AN ANALYSIS OF SOCIAL IMPACT ON USAGE OF MOBILE APPS IN INDIA: USING STRUCTURAL
EQUATION MODELLING
Dr. Nilam Panchal
Associate Professor, B .K. School of Business Management,
Gujarat University, Ahmedabad
ABSTRACT
The intention of this study is to investigate how Smartphone‘s and use of Mobile applications are
impacting the society and also how it impact on transforming the culture, social life, technology landscape and other diverse aspects of modern society. The intention of this study is to understand all the positive and negative aspects of Mobile applications on the society. This study is an analysis of social impact on usage of mobile apps in India with the help of confirmatory factory analysis. The study tries to identify the social influencing factors for usage of mobile usage.
KEY WORDS: Mobile Apps, Social Impact
INTRODUCTION
Information technology (IT) is gaining momentum in today’s business life as every business is having
the tendency of reaching and serving their customers most effectively, to be in the pace of competition. These requirements have forced each business to adopt Information technology to reach to their goals. Banking business is also adopting the technology very rapidly. ATM, internet banking and mobile banking are the most widely used technology innovations (Wessels & Drennan, 2010).
LITERATURE REVIEW
mobile phones has opened the era of mobile banking as one of the greatest tool for penetration of financial inclusion(Technical committee, RBI , 2014). But the Mobile banking is adopted by a small part of the population, it was only 10%in 2011 and 16%in 2012, but this is showing the growing potentials in the mobile banking (Power, 2011). Mobile Apps can be accessed by mobile phones with the help of technological means such as mobile browser, text messaging, downloadable applications and via preloaded applications.(Lin, 2011). In India mobile banking is supported by multiple channels these are- SMS based channel, Application (app) based channel and USSD (Unstructured Supplementary Service Data) based channel (Technical committee, RBI , 2014). RBI has given approval to 80 banks to start Mobile Banking, and 64 banks have already started operations. All sectors are increasing its spending on the information technology in terms of establishing new applications for the different areas in the era of economic downturns due to demonetisation. These economic conditions and increasing competition among the various financial institutions are putting pressure on the organisations to reduce their cost, so it becomes necessary for organisations to analyse the success and to do the cost-benefit analysis of the Information System (IS) used. The impact of this Information System (IS) is always indirect and can be measured and influenced by human aspect of the organisation, making the measurement of success and working of information System (IS) a complex and necessary step in any of the organisation. There are many ways to measure the success of Information System like return on investment as traditional method, and mesuring all the tangible and intangible benefits of the IS by adopting models created by researchers like model of Delone and McLean, 1992;model of Seddon,1997; and model of Ballantine et al., 1996. This study is concerned with analysing the socio-economic impact of usage of different mobile apps by youth in India.
RESEARCH METHODOLOGY
ANALYSIS OF SOCIAL IMPACT OF USAGE OF MOBILE APPs
1. Social Influence
Social Influence
Minimum Maximum Mean
Std.
Deviation
In general, people have supported the use of
apps
1 5 3.47 1.089
Using mobile app is a fashion now 1 5 3.39 1.226
Using Mobile Apps reduces the interaction
among family members
1 5 3.31 1.241
Using Mobile Apps individualizes the members
of the family
1 5 3.24 1.145
Those who have a great experience of using
these apps have been helpful in the usage of
apps
1 5 3.22 1.066
Using Mobile Apps decreases the group
decision making in the family
1 5 3.04 1.114
Using Mobile Apps influences the
decision-making process in the family
1 5 3.04 1.119
My family relationships have been changed
due to use of mobile apps
1 5 2.87 1.200
People who influence my behaviour think that I
should use media apps
1 5 2.85 1.057
The Statement “In general, people have supported the use of apps” is given highest agreement. It
can mean that respondents agree that in general, people supported use of apps. The statement”
People who influence my behaviour think that I should use media apps” is given lowest agreement
Social Ties
Minimu
m Maximum Mean
Std.
Deviation
I have frequent communication with my friends by
using this social app
1 5 3.37 1.119
I Can maintain close relationships with my friends
and relatives by using this apps
1 5 3.25 1.144
I spend a lot of time interacting with my friends and
social groups by using this social app
1 5 3.04 1.126
The Statement “I have frequent communication with my friends by using this social app” is given
highest agreement. It can mean that respondents are using mobile apps for frequent
communications with their friends. The statement” I spend a lot of time interacting with my friends
and social groups by using this social app” is given lowest agreement among the statements.
CONFIRMATORY FACTOR ANALYSIS: SOCIAL IMPACT ON USAGE OF MOBILE APPS
The Model under Study:
• The model of the social impact on usage of mobile apps has 2 factors, as indicated by the
ellipses.
• There are 12 observed variables, as indicated by the 12 rectangles. • The observed variables load on the factors in the given pattern: • Each observed variable loads on one and only one factor.
• Errors of measurement associated with each observed variable are also shown in the figure.
Standardized Regression Weights: The table below shows the Standardized Regression weight for each of the variables. It can be seen that all the standardized regression weights are above 0.5 indicating high level of convergent validity. It can be concluded that all variables are contributing in explaining the fair amount of variance in factors. Hence scale of social impact on usage of mobile apps is to be considered as Valid.
Standardized Regression Weights - Social Impact
Estimate
SI9 <--- SI .562
SI8 <--- SI .660
SI7 <--- SI .670
SI6 <--- SI .634
SI5 <--- SI .633
SI4 <--- SI .794
SI3 <--- SI .724
SI2 <--- SI .716
SI1 <--- SI .630
ST3 <--- ST .863
ST2 <--- ST .786
ST1 <--- ST .728
Correlations between Factors: The table below shows the correlation between factors. All factors
are assumed to have correlation. The correlation coefficient between all factors are found positive indicating high level of dependency among each other.
Correlations of Social Impact
Estimate
SI <--> ST .600
Model Fit Summary: The table below shows the Model Fit.
Absolute Fit Measures for Assessing Social Impact Absolute Fit Measures
Test Recommended Value Model Under Study
χ2 p> 0.05 p=0.000
CMIN/DF < 5 3.69
RMSEA <0.10 0.07
Relative Fit for Assessing Social Impact Relative Fit Measures
Test Recommended Value Model Under Study
CFI >0.90 0.91
NFI >0.90 0.90
RFI >0.90 0.90
IFI >0.90 0.90
Parsimonious Fit for Assessing Social Impact Parsimonious Fit Measures
Test Recommended Value Model Under Study
Pcfi >0.50 0.68
Pnfi >0.50 0.67
Note : All Recommended values are based on Hair et al.( 2000), Ullman ( 1996) recommended
CMIN/DF < 5
χ2 = Chi- Square Test , CMIN/DF = Chi square test / Degree of freedom ,
RMSEA = Root Mean Square Error of Approximation, CFI = Comparative Fit Index
NFI = Normed Fit Index, RFI = Relative Fit Index , IFI = Incremental Fit Index,
PCFI= parsimony Comparative Fit Index , PNFI= Parsimony Normed Fit Index
CONCLUSION
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