The amplification effect of mobile phones on female-owned microenterprises

Abstract

Based on a survey of urban female-owned microentrepreneurs in Chennai, India, we contend that access to mobile phones is a necessary but not sufficient condition for achieving certain development goals. Through the analysis of a survey completed by 335 female microentrepreneurs who owned mobile phones, we explicate an innovative concept, entrepreneurial expectations, and explore its linkage with mobile phones and microenterprise growth. We found that among microentrepreneurs with high entrepreneurial expectations (14% of the sample), business use of mobile phones amplified the impact of entrepreneurial expectations and was associated with greater microenterprise growth.

Keywords

Amplification ICT4D livelihoods microenterprise mobile phones

Like all technologies throughout time, mobile phones are tools that can improve or worsen the human condition, or perhaps even both simultaneously. Transformative changes, positive or negative, do not occur however because of technology alone; they come about because communication processes are modified by the interplay between social structure, human intent, values, and the technologies. As the social shaping of technology approach suggests, humans enact technologies and technologies in turn amplify human intent and capacity (Bijker, Hughes, & Pinch, 1987; Lievrouw & Livingstone, 2002). Indeed, as Toyama (2011) put it, “technology projects in global development are most successful when they amplify already successful development efforts or positively inclined intent” (p. 75).

The research reported here introduces one form of intent, a concept that we call “entrepreneurial expectations.”¹ We explore entrepreneurial expectations in the context of mobile phones and microenterprises owned by women. In sheer number, microenterprises are the most common type of business in the developing world and the overwhelming majority is found in the informal sector. Various estimates, both country-specific and regional, suggest that women own upward of half of all microenterprises in developing countries (Chen, 2001). Despite the ubiquity of microenterprises and their potential role in sustaining the livelihoods of many people, a consensus has emerged, particularly in the development economics literature, that only a miniscule percentage of microenterprises are ever likely to experience growth in their businesses (Banerjee, & Duflo, 2007; de Mel, McKenzie, & Woodruff, 2009; Duncombe & Heeks, 2005; Schoar, 2010). Extant research shows that owners of subsistence microenterprises are often “reluctant entrepreneurs” who lack the skills and opportunities to find stable jobs and hence start businesses only to supplement family income, often temporarily (Aspen Network of Development Entrepreneurs [ANDE], 2012; Banerjee & Duflo, 2011). This study challenges the academic consensus regarding the limited growth potential of microenterprises. We do so because we contend that most previous economics scholarship has overlooked the role that mobile phones might play in facilitating nontrivial microenterprise growth, especially when entrepreneurial expectations are brought into consideration. We offer evidence here that it is possible to identify microentrepreneurs who have the necessary access to mobiles and who manifest sufficiently high levels of entrepreneurial expectations to create a set of conditions leading to positive economic outcomes.

Entrepreneurial expectations

We see business growth in microenterprises as a two-step process: a cognitive stage—in this instance, expecting business growth and planning for it—and a behavioral stage, utilizing an available technology, the mobile phone, in an effort to make growth happen. In this section we will discuss the cognitive stage of microenterprise development and in the subsequent section we will consider a relevant set of communication behaviors. With regard to cognition, recent research has found that certain psychological traits of microenterprise owners play a role in microenterprise development and suggests that having an entrepreneurial mindset enables some business owners to be more productive (Neneh, 2012; Perren, 1999). This entrepreneurial mindset includes traits such as a greater motivation to succeed (de Mel et al., 2009; Schoar, 2010), an enhanced desire to innovate (Kortum & Lerner, 2000), a greater willingness to take risks (Acharya, Rajan, & Schoar, 2007; de Mel et al., 2009), and higher levels of optimism (Kahneman, 2011; Sharot, 2011). By and large, the entrepreneurial mindset appears to be a key factor in the creation and success of small and medium enterprises, both in the developed and developing worlds. However, given the economic, cultural, and social constraints on microenterprise growth in the Global South, the role of psychological traits in microenterprise dynamics remains unclear.

In this paper, we explicate the notion of “entrepreneurial expectations.” We conceptualize entrepreneurial expectations as a construct with two dimensions: (a) a businessperson’s predictions about the likelihood of future, hoped-for economic outcomes and (b) a businessperson’s intentions regarding the behaviors necessary to achieve those economic goals. Entrepreneurial expectations are part of an individual’s mindset, a collection of an individual’s personality traits and attitudes which also include notions such as propensity for risk-taking, optimism, motivation to succeed, etcetera.

Entrepreneurial expectations are based on two types of information: (a) the individual’s private information about his or her capabilities and social situation and (b) public information about the market. We postulate that a microentrepreneur’s subjective expectations about desired, future states of their business are an important factor driving a microentrepreneur’s decision making, particularly with regard to her or his allocation of current assets and effort. We expect for example that ceteris paribus a microenterprise owner who anticipates being able to hire more paid workers or to have increased profits in the future would engage in behaviors, including greater mobile phone use, that might lead to greater efficiency and productivity of their business. However, we also recognize that microenterprise ownership is embedded in a range of historic, cultural, social, and political constraints (Nichter & Goldmark, 2005). Female microentrepreneurs, even with high levels of entrepreneurial expectations, may be significantly limited in both their aspirations and their ability to enact behaviors leading to microenterprise growth. Still, as Duflo (2012) observed, “A little bit of hope and some reassurance that an individual’s objectives are within reach can act as a powerful incentive” (p. 51). In this study, we investigate entrepreneurial expectations about the future economic well-being of the microenterprises. The measures of entrepreneurial expectations employed in this study go beyond the generalized indicators that have been commonly used to examine various aspects of entrepreneurial mindset. We operationalize entrepreneurial expectations with measures that explicitly ask microenterprise owners to make judgments on how they expect their businesses to develop over the next 5 years. Since we assume that notions of business success will vary considerably across a population of microentrepreneurs, a wide-ranging set of predictors for entrepreneurial expectations was assessed in the questionnaire.

Mobile phones and microenterprises

Information and communication technologies (ICTs), especially mobile phones, have been considered both an encouraging instrument for reaching development goals (Rashid & Elder, 2009; UNCTAD, 2010, 2011; Waverman, Meschi, & Fuss, 2005) and a failed and unproven remedy (Aker & Mbiti, 2010; Heeks, 2010). Three, often cited studies make the case for the positive consequences of mobile use at the micro level (Abraham, 2007; Aker, 2008; Jensen, 2007). These studies demonstrate how information acquired by mobile phone reduces producer risk, price variability, and cost to consumers. Mobile phones can also create new sources of income as was the case of the Grameen Village Phone (Sullivan, 2007). Two wide-ranging reviews of the literature (Donner, 2008; Donner & Ecobari, 2010) come to much the same conclusions. Still, with the exception of the studies mentioned before, it is unusual to find strong evidence to support the idea that important increases in microenterprise productivity are causally linked to the near universal diffusion of mobiles. This “productivity paradox” arises in part because most users of mobile phones, including owners of subsistence microenterprises, tend to make greater social than business use of their mobiles (Donner, 2010). Nevertheless, a recent study of microenterprises based in the Philippines found that mobile phones increased business profitability mostly through the reduction of travel and transaction costs (Mwangi & Acosta, 2013) and that there appears to be a weak but statistically significant effect between mobile use for business and enterprise growth (Chew, Levy, & Ilavarasan, 2011).

Research method

Sample design

We chose urban India as our general research site: first because there are 4.2 million microenterprises in urban centers or 3 times the number in India’s vast rural areas and the overwhelming majority are owned by women (National Sample Survey Organization [NSSO], 2000); second because urban mobile teledensity is 163%, leading us to expect substantial mobile penetration even among the less well off (Telecom Regulatory Authority of India [TRAI], 2012). More specifically, we selected Chennai, India as the specific research location because of its mix of microenterprises across service, trade, and manufacturing sectors.

A survey comprising three subsamples was conducted in Chennai, India: one subsample, 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), where government and trade association reports suggested there would be a preponderance of microenterprises. The questionnaires were administered by trained interviewers from a local marketing firm. The surveys were conducted from March to May, 2011. The Appendix contains the questions used in the analysis.

For data-gathering purposes, a microenterprise was defined as a business that has between zero and 10 hired workers.² Data for the general survey was gathered using a multistage random sampling technique,³ coupled with a random walk procedure. Interviewers were given quotas that approximated the distribution of microenterprises by number of hired employees as reported by the Indian government’s National Sample Survey Office (NSSO). The sample size for this general survey was 298 respondents and the response rate was about 80% based on the field reports from the interviewers and the survey agency.

For the microloans sample, 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). The snowball sampling method yielded a total of 150 completed surveys.

Taken together, the general survey, the microloan survey, and the industrial sector survey produced an initial N = 598 female microentrepreneurs that included both mobile phone owners and nonowners. 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, ns, thereby providing support for further analysis based on the combined samples. The findings of the current study are then based on 335 female mobile phone owners drawn from the composite sample.

Slightly more than half (56.0%) of the 598 microentrepreneurs surveyed owned at least one mobile phone. Upwards of 90% of those mobiles were basic models and less than 10% had Internet capability. Table 1 shows the descriptive statistics of the 335 mobile phone owners among the female microentrepreneurs. The majority of female-owned microenterprises (54.2%) were in the service sector, with 19.5% in trade and 26.3% in manufacturing.

Table 1.

Descriptive statistics for subsample of mobile phone owners among the female microentrepenurs.

Microenterprise	Minimum	Maximum	M	SD
No. of hired workers	0	10	1.50	2.12
No. of years in business	0.25	50	9.29	8.72
Female microentrepreneur
Age	18	65	37.60	8.90
No. of children	0	7	1.93	1.05
Other characteristics
Married (%)	81.5
Education (%)	None (5.4)	High school (85.1)	Postsecondary (7.5)
Caste (self-identified, %)	Lower (25.4)	Middle (66.0)	Upper (8.7)
Class (self-identified, %)	Poor (10.7)	Lower (20.9)	Middle (56.7)	Upper (11.6)

Operational measures

The following section describes the operationalization of the dependent variable and the 13 independent variables. The Appendix 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 next. Where appropriate, the most common measure of reliability, Cronbach’s (1951) alpha, is listed.

Dependent variable

Business growth was operationalized as the percentage by which microenterprise revenues changed over a 1 year period as reported by the microentrepreneur. For this sample, the mean business growth was 6.26%, SD = 12.92, range = −50–80. About 7% of the microentrepreneurs reported negative growth of 1% to 50%. About 2 in 5 (38.2%) of the microentrepreneurs reported no growth. Another 2 in 5 (38.2%) reported growth of between 1 to 10%. Another 11.3% reported growth between 10 and 20%. The rest (2.7%) reported growth between 20 to 80%.

Independent variables

Respondent attitudes and behaviors

Based on previous research, we delineated several variables that might predict business growth. In this study, 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 entrepreneurial expectations of the businesswomen. This variable measured the attitudes of the entrepreneurs towards their future growth in terms of anticipated workforce expansion and anticipated profits and comprised six items (α = .80, M = 3.61, SD = 0.69). About 14.3% (81 of 335) of the entrepreneurs had entrepreneurial expectations that were above one standard deviation of the mean. For the purposes of subsequent analyses, this group is considered as business owners with high entrepreneurial expectations. About 18.5% (62 of 335) of the business owners had entrepreneurial expectations that were below one standard deviation of the mean. For further analysis, this group is considered as business owners with low entrepreneurial expectations.

The business use of mobile phones is a key independent variable in the analysis. The items that comprise this measure included 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 composite measure allows 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.

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.

In keeping with the general thrust of the technology adoption model (Davis, 1993) and its focus on perceived usefulness of an innovation as a factor in its adoption, we created two indexes of the perceived benefits of mobile phones for business: first, the perceived benefit of relationship maintenance comprised three items (α = .80, M = 2.50, SD = 1.22). The perceived benefit of increased business productivity was indexed by seven items (α = .97, M = 3.62, SD = 1.21).

Independent variables: Microenterprise characteristics

The second category of independent variables comprised different characteristics of the businesses. We included these measures because each has been variously identified in the research literature cited before as potentially influencing microenterprise growth. These included the 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 the number of hired workers may be enterprise-specific and indicates only the size of the microenterprise at the time of our survey, not microenterprise growth. 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 ID) number?”; “Does your business have a bank account to use just for business purposes?” and whether financial records 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 self-identified 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 nonmanual occupations, situated between a tiny, rich upper class and a majority of low income and manual occupation groups” (p. 411). Iyer (1999) reported 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 part-time or full-time domestic help and whether other members of the family (mother, in-laws, and husband) shared the domestic chores. 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 be understood in the context of dual home–work challenges (Banerjee & Mullainathan, 2008; World Bank, 2011).

Results

Pearson product–moment correlations were calculated using SPSS (2011) Version 19.0. Table 2 shows the Pearson product–moment correlations among the dependent and independent variables. For the dependent variable, business growth, the correlation matrix indicated that microenterprise growth was correlated with the entrepreneurial expectations of the business women, the business use of mobile phones, the perceived affordances of mobiles for maintaining business relationships and for increasing microenterprise productivity. Business growth was also positively correlated with the number of hired workers, formality, the education level of the businesswomen, and the amount of domestic help available.

Table 2.

Pearson product–moment correlations among the dependent and independent variables.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	M	SD
Business growth	1														6.25	12.92
Entrepreneurial expectations	.22**	1													3.72	0.69
Business use of mobile phones	.16**	.12*	1												1.64	0.72
Social use of mobile phones	.080	.087	.021	1											3.53	0.82
Relationship maintenance	.16**	.13*	.43**	−.21**	1										2.50	1.22
Productivity	.23**	.29**	.50**	−.24**	.54**	1									3.52	1.21
Customer reach	.08	−.01	.37**	−.13*	.28**	.17**	1								1.21	0.53
Hired workers	.12*	.19**	.40**	.03	.20**	.25**	.31**	1							1.50	2.12
Formality	.17**	.39**	.17**	.13*	.03	.26**	.15**	.20**	1						0.52	0.73
Education	.12*	.17**	.32**	.07	.03	.23**	.04	.10	.36**	1					2.98	1.26
Caste	.09	.12*	.14*	.14*	.02	.10	.15**	.17**	.24**	.26**	1				1.83	0.56
Class	.05	.20**	.23**	.25**	−.01 <	.04	.03	.18**	.24**	.35**	.47**	1			3.16	1.20
Age	−.08	−.10	−.17**	.02	−.03	−.15**	.12*	.02	−.06	−.26**	.02	−.10	1		37.60	8.90
Domestic help	.121*	.05	.09	.07	.03	.03	.03	−.07	.08	.10	.01 <	.06	.01 <	1	1.01	0.27

Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).

The independent variable of entrepreneurial expectations was positively correlated with the business use of mobile phones, perceived benefits of mobile phones for maintaining business relationships, increasing microenterprise productivity, number of hired workers, business formality, education, caste, and class. It was particularly significant that entrepreneurial expectations were positively correlated with the business use of mobile phones but uncorrelated with the social use of mobile phones. This suggests that female microentrepreneurs who had higher anticipated growth also tended to use mobile phones in their business processes more. Their use of mobile phones for social calls was no different when compared to other female microentrepreneurs with lower anticipated growth.

A hierarchical multiple regression was conducted to determine the best linear combinations of the independent variables and the dependent variable. To examine the relationship between microentrepreneurs’ use of mobile phones, their entrepreneurial expectations, and economic outcomes for their businesses, a multiple regression was conducted with business growth as the dependent variable. Since using mobiles for business communication might be working with other variables to generate business growth or be suppressed by other underlying explanatory variables, the interaction term of mobile phone use and entrepreneurial expectations was also included (see Table 3).

Table 3.

Hierarchical multiple regression analysis summary predicting business growth with the addition of the interaction term.

		β	t	sig
Model	(Constant)		−3.86	.000
	Hired workers	.03	0.47	.639
	Formality	.04	0.57	.571
	Education	.02	0.35	.726
	Domestic help	.12	2.16	.032
	Entrepreneurial expectations	.17	2.78	.006
	Business use of mobiles	.02	0.30	.763
	Relationship maintenance	.05	0.75	.454
	Productivity	.13	1.86	.064
	Interaction of entrepreneurial expectations and business use of mobiles	.13	2.34	.020

Note. Dependent variable: Business growth.

The entire group of variables significantly predicted business growth, F(9, 325) = 4.59, p < .001, The adjusted R² of the model was .09, a small effect according to Cohen (1988). The assumptions of linearity, normally distributed errors, and uncorrelated errors were checked and met.

The beta weights and significant values, presented in Table 3, indicate which variables contributed most to business growth. Business use of mobiles by itself was not significantly linked to microenterprise growth. However, entrepreneurial expectations was (β = .17, p < .01); and of greatest importance to our thesis, the interaction term of entrepreneurial expectations and business use of mobile phones was statistically significant, β = .13, p < .05. To investigate the nature of the interaction effect between entrepreneurial expectations and business use of mobile phones, the predicted values of business entrepreneurial expectations by business use of mobile phones were plotted (Figure 1).

Figure 1.

Plot of interaction effects of entrepreneurial expectations and business use of mobile phones.

Figure 1 suggests that entrepreneurs with low levels of entrepreneurial expectations do not seem to benefit from the business use of mobile phones while those with high levels of entrepreneurial expectations benefited the most from the business use of mobile phones in terms of their business growth.

In the analysis, growth-oriented microentrepreneurs are those who are one standard deviation above the mean entrepreneurial expectations of the sample. Microentrepreneurs who are not growth oriented are one standard deviation below the mean entrepreneurial expectations 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 microentrepreneurs with higher entrepreneurial expectations benefitted more from business use of mobile phones compared to those with lower expectations.

It is not possible given the cross-sectional nature of our data to directly investigate questions of causality, whether, for example, entrepreneurial expectations and mobile phone use are recursively linked to microenterprise growth. However, we were able to examine the endogeneity of key variables through a treatments-effects model (Cong & Drukker, 2001) and that analysis lends support to our general conclusion about an amplification effect.

The term “treatment effect” refers to the causal effect of a binary (0–1) variable on an outcome variable. The general form of a treatment-effects model is:

Y_{j} = X_{j} β + δ z_{j} + ε_{j}

z_{j}^{*} = W_{j} γ + u_{j}

z_{j} = {\begin{matrix} 1 i f z_{j}^{*} > 0 \\ 0, o t h e r w i s e \end{matrix} [\begin{matrix} δ^{2} & ρ δ \\ ρ δ & 1 \end{matrix}]

where Y_j is the dependent variable; X_j and W_j are independent variables; z_j is the endogenous dummy treatment variable, ε_j is the error term, and z_j* is an unobserved latent variable that is assumed to be a linear function of the exogenous covariates W_j and a random component u_j.

Using STATA12 we performed a two-step estimate of the effect of an endogenous binary treatment (use or nonuse of mobile phones for business purposes) on the dependent variable, business growth, conditional on independent variables (entrepreneurial expectations, education, number of respondent’s children, and caste). STATA12 first estimated the Z* and then the equations for mobile phone use. The analysis is based on all 598 respondents. Women without mobile phones and women with mobiles but who did not use them for business were coded 0 (N = 380), while respondents with mobile phones and who used them for business purposes were coded 1 (N = 218).

As the coefficients in the top half of Table 4 demonstrate, use or nonuse of mobile phones is a strong and statistically significant predictor of microenterprise growth. By contrast, neither education nor number of children is significantly associated with business growth. When the model then tested for predictors of mobile phone use for business purposes, all four variables (entrepreneurial expectations, education, caste, and number of children) were statistically significant. However, for our purposes here, we wish only to point out that the greatest impact on mobile phone use for business was entrepreneurial expectations. In short, the treatment effects model clearly indicates that the greater a microentrepreneur’s entrepreneurial expectations, the more likely it is that she will use her mobile phone for business. Although the model does not allow for the assessment of precisely how much entrepreneurial expectations add to business growth, the model does suggest that the combination of expectations and use amplifies the likelihood of microenterprise growth.

Table 4.

Treatment effects model of business growth predictors.

	Coefficient	SE	p
Business growth
Education	−.213	.671	0.751
Children	−.025	.614	0.968
Mobile business use	14.101	3.604	0.000
Constant	5.977	2.298	0.009
Mobile business use
Entrepreneurial expectations	.412	.080	0.000
Caste	.199	.096	0.039
Education	.251	.051	0.000
Children	−.126	.055	0.023
Constant	−2.644	.380	0.000
Rho	−.468	.123

Note. The treatment-effects model does not evaluate goodness of fit. However, when Rho = 0, the model is said to be mis-specified. By contrast, a non-zero Rho suggests that the underlying formulas and variables were validly chosen.

Discussion

The preceding analyses and findings examined how mobile phone use for business amplifies entrepreneurial expectations and the resulting impact on the economic development of microenterprises owned by women. Specifically, this paper found that 1 in 7 female microentrepreneurs had above average entrepreneurial expectations and have used mobiles phones in their operations to achieve some measure of business growth. Indeed, we suggest that the most important finding in the study is the interaction effect between entrepreneurial expectations 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 that variable with entrepreneurial expectations was. In short, the development outcome of business growth was greatest at the confluence of a microentrepreneur’s strong desire to grow her business and her active use of mobile phones to support her business activities. This conclusion lends perspective to the failures of earlier ICT4D interventions which often operated on the assumption that access and use of technology would be sufficient to alleviate poverty. From an ICT4D perspective, this study lends further credence to the notion that access and use of technology is not sufficient to achieve development outcomes. It also represents a rigorous testing of a widely held intuition in the field that technology only amplifies preexisting intent and capabilities (Toyama, 2011).

Although data for this study were collected through a rigorous sampling design, care should still be exercised in interpreting the findings. The evidence presented is limited to self-report derived from a cross-sectional survey, completed at a time when India’s long-running economic growth was beginning to slow (“GDP Growth Slows Down,” 2012) and in Chennai, India, home of female microentrepreneurs with their own set of personal and business characteristics. The cross-sectional nature of the data meant that the time order of key variables modeled in this study could not be fully tested. That is, whether historical business performance have influenced current expectations of the future (the study posits that expectations have influenced business performance). On the other hand, the claims in this study are buttressed by rigorous statistical modeling of causal relationships and such statistical modeling is thought to generate plausible statements about causality (Pearl, 2000). Future longitudinal studies could examine the relationships between expectations about the business in the future, the subsequent adoption of business practices such as mobile phone use, and the business outcome.

By providing a tool (the concept, entrepreneurial expectations) and with new evidence about the effects of mobiles in the informal sector, governments and NGOs might be better able to channel resources to those female microentrepreneurs who wish to transform their businesses beyond the subsistence level. Indeed, this study identified that female microentrepreneurs with above average entrepreneurial expectations seemed best positioned to make the most of mobile phone use for business communication.

However, we are not suggesting that candidates for programs be screened initially to assess their entrepreneurial mindset. Instead, we suggest a more inclusionary approach that evaluates the abilities and entrepreneurial expectations of female microentrepreneurs using measures such as reliable attendance at multiple, rudimentary business skills classes, group discussions about work history or family situation suggesting a desire and a situation conducive to growing their business, or even previous, positive experiences with microloans. It is possible then that over the course of multiple interactions with program practitioners some participants will self-select out of further training, etcetera, while others will clearly emerge as having an entrepreneurial mindset and thus signal that they are most likely to benefit from more sophisticated interventions.

Mobile phones are not a panacea that lifts all people out of poverty, but they certainly have the potential to bring about important change by improving the lot of certain working poor. Insofar as technology was found to drive some measure of economic change, questions still remain about the magnitude of the changes and whether those changes make a meaningful difference to individuals, their families, and to society at large. From a strictly statistical standpoint, the changes attributable solely to technology in this paper are modest. Nevertheless, for women microentrepreneurs who expect better things from their very small businesses, the mobile phone might suggest one path to a better life for those women and their families.

Footnotes

Appendix

Appendix.

Independent variables and their reliability

Entrepreneurial expectations (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 the 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.

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 mobile 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 ID) number?

4. Does your business have a bank account to use just for business purposes?

5. Are financial records 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.

Funding

This research was carried out with the aid of a grant from the International Development Research Centre, Ottawa (project number: 104170).

Notes

Author biographies

Han Ei Chew (PhD, Michigan State University) is a visiting scholar at the Singapore Internet Research Centre, Nanyang Technological University, Singapore. He was most recently a research specialist at the United Nations University in Helsinki, Finland. His research focuses on the impact of information and communication technology on livelihoods, education, and social well-being particularly in developing countries. He is the principal analyst for an international mobiles-for-learning project in collaboration with UNESCO and a consultant for the International Telecommunications Union and Worldreader. He also coleads a mobile phones impact study in Coimbatore, India, funded by the International Development Research Centre, Canada. Dr Chew has also been a consultant to the Swiss International Development Cooperation Agency and the Cherie Blair Foundation for Women.

P. Vigneswara Ilavarasan (PhD, Indian Institute of Technology Kanpur) is Associate Professor at the Department of Management Studies, Indian Institute of Technology Delhi. He researches and teaches about production and consumption of information and communication technologies (ICTs) with a special focus on India. Dr. Ilavarasan is a recipient of the Outstanding Young Faculty Fellowship Award at IIT Delhi and Prof. M. N. Srinivas Memorial Prize of the Indian Sociological Society. His specific research interests are information and communication technologies & development (ICTD; use of mobiles and other ICTs by women microentrepreneurs in India); information technology industry in India (labour, R&D centers of MNCs, interfirm linkages, clusters and subnational policy); and ICTs and government (electronic governance). His publications and other details are available at

Mark R. Levy (PhD, Columbia University) is a Professor in the Department of Media and Information. Dr. Levy has authored and coauthored numerous scientific articles and 10 books, including The VCR Age; Global Newsrooms, Local Audiences; Mass Communication Review Yearbooks 5 and 6; and the award-winning Communication in the Age of Virtual Reality. Dr. Levy has been editor of the Journal of Communication and an Associate Editor of Human Communication Research and Critical Studies in Mass Communication. His research has examined the audience for mass communications and the users of new communication technologies. Most recently, Dr. Levy has been studying information and communication technologies for development (ICT4D), most especially mobiles for development (M4D).

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