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Perceived Impact of Internet Use on Individuals in Rural India

Introduction

The Internet has greatly influenced the way individuals socialise, create and exploit economic opportunities and knowledge resources. Previous studies on impact assessment have been limited to examining social and economic factors influencing the adoption and use of the Internet. These studies do not consider the role of knowledge enhancement and exchange as a factor in assessing impact. Consequently, factors influencing impact, their inter-relationships and their intensity have not been articulated. This study fills the gap by providing such a foundation and examining the aspects of social, economic and knowledge enhancements in an integrated manner to help understand the ‘Perceived Impact’ of Internet use.

Another driver for this study was the lack of such studies in developing countries, especially in rural areas. Further, most studies have focused on the household as the unit of analysis. Recent government policies of deployments in rural areas and availability of Internet on mobiles have created the need to focus on the ‘Perceived Impact’ of Internet for rural individuals.

To identify the dimensions that influence ‘Perceived Impact’, we have examined past studies and augmented these with two theoretical and complementary domains: Social Capital Theory (Lin, 2001) and Social Cognitive Theory (Bandura, 1997). We use Social Capital Theory to draw upon the concept of Social Capital. In this study Social Capital refers to Structural Social Capital. Social Capital refers to a network of near and distant social ties that individuals draw upon for enhancing their information base, knowledge, influence, solidarity for economic or other benefits such as better status or professional standing. Such networks enhance both Knowledge and Economic Capital by providing the underlying mechanism for individuals to enhance their knowledge and support knowledge seeking behaviour by providing an environment for knowledge exchange. Such knowledge could increase the economic capital and social networks. Knowledge exchanges within the social network could play an important part in influencing economic outcomes. Thus Social Capital embeds Knowledge and Economic Capital. Although prior studies indicate the influence of Social Capital on Internet use, imputing causation is often difficult.

Social Cognitive Theory posits that an individual’s personal cognition (comprising of knowledge and beliefs) and social influence control behaviour. The related concept of self-efficacy—beliefs regarding one’s ability to perform specific tasks—and outcome expectations—judgment regarding the consequences of performance—are two constructs used within the Social Cognitive Theory to study computer use and Internet behaviours. Outcome expectations, level of social interactions, shared knowledge and language drive the quality and quantity of knowledge sharing.

Thus, ‘Perceived Impact’ is a complex construct that is broadly influenced by Social, Economic and Knowledge Capital. The role of outcome expectations and self-efficacy in driving Internet use is used in operationalising the measures. The objective of this study is to uncover the underlying dimensions that influence ‘Perceived Impact’, their inter-relationships and the strengths of their influence.

Methodology

A survey-based instrument was used as underlying tool for this study. The main dimensions on which data were collected were on social, economic and knowledge enhancements due to Internet use. Questions were asked to measure ‘Perceived Impact’ of Internet use. Items for data collection were based on the literature survey, and a Principal Component Analysis (PCA) was used to identify the latent perceptual dimensions that contribute to ‘Perceived Impact’. PCA helped to reveal the internal structure of the data in a way that best explains the variance in the data. Subsequently, using the dimensions uncovered in PCA, linear regression was used to posit the strengths of each identified dimension in contributing to ‘Perceived Impact’.

Design and Measurement

In the design of the instrument, Social Capital Theory was used and the three underlying dimensions (Social, Knowledge and Economic), as a basis for question framing and Social Cognitive Theory to measure outcome expectations and self-efficacy.

For measuring ‘Social Capital’, the concept of bridging, bonding and linking1 was used by assessing how Internet use influenced perceptions in change in modes of social engagement, enhanced communication with friends, and enhanced bonding with the community. For measuring ‘Knowledge Capital’, the Influence of Internet use on users’ perceptions was rated on their ability to search for and understand the subjects that they would not have been able to do otherwise, exchange ideas about work with other people, chance to share knowledge with others who have the same area of interest, the extent of usage of video for increasing their understanding, extent of facilitation regarding understanding the linkages among different topics. For ‘Economic Capital’, the influence of Internet use was measured on i) the scope of enhancing business such as increased number of customers/suppliers, selling of new products, geographical reach, intensified competition, existing business and new business opportunities; ii) increased efficiency of business for business related transactions, reduced travel time, waiting time and cost of supplies; iii) scope of collaboration and feedback; and iv) facilitating business and work related information. All the survey questions were oriented towards measuring Outcome Expectations and Self-efficacy along the above three dimensions.

Sampling

A survey of Users of Internet was carried out in two rural areas of India: (a) Ranchi district, in the state of Jharkhand; and (b) Guna district, in the state of Madhya Pradesh. (See Appendix 1 for details about both the locations.)

Since the research was based on ‘Perceived Impact’ and perceptions are contextual, the researchers conducted focused group discussions (FGDs) of Internet users to assess the specific relevant dimensions for the study. The survey covered users who had used the mobile Internet or data card or wired Internet for surfing/browsing. The sampling technique used in the case was systematic random sampling.

Survey Instrument

Table 1 gives an overview of respondent profile. A five-point Likert-type scale was used where 1= Strongly Disagree, 2= Disagree, 3= Neither Agree or Disagree, 4=Agree and 5= Strongly Agree to measure the items. The number of survey respondents was 319. The significance threshold was set at .05.

Table 1: Respondents’ Profile

Category Sub-Category Number %
District Guna  149  47
Ranchi  170  53
Age Up to 25 years  106  33
Above 25 years  213  67
Occupation Business  143  45
Others  176  55
Digital Literacy Digitally Less Literate  112  35
Digitally More Literate  207  65
Earnings Up to Rs 15,000  211  66
Above Rs 15,000  108  34
Education Up to SSC/HSC and College  168  53
Graduation/Post Graduation 151 47
Gender Male  295  92
Female  24  8

Results of the Principal Component Analysis

PCA helped to reveal the internal structure of the data in a way that best explained their variance. Subsequently, using the dimensions uncovered in PCA, linear regression was used to posit the strengths of each identified dimension in contributing to ‘Perceived Impact’.

The PCA gave three components that explained 40.89 percent, 36.53 percent and 8.71 percent of the variation. Based on the underlying semantics of the attributes that respectively loaded on to each of these dimensions, these were labeled as following:

  1. Enhanced Scope of Work

  2. Empowerment

  3. Transactional Efficacy

Enhanced Scope of Work: This component explained the highest level of variance (40.89 percent). Most elements from ‘Economic Capital’ mapped onto this component. This component reflects growth in business or support for professional growth. The attributes that loaded on this relate to skill enhancement and selling of new products, increase in business, new opportunities, geographical reach, reduction in travel time, availability of new information, intensified competition, efficiency, reduction in waiting time and bringing down the expenses, professional contacts and searching for new topics.

Empowerment: This component explains the second highest level of variance (36.53 percent). Most elements from ‘Social Capital’ and ‘Knowledge Capital’ mapped onto this component. Elements in this component reflect the ability to manage rural vulnerabilities. These vulnerabilities are not just related to the poor information availability that is characteristic of rural areas but also that arise due to lack of physical infrastructure and poor earning opportunities. The aspect of Internet use for managing vulnerabilities has not been considered in previous studies. This study groups the elements that mapped on this component into four categories that characterise the vulnerabilities:

      1. Informational: This arises due to being able to get current information on the Internet. This has to be seen in the rural context where respondents have challenges in accessing information. Nearly 60 percent of the respondents had stated that they perceive that the information that they get is late or not current and Internet use helps them to overcome this barrier.

      2. Linkage: This is measured by attributes such as ease of staying in touch, and ability to maintain near and distant social ties. This ability must be viewed in the rural context where organising face-to-face meetings can be a huge challenge due to poor road infrastructure and transport availability.

      3. Institutional: This is related to the ability to contact people during emergencies, improving current ability to earn and managing hardships associated with physical travel related to work (in rural areas, infrastructure and services related to travel are poor).

      4. Knowledge Creation and Cognition: This relates to facilitating users to understand a subject matter better, the use of videos for the same, sharing knowledge with other similarly interested people, higher preparedness and confidence with respect to the work environment, and ability to better understand the linkages amongst different topics. This was captured through assessing impact by viewing videos for learning and understanding subjects, getting a chance to talk to people interested in the same topics, understanding linkages among related topics, exchanging ideas about work, help in being more confident, in expectation of work/job requirement. These aspects highlight the vulnerability of accessing knowledge in a rural area, where there is poor access to knowledge resources like schools, libraries, and experts. This study shows that knowledge creation and cognition loads on the dimension that has many variables from the social dimension.

Transactional Efficacy: This is the third dimension and it explains 8.7 percent of variance. The two attributes related to it are, a) Extent of on-line transactions; and b) Getting feedback on business/work related issues.

Regression

To uncover how the three latent dimensions identified through PCA contribute to ‘Perceived Impact’, we ran a multiple regression, using the principal components identified earlier as the independent variables and the ‘Perceived Impact’ as the dependent variable.

Out of the three factors, only ‘Empowerment’ and ‘Enhanced Scope of Work’ were significant with p-values < 0.001 and 0.004. The path loading of ‘Transactional Efficacy’ despite being positive was insignificant with a p-value of 0.117.

R2 value for model was 0.907, showing that a high degree of variation is explained by the model.

Figure 1:

Results

a) The effect of ‘Empowerment’ on ‘Perceived Impact’ is significant and positive. For the rural user, the highest impact of the Internet is through ‘Empowerment’.

b) The effect of ‘Enhancement of Scope of Work’ on ‘Perceived Impact’ is significant and negative. The negative sign is counter-intuitive. However, this could be explained by understanding the theory behind satisfaction formation. This study uses Disconfirmation Theory that stipulates that satisfaction from Internet use is mainly determined by the gap between cognitive standards and desires or expectations, and perceived performance to explain the negative sign. Negative disconfirmation arises when the perceived performance, especially for Internet-based services, is below expectation or desires. In the context of this study, the above indicates that possibly individuals who used the Internet had high levels of desires and expectation on the dimension of ‘Enhancement of Work Scope’ by using the Internet. The outcomes on this dimension were lower than their desires and expectations, leading to a negative perception. This gap could be due to the novelty factor and the changing nature of scope of features and services available on the Internet that create dynamic determinants of satisfaction. Such changes could lead to users possibly having low self-efficacy and higher negative disconfirmations. An alternative explanation could be that the gap was due to the individuals not getting enough support for enhancing their scope of profession as there may not be enough or relevant content for individuals in rural areas. In addition, lack of content in local language, poor presence of local websites, inadequate quality of Internet connectivity and meagre Internet penetration lead to low levels of perceived performance. High expectations and desires could be driving the negative disconfirmation and thus the negative sign on this dimension. On the other hand, the ‘Enhancement of Scope of Work’ is significant in terms of its ‘Perceived Impact’.

c) The effect of ‘Transaction Efficacy’ on ‘Perceived Impact’ is insignificant. This could be due to the low levels of transactions by the survey respondents. The reason for this could be because Internet services in the survey area had become available only a few months earlier and may not have had high levels of service quality in the initial phases. Studies of Internet adoption indicate that users initially begin with the usage of Internet for social purposes. Only when they feel comfortable with various uses of Internet and see the benefits of online transactions, they may graduate to it. Online transactions for e-commerce are a relatively new phenomena in India and many individuals in rural areas may not be able to participate on account of not having Internet banking, delivery of services to rural area, and lack of trust in online transactions.

Conclusion

This study developed a model for identifying constructs that influence ‘Perceived Impact’ of the Internet on rural users in India. The two constructs that influence ‘Perceived Impact’ are ‘Empowerment’ and ‘Enhanced Scope of Work’. While highlighting the role of Social, Economic and Knowledge capital, Internet users in rural India emphasised the aspect of ‘Empowerment’ on ‘Perceived Impact’. ‘Empowerment’ embeds the social, knowledge creation and cognition aspects and highlights the role of Internet use in managing vulnerabilities of a rural context. The model used in this study highlighted that knowledge creation and cognition on the Internet is perceptually recognised as having a social dimension.

The specific role of the Internet in overcoming vulnerabilities, was in terms of overcoming the informational, linkage, institutional, and knowledge creation and cognition gaps in rural areas. This aspect had not been considered in previous studies.

Theory of Disconfirmation regarding satisfaction of Internet services vis-à-vis desired and cognitive expectations in relation to the perceived performance of Internet services at the current levels of Internet penetration and adoption helped to explain the negative effect of ‘Enhanced Scope of work on ‘Perceived Impact’.

Areas of Further Study

This study was done at an early stage of Internet deployment in India’s rural areas. At this stage adoption may not have been high and service quality may not have been adequate. These factors could influence the ‘Perceived Impact’. Although the model used for this study does not take into account the Quality of Service (QoS) explicitly, it is possible that users’ decision to adopt certain features of Internet services may depend on it. For example, poor QoS could lead individuals to not adopt online banking, as they may not be sure whether their transaction would go through given the poor quality of services.

A longitudinal study to study how the different dimensions of ‘Perceived Impact’ change over time would provide rich data on the stages of ‘Perceived Impact’ of the Internet. This study focused only on Internet users. Further work needs to be done to make it applicable to a wider population.

References:

  1. Bandura, A. (1997). Self-Efficacy: The Exercise of Control, New York: Freeman.

  2. Lin, N. (2001). Social capital: A theory of social structure and action, Cambridge, UK: Cambridge University Press.

Appendix 1: Pilot Project Details

  1. Ranchi: It is situated in one of India’s most backward states (Figure 2). As in most backward rural areas, many villages in Ranchi district had poor connectivity. Airjaldi has covered around 60 villages in five blocks near Ranchi (Ormanjhi, Kanke, Angara, Gola, Patratu), by providing them low-cost wireless Internet broadband. The population of all the five blocks is approximately 14,34,649.

    Figure 2: Ranchi district map. Source: Mapsofindia.com, accessed on February 28, 2015

  2. Guna: The second site was also in an economically backward part of India, at Guna, in Madhya Pradesh (Figure 3). The population of Guna is 137,175. DEF has provided wireless Internet broadband in this part through innovative low-cost technology. DEF largely provided connectivity on the periphery of the two small towns of Guna and Shivpuri and six villages around them that were away from the city.

    Figure 3: District map of Guna. Source: Mapsofindia.com, accessed on February 28, 2015

Acknowledgements

This working paper is part of the project titled, “Evolving Policy for Spectrum Management through Impact Assessment of Wireless Technology and Broadband Connectivity in Rural India”. The research team would like to acknowledge the funding support provided by the Ford Foundation.

We would also like to acknowledge the research assistance provided by Ms Kavita Tatwadi, Ms Shivangi Mishra and Ms Sneha Jhala, Research Associates at IITCOE, and Mr Rishabh Dara, PhD Scholar at IIMA.

Endnotes

  1. http://www.oecd.org/insights/37966934.pdf, accessed on October 6, 2014

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