the respondents namely Gender, Age, Educational qualification, marital status Occupation, Monthly income, Family size, Family type and location within Kanpur.
Table No. 4.1 Demographic Profile of the Respondents
Demographic variables | Frequency | Percentage |
Gender | Male | 614 | 85.30 |
Female | 106 | 14.70 |
Age | Less than 20 years | 306 | 42.50 |
21-30 years | 218 | 30.30 |
31-40 years | 73 | 10.10 |
41-50 years | 96 | 13.30 |
Above 50 years | 27 | 3.80 |
Educational Qualification | School Level | 69 | 9.60 |
Diploma | 178 | 24.70 |
Graduate | 370 | 51.40 |
Postgraduate | 98 | 13.60 |
Others | 5 | 0.70 |
Marital Status | Married | 248 | 34.40 |
Unmarried | 472 | 65.60 |
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 |
Analyses of consumer knowledge of mobile commerce, including smartphone kinds, connection types, the network they're on, grocery stores, and dealers, are reported in Table No. 4.2 below:
Table 4.2 Level of Awareness of Mobile Commerce
Particulars | Frequency | Percentage |
Type of your Smartphone | Android | 407 | 56.50 |
Windows | 214 | 29.70 |
Apple | 80 | 11.20 |
Others | 19 | 2.60 |
Type of connection | Post paid | 335 | 46.50 |
Prepaid | 385 | 53.50 |
Network provider | Airtel | 371 | 51.50 |
BSNL | 164 | 22.80 |
Idea | 41 | 5.70 |
Reliance | 13 | 1.80 |
Tata | 29 | 4.00 |
Vodafone | 75 | 10.40 |
Others | 27 | 3.80 |
Grocery Store | Yes | 434 | 60.30 |
No | 286 | 39.70 |
Dealer | Yes | 460 | 63.90 |
No | 260 | 36.10 |
Internet | Yes | 477 | 66.20 |
No | 243 | 33.80 |
Others | Yes | 295 | 41.00 |
No | 425 | 59.00 |
Mobile Usage Per Month (Rs.) | Less than 300 | 310 | 43.10 |
301 – 500 | 248 | 34.40 |
501 – 700 | 71 | 9.90 |
701 – 1000 | 47 | 6.40 |
More than 1000 | 44 | 6.20 |
Distance | Less than 2 kms | 245 | 34.00 |
2.1 – 4 km | 167 | 23.20 |
4.1 – 6 km | 116 | 16.10 |
6.1 – 8 km | 79 | 11.00 |
More than 8 km | 113 | 15.70 |
- It was found that 56.50 percent of the respondents owned Android-based smart phones, 29.70 percent owned Windows phones, 11.20 percent owned Apple phones, and 2.60 percent of respondents said that they used other operating systems.
- Pre-paid connections are used by 53.50 percent of the respondents, while post-paid connections are used by 46.50 percent of respondents.
- 53.50 percent of those polled use Airtel as their service provider, while 22.80 percent use BSNL and 10.40 percent use Vodafone; 5.70 percent use Idea; 4.00 per cent use Tata; 1.80 per cent utilize Reliance; and 3.80 per cent use another provider. MTS/Aircel was cited as a network provider by 3.80 percent of respondents.
- Over 60% of respondents have recharged/topped up at grocery shops, via a dealer and on the internet, as well.
- Most people (43.10 percent) say they pay less than Rs.300 per month for their cell phones. They use between Rs.300 and Rs.500 per month, which is 34.40 percent of their total monthly consumption. Between Rs.500 and Rs.700, 9.90 percent of the respondents use the service. Only 6.40 percent of those polled had spent between Rs.700 and Rs.1000 on their mobile phone use every month, while only 6.20 percent have spent more than Rs.1000.
- One-third of the respondents (34.00 percent) live within a two-kilometer radius of the Bank. More than two-thirds of respondents' distance from the bank to their home is between 2 and 4 kilometers. For 16.10 percent of the respondents, the distance between the bank and their home is between 4 and 6 kilometers. A total of 15.70 percent of respondents live more than 8 kilometers away from both their home and their bank. Nearly one out of every ten responders must travel a distance of six to eight kilometers in order to access their local bank. This suggests that even if the Bank is located close to their home, they are still utilizing a mobile commerce application.
Mobile commerce service utilization is examined in Table No. 4.3 below, including the dependability of network operator, internet connectivity, use of mobile transactions and the use of numerous SIM cards by the survey participants.
Table 4.3 Opinion about Mobile Commerce Services
Mobile Commerce Services/ scale | Always | Often | Some-times | Rarely | Never | Mean | S.D |
Network Operator Reliable | 185 (25.60%) | 190 (26.40%) | 139 (19.30%) | 112 (15.60%) | 94 (13.10%) | 3.36 | 1.357 |
Internet Access | 277 (38.50%) | 164 (22.80%) | 113 (15.70%) | 88 (12.20%) | 78 (10.80%) | 3.66 | 1.375 |
Usage of Mobile Transactions | 222 (30.90%) | 142 (19.70%) | 140 (19.40%) | 108 (15.00%) | 108 (15.00%) | 3.36 | 1.432 |
Usage of Multiple SIM | 279 (38.80%) | 119 (16.50%) | 107 (14.90%) | 83 (11.50%) | 132 (18.30%) | 3.46 | 1.535 |
These findings are based on a study of descriptive statistical data.
The fact that the mean values of all the assertions are above average suggests that the people who took the survey make use of mobile commerce services. Using the above data, it is evident that the network operator is trustworthy, that respondents have access to the internet, and that majority of them use numerous SIM cards. This indicates that the majority of those who took the survey are in favor of mobile commerce.
There are five categories of Mobile Commerce service use, including internet browsing, entertainment, messaging, purchasing, and more, in Table No. 4.4 below.
Table 4.4 Utilisation of Mobile Commerce Services
Usage / Scale | Always | Often | Some- times | Rarely | Never | Mean | S.D |
Internet | 295 (41.00%) | 139 (19.30%) | 112 (15.60%) | 102 (14.20%) | 72 (10.00%) | 3.67 | 1.388 |
Entertainment | 309 (42.90%) | 135 (18.80%) | 129 (17.90%) | 87 (12.10%) | 60 (8.30%) | 3.76 | 1.336 |
Message (Whats App / Hike) | 285 (39.60%) | 115 (16.00%) | 108 (15.00%) | 107 (14.90%) | 105 (14.50%) | 3.51 | 1.489 |
Video Calling (Skype /Viber/IMO) | 183 (25.40%) | 110 (15.30%) | 145 (20.10%) | 135 (18.80%) | 147 (20.40%) | 3.07 | 1.474 |
Shopping (eBay/Amazon / Myntra / Snapdeal) | 147 (20.40%) | 123 (17.10%) | 141 (19.60%) | 135 (18.80%) | 174 (24.10%) | 2.91 | 1.462 |
Ola/Uber | 127 (17.60%) | 107 (14.90%) | 126 (17.50%) | 149 (20.70%) | 211 (29.30%) | 2.71 | 1.467 |
Navigation (GPS) | 106 (14.70%) | 107 (14.90%) | 129 (17.90%) | 129 (17.90%) | 249 (34.60%) | 2.57 | 1.456 |
Sodexo | 84 (11.70%) | 78 (10.80%) | 100 (13.90%) | 135 (18.80%) | 323 (44.90%) | 2.26 | 1.417 |
Virtual Money (Paytm Wallet/mPesa) | 184 (25.60%) | 141 (19.60%) | 125 (17.40%) | 95 (13.20%) | 175 (24.20%) | 3.09 | 1.522 |
The most common use of mobile commerce is for amusement, with a mean score of 3.76. Next up is 3.67, which is for perusing the Internet. Then, for the purpose of communication, they turn to mobile commerce (3.51). Virtual currency (3.09), video calling (3.07), and online purchasing come next (2.91). Respondents used it to make OLA/Uber reservations and even for navigating (2.71). (2.57). Few respondents have used Sodexo (2.26) in the characteristics of usage described (11.70 percent ). We use less than average services like Sodexo and GPS, as well as Uber, OLA, and OLA, on the whole. It was utilized by half of the respondents for entertainment, the internet, and communications purposes. Standard deviation is more than one, which indicates that there is a greater degree of agreement for use.
There are several factors that influence mobile commerce service adoption (Louzi and colleagues, 2012), including ease of use, advantages, privacy, time savings and cost savings.
Table 4.5 Variables Influencing Adoption of Mobile Commerce Services
Variables / Scale | Strongly Agree | Agree | Neither agree nor | Disagree | Strongly disagree | Mean | S.D |
User Friendliness | 215 (29.90%) | 301 (41.80%) | 128 (17.80%) | 23 (3.20%) | 53 (7.30%) | 3.84 | 1.114 |
Easy to Use | 182 (25.30%) | 328 (45.60%) | 166 (23.10%) | 35 (4.90%) | 9 (1.10%) | 3.89 | 0.882 |
Beneficial | 179 (24.90%) | 278 (38.60%) | 191 (26.50%) | 54 (7.50%) | 18 (2.50%) | 3.76 | 0.991 |
Privacy | 147 (20.50%) | 266 (36.90%) | 236 (32.80%) | 55 (7.60%) | 16 (2.20%) | 3.66 | 0.960 |
Mobile Future | 163 (22.60%) | 299 (41.60%) | 189 (26.30%) | 50 (6.90%) | 19 (2.60%) | 3.75 | 0.970 |
Flexibility | 157 (21.80%) | 285 (39.60%) | 190 (26.40%) | 62 (8.60%) | 26 (3.60%) | 3.67 | 1.023 |
Financial Transactions | 112 (15.60%) | 272 (37.70%) | 255 (35.40%) | 59 (8.20%) | 22 (3.10%) | 3.55 | 0.953 |
Inadequate Information | 114 (15.80%) | 245 (34.00%) | 255 (35.40%) | 84 (11.70%) | 22 (3.10%) | 3.48 | 0.992 |
Search for Information | 139 (19.40%) | 302 (41.90%) | 209 (29.00%) | 55 (7.60%) | 15 (2.10%) | 3.69 | 0.938 |
Data Loss | 118 (16.40%) | 276 (38.30%) | 239 (33.20%) | 63 (8.80%) | 24 (3.30%) | 3.56 | 0.975 |
Account Status | 165 (22.90%) | 310 (43.10%) | 171 (23.80%) | 55 (7.60%) | 19 (2.60%) | 3.76 | 0.976 |
Compatible | 157 (21.80%) | 316 (43.90%) | 165 (22.90%) | 61 (8.50%) | 21 (2.90%) | 3.73 | 0.989 |
Easy Banking | 143 (19.90%) | 309 (42.90%) | 184 (25.60%) | 65 (9.00%) | 19 (2.60%) | 3.68 | 0.977 |
Expertise Training | 137 (19.00%) | 281 (39.00%) | 195 (27.10%) | 81 (11.30%) | 26 (3.60%) | 3.59 | 1.033 |
Save Time | 168 (23.30%) | 273 (37.90%) | 183 (25.50%) | 70 (9.70%) | 26 (3.60%) | 3.68 | 1.048 |
Third Person Access | 130 (18.10%) | 218 (30.20%) | 229 (31.80%) | 100 (13.90%) | 43 (6.00%) | 3.41 | 1.114 |
M Commerce Facilities | 120 (16.70%) | 287 (39.90%) | 222 (30.70%) | 66 (9.20%) | 25 (3.50%) | 3.57 | 0.986 |
Fewer Risks | 135 (18.80%) | 241 (33.50%) | 241 (33.50%) | 77 (10.60%) | 26 (3.60%) | 3.53 | 1.028 |
Cheap | 143 (19.90%) | 303 (42.10%) | 173 (24.00%) | 73 (10.10%) | 28 (3.90%) | 3.64 | 1.032 |
Transportation Cost | 157 (21.80%) | 268 (37.30%) | 203 (28.20%) | 60 (8.30%) | 32 (4.40%) | 3.64 | 1.050 |
Save | 150 (20.70%) | 282 (39.20%) | 187 (26.00%) | 66 (9.20%) | 35 (4.90%) | 3.62 | 1.063 |
Faster Transaction | 173 (24.00%) | 267 (37.10%) | 183 (25.40%) | 61 (8.50%) | 36 (5.00%) | 3.67 | 1.083 |
Low Cost | 137 (19.00%) | 268 (37.20%) | 215 (29.90%) | 66 (9.20%) | 34 (4.70%) | 3.57 | 1.046 |
Convenience | 115 (16.00%) | 294 (40.80%) | 221 (30.70%) | 59 (8.20%) | 31 (4.30%) | 3.56 | 0.995 |
Trust Evidence | 142 (19.70%) | 233 (32.30%) | 252 (35.00%) | 63 (8.80%) | 30 (4.20%) | 3.55 | 1.034 |
Fraud | 98 (13.60%) | 244 (33.90%) | 295 (41.00%) | 47 (6.50%) | 36 (5.00%) | 3.45 | 0.975 |
Security Threats | 73 (10.10%) | 260 (36.10%) | 296 (41.10%) | 58 (8.10%) | 33 (4.60%) | 3.39 | 0.937 |
Protect Information | 128 (17.80%) | 263 (36.50%) | 267 (37.10%) | 34 (4.70%) | 28 (3.90%) | 3.60 | 0.962 |
Account Info Disclosure | 175 (24.30%) | 296 (41.10%) | 210 (29.20%) | 24 (3.30%) | 15 (2.10%) | 3.82 | 0.908 |
Processing Transaction | 129 (17.90%) | 286 (39.70%) | 255 (35.40%) | 33 (4.60%) | 17 (2.40%) | 3.66 | 0.904 |
Secure Environment | 141 (19.60%) | 286 (39.70%) | 236 (32.80%) | 45 (6.20%) | 12 (1.70%) | 3.69 | 0.911 |
Reach Target Bank | 132 (18.30%) | 247 (34.30%) | 268 (37.30%) | 54 (7.50%) | 19 (2.60%) | 3.58 | 0.959 |
No Unauthorised Access | 151 (21%) | 278 (38.6%) | 224 (31.10%) | 41 (5.70%) | 26 (3.60%) | 3.68 | 0.985 |
Both practitioners and academics are interested in mobile commerce. After the year 2000, research into mobile commerce saw a substantial uptick. The retail sector has been at the forefront of the shift from a conventional paradigm of commerce to e-commerce and now mobile commerce. Digital commerce is just a small portion of the retail sector's overall revenues, according to industry sources. Considering this, it is clear that mobile commerce has a lot of room to grow, particularly in the areas of health care, entertainment, and on-demand services. As technology advances and new methods are developed, mobile commerce is rapidly rising as the future trend. More people than ever before are always on the go, therefore being able to do business on the go has never been more desirable. There hasn't been a lot of study done on how culture and mobile business interact. We can be sure of one thing: mobile commerce is going to take off soon.
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