ANALYSIS OF FACTORS AFFECTING MOBILE COMPUTING ADOPTION IN THE NIGERIAN ECONOMY

Journal for Advanced Research in Commerce and Management Studies 68

RESEARCH ARTICLE E-ISSN: 2394-837X Vol.2.Issue.1.2015

Journal for Advanced Research in

Commerce and management studies

A Peer Reviewed International Research Journal A Journal of

S S Publications, Repalle-522265, Guntur (Dt), Andhra Pradesh, India

ANALYSIS OF FACTORS AFFECTING MOBILE COMPUTING ADOPTION

IN THE NIGERIAN ECONOMY

Oluigbo Ikenna Victor1, Ajere Ikenna

2, Okpara Chinyere

3, Ujunwa Anuli

4

1-3Department of Information Management Technology, Federal University of Technology Owerri, Imo State, Nigeria.

4Department of Computer Science, Federal Polytechnic Nekede, Imo State, Nigeria.

ABSTRACT

Mobile computing is term used to describe technologies that enable people to access network

services anyplace, anytime and anywhere. This study analyses the factors affecting Mobile

Computing Adoption in the Nigeria Economy; and to determine if these factors will contribute to

or mitigate adoption of mobile computing in Nigeria. The data used for the study were mainly

primary data, sourced through structured questionnaire to rate the opinion of the selected

population. These data were subjected to multiple regression analysis and the Analysis of

Variance (ANOVA). In the analysis, Mobile computing was regressed against the components

that affect its adoption - High cost of data, Poor network coverage and Irregular power supply.

The result discloses that these factors have a significant effect on mobile computing adoption;

with 97% of respondents agreeing to high mobile computing adoption if these factors are

mitigated. The researchers recommend that to ensure high level mobile computing adoption and

acceptance, government and relevant agencies should work towards providing an enabling

environment devoid of these components that affects mobile computing adoption; as mobile

computing can be a major factor for economic development.

Keywords: Mobile Computing, Nigerian Economy, Economic development, Mobile computing

adoption, Network Providers


http://www. jarcms.sspublications.co.in

INTRODUCTION:

The emergence of mobile computing is an innovative revolution the world has seen,

expanding the reaches of computing and telecommunication around the world. Its simplicity

and apparent ease of use, has facilitated its use and adoption by people from all walks of life.

Mobile Computing generally involves taking a computer and all necessary files and software

out, onto relatively smaller more portable devices. It is primarily classified into portable

computers, mobile phones and wearable computers.

The study of [1] reveals that there is considerable growth in the use of mobile phones in

recent years, leading to increasing demands for land, energy and labour, for setting up and

maintaining efficient state of the art telecommunication base stations, and their associated

infrastructures. Currently, there are about 100 million telephone subscribers in Nigeria. This

exponential growth in mobile phone usage, has not only brought about rapid and sustained

economic growth to Nigeria, but has also made available cheaper and economically

affordable telecommunication services to Nigerians. [2] Highlighted that the forage of the

Nigerian nation into the world of telephony at large, began in 1985, when the telecom

market reform of the administration, evolved from the several administrative structure and

operational changes, through three successive natural developments. Various studies

indicates that in the space of 20 years, the mobile computing discipline has witnessed rapid

evolving innovations, and has moved from being technology for the privileged few, to a

main stream technology. [3] In his work describes perceived usefulness of mobile

computing as the degree to which a person believes that adopting the technology would

enhance his or her job performance. According to [4], five factors influence the growth and

impartation of Mobile computing in any economy;

1. Economic Factor: Available data as well as extensive research, shows that initially,

a county’s GDP affects its ability to develop or adopt wireless technology. This must

be considered in relative terms however, as poorer countries may in fact be more

enthusiastic adopters of mobile technology to replace the inadequate and unreliable

mobile and wireless infrastructures already in place. Developing economy will

always seek to consolidate its fragile mobile technology facilities, trying to improve

the speed, efficiency and communality of the services it provides its citizens.



2. Geographic Factor: Countries with small land mass are able to speed up the

adoption of wireless infrastructure, the greater the geographical landscape, the

greater the capital and components required to set it up. For example the amount of

requirements needed to set up a mobile service in Nigeria (937,000 sq. km) would be

greater than that required for a smaller country like Ghana (92,099 sq. km) [9].

3. Industry Factor: A variety of industry related factors contribute to the increase of

mobile adoption levels in various countries, usually stemming from the level of

expertise the industry players have achieved.

4. Government Policy: In Nigeria, the government have particularly been participating

actively in favourable promotion and encouragement of wireless technology, thus

leading to their high adoption rates. For example, Nigeria’s mandatory registration

and regulation on the mobile operators via the NCC has ensured that everyone no

matter their income level would be comfortable with owning a mobile phone [10].

5. Socio-Cultural Factor: In a country like Nigeria with numerous popular media

personnel, advertisers believe that whatever products these famous people endorse

would be attractive to the masses. Mobile phone production companies and mobile

operators alike, flood the media outlets with such adverts, aimed at making their

products and services more attractive to the masses, and hoping they would acquire

and adopt these products and services, thus increasing the adoption of mobile devices.

2. Objectives of the Study

The broad objective of this study is to analysis the factors affecting mobile computing

adoption in the Nigerian economy. The specific objectives are:

1. To identify the factors affecting mobile computing adoption.

2. To evaluate the effect of factors as a whole that affect mobile computing adoption.

3. To evaluate the effect of each individual factor that affects mobile computing

adoption.



3. Research Questions

In order to carry out this investigation, the following questions formed the basis for data

collection:

1. What are the possible factors that affect mobile computing adoption?

2. To what extent do all the factors affecting mobile computing have on mobile computing

adoption?

3. To what extent does each of the individual factors affecting mobile computing have on

mobile computing adoption?

4. Research Hypotheses

HO1: There is no significant effect of the factors affecting mobile computing as a whole on

mobile computing adoption.

HA1: There is significant effect of the factors affecting mobile computing as a whole on


HO2: There is no significant effect of the individual factors affecting mobile computing on


HA2: There is significant effect of the individual factors affecting mobile computing on


5. Research Methodology and Design

This research took the form of a survey research of explanatory type. The requirement of

explanatory survey was fulfilled through a questionnaire and secondary sources of data.

Primary and secondary data were used in this work. The primary data was generated from

administered questionnaires to the population under study, while secondary data was

collected from the relevant literatures, journals, texts, and the electronic sites. The population

of a study is a census of all items or subjects that possess the characteristics or that has

knowledge of the phenomenon being studied [6].

Of the One Hundred and Twenty (120) questionnaires fully administered, 104 samples

responded; giving a response rate of 86 percent, while four (4) were discarded because they

contained errors such as incomplete answers and inconsistency. 100 questionnaires,

amounting to eighty four percent (84%), were found suitable for the analysis.



6. Method of Data Analysis

Simple percentage and regression analysis are used in analysing the data collected from the

survey. Regression analysis is a statistical tool, which helps to predict one variable from the

other variable on the basis of assumed nature of the relationship between variable [6].

In the analysis, Mobile Computing was regressed against factors that affect its adoption

which are: High Cost of Data, Poor Network Coverage and Irregular Power Supply.

Multiple regression analysis and Analysis of variance (ANOVA) were the tools used in this

study to evaluate the factors affecting adoption of Mobile computing, with F-test utilized in

determining level of significance. The student t-test was used to test for level of significance

of each individual factor.

In multiple regressions, the model describing the relationship between the dependent variable

and a set of independent variables X1, X2 . . . Xn can be expressed as:

Y = a +b1X1 + b2X2 + . . . + bkXk

Where: a, b1 and b2 are the unknown parameters to be estimated

Y = Mobile Computing = Dependent Variable

X1 = High Cost of Data = Independent Variable

X2 = Poor Network Coverage = Independent Variable

X3 = Irregular Power Supply = Independent Variable

The decision rule is to accept the null hypothesis if the critical tabulated value is greater than

the calculated value, otherwise reject. H0 is accepted at the 5% significance level, if F * < F1

– α (n-k-1). Otherwise, H0 is rejected in favour of HA. H0 is accepted at 5% significant level if

/t/< t0.05. Otherwise, H0 is rejected in favour of HA. Alternatively, at 5% significant level, we

reject the null hypothesis H0 if (P > 0.05), or to accept the alternative hypothesis HA if (P <

0.05).



Results and Discussions

7.1 Respondents Characteristics and Classification

Table 1: Sex of Respondents.

SEX PERCENTAGE

MALE 61%

FEMALE 39%

TOTAL 100%

Table 2: Age Group of Respondents.

AGE GROUP PERCENTAGE

18 – 27 64%

28 – 37 30%

38 and above 6%

Total 100%

Table 3: Preferred Network Provider.

Network Provider PERCENTAGE

MTN 40%

GLO 34%

AIRTEL 18%

ETISALAT 8%

Total 100%

Table 4: Time Spent daily on mobile device.

Occupation PERCENTAGE

Below 1 hour 5%

2 – 4 hours 17%

5 – 8 hours 68%

Above 8 hours 10%

Total 100%

Table 5: Years of using mobile computing



Years of usage PERCENTAGE

Less than one year 5%

1 – 4 years 70%

5 – 9 years 22%

Greater than 10 years 3%

Total 100%

Table 6: Factors that affect mobile computing adoption

Factors PERCENTAGE

High Cost of Data 41%

Poor network coverage 38%

Irregular power supply 21%

Total 100%

Table 7: Adoption of mobile computing, if constraining factors were minimized.

Response PERCENTAGE

Yes 97%

No 3%

Total 100%

8. Model Estimation and Hypothesis Testing

The result obtained from the multiple regression analysis is shown in Tables 8, 9, 10

Table 8: Model Summary

Model R R Square

Adjusted R

Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .966a .932 .930 .660 .932 440.850 3 96 .000

a. Predictors: (Constant), Irregular power, High cost of data, Poor network coverage



8.1 Estimation of relationship model and Interpretation

With reference to Table 8, the meaning of the various statistical tool used in analysing the

model of this research work were given as follows:

1. The co-efficient of correlation (R) shows the degree or extent of relationship

between the dependent and the independent variables. The value 0.966 shows the

existence of a positive relationship between these variables.

2. The co-efficient of determination (R2) explains the proportion of the total variations

in the dependent variable that is attributable to the variations in the independent

variable. From Table 8, it was observed that about 93.2% (0.932) of the variation is

the dependent variables are attributable to variations in the independent variables.

3. The adjusted co-efficient of Determination (R2 Adjusted) is 0.930 which implies that

the actual variation is 93.0% as against 93.2% suggested by normal R2.

Table 9: ANOVA

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 575.245 3 191.748 440.850 .000a

Residual 41.755 96 .435

Total 617.000 99

a. Predictors: (Constant), Irregular power, High cost of data, Poor network coverage

b. Dependent Variable: Mobile computing

From Table 9 above, the model reaches statistical significance at (sig = 0.000), therefore we

reject the null hypothesis H0 and accept the alternative hypothesis HA.



Table 10: T-test for the constructs

Model

Unstandardized

Coefficients

Standardiz

ed

Coefficient

s

T Sig.

95.0% Confidence

Interval for B

B Std. Error Beta

Lower

Bound

Upper

Bound

1 (Constant) .279 .228

1.223 .224 -.174 .733

High cost .354 .025 .454 14.222 .000 .304 .403

Poor network .241 .033 .274 7.290 .000 .176 .307

Irregular

Power

.364 .029 .435 12.390 .000 .305 .422

a. Dependent Variable: mobile computing

Table 10 shows that the unstandardized Beta Coefficients that present the contributions of

each variable to the model. The t and p-values shows the impact of the independent variables

on the dependent variable.

Using the regression output on Table 4.10 we estimated the following equation.

Y = 0.279 + 0.354X1 + 0.241X2 + 0.364X3

Where Y = Mobile Computing = Dependent Variable

X1 = High Cost of Data = Independent Variable

X2 = Poor Network Coverage = Independent Variable

X3 = Irregular Power Supply = Independent Variable

9. Hypothesis Testing

The formulated hypotheses are tested as follows:

HO1: There is no significant effect of the factors affecting mobile computing as a whole on


HA1: There is significant effect of the factors affecting mobile computing as a whole on




Using the decision rule, we found that the calculated F-ratio is 440.850 and the tabulated

value of F-ratio is 2.53. Since Fcal > Ftab, we reject the null hypothesis (H0) and accept

alternative hypothesis (HA) and conclude that there is significant effect of the factors

affecting mobile computing as a whole on mobile computing adoption.

HO2: There is no significant effect of the individual factors affecting mobile computing on


HA2: There is significant effect of the individual factors affecting mobile computing on


In order to properly determine the effect of the individual factors affecting mobile computing

on the adoption of mobile computing, we breakdown the hypothesis (HO2) and (HA2) into the

following sub-sections:

HO2 (a): High cost of data has no significant effect in adoption of mobile computing.

HA2 (b): High cost of data has significant effect in adoption of mobile computing.

Using the decision rule, we found that the tabulated value of t-statistics is 1.665 and the

calculated t-value of High cost of data is 14.222. Since t cal > t tab, we reject the null

hypothesis (HO2 (a)) and accept the alternative hypothesis (HA2 (b)) and conclude that High

cost of data has significant effect in adoption of mobile computing.

Alternatively, at significant level of 0.000 for High cost of data, this variable is also

significant because this probability value 0.000 is less than 0.05 significant level (P < 0.05).

We therefore reject the null hypothesis (HO2 (a)) and accept the alternative hypothesis (HA2

(b)) and conclude that High cost of data has significant effect in adoption of mobile

computing.

HO3 (a): Poor network coverage has no significant effect in adoption of mobile computing.

HA3 (b): Poor network coverage has significant effect in adoption of mobile computing.

Using the decision rule, we found that the tabulated value of t-statistics is 1.665 and the

calculated t-value of Poor network coverage is 7.290. Since t cal > t tab, we reject the null

hypothesis (HO3 (a)) and accept the alternative hypothesis (HA3 (b)) and conclude that Poor

network coverage has significant effect in adoption of mobile computing.



Alternatively, at significant level of 0.000 for Poor network coverage, this variable is also

significant because this probability value 0.000 is less than 0.05 significant level (i.e P <

0.05). We therefore reject null hypothesis (HO3 (a)) and accept the alternative hypothesis

(HA3 (b)) and conclude that Poor network coverage has significant effect in adoption of

mobile computing.

HO4 (a): Irregular power supply has no significant effect in adoption of mobile computing.

HA4 (b): Irregular power supply has significant effect in adoption of mobile computing.

Using the decision rule, we found that the tabulated value t-statistics is 1.665 and the

calculated t-value of Irregular power supply is 12.390. Since t cal > t tab, we reject the null

hypothesis (HO4 (a)) and accept the alternative hypothesis (HA4 (b)) and conclude that

Irregular power supply has significant effect in adoption of mobile computing. Alternatively,

at significant level of 0.000 for irregular power supply, this variable is also significant

because this probability value 0.000 is less than 0.05 significant level (i.e P < 0.05). We

therefore reject null hypothesis (HO4 (a)) and accept the alternative hypothesis (HA4 (b)) and

conclude that Irregular power supply has significant effect in adoption of mobile computing.

9.1 Result Discussion

Results are being discussed here in context of research questions.

Question One: To what extent do all the factors affecting mobile computing have on mobile

computing adoption?

The test of hypothesis on this research question shows that the collective factors affecting

mobile computing have a significant effect on mobile computing adoption. The conclusion

was drawn from the result of the F-test in which F-calculated value of 440.850 is greater than

the F-tabulated value of 2.53 at 5% significance level which according to decision rule

implies that the collective factors affecting mobile computing have a significant effect on


The positive relationship of the factors affecting mobile computing usage on its adoption

shows that these factors can greatly hinder students, IT workers and various individuals at

large from adopting its solutions in their daily endeavours. This is in accordance with [11]

that posits although mobile computing has greatly improve the level of uptime a user can

use in accessing relevant information through the internet, its constraining factors still stands

as a limiting factor towards its adoption.



Question Two: To what extent does each of the individual factors affecting mobile

computing have on mobile computing adoption?

In order to examine the significance of each individual factor, this research question is broken

down into sub question:

2a) How significant is High cost of data as a factor affecting mobile computing adoption?

The test of hypothesis on this research question shows that high cost of data has a significant

effect on, since the probability value of 0.000 is less than 0.05 significance level. This result

is in tandem with respondent’s perception that High cost of data has a significant effect on

mobile computing adoption. Students, Experts and Individuals at large adopt technology

based on a number of reasons, one of which is the extent to which an individual feels it helps

reduce their workload. Perceived benefits refer to the extent which an individual feel that

adopting technology would actually reduce the effort in his daily activities. Mobile

computing helps individuals to be in constant contact and communication even when on the

go, people can have access to quality information with just a limited time. However the

problem of high cost of access is a limiting factor towards adoption of mobile computing.

2b) How significant is Poor network coverage as a factor affecting mobile computing

adoption?

The test of hypothesis on this research question shows that Poor network coverage has a

significant effect on mobile computing adoption, since the probability value of 0.000 is less

than 0.05 significance level. This result is in tandem with respondent’s perception that Poor

network coverage has a significant effect on mobile computing adoption access

Poor network coverage is an evident problem in mobile computing, some areas in Nigeria

suffer low network footprint coverage, and as a result individuals cannot make a phone call

or access the internet. This is in accordance with [7] who posits that a major benefit of

mobile computing is to be able to access information on the go, however some areas in

Nigeria that suffer poor network coverage makes this unattainable.

2c) How significant is Irregular power supply as a factor affecting mobile computing

adoption?

The test of hypothesis on this research question shows that irregular power supply has a

significant effect on infusion and diffusion of IT platforms, since the probability value of

0.000 is less than 0.05 significance level. This result is in tandem with respondent’s

perception that complexity and maintainability of devices has a significant effect on mobile

computing adoption.



This problem of irregular power supply is also a trivial issue in the use of mobile computing,

because the devices through which the individuals access the internet work on batteries. And

due to dilapidated power supply these devices are usually very low leading to low usage of

mobile computing. This is in accordance with [7] which posit that shortage power supply

usually constraints people for having maximum utility with mobile computing.

10. Summary and Conclusion

Mobile computers, have greatly enhanced the chances and opportunities of interpersonal

sociability and shared practices. Mobile computers have created employment opportunities

and provided people various means of gaining knowledge and enhancing themselves, as well

as created means for people to conduct activities which would have not been previously

possible, over the internet, all from the comfort of the palms of your hands.Even with the

rapid advancements that the mobile computing industry has provided, the findings in this

research have proven that certain factors affecting mobile computing has greatly reduced its

adoption. Mobile Computing is the basis of the infrastructure of emerging global economies;

there is therefore the need to find ways to promote this ever growing industry, and

emancipate people economically and intellectually.

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http://www.directionsmag.com/entry/spatial-analysis-of-gsm-subscriber-call-data-records/123196

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ANALYSIS OF FACTORS AFFECTING MOBILE COMPUTING ADOPTION IN THE NIGERIAN ECONOMY

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