THE EFFECTS OF MOBILE PHONES IN SOCIAL
AND ECONOMIC DEVELOPMENT:
THE CASE OF FEMALE MICROENTREPRENEURS IN CHENNAI, INDIA
By
Han Ei Chew

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILSOPHY
Communication Arts and Sciences – Media and Information Studies
2012

ABSTRACT
THE EFFECTS OF MOBILE PHONES IN SOCIAL AND
ECONOMIC DEVELOPMENT:
THE CASE OF FEMALE MICROENTREPRENEURS IN CHENNAI, INDIA

By
Han Ei Chew
This dissertation participates in the grand debate on whether ideas or technology change social
structures that affect the lives of individuals. At its broadest and based on its findings, this
dissertation makes the argument that neither ideas nor technology takes precedence. While
technology can drive economic and social changes, it cannot do so in the absence of human
agency. Insofar as technology drives social change, it does so by amplifying human intent and
capacity. Through an examination of mobile phone use by 335 female microentrepreneurs in
Chennai, India, this dissertation found that: (1) microentrepreneurs who are highly motivated to
grow their businesses experience higher business growth, demonstrating a fairly strong link
between attitudes and desired outcomes; (2) business growth is a function of both the use of
mobile phones for business processes and the entrepreneurial intent to grow one’s business; (3)
the economic consequences of mobile phones use may sometimes be over-estimated by the users
themselves; and (4) the social use of mobile phones may have a social development outcome in
that female microentrepreneurs who use mobile phones for social purposes more frequently also
reported a greater sense of self-worth. This dissertation also contributes to the Information and
Communication Technology for Development (ICT4D) field by introducing and testing the
concept of mattering, a construct that measures self-perception of how significant one is to

others. The generalizability of the findings in this dissertation is limited in part by the crosssectional survey method that was used to collect the data. While claims of causality are made,
they should be verified through longitudinal data and can be tested using more powerful
statistical procedures. The generalizability of the findings are also limited to historic time and to
Chennai, India, home of female microentrepreneurs with their own set of personal and business
characteristics. Policy recommendations arising from the findings are also made.

Copyright by
HAN EI CHEW
2012

DEDICATION

I dedicate this dissertation to my family, especially…
to Father, Mother and Aunts for supporting this dream;
to my one and only Brother who has always been there for me;
to Mark and Diane for the honor and privilege of being one of their sons;
to Michigan State Men’s Volleyball for allowing me to serve and lead.
May all dreams be fulfilled in the love of great family and friends.

v

ACKNOWLEDGEMENTS
I would like to thank all of those people who helped make this dissertation possible.
First, I wish to thank my dissertation chair, Dr. Mark Levy, for all his guidance,
encouragement, support, and patience. His passion for research in ICT4D has been a great
inspiration to me.
I would also like to thank my dissertation committee members - Drs. Robert LaRose,
Hairong Li, and Kurt DeMaagd - for their guidance. This dissertation would not have been
possible without their invaluable feedback.
Thanks to Dr. P. Vigneswara Ilavarasan, IIT-Delhi, for his permission to use the dataset
and his reviews and suggestions for the dissertation.
I would also like to thank my fellow graduate students Paul Curran from the MSU Center
for Statistical Training and Consulting for his invaluable support in statistics, Yu-hao Lee, and
Kanni Huang for their reviews of the dissertation.
In the course of developing my dissertation, I also benefited from the reviews and
suggestions of the editors and anonymous reviewers of Information Technology & International
Development, Media Asia, and The Electronic Journal of Information Systems in Developing
Countries for the manuscripts I submitted.
This research was carried out with the aid of a grant from the International Development
Research Centre, Ottawa and a dissertation completion fellowship from The Graduate School at
Michigan State University.

vi

TABLE OF CONTENTS
LIST OF TABLES ......................................................................................................................... ix
LIST OF FIGURES ........................................................................................................................ x
INTRODUCTION .......................................................................................................................... 1
Mobile Phones, ICT4D, and Social Change ............................................................................... 3
CHAPTER 1 ................................................................................................................................... 9
ICTs and Economic Development .............................................................................................. 9
Effects across Levels of Analysis.............................................................................................. 12
Micro level evidence. ........................................................................................................ 13
Microenterprises and Development .......................................................................................... 15
Limitations of Mobile Phones for Effective Development ....................................................... 18
Limited economic use. ....................................................................................................... 18
Business attributes. ........................................................................................................... 19
The Matthew effect of mobile phones use. ........................................................................ 21
Social uses of mobile phones. ........................................................................................... 22
CHAPTER 2 ................................................................................................................................. 24
Unintended and Unrecognized Effects of Mobile Phone Use................................................... 24
Mobile Phones and Social Development .................................................................................. 27
Applying the Senian Approach Using the Concept, Mattering ................................................. 30
Mattering as a social effect. .............................................................................................. 31
CHAPTER 3 ................................................................................................................................. 34
Women Entrepreneurs in India ................................................................................................. 34
RESEARCH METHOD................................................................................................................ 36
Sample Design........................................................................................................................... 36
Questionnaire Construction ....................................................................................................... 39
Data Handling and Analysis ...................................................................................................... 40
Operational Measures ................................................................................................................ 40
Dependent variables. ........................................................................................................ 41
Independent variables: Respondent attitudes and behaviors. .......................................... 50
Independent variables: Microenterprise characteristics. ................................................. 53
Independent variables: Microentrepreneur characteristics. ............................................ 55
Sample Characteristics .............................................................................................................. 56
Owners and non-owners of mobile phones ....................................................................... 58
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RESULTS ..................................................................................................................................... 64
DISCUSSION ............................................................................................................................... 82
Limitations ................................................................................................................................ 89
Policy Recommendations .......................................................................................................... 92 
CONCLUSION ............................................................................................................................. 95
APPENDICES .............................................................................................................................. 99
BIBLIOGRAPHY ....................................................................................................................... 164

viii

LIST OF TABLES
Table 1: Factor Loadings for the Rotated Factors of Mattering .................................................. 46 
Table 2: Conceptual Mapping of Interpersonal Mattering to Mattering in Current Research
Context .......................................................................................................................................... 48 
Table 3: Factor Loadings for the Rotated Factors of Growth Orientation .................................. 51 
Table 4: Number of Hired Workers and Economic Sector by Owners and non-Owners of Mobile
Phones ........................................................................................................................................... 57 
Table 5: Comparison between Owners and non-Owners of Mobile Phones ................................ 59 
Table 6: Two-Way Analysis of Variance for Business Growth as a Function of Mobile Phone
Ownership and Growth Orientation ............................................................................................. 61 
Table 7: Means, Standard Deviations, and n’s for Business Growth as a Function of Mobile
Phone Ownership and Growth Orientation .................................................................................. 61 
Table 8: Pearson Product–moment Correlations among the Dependent and Independent
Variables ....................................................................................................................................... 67 
Table 9: Hierarchical Multiple Regression Analysis Summary Predicting Business Growth...... 69 
Table 10: Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with
the Addition of the Interaction between Growth Orientation and Business of Mobile Phones .... 70 
Table 11: Predicted Values of Business Growth by Growth Orientation and Business Use of
Mobile Phones .............................................................................................................................. 71 
Table 12: Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with
the Addition of Economic Sector Dummy Variables..................................................................... 73 
Table 13: Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with
the Addition of Interaction Terms of Social Economic Variables ................................................ 74 
Table 14: Predicted Values of Business Growth by Education and Business Use of Mobile
Phones ........................................................................................................................................... 75 
Table 15: Predicted Values of Business Growth by Age and Business Use of Mobile Phones .... 76 
Table 16: Hierarchical Multiple Regression Analysis Summary Predicting Growth Linked to
Mobile Phones .............................................................................................................................. 79 
Table 17: Hierarchical Multiple Regression Analysis Summary Predicting Mattering ............... 80
ix

LIST OF FIGURES
Figure 1: Plot of the Estimated Marginal Means of Business Growth as a Function of Mobile
Phone Ownership and Growth Orientation .................................................................................. 62 
Figure 2: Plot of Interaction Effects of Growth Orientation and Business Use of Mobile Phones
....................................................................................................................................................... 71 
Figure 3: Plot of Interaction Effects of Education and Business Use of Mobile Phones.............. 76 
Figure 4: Plot of Interaction Effects of Age and Business Use of Mobile Phones ........................ 77
Figure 5: Confirmatory Factor Analysis of Operational Measures of Mattering .......................100

x

INTRODUCTION
This dissertation participates in a small way in one of the longest running and most
highly-contested conversations in the history of ideas. That grand debate centers on how social
structures are created, maintained, replaced, and, most importantly for this study, how such
social processes affect the lives of individuals. From the classical Greek philosophers, to
th

th

Durkheim, Weber and Marx in the 19 century, to Giddens in the 20 century, two poles of the
argument are visible (Noble, 2000). In broad terms, the argument is about which factors are the
principal agents of social maintenance and social change: “ideas” and other cognitive
manifestations such as attitudes and common-sense theories; or “atoms”, that is, things-material,
tools, and technologies.
The idealist position is best exemplified by Weber’s study of the Protestant ethic and how
that powerful theology “created” capitalists and capitalism (Weber, trans. 1958). In essence,
idealists assert that there is nothing so powerful as an idea, an ideology, or even a theology
whose time has come. By contrast, the materialist camp contends that both the continuation of
the social status quo and its opposite, social change, is fundamentally a matter of control of and
access to tangible economic resources. A materialist might ask, for example, how does
ownership of telecommunication systems benefit some and exclude others from the social and
economic benefits of the network society?
An important stream of thought that builds on materialist assumptions is that of
technological determinism. In its least nuanced form or so-called “hard” form, the
technologically deterministic position privileges tools and their affordances as the prepotent
driver of history (Marx & Smith, 1998). Proponents of hard technological determinism might
1

claim, for example, that the technologies of online connectivity inexorably lead to massive and
negative social change as “real” social bonds are replaced with inauthentic, mediated
interactions.
Hard technological determinism is an implicit assumption of communication research. As
McQuail (2005) observed, ‘‘the entire study of mass communication is based on the assumption
that the media have significant effects’’ (p. 456). Moreover, as Chaffee (Rogers & Chaffee,
1983) noted, “the history of communication research has largely been one of response to
technological innovation…” (p. 20). If the hard version of technological determinism silently
underwrote the institutionalization and growth of the communication research enterprise, it is
nevertheless not without its academic critics. Castells, Fernandez-Ardevol, Qiu, and Sey (2007)
cautioned, for example, that technology should only be understood as a part of social practices.
People use and therefore shape communication devices based on their own motivations, values
and goals. A key insight of the soft determinist strategy is that technologies may be created by
engineers, but individuals give technologies meaning by choosing how and why to use those
technologies. As science fiction novelist William Gibson (1989) observed, “The Street finds its
own uses for things – uses the manufacturers never imagined” (p.85).
In short, the lens of soft determinism alerts us to consider that, to the extent that the
transformative social changes that are being observed worldwide are the result of communication
technologies and processes, researchers should consider technology to be only one of many
factors influencing social life. Indeed, only by understanding the interaction of technology with
history, culture, gender, laws, etc. can the most complete understanding of contemporary life be
achieved.
2

Mobile Phones, ICT4D, and Social Change
As the most rapidly diffused information and communication technology (ICT) in
history, the mobile phone is potentially an especially fruitful research site for exploring the
1

relationship between technology and social change (Castells et al., 2007) . First diffused and
studied in the United States and similarly advantaged countries of Europe and Asia, the mobile
phone was seen to support the activities of daily life (Katz, 2008; Ling & Donner, 2009).
However, the mobile soon diffused in developing countries. Between 2000 and 2004, mobile
phone ownership rates in developing countries almost doubled and low-income countries
reached the same average level of mobile phone penetration as was observed in high-income
countries in 1995 (World Bank, 2008). With this diffusion, scholarly attention was extended to
consideration of access to mobiles by the world’s poorest persons.
According to the latest projections, there are more than six billion mobile phone
subscribers worldwide, with roughly three-quarters of those subscriptions in the developing
world (International Telecommunication Union [ITU], 2011). Also, the number of mobile users
in developing countries far outstrips the number of people with Internet access. In 2011, the ITU
estimated that there were 70 mobile phone users compared to 21 internet users per 100
inhabitants in the developing world. In India, there were 61 mobile phone users per 100
inhabitants and only four in 100 households had internet in 2010. An earlier study estimated that
one-fifth of the world’s most disadvantaged citizens are mobile subscribers (Heeks, 2008a).

1

Throughout this dissertation, I will use the terms “mobile phone” or “mobiles”, rather than
“cell phone.” With the exception of mobile phone subscribers in the United States, users
worldwide identify this communication technology as a mobile phone or mobile.
3

Initially, development practitioners, policymakers, and scholars expressed optimistic,
some would say naïve, hopes that information and communication technologies, especially
mobile phones, would dramatically facilitate the accomplishment of development goals. For
example, the United Nations Millennium Development Goals include Target 8F – [M]ake
available benefits of new technologies, especially information and communications (United
Nations, 2000). Indeed, the dominant belief in the development community was that access to
technology would ameliorate the economic condition of the poorest in the world. In fact, that
belief was not completely unfounded. There were well-established and well-known findings that
macro-level investments in telecommunication infrastructure (fixed line telephones especially)
and the growth of the telecommunication market lead to increased GDP figures in developing
countries (Saunders, Warford, & Wellenieus, 1994). With the early diffusion of the mobile
phone in countries of Africa, a 10% increase in the availability of mobile phones resulted in a
0.6% increase in per capita GDP (Waverman, Mesci, & Fuss, 2005).
Pioneering research into the growth of access to mobile phones in the developing world
was framed early on by communication scholars drawing on the diffusion of innovation
paradigm (Rogers, 1986; Singhal & Rogers, 2003; Melkote, 2006) or theories of the digital
divide (van Dijk, 2005; Warschauer, 2004). The digital divide refers to gaps in ICT access, and
uses between groups (van Dijk, 2005). Early digital divide studies focused on narrowing these
gaps and were often framed as efforts at digital inclusion (Warschauer, 2004).With time,
communication researchers who studied the role of technology in development coalesced in the
specialist field called Information Communication Technology for Development (ICT4D) in

4

which mobile phones, computers, and public access to those communication technologies are
investigated.
Typically, ICT4D research examined those relationships at the level of economic
markets. Prominent studies in the ICT4D literature demonstrate that the use of mobile phones
can improve market efficiency in such diverse markets as fishing, grain trade and artisan
industries (Jensen, 2007; Aker, 2008; Molony, 2006). Although the dynamics of mobile phone
use and consequences at the macro- and market-levels are important to understand, small and
microenterprises are also important for developing economies since various estimates, both
country specific and regional, suggest that women own upward of half of all microenterprises in
the developing world (Chen, 2001; Peebles, 2006; Wasihun & Paul, 2010) and a large percentage
of the local workforce is employed by those businesses. In India, the National Sample Survey
Organization (NSSO, 2000) puts the number of microenterprises with at least one employee at
more than four million. More recent surveys estimate the number of workers in the urban,
informal sector of the Indian economy to be 74 million, with 16 million of that total being
women (Raveendran, 2006).
In the past half-decade or so, enthusiasm for the ICT4D-project has become more
restrained, as funders and international organizations now talk of “mainstreaming” mobilecentered research as secondary to the delivery of services such as education, health, or
agriculture (Heeks, 2010a). In large part, this growing disregard for the potential value of the
mobile phone has come about as a paradox for, despite the explosion of mobile phone
ownership; developing countries have not by and large experienced the widespread and
transformative economic impacts that ubiquitous access to ICTs was supposed to bring about
5

(Heeks, Gao, & Ospina, 2010). In response to that paradox, then, this dissertation examines why
we see mobile phones everywhere except in the productivity of microenterprises. In fact, the
general use of technology may not be related to economic development at all unless that use is
directed at economic activity. Intuitively, the adoption of mobile phones by all the workers in a
business would not result in increased business productivity especially if the calls placed are
non-business related. Thus, when considering the impact of ICT use, just as important and
perhaps more important are the intents of the users and the purposes to which the technology is
being put to.
For years, the “development” in ICT4D has been synonymous with economic
development and researchers have found evidence that ICTs can indeed result in economic
growth at various levels of analysis. At the macro level, investments in telecommunication
infrastructure and the growth of the telecommunication market benefit the overall economic
growth of developing countries. At the market level, prominent studies in ICT4D demonstrate
that the use of mobile phones can improve market efficiency in the fishing, grain and artisan
industries.
Compared to research at the macro and market level, studies focused on firms or
individuals are neither large in number nor cumulative (Donner & Escobari, 2010). Thus, one
objective of this dissertation is to address this shortcoming in the ICT4D literature. Moreover,
much research about mobiles was for marketing purposes or as atheoretical, after-the-thought
evaluations of mobile interventions. As Heeks (2008a) observed, mobile phones are only being
incorporated into development projects adventitiously and there has not been crosscutting

6

initiatives to learn about their transformative possibilities. A second goal of this dissertation then
is to bring the social psychological concept of mattering to ICT4D research.
In addition, the current examination of small-scale enterprises may have policy
implications, since the majority of the workforce in the developing world is engaged by these
microenterprises. Aside from their numbers, microenterprises may be a particularly fruitful site
for research into non-economic development goals. Donner (2009), for example, reported that
business owners use their mobile phones for both social and business purposes. The different
uses of mobile phones would presumably have different types of outcomes that may not always
be economic in nature. This dissertation argues that just because some of the outcomes are noneconomic in nature does not mean that other developmental goals are not being served. For many
international aid agencies, development is more than just economic development; development
can also be about social development (ADB, 2003; GSMA, 2011; UNCTAD, 2011). Thus,
another objective of this dissertation is to expand the current definition of development beyond
that of only economic growth. In the literature review that follows, it is argued that development
entails both economic and social development, and the use of mobile phones has an impact on
both economic and social developmental outcomes.
By situating this dissertation in the technocentric paradigm of ICT4D research, and by
specifically investigating the effect of mobile phones on development goals, this dissertation
becomes a vehicle to engage with the “big ideas” of technology, social continuity, and social
change. In terms of research goals, this dissertation will: (1) Explore the economic and social
consequences of using mobile phones by microentrepreneurs; (2) Examine possible reasons for
the mobile phone paradox; and (3) Enrich the ICT4D field with concepts pertaining to economic
7

and social development. Accordingly, the research outcomes of this dissertation are: (1)
Statistical models that explain the mechanisms and consequences of mobile phone use; (2)
Boundary conditions for microentrepreneurs to benefit from the use of mobile phones; and (3)
Concepts and relationships that explicate the role of mobile phones in both economic and social
development. In the broadest sense, the research questions and hypotheses posed here were
generated in order to interrogate the mobile phone paradox. In answering these research
questions and testing these hypotheses, this dissertation seeks to contribute to the ICT4D field by
systematically examining the challenges of delivering development outcomes through mobile
phones.

8

CHAPTER 1
ICTs and Economic Development

2

The role of ICTs, especially mobile phones as a facilitator of development for the poor
has become an axiom of scholarly and public discourse. International organizations (ITU, 2009;
United Nations, 2009; World Bank, 2009), governments (e.g., Republic of Rwanda, 2004;
Government of India, 2004; Jamaica Ministry of Industry, Technology, Energy, and Commerce,
2007), and even the news media (Agence France-Press, 2009; The Economist, 2009; The New
York Times, 2009) now take it as an article of faith that ICTs, especially mobile phones, can be
significant enablers of positive change in the developing world.
In a surprisingly communication-centric approach, the Asian Development Bank (ADB)
suggests that one cause of poverty and hardship in the developing world is the lack of access to
information (ADB, 2003), and it is in this area that mobile phones have the greatest potential to
make a difference in the desperate lives of the world’s poor (Collier, 2007). In particular, mobile
phones have been found to reduce transactional costs, increase income and productivity, and
reduce uncertainty and risk (Donner & Escobari, 2010; Esselaar, Stork, Ndiwalana, & DeenSwarray, 2007; Eurostat, 2008; Heeks, 2008a; 2010b; Indjikian & Segel, 2005; OECD, 2004).
Accelerated information diffusion can enhance market efficiency and competition. The same
phones could also “extend social and business networks and substitute journeys and

2

Throughout this dissertation, the term “ICT” is used for more general discussions of the effects
of technological use. The focus of this dissertation is specifically the mobile phone. Mobile
phones are, for now, the technology of the moment. With the diffusion of data-enabled phones
and even tablets, some of the consequences discussed in this dissertation are likely to evolve as
microentrepreneurs find different uses for these devices.
9

intermediaries” (Duncombe & Heeks, 1999, p.18). Indeed, in countries where mobile phones
constitute the primary form of access, increased exchange of information on trade or health
services appears to be contributing to development goals (ITU, 2008a).
Despite the general optimism about the promise of ICTs, there is still a paucity of
rigorous research on their role for development that would inform practitioners and policy
makers. Until recently, evidence linking ICTs to creating new economic activities or changing
existing activities has been very weak despite the growing availability of the technology
(UNCTAD, 2010, p. xi).
From the perspective of policy makers, the most critical link between ICTs and
development is whether the growing availability of ICTs, especially mobile phones, helps
achieve development goals. ICT effects have been conceptualized in different ways by different
international development organizations. For example, the Asian Development Bank (2003)
identified two possible kinds of impacts – vertical and horizontal. Vertical impacts refer to the
economic growth that is associated with the ICT industrial sector. ICTs here primarily refer to
the goods and services that are being exchanged in this sector. Impact here refers to the increased
economic activities that result from investments in the telecommunication sector. The second
kind of impact is horizontal and refers to the “means of supporting activities that benefit from
prompt and reliable information (p.5).” Horizontal impact is characterized by cost reduction in
information searches and transactions as well as the improvement of communication within
supply chains. The most valuable contribution of ICTs for enterprises is when “tangible benefits
are accrued from greater efficiencies” (UNCTAD, 2010, p. xiv). It is to the second of these two
impacts that this dissertation addresses.
10

Different organizations have termed horizontal and vertical impacts differently. The
UNCTAD (2010) refers to direct employment generation in the ICT sector as first order effects
and horizontal impact as second order effects. The same study also reported that the greatest
business involvement of poor people in the ICT sector was the vending of prepaid cards.
However, UNCTAD also conceded that the prepaid card business is fraught with price volatility
and low returns, thus raising hard questions about whether such enterprises were likely to be a
path out of poverty.
Scholars in the development communication tradition by contrast conceive of ICT effects
as the “ultimate dependent variables” (Rogers, 1986, p. 148). Broadly speaking, the study of
consequences from that perspective entails examining the changes in an individual’s behavior
(knowledge, attitudes, or actions) that occur as a result of a communication message (Rogers,
1986, p. 151). Depending on the level of analysis (individual, firm, societal), the consequences of
communication technologies can be considered positive (e.g. increased gross domestic product
or greater income for very small businesses) or negative as access to online information
exacerbates inequality between the information-rich and information-poor.
In the context of this dissertation, desirable outcomes are the functional effects of the
mobile phones on the microenterprises owned by individuals and include consequences such as
increased productivity and reduced transactional costs. Direct impacts are the changes that occur
in immediate response to the mobile phone and indirect impacts are the changes that result from
the direct impacts of the mobile phones. If reduced transaction costs are the direct impact, then
indirect impacts could be increased business growth or profits. Anticipated impacts are changes
caused by the mobile phones that are recognized and intended by the members of the social
11

system. Unanticipated effects are changes that are neither intended nor recognized. To
foreshadow a later section of this dissertation, my dissertation uncovered social development
outcomes of mobile phone use that seem to be relatively unrecognized by the ICT4D research
community.
Effects across Levels of Analysis
Impact can be measured at different levels of analysis. Some researchers have looked at
the impact of ICTs at the macro level while others have looked at the issue from the micro level.
If one considers the existing ICT4D literature as well as ICT impact literature in developed
countries, one can find much more empirical evidence at the macro level than the micro level of
enterprises.
Macro level evidence. Extent economic research in developed countries on the impact of
telecommunication investments has yielded fairly convincing evidence of a causal relationship
between IT investment and economic growth. Hardy (1980) investigated the impact of
telephones per capita on economic growth and concluded that telecommunications was an
essential component of the economic infrastructure, fostering productivity and economic growth.
Causal relationships between IT equipment investment and rapid economic growth have also
been reported (de Long & Summers, 1991; Kathuria, Uppal, & Mamta, 2009). Examining
country level data, Cronin, Parker, Collerau, and Gold (1991) confirmed a two-way causal
relationship in the U.S. between telecommunications infrastructure investment and economic
growth. In their study, the researchers used statistical procedures to confirm the existence of
feedback process in which economic activity and growth stimulates demands for
telecommunication services. Their explanation for the two-way causal relation was that as the
12

economy grows, more telecommunications facilities are needed to conduct the increased
business transactions. More recently, strong links between mobile phones and economic growth
have also been reported in a variety of other geographic contexts (Deloitte, 2007; Lee, Cho, &
Jin, 2009; Ovum, 2006; Qiang & Rossotto, 2009).
The economies of developing countries are not excluded from the benefits of ICT
investments. In the countries of Africa, a 10% increase in the availability of mobile phones
resulted in a 0.6% increase in per capita GDP (Waverman et al., 2005). In fact, this impact may
be twice as large in developing countries compared to developed countries. Across the Global
South, a 10% increase in broadband penetration was projected to produce a 1.4% increase in per
capita GDP (Qiang & Rossoto, 2009). Other macro-level studies also found a direct relationship
between ICT growth and economic growth (Forestier, Grace, & Kenny, 2002). Kenny (2006)
found that ICT investments can account for 0.4% of additional annual growth in labor
productivity.
Micro level evidence. Despite the studies cited above, Donner (2010) argued that there is
still a scarcity of ICT4D quantitative research. In his review of ICT4D studies, he found that
most existing ICT4D studies are qualitative in nature and even among quantitative ones, there is
little agreement in terms of the variables being examined. As such, Donner concluded that a
statistical meta-analysis would not be feasible given the lack of commonality in available studies
(Light & Pilleman, 1984) and suggested that more research be conducted.
At the micro level, a number of economic outcomes are possible. ICTs can directly
eliminate brokers and intermediaries. ICTs can also boost the household incomes of workers who
are employed by the ICT sector. Technology can also support the ways that development
13

projects are implemented in the form of databases and software platforms. These possibilities are
wide-ranging but the evidence that demonstrate positive impact has been relatively scarce until
recently. The few studies that have been successful in reporting a direct impact have been cited
ad nauseum by proponents of ICT4D interventions.
In a study of the fishermen of Kerala, India, Jensen (2007) famously found that mobile
phones resulted in positive developmental impacts. Working with five-year time series data from
three fish markets in coastal India, Jensen found that because the fishermen were able to get
mobile phone coverage out at sea, they started to make calls to inquire about pricing and that
helped them decide on the best market to take their fish to. This adoption of mobile phones by
fishermen and wholesalers was “associated with a reduction in price dispersion. Both consumer
and producer welfare increased” (Jensen, 2007, p. 879).
Another often-cited study focused on grain traders in Niger. Aker (2008) found that
introduction of cell phone towers in Niger reduced differences in grain prices across markets by
20% and the intra-annual variation of grain prices by 12%. Akers reported that “grain traders in
markets with cell phone coverage search over a greater number of markets, have more contacts
and sell in more markets. This underscores the fact that the primary mechanism by which cell
phones affect market efficiency is a reduction in search costs and hence transaction costs” (p. 45). Mobile phones were also found to have augmented communication in existing supply chains
in the Nigerian market for traditional handwoven ceremonial cloth (Jagun, Heeks, & Whalley,
2008). In addition, Donner (2006), and de Silva and Ratnadiwakara (2008) also found evidence
that mobiles reduce transactional costs and made information more readily available. Other
research suggests that the extent to which individual enterprises benefit from enhanced access to
14

ICT depend on: size, industrial sector, location, workforce skills, availability of relevant content
and whether suppliers and customers are also frequent users of ICTs (UNCTAD, 2010, p.64).
Taken together, these studies give the ICT4D practitioner some optimism that with better access
to information through the use of mobile phones, the economic lot of the poor can be improved.
Nevertheless, the evidence of ICT4D effects at the micro level has not been cumulative,
first, because as mentioned above, little energy has been expended on theory building and
second, because ICT4D researchers have failed to learn from mistakes made in the past and
continue to impose preexisting designs on the poor (Heeks, 2002). Macro level ICT4D findings
may apply to small-scaled enterprises but the effects may be very different since the
entrepreneurs could have different purposes for ICT acquisition and different entrepreneurial
objectives. As such, the dynamics of microenterprise growth require additional evaluation.
Microenterprises and Development
Microenterprises constitute a considerable proportion of the businesses in the developing
world. In India alone, there are an estimated 1.5 billion microenterprises (NSSO, 2012), making
microenterprises the most common type of business in the country. Just by sheer numbers,
microenterprises have the potential to make a difference in the lives of the bottom billion –
people whose income is less than one dollar a day (United Nations, 2000). However, this
optimistic view of microenterprises needs to be tempered by the fact that only a small minority
of enterprise are growth oriented and most remain at the subsistence level, yielding low returns
on labor and capital (Duncombe & Heeks, 2002). Furthermore, these smaller enterprises are
more likely to remain unproductive relative to the larger ones (ILO, 2009; La Porta & Shleifer,

15

2008). Besides, the overall contribution to the economy’s net growth by microentrepreneurs is
still unclear (La Porta & Shleifer, 2008).
But just because microenterprises do not transform a country’s economy does not mean
that they do not warrant rigorous academic study. Microentrepreneurs have long been of interest
to the development community (Mead & Liedholm, 1998; Nichter & Goldmark, 2005) and from
a poverty reduction perspective, it is important to focus attention on small-scaled enterprises
because they typically have the greatest involvement of the poor and collectively sustain the
livelihoods of many of those at the bottom of the pyramid (UNCTAD, 2010). Among the
microenterprises, those that are growth oriented typically aim to extend beyond their current
market reach (UNCTAD, 2010, p. 66) and are generally better-placed to make use of ICTs.
Besides, owning a microenterprise may have social benefits for the business owners in additional
to economic ones.
3

People at the bottom of the pyramid have voted with their wallets for their technology of
choice. Even though they number among the world’s poorest, this demographic group has
demonstrated that they desire access to mobile phones and are willing to pay (Quadir &
Mohaiemen, 2009). Mobile phones appear to be the technology of the moment and the increased
availability of the devices is “creating opportunities for ICTs in the enterprise sector to contribute
to development and reduce poverty” (UNCTAD, 2010, p. xi).

3

In the GSMA study, people with a household income of less than US$75 per month were
defined as being at the bottom of the pyramid (BOP). The original notion that such persons
represent a large, untapped market is the work of Prahalad (2005).
16

There is some attitudinal evidence that microenterprise owners have very positive
attitudes toward mobile phones use for business. The GSM Association (2011) reported that 80%
of business owners say that they benefit from mobile phone ownership compared to only 63% of
non-business owners. Business owners are two and a half times more likely to use mobile phones
to earn income than non-business owners. Other researchers describe the key utility of mobile
phones as the improved coordination of social and economic activities (Ling & Yittri, 2002).
Donner (2010) suggests that it is important to rigorously examine mobile use by entrepreneurs
because different uses of the devices may lead to different outcomes. It is particularly important
for ICT4D researchers to distinguish different uses and outcomes so as to understand how
entrepreneurs can use technology to serve customers more effectively, start a new business, and
check market prices or to bypass brokers. Current studies indicate that mobile phones are best at
intensifying current relationships compared to changing the fundamental structure of business
relationships and this led some researchers to conclude that mobile phones help some businesses,
some of the times (Donner, 2005; 2006; Samuel, Shah, & Hadingham, 2005). What would be
helpful for ICT4D practitioners are clearer distinctions of which business processes are best
augmented by the use of technology and what kinds of comparative advantages are afforded.
A caveat to the optimistic take that mobiles for development is that despite the
widespread ownership of mobile phones and their potential, global poverty does not seem to
have decreased noticeably. This seems to suggest that access to mobile phones is not the magic
bullet to poverty and studies that inform the ICT4D field about why economic growth is limited
despite widespread access would be particularly useful. This dissertation is positioned as one
such study. In addition, it should be noted that all the effects discussed thus far are economic
17

ones. It may seem intuitive to only consider economic consequences in the case of
microentrepreneurs but research in developing contexts have found that the same mobile devices
are used for both business and social purposes. As such, it is plausible that the use of the devices
will have both economic and non-economic effects. Accordingly, this study will attempt to
identify other non-economic effect of technology use by business owners that have been
unrecognized by ICT4D researchers and “unintended” by ICT4D interventionists.
Limitations of Mobile Phones for Effective Development
The ICT4D community is coming to a better understanding of the limitations of mobile
phones in enacting effective economic development. The following section discusses four of
these limitations. There is limited economic effect because of: (1) Limited economic use; (2)
Business attributes; (3) The Matthew effect of technology use; and (4) Social uses of mobile
phones far outstrips economic uses.
Limited economic use. The ICT4D literature does not always support the notion that
ICTs lead to positive development. Some researchers have not been able to find the same
relationship between IT investments and rates of economic growth (Pohjola, 2001). Other microlevel studies also did not find strong evidence of ICT use and increased productivity. Kenny
(2006) was unable to find increased productivity resulting from ICT use among SMEs in East
Africa and Chowdhury (2006) also noted that the productivity gains in his study were not
demonstrably strong. Similarly, in a study of microentrepreneurs in Mumbai, India, Chew, Levy,
and Ilavarasan (2011) also found that productivity gains from ICT use were small due to limited
use in business practices. The researchers did however find evidence to suggest a causal
relationship between ICT use and increased business profits in the case where mobile phones
18

were used for business practices. This observation of limited impact from limited use is also
supported by a study in Rwanda which reported that only one-third of calls made by micro and
small enterprise owners (MSEs) were business related (Donner, 2006).
The lesson that can be drawn from these studies is that improving access to ICTs does not
always guarantee poverty reduction. The information that is being exchanged in the ICT
channels has to be relevant (UNCTAD, 2010). The technology itself does not create the
information and neither does it validate the utility of the information. Understanding the
information that is exchanged and the utility of that information is still a human endeavor (ADB,
2003). As a development tool, ICTs cannot improve conditions or create jobs on its own. ICTs
have to be linked to the efforts and motivations of their users.
Business attributes. Another limitation to the economic effect of mobile phones relates
to the nature of the enterprise - whether they are growth or livelihoods enterprises (Duncombe &
Heeks, 2005). Growth enterprises show a greater business focus and deliver broader/longer-term
benefits of competitiveness, innovation, exports, etc. Livelihood enterprises are typically for the
majority of the poor, delivering benefits in terms of livelihood assets. The communicative needs
of livelihood enterprises tend to be limited and hence the investments in mobile phones would be
unlikely to make a significant impact in the profitability of these businesses (Duncombe &
Heeks, 2005). In fact, Schoar (2010, p. 62) noted that there is “a vanishingly small number of
individuals who transition from subsistence to transformational entrepreneurs.” On the other
hand, for growth enterprises that have entrepreneurial goals that are more proximal to large
organizations, the increase in business efficiency afforded by technology might be more
applicable, although the general consensus in the literature is not at all optimistic in this regard.
19

Furthermore, a review of mobiles for development studies suggested that mobile devices
are mostly used for existing business networks rather than business creation (Donner, 2010).
Other studies showed that mobiles are mostly used to keep in touch with customers and suppliers
(Esselaar et al., 2007), so perhaps one explanation for the scarce evidence of impact is that the
majority of subsistence-based enterprises uses ICTs at best as a communication tool rather than a
tool of commerce (UNCTAD, 2010, p.84).
In his literature review of mobile phones for development, Donner and Escobari (2010)
observed that mobile phones “amplify existing material and informational flows” (p. 641) rather
than change them fundamentally. Indeed, mobiles have generally not been found to be very
successful in growing businesses by way of reaching out to new customers (Donner, 2007).
Micro and small entrepreneurs were found to prefer face-to-face interaction over mobile phone
use especially when they are reaching out to new clients and the phones are mostly seen as a
facilitating technology to sustain existing trust-based relationships (Donner, 2006, p.78).
Molony (2006) also found that mobiles help intensify and solidify existing relationships but the
augmentation of relationships only occur for business networks that already have face-to-face
connections. For business relationships in Tanzania, face-to-face interactions and informal
relationships are still very important despite the affordances of the mobile (Molony, 2006). As
noted by the UNCTAD (2010), the magnitude of ICTs may be more limited in the context of
local value chain systems that rely predominantly on “pre-existing, informal and culturallyrooted communication where the exchange of valued information is by means of personal
contact” (p. xv). For these enterprises, it may be that combining mobile phones and face-to-face
channels will boost the effectiveness of both (GSMA, 2011).
20

The economic sector that the microenterprise operates in may also influence the effect of
mobile phone use on business growth. Different industrial sectors might have different
communication needs and thus use mobile phones in different business processes. For instance,
microentrepreneurs in the service sector might make more intensive use of mobile phones to
communicate with local customers and suppliers. This is evidenced by in South Africa, Egypt
and Tanzania which found that mobile phone use increased profits particularly in the service
sector (Samuel et al., 2005). In the manufacturing sector, non-exporting manufacturers had
highest information needs because they often struggle to survive (Duncombe, & Heeks, 2001).
By contrast, manufacturing exporters who are part of the global value chain often seek
information for growth through new markets. Microentrepreneurs in the trade sector might use
mobile phones to check the market for the best prices and optimize their profits in their business
transactions (Jensen, 2007; Aker, 2008).Previous research also found that microenterprises in the
trade sector of the informal economy were more likely to experience economic growth from the
combination of ICT access and owner motivation to use ICTs for business (Chew, Ilavarasan, &
Levy, 2010). Given these findings, economic sectors could be an important consideration in the
examination of the effect of mobile phones and form the basis of one of the research questions
posed later.
The Matthew effect of mobile phones use. ICT4D research in the past decades suggests
one unintended consequence: those who are better placed in society might use technology to
accentuate existing power relations and inequalities (UNCTAD, 2010, p. 83). For instance,
Abraham (2006) noted that in closing the digital divide, another may open in that the power of
trade intermediaries was enhanced because they were better able to maximize the benefits
21

afforded by technology. This “the-rich-get-richer” effect is sometimes referred to as the Matthew
effect (Merton, 1968), thusly named from the biblical verse in which it was proclaimed:
“For whosoever hath, to him shall be given, and he shall have more abundance; but
whosoever hath not, from him shall be taken away even that he hath” (Matthew 13:12,
The Holy Bible: King James Version)
The Matthew effect has also been noted in research on the effects of traditional mass
media. Past research has shown that mass media tend to widen differences between the
information-rich and information-poor among their audiences (Gaziano, 2010; Tichenor,
Donohue, & Olien, 1970). The information transmitted by the mass media very often has
“greater effect on individuals who are already better informed, better educated and of a higher
socio-economic status” (Rogers, 1986, p. 169). In other words, demography determines the
differences in the ability to process media information and may in turn affect the extent of the
effect of technological use on business growth.
Social uses of mobile phones. Another possible explanation for the lack of studies
demonstrating strong effects on economic growth might be that microentrepreneurs do not make
extensive use of technology for business purposes. Donner (2004) found that microentrepreneurs
in Africa used their mobile phones for both business and social purposes and hence mobile
phones support their “livelihoods” as well as their “lives.” Souter et al. (2005) also found that
mobile phone users in Africa and India used their devices in times of crises and to connect to
their social networks in their everyday lives. Few if any used mobile phones solely for economic
activity. Despite the recognition that the users of mobile phones may not distinguish between
business and social uses, most ICT4D studies have not looked into the social consequence of
22

mobile use as it relates to developmental goals. Most international aid agencies acknowledge the
importance of technology and now understand that the effectiveness of the devices would
naturally depend on the content they carry (UNCTAD, 2011). The tendency for mobile phones to
be used for social calls begs the question of whether the development outcomes would still be
served.
Two research questions and three hypotheses are posed here, with one research question
to follow later:
RQ1: What is the relationship between female microentrepreneurs’ use of mobile phones
and the economic growth of their enterprises?
H1: The more a female microentrepreneur uses mobile phones for business purposes, the
more her business will show business growth.
H2: The higher the growth orientation of a female microentrepreneur, the greater the
likelihood that her microenterprise will experience economic growth.
H3: Female microentrepreneurs with a higher socioeconomic status will benefit more
from the business use of mobile phones than female microentrepreneurs with lower
socioeconomic status.
RQ2: What are the differences, if any, in the relationship between use of mobile phones
and business growth for microentrepreneurs in different economic sectors?

23

CHAPTER 2
Unintended and Unrecognized Effects of Mobile Phone Use
The preceding discussion examined the role of mobile phones on economic growth. As
was shown, the most popular narrative of ICT4D centered on economic growth, but as prior
ICT4D inventions have demonstrated, access to technology does not insure economic growth.
There are two issues at hand here, the first being that ICT4D means more than putting
technology in the hands of the poor and expecting that their economic well-being will be
improved. The second issue is about understanding that there are possibly other effects that have
been consigned to the penumbra of economic effect. It may be that social effects of mobile
phone use will also help improve the broader well-being of the world’s poorest.
Understandably, the foremost concern of development should be about alleviating poverty.
Collier (2007) argued that we should be focusing on the “bottom billion” because, without
external aid, the 58 nations in which the poorest reside will most likely remain static or decline
as the rest of the world progresses. However, development is not just about the “accumulation of
wealth and the growth of gross national product and other income-related variables” (Sen, 1999,
p.14). Economic development will always be important but development is also about other
related indicators of development like primary education and being free from diseases. This
multi-dimensional view of development is reflected in the different Millennium Development
Goals where both economic and non-economic factors are listed. As Hayek (1976, p.35) pointed
out, “economic considerations are merely those by which we reconcile and adjust our different
purposes, none of which, in the last resort, are economic (excepting those of the miser or the man

24

for whom making money has become an end in itself).” Economic development is a means to a
greater end and should certainly not be seen as the only ends of development.
This philosophical argument that development is not just about economic growth is
reflected in how mobile phones are used in everyday lives. While economic activity may be
facilitated by the affordances of the mobile in terms of improving response time and information
exchange, the technology may also be generating non-economic benefits for the individuals. For
instance, fishermen report the peace of mind from increased contact with their families onshore
as a benefit of mobile ownership. The mobile phone in the case of SMEs is not just a tool for
maximizing profits or improving business efficiency; it serves other social functions that may be
tied in with the economic benefits as well.
Indeed, research indicates that people in the developing world tend to use mobiles more
for social than business interactions (Castells et al., 2007; Souter et al., 2005). Another
noteworthy observation is that the users themselves do not distinguish social and business use
and that the line between the two is often blurred (Best, 2010; Donner 2004). ICT4D researchers
are the ones who have been examining business uses almost exclusively and perhaps ignoring the
fact that individuals may not always been driven to use ICTs in “exclusively instrumental ways”
(Duncombe, 2009). If users mostly communicate via mobile phones for social purposes, it is the
social networks rather the business networks that may be augmented.
From a broader perspective, international aid agencies such as the UNCTAD (2010) also
noted that ICT interventions for subsistence-based enterprises should not be judged solely on
their economic effect since “social benefits cannot be readily separated from enhancements to
their communication and information systems for enterprise purposes” (p.85). This is also
25

reflected in the Asian Development Bank’s development framework (2003, p.2) which includes
strategies that would promote pro-poor sustainable economic growth as well as inclusive social
development for the disadvantaged groups in society. In Bangladesh, women now earn money
for their family using ICTs and these earnings have elevated their positions in the household
(Ahmed, Islam, Hasan, & Rahman, 2006). Their role in decision-making for family affairs is
now more important and this increase in respect for women has created a virtuous cycle in
society by inspiring other women to push for changes to their own social status. Furthermore,
ICTs can provide accessible public spaces for underprivileged women to have their own voice
and publicize their experiences and perspectives (Gurumurthy, 2006). Other researchers have
found that social uses of mobile phones can reinforce economic uses (Donner, 2009; Burrell,
2008). The implication for researchers is that the use of technology by enterprises should not be
studied behind a walled garden of economic effects.
Thus, this dissertation seeks to identify social benefits that are generated by enhanced
communication within social networks. These social benefits may be tied in with economic and
business outcomes, For instance, when women entrepreneurs remain in touch with family while
they are at work, they may feel more at ease that household chores are taken care of.
Consequently, they may be better positioned to take care of their businesses. So while the social
calls are not generally considered in ICT4D research as important, these calls could possibly
generate benefits that are tied in with economic outcomes. In order for mobile phones to benefit
the poor, the devices need to address their livelihood concerns as well as other concerns of their
daily lives. The separation of business and social effect may be an artifact of research agendas

26

rather than the actual ways in which the technology is used. At the micro level, the entrepreneurs
simply do not separate mobile use for work and for life.
Mobile Phones and Social Development
ICTs can be used as productivity tools and as prestige objects (Unwin, 2009; Castells et
al., 2007). Mobiles can also serve as symbols or vehicles of globalization. Accordingly, while
entrepreneurs might use mobile phones for instrumental economic purposes such as information
seeking or connecting with their business networks, they can also be using mobile phones for
intrinsic uses that are “social or emotionally related” (Ling & Donner, 2009, p.189). This point
might be illustrated in a study of mobile advertisements in India. Kavoori and Chanda (2006)
found that mobile phones are depicted as capitalist, masculine and corporate, and serve as
symbols of modernity and globalization for many users. In their study, the scholars found that a
sizeable number of advertisements focused on the esteem-enhancing aspect of mobile phones
rather than the possibilities of increased economic efficiency.
At a broader level, scholars are gradually recognizing that development is not strictly
economic. As noted by Sridhar and Sridhar (2006), mobile phones have the potential to improve
broader social well-being as well and scholars should also examine the role of mobile phones in
other non-economic aspects of life such as health, education and governance (Horst & Miller,
2006). In another study on the capacities to use and actual uses of technology, Alampay (2006)
noted that the primary use of mobile phones is social in nature. He observed that the use of
mobile phones can intensify social ties and improve connections with clients but this use of the
devices does not necessarily translate to more business or more income for microentrepreneurs.

27

In this last instance, the investment in mobile phones is having a social outcome as opposed to
the economic outcome for which it was intended.
Thus, the social consequences of mobile phone use might be just as important, if not
more important than economic ones. Smith, Spence, and Rashid (2011) report that mobile
phones maintain family and social relations, allow users to act in an emergency and increased the
efficiency of everyday living. For business owners, mobile phones increase profits particularly in
the service sector (Samuel et al., 2005) and they also contribute to the overall well-being of the
entrepreneurs. Beyond the potential of generating increased revenues, there are other benefits.
For instance, connection to their social networks can give the tradespeople a peace of mind while
they conduct their business. Abraham (2007) reported that while mobile phone use improved the
market efficiency of fishermen in India, what has been overlooked is that the devices also allow
them to feel safer and less isolated while they are out at sea. The use of mobile phones for family
interactions can also reduce family transaction costs such that the fishermen can deal with
quotidian activities while they are out at sea or in the harbor.
Other major international organizations such as the UNCTAD also acknowledge the role
of ICTs in augmenting social and human capital assets in addition to physical capital. ICTs have
the potential to increase the self-confidence of their users and can facilitate the participation of
women in the economy (UNCTAD, 2011). For women in low and middle income countries,
research has found that the benefits of mobile phone ownership are more social than economic.
Women in these countries reported that they felt safer, more connected and more independent as
a result of their mobile phones (GSMA, 2011). Their income or professional opportunities also
increased. The respondents reported that communication with family and friends was a core
28

activity of daily life. Another case study also provided some evidence that woman entrepreneurs
benefit socially from ICT usage (Guihuan, 2005). Other studies about women and ICTs also
suggest that ICTs may have an empowering function for women users, improving their sense of
agency and control, increasing self-esteem and self-confidence, and improving power
relationships (Garrido & Roman, 2006; Huyer, 2005; Maier & Nair-Reichert, 2007; Slater &
Tacchi, 2005). The Grameen Village Phone Project did not just create business opportunities for
women in rural villages; Bayes, von Braun, & Akhter (1999) found that there were also social
development outcomes. The village phone ladies reported higher self-confidence, improved
mobility, knowledge and a greater sense of empowerment in making decisions that matter to
them.
The social consequences are not limited to perceptions of empowerment and security.
ICT4D scholars have also examined the use of mobile phones to improve education (Rashid &
Elder, 2009; Traxler & Leach, 2006; Stone, Lynch, & Poole, 2003), and to provide mobile
healthcare applications (Chib, 2010; Idowu, Ogunbodede, & Idewo, 2003). In his study of
midwives working in rural communities, Chib (2010) reported that midwives used mobile
phones to transmit health statistics to a central database, contact coordinators and peers for health
advice and information, and communicate with doctors and patients. In their study of Nigerian
doctors, Idowu et al. (2003) described how the doctors used mobiles to communicate with one
other within a large hospital, and to respond to emergencies when offsite. In both studies, the
mobile phone infrastructure facilitated the delivery of basic healthcare efficiently while
overcoming the challenges of constrained access and delayed interventions.

29

As the uptake of mobile mobiles increases globally, researchers have also observed that
mobile phones can create rural-urban and international linkages that could add to the social
capital of marginalized populations (Heeks, 2008b). In these cases, better flow of information
through social ties that are maintained by mobile phones can help the poor make the best of
opportunities that they might not have been aware of.
Applying the Senian Approach Using the Concept, Mattering
In Development as Freedom, Amartya Sen (1999) argued that indicators of development
are both economic and non-economic in nature. Sen’s freedoms approach contends that
development is about freedom from the vicissitudes of poverty as well as from other societal
features that constrain the ability of the poor to make choices that will better their lives.
The Senian approach takes into account that development is so much more than economics.
Mobile phone use may make a difference for economic growth but its effects may be enhanced
when the social milieu supports the development efforts. Defining development as more than
economic development is important because community norms and values can determine
developmental outcomes. Sen highlighted how “values and social mores affect the presence or
absence of corruption and the role of trust in economic or social or political relationships” (p. 9).
Consistent with the Senian view of development, the Asian Development Bank (2003) also
believes that development goes beyond alleviating poverty and the provision of basic services.
When access to information is increased, the poor can gain access to a wider range of life options
and therefore play a greater role in determining their future. Equally important are other
intangibles such as the freedom to participate in the wider society and feelings of empowerment.

30

The Senian approach focuses on development as the expansion of a freedom of choice
not just in the economic domain but other domains such as healthcare, education and political
participation. In fact, the Senian approach seems to be more closely aligned with the Millennium
Development Goals which encompass development in different domains of daily life. Smith et
al. (2011) made the case for the potential of mobile phones in extending human capabilities in a
paper that applies Sen’s capability approach. The authors argued that mobile phones increased
access to timely and relevant information, and expanded possibilities for increase connectedness
between people. Beyond this recent study, empirical research that applies the Senian approach to
ICT4D is scarce, presumably because Sen himself has not suggested any methods of
operationalizing his largely normative insights.
Mattering as a social effect. Related in a sense to several of Sen’s “substantive
freedoms” (e.g. freedom of thought, of emotions, and of affiliation) is a concept from the field of
social psychology, “mattering.” Mattering is defined as the perception that people have of how
significant they are to others (Rosenberg & McCullough, 1981). Mattering has been said to be
integral to people’s self-concept and their sense of where they fit in their social networks and
mattering is profoundly important for both self and society. For individuals, the perception that
who they are and what they do are relevant to others can keep feelings of alienation at bay. For
society, mattering is an essential element of feelings of social bonding in the social networks and
structures.
Empirical verification of the construct of mattering in social psychology has thus far
uncovered three dimensions – awareness, importance and reliance. Awareness refers to the
“merest of senses if others realize that we exist” (Elliot, Kao, & Grant, 2004, p. 340). Importance
31

refers to the extent to which people are the object of others’ interest and concern. Reliance refers
to the extent to which others depend on us. In an empirical validation the concept of mattering,
Elliot et al. (2004, p. 353) established the discriminant validity of three components of mattering
and concurred with Rosenberg and McCullogh (1981) that mattering is one of the primary
motivators in the self-concept and expected that the concept would be relevant to a wide range of
social phenomena.
In previous examinations of female microentrepreneurs in India, the construct of
perceived empowerment was found to be related to mobile phone use (Ilavarasan & Levy, 2010;
Chew, Ilavarasan, & Levy, 2010; Chew, Levy, & Ilavarasan, 2011). The construct comprised
items such as “because of my business, I am feeling more confident”, “because of my business, I
have gained respect among my friends and in my neighborhood”, and “because of my business,
my parents-in-law are proud of me.” These items share conceptual similarities with particularly
the important and awareness components of the mattering construct. If measures of reliance had
been deployed in the Mumbai study and if the Mumbai data were then re-analyzed, it is possible
that what was conceptualized as perceived empowerment is actually mattering.
The conceptual clarification of perceived empowerment and mattering might be of
interest to the ICT4D field in two ways. First, it will introduce a social psychological construct
that has already been empirically validated and it will clarify the somewhat vague concept of
perceived empowerment. This endeavor will improve the parsimony of the social psychological
construct. Second, by using the term mattering, we can focus on the empirical examination of a
self-construct without getting into the ideologically-tainted rhetoric that sometimes surrounds
discussions of the enfranchisement of women (Kabeer, 1999; Rowlands, 1997).
32

Thus, inspired by the Senian capabilities framework, this dissertation will examine the
relevance of the construct of mattering in ICT4D research. At the framework level, this endeavor
will be an attempt to bridge the economic goals of development to the other goals that address
the broader well-being of the poor. At the theoretical level, the dissertation will examine one key
social consequence of mobile phone use for microentrepreneurs in the form of mattering. As
such, the following research question is posed:
RQ3: What is the relationship between mobile phone use and mattering?

33

CHAPTER 3
Women Entrepreneurs in India
India holds great promise as a site for research about the mobile phone productivity
paradox for three reasons. First, the country currently has an overall mobile teledensity of 79%
(TRAI, 2012). Average price per minute of mobile phone use in India is one cent (in USD) and is
among the lowest in the world (UNCTAD, 2011). In urban areas, the total number of mobile
subscribers is 596 million and the mobile teledensity is 163%. With the number of mobile
phones exceeding the number of people in Indian cities, claiming that mobile phones are
“everywhere” in this context would not be an exaggeration, at least not by far.
Second, Indian cities are home to a great number of microenterprises that provide
employment for impoverished workers. According to the NSSO (2000), the greatest number of
“establishments” (in practice, microenterprises) – some 4.2 million – are found in the cities of
India, a figure that is three times that of the number located in India’s vast rural areas. This
informal sector is also estimated to account for two-thirds of total employment (Srivastava,
2005). Currently, the vast majority of new connections to mobiles worldwide occur amongst the
urban poor, a trend that is only projected to continue. Therefore, the frontier of socio-economic
exclusion and inclusion in emerging networked societies is in urban spaces (Qiu, 2009). A study
in this research context will thus shed light on how mobile phones are used by urban
microentrepreneurs and perhaps start to unravel the mobile phone paradox.
Third, despite widespread access to mobile telephony and multitudes of microenterprises,
India still has one of the largest income gaps in the world (GSMA, 2011). This mismatch
between mobile access and income makes India an archetypal context of the mobile phone
34

paradox. Indeed, in the urban areas of India, we seem to be seeing mobile phones everywhere but
in poverty alleviation. Also, the case of India is particularly relevant to this dissertation because
the country has one of the most profound gender gaps in South Asia. There are 137 million fewer
women than men, and women are 31% less likely than men to own a mobile phone (GSMA,
2011). The presence of this gender gap makes India an ideal site for examining the role of mobile
phones in achieving social development outcomes.
Returning to the three research goals of this dissertation, urban India is a suitable context
because: (1) There is a large number of microentrepreneurs; (2) It is an archetype of the mobile
phone paradox with its huge income gap despite the widespread access to mobile phones; and (3)
There is both an income gap and a gender gap that would allow this dissertation to examine the
role of mobile phones in achieving economic and social development outcomes.

35

RESEARCH METHOD
Sample Design
One survey comprising three samples were conducted in Chennai, India: one, a general
survey of women who own microenterprises, one of women who own microenterprises who had
received microloans, and one of women who own microenterprises in three specific industrial
sectors (leather, plastics, and engineering). The “industrial sectors” survey involved
microenterprises in the manufacturing sector (MOSPI, 2008) as we wanted to investigate if the
use of mobile phones differed among industrial sectors. In our previous study in Mumbai, India,
the manufacturing sector was omitted from the analysis because the number of microenterprises
in this sector was too small. The inclusion of manufacturing firms in these industrial sectors was
to ensure that there would be a sufficient number of them to run the sector level analyses.
The surveys were administered by trained interviewers from a local marketing firm, selected
through the competitive bidding policies of the Indian Institute of Technology-Delhi. The
surveys were in the field from March to May, 2011. Permission to use data from the three
surveys was granted by Dr. P. Vigneswara Ilavarasan, principal investigator, Indian Institute of
Technology, Delhi. The data-gathering was supported by a research grant from the International
Development Research Centre (IDRC), a Crown corporation in Ottawa, Canada.
4

For data-gathering purposes, a microenterprise was defined as a business that has
between one and ten hired workers. Data for the general survey was gathered using a multi-stage

4

There is no consensus among researchers about the definition of a “microenterprise” and
reports often conflate micro-, small-, and even medium-sized businesses. Some researchers cap
microenterprises at five or fewer workers or, invoking International Labor Organization, United
36

random sampling technique, coupled with a random walk procedure. The first stage was
composed of 31 “investigative units” (IV) of the Indian government’s National Sample Survey
Office (NSSO), selected by simple random sampling from the 305 IV units in Chennai City. Two
reserve IV units were selected at the same time. The IV is a geographic area typically made up of
15-20 “blocks,” where a block has a population of 600 to 800 persons and has clear cut
identifiable boundaries and landmarks. Nine interviews were conducted in each of the 31 IVs, a
clustering method that is generally consistent with common survey practice.
All the blocks in a given IV were numbered. As the second stage in the sampling frame,
one block in each IV unit was selected by simple random sampling. A number was assigned to
each corner of each block selected, going clockwise around the block starting in the uppermost
left-hand corner. One of the corners was then chosen by simple random sampling. That corner
became the final stage in the multi-stage cluster sample and was the starting point for the
interviews.
Interviewers were instructed to follow a predetermined, random walk from the starting
point. As a part of providing instructions to the survey interviewers, four possible directions (left,
right, forward, back) were listed and all possible ways of walking in any Indian road/street. A
direction was chosen by simple random sampling and the interviewer proceeded in that direction
along the flow of traffic until the first interview is completed. For each subsequent interview, a
new direction (left, right, forward, back) was again selected by simple random sampling and the

Nations, or British government criteria, set the limit at ten employees. In India, the site for this
dissertation, the National Survey Sample Organization (NSSO, 2000 and 2012) categorizes very
small businesses by the number of workers, while the Indian Ministry of Micro, Small, &
Medium Enterprises bases its definition on the value of physical plant and machinery.
37

interviewer continued walking within the selected IV until all interviews have been completed.
In order to ensure adequate representation of various size microenterprises, the following quotas
within each IV unit were established: three enterprises with 1–5 hired workers and six with 6–9
hired workers. These quotas approximate the distribution of microenterprises by number of hired
employees as reported by the NSSO.
The final sample size for the general survey, which was based on the multi-stage random
sample described above, was 298 respondents and the response rate was about 80% based on the
field reports from the interviewers and the survey agency. This rate was slightly higher than the
67% reported in previous research using the same sampling design (Ilavarasan & Levy, 2010).
For the microloans survey, the list of respondents was generated from three sources – the
City Commissioner, the Working Women’s Forum, and the Sornammal Educational Trust. This
list generated 150 microentrepreneurs who received microloans and the response rate was about
79%. For the “industrial sectors” survey, there was no existing list of microenterprises in each of
these sectors and it was more efficient to use a snowball sampling method. Seventy enterprises
were first selected from each of the three industrial sectors (leather, plastics, and engineering).
With the assistance of local informants, geographically contiguous neighborhoods in which a
substantial number of microenterprises can be clearly identified as being in one of the three
industrial sectors were sought out. In developed countries, industrial business sectors located in
the same geographic area have been found to improve business performance by endowing certain
localities with resource advantages while simultaneously sparking innovation through
competition among geographically proximate members (Breschi & Malerba 2001; Porter, 2000;
Pratt, 2000). As this phase is an exploratory one, the size of the sample (n = 150) was fixed by
38

limitations of time and resources. Taken together, the general survey, the microloan survey, and
the industrial sector survey produced an initial N of 598 that included both mobile phone owners
and non-owners.
Questionnaire Construction
The questionnaire drew heavily on a questionnaire developed for a previous study of
microenterprises in Mumbai, India (Ilavarasan & Levy, 2010). For this dissertation, items that
were inserted included better indicators of overall mobile phone use: “how often do you receive
calls from your business suppliers?” and “how often do you receive calls on your mobile from
your customers?” Two new measures of perceived benefits mobile phones – relationship
maintenance and productivity – were also conceptualized. Items for these measures included:
“my mobile phone has improved my relationships with my business suppliers” and “because of
my mobile phone, I do not travel much for business purposes”. According to the manager of the
survey field staff, each interview in Chennai ran for no more than 45 minutes. To reduce
respondent burden and interviewer fatigue, three short breaks of a few minutes were built into
the survey.
With initial approval from the Michigan State Institutional Review Board, a draft
questionnaire was pilot tested in Chennai by Dr. Ilavarasan with a convenience sample of 10
women who own microenterprises. Modifications were subsequently made to the questionnaire
and the final version was approved by the MSU IRB on January 6, 2011. On December 21, 2011,
the MSU IRB issued a closure of the protocol. The final version of the questionnaire was
translated into Tamil, the predominant language of Chennai. To ensure that a linguistically and
culturally Tamil text had been created, the Tamil version of the questionnaire was then back39

translated into English by a local translation service in Chennai. Interviewers carried both a
Tamil and an English version of the questionnaire and respondents were given the opportunity to
indicate from which version they preferred to be interviewed.
Data Handling and Analysis
Completed interviews were returned to P.I. Ilavarasan once a week. The physical
questionnaires are kept in a locked office at the Indian Institute of Technology, Delhi.
Respondents were contacted by Dr. Ilavarasan and a research assistant to verify that the
interview had taken place. Once the interview had been verified, the signed consent form on
page one of the survey which contained identifying information was removed from the
remaining pages and keep in a locked file cabinet. A computer data file was then created from
the questionnaire. Since the signed consent form had been separated from the body of the
questionnaire, the data file did not contain any information linking individual respondents to
their responses.
Operational Measures
The following section describes the operationalization of the three dependent variables
and the 13 independent variables. Appendix 2 lists eight of these independent variables and the
respective items that comprise these variables using the exact wording from the questionnaire.
The operationalization of the other five single-item independent variables is detailed below.
Where appropriate, the most common measure of reliability, Cronbach’s alpha (Cronbach,
1951), is listed.
The rationale for choosing the three dependent variables was twofold. On their own, the
analysis of the individual dependent variables addresses the research questions and hypotheses in
40

this dissertation. As a set, the three dependent variables collectively will offer a narrative of the
overall effect of mobile phones which includes both economic and social effect. This broader
narrative calls to attention of ICT4D researchers and policymakers that the effect of mobile
phones will vary considerably depending on how effect is defined, measured and analyzed.
Dependent variables. The first dependent variable, business growth, is operationally
measured the year-over-year increase in revenue reported by the microentrepreneur. For this
sample, the mean business growth was 6.26, SD = 12.92, range = -50 – 80. About seven percent
of the microentrepreneurs reported negative growth of one percent to fifty percent. About two in
five (38.2%) of the microentrepreneurs reported no growth. Another two in five (38.2%) reported
growth of between one to ten percent. Another 11.3% reported growth between ten and 20%.
The rest (2.7%) reported growth between 20 to 80%.
The use of the year-over-year change in revenue in this dissertation is consistent with
previous work in another research site (Chew, Ilavarasan, & Levy, 2010; Chew, Levy, &
Ilavarasan, 2011). From these studies, we found that the majority of microenterprises do not keep
financial records, making the data collection of revenue reliant on recall. This use of the yearover-year change in revenue originated from the pilot study in 2008. In that pilot study, the
survey interviewers found that entrepreneurs were reluctant to disclose the amount of business
income in real money terms. Presumably, they are concerned about the information being used
for tax purposes (de Mel, McKenzie, & Woodruff, 2009). Attempts to ask indirect questions such
as “how much money would it take to start a business like this now?” were also unsuccessful in
gaining compliance. The respondents were more willing to comply when the question of
business income was measured in terms of percentage change. This measure of business growth
41

has been used consistently in the published research. In previous research (Chew et al., 2010;
Chew et al., 2011), the logarithmic transformation of the measure of business growth was
calculated to correct for skewness in the dataset. In the current dataset, the distribution of
5

business growth is close to normal (skewness = .76) and the measure of business growth was
retained in its original form (year-over-year percentage change in income).
Two attempts were made to improve the reliability of the measure of business growth for
this dissertation. In the first, self-reports of the year-over-year change in the number of hired
workers was explored as another possible indicator of business growth. However, the change in
number of hired workers was not significantly correlated with business growth and produced a
low correlation coefficient, r(335) = -.016, n.s. Indeed, the correlation between change in hired
workers and business growth was negative. One possible explanation might be for this seeming
contradiction is that the increase in the number of hired workers entailed higher labor costs and
thus decreases annual income (LaPorta & Schliefer, 2008).
In the second attempt to increase the reliability of the indicator of business growth, six
additional items based on the assumption that year-over-year profits had increased were
considered: the use of increased revenue to buy more supplies, to start a new branch, to hire
more workers, to save money to reinvest later in business, to buy things for the family, and to
save money for health emergencies. This exploratory index of business growth was a count
variable (1 = yes, 0 = no; M = 2.60, SD = 1.14, range = 0 – 6). This variable was moderately
correlated with the percentage change in annual income, r(335) = .27, p < .001. However, the

5

One statistical rule of thumb is that distributions with skewness values between -1 and 1 are
close to normal.
42

composite measure of growth indicators and business growth had very low reliability (α = .241)
even when both were standardized and scaled. One possible explanation for the low reliability
may be that in scaling to prevent over-weighting of either variable, each growth indicator (e.g.
relocation to a larger premise) is invariably equated to a percentage change in business growth.
This matching may not be valid as increased profitability is often the pre-requisite for the other
business growth indicators. In statistical terms, the two variables are not measuring the same
latent variable of business growth (Hays, 1981). In the interest of maintaining statistical validity,
neither the change in number of hired workers nor growth indicators were added to the measure
of business growth.
The second dependent variable is economic growth linked to mobile phones. This
dependent variable is the perception of increased business growth that the entrepreneurs
specifically attribute to mobile phones. Growth linked to mobile phones was based on a fouritem index comprising: “my mobile is an important tool to help my business grow”, “after I
bought my mobile phone, my profits increased”, “using your mobile for business helped improve
the way you do business”, “using my mobile phone is helping my business make a bigger profit
right now” (α = .71, M = 3.02, SD = 1.08).This variable was constructed by summing and
averaging the component items. Missing data were replaced with mean values but only when
fewer than 10% of the cases were missing for a component item and only for cases in which no
more than a third of the items in an index would be replaced.
The third dependent variable is mattering. The current examination of mattering is based
on the premise that the use of mobile phones may yield social development outcomes in addition
to economic ones. Indeed, the literature reviewed above about women and mobile phones use
43

suggested that mobile phones may have an empowering function for women users by improving
their sense of agency and control, increasing self-esteem and self-confidence, and improving
power relationships (Garrido & Roman, 2006; Huyer, 2005; Maier & Nair-Reichert, 2007; Slater
& Tacchi, 2005). However, the use of the umbrella term of empowerment may pose problems of
conceptual obscurity as can be seen in the multiple ways that empowerment is operationalized in
the current literature. One purpose of this dissertation is to maintain conceptual clarity and
precision in the measurement of the social development consequence of mobile phone use. To
this end, mattering is used for the reason that it has been empirically validated and examined in
health and interpersonal communication over the past three decades.
To create indices of mattering that were appropriate to the research context, the three
factors that comprise mattering – importance, awareness and reliance, were first adapted from
Elliot et al. (2004). To recap, importance refers to the extent to which people are the object of
others’ interest and concern. In the research context, survey items that were selected for the
factor analysis probed the significance of entrepreneurs’ business activities to their family and
social networks. Awareness refers to the merest of senses if others realize that we exist. In the
research context, survey items that were selected for the factor analysis probed the social
acceptance of the women’s business activities by males in their social networks. Reliance refers
to the extent to which others depend on us. In the research context, survey items that were
selected for the factor analysis probed the financial independence of the women as a proxy to the
extent to which their family can depend on their business success.
Although all the survey items were selected based on their conceptual similarities to the
three factors of mattering, they were not assigned a priori into the respective subscales. Instead,
44

a principal axis factor analysis with varimax rotation was conducted to assess the underlying
structure for the 14 items measuring the concept of mattering. (The assumption of independent
sampling was met. The assumptions of normality, linear relationships between pairs of variables,
and the variables being correlated at a moderate level were checked). Three factors were
requested, based on the fact that the items were designed for the three constructs: importance,
awareness, and reliance. After rotation, the first factor accounted for 26.8% of the variance, the
second factor accounted for 19.9%, and the third factor accounted for 15.4%. Table 1 displays
the items and factor loadings for the rotated factors, with loadings less than .40 omitted to
improve clarity. The first factor, which seems to index importance, had strong loadings on items
5-10. Item 4 had a cross-loading on both the first and third factors and was dropped from the
sub-scales construction. The second factor, which seemed to index others’ awareness of the
women’s business activities, had high loadings on items 11-14. The third factor, which seemed
to index the microentrepreneurs’ financial independence, had high loadings on the first three
items.
The first factor of mattering, importance, comprised six items on the first factor (α = .84,
M = 4.30, SD = 0.45). The second factor of mattering, awareness, comprised three items on the
second factor (α = .84, M = 3.88, SD = 0.89). The third factor of financial independence,
comprised three items on the first factor (α = .63, M = 3.72, SD = 0.75). The three factors of
mattering had medium to high reliability for the respective subscales.

45

Table 1
Factor Loadings for the Rotated Factors of Mattering
Factor Loading
1
2
3

Item

1. My business generates some additional income for
my family, but it is not the only source of money we
.75
have
2. I'm hoping my business will make me financially
.67
independent
3. I started my business because I didn't want to be idle
.71
at home
4. Because of my business, I am feeling more confident
.51
.51
about my life in general.
5. Because of my business, I have gained respect
.77
among my friends and in my neighborhood
6. Because of my business, my parents feel proud of
.81
me.
7. Because of my business, my parents-in-law are
.77
proud of me.
8. Because of my business, my husband shows me
.68
more respect.
9. Because of my business, my opinions are considered
.63
to be important in family decisions.
10. I am confident that I can run a successful business.
.64
11. Male customers think that a woman can run a
.67
successful business
12. Male suppliers think that a woman can run a
.85
successful business.
13. I think the men in my family approve of my dealing
.86
with male customers and suppliers in my business
14. My neighbors approve of my dealing with male
.86
customers and suppliers in my business
Eigenvalues
4.90 2.31 1.41
% of variance
26.58 20.07 14.90
Note. Loadings < .04 are omitted

Communality
.59
.46
.63
.59
.63
.65
.61
.55
.49
.55
.49
.76
.80
.82

The awareness and importance sub-scales had reliability coefficients comparable with
those found in existing studies (see Table 2 below). The reliance sub-scale had a lower reliability
46

coefficient than the ones found in existing studies. Table 2 provides a conceptual mapping of the
sub-scales from interpersonal communication to the research context. Indeed, the third factor in
the factor analysis seems to index the financial independence of the women entrepreneurs rather
than the reliance of others on them. However, it should be mentioned that the focus of the other
sub-scales are the businesses in that the first indexed the importance the women entrepreneurs
feel as a result of their business and the second indexed the others’ awareness of their business
activities. Likewise, the financial independence of female microentrepreneurs can be considered
a proxy for the extent to which their families will benefit from the success of the business. In a
patriarchal culture where women are often perceived as a liability on the family’s finances,
financial independence makes them less of a liability on the family’s finances and more of an
asset to the family. Thus, reliance in this context does not index the extent to which others are
dependent on the business but rather the extent to which the women entrepreneurs are not a
financial liability. The reliability coefficient of .63 is less than desired values of greater than .70
but given the exploratory nature of mattering in ICT4D, this sub-scale in the concept is deemed
acceptable (Cronbach, 1951) and can be refined in future studies.

47

Table 2
Conceptual Mapping of Interpersonal Mattering to Mattering in Current Research Context
Interpersonal mattering
Awareness (α = .82 to .87)
Most people do not seem to notice when I
come or in a social gathering,
Sometimes when I am with others, I feel
almost as if no one recognizes me
People are usually aware of my presence

Mattering in research context
Awareness (α = .82)
Male customers think that a woman can
run a successful business
Male suppliers think that a woman can run
a successful business.
I think the men in my family approve of
my dealing with male customers and
suppliers in my business
My neighbors approve of my dealing with
male customers and suppliers in my
business

For whatever reason, it is hard for me to
get other people’s attention
Whatever else may happen, people do not
ignore me
For better or worse, people generally know
when I am around
People tend not to remember my name
Importance (α = .79 to .86)
People do not care what happens to me

Importance (α = .85)
Because of my business, I have gained
respect among my friends and in my
neighborhood
Because of my business, my parents feel
proud of me.

There are people in my life who react to
what happens to me in the same way they
would if it had happened to them
My successes are a source of pride to
people in my life
I have noticed that people will sometimes
inconvenience themselves to help me
When I have a problem, people usually
don’t want to hear about it

Because of my business, my parents-inlaw are proud of me.
Because of my business, my husband
shows me more respect.
Because of my business, my opinions are
considered to be important in family
decisions.
I am confident that I can run a successful
business.

Much of the time, other people are
indifferent to my needs
There are people in my life who care
enough about me to criticize me when I
need it
There is no one who really takes pride in
my accomplishments
48

Table 2 (cont’d)
No one would notice if one day I
disappeared
If the truth be known, no one really needs
me
Reliance (α = .83 to .87)
Quite a few people look to me for advice
on issues of importance
I am not someone people turn to when they
need something
People tend to rely on me for support

Financial independence (α = .65)
My business generates some additional
income for my family, but it is not the only
source of money we have
I’m hoping my business will make me
financially independent
I started my business because I didn’t want
to be idle at home

When people need help, they come to me
People count on me to be there in times of
need
Often people trust me with things that are
important to them
A confirmatory factor analysis indicated that the three-factor concept of mattering in this
6

dissertation was an adequate fit, χ2 = 242.59, df = 63, p < .001, RMSEA = .069, CFI = 0.940
(see Appendix 1). Modification indices suggested that dropping item 11 from the sub-scale of
awareness would improve the model fit slightly, χ2 = 188.03, df = 52, p < .001, RMSEA = .066,
CFI = 0.951. As this examination of mattering is an exploratory one, item 11 was retained given
the very slight increase in model fit. The inclusion or omission of the item in the awareness subscale should be re-assessed when the concept of mattering is replicated in future studies.

6

RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index. Three
measures of goodness-of-fit were used: the overall chi-square test of fit; as Byrne (1989) cited;
RMSEA as Browne and Cudeck (1993) cited, and CFI as Hu and Bentler (1999) cited.
49

Furthermore, in the interest of having fewer predictors in the regression models later, a grand 14item mattering measure was constructed (α = .84, M = 4.10, SD = 0.45).
By conceptualizing mattering as being a function of mobile phone use, this dissertation
does not rule out the possibility of the reverse causal relationship in which increased perceptions
of mattering lead to greater mobile phone use. This question of endogeneity has been posed in
our previous work on business growth. In subsequent analyses, we were able to assess the
statistical support for the plausibility of a recursive relationship (Chew et al., 2011). Since this
dissertation is primarily concerned with examining the effect of mobile phone use, the causal
effect of mobile phone use on mattering serves as the logical start point of the analysis. The
reverse causal relationship or a recursive relationship will be the subject of subsequent research
on the concept.
Independent variables: Respondent attitudes and behaviors. Based on previous
research, several other variables predict business growth. These variables are grouped into
characteristics of the entrepreneurs, characteristics of the business, and the demographics of the
entrepreneurs.
Characteristics of the entrepreneurs include the growth orientation of the businesswomen.
This variable measured the attitudes of the entrepreneurs towards their future growth in terms of
anticipated workforce expansion and anticipated profits. In recent studies of entrepreneurial
characteristics, growth orientation might also relate to risk-taking behavior (Acharya, Rajan, &
Shoar, 2007; de Mel et al., 2009). While this dissertation did not assess risk-taking per se, the
current measurement of anticipated growth follows an approach similar to a recent study by
Verheul, Thurik, Grilo, and van der Zwan (2012) who referred to these attitudes as “wanting it”
50

(business growth). To assess growth orientation, seven items measuring the anticipated growth
by the entrepreneurs were identified and a principal axis factor analysis with varimax rotation
was conducted to assess the underlying structure of the items. After rotation, the first factor
accounted for 43.3% of the variance and the second factor accounted for 20.4%. Table 3 displays
the items and factor loadings for the rotated factors, with loadings less than .40 omitted to
improve clarity.
Table 3
Factor Loadings for the Rotated Factors of Growth Orientation
Item
1. There is substantial demand for our product/services.
2. I won't think of myself as a successful businessperson unless
my profits grow every year.
3. I won't think of myself as a successful businessperson unless
I can hire some new workers every year.
4. One year from now, I expect to be making more money in
my business.
5. One year from now, I expect to have more hired workers in
my business.
6. Five years from now, I expect to be making more money in
my business.
7. I will have more employees in next 5 years
Eigenvalues
% of variance
Note. Loadings < .040 are omitted

Factor
Loading
1
2
.72
.59
.89

.82
.80

.89

.65
.81

.73
.82
3.03
43.3

Communality
.52
.39

.56
.72

1.43
20.4

The first factor, which seems to index anticipated workforce expansion, had strong
loadings on items 3, 5 and 7. The second factor, which seemed to index anticipated increased
profits, had high loadings on items 1, 4 and 6. Item 2 had loading below .6 and was dropped
from the sub-scales construction. Growth orientation in terms of anticipated workforce expansion
51

comprised three items on the first factor (α = .85, M = 3.08, SD = 1.13). The second factor of
anticipated increased profits, comprised three items on the second factor (α = .71, M = 4.14, SD
= 0.53).
As with mattering, a grand growth orientation index was calculated in order to reduce the
number of factors in the subsequent regression models. The index of growth orientation
comprised all six items in the two subscales (α = .80, M = 3.61, SD = 0.69). About 14.3% (81 of
335) of the entrepreneurs had growth orientation that was above one standard deviation of the
mean. For the purposes of subsequent analyses, this group is considered as entrepreneurs with
high growth orientation. About 18.5% (62 of 335) of the entrepreneurs had growth orientation
that was below one standard deviation of the mean. For the purposes of subsequent analyses, this
group is considered as entrepreneurs with low growth orientation.
The business use of mobile phones is a key independent variable in the analysis. The
questionnaire in the Mumbai study asked only about the frequency with which respondents used
mobiles to call customers, suppliers, etc. (Ilavarasan & Levy, 2010). As a methodological
improvement, the Chennai questionnaire included three additional items that indexed both the
frequency with which respondents used mobiles to call their customers, employees, and business
suppliers; and the frequency with which the female microenterprises owners received calls from
their customers, employees and business suppliers, α = .82, M = 1.64, SD = 0.72. This expanded
composite measure not only increased the reliability of the previous measure, it also allowed for
a more fine-grained examination of the actual business processes. This detailed examination may
shed light on the exact business processes that are associated with entrepreneurs with higher
business growth.
52

The social use of mobile phones was a two-item measure that indexed the frequency that
the business owners call their family and friends to talk about non-business related things and
how often they receive calls from family and friends to talk about non-business related things,
r(335) = .556, M = 3.53, SD = 0.82. The inclusion of the social use of mobile phones is based on
existing literature that suggests that even non-business related calls can have a business effect.
For instance, Jensen (2007) found that fishermen in Kerala, India reported better “peace of mind”
while they are working at sea because they can use mobile phones to call home to make sure that
the family is all right.
An index of the perceived benefits of mobile phones was created using two multi-item
measures. First, the perceived benefit of relationship maintenance comprised three items:
“having a mobile phone makes it easier for me to deal with male customers,” “having a mobile
phone makes it easier for me to deal with male suppliers,” and “my mobile phone has improved
my relationships with my business suppliers” (α = .80, M = 2.50, SD = 1.22). The perceived
benefit of increased business productivity was indexed by seven items: “having a mobile phone
makes it easier for me to balance my business life and my home life,” “I get more work done
because I own a mobile phone,” “because of my mobile phone, I do not travel much for business
purposes,” “because of my mobile phone, I receive business calls at any time,” “because I own a
mobile phone, I feel more confident in running my business,” “because of my mobile phone, I
feel more self reliant,” and “because of my phone, I am able to do business with strangers
without much hesitation” (α = .97, M = 3.62, SD = 1.21).
Independent variables: Microenterprise characteristics. The second category of
independent variables comprised different characteristics of the businesses. These included the
53

customer reach of the microenterprise, the number of hired workers, and the formality of
business operations. The customer reach of the business was a single item measure that was
indicated by the geographical locations of the customers. The business owners were asked to
indicate if customers came primarily from the neighborhood (coded as 1), other parts of Chennai,
outside Chennai or outside India (coded as 4). There are two ways that customer reach may be
related to the other variables in the analysis. First, businesses with a higher customer reach may
be associated with higher business growth since they could be servicing more clients in a larger
geographical area. Second, a higher customer reach may require the business owners to use their
mobile phones more extensively in order to stay connected to their clients. In these instances, the
mobile phones are reducing the transactional costs between businesses and their customers by
eliminating the need to travel or allowing business-owners to market their services to more
potential customers.
Another characteristic of the businesses, the number of hired workers, was
operationalized as the number of hired, full-time employees in the businesses who were not
immediate family members. The number of hired workers may be associated with business
growth in conflicting ways. Businesses with higher growth might hire more workers but the
more hired workers a business has, the higher the labor costs. Thus, the net effect of hired
workers may be enterprise-specific and bears further exploration in this dissertation.
Business formality was a count variable (yes coded as 1, no coded as 0) comprising five
items: “is your business registered with the government?”, “is your business registered with an
association?”, “does your business have a PAN (unique taxpayer I.D) number?”, “does your
business have a bank account to use just for business purposes?” and whether financial records
54

are kept for business transactions. The mean for business formality was 0.52, SD = .73. This low
mean is consistent with the findings from existing studies that the majority of microentrepreneurs
are located in the informal sector of the economy.
Independent variables: Microentrepreneur characteristics. The third category of
independent variables comprised the demographics of the women entrepreneurs. These included
the education, caste, class, age, and the availability of domestic help. Education was indicated
by how much formal education the women entrepreneurs had. This varied between “never been
to school” to “master’s degree or higher.” Caste was indicated by whether the respondents selfidentified as being members of a lower, middle or upper caste. Respondents also classified
themselves as being poor, middle or upper class. Caste and class are relevant to the analysis
because they are related to the economic sector the respondents tend to operate in. As Sridharan
(2004) noted, the middle class tends to be “intermediate income groups in non-manual
occupations, situated between a tiny, rich upper class and a majority of low income and manual
occupation groups” (p. 411). Vissa (2011) also found that Indian entrepreneurs have a preference
to work with those from their own caste group when forming a business because they share a
common language. Age was measured by how old the women entrepreneurs were in years. The
availability of domestic help was a count variable of whether the women entrepreneurs had parttime or full time domestic help and whether other members of the family (mother, in-laws and
husband) share the domestic chores. Availability of domestic help was included in the analysis
because of its relevance to the research context. Indeed, given the patriarchal nature of Indian
society, women are typically prescribed home-based roles. Women who own and run a
microenterprise are still not free from domestic work and their business activities must
55

understood in the context of dual home-work challenges (Sulandjari, 1998; Maier & NairReichert, 2007).
Sample Characteristics
Taking the microenterprise as the unit of analysis, the mean number of full time hired
workers (excluding members of the immediate family) was 1.12, SD = 1.85, range = 0 – 10.
More than half (56.7%) of the microenterprises did not have any hired workers other than their
immediate family members. About one-third of the microenterprises (37.1%) had between one
and five hired workers, 6.2% had between six and ten workers. Compared to the national sample
based on the most recent data (2009 – 2010) from the NSSO (2012), the businesses in the
Chennai sample appear to have smaller operations. In the latest national survey, 66% of urban
establishment had employees between one and five workers (including the owner), and 24%
have between six and ten workers on their payrolls (NSSO, 2012). The daily wage rate for an
urban worker was Rs. 125 (2.38 USD) for a male and Rs. 71 (1.35 USD) for a female.
Of the 598 microenterprises surveyed, only 6.4% had a landline phone in the workplace;
while slightly more than half (56.0%) of women who owned those businesses had at least one
mobile phone. Mobile phone ownership by Chennai female microentrepreneurs was twice as
high as the national average of 28% for all Indian women (GSMA, 2011). The use of landline
phones was excluded from the analysis in this dissertation given that less than one in ten
microenterprises had them in the workplace. Also, our previous research also found that they are
being used to meet business communication needs in an extremely limited manner (Chew et al.,
2010). Indeed, many microenterprises maintained their landlines primarily because of the
historically long waiting period to acquire a landline. Also, there is a belief that businesses need
56

to have a landline in order to maintain the business’ presence in the business space, however
infrequently these landlines are used (Ilavarasan & Levy, 2010).
A majority of female-owned microenterprises (47.3%) were in the service sector.
Examples of services offered by respondents included beauty parlors, academic tutoring,
laundry, and equipment rental. Some 29.4% of the microenterprises were trade sector businesses.
As reported by respondents, typical businesses in trade included sellers of clothing, groceries,
small electronics, dress materials, or jewelry. Some 23.3% of the microenterprises were in the
manufacturing sector, including tailoring, dressmaking, toy making, tobacco products and
bamboo furniture. Table 4 shows the breakouts of these business characteristics by mobile phone
owners and non-owners. The average microenterprise had been in business for a decade (mode =
5 years, range = 3 months – 50 years), and only 7.4% had been started within a year of the
survey. Nearly all (86.8%) of the microenterprise customers came from the “neighborhood,” one
in ten came from other parts of Chennai and 3.3% were from outside Chennai.
Table 4
Number of Hired Workers and Economic Sector by Owners and non-Owners of Mobile Phones
Number of hired workers
0
1–5
6 – 10
Economic sector
Service
Trade
Manufacturing

Mobile phone
owners
155 (46.3%)
148 (44.2%)
32 (9.6%)
335 (100.0%)
140
124
70
334

(41.9%)
(37.1%)
(21.0%)
(100.0%)
57

Mobile phone nonowners
184 (70.0%)
74 (28.1%)
5 (1.9%)
263 (100.0%)
142
51
69
262

(54.2%)
(19.5%)
(26.3%)
(100.0%)

Overall
339 (56.7%)
222 (37.1%)
37 (6.2%)
598 (100.0%)
282
175
139
596

(47.3%)
(29.4%)
(23.3%)
(100.0%)

Of the 598 female microentrepreneurs sampled, 80.3% were married and had an average
of two children (mode = 2.0, range = 0 – 7). The mean age was 38.9, SD = 9.97. Only 3.8% had
full-time or part-time domestic help although 37.8% said their husband shared in the work of
maintaining the home. About four out of ten (38.3%) businesswomen had a personal bank
account from which they could make withdrawals or payments on their own. One in ten of the
women entrepreneurs had never been to school, 68.3% had a high school or higher secondary
school education and only 6.3% earned a diploma, bachelor’s degree or higher.
All of the respondents said they speak Tamil and 73.4% can write the language. Less than
two in ten (17.7%) spoke English, 23.2% could read English, and 19.4% could write it. In terms
of numeracy, only two in five (18.1%) of respondents said they could calculate taxes or interest,
another 59.7% said they could do simple arithmetic, while only 3.7% said they could recognize
or write numbers but could not do calculations.
In terms of caste, 36.3% identified themselves with the lower caste, 57.4% with the
middle caste and only 6.4% with the upper caste. In terms of social class, 15.7% identified
themselves as the poor class, 29.9% as lower class, 21.6% as the lower middle class, 25.4% as
the middle middle class, 7.2% as the upper middle class and only 0.2% as the upper class.
Owners and non-owners of mobile phones
Since a principal objective of this dissertation is to investigate the dynamics of mobile
phone use, much of the analysis that follows draws only on the 335 female microentrepreneurs
who were also mobile phone owners. In addition, to provide baseline comparisons between
owners of mobiles and non-owner, independent samples t-tests were conducted for the key
variables and presented in Table 5 below.
58

Table 5
Comparison between Owners and non-Owners of Mobile Phones
Mobile phone
ownership
No
Domestic help
Yes
No
Caste
Yes
No
Class
Yes
No
Age of business owner
Yes
No
Education
Yes
No
Number of full-time hired
workers
Yes
No
Age of business
Yes
No
Formality
Yes
No
Customer reach
Yes
No
Growth orientation
Yes
No
Business growth
Yes

n

M

SD

263
335
263
335
263
335
263
335
263
335
263
335
263
331
263
335
263
335
263
335
263
335

0.99
1.01
1.53
1.83
2.32
3.16
40.54
37.60
2.33
2.98
0.65
1.50
12.05
9.29
0.25
0.52
1.10
1.21
3.46
3.72
6.62
6.26

0.25
0.27
0.56
0.56
1.03
1.20
10.98
8.90
1.05
1.26
1.29
2.12
11.09
8.73
0.44
0.73
0.32
0.53
0.67
0.69
15.50
12.92

t

df

sig. (2tailed)

-0.81

596

.421

-6.50

596

.000

-8.95

596

.000

3.61

596

.000

-6.74

596

.000

-5.71

596

.000

3.40

592

.001

-5.37

596

.000

-3.06

596

.002

-4.55

596

.000

-0.31

596

.754

It appears that owners and non-owners of mobile phones divide along economic lines.
Mobile phone owners are of a higher caste and class. Owners of mobile phones are also younger
and more educated. In terms of business characteristics, owners of mobile phones have more
hired workers; newer businesses; more formal operations and customers from a larger
geographical locale. They also report a stronger desire to grow their businesses.

59

Curiously however, female microentrepreneurs without mobile phones scored higher in
terms of raw numbers on the index of business growth, although this difference was not
statistically significant. This observation runs counter to the dissertation’s premise that mobile
phones have a positive effect on business growth. Possible explanations for this finding include,
first, the costs of maintaining a mobile phone may offset any increase in business growth.
Indeed, Heeks (2008b) noted that there are concerns that the poor could be spending high
proportions of their disposable income on airtime, to the detriment of a family’s financial wellbeing. Second, owners of mobile phones may not always be using the phones for business
purposes. Time spent on the phone chatting is time that could be spent earning money. Also, if
the mobile phones are used primarily for non-business calls, then we would not expect a
consequent growth in business.
To examine the possible effect of mobile phone ownership and growth orientation on
business growth, business growth scores were subjected to a two-way analysis of variance (see
Table 6) having two levels of mobile phones ownership (owners, non-owners) and three levels of
growth orientation (low, average, high). Only the main effect of growth orientation was
statistically significant at the .05 significance level. The main effect of growth orientation
yielded an F-ratio of F(2, 592) = 12.141, p < .001, indicating that the mean growth orientation
score was significantly greater for high growth orientation (M = 4.78, SD = 1.99) compared to
average growth orientation and low growth orientation. The main effect of mobile phone
ownership, F(1, 592) = 1.296, and the interaction effect of the two variables, F(2, 592) = 0.06,
were not significant. Table 7 shows the descriptives for the two-way analysis of variance and
Figure 1 shows the plot of the estimated marginal means.
60

Table 6
Two-Way Analysis of Variance for Business Growth as a Function of Mobile Phone Ownership
and Growth Orientation
Variable and source
Mobile phone ownership
Growth orientation
Mobile phone ownership*growth
orientation
Error

df
1
2
2

MS
248.83
2336.90
11.66

592

F
1.29
12.14
0.06

p
.256
< .001
.941

192.49

Table 7
Means, Standard Deviations, and n’s for Business Growth as a Function of Mobile Phone
Ownership and Growth Orientation

Growth orientation
Low
Average
High
Total

Mobile phone owners
n
M
SD
36
-0.74 10.73
218 6.44 12.04
81
8.88 14.99
335 6.26 12.92

61

n
57
165
41
263

Non-owners
M
SD
1.26
14.57
7.48
13.63
10.64 21.30
6.62
15.50

M
0.48
6.89
9.47
6.42

Total
SD
13.19
12.74
17.30
14.10

Figure 1
Plot of the Estimated Marginal Means of Business Growth as a Function of Mobile Phone
Ownership and Growth Orientation
For interpretation of the references to color in this and all other figures, the reader is referred to
the electronic version of this dissertation.
12.00

10.64

10.00
8.00
Business growth

8.87

7.48

6.00

Do you own
a mobile phone?
Yes

6.44

4.00
2.00

No
1.26

0.00
-0.74
-2.00
Low growth
orientation

Average growth
orientation

High growth
orientation

The results of the two-way factorial analysis of variance are consistent with what decades
of ICT4D interventions have eventually found – mere access to technology does not improve the
well-being of the poor. In the analysis above, neither the main effect of mobile ownership nor the
interaction effects of mobile ownership with growth orientation on business growth were
statistically significant.
However, in order to continue endeavors in narrowing the digital divide, funding
agencies need to be convinced that there will be positive effect for the poor. What existing
62

research and the preceding analysis have found is that the assumption that access facilitates
development does not seem to be supported by empirical data. Certainly in the case of mobile
phones, the near ubiquity of access to these devices did not create transformative improvements
in the lives of poor. Since the primary interest of this dissertation is to understand the effect of
mobile phones, the following analyses and results were conducted in service to this research
problem. To foreshadow these analyses, the results will identify the boundary conditions for
when mobile phones will make an impact on the economic and social development of the poor.

63

RESULTS
To begin to address the research questions and hypotheses, Pearson product-moment
correlations were calculated using SPSS (2011) version 19.0. Table 8 shows the Pearson
product-moment correlations among the dependent and independent variables. The results
reported from this point forth are for the 335 microentrepreneurs who own mobile phones. Also,
recall that the entire sample comprised three sub-samples: one general, one of
microentrepreneurs who had microloans and one of microentrepreneurs in the manufacturing
sector. To ensure that the three samples used were comparable in terms of their business growth,
an F-test was conducted for the respondents who had microloans and those who did not. The
groups were not statistically different, F(1, 334) = 1.10, n.s. An a apriori check for the industrial
sector was not done since industrial sector is an independent variable listed in one of the research
questions.
For the dependent variable, business growth, the correlation matrix indicated that
microenterprise growth was correlated with perceived growth linked to mobile phones, the
growth orientation of the business women, the business use of mobile phones, the perceived
affordances of mobiles for relationship maintenance, and increased productivity. Business
growth was also positively correlated with the number of hired workers, formality, the education
level of the business women, and the amount of domestic help available.
The second dependent variable, perceived growth linked to mobile phones, was
positively correlated with mattering, the growth orientation of the entrepreneurs, customer reach,
number of hired workers, formality, education, and caste. This dependent variable was also
highly correlated to the business use of mobile phones, the perceived affordances of mobiles for
64

relationship maintenance, and increased productivity. There was a negative correlation between
age and perceived growth linked to mobile phones.
The third dependent variable, mattering, was positively correlated with the growth
orientation of the business women, the social use of mobile phones, perceived benefit of mobile
phones for relationship maintenance, number of hired workers, formality, and the availability of
domestic help.
Hypothesis 1 posited that microentrepreneurs who use mobile phones for business
purposes more will experience better business growth. This was consistent with the data and the
zero-order correlation indicated in the matrix. There was a positive correlation between business
use of mobile phones and business growth, r(335) = .16, p < .001.
To answer the other research questions and hypotheses, hierarchical multiple regressions
were conducted to determine the best linear combinations of the independent variables and the
three dependent variables. Four independent variables had non-normal distributions and the BoxCox power transformation (Box & Cox, 1964; Osbourne, 2010) was performed using SPSS to
render the distributions more nearly normal. The four independent variables were: number of
hired workers, customer research, availability of domestic help, and formality. After the BoxCox transformations, the assumptions of linearity, normally distributed errors, and uncorrelated
errors were checked in each model and met.
To examine the relationship between microentrepreneurs’ use of mobile phones and
economic growth in the businesses they own (RQ1), the first hierarchical multiple regression was
conducted with business growth as the dependent variable. When the number of hired workers,
education, formality and the availability of domestic help, were entered, they significantly
65

predicted business growth, F(4, 330) = 4.58, p = .001, with two variables significantly
contributing to the prediction. When the other variables were added, the prediction was
2

improved: R change = .05, F(4,326) = 4.09, p < .01. The entire group of variables significantly
2

predicted business growth, F(8, 326) = 4.42, p < .001, adjusted R = .08, however the predicted
effect was small (Cohen, 1988).
The beta weights and significant values, presented in Table 9, indicate which variables
contributed most to business growth. With this combination of predictors, the only significant
predictor was the growth orientation of the entrepreneurs, β = .14.

66

Table 8
Pearson Product-moment Correlations among the Dependent and Independent Variables

1. Business
growth
2. Growth
linked to
mobiles
3. Mattering
4. Growth
orientation
5. Business use
of mobile
phones
6. Social use of
mobile
phones
7. Relationship
maintenance
8. Productivity
9. Customer
reach
10. Hired
workers
11. Formality
12. Education
13. Caste

1
1

2

3

4

5

6

7

8

9

10

11

12

13

.24** 1

.03
.14 * 1
.22** .30 ** .48**

1

.16** .55** .06

.12* 1

.080

-.091

.224** .087 .021

1

.16** .51** .14**

.13* .43** -.21** 1

.23** .81** .10
.08
.19** .05

.29** .50** -.24** .54** 1
-.01 .37** -.13* .28** .17** 1

.12*

.19** .40** .03

.20** .25** .31** 1

.39** .17** .13*
.17** .32** .07
.12* .14* .14*

.03
.03
.02
67

.24** .13*

.17** .28** .21**
.12* .28** .07
.09
.15** .03

.26** .15** .20** 1
.23** .04 .10
.36** 1
.10
.15** .17** .24** .26** 1

14

15

16

Table 8 (cont’d)
14. Class
.05
.10
.09
.20** .23**
15. Age
-.08 -.13** -.06
-.10 -.17**
16. Domestic
.121* .09
.11*
.05 .09
help
*. Correlation is significant at the 0.05 level (2-tailed).
Variable
1. Business growth
2. Growth linked to mobiles
3. Mattering
4. Growth orientation
5. Business use of mobile phones
6. Social use of mobile phones
7. Relationship maintenance
8. Productivity
9. Customer reach
10. Hired workers
11. Formality
12. Education
13. Caste
14. Class
15. Age
16. Domestic help

M
6.25
3.03
4.20
3.72
1.64
3.53
2.50
3.52
1.21
1.50
0.52
2.98
1.83
3.16
37.60
1.01

.25** -.01 < .04
.03 .18** .24** .35** .47** 1
.02
-.03 -.15** .12* .02
-.06 -.26** .02 -.10
.07
.03
.03
.03 -.07 .08 .10
.01 < .06
**. Correlation is significant at the 0.01 level (2-tailed).

SD
12.92
1.08
0.45
0.69
0.72
0.82
1.22
1.21
0.53
2.12
0.73
1.26
0.56
1.20
8.90
0.27

68

1
.01 < 1

Table 9
Hierarchical Multiple Regression Analysis Summary Predicting Business Growth
Sig
Β
t
Model 1 (Constant)
-0.95
.341
Hired workers
.10
1.77
.078
Formality
.12
2.08
.038
Education
.06
0.96
.340
Domestic help
.11
2.10
.037
Model 2 (Constant)
-3.41
.001
Hired workers
.04
0.69
.492
Formality
.06
0.89
.376
Education
.03
0.50
.617
Domestic help
.10
1.95
.053
Growth orientation
.14
2.40
.017
Business use of mobiles
.02
0.21
.832
Relationship maintenance
.06
0.85
.395
Productivity
.11
1.60
.111
Dependent variable: Business growth
2

2

F(8, 326) = 4.42, p < 0.001; Adjusted R for Model 2 = .08; Δ R for additional variables = .05
From the regression analysis, the use of mobile phones and the perceived benefits of the
mobile phones appear to have no effect on business growth. However, using mobiles for business
communication might be working with other variables to generate business growth or suppressed
by other underlying explanatory variables. As the correlation matrix in Table 8 indicated, the
business use of mobile phones was positively correlated with business growth, r(335) = .16,
p<.001. To investigate the possibility of an interaction effect, the interaction term of mobile
phone use and growth orientation was included in the third step of the previous regression
analysis (see Table 10).

69

Table 10
Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with the
Addition of the Interaction between Growth Orientation and Business of Mobile Phones

Β
Model 3

t
-3.86
0.47
0.57
0.35
2.16
2.78
0.30
0.75
1.86
2.34

sig
.000
.639
.571
.726
.032
.006
.763
.454
.064
.020

(Constant)
Hired workers
.03
Formality
.04
Education
.02
Domestic help
.12
Growth orientation
.17
Business use of mobiles
.02
Relationship maintenance
.05
Productivity
.13
Interaction of growth orientation
.13
and business use of mobiles
Dependent variable: Business growth
2
2
F(9, 325) = 4.59, p < 0.001; Adjusted R for Model 3 = .09; Δ R for interaction term = .02
In this model, the interaction term of growth orientation and business use of mobile
2

phones was statistically significant, β = .13, p < .05. The overall R of the model was .09. With
the addition of the interaction term, the availability of domestic help was also a significant
predictor. To investigate the nature of the interaction effect between growth orientation and
business use of mobile phones, an interaction analysis was conducted (see Table 11 and Figure
2).
Table 11 provides the means for the predicted values of business growth orientation by
business use of mobile phones and Figure 2 graphically represents the interaction effects
between growth orientation and business use of mobile phones on business growth. The figure
70

suggests that entrepreneurs with a low growth orientation do not seem to benefit from the
business use of mobile phones while those with a high growth orientation benefited the most
from the business use of mobile phones in terms of their business growth.
Table 11
Predicted Values of Business Growth by Growth Orientation and Business Use of Mobile Phones

Business use of
mobile phones

Low (-1 SD)
3.11
3.19
3.27

Low
Mean
High

Growth Orientation
Mean
High (+1 SD)
4.34
5.56
6.07
8.95
7.80
12.34

Figure 2
Plot of Interaction Effects of Growth Orientation and Business Use of Mobile Phones
14.00
12.34

12.00

Business growth

10.00

Low Growth
Orientation

8.95

Mean Growth
Orientation

8.00
6.00
4.00
2.00

7.80

5.56

6.07
4.33

3.11

3.19

3.27

0.00
Low use of mobile Mean use of mobile High use of mobile
phones for business phones for business phones for business
communication
communication
communication
71

High Growth
Orientation

Hypothesis 2 posited that, compared to livelihood microentrepreneurs, growth-oriented
microentrepreneurs will benefit more from business use of mobile phones. The results of the
interaction analysis above were consistent with hypothesis 2. In the analysis, growth-oriented
microentrepreneurs are operationalized as those who are one standard deviation above the mean
growth orientation of the sample. Livelihood microentrepreneurs are those who are one standard
deviation below the mean growth orientation of the sample. This trifurcation is an analytical
convention for interaction analysis in regression models (Aiken & West, 1991). The results from
the regression and the subsequent simple slopes analysis suggested that growth-oriented
microentrepreneurs benefitted more from business use of mobile phones compared to livelihood
microentrepreneurs.
To address RQ2 which asked whether the economic sector in which the microenterprise
was located made a difference to the relationships between mobile phones and business growth,
a fourth step was added to the regression model. Here, the economic sectors of manufacturing,
trade, and services were included as predictors of business growth. Each economic sector was
dummy-coded as one or zero before it was added to the model (see Table 12).
None of the beta coefficients of the economic sector dummy variables were statistically
significant. This suggests that, controlling for other variables, the average business growth of
microentrepreneurs in the three economic sectors did not differ by sector. This is not to say that
the economic sector is not important. The null findings for the economic sector may be due to the
regression procedure; that is, the other variables in the model were accounting for most of the
variance and when they were controlled, economic sector did not account for any more variance
that were statistically significant.
72

Table 12
Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with the
Addition of Economic Sector Dummy Variables
sig
B
t
Model 4
(Constant)
2.45
.015
Hired workers
.02
0.27
.788
Formality
.04
0.59
.558
Education
.02
0.27
.790
Domestic help
.11
2.10
.037
Growth orientation
.16
2.68
.008
Business use of mobiles
.02
0.31
.760
Relationship maintenance
.05
0.81
.417
Productivity
.13
1.86
.064
Interaction of growth orientation .12
2.23
.027
and business use of mobiles
Manufacturing sector
.06
0.31
.753
Trade sector
.06
0.26
.804
Service sector
.02
0.07
.947
Dependent variable: Business growth
2

2

F(12, 322) = 3.50, p < 0.001; Adjusted R for Model 4 = .12; Δ R for economic sector variables
= .003
Hypothesis 3 inquires whether female microentrepreneurs with a higher social economic
status would benefit more from the business use of mobile phones. To address this hypothesis,
interaction terms of the social economic variables (education, caste, class and age) with business
growth were added in the model. When the interaction terms of the socio-economic variables
2

were added, they improved the prediction, R change = .034, F(4.321) = 3.16, p < .05. The entire
group of variables significantly predicted business growth, F(13, 321) = 4.234, p < .001, adjusted
2

R = .11. This is a small to medium effect (Cohen, 1988). The beta weights and significant
values, presented in Table 13, indicate which variables contribute most to business growth. With
this combination of predictors, the interaction terms of the socio-economic variables show
73

significant predictors to be education, β = -14 and age, β = -.12. This suggests that business use
of mobile phones was different for women of different levels of education and age groups. To
investigate the nature of these two interaction effects, two interaction analyses were conducted.
Table 13
Hierarchical Multiple Regression Analysis Summary Predicting Business Growth with the
Addition of Interaction Terms of Social Economic Variables
B
Model 5

(Constant)
Hired workers
Formality
Education
Domestic help
Growth orientation
Business use of mobiles
Relationship maintenance
Productivity
Interaction of growth orientation and business use of mobiles
Interaction of education and business use of mobiles
Interaction of caste and business use of mobiles
Interaction of class and business use of mobiles
Interaction of age and business use of mobiles
Dependent Variable: Business growth
2

.02
.05
.06
.13
.17
.10
.04
.12
.16
-.14
-.04
-.08
-.12

t
-4.42
0.39
0.73
0.90
2.36
2.87
1.32
0.64
1.64
2.85
-2.30
-0.69
-1.21
-2.08

sig
.000
.694
.464
.367
.019
.004
.189
.523
.102
.005
.022
.491
.228
.039

2

F(13, 321) = 4.23, p < 0.001; Adjusted R for Model 5 = .11; Δ R for interaction terms = .03
Table 14 provides the means for the predicted values of education by business use of
mobile phones and Figure 3 graphically represents the interaction effects between education and
business use of mobile phones on business growth. The figure suggests that entrepreneurs who
are more educated benefit less from the business use of mobile phones, while those with less
educated are able to experience comparable business growth if they use their mobile phones for
business purposes more intensively. Referring to Table 14, for women entrepreneurs who were
74

less educated, business use of mobile phones could make a difference of as much as 7.3% growth
in their business. For the more educated group, the business use of mobile phones only made a
difference of less than one percent in business growth.
Table 14
Predicted Values of Business Growth by Education and Business Use of Mobile Phones

Business use of mobile phones

Low (-1 SD)
1.57
5.20
8.83

Low
Mean
High

Education
Mean
4.72
6.76
8.81

High (+1 SD)
7.87
8.33
8.78

Table 15 provides the means for the predicted values of the age of the female
microenterprise owner by business use of mobile phones and Figure 4 graphically represents the
interaction effects between microentrepreneur age and business use of mobile phones on
business growth. The figure suggests that, compared to microenterprises owned by older women,
the microenterprises owned by younger women benefit more if the owner uses her mobile
phones for business purposes. In this analysis, younger entrepreneurs are between 18 to about 29
years old.

75

Figure 3
Plot of Interaction Effects of Education and Business Use of Mobile Phones

10.00
9.00
8.00

7.87

8.81
8.83

6.76

7.00
Business Growth

8.78

8.33

6.00
5.00

4.72

5.20
Low education

4.00

Mean education
High education

3.00
2.00

1.57

1.00
0.00
Low use of mobile Mean use of mobile High use of mobile
phones for business phones for business phones for business
communication
communication
communication

Table 15
Predicted Values of Business Growth by Age and Business Use of Mobile Phones

Business use of mobile phones

Low (-1 SD)
4.39
6.91
9.42

Low
Mean
High

76

Age
Mean
4.32
6.14
7.96

High (+1 SD)
4.25
5.38
6.51

Figure 4

Business growth

Plot of Interaction Effects of Age and Business Use of Mobile Phones
10.00
9.50
9.00
8.50
8.00
7.50
7.00
6.50
6.00
5.50
5.00
4.50
4.00
3.50
3.00

9.42

7.96

6.91
6.14
4.39

6.51
Low Age

5.38

Mean Age

4.32

High Age
4.25

Low use of mobile
phones for business
communication

Mean use of mobile
phones for business
communication

High use of mobile
phones for business
communication

Hypothesis 3 posited that microentrepreneurs with a higher social economic status will
benefit more from the business use of mobile phones. The findings from the preceding
regressions and interaction analyses do not support that hypothesis. The microenterprises owned
by women who belong to a higher caste or higher social class do not benefit more from the
business use of mobile phones. In terms of education, a Matthew effect was not evident in the
group who are more educated, i.e., those who are more educated do not report higher business
growth even when they use their mobile phones for business purposes more intensively. On the
contrary, the plot of the interaction effect in Figure 3 suggests that microenterprises owned by
77

women who are less educated but use their mobile phones for business purposes more
intensively will experience business growth that is comparable with the more educated group,
controlling for all other variables. In terms of age, the interaction effect of age and business use
of mobile phones is consistent with the well-documented phenomenon that the younger
generation worldwide are early adopters of new communication technologies and use them more
intensively (ITU, 2008b; Locksley, 2009). The simple slopes in Figure 4 suggest that business
growth is highest when younger women entrepreneurs use mobile phones for business purposes
more frequently.
To address RQ1 from an alternative perspective, a second hierarchical multiple
regression was conducted in which an alternative measure of business growth was tested. In this
set of analyses, perceived business growth linked to mobile phones was considered the
dependent variable and mobile phone use was the independent variable (see Table 16). The key
difference between this dependent variable and the previously used business growth is that it
indexes the retrospective attribution of the effect of mobile phone use on the microentrepreneurs’
business growth. This dependent variable is constructed from questions that probed the
microentrepreneurs’ perceptions and recall of how much their business growth has benefited
from the use of mobile phones.

78

Table 16
Hierarchical Multiple Regression Analysis Summary Predicting Growth Linked to Mobile
Phones
sig
B
t
Model 1
(Constant)
7.31
.000
Hired workers
.15
2.73
.007
Customer reach
.13
2.38
.018
Formality
.16
2.82
.005
Education
.16
2.84
.005
Caste
Age
Model 2

.03
-.12

.596
.029

-0.84

(Constant)

0.53
-2.20

.404

Hired workers

-.04

-1.25

.213

Customer reach
Formality
Education
Caste
Age

-.01
.04
.04
.04
.01

-0.32
1.04
1.16
1.20
0.16

.746
.297
.247
.247
.871

Growth orientation
Business use of mobiles
Relationship maintenance
Productivity

.06
.18
.09
.65

1.83
4.48
2.22
15.85

.068
.000
.027
.000

Dependent variable: Growth linked to mobile phones
2

2

F(10, 324) = 76.48, p < 0.001; Adjusted R for Model 2 = .693; Δ R for additional variables =
.533
When the number of hired workers, customer reach, formality, education, caste and age
were entered into the model, five of the six independent variables were significant predictors of
business growth linked to mobile phones, F(6, 328) = 11.12, p < .001. Only caste did not predict
business growth linked to mobile phones. With the inclusion of the five variables, the prediction
2

was improved substantially: R change = .53, F(4,324) = 145.18, p < .001. The entire group of
79

2

variables significantly predicted business growth, F(10, 324) = 76.48, p < .001, adjusted R =
.69. This is a large effect according to Cohen (1988). The beta weights and significant values,
presented in Table 16, indicate which variables contribute most to business growth. With this
combination of predictors, there are three significant predictors of growth linked to mobile
phones: business use of mobile phones, β = .18, perceived benefit of mobile phones for
relationship maintenance, β = .09 and the perceived benefit of mobile phones for increased
productivity, β = .65.
To address RQ3 which focused on the antecedents of mattering, a third hierarchical
multiple regression was conducted for the dependent variable of mattering (see Table 17). In
contrast to the two earlier regression analyses which examined economic effect, the current
analysis focuses on the social development consequence of mattering.
Table 17
Hierarchical Multiple Regression Analysis Summary Predicting Mattering
B
(Constant)
Hired workers
Formality
Domestic help
Model 2
(Constant)
Hired workers
Formality
Domestic help
Growth orientation
Social use of mobiles
Relationship maintenance
Dependent variable: Mattering

t
39.66
1.85
3.34
1.92
14.15
0.44
0.04
1.52
8.41
4.21
2.46

Model 1

.10
.18
.10
.02
.01
.07
.43
.21
.12
2

sig
.000
.065
.001
.056
.000
.659
.971
.129
.000
.000
.014
2

F(6, 328) = 21.37, p < 0.001; Adjusted R for Model 2 = .27; Δ R for additional variables = .22
80

When the number of hired workers, formality, and the availability of domestic help were
entered, a significant linkage between business growth and mobile phones use was found, F(3,
331) = 7.37, p < .001, with formality significantly contributing to the prediction. When growth
orientation, social use of mobiles, and the perceived benefit of relationship maintenance were
2

added, they improved the prediction, Δ R = .22, F(3,328) = 33.22, p < .001. The entire group of
2

variables significantly predicted business growth, F(6, 328) = 21.37, p < .001, adjusted R =
.268, a medium effect (Cohen, 1988).
The beta weights and significant values, presented in Table 17, indicate which variables
contribute most to mattering. With this combination of predictors, there are three significant
predictors of growth linked to mobile phones: growth orientation, β = .43, social use of mobile
phones, β = .21 and the perceived benefit of mobile phones for relationship maintenance, β = .12.
Conceptually, business growth could be a predictor of mattering in that greater business success
would be associated a greater sense of self worth. However, business growth was not included in
this regression model because it was uncorrelated with mattering, r(335) = .03, n.s. (see Table 8).

81

DISCUSSION
The preceding analyses and findings can best be understood as a series of smaller
narratives that collectively tell a broader story about the effects of mobile phones on the female
microentrepreneurs of Chennai and their businesses. These smaller narratives are derived largely
from the three sets of multiple regressions. Collectively, these findings identify boundary
conditions for the type and extent of mobile phone effects on the economic and social
development of female microentrepreneurs in Chennai.
The first set of regression models examined the antecedents of business growth.
Controlling for all the other variables, business growth was significantly predicted by: the index
of growth orientation of the women entrepreneurs, perceived benefit of mobile phones for
increased productivity, the availability of domestic help, and the interaction effects of the
business use of mobile phones with growth orientation, education, and age.
The growth orientation of the women entrepreneurs featured prominently in the
regression models. Insofar as the women entrepreneurs had a strong desire to garner greater
profits, hire more workers or expand their operations in the future, their businesses also tended to
do better economically. In the dataset, 14.3% of the microentrepreneurs were highly growth
oriented. This is one of the two most significant finding reported in my dissertation. The
literature reviewed earlier suggested that microenterprises generally do not grow. However, this
finding suggests that microentrepreneurs who were highly motivated to grow their businesses did
experience higher business growth, demonstrating a fairly strong link between attitudes and
desired outcomes.

82

Another set of attitudes, female microentrepreneurs’ perceptions of the benefits of
mobile phones for increased productivity, also predicted business growth. Although the
verification of a causal chain between this set of attitudes and greater microenterprise revenues,
etc. was beyond the scope of this dissertation, it is possible that the perceived benefit of
increased productivity might have driven mobile phone use and higher mobile phone use for
business-related activities in turn increased business growth.
The findings also indicated that greater availability of domestic help predicted higher
business growth. It is possible that when women entrepreneurs had a lighter domestic burden,
their businesses might perform better economically. Time that the women entrepreneurs did not
spend doing household chores was time that could potentially be spent on growing their
enterprises. This finding supported a long-held feminist belief that for women to succeed
professionally, “involvement and support from immediate family were also valuable, especially
where the women had children and other household responsibilities” (Maier & Nair-Reichert,
2007, p.52).
The second of the two most important findings in my dissertation was the interaction
effect between growth orientation and the business use of mobile phones. While business use of
mobile phone on its own was not a significant predictor of business growth, the interaction of
this variable with growth orientation was. What this interaction analysis showed was that the use
of mobile phones in business activities had the greatest effect on business growth when it was
coupled with a strong entrepreneurial spirit. For women entrepreneurs who, for whatever the
reasons, did not or could not grow their businesses, the use of mobile phones did not have a
strong effect on their business growth. On the other hand, for women entrepreneurs with a strong
83

desire to grow their businesses, the use of mobile phones had a relatively stronger positive effect
on their business growth. Thus, business growth was strongest at the confluence of a
microentrepreneur’s strong desire to grow her business and her active use of mobile phones to
support one’s business activities. Earlier, the findings suggested that “wanting it” (business
growth, Verheul et al., 2012) predicted business growth; that effect appeared to be amplified
when microentrepreneurs use mobile phones for business purposes.
In short, the overall conclusion based on the first set of regressions is that there are two
boundary conditions that need to be present for mobile phones to have a substantial effect on
business growth: women who own microenterprises must be both motivated to grow their
businesses and they must be using the mobile phones for business purposes. This conclusion
lends perspective to the failures of earlier ICT4D interventions which operated on the
assumption that access and use of technology would alleviate poverty. In the absence of a strong
motivation to better one’s life and aided by the appropriate use of communication technology
developmental outcomes would not be achieved. As Toyama (2011; 2012) puts it, technology is
really only 10% of the solution and “technology projects in global development are most
successful when they amplify already successful development efforts or positively inclined
intent” (p.75).
One important and often neglected issue in ICT4D research centers on possible negative
consequences that might occur when a new communication technology is introduced (Agre,
2002; Warshauer, 2004). The relevant question here is, given existing patterns of social,
economic, or political discrimination, does the communication technology in question help
perpetuate the marginalizing status quo, does it strengthen the power of the already powerful
84

and thereby enhance inequality, or does it facilitate steps toward greater economic, social, or
political inclusion? The interrogation of a possible rich-get-richer effect in this dissertation
instead demonstrated an outcome that can be seen in a positive light. While great education
disparities exist in India (Desai & Kulkarni, 2008), the interaction analysis of education and
business use of mobile phones on business growth demonstrated that greater use of mobile
phones for business activities had an enabling effect for those who are less educated. Controlling
for the other variables, the microenterprises owned by less educated women benefitted more
from business use of mobile phones than female microentrepreneurs with higher levels of
education. Thus, far from supporting a Matthew effect for which we would expect that more
educated entrepreneurs would benefit more from business use of mobile phones, this finding
suggested that less educated entrepreneurs narrowed income discrepancies due to the literacy gap
and enjoyed comparable business growth as those who were more educated.
Returning to the data analysis, the second multiple regression tested the antecedents for
the dependent variable of perceived business growth linked to mobile phones. On the whole, the
findings supported the earlier narrative that the business use of mobile phones and the perceived
benefits of mobile phones contributed to overall business success. However, the insights that the
second regression offered go beyond reinforcing the earlier finding. The results of the second
regression pointed to a very important methodological consideration: the variance explained and
the effect sizes of the two regression models were vastly different because of the different
operationalization of business growth. In the first regression model, business growth was the
year-over-year increase in income and the variance explained by the predictor variables of
2

mobile phone use was small (R = .11). This was consistent with our previous work which found
85

that the effect of mobile phones was modest. In the second regression model, business growth
was the perceived growth attributed to the use of mobile phones. The variance explained by the
2

predictors related to mobile phones was large (R = .69). To recap, this dependent variable
consisted of self-reported items such as “my mobile is an important tool to help my business
grow,” “after I bought my mobile phone, my profits increased,” and “using your mobile for
business helped improve the way you do business”. In terms of the magnitude of effect, the two
regression models told very different stories about what mobile phones can do for business
growth. Our previous research suggests that the first regression model may be presenting a more
accurate picture.
The discrepancy in effect sizes offers a cautionary note to researchers seeking to measure
the effects of mobile phones by using self-reported retrospection of technological effect. In the
latest multi-country survey on the effect of mobile phones conducted by the GSMA (2011), four
in ten women across low and middle-income countries reported increased economic or
professional opportunities due to their ownership of a mobile phone (p.22). Eighty percent of
women business owners reported that they benefited from mobile phone ownership compared to
only 63% of non-business owners (p.23). These findings corroborated with the findings of the
second regression in that, based on self-reported benefits of mobile phones on business growth,
technology had a huge effect.
The juxtaposition of the findings from the first and second regression models produces
the second narrative that points to one possible pitfall in ICT4D research. The huge effect size
suggests that respondents may have over-estimated the positive effect of mobile phones (and
other technologies) on their businesses. If the findings of the second set of regressions are used
86

to inform policy in the absence of earlier findings, policy makers may anticipate effect that are
overly optimistic and this could lead to overly ambitious technological interventions. For ICT4D
researchers, one methodological implication from this finding is that survey questions that
require respondents to report the effect of technology on business growth may be particularly
susceptible to the cooperative subject bias and marketing hype about the benefits of technology.
The combined findings from the two regressions highlight the need to examine both perceived
effect and other indicators of effect that are less prone to survey biases.
The third regression with mattering as the dependent variable shed light on one social
development consequence of mobile phone use that is often consigned to the penumbra of
ICT4D effects studies. In this analysis, the entrepreneurs’ growth orientation, the use of mobile
phones for social purposes, and their perception that mobile phones facilitate relationship
maintenance all predicted a greater sense of mattering. One key insight from this set of analyses
is that the use of mobile phones can have a social development impact. When women
entrepreneurs used mobile phones to make social calls to their family and friends, they also
reported a heightened sense that their business activities mattered to these significant others.
These results are consistent with observations that mobile phones are not merely used for
economic purposes but also as symbols of prestige and status. Interestingly, even though the
women entrepreneurs use mobile phones to make non-business related calls to family and
friends, the respondents in this study also reported that they felt a greater sense of importance,
awareness, and reliance of their social position as a businesswoman. In other words, their social
calls to family and friends heightened awareness of their status as businesswomen and

87

contributed to a more positive self-image that their business activities “mattered” to their
significant others.
At most, there are only a handful of large-sample studies in the literature that report
mobile phones being used as enhancers of status for female microentrepreneurs (Bhavnani, Chiu,
Janakiram, & Silarszky, 2008). In the latest GSMA study (2011), the majority of the women
surveyed – between eight and nine in ten, depending on location – reported that mobile phones
allowed them to lead more secure, connected and independent lives. In a similar vein, the
findings on mattering provided empirical and quantitative support for the notion that mobile
phones can have a social development consequence by increasing feelings of mattering.
The question for other ICT4D researchers is whether mattering matters. This dissertation
contends that it does. The social development consequence of mattering has promise for the
ICT4D field and perhaps future research could explore the relationship between mattering, social
networks, and the generation of social capital. For researchers trying to address the third
Millennium Development Goal of promoting gender equality and empowering women, the
concept of mattering is a viable option. Thus, the methodological contribution of this dissertation
is that it offers a possibly more precise conceptualization and operationalization of
“empowerment” – a vague and often politicized concept (Rowlands, 1997; Williams, 2005). The
validity and reliability of the concept of mattering has been verified for the past three decades in
the field of psychological health. The confirmatory factor analysis in this dissertation indicated
that items that were adapted for this research context from the original mattering scales were
reliable and are viable for use in future research.

88

To sum up, the common thread that ran through the findings of this dissertation is that all
provided some additional understanding of the mobile phone paradox. This dissertation did not
find that mobile phones were a change agent that transformed the lives of massive numbers of
women who own microenterprises. It did however identify important boundary conditions that
determined the effects of mobile phones. While those effects were not large, they specified the
conditions under which mobile phones can make the greatest difference for women operating
very small businesses in developing countries. To achieve such an effect, female
microentrepreneurs must use mobile phones for business purposes and that use must be
accompanied by a strong motivation to better one’s economic condition. In terms of social
development, the use of mobile phones can also potentially increase the psychological wellbeing of the users. When female microentrepreneurs used the mobiles for social calls, they also
reported a greater sense that their business activities are of consequence to their significant
others.
Limitations
Like all scientific investigations, this dissertation has its limitations. First, although the
dissertation examined effects, the findings come from a cross-sectional survey and not from a
longitudinal data-gathering design. Without data collected over time, it is not possible to
definitively address questions about which independent variables are linked to the main
dependent variable, microenterprise growth. However, the claims to effects in this dissertation
are substantiated by statistical modeling of causal relationships and such statistical modeling is
thought to generate plausible statements about causality (Pearl, 2000). For future research, this
lack of more certain proof of causality can be overcome either through panel studies of
89

microentrepreneurs over multiple years or by field experiments where key variables can be
isolated and manipulated.
Second, questions of endogeneity and recursivity may not have been fully addressed in
this dissertation and are the subject of future work. It may be that the dependent variables
examined in the dissertation also have an impact on the independent variables that were
examined. In the case of mattering, even though it has been conceptualized as an effect of mobile
phone use, it may be that a heightened sense of self-concept would also encourage greater mobile
phone use. In the case of business growth, it may be that greater business growth would allow
the microentrepreneurs to afford domestic help. Nevertheless, the question of bi-directional
causality for business growth and business use of mobile phones was addressed using more
powerful statistical tools of structural equation modeling and path analysis in previous research
(Chew et al., 2011). In further research, the verification of the plausibility of the causal
relationships would be replicated for this data set for all three dependent variables. For now, due
to the significant interaction relationships found in the dataset, AMOS (version 19) could not be
used to verify the statements of causality. AMOS is not capable of modeling interactions among
latent variables, since interactions among continuous latent variables require nonlinear
constraints among the parameters (Joreskog & Yang, 1996). Nonetheless, the overall
conceptualization of business growth and mattering as effects in this dissertation aligns my
research with a central theme in recent ICT4D research which focuses on impact assessment of
technology (Heeks, 2010c).
Third, the generalizability of the findings in this study are limited to historic time and
st

place – roughly a decade into the 21 century, a time when India’s long-running economic
90

growth was beginning to slow (India Today, 2012) and to Chennai, India, home of female
microentrepreneurs with their own set of personal and business characteristics. Whether the
macro-level decline in India’s growth influenced the business growth of microenterprises owned
by women is well beyond the scope of this dissertation and was not investigated.
As to a comparison of the women and the microenterprises they own in Chennai, an
earlier study from Mumbai, India, (Ilavarasan & Levy, 2010) also found results that are broadly
similar to those that have been discussed in this dissertation. In both studies, ICT use was limited
to the mobile phone and business use of mobile phones had a minimal impact on the income of
the microentrepreneurs. For now, the findings and discussion points of this dissertation should be
considered as arising from the limited geographic context of urban India and from a study of
mobile phone use by a specific type of female business owners. These findings might apply to
urban microenterprises in other developing countries. However, cultural differences (e.g.,
preference for face-to-face interactions or bargaining, Effah, 2012), or differences in economic
freedom (compare, for example, the role of the government in economies of Hong Kong,
Vietnam, or Indonesia) might require revisions to these findings. Also, this dissertation focused
on mobile phones and the voice interactions enabled by these devices. Although this form of
mobile phones is the technology of the moment, data-enabled mobiles or even tablet personal
computers might be worth studying in the near future. For microenterprises tied to global value
chains, data-enabled mobile devices might offer benefits or facilitate cost-effective participation
in the global economy.

91

On a related note, even though the comparison in this study of business growth between
owners and non-owners of mobile phones showed that their scores were not significantly
different; this dissertation does not claim that access to mobile phones is unimportant. For other
populations such as rural and coastal communities who have poorer access to telecommunication
networks, access and use remains an important item on the development agenda.
Finally, the findings of this dissertation are derived from self-reports of survey
respondents. Like all data about behaviors that occur over time, some responses to survey items
in this study (e.g., cumulative estimates of mobile phone use for specific business tasks) should
be interpreted cautiously, since the survey item might exceed the cognitive capacity of certain
respondents to accurately recall communication behavior (Wei, 2007). Moreover, in some cases,
responses to might even be deliberately deceptive, as certain female microentrepreneurs were
reluctant to disclose true business revenues or other sensitive items. Obtaining valid indicators
of business income and growth remains difficult of course, even for the rigorous models so
highly valued by development economists (de Mel et al., 2009). Although the year-over-year
percentage change in income was used as a proxy for business growth, we do not know what the
dollar value of these percentages is and whether any increases in income is sufficient to lift
people out of poverty. Without the actual Rupee value of the percentages, the question to
whether the increases would make the difference in the lives of the women and their families
remains partly unanswered and should be examined in future research.
Policy Recommendations
The findings of this dissertation offer several policy recommendations that are germane
to the space of female microentrepreneurs. Subject to the limitations discussed above, the
92

research can be used to inform policy debates around Millennium Development Goal One:
Eradicate extreme poverty and Millennium Development Goal Three: Promote Gender Equality
and Empower Women (United Nations, 2011). By providing a tool (the index of growth
orientation) and with new evidence about the effects of mobiles in the informal sector,
governments and NGOs will be better able to channel resources to those female
microentrepreneurs who wish to transform their businesses beyond the subsistence level.
This dissertation identified one of those traits, an above average business growth
orientation; and in terms of age, younger female microentrepreneurs, say 18 to 29, seemed best
positioned to make the most of mobile phone use for business communication. This dissertation
is not suggesting that programs targeting subsistence microenterprises should be discontinued,
for it is always possible that such efforts might have some positive consequence. But, when
policy makers are faced with competing demands on limited budgets, the analysis, and
subsequent replications in other developing countries, may help guide them in setting budget
priorities.
Another potentially contentious recommendation that follows from this dissertation is
that a revision might be necessary to the long-standing development goal that increasing access
for young girls and women to formal education is necessary to remediate the lives of young
women (Summers, 1994) and that continues to be on the policy agenda as Millennium Goal
Two. Findings from the research reported here suggests that limited educational attainment may
be less of a handicap than generally thought and that women with comparatively low levels of
formal schooling need not be excluded from ICT4D interventions. The finding that supports this
recommendation is that the income gap associated with differences in literacy can be narrowed
93

by the purposive use of mobile phones for business activities. In fact, while higher levels of
mobile use for business purposes did result in microenterprise growth for businesses owned by
poorly educated women, the amount of mobile phone use for business purposes did not
significantly affect the income of the more educated group.
One additional recommendation pertains to increasing the efficacy of the
microentrepreneurs in using mobile phones in their business activities. Several of the findings
highlighted the role that business use of mobile phones played in increasing business growth. To
encourage greater business use of mobile phones, policy makers could offer entrepreneurial
training for motivated business owners. The foci of the training might include how to use the
mobile phones to make more money and reduce costs. The training might also seek to increase
the entrepreneurs’ awareness of the benefits of mobile phones such as increased productivity or
enabling relationship maintenance. This heightened awareness of the benefits of mobile phones
was associated with higher business use of mobile phones and might reinforce the other
pathways to greater business growth identified in this dissertation.
While the discussion of this dissertation focused on the effects of mobile phone use, this
is not to say that access is not an important condition for mobile phones to improve the lives of
the poor. For parts of the world that are still too isolated or too sparsely populated for
telecommunication companies to want to serve them, access continues to be a question.
Policymakers and other practitioners should continue to work at narrowing the digital divide in
access for these marginalized groups.
My final recommendation is about the use of the concept, mattering, in future ICT4D
research and practice. While this dissertation only carried out an initial exploration of mattering,
94

the analysis showed that concept was a robust measure for the social development goal of
promoting gender equality. As a substitute or possibly just an adjunct to the concept of
empowerment, the term mattering would allow practitioners and researchers to measure the
social development outcomes of ICT4D interventions more consistently from project to project.
This push for the social development of the poor has its supporters in popular press. The Free
Exchange blog in The Economist (2012), citing a speech by the noted development economist
Esther Duflo, noted that the infusion of hope among poor people can help break them out of the
poverty cycle.

95

CONCLUSION
At the beginning of this dissertation, a promise was made to examine the role of
technology in driving economic and social changes. Embedded in this examination was an
assumption that the change agents under scrutiny – mobile phones – can enact a change. Indeed,
many theories of social change, especially those with a materialist perspective, assume that
change driven by technology is inevitable. Others, arising from an idealist point of view, are
much less certain that technologically-driven social change will always occur.
Broadly speaking, this dissertation did not find definitive evidence that supported one
view over the other. Rather, the statement that dissertation makes is that technology can drive
economic and social changes; but not in the absence of human agency. Transformative social
changes do not occur because of technology alone; they occur because communication processes
are modified by the interplay between social structure, human intent, values, and the new
communication technologies (Castells et al., 2007). Mobile phones are not a panacea that lifts
people out of poverty, but they certainly have the potential to bring about important change by
improving the lot of the poor. Like all technologies throughout time, mobile phones are tools that
can improve or worsen the human condition. The findings from this dissertation resonates with
the social shaping of technology approach and its core observations that human enact
technologies and that technologies amplify human intent and capacity (Lievrouw & Livingston,
2002).
The inter-relationships between technology and human networks are complex and it is
important that considerations of human factors are not overlooked in the excitement to pin our
hopes on technology to alleviate poverty. In his book The Shock of the Old, Edgerton (2006)
96

observed that we have been enamored of technology as innovations and have overlooked
technology-in-use. When we are obsessed with what the technology is and what it can do, we
ignore the fact that technology on its own cannot overcome inequalities that are embedded in
social structures. On the other hand, when we only romanticize grand ideas such as eradicating
global poverty, we ignore the fact that ideas need agents and tools to bring them to fruition. In
the endeavor to better the condition of the poor, both ideas and tools are needed.
Another parallel commentary that this dissertation makes is just because technology has
the potential to amplify human capacity and intent in beneficial ways does not mean that the
users would choose to make use of these opportunities. While the development community
would like to see the poor make use of the devices to lift themselves out of poverty, there is no
certainty that this will happen. As White (1978) notes, “a new device merely opens a door; it
does not compel one to enter” (p. 28). This dissertation takes this one step further and suggests
that there might be other doors that we have been blindered to. While some see technologicallydriven change exclusively in economic terms, the effects of mobile phone use may not always be
economic in nature. The assumption that mobile phones would be dedicated to economic
betterment by the poor reflects certain hubris on the part of some in the development community.
Just like the people living in developed countries, poor people living in developing countries use
their phones for the betterment of both their economic and social conditions. From their
perspective, one use may be just as important as the other. The seemingly frivolous social calls
may contribute to the achievement of a social development outcome in that when social calls are
made, the social well-being of women entrepreneurs may be improved because they feel that
what they do matter.
97

Insofar as technology was found to drive some measure of economic and social change;
the questions that naturally follow are what the magnitudes of the changes are and whether these
changes make a difference. Strictly from a statistical standpoint, the changes solely attributable
to technology are modest. However, for women microentrepreneurs who are striving to improve
their own and their families’ lives, even the modest changes would help them take a positive step
towards their aspirations. We stand at the crossroads of technological history where mobile
phones are now firmly in the hands of the poor. For those deemed poor and destitute, perhaps the
real difference that these devices make is not just improving income or increasing a sense of selfworth; perhaps the real difference is giving hope. However minute the changes may be, the
devices are also giving hope that the poverty cycle may be overcome. Like the proverbial
raindrops that collect to form an ocean, perhaps hope too can coalesce into a wellspring of
entrepreneurial spirit that would help these businesswomen transcend poverty and marginality.

98

APPENDICES

99

Appendix 1
Figure 5
Confirmatory Factor Analysis of Operational Measures of Mattering

χ2 = 242.59, df = 63, p < .001, RMSEA = .069, CFI = 0.940
Items 1 – 14 correspond to the respective items listed in Table 1.
100

Appendix 2
Independent Variables and their Reliability
Growth Orientation (α = .80) 1 = strongly disagree, 5 = strongly agree
1. I won't think of myself as a successful businessperson unless I can hire some new workers
every year.
2. One year from now, I expect to have more hired workers in my business.
3. I will have more employees in next 5 years
4. There is substantial demand for our product/services.
5. One year from now, I expect to be making more money in my business.
6. Five years from now, I expect to be making more money in my business.
Business Use of Mobiles (α = .83) 1 = never, 5 = very often
1. How often do you use your mobile to call your customers?
2. How often do you receive calls on your mobile from your customers?
3. How often do you use your mobile to call the employees of your business?
4. How often do you receive calls on your mobile from your business employees?
5. How often do you use your mobile to call your business suppliers?
6. How often do you receive calls from your business suppliers?
Social Use of Mobiles (r[335] = .56) 1 = never, 5 = very often
1. How often do you use your mobile to call your family and friends to talk about things not
connected to your business?
2. How often do your family and friends call you on your mobile to talk about things not
connected to your business?
Perceived Benefit of Relationship Maintenance (α = .80) 1 = strongly disagree, 5 = strongly
agree
1. Having a mobile phone makes it easier for me to deal with male customers
2. Having a mobile phone makes it easier for me to deal with male suppliers
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3. My mobile phone has improved my relationships with my business suppliers
Perceived Benefit of Productivity (α = .97) 1 = strongly disagree, 5 = strongly agree
1. Having a mobile phone makes it easier for me to balance my business life and my home life
2. I get more work done because I own a mobile phone
3. Because of my mobile phone, I do not travel much for business purposes
4. Because of my mobile phone, I receive business calls at any time.
5. Because I own a mobile phone, I feel more confident in running my business
6. Because of my mobile phone, I feel more self reliant
7. Because of my phone, I am able to do business with strangers without much hesitation
Customer Reach
1 = Customers are people who walk in
2 = Customers are from other parts of Chennai
3 = Customers are from outside Chennai
4 = Customers are from outside India
Business Formality (1 = yes, 0 = no)
1. Is your business registered with the government?
2. Is your business registered with an association?
3. Does your business have a PAN (unique taxpayer I.D) number?
4. Does your business have a bank account to use just for business purposes?
5. Are financial records are kept for business transactions?
Domestic Help (1 = yes, 0 = no)
1. I have part-time domestic help.
2. I have full-time domestic help.
3. My mother and /or my in-laws share the work at home
4. My husband shares the work at home
102

Appendix 3
Questionnaire

Study on Women Microentrepreneurs and ICTs in Chennai
Indian Institute of Technology Delhi
IV No.
Block No.
Interview No.
Que ID.
Dataset : ____________________
Date of Interview ___/____/2010 Time of Interview: Start: ____________ End:
Interviewer sign & date: _____________________ Reviewer sign & date:
You are being asked to participate in a research study about businesses owned by women in
Chennai. You have been scientifically selected as a possible participant in this study because you
own a small business.
1. The goal of this research is to understand more about the factors that impact on businesses
like yours. You will be asked questions about your business. The interview will take
approximately half an hour.
2. There are no obvious physical, legal, or economic risks associated with participating in this
study because your answers will be kept strictly confidential. The Michigan State University
Institutional Review Board will have access to the data in the event of an audit. However,
your confidentiality will be protected to the maximum extent of the law Participation in this
study does not benefit you personally.
3. By participating, you will help us understand the needs and wants of small businesswomen in
general. You must be at least 18 years old but no older than 70 to participate. Your
participation is voluntary. You will receive a gift worth 500 rupees if you chose to
participate. You are free to discontinue your participation at any time or refuse to answer any
question without consequences. There will be no way to link you personally to your answers.
4. If you have concerns or questions about this study, such as scientific issues, how to do any
part of it, or to report an injury, please contact Prof. Mark Levy, Department of
Telecommunication, Information Studies & Media, Michigan State University, East Lansing,
MI 48824 USA, Email: mlevy@msu.edu, Tel: 00 1 517 432 6747.
5. If you have any questions or concerns about your role and rights as a research participant, or
would like to register a complaint about this study, you may contact (anonymously if you
wish), MSU Human Research Protection Program at Tel: 00 1 517 355 -2180, Fax: 00 1 517
432 4503, or e-mail irb@msu.edu or regular mail at 207 Olds Hall, MSU, East Lansing, MI
48824 USA
You indicate your voluntary agreement to participate in this research and have your answers
included in the data set by providing your signature below.
103

Signature with date
Name & mobile number, if any
Please attach proof of gift receipt (Letterhead / Visiting Card / Rubber Stamp), signed
This consent form was approved by the Social Science/Behavioral/Education Institutional
Review Board (SIRB) at Michigan State University. Approved 1/6/11 – valid through 1/5/12.
This version supersedes all previous versions. IRB # 10-1151.
Part A
A1. Thank you once again for agreeing to participate in this survey. To start with, purely for
classification purposes, please tell me how many full-time hired workers does your business
have, not counting members of your immediate family? _________
Note: Hired workers are full-time employees who are working on a regular basis. Husband,
wife, parents, own siblings, sons, and daughters should NOT be counted as hired workers.
More distant relatives who work in the business and who are paid should be included in this
count.]
A2. Not counting members of your family, how many part-time workers does your business
have? __________
[Note: Part time workers are either getting paid on piece-meal basis or come for only limited
hours in a day.]
A3. Out of the total hired workers in your business, how many are women? __________
A4. Are there any women managers or supervisors?
a. Yes
b. No
A5. [Ask only if hired worker(s) is (are) present] How much do you agree or disagree with this
statement: My hired workers have specialized skills that are often hard to replace.
[Use SHOW CARD 1] ___________
SECTION 1
ENTERPRISE/BUSINESS DETAILS
A6.

How many years ago was your business started? __________ years ago.

104

A7.

Please complete this sentence. “Money to start my business came from_______.”
[Check all that apply]
a. My own personal savings
b. My husband gave me money
c. I mortgaged my jewelry
d. My parents gave me money
e. My brother(s) gave me money
f. A private organization (NGO) loaned me money
g. A microfinance organization loaned me money
h. A private money lender loaned me money
i. A government organization gave me money
j. I do not remember
k. Others___________________

A8.

Over the years, have you taken any loans to help your business?
a. Yes
b. No
[If Yes, ask QA9; else go to QA13]

A9.

Tell me about your loans. Thinking about your most recent business loan:
a.
b.
c.
d.
e.
f.

From whom did you receive the loan?
When did you receive the loan?
For what purpose was the money used?
How much was the loan for?
What is the interest rate for this loan?
Was this interest rate:

a. Much higher than the market rate
b. Same as the market rate
c. Lower than the market rate
g. Did you provide any collateral security for this loan?
a. Yes
b. No

105

A10.

Now please tell me about the loan before your most recent loan. [If none, SKIP to QA11]
a. From whom did you receive the loan?
b.When did you receive the loan?
c. For what purpose was the money used?
d.How much was the loan for?

A11.

Do you think you will borrow money again for your business?
a. Yes
b. No

A12. Why do you say that? _______________________________________
A13. Which of the following categories best describes your business?
a. Manufacturing
b. Services
c. Trade
d. Other: ____________________________
A14. Please describe your product/services/trade. [Use product, service, trade, depending on
answer to A13]
a. ____________________________________
b. ____________________________________
c. ____________________________________
A15. In your business, what are the top three top revenue generators?
a. _____________________________________
b._____________________________________
c. _____________________________________

106

A16. How do you describe customers of your business? [Check all that apply]
a. Customers are people who walk in.
b. Customers are from other parts of Chennai
c. Customers are from outside Chennai
d. Customers are from outside India.
e. Customers come through my sales representatives.
f. Customers come through the mobile phone.
g. Customers come through e-mail.
h. Customers are through the retail vendors.
i. Other: ________________________________
A17. Do you have any paid employee who is responsible for running the business in your
absence?
a. Yes
b. No
A18.

Is this the only business you own?
a. Yes
b. No
[If No ask QA19 and QA20; else go to QA21]

A19.

What are your other businesses? [Probe for type of business]
a. ___________________________________
b. ___________________________________

A20.

To manage your other businesses, do you:
a. Make regular visits in person
b. Use a mobile phone
c. Use e-mail
d. Use Internet online chatting
e. Other: ______________________

Yes
Yes
Yes
Yes
Yes

107

No
No
No
No
No

A21. Please tell me how much you agree or disagree with each of the following statements:
[Use Show Card 1]
a. My business generates some additional income for my family, but it is not the only
source of money we have.
b.I’m hoping my business will make me financially independent.
c. I started my business because I didn’t want to be idle at home.
A22.

Do you have landline phones at your business?
a. Yes
b. No
[If Yes askQA23, else go to QA29]

A23.

How many landline phones do you have in your workplace? _____________

A24.

How often do you use your business landline to call your customers?
[Use SHOW CARD 2]

A25.

Why do / don’t you use your business landline to call your customers?
a. _____________________________________
b. _____________________________________

A26.

[Ask only if use business landline to call customers] How many days in the past week did
you use your business landline to call your customers? [Use SHOW CARD 3]

A27.

How often do you receive calls from your customers on your business landline?
[Use SHOW CARD 2]

A28.

How many days in the past week did you receive calls from your customers on your
business landline? [Use SHOW CARD 3]

A29.

Do you ever use your friends’/other people’s landline phones to receive calls for your
business?
a. Yes
a. No
[If Yes ask QA30, else go to QA31]

108

A30.

Why do you use friends’/others’ landlines to receive business calls?
a. _____________________________________
b. ___________________________________

A31.

Do you ever use external PCOs/STD booths to make phone calls for your business?
a. Yes
b. No
[If Yes ask QA32, else go to QA33]

A32.

Why do you use PCOs/STD booths?
a. _____________________________________
b. ___________________________________

A33.

Do you ever use your friends’/other people’s mobile phones to make or receive calls for
your business?
a. Yes
b. No
[If Yes ask QA34 else go to QA35]

A34.

Why do you use friends’/others’ mobile phones to make or receive business calls?
a. _____________________________________
b. ___________________________________

A35.

Have you ever given mobile phones to your employees to use for business purposes?
a. Yes
b. No

A36. Please tell me the main reasons for providing or not providing mobile phones to your
employees to use for business purposes.
a. _____________________________________
b. ___________________________________
A37.

In general, do most of your employees have personal mobile phones?
a. Yes
b. No
[If Yes, ask QA38 and QA39, else go to SECTION 2
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A38.

Do they ever use their personal mobile phones for business purposes?
a. Yes
b. No

A39.

Do you pay the phone bills for your employees when they use their own mobiles for
business purposes?
a. Yes
b. No

SECTION 2
OPINION ON BUSINESS
A40.

On a normal working/business day, how many hours do you spend on:
a. Business-related activities
b. Home-related activities

A41.

_____ hours
_____ hours

Would you say that… [Check only one]
a. Almost all of your customers are men.
b. Some of your customers are men and some of your customers are women.
c. Almost all of your customers are women

A42.

Would you say that… [Check only one]
a. Almost all of your suppliers are men.
b. Some of your suppliers are men and some of your suppliers are women.
c. Almost all of your suppliers are women.

A43.

Would you say that… [Check only one]
a. Almost all of your friends who are in business are men.
b. Some of your friends who are in business are men and some of your friends
in business are women.
c. Almost all of your friends who are in business are women.

110

A44.

How much do you agree or disagree with the following statements? [Use Show Card 1]
a.
b.
c.
d.

A45.

I feel more comfortable doing business with women customers.
I feel more comfortable doing business with women suppliers.
In the future, I would like to have more men as customers.
In the future, I would like to have more men as suppliers.

As a business person, have you ever received any support or extra benefits from the
government because you are a woman?
a. Yes
b. No
[If Yes ask QA46 and QA47, else go to QA49; if response to QA46 is more than 1, ask
QA48]

A46.

How many times? _____

A47.

Please tell me why you received special support or extra benefits [the first time].
a. ___________________________________
b. _________________________________

A48.

Please tell me about the most recent special support or benefits you received because you
are a woman.
a. ___________________________________
b. _________________________________

A49.

Have you had any kind of training related to your business?
a. Yes
b. No
[If yes ask QA50, else go to SECTION 3]

111

A50.

Please tell me about the training.
What was the training
about?
a.
b.
c.

Who gave the
training?

For how many days was the
training?

SECTION 3
ACCESS TO AND OPINIONS ABOUT MOBILES
A51.

Do you own a mobile phone?
a. Yes
b. No
[If Yes ask QA52; else go to SECTION 4]

A52.

How many mobiles phones do you own? ___________

A53.

[Ask only if number of mobile phone is more than one] Do you use [any of] the mobile
phone(s) only for business?
a. Yes
b. No
[If Yes, ask QA54; else go to QA55]

A54.

Why do you use certain mobile phones only for business?
a. ____________________________________
b.____________________________________

112

A55.

Whether you use your mobile(s) only for business or not, please tell me about the mobile
phone you use most often for business:
a. What is the brand name?
_____________________
b. How many years have you been using this phone? _____________________
c. How much did the phone cost?
d.Is it a prepaid / recharge connection?
Yes
No
e. Who is the service provider
SIM 1: ________________
SIM 2: ________________
(if applicable):
f. Did you buy it “second-hand”?
Yes
No
[For each of the following items, first ask whether the mobile used most for business has
the function and then how comfortable or uncomfortable respondent is with each function,
using SHOW CARD 5]
Comfort Level
Use Show Card 5
g. Can it play MP3 songs or other audio?
h. Does it have a video recorder?
i. Is it Internet enabled?
j. Does it have a dual SIM?
k. Does it have Bluetooth?
l. Does it have a camera?
m.Does it have MMS, picture messaging facility?

A56.

Yes
Yes
Yes
Yes
Yes
Yes
Yes

No
No
No
No
No
No
No

Beside talking on your mobile, which functions, if any, do you use on this mobile
phone?[Probe]
a. _____________________________________
b._____________________________________
c. _____________________________________

A57.

Do you ever get unsolicited SMS text messages from some external person or agency? [If
Yes, ask Q A58; else skip to QA59]
a. Yes
b. No

113

A58.

What was the SMS about?
a. ____________________________________
b.____________________________________

A59.

As a business woman, do you get any special discounts from your mobile company?
a. Yes
b. No

A60.

What mobile service provider do your customers use? [Ask all]
a. Do most of your customers use the same mobile service
provider that you do?
b.Do most of your customers use a different mobile service
provider than you do?
c. Do some of your customers use the same mobile service
provider and some use a different one than you do?

A61.

Yes

No

Yes

No

Yes

No

Some people say that it is cheaper to call within the same mobile service provider
network. In your experience is that correct or not correct?
a. Yes it is correct
b. No, it is not correct
c. DON’T KNOW

114

Don’t
Know
Don’t
Know
Don’t
Know

A62.

How much do you agree or disagree with the following statements?
[Use SHOW CARD 1]
a. Having a mobile phone makes it easier for me to deal with male customers.
b. Having a mobile phone makes it easier for me to deal with male suppliers.
c. Having a mobile phone makes it easier for me to balance my business life and
my home life.
d. I get more work done because I own a mobile phone
e. Because of my mobile phone, I do not travel much for business purposes.
f. Because of my mobile phone, I receive business calls at any time.
g. My mobile phone has improved my relationships with my customers.
h. My mobile phone has improved my relationships with my business suppliers.
i. My mobile is an important tool to help my business grow.
j. My mobile phone helps me to relax by playing games or listening to music.
k. Because I own a mobile phone, I feel more confident in running my business.
l. Because of my mobile phone, I feel more self-reliant.
m. Because of my mobile phone, I am able to do business with strangers without
much hesitation.
n. After I bought my mobile phone, my profits increased.

A63.

Sometimes it takes a while for people to figure out how to best use their mobile phones
for business. Do you think that using your mobile for business helped improve the way
you do business [Read all. Check only one]
a. Almost immediately
b. Within a month or two
c. In six months to one year
d. In one to two years
e. Even longer than two years
f. It hasn’t improved the way I do business

115

SECTION 4
MOBILE USE
[Ask only if the respondent has a mobile phone, else go to Section 5]
A64.

How often do you use your mobile to call your customers? [Use SHOW CARD 2] [If
NEVER, go to QA66; else go to QA65]

A65.

How many days in the past week did you use your mobile to call your customers?
[Use SHOW CARD 3]

A66.

Why do / don’t you use your mobile to call customers?
a. ___________________________________
b. ____________________________________

A67.

How often do you receive calls on your mobile from your customers?
[Use SHOW CARD 2] [If NEVER, go to QA69; else go to QA68]

A68.

How many days in the past week did you receive calls on your mobile from your
customers? Use SHOW CARD 3] __________

A69.

How often do you use your mobile to call the employees of your business?
[Use SHOW CARD 2] __________

A70.

Why do / don’t you use your mobile to call business employees?
a. _____________________________________
b.______________________________________

A71.

How often do you receive calls on your mobile from your business employees?
[Use SHOW CARD 2] __________

A72.

How often do you use your mobile to call your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA74; else go to QA73]

A73.

How many days in the past week did you use your mobile to call your business suppliers?
[Use SHOW CARD 3]

116

A74.

Why do / don’t you use your mobile to call business suppliers?
a. _____________________________________
b. _____________________________________

A75.

How often do you receive calls on your mobile from your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA77; else go to QA76]

A76.

How many days in the past week did you receive calls on your mobile from your
business suppliers? [Use SHOW CARD 3]

A77.

How often do you use your mobile to call business-people in other parts of Chennai?
[Use SHOW CARD 2] [If NEVER, go to QA80, else go to QA78]

A78.

How many days in the past week did you use your mobile to call your business-people in
other parts of Chennai? [Use SHOW CARD 3]

A79.

Why do / don’t you use your mobile to call business-people in other parts of Chennai?
a. ____________________________________
b. ____________________________________

A80.

How often do you receive calls on your mobile from business-people in other parts of
Chennai? [Use SHOW CARD 2] [If NEVER, go to QA82; else go to QA81]

A81.

How many days in the past week did you receive calls on your mobile from businesspeople in other parts of Chennai? [Use SHOW CARD 3]

A82.

How often do you use your mobile to call business-people in other parts of India?
[Use SHOW CARD 2] [If NEVER, go to QA84; else go to QA83]

A83.

How many days in the past week did you use your mobile to call business-people in other
parts of India? [Use SHOW CARD 3]

A84.

Why do / don’t you use your mobile to call business-people in other parts of India?
a. ____________________________________
b.____________________________________

A85.

How often do you receive calls on your mobile from business-people in other parts of
India? [Use SHOW CARD 2] [If NEVER, go to QA87; else go to QA86]

A86.

How many days in the past week did you receive calls on your mobile from businesspeople in other parts of India? Use SHOW CARD 3]

A87.

How often do you use your mobile to call business-people outside India?
[Use SHOW CARD 2] [If NEVER, go to QA89; else go to QA88]
117

A88.

How many days in the past week did you use your mobile to call business-people outside
India? [Use SHOW CARD 3]

A89.

Why do / don’t you use your mobile to call business-people outside India?
a. ____________________________________
b.____________________________________

A90.

How often do you receive calls on your mobile from business-people outside India?
[Use SHOW CARD 2] [If NEVER, go to QA92; else go to QA91]

A91.

How many days in the past week did you receive calls on your mobile from your
business-people outside India? Use SHOW CARD 3]

A92.

How often do you use your mobile to call your family and friends to talk about things not
connected to your business? [Use SHOW CARD 2]

A93.

Why do / don’t you use your mobile to call family and friends to talk about things not
connected to your business?
a. ___________________________________
b.___________________________________

A94.

How often do your family and friends call you on your mobile to talk about things not
connected to your business? [Use SHOW CARD 2]

A95.

How often do you use your mobile to send SMS text messages to your family and friends
about things not connected to your business? [Use SHOW CARD 2]

A96.

Why do / don’t you use your mobile to send SMS text messages to your family and
friends about things not connected with your business?
a. ____________________________________
b. ____________________________________

A97.

How often do your family and friends send you SMS text messages on your mobile phone
that are not connected with your business? [Use SHOW CARD 2]

A98.

How often do you use your mobile to take pictures of your family and friends?
[Use SHOW CARD 2]

118

A99.

Why do / don’t you use your mobile to take pictures of your family and friends?
a. ___________________________________
b.____________________________________

A100. How often do you use your mobile to send email to your family and friends about things
not connected to your business? [Use SHOW CARD 2]
A101. Why do / don’t you use your mobile to send email to your family and friends?
a. ____________________________________
b.____________________________________
A102. How often do you receive email on your mobile from your family and friends about
things not connected to your business? [Use SHOW CARD 2]
A103. How often do you use your mobile to send SMS text messages to your customers?
[Use SHOW CARD 2] [If NEVER, go to QA105; else go to QA104]
A104. How many days in the past week did you use your mobile to send SMS text messages to
your customers? [Use SHOW CARD 3]
A105. Why do / don’t you use your mobile to send SMS text messages to your customers?
a. ____________________________________
b.____________________________________
A106. How often do you receive SMS text messages on your mobile from your customers?
[Use SHOW CARD 2] [If NEVER go to QA108, else go to QA107]
A107. How many days in the past week did you receive SMS text messages on your mobile from
your customers? [Use SHOW CARD 3]
A108. How often do you use your mobile to send SMS text messages to your business
employees? [Use SHOW CARD 2] [If NEVER, go to QA110; else go to QA109]
A109. How many days in the past week did you use your mobile to send SMS text messages to
your business employees? [Use SHOW CARD 3]

119

A110. Why do / don’t you use your mobile to send SMS text messages to your business
employees?
a. ___________________________________
b.____________________________________
A111. How often do you receive SMS text messages on your mobile from your business
employees? [Use SHOW CARD 2] [If NEVER, go to QA113; else go to QA112]
A112. How many days in the past week did you receive SMS text messages on your mobile from
your business employees? [Use SHOW CARD 3]
A113. How often do you use your mobile to send SMS text messages to your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA115; else go to QA114]
A114. How many days in the past week did you use your mobile to send SMS text messages to
your business suppliers? [Use SHOW CARD 3]
A115. Why do / don’t you use your mobile to send SMS text messages to your business
suppliers?
a. ____________________________________
b.____________________________________
A116. How often do you receive SMS text messages on your mobile from your business
suppliers? [Use SHOW CARD 2] [If NEVER, go to QA118; else go to QA117]
A117. How many days in the past week did you receive SMS text messages on your mobile from
your business suppliers? [Use SHOW CARD 3]
A118. How often do you use your mobile to send SMS text messages to business-people in other
parts of Chennai? [Use SHOW CARD 2] [If NEVER, go to QA120; else go to QA119]
A119. How many days in the past week did you use your mobile to send SMS text messages to
business-people in other parts of Chennai? [Use SHOW CARD 3]
A120. Why do / don’t you use your mobile to send SMS text messages to business-people in
other parts of Chennai?
a. ____________________________________
b.____________________________________
A121. How often do you receive SMS text messages on your mobile from business-people in
other parts of Chennai? [Use SHOW CARD 2] [If NEVER, go to QA123; else go to
QA122]
120

A122. How many days in the past week did you receive SMS text messages from businesspeople in other parts of Chennai on your mobile? [Use SHOW CARD 3]
A123. How often do you use your mobile to send SMS text messages to business-people in other
parts of India? [Use SHOW CARD 2]
A124. Why do / don’t you use your mobile to send SMS text messages to business-people in
other parts of India?
a. ____________________________________
b.____________________________________
A125. How often do you receive SMS text messages on your mobile from business-people in
other parts of India? [Use SHOW CARD 2] [If NEVER, go to QA127; else go to QA126]
A126. How many days in the past week did you receive SMS text messages from businesspeople in other parts of India on your mobile? Use SHOW CARD 3]
A127. How often do you send SMS text messages on your mobile to business-people in other
parts of India? [Use SHOW CARD 2] [If NEVER, go to QA129; else go to QA128]
A128. How many days in the past week did you use your mobile to send SMS text messages to
business-people in other parts of India?
[Use SHOW CARD 3]
A129. How often do you use your mobile to send SMS text messages to business-people outside
of India? [Use SHOW CARD 2]
A130. Why do / don’t you use your mobile to send SMS text messages to business-people
outside of India?
a. ____________________________________
b.____________________________________
A131. How often do you receive SMS text messages from business-people outside of India on
your mobile? [Use SHOW CARD 2]
A132. How often do you use your mobile to take pictures for your business?
[Use SHOW CARD 2]

121

A133. Why do / don’t you use your mobile to take pictures for your business?
a.
b.

________________________________
________________________________

A134. How often do you use your mobile to send email to your customers?
[Use SHOW CARD 2]
A135. Why do / don’t you use your mobile to send email to your customers?
a.
b.

__________________________________
__________________________________

A136. How often do your customers send you email on your mobile phone?
[Use SHOW CARD 2]
A137. How often do you use your mobile to browse the Internet for information about prices
and other business news? [Use SHOW CARD 2]
A138. How often do you download new programs or apps for your mobile to use in your
business?[Use SHOW CARD 2]
A139. How often do you use computer programs or apps on your mobile to keep track of
business inventory? [Use SHOW CARD 2]
A140. How often do you use computer programs or apps on your mobile, things like address
book or contact list, to keep track of your customers?
[Use SHOW CARD 2] [If NEVER, go to QA142; else go to QA141]
A141. About how many days in the past week did you use computer programs or apps on your
mobile to keep track of your customers? [Use SHOW CARD 3]
A142. Why do / don’t you use computer programs or apps on your mobile to keep track of your
customers?
a.
b.

__________________________________
__________________________________

A143. [Ask only if use computer programs or apps on mobile to keep track of customers]
Compared to a year ago, would you say you are using computer programs or apps on
your mobile more or less or about the same amount to keep track of customers?
[Use SHOW CARD 4]
A144. How often do you use computer programs or apps on your mobile to keep track of money
in your business? [Use SHOW CARD 2]
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A145. Why do / don’t you use computer programs or apps on your mobile to keep track of
money in your business?
a.
b.

__________________________________
__________________________________

A146. [Ask only if use computer programs or apps on mobile to keep track of money]
Compared to a year ago, would you say you are using computer programs or apps on
your mobile more or less or about the same amount to keep track of money in your
business? [Use SHOW CARD 4]
A147. How often do you use computer programs or apps on your mobile to keep records about
your business employees? [Use SHOW CARD 2]
A148. Why do / don’t you use computer programs or apps on your mobile to keep records about
your business employees?
a. ________________________________
b. ________________________________
A149. [Ask only if use computer programs or apps on mobile to keep track of business
employees] Compared to a year ago, would you say you are using computer programs or
apps on your mobile more or less or about the same amount to keep records about your
business employees? [Use SHOW CARD 4]
A150. How often do you use your mobile to…[Use SHOW CARD 2] [Ask all]
a.
b.
c.
d.
e.
f.
g.
h.

send money to your family and friends?
send money to suppliers to pay bills?
send money to your business employees?
send or receive money from your customers?
send or receive money from business-people in other parts of Chennai?
send or receive money from business-people in other parts of India?
send or receive money from business-people outside India?
deposit or withdraw money from your business into a banking account?

123

A151. In general, what language do you use for…
a. Texting b. Email

c. Looking at d. storing
websites
contacts

Tamil
English
Combination of Tamil and English
Some other language

SECTION 5
COMPUTERS AT HOME
[If there are no computers at home, please go to SECTION 9]
Now we would like to talk about computers at home.
A152. Please tell me whether you have any of the following at home:
a. Desktop computer at home
b. Laptop computer at home

Yes
Yes

No
No

[If No to both a and b, go to SECTION 9
A153. How many years ago did you get first get a: [Ask only applicable questions]
a. Desktop computer at home
b. Laptop computer at home

_____
______

A154. How often do you use your computer at home to play video games?
[Use SHOW CARD 2] [If NEVER, go to QA156; else go to QA155]
A155. About how many days in the past week did you use your computer at home to play video
games? [Use SHOW CARD 3]
A156. [If Yes to QA152b] Do you ever bring your laptop to work for business purposes?
a. Yes
b. No
A157. What specific things do you do with your computer at home” [Probe: Anything else?]
a. _________________________________
b._________________________________
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A158. Do you use your computer at home for business purposes?
a. Yes
b. No
[If No, please go to SECTION 7]
SECTION 6
USE OF HOME COMPUTER FOR BUSINESS
A159. How often do you use your computer at home to keep track of business supplies?
[Use SHOW CARD 2] [If NEVER, go to QA161; else go to QA160]
A160. About how many days in the past week did you use your computer at home to keep track
of business supplies?[Use SHOW CARD 3]
A161. Why do / don’t you use your computer at home to keep track of business supplies?
a. ___________________________________
b. ___________________________________
A162. [Ask only if use home computer to keep track of business supplies] Compared to a year
ago, would you say you are using your computer at home more or less or about the same
amount to keep track of business supplies?[Use SHOW CARD 4]
A163. How often do you use your computer at home to keep track of your business inventory?
[Use SHOW CARD 2] [If NEVER, go to QA165; else go to QA164]
A164. About how many days in the past week did you use your computer at home to keep track
of your business inventory? [Use SHOW CARD 3]
A165. Why do / don’t you use your computer at home to keep track of your business inventory?
a. __________________________________
b. __________________________________
A166. [Ask only if use home computer to keep track of business inventory] Compared to a year
ago, would you say you are using your computer at home more or less or about the same
amount to keep track of your business inventory? [Use SHOW CARD 4]
A167. How often do you use your computer at home to keep track of money in your business?
[Use SHOW CARD 2] [If NEVER, go to QA169; else go to QA168]
A168. About how many days in the past week did you use your computer at home to keep track
of money in your business? [Use SHOW CARD 3]
125

A169. Why do / don’t you use your computer at home to keep track of money in your business?
a. ___________________________________
b. ___________________________________
A170. [Ask only if use home computer to keep track of money in business ] Compared to a year
ago, would you say you are using your computer at home more or less or about the same
amount to keep track of money in your business?[Use SHOW CARD 4]
A171. How often do you use your computer at home to keep records about your business
employees? [Use SHOW CARD 2] [If NEVER, go to QA173; else go to QA172]
A172. About how many days in the past week did you use your computer at home to keep
records about your business employees? [Use SHOW CARD 3]
A173. Why do / don’t you use your computer at home to keep records about your business
employees?
a. __________________________________
b. __________________________________
A174. [Ask only if use home computer to keep records about business employees] Compared to
a year ago, would you say you are using your computer at home more or less or about the
same amount to keep records about your business employees? [Use SHOW CARD 4]
A175. How often do you use your computer at home to keep track of your customers?
[Use SHOW CARD 2] [If NEVER, go to QA177; else go to QA176]
A176. About how many days in the past week did you use your computer at home to keep track
of your customers? [Use SHOW card 4]
A177. Why do / don’t you use your computer at home to keep track of your customers?
a.__________________________________
b.__________________________________
A178. [Ask only if use home computer to keep track of your customers] Compared to a year
ago, would you say you are using your computer at home more or less or about the same
amount to keep track of your customers? [Use SHOW CARD 4]

126

SECTION 7
INTERNET USE AT HOME
A179. Do you have an Internet connection at home? [If No, go to PART B Section 9]
a. Yes
b. No
A180. [If yes] What specific things do you do with the Internet connection at home”
[Probe: Anything else?]
a. _________________________________
b. __________________________________
A181. How often do you use your computer at home to send email to your family and friends
about things not connected to your business?
[Use SHOW CARD 2] __________
A182. Why do / don’t you use your computer at home to send email to your family and friends
about things not connected to your business?
a. ___________________________________
b. ___________________________________
A183. How often do you receive email on your computer at home from your family and friends
about things not connected to your business? [Use SHOW CARD 2]
A184. How often do you use your computer at home to look at online web sites for news, sports,
or stories about famous people?
[Use SHOW CARD 2] [If NEVER, go to QA186; else go to QA185]
A185. About how many days in the past week did you use your computer at home to look at
web sites for news, sports, or stories about famous people? [Use SHOW CARD 3]
A186. Why do / don’t you use your computer at home to look at to online web sites for news,
sports, or stories about famous people?
a. ___________________________________
b. ___________________________________
A187. How often do you use your computer at home to chat online with your family and friends
about things not connected to your business? [Use SHOW CARD 2][If NEVER, go to
QA189; else go to QA188]

127

A188. About how many days in the past week did you use your computer at home to chat online
with your family and friends about things not connected to your business?
[Use SHOW CARD 3]__________
A189. Why do / don’t you use your computer at home to chat online with your family and
friends about things not connected to your business?
a. __________________________________
b. __________________________________
A190. How often do you use your computer at home to buy something online for your personal
use? [Use SHOW CARD 2] [If NEVER, go to QA192; else go to QA191]
A191. About how many days in the past week did you use your computer at home to buy
something online for your personal use? [Use SHOW CARD 3]
A192. Why do / don’t you use your computer at home to buy something online for your
personal use?
a. ___________________________________
b. ___________________________________
A193. How often do you use your computer at home to buy something online for your family
and friends? [Use SHOW CARD 2] [If NEVER, go to QA195; else go to QA194]
A194. About how many days in the past week did you use your computer at home to buy
something online for your family and friends? [Use SHOW CARD 3]
A195. Why do / don’t you use your computer at home to buy something online for your family
and friends?
a. ___________________________________
b. ___________________________________
A196. How often do you use your computer at home to download music?
[Use SHOW CARD 2] [If NEVER, go to QA198; else go to QA197]
A197. About how many days in the past week did you use your computer at home to download
music? [Use SHOW CARD 3]
A198. Why do / don’t you use your computer at home to download music?

128

SECTION 8
INTERNET USE AT HOME FOR BUSINESS
A199. Do you ever connect to the Internet on your computer at home to do business? [If No, go
to PART B SECTION 9]
a. Yes
b. No
A200. How often do you use your computer at home to send email to your customers?
[Use SHOW CARD 2] [If NEVER, go to QA202; else go to QA201]
A201. How many days in the past week did you use your computer at home to send email to
your customers [Use SHOW CARD 3]
A202. Why do / don’t you use your computer at home to send email to your customers?
a. ___________________________________
b. ___________________________________
A203. How often do you get email on your home computer from your customers?
[Use SHOW CARD 2] [If NEVER, go to QA205; else go to QA204]
A204. How many days in the past week did you get email on your home computer from your
customers [Use SHOW CARD 3] __________
A205. How often do you use your computer at home to send email to your business employees?
[Use SHOW CARD 2] [If NEVER, go to QA207; else go to QA206]
A206. How many days in the past week did you use your computer at home to send email to
your business employees? [Use SHOW CARD 3]
A207. Why do / don’t you use your computer at home to send email to your business
employees?
a. ___________________________________
b. ___________________________________
A208. How often do you get email on your home computer from your business employees?
[Use SHOW CARD 2] [If NEVER, go to QA210; else go to QA209]
A209. How many days in the past week did you get email on your home computer from your
business employees? [Use SHOW CARD 3]

129

A210. How often do you use your computer at home to send email to your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA212; else go to QA211]
A211. How many days in the past week did you use your computer at home to send email to
your business suppliers? [Use SHOW CARD 3]
A212. Why do / don’t you use your computer at home to send email to your business suppliers?
a. ___________________________________
b. ___________________________________
A213. How often do you get email on your home computer from your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA215; else go to QA214]
A214. How many days in the past week did you get email on your home computer from your
business suppliers? [Use SHOW CARD 3]
A215. How often do you use your computer at home to send email to business-people in other
parts of Chennai? [Use SHOW CARD 2] [If NEVER go to QA217; else go to QA216]
A216. How many days in the past week did you use your computer at home to send email to
business-people in other parts of Chennai? [Use SHOW CARD 3]
A217. Why do / don’t you use your computer at home to send email to business-people in other
parts of Chennai?
a. ___________________________________
b. ___________________________________
A218. How often do you get email on your home computer from business-people in other parts
of Chennai? [Use SHOW CARD 2] [If NEVER, go to QA220; else go to QA219]
A219. How many days in the past week did you get email on your home computer from
business-people in other parts of Chennai? [Use SHOW CARD 3]
A220. How often do you use your computer at home to send email to business-people in other
parts of India? [Use SHOW CARD 2]n[If NEVER, go to QA222; else go to QA221]
A221. How many days in the past week did you use your computer at home to send email to
business-people in other parts of India? [Use SHOW CARD 3]

130

A222. Why do / don’t you use your computer at home to send email to business-people in other
parts of India?
a. ___________________________________
b. ___________________________________
A223. How often do you get email on your home computer from business-people in other parts
of India? [Use SHOW CARD 2] [If NEVER, go to QA225; else go to QA224]
A224. How many days in the past week did you get email on your home computer from
business-people in other parts of India? [Use SHOW CARD 3]
A225. How often do you use your computer at home to send email to business-people outside
India? [Use SHOW CARD 2] [If NEVER go to QA227; else go to QA226]
A226. How many days in the past week did you use your computer at home to send email to
business-people outside India? [Use SHOW CARD 3]
A227. Why do / don’t you use your computer at home to send email to business-people outside
India?
a. __________________________________
b. __________________________________
A228. How often do you get email on your home computer from business-people outside India?
[Use SHOW CARD 2] [If NEVER, go to QA230; else go to QA229]
A229. How many days in the past week did you get email on your home computer from
business-people outside India? [Use SHOW CARD 3]
A230. How often do you use your computer at home to browse the Internet for information
about prices and other business news? [Use SHOW CARD 2] [If NEVER, go to QA232;
else go to QA231]
A231. About how many days in the past week did you use your computer at home to browse the
Internet for information about prices and other business news? [Use SHOW CARD 3]
A232. Why do / don’t you use your computer at home to browse the Internet for information
about prices and other business news?
a. __________________________________
b. __________________________________

131

A233. [Ask only if use home computer to browse for information about prices and other
business news] Compared to a year ago, would you say you are using your computer at
home more or less or about the same amount to browse the Internet for information about
prices and other business news? [Use SHOW CARD 4]
A234. How often do you use your computer at home to search the Internet for information about
ways to do things better in your business? [Use SHOW CARD 2] [If NEVER, go to
QA236; else go to QA235]
A235. About how many days in the past week did you use your computer at home to search the
Internet for information about ways to do things better in your business?
[Use SHOW CARD 3]
A236. Why do / don’t you use your computer at home to search the Internet for information
about ways to do things better in your business?
a. __________________________________
b. __________________________________
A237. [Ask only if use home computer to search for information about ways to do things better
in your business] Compared to a year ago, would you say you are using your computer at
home more or less or about the same amount to search the Internet for information about
ways to do things better in your business? [Use SHOW CARD 4]
A238. How often do you use your computer at home to download new computer programs to
use in your business? [Use SHOW CARD 2]
A239. Why do / don’t you use your computer at home to download new computer programs to
use in your business?
a. __________________________________
b. __________________________________
A240. [Ask only if use home computer to download new computer programs to use in business]
Compared to a year ago, would you say you are using your computer at home more or
less or about the same amount to download new computer programs to use in your
business? [Use SHOW CARD 4]
A241. How often do you use your computer at home to chat online with business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QA243; else go to QA242]
A242. About how many days in the past week did you use your computer at home to chat online
with your business suppliers? [Use SHOW CARD 3]

132

A243. Why do / don’t you use your computer at home to chat online with your business
suppliers?
a. ___________________________________
b. ___________________________________
A244. [Ask only if use home computer to chat with business suppliers] Compared to a year ago,
would you say you are using your computer at home more or less or about the same
amount to chat online with your business suppliers?
[Use SHOW CARD 4]
A245. How often do you use your computer at home to chat online with customers?
[Use SHOW CARD 2] [If NEVER, go to QA247; else go to QA246]
A246. About how many days in the past week did you use your computer at home to chat online
with customers? [Use SHOW CARD 3]
A247. Why do / don’t you use your computer at home to chat online with customers?
a. ____________________________________
b. ___________________________________
A248. [Ask only if use home computer to chat online with customers] Compared to a year ago,
would you say you are using your computer at home more or less or about the same
amount to chat online with customers? [Use SHOW CARD 4]
A249. How often do you use your computer at home to chat online with employees?
[Use SHOW CARD 2] [If NEVER, ask A251; else go to QA250]
A250. About how many days in the past week did you use your computer at home to chat online
with employees? [Use SHOW CARD 3]
A251. Why do / don’t you use your computer at home to chat online with employees?
a. ___________________________________
b. ________________________________
A252. [Ask only if use home computer to chat online with employees] Compared to a year ago,
would you say you are using your computer at home more or less or about the same
amount to chat online with employees? [Use SHOW CARD 4]
A253. How often do you use your computer at home to chat online with business-people in
other parts of Chennai? [Use SHOW CARD 2] [If NEVER, ask QA255; else go to
QA254]

133

A254. About how many days in the past week did you use your computer at home to chat online
with business-people in other parts of Chennai? [Use SHOW CARD 3]
A255. Why do / don’t you use your computer at home to chat online with business-people in
other parts of Chennai?
a. ___________________________________
b. ___________________________________
A256. [Ask only if use home computer to chat online with business people in other parts of
Chennai] Compared to a year ago, would you say you are using your computer at home
more or less or about the same amount to chat online with business-people in other parts
of Chennai? [Use SHOW CARD 4]
A257. How often do you use your computer at home to chat online with business-people in
other parts of India?
[Use SHOW CARD 2] [If NEVER, ask QA259; else go to QA258]
A258. About how many days in the past week did you use your computer at home to chat online
with business-people in other parts of India? [Use SHOW CARD 3]
A259. Why do / don’t you use your computer at home to chat online with business-people in
other parts of India?
a. _______________________________
b. _______________________________
A260. [Ask only if use home computer to chat online with business people in other parts of
India] Compared to a year ago, would you say you are using your computer at home
more or less or about the same amount to chat online with business-people in other parts
of India? [Use SHOW CARD 4]
A261. How often do you use your computer at home to chat online with business-people outside
India? [Use SHOW CARD 2] [If NEVER, ask QA263; else go to QA262]
A262. About how many days in the past week did you use your computer at home to chat online
with business-people outside India? [Use SHOW CARD 3]
A263. Why do / don’t you use your computer at home to chat online with business-people
outside India?
a. __________________________________
b. __________________________________

134

A264. [Ask only if use home computer to chat online with business people outside of India]
Compared to a year ago, would you say you are using your computer at home more or
less or about the same amount to chat online with business-people outside India?
[Use SHOW CARD 4]
A265. How often do you use your computer at home to buy something online for your business?
[Use SHOW CARD 2] [If NEVER, go to Q A267; else go to QA266]
A266. About how many days in the past week did you use your computer at home to buy
something online for your business? [Use SHOW C ARD 4]
A267. Why do / don’t you use your computer at home to buy something online for your
business?
a. __________________________________
b. __________________________________
A268. [Ask only if use home computer to buy something online for business] Compared to a
year ago, would you say you are using your computer at home more or less or about the
same amount to buy something online for your business? [Use SHOW CARD 4]
Please go to Part B
Part B
SECTION 9
COMPUTERS IN THE BUSINESS WORKPLACE
Now I’d like to talk about computers in the business workplace.
B1.

Does your business have computers in the workplace?
a. Yes
b. No
[If No, go to SECTION 12]

B2.

How many computers does your business have now? ________

B3.

Please tell me the details about your computer(s). How many years ago was the (first)
computer bought for your business? _____________

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B4.

Why was the (first) computer bought? [Probe: Any other reasons?]
a. _____________________________________
b. ___________________________________

B5.

Why do you use computers at work? [Probe: Any other reason?]
a. _____________________________________
b. ___________________________________

B6.

Who uses the business computer(s)? [Ask all]
a. You as owner
b. Managers / supervisors
c. Workers

Yes
Yes
Yes

No
No
No

[If Yes for QB6b or c, ask QB7; else skip to Q B8]
B7.

[Ask only if response to QB6 b or c is Yes] Please tell me the main reasons for providing
computers for your employees.
a. _____________________________________
b. ___________________________________

B8.

[Only ask if the business is an Internet café or desktop publishing] If your computers are
used by your customers, for what purpose(s) are they used?
a. __________________________________
b. __________________________________

B9.

Please answer the following questions about the computer at work that is used most.
a. Cost of the computer
b. Is it branded?
c. Did you buy it “second-hand”?
d. Did you buy it in installments?

Yes
Yes
Yes

136

No
No
No

[For the following items, ask how comfortable or uncomfortable respondent is with each
function. Use SHOW CARD 5]
Comfort
Level
a. Does the most used computer at work have CD/ DVD
ROM facility?
b. Does it have USB port (to connect pen/ external drive)?

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

c. Does it have the Microsoft office software (Word, Excel)?
d. Does it have other software like Tally, CAD, ERP,
Inventory, Adobe or Photoshop?
e. Does it have Internet calling facility? (SKYPE, GOOGLE
VOICE, MAGIC BOX)
B10.

Is your workplace computer [Read all. Check only one]:
a. Always switched on during the day
b. Only switched on when it is needed

B11.

How often do you use the computer at work to keep track of business supplies?
[Use SHOW CARD 2][If NEVER, go to QB13; else go to QB12]

B12.

About how many days in the past week did you use the computer at work to keep track of
business supplies? [Use SHOW CARD 3] _____________

B13.

Why don’t / do you use the computer at work to keep track of business supplies?
a. ___________________________________
b. ___________________________________

B14.

[Ask only if use computer to keep track of business supplies; else skip to B15]Compared
to a year ago, would you say you are using the computer at work more or less or about
the same amount to keep track of business supplies?[Use SHOW CARD 4]

B15.

How often do you use the computer at work to keep track of inventory?
[Use SHOW CARD 2] [If NEVER, go to QB17; else go to QB16]

B16.

About how many days in the past week did you use the computer at work to keep track of
business inventory? [Use SHOW CARD 3] ________________

137

B17.

Why don’t / do you use the computer at work to keep track of business inventory?
a. ___________________________________
b. ___________________________________

B18.

[Ask only if use computer to keep track of inventory; else skip to B19] Compared to a
year ago, would you say you are using the computer at work more or less or about the
same amount to keep track of business inventory? [Use SHOW CARD
4]_______________

B19.

How often do you use the computer at work to keep track of money in your business?
[Use SHOW CARD 2] [If NEVER, go to QB21; else go to QB20]

B20.

About how many days in the past week did you use the computer at work to keep track of
money in your business? [Use SHOW CARD 3] _______________

B21.

Why do / don’t you use the computer at work to keep track of money in your business?
a. ____________________________________
b. ____________________________________

B22.

[Ask only if use computer to track money; else skip to B23] Compared to a year ago,
would you say you are using the computer at work more or less or about the same amount
to keep track of money in your business? [Use SHOW CARD 4]

B23.

How often do you use the computer at work to record information about employees?
[Use SHOW CARD 2] [If NEVER, go to QB25; else go to QB24]

B24.

About how many days in the past week did you use the computer at work to keep records
about employees? [Use SHOW CARD 3] ________________

B25.

Why do / don’t you use the computer at work to keep records about employees?
a. ___________________________________
b. ___________________________________

B26.

[Ask only if use computer to keep records about employees; else skip to B27] Compared
to a year ago, would you say you are using the computer at work more or less or about
the same amount to keep records about employees? [Use SHOW CARD 4]

B27.

How often do you use the computer at work to keep track of customers?
[Use SHOW CARD 2] [If NEVER, go to QB29; else go to QB28]

B28.

About how many days in the past week did you use the computer at work to keep track of
customers? [Use SHOW CARD 3] _______________
138

B29.

Why do / don’t you use the computer at work to keep track of customers?
a. ____________________________________
b. ____________________________________

B30.

[Ask only if use computer to keep track of customers; else skip to B31] Compared to a
year ago, would you say you are using the computer at work more or less or about the
same amount to keep track of customers? [Use SHOW CARD 4] ____________

B31.

How often do you use the computer at work to play video games? [Use SHOW CARD 2]

SECTION 10
INTERNET USE AT WORK PLACE
B32.

Does your business have an Internet connection?
a. Yes
b. No
[If Yes ask QB33; if No go to SECTION 11]

B33.

How many years ago did your business first get an Internet connection? ________ years

B34.

Why did your business first get an Internet connection?
a. ______________________________________
b. ______________________________________

B35.

Is your workplace computer:
a. Always connected to the Internet during the day
b. Only connected to the Internet when it is needed

B36.

Does your business have wireless Internet capability?
a. Yes
b. No

139

B37.

Who uses the Internet at work? [Ask all]
a. You as owner
b. Managers / supervisors
c. Workers

B38.

Yes
Yes
Yes

No
No
No

What are the main reasons you use the Internet at work? [Probe: Any other reasons?]
a. ______________________________________
b. ______________________________________

B39.

Does your business have an e-mail address?
a. Yes
b. No
[If Yes ask QB40 and QB41; else go to QB42]

B40.

For how many years has your business had an e-mail address? _____________ years

B41.

Why did you set up an e-mail address for your business?
a. ______________________________________
b. ______________________________________

B42.

Do you have a personal email address?
Yes
No

B42a. [If Yes], Do you use it for your business?
Yes
No
B43.

How often do you use the computer at work to send email to your family and friends
about things not connected with your business? [Use SHOW CARD 2] ______________

B44.

Why do / don’t you use the computer at work to send email to your family and friends
about things not connected with your business?
a. ______________________________________
b. ______________________________________

B45.

How often do you get email on the computer at work from your family and friends about
things not connected to your business? [Use SHOW CARD 2] ___________
140

B46.

How often do you use your computer at work to send email to customers?
[Use SHOW CARD 2] [If NEVER, go to QB48; else go to QB47]

B47.

How many days in the past week did you use your computer at work to send email to
customers? [Use SHOW CARD 3] ___________

B48.

Why do / don’t you use your computer at work to send email to customers?
a. _____________________________________
b. _____________________________________

B49.

How often do you get email from your customers on your computer at work?
[Use SHOW CARD 2] [If NEVER, go to QB51; else go to QB50]

B50.

How many days in the past week did you get email from your customers on your
computer at work? [Use SHOW CARD 3] __________

B51.

How often do you use your computer at work to send email to employees? [Use SHOW
CARD 2] [If NEVER, go to QB53; else go to QB52]

B52.

How many days in the past week did you use your computer at work to send email to
employees? [Use SHOW CARD 3] __________

B53.

Why do / don’t you use your computer at work to send email to employees?
a. ______________________________________
b. ______________________________________

B54.

How often do you receive email from your employees on your computer at work?
[Use SHOW CARD 2] [If NEVER, go to QB56; else go to QB55]

B55.

How many days in the past week did you receive email from your employees on your
computer at work? [Use SHOW CARD 3] __________

B56.

How often do you use your computer at work to send email to business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QB58; else go to QB57]

B57.

How many days in the past week did you use your computer at work to send email to
business suppliers? [Use SHOW CARD 3] __________

141

B58.

Why do / don’t you use your computer at work to send email to business suppliers?
a. ______________________________________
b. ______________________________________

B59.

How often do you receive email from business suppliers on your computer at work?
[Use SHOW CARD 2] [If NEVER, go to QB61; else go to QB60]

B60.

How many days in the past week did you receive email from business suppliers on your
computer at work? [Use SHOW CARD 3] __________

B61.

How often do you use your computer at work to send email to business-people in other
parts of Chennai? [Use SHOW CARD 2] [If NEVER, go to QB63; else go to QB62]

B62.

How many days in the past week did you use your computer at work to send email to
business-people in other parts of Chennai? [Use SHOW CARD 3] __________

B63.

Why do / don’t you use your computer at work to send email to business-people in other
parts of Chennai?
a. ______________________________________
b. ______________________________________

B64.

How often do you receive email from business-people in other parts of Chennai on your
work computer? [Use SHOW CARD 2] [If NEVER, go to QB66; else go to QB65]

B65.

How many days in the past week did you receive email from business-people in other
parts of Chennai on your computer at work? [Use SHOW CARD 3] __________

B66.

How often do you use your computer at work to send email to business-people in other
parts of India? [Use SHOW CARD 2] [If NEVER, go to QB68; else go to QB67]

B67.

How many days in the past week did you use your computer at work to send email to
business-people in other parts of India? [Use SHOW CARD 3] __________

B68.

Why do / don’t you use your computer at work to send email to business-people in other
parts of India?
a. ____________________________________
b. ____________________________________

B69.

How often do you get email from business-people in other parts of India on your
workplace computer? [Use SHOW CARD 2] [If NEVER, go to QB71; else go to QB70]

B70.

How many days in the past week did you use your computer at work to receive email
from business-people in other parts of India? [Use SHOW CARD 3] __________
142

B71.

How often do you use your computer at work to send email to business-people outside
India? [Use SHOW CARD 2] [If NEVER, go to QB73; else go to QB72]

B72.

How many days in the past week did you use your computer at work to send email to
business-people outside India? [Use SHOW CARD 3] __________

B73.

Why do / don’t you use your computer at work to send email to business-people outside
India?
a. ______________________________________
b. ______________________________________

B74.

How often do you receive email from business-people outside India on your computer at
work? [Use SHOW CARD 2] [If NEVER, go to B76; else go to B75]

B75.

How many days in the past week did you receive email from business-people outside
India on your computer at work? [Use SHOW CARD 3] __________

B76.

Have you ever used Google Translate or some other computer program to translate your
emails or documents from one language to another?
a. Yes
b. No
[If Yes, ask QB77; else skip to QB78]

B77.

How often have you used a translation option? [Use Show card 2] _______

B78.

How often do you use the computer at work to browse the Internet for information about
prices and other business news? [Use SHOW CARD 2] [If NEVER, go to QB80; else go
to QB79]

B79.

About how many days in the past week did you use the computer at work to browse the
Internet for information about prices and other business news? [Use SHOW CARD 3]

B80.

Why do / don’t you use the computer at work to browse the Internet for information
about prices and other business news?
a. _____________________________________
b. ____________________________________

143

B81.

[Ask only if use computer at work to browse Internet for information about prices and
business news; else go to QB82] Compared to a year ago, would you say you are using
the computer at work more or less or about the same amount to browse the Internet for
information about prices and other business news? [Use SHOW CARD 4]

B82.

How often do you use the computer at work to search the Internet for information about
ways to do things better in your business? [Use SHOW CARD 2] [If NEVER, go to
QB84; else go to QB83]

B83.

About how many days in the past week did you use the computer at work to search the
Internet for information about ways to do things better in your business?
[Use SHOW CARD 3]

B84.

Why do / don’t you use the computer at work to search the Internet for information about
ways to do things better in your business?
a. _____________________________________
b. ____________________________________

B85.

[Ask only if using work computer to search Internet for ways to do things better in
business] Compared to a year ago, would you say you are using the computer at work
more or less or about the same amount to search the Internet for information about ways
to do things better in your business? [Use SHOW CARD 4]

B86.

How often do you use the computer at work to download new computer programs or apps
to use in your business? [Use SHOW CARD 2]

B87.

Why do / don’t you use the computer at work to download new computer programs or
apps to use in your business?
a. _____________________________________
b. _____________________________________

B88.

[Ask only if downloading programs or apps to use in business; else go to B88] Compared
to a year ago, would you say you are using the computer at work more or less or about
the same amount to download new computer programs or apps to use in your business?
[Use SHOW CARD 4]

B89.

How often do you use the computer at work to look at online web sites for news, sports,
or stories about famous people?
[Use SHOW CARD 2] [If NEVER, go to QB91; else go to QB90]

B90.

About how many days in the past week did you use the computer at work to look at
online web sites for news, sports, or stories about famous people?
[Use SHOW CARD 3]
144

B91.

Why do / don’t you use the computer at work to surf to online web sites for news, sports,
or stories about famous people?
a. _____________________________________
b. _____________________________________

B92.

How often do you use the computer at work to chat online with your family and friends
about things not connected to work? [Use SHOW CARD 2] [If NEVER, go to QB94;
else go to QB93]

B93.

About how many days in the past week did you use the computer at work to chat online
with your family and friends about things not connected to work? [Use SHOW CARD 3]

B94.

Why do / don’t you use the computer at work to chat online with your family and friends
about things not connected to work?
a. _____________________________________
b. _____________________________________

B95.

How often do you use the computer at work to chat online with your business suppliers?
[Use SHOW CARD 2] [If NEVER, go to QB97; else go to QB96]

B96.

About how many days in the past week did you use the computer at work to chat online
with your business suppliers? [Use SHOW CARD 3]

B97.

Why do / don’t you use the computer at work to chat online with your business suppliers?
a. _____________________________________
b. _____________________________________

B98.

[Ask only if use computer at work to chat online with business suppliers; else go to B99]
Compared to a year ago, would you say you are using the computer at work more or less
or about the same amount to chat online with your business suppliers? [Use SHOW
CARD 4]

B99.

How often do you use the computer at work to chat online with customers?
[Use SHOW CARD 2] [If NEVER, go to QB101; else go to QB100]

B100. About how many days in the past week did you use the computer at work to chat online
with customers? [Use SHOW CARD 3]

145

B101. Why do / don’t you use the computer at work to chat online with customers?
a. _____________________________________
b. _____________________________________
B102. [Ask only if use workplace computer to chat online with customers; else go to B103]
Compared to a year ago, would you say you are using the computer at work more or less
or about the same amount to chat online with customers? [Use SHOW CARD 4]
B103. How often do you use the computer at work to chat online with your business
employees? [Use SHOW CARD 2] [If NEVER, go to QB105; else go to QB104]
B104. About how many days in the past week did you use the computer at work to chat online
with your business employees? [Use SHOW CARD 3]
B105. Why do / don’t you use the computer at work to chat online with your business
employees?
a. _____________________________________
b. _____________________________________
B106. [Ask only if use workplace computer to chat with business employees; else go to B107]
Compared to a year ago, would you say you are using the computer at work more or less
or about the same amount to chat online with your business employees? [Use SHOW
CARD 4]
B107. How often do you use the computer at work to chat online with business-people in other
parts of Chennai? [Use SHOW CARD 2] [If NEVER, go to QB109; else go to QB108]
B108. About how many days in the past week did you use the computer at work to chat online
with business-people in other parts of Chennai? [Use SHOW CARD 5]
B109. Why do / don’t you use the computer at work to chat online with business-people in other
parts of Chennai?
a. _____________________________________
b. _____________________________________
B110. [Ask only if use workplace computer to chat with business-people in other parts of
Chennai; else go to B111] Compared to a year ago, would you say you are using the
computer at work more or less or about the same amount to chat online with businesspeople in other parts of Chennai? [Use SHOW CARD 4]
B111. How often do you use the computer at work to chat online with business-people in other
parts of India? [Use SHOW CARD 2][If NEVER, go to QB113; else go to QB112]
146

B112. About how many days in the past week did you use the computer at work to chat online
with business-people in other parts of India? [Use SHOW CARD 3]
B113. Why do / don’t you use the computer at work to chat online with business-people in other
parts of India?
a. ___________________________________
b. ___________________________________
B114. [Ask only if use workplace computer to chat with business-people in other parts of India;
else skip to B114] Compared to a year ago, would you say you are using the computer at
work more or less or about the same amount to chat online with business-people in other
parts of India? [Use SHOW CARD 4]
B115. How often do you use the computer at work to chat online with business-people outside
India? [Use SHOW CARD 2] [If NEVER, go to QB117; else go to QB116]
B116. About how many days in the past week did you use the computer at work to chat online
with business-people outside India? [Use SHOW CARD 3]
B117. Why do / don’t you use the computer at work to chat online with business-people outside
India?
a. ___________________________________
b. ___________________________________
B118. [Ask only if use workplace computer to chat with business-people outside India; else skip
to B119] Compared to a year ago, would you say you are using the computer at work
more or less or about the same amount to chat online with business-people outside India?
[Use SHOW CARD 4]
B119. How often do you use the computer at work to buy something online for your personal
use? [Use SHOW CARD 2]
B120. Why do / don’t you use the computer at work to buy something online for your personal
use?
a. ___________________________________
b. ___________________________________
B121. How often do you use the computer at work to buy something online for your family and
friends? [Use SHOW CARD 2] [If NEVER, go to QB123; else go to QB122]

147

B122. Why do / don’t you use the computer at work to buy something online for your family
and friends?
a. ___________________________________
b. ___________________________________
B123. How often do you use the computer at work to buy something online for your business?
[Use SHOW CARD 2] [If NEVER, go to QB125; else go to QB124]
B124. About how many days in the past week did you use the computer at work to buy
something online for your business? [Use SHOW CARD 3]
B125. Why do / don’t you use the computer at work to buy something online for your business?
a. ___________________________________
b. ___________________________________
B126. [Ask only if use workplace computer to buy something for business; else skip to B127]
Compared to a year ago, would you say you are using the computer at work more or less
or about the same amount to buy something online for your business? [Use SHOW
CARD 4]
B127. How often do you use the computer at work to download music? [Use SHOW CARD 2]
[If NEVER, go to QB129; else go to QB128]
B128. Why do / don’t you use the computer at work to download music?
a. ___________________________________
b. ___________________________________
B129. How often do you use the computer at work to play video games online? [Use SHOW
CARD 2]
B130. Why do / don’t you use the computer at work to play video games?
a. ___________________________________
b. ___________________________________
[Go to SECTION 11]

148

SECTION 11
OPINION ON COMPUTERS AT WORKPLACE
B131. I am going to read you some statements about computers at work and I would like you to
tell me how much you agree or disagree with each. [Use SHOW CARD 1]
a.
b.
c.
d.

I get more work done because I have a computer at work.
Because of my computer at work, it takes me less time to do accounting.
Because of my computer at work, it takes me less time to do my inventory list.
Because of my computer at work, my employees are able to work more
efficiently.
e. My computer at work helps me to build better relationships with my customers.
f. With a computer at work, my interactions with suppliers have become easier.
g. Having a computer at work makes my suppliers more confident in doing
business with me
h. Having a computer at work makes e my customers more confident in doing
business with me.
i. A computer at work is an important tool to help my business grow.
j. A computer at work helps me to relax by playing games or listening to music.
k. My computer at work helps me to balance my responsibilities at home and in my
business.
l. Because of my computer at work, I feel more confident in running my business.
m. Because of my computer at work, I feel more self-reliant
n. Through my computer at work, I am able to do business with strangers without
much hesitation
o. Having a computer at work makes it easier to deal with male customers.
B132. Does your business have a permanent website online?
a. Yes
b. No
[If No, go to go to SECTION 12]
B133. For how many years have you had a permanent website? _____________ years

149

B134. Why was a website created for your business?
a. ___________________________________
b. ___________________________________
B135. Do you update the website regularly?
a. Yes
b. No

B136. What language is used on your business website? [Check ALL that apply]
a. Tamil
b. English
c. Other: __________________________
d. Other: __________________________
B137. Have you ever advertised your business on some other website?
a. Yes
b. No
SECTION 12
INTERNET CAFÉ
B138. How often do you go to Internet café to use the computers for personal purposes? [Use
SHOW CARD 2] _______________
B139. Why do / don’t you go to Internet café to use the computers for personal purposes?
a. ___________________________________
b. ___________________________________
B140. Do you ever use computers at an Internet café for business purposes?
a. Yes
b. No
[If No, go to SECTION 13; else ask B141]
B141. For how many years you have been using Internet cafés for business purposes?
__________ years
150

B142. Why did you first start going to Internet cafés for business purposes?
a. ___________________________________
b. ___________________________________
B143. Approximately many kilometers away from your business is the Internet café you use?
_____________KM
B144. Do you ever feel uncomfortable going to the Internet café, because most of the customers
are men?
a. Yes
b. No
B145. Please tell me how much you agree or disagree with the following statements. [Use
SHOW CARD 1]
a. When I go to the Internet café, I hardly ever need help from an Internet café
worker on how to browse the Internet.
b. I always take a relative or friend with me so I will feel more comfortable.
c. I hardly ever get help using the computers from other people using the
computers at the Internet café.
d. Instead of going to the Internet café myself, I always send a worker to use the
computers there for business.
B146. Why do you use the Internet café for business now? [Probe: Any other reasons?]
a. ____________________________________
b. ____________________________________
B147. Please tell me about the Internet cafés that you use:
a. Is the Internet café is a branch of Internet café chains. (Example: Sify,
Webworld of Reliance)?
b. Is the Internet café an independent private enterprise?

Yes

No

Yes

No

Yes

No

Yes

No

c. Is the Internet café run by the government?
d. Is the Internet café run by a non-governmental organization?

151

B148. Does anyone else from your business use an Internet café for work matters? [Ask all]
a. Managers / supervisors
b. Other workers

Yes
Yes

No
No

SECTION 13
BUSINESS GROWTH
B149. How much do you agree or disagree with the following statement: I think my business is
a success. [Use SHOW CARD 1]
B150. Why do you say that?
a. ____________________________ __
b. ____________________________ __
B151. How much do you agree or disagree with this statement: I am not satisfied with how my
business is doing now. [Use SHOW CARD 1]
B152. Why do you say that?
a. ____________________________ __
b. ____________________________ __
B153. Have you moved your business to a larger location in the past year?
a. Yes
b. No
B154. How much do you agree or disagree with these statements: [Use SHOW CARD 1]:
a. I am thinking it may be necessary soon to move my business to a larger location.
b. I am thinking that it may necessary soon to hire more paid employees for my
business
c. Five years from now, I think I will be satisfied with how much money my
business is making.
d. Five years from now, I think my business will have more hired workers than it
does now.
B155. Last month approximately how much money did your business make before expenses?
___________

152

B156. Do you think that next year, profits in your business are likely to:
a. Increase
b. Stay about the same
c. Decrease
B157. If someone wanted to start a business like yours today, how much of a bank loan would
be needed?
_______________________
B158. [Ask only applicable questions] How much do you agree or disagree with the following
statements? Using [Ask all] is helping my business make a bigger profit right now. [Use
SHOW CARD 1]
a. My mobile phone(s)
b.Computer at work
c. Computer at home
d.Internet at work
e. Internet at home
f. Internet cafés
B159. How much do you agree or disagree with the following statements? In the future, using
[Ask all] will help my business make a bigger profit. [Use SHOW CARD 1]
a. My mobile phone(s)
b.Computer at work
c. Computer at home
d.Internet at work
e. Internet at home
f. Internet cafés

153

B160. How much do you agree or disagree with the following statements? [Ask all. Use SHOW
CARD 1]
a. There is substantial demand for our product/services.
b.I will be content if my business stays about the same size as it is now.
c. I won’t think of myself as a successful businessperson unless my profits grow every
year.
d.I won’t think of myself as a successful businessperson unless I can hire some new
workers every year.
e. One year from now, I expect to be making more money in my business.
f. One year from now, I expect to have more hired workers in my business.
g.Five years from now, I expect to be making more money in my business.
h.Five years from now, I expect to be having more workers in my business
B161. Compared to a year ago, has the annual income of your business:
a. Increased [By what percentage?]
b. Remained the same
c. Decreased [By what percentage?]

Yes, by ____%
Yes
Yes, by____%

B162. Why do you think the annual income of your business has increased/decreased/remained
the same?
a. ___________________________________
b. ___________________________________

154

B163. Businesses like yours have many choices about what to do with increased income. Did
you ever use part of your increased revenue to… [Check all that apply. Probe]
Buy more supplies
b. Start a new branch
c. Pay off loans
d. Hire more workers
e. Move to another location
f. Get a better mobile for business use
g. Get a better computer for use in your business
h. Get an Internet connection for the computer at work
i. Get an Internet connection for your computer at home
j. Save some money to reinvest later in your business
k. Save money for things you need at home
l. Buy some things for your family
m. Pay for more education for your family
n. Save money for health emergencies
o. Anything else? ____________________________
a.

B164. Compared to a year ago, has the number of hired workers in your business:
a. Increased [How many?]
b. Remained the same
c. Decreased [How many?]

Yes, by _____
Yes
Yes, by_____

B165. Why has the number of hired workers in your business increased/ decreased/stayed the
same?
a. ___________________________________
b. ___________________________________
B166. Are you a member of any self-help group?
a. Yes
b. No
[If Yes, ask QB167, else go to QB168]
B167. Please give the name of the group: ______________________________________

155

B168. Is your business registered with the government?
a. Yes
b. No
[If Yes ask QB169 and Q B170; else go to Q B171]
B169. What kind of registration do you have?
B170. How many years ago did you register? ___________ years
B171. Is your business registered with an association?
a. Yes
b. No
[If Yes ask Q B172 and Q B173, else go to Q B174]
B172. Please name the association(s):
a. ________________________________
b. ________________________________
B173. When was the first time you registered with an association? _______________
B174. Does your business have a PAN number?
a. Yes
b. No
B175. Does your business have a bank account to use just for business purposes?
a. Yes
b. No
[If Yes, ask B176, B177, and B178; else go to QB179]
B176. How many years ago did you get that bank account? __________ years
B177. Can you make deposits to that bank account using your mobile phone?
a. Yes
b. No

156

B178. Can you withdraw money from that bank account using your mobile phone?
a. Yes
b. No
B179. For maintaining the financial and business records of your business, which of the
following statements is most applicable? [Select only one]
a. Financial accounts and business records are not kept for this business.
b. Accounts are written by hand but not given to an accountant.
c. Accounts and business records are written by hand and then given to an
accountant.
d. Accounts and business records are kept in a computer located at home.
e. Accounts and business records are kept in a computer at this business.
Please go to PART C
PART C
Now we have a few questions about you and then we will be finished with this interview.
C1.

Please tell me about your language abilities. Do you speak, read, or write… [Ask all]
Language
a. Tamil
b. English
c. Other(s) (specify):
__________________
__________________

Speak Read Write

157

C2.

Please tell me about your numerical abilities. [Check only one]
a. I cannot recognize or write numbers.
b. I can recognize numbers but cannot read or write them.
c. I can read and write numbers but cannot do any calculations.
d. I can do simple addition, subtraction & multiplication, etc.
e. I can calculate interest rates, tax calculations etc.

C3.

How old are you? __________ years

C4.

According to you, do you belong to:[Mark only ONE]
a. An upper caste group
b. A middle caste group
c. A lower caste group

C5.

According to you, do you belong to: [Mark only ONE]
a. The upper class
b.The upper middle class
c. The middle middle class
d.The lower middle class
e. The lower class
f. The poor class

C6.

What is your religion? ____________________

C7.

Are you physically disabled? :
d. Yes
e. No

C8.

Do you have a personal bank account that you do not use for business?
a. Yes
b. No
[If Yes, ask QC9and QC10; else go to QC12]

158

C9.

Do you make withdrawals or payments on your own from that account?
a. Yes
b. No

C10.

Is it a joint account?
a. Yes
b. No
[If Yes, ask Q C11, else go to QC12]

C11.

With whom is the joint account? _______________________________________

C12.

How much formal education have you had? [Check all that apply]
a.
b.
c.
d.
e.
f.
g.
h.
i.

C13.

Never been to a school
Primary school
High school/Std. 10
Higher secondary school/Std. 11
Bachelor’s degree/BA/BCOM/ BSc/BE/BTech/MBBS
Master’s degree/MA/MSc/ME/MTech/MS
Diploma
Certificate holder
Other: _____________________________

Have you undertaken any formal computer training?
a. Yes
b. No

C14.

Does anyone in your family have experience running a business? .
a. Yes
b. No
[If Yes, ask QC15. Probe: Anybody else? If No, go to QC19]

C15.

Who else?
a. ___________________________________
b.___________________________________
159

C16.

Do they ever give you advice about running your business?
a. Yes
b. No

C17.

Do any of them work in your business?
a. Yes
b. No
[If Yes, ask QC18; else go to QC19]

C18.

Who?
a. ____________________________________
b.____________________________________

C19.

Here are some things that people who own businesses sometimes say. Please tell me how
strongly you agree or disagree with each statement. [Use SHOW CARD 1]
a. Because of my business, I am feeling more confident about my life in general.
b. Because of my business, I have gained respect among my friends and in my
neighbourhood.
c. Because of my business, my parents feel proud of me.
d. Because of my business, my parents-in-law are proud of me.
e. Because of my business, my husband shows me more respect.
f. Because of my business, my opinions are considered to be important in family
decisions.
g. I am confident that I can run a successful business.
h. Male customers think that a woman can run a successful business.
i. Male suppliers think that a woman can run a successful business.
j. I think the men in my family approve of my dealing with male customers and
suppliers in my business.
k. I sometimes wonder whether my business interferes with my responsibilities at
home.
l. My neighbors approve of my dealing with male customers and suppliers in my
business.

160

C20.

Are you: [Check ONE]
a. Married
b. Unmarried
c. Divorced
d. Widow
e. Other:
[If the response is b, go to QC 28; else continue with QC21.

C21.

How many children do you have?
Boys
1. Age ________yrs
2. Age ________ yrs
3. Age ________ yrs
4.Age ________ yrs

1.
2.
3.
4.

Girls
Age ________ yrs
Age ________ yrs
Age ________ yrs
Age ________ yrs

C22.

[Ask only if boys are listed in QC21] How much do you agree or disagree with this
statement: In the future, I want my son(s) to take over my business. [Use SHOW CARD
1]

C23.

Please explain your answer: _________________________________________________

C24.

[Ask only if girls are listed in QC21] How much do you agree or disagree with this
statement: In the future, I want my daughter(s) to take over my business. [Use SHOW
CARD 1]

C25.

Please explain your answer: _________________________________________________

C26.

Do you usually bring any of your children to this business? [If Yes, ask QC27; else, go to
QC28]
a. Yes
b. No

C27.

What is the age of child/children who is/are usually brought to work?

161

C28.

How do you manage things at home (cooking, caring for guests, children, elders etc.)?
[Check all that apply.]
a. I have part-time domestic help.
b.I have full-time domestic help.
c. My mother and/or my in-laws share the work at home.
d.My husband shares the work at home.
e. I manage things alone myself
f. I manage things at home some other way. (Please specify)

C29.

How many kilometres from this business do you live? ______________ kilometres [If
business is part of respondent’s house, record as “0” kilometres]

C30.

Among the relatives listed below, who lives with you? How many? [Write number for all
that apply]
a. Parents
b.Parents-in-law
c. Brothers/sisters
d.Brothers/sisters-in-laws
e. Grandparents
f. Others:

Please tell us about your business background?
C31.

Are you the first person in your family to own a business?
a. Yes
b. No

C32.

Is your husband’s family in business?
a. Yes
b. No

C33.

Are your parents in business?
a. Yes
b. No

162

C34.

Are you the only member of your family who earns money?
a. Yes
b. No
[If No, ask QC35, else go to QC37]]

C35.

Who else earns money?
a.
b.
c.
d.

C36.

_________________________________
_________________________________
_________________________________
_________________________________

Compared to other earning members, do you…
a. Earn more than others do.
b. Earn less than others do.
c. Earn just about the same amount others do.

C36.

Do you have a television set at home?
a. Yes
b. No
[If No, go to QC43; else go to QC38]

C37.

Please tell me about your television:
a. Was it given to you by the government?
b.Did you buy it?
c. Do you have a cable connection?
d.Do you have Direct to Home (DTH) / satellite dish connection?

C38.

How many hours did you watch television yesterday? _________hrs

C39.

Do you think watching television has helped your business grow?
a. Yes
b. No
[If Yes, ask Q C41, else go to QC42]

163

Yes
Yes
Yes
Yes

No
No
No
No

C40.

How has watching television helped your business grow?
a. _________________________________
b. _________________________________

C41.

How many times in the past week have you watched each of the following kinds of
television programs? [Use Show Card 3]
a. News
b. Talk shows

C42.

Do you have a radio at home? [If No, end survey]
a. Yes
b. No

C43.

How many times in the past week have you listened to each of the following kinds of
radio programs? [Use Show Card 4]
a. News
b. Talk shows

C44.

Do you think listening to the radio has helped your business to grow?
a. Yes
b. No
[If Yes, ask QC46, if No, end survey]

C45.

How has listening to the radio helped your business grow?
a. _________________________________
b. _________________________________

This is the end of the survey.
Thank you very much!

164

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