Factors Influencing a Consumers Intention to Use a Mobile Wallet (Essay Sample)

FACTORS INFLUENCING A CONSUMER’S INTENTION TO USE A MOBILE WALLET
Student Name:
Student ID:
Date:
INTRODUCTION
Mobile wallets have been known for storage of money cards information in mobile devices. In the recent past, the technology has highly been accepted at its growth witnessed. Research has shown that from inception to about year 2003, 95million people had embraced the technology (Amoroso & Magnier-Watanabe, 2012). Google was the first company to launch the wallet which by 2016 witnessed a growth of 7%. It has widely been used for its convenient, safeness, and reduces time spent by consumers thus creating efficiency (Shaw, 2014).
The use of the mobile technology and more specifically the wallets, have been influenced by a wide range of factors. Acceptance models argue that the rate of adoption is determined by the rate of consumer’s acceptance and thus a decision to purchase the technology. In the case of wallets that rely on smartphone technology, consumers’ willingness to accept the technology has been seen to influence purchase of phone by 2% (Madan & Yadav, 2016).
The current analysis will hence seek to evaluate the mobile wallet technology from different aspects to help explain consumer’s behaviour.
Objectives
The analysis will be undertaken with a general purpose of assessing the factors influencing consumer’s intention to use a mobile wallet.
The objective will be assessed using various specific objectives including:
1 To characterise the mobile wallet users
2 To assess social economic factors influencing use of mobile wallet
3 To assess the factors influencing intention to use of mobile wallets
Hypothesis/research questions
Research question
1 What are the characteristics of mobile wallet users?
Hypotheses
2 Social, economic, and personal attributes have not influence on use of mobile wallets.
3 Perceived usefulness, perceived ease to use, and external variables have no influence on intention to use of mobile wallet.
Literature review
Technology acceptance model has widely been applied in the field of technological advancement with a major intent of assessing how users come to accept a technology. The model argues that the behaviour of individuals lead them to adoption of technologies (Junadi, 2015). Behaviour is influenced by attitude of the individual. The model argues for a variety of factors that influence the intention to use the technology including; perceived usefulness, perceived ease to use, and external variables.
Therefore, presented with a few alternatives, modelling the intentions of consumers follows the random utility theory with looks at choices consumers make when presented with discrete choices. It has been argued, that the consumer preference can be modelled using their utilities where they make use of the highest utility. Therefore, allowing the use of lineally related models in determination of the latent utility (Sardar, 2016). In assessment of the factors influencing acceptance, which has discrete choices, the analysis will embrace the use of the model.
RESULTS AND ANALYSIS
The analysis was undertake making use of statistical package of social sciences (S.P.S.S). The models used were descriptive statistics for objective 1, logistic regression for objective 2, and component factor analysis for objective 3.
Objective 1: Characterisation of mobile wallet users
The mobile wallet users were assed and the results revealed that males making use of the wallets (51.3%) were more compared to females (48.7%) as shown in table 1 below.
Variable

frequency

Percent

Male

186

48.7

Female

196

51.3

Total

382

100

In addition, to having both gender having used the wallets, the most frequently used wallets type was the pay pal (34.6%) as shown in the table 2 below.
Variable

Frequency

Percent

Google pay

18

4.7

Apply pay

18

4.7

Pay pal

132

34.6

Samsung pay

19

5.0

Others

9

2.4

However, despite having used the wallet, there was a noted lack of continuity in use of the wallet as most people had stopped using their wallets. That is they were not regularly using the technology (51.3%). This is an indication of poor technology acceptance which need be assessed.
Variable

frequency

Percent

Yes

186

48.7

No

196

51.3

Those who were using the wallet were assessed for frequency of use. The results showed that most people were using the wallet after some days. This is an indication that there is a low level of use among users.
Variable

Frequency

Percent

More than once a day

17

4.5

Daily

48

12.6

Every 2-3 days

58

15.2

Once a weak

40

10.5

Once a month

13

3.4

Once every few months

7

1.8

Once or twice a year

2

0.5

Never

1

0.3

Variable

N

Min

Max

Mean

Sd. Dev

Age

382

18

88

43.17

14.66

The analysis also revealed that most respondent had a mean age of 43 years with the youngest being 18 years and the oldest being 88 years as shown in the table below
Objective 2: Social, economic, and personal attributes have not influence on use of mobile wallets usage
Making use of gender, age and employment as the main dependent variables that would be used to explain use of the mobile wallet, results show that age and employment have a significant impact of mobile wallet usage as shown on the table below.
Chi-square

Df

Sig.

Cos R-squared

Negalkerke R


59.72

3

0.000

0.145

0.193


Variable

Β

S.E

Wald

D.F

Sig

Gender

-0.316

0.232

1.843

1

0.729

Age

0.044

0.009

24.88

1

0.000

Employment

0.212

0.059

12.72

1

0.000

Constant

-1.88

0.539

12.20

1

0.000

As the age of the respondent increases, use of mobile wallet is likely to increase by 4.4%. In addition, people with a better employment position thus earning better income are 21.2% more likely to use mobile wallets as compared to people of lower employment levels.
Objective 3: Assessment of factors influencing acceptance
Using the principal component analysis, the analysis, from the variance difference table shown below, only 6 of the variables were able to meet the criteria of eigenvalues. The total variability that can be explained by these factors as shown are; age can explain 30.31%, current use 12.53%, employment 12.48%, efficiency in conducting payments 8.28%, usefulness7.78%, and speed 4.07%. The variances are attached at the appendix but this can be seen from the scatter plot below where it levels at about 6 factors.
Finally we make use of the rotated matrix which help in assessing the factors that influence acceptance and their highest loading. As shown in the figure 2 at the appendix. Age, use of wallet, employment, information leakage, errors, fraud, personal data hackers, security and apprehensive feeling loaded more in component 2. Improved efficiency, usefulness, enhanced payments, and improved control loaded more in component 1.<...