Context-Driven Diversification in Social Enterprises

Abstract

Recent work has emphasized the role of context in shaping the diversification strategies of social enterprises (SEs), but our understanding remains superficial. We identify two types of context-driven diversification strategies—market development diversification (MDD) and market functioning diversification (MFD)—depending on the type of voids being addressed. We then empirically test how these diversification strategies impact the performance of SEs on the twin dimensions of financial growth and social impact. Using a mixed-method approach of qualitative interviews and a longitudinal database of Indian microfinance firms (MFIs), we find that while MFD positively impacts financial growth, MDD has a positive effect on social impact. Furthermore, we find that the strategic fit (or lack of it) between the SE’s legal form and the type of diversification enhances (undermines) financial growth. However, the strategic fit between the legal form and diversification choice does not amplify social impact. The study contributes to product diversification literature, which has paid limited attention to the role of context.

Keywords

context-driven diversification social enterprises institutional voids microfinance institutions (MFI)

Social enterprises (SEs) are firms that integrate financial and social objectives—they address social problems by leveraging market forces. While solving social needs in a limited manner may be a worthwhile endeavor, the growth of SEs is essential to address social problems in a more comprehensive manner (Bloom, 2012; Bruton et al., 2015; Deng et al., 2020; Klarin & Suseno, 2023; Porter et al., 2020). Growth is the process by which a firm with an effective solution to a problem expands or extends the solution to a broader potential market, allowing the firm to do well financially while having a big social impact (Bloom, 2012). Notwithstanding its desirability, growth has often proved to be elusive for SEs—for every Grameen Bank, Teach for America, and Habitat for Humanity, there exist many SEs that stagnate or fail to deliver on their social and/or financial promise (Bloom, 2012; Bloom & Chatterji, 2009; Bruton et al., 2015; Chowdhury, 2021; Klarin & Suseno, 2023).

Walking a tightrope between meeting social goals and ensuring financial performance can be taxing and often keeps SEs stunted and unable to grow (Battilana & Dorado, 2010; Battilana & Lee, 2014; Cornelissen et al., 2021; McMullen & Warnick, 2016; Ometto et al., 2019). Drawing on the work in strategic management, product diversification has recently been proposed as a possible strategy to sidestep the constraints that social-business tension imposes on growth (Fosfuri et al., 2015, 2016), allowing SEs to successfully pursue the twin goals of financial growth and social impact. While this is a promising approach, several gaps exist in our understanding of diversification.

First, the emerging scholarly work suggests that the diversification strategy in SEs may be shaped by factors “outside the company,” that is, demand-side or contextual factors (Fosfuri et al., 2016; Jha et al., 2021). While the study of diversification has a rich legacy and spans nearly 6 decades of scholarly research in strategic management (Ansoff, 1958; Benito-Osorio et al., 2012; Rumelt, 1982), it has primarily focused on resource or capability-led diversification, and the role of operating context has remained underexplored (Benito-Osorio et al., 2012; Bruton et al., 2022; Deng et al., 2020; Prashantham et al., 2018; Wan et al., 2011; White et al., 2021). Recent conversations in the field have recognized this organization-centric bias in theories built around capitalist frameworks (Bruton et al., 2022; Chowdhury, 2021; Deng et al., 2020; White et al., 2021) and called for research that builds on new contextual frontiers and advances or identifies the boundaries for current theories (Bruton et al., 2022; Deng et al., 2020; Khoury & Prasad, 2016; Ometto et al., 2019). Therefore, it is time to open the black box of context-led diversification and enrich our understanding of diversification. It is particularly interesting and important to explore context-led diversification in SEs because they are deeply rooted in the local contexts in which they operate, and these contexts are often fraught with underdeveloped institutions (Khanna & Palepu, 2005; Mair & Marti, 2009). Therefore, they present an ideal setting to study context-led diversification.

Second, the inquiry into diversification and its effects on the performance of SEs (Fosfuri et al., 2016; Jha et al., 2021; Mendoza-Abarca & Gras, 2019) is limited in number and mostly conceptual in nature (Cornelissen et al., 2021; Fosfuri et al., 2016). Very little work has sought to empirically test the effects of diversification on twin outcomes that SEs seek to achieve—financial growth and social impact (Rawhouser et al., 2017; Saebi et al., 2019). There are also unexplored contingencies that might impact the relationship between diversification strategy and SE performance. A salient one is the legal form of incorporation of the SE that is known to affect its ability to successfully implement a strategy (Battilana et al., 2015; Haigh et al., 2015).

Addressing these gaps, we explore the following research questions—What are the types of context-driven diversification strategies in SEs? How does context-driven diversification impact the financial growth and social impact of SEs? We propose a typology of context-led diversification based on the literature on institutional voids (Khanna & Palepu, 1997; Mair & Marti, 2009). Our hypotheses connect this typology of context-driven diversification to financial growth and social impact of SEs using two streams of literature—the extensive work on hybrid SEs (André & Pache, 2016; Battilana & Dorado, 2010; Battilana et al., 2015; Deng et al., 2020; Haigh et al., 2015; Kannothra et al., 2018) and the literature on institutional voids (Khanna & Palepu, 1997, 2010; Mair et al., 2012; Mair & Marti, 2009; Webb et al., 2020). We adopt a mixed-method approach, beginning with the validation of our diversification typology through 10 qualitative interviews with CEOs of microfinance institutions (MFIs). Following this, we test our hypotheses on a unique longitudinal dataset of 155 MFIs in India from 2013 to 2018.

MFIs are a particular type of SEs that seek to drive financial inclusion for communities that have been ignored by mainstream financial institutions by providing small loans and other financial products that can unlock the economic potential of marginalized communities. They sustain and grow their operations through the interest income they generate. MFIs have been criticized for deploying dubious practices to extract higher income, which has raised questions about their commitment to the social cause (Karim, 2008; Mader, 2013; Wichterich, 2012). However, following a tumultuous period where the raison d’etre of MFIs was called into question, they have been streamlined through a regime of regulation (Hulme & Arun, 2011). Today, it is well accepted that MFIs represent an ideal type of SE with a mission to bring about financial inclusion in a sustainable manner (Abraham & Kalamkar, 2016; Cull et al., 2009; Mersland et al., 2019; Quayes, 2021).

We make the following contributions to theory and practice. First, we contribute to the literature on product diversification by considering the role of the operating context on the organization’s diversification strategy. We find that diversification in MFIs is localized and tied to the type of institutional voids that they seek to address (Khanna & Palepu, 2010; Mair et al., 2012; Mair & Marti, 2009; Prashantham et al., 2018). We identify two diversification strategies—market development diversification (MDD) and market functioning diversification (MFD)—that bridge market development, market participation, and market functioning voids (Mair & Marti, 2009). This type of context-centered diversification is a departure from the firm-centered capability-led diversification that has been the focus of strategic management research (Bruton et al., 2022; Ng, 2007; White et al., 2021). Second, findings from our study add nuance to the prevailing debates on the dialectic tensions in SEs with dual goals (Battilana & Dorado, 2010; Battilana et al., 2015; Cornelissen et al., 2021; McMullen & Warnick, 2016; Pache & Santos, 2013; Saebi et al., 2019) and the role of product diversification in sidestepping the social-business tensions (Fosfuri et al., 2016; Santos et al., 2015). Our findings lend qualified support to the general idea that product diversification can help SEs achieve both financial growth and social impact (Fosfuri et al., 2016). We find that MFD increases financial growth, while MDD enhances social impact. This finding implies that an exclusive focus on either type of diversification can compromise either financial or social goal, highlighting the need for a deliberate, balanced approach. We also find that a strategic fit (Zajac et al., 2000) between the MFI’s legal form and the type of diversification accentuates financial growth but not social impact. Third, we make an empirical contribution by operationalizing diversification in the context of SEs. We also consider the impact of diversification on the twin dimensions of SE performance—financial growth and social impact—addressing calls by researchers to go beyond organizational outcomes and examine the external impact of SEs (Bruton et al., 2015; Klarin & Suseno, 2023; Rawhouser et al., 2017; Saebi et al., 2019; Wickert et al., 2021). Our measurement of internal growth and external impact not only affords a better measure of performance when studying SEs but also allows us to explore inter-relationships between financial and social outcomes.

The rest of the article is organized as follows. First, we lay the theoretical groundwork for the study in three parts by (a) discussing financial and social goals in SEs; (b) developing a typology of context-driven diversification by drawing on institutional voids literature and grounding it with examples from our interviews with MFI principals; and (c) formulating hypotheses to examine the relationship between context-driven diversification and the twin goals of SE performance. Next, we discuss our data and methods in detail. Finally, we present our results and conclude with a discussion of the implications of the study.

Theoretical Background and Hypotheses

Financial Growth and Social Impact

SEs typically strive to pursue twin goals—social impact and financial growth (Battilana & Lee, 2014; Haigh et al., 2015; Santos et al., 2015). There is an inherent tension between these goals. The tension stems from the fact that there is often a tradeoff between the two goals, and the pursuit of one goal comes at the expense of the other and can derail the hybrid nature of the organization (Battilana & Dorado, 2010; Battilana et al., 2012; Pache & Santos, 2013). This is especially true in cases where the beneficiaries are also the paying customers, like in the case of an MFI. Such SEs constantly strive to strike a delicate balance between the dual goals of social and financial performance.

This duality of goals persists through the life cycle of SEs, from inception to the growth phase (Battilana & Lee, 2014; Cornelissen et al., 2021; Im & Sun, 2015; Kannothra et al., 2018). Once an SE has developed a sustainable model to address a social problem, the next frontier is to build on that foundation to expand the scale of operations such that the social benefits are amplified. By replicating or expanding a validated solution to a social problem, the SE can do both, that is, register financial growth while enhancing social impact (André & Pache, 2016; Bloom & Chatterji, 2009; Dees et al., 2004).

While this is sustainable in theory, it has been found to be problematic for SEs and their commitment to maintaining dual and often dialectic goals (André & Pache, 2016; Ebrahim et al., 2014; Fosfuri et al., 2016). The challenges come from multiple sources—the substantial amount of resources needed (Bradach, 2003), management competence required for large-scale execution and to overcome diseconomies of growth (Bloom & Chatterji, 2009; Weber et al., 2012), and the highly contextual nature of the product or service offered, all of which make replication difficult (Bradach, 2003). These challenges imply that SEs need to invest significant amount of time and capital to undertake the expansion and localization of their operations, which puts pressure on their financial performance and time to breakeven. The thrust on financial performance can compromise the social goals of the organizations, leading to mission drift and threatening their core purpose (André & Pache, 2016; Ebrahim et al., 2014; Fosfuri et al., 2016).

Take the example of MFIs that extend credit to the unbanked population at the base of the economic pyramid with the aim of empowering them to engage in productive activities. To scale up, an MFI could extend credit services to a large number of bankable people in different geographies, expanding its net loan portfolio and income. However, in an attempt to become profitable quickly, it might end up extending credit to only the “more credit worthy” individuals, compromising on its social goal of offering credit to the people at the base of the economic pyramid. By the same token, a singular focus on enhancing social impact may come at the expense of financial growth. An MFI could work closely with its beneficiaries to address various other problems affecting their lives, such as healthcare and education, and lifting them out of poverty. This might create a positive social impact but hurt the financial prospects of the organization.

To grow successfully, MFIs need to simultaneously expand their financial performance and social impact. In other words, MFI performance is a multidimensional concept that involves two distinct elements—(a) financial growth, which measures the organization’s ability to sustain its operations and the profits it can generate to invest additional resources toward the pursuit for its social goals, and (b) social impact, which captures the extent to which the organization empowers its beneficiaries to enhance their income-generating capacity and contributes to their overall well-being (André & Pache, 2016; Battilana et al., 2015; Ebrahim et al., 2014; Fosfuri et al., 2016). Recently, product diversification has been proposed as an alternate strategy that allows the organization to grow while pursuing financial and social goals (Fosfuri et al., 2016) by taking advantage of demand needs. The organization can grow by expanding their offerings to their potential market—the social objective is not compromised as the products often meet essential needs, but neither is the financial growth stymied.

Context-Driven Product Diversification

Diversification refers to a growth strategy where an enterprise broadens the range of products and services offered¹ (André & Pache, 2016). Diversification has received considerable scholarly attention in strategic management (Benito-Osorio et al., 2012; Wan et al., 2011), as a means to grow the firm and maximize profits and shareholder value. But the bulk of this literature takes an organization-centric view where firms expand value creation and value capture by leveraging their unique bundle of resources and capabilities across new businesses to realize economies of scope and integration (Wan et al., 2011).

SEs, unlike commercial firms, are not driven solely by the motivation to maximize profits and shareholder value. To the contrary, their primary goal is to address social problems and improve the lives of marginalized communities. Profits are secondary and a means to further their social agenda. Therefore, the type of diversification they undertake is likely to be less dictated by the organizational need for profit and more by the needs of the communities they serve. In other words, diversification is community-centered as opposed to organization-centered.

The communities served by SEs are characterized by significant institutional voids (Khanna & Palepu, 1997, 2010), that is, formal and informal institutions that support the emergence and functioning of markets are weak or absent (Khanna & Palepu, 2005; Mair & Marti, 2009; Webb et al., 2020). Voids pose a challenge for both supply and demand, creating a sub-optimal economic structure. While voids can and do exist everywhere, they are more prevalent in emerging country contexts (Khanna & Palepu, 1997, 2010) either because formal institutions are underdeveloped or because they conflict with deeply entrenched informal institutions (Mair et al., 2012; Mair & Marti, 2009). SEs often operate in and around these voids, and in many instances, their raison d’etre is to span an institutional void and develop or strengthen markets (Chakrabarty & Bass, 2014; P. A. Dacin et al., 2010; Mair et al., 2012; Mair & Marti, 2009; Tracey & Phillips, 2011).

Institutional voids originate from gaps in formal and informal institutions (Liedong et al., 2020; Webb et al., 2020). Formal institutional voids include poor legal and regulatory frameworks, weak mechanisms to govern economic activities, and underdeveloped hard (e.g., roads, railways) and soft infrastructure (e.g., education, access to information) necessary for economic activities to flourish (Khanna & Palepu, 1997; Stephan et al., 2015; Webb et al., 2020). Informal institutional voids are present when local norms, beliefs, and values and their local representations fail to facilitate economic transactions (Webb et al., 2020). For instance, socio-cultural norms and values related to business ethics, corruption, and lack of trust among market actors in relational contracts hamper efficient and effective transactions (Mair et al., 2012; Mair & Marti, 2009; Webb et al., 2020). Often, voids do not operate in isolation and can combine and reinforce one another (Khoury & Prasad, 2016; Stephan et al., 2015; Webb et al., 2020), magnifying their adverse effect on economic activities.

Institutional voids impact markets in three ways (Mair & Marti, 2009). First, they hamper market development. There may be limited demand, supply, or both, owing to the lack of infrastructure, information, education, and other fundamental building blocks necessary for a market to thrive. Consequently, markets remain small and stunted. Market-development voids represent a deficiency in the overall market development and keep it from growing to its full potential. Second, voids hamper smooth functioning of markets. In this scenario, supply and demand exist, but there is typically a lacuna of intermediaries that connect supply and demand, govern economic transactions, and reduce the friction involved in economic transactions. Market functioning voids make markets inefficient. Finally, voids can also create challenges for certain vulnerable populations to participate in market activities. In this case, markets exist and operate well for a majority of the population, but they exclude certain sections of the society, primarily due to deeply entrenched socio-cultural norms. These are called market participation voids.

As organizations embed themselves in the communities they serve and in the lives of their customers, they develop an intimate understanding of the context and the range of institutional challenges (Bhawe & Jha, 2024), which creates the impetus for diversification. We posit that such context-driven diversification can fall into two categories—MDD that address market development and market participation voids, and MFD that fill market functioning voids. MDD refers to products and services that fill institutional voids that hamper market development. They build capacity in target communities—creating awareness, imparting skills, providing support to the weaker sections of society—thereby enabling market development. These products, directly or indirectly, strengthen individuals and communities and empower them to effectively engage in market activities. Most often, the need for these products is sensed by the organization due to their familiarity with the context. MFD refers to products and services that fill institutional voids that hamper market functioning. These products improve the quality of life of customers by making markets work better for customers. These products address different needs and aspirations of the customers, enabling them to transact across a variety of products and services. These products and services are usually demanded by the consumers as they start to engage in market activities.

In summary, MDD products and services cater to markets that are not sufficiently developed due to the presence of institutional voids that inhibit market development and participation. On the other hand, MFD products and services facilitate efficient market functioning. Both MFD and MDD are driven by the demands of the context and represent a departure from the traditional view of diversification that is driven by organizational capabilities. Next, we will argue how these two types of context-driven diversification impact the growth of SEs along financial and social dimensions.

Diversification and SE Performance

Financial growth

Let us first consider the impact of the two types of diversification on the financial performance of the SE. The impact of MFD on the financial growth of the SE occurs through one primary mechanism—access to markets which lowers transaction costs. By engaging in MFD, SEs play the role of an intermediary and enable easy access to new product and capital markets (Khanna & Palepu, 2010, p. 57). This allows customers to transact across a variety of products and services with minimal friction, that is, lower transaction costs (Khanna & Palepu, 2005; Mair & Marti, 2009). Take for instance, savings, insurance, consumer durables, and other products that are offered by MFIs. These products bridge the absence of aggregator and distributor institutions such as banks, insurance companies, and mass retailers (Khanna & Palepu, 2010) at the base of the pyramid (BoP).

Access to these new products and services builds resilience and enhances the ability of those at the BoP to engage in productive economic activities. The improving economic status of the customers will in turn positively impact the financial performance of the organization. As an example, consider an MFI borrower who has health insurance. If there is a health episode in the family, the insurance acts as a shield and protects the financial well-being of the family without adversely impacting the ability to engage in income generation. Similarly, purchasing a mobile phone can help with increasing the efficiency and effectiveness of the productive activities of the borrower. Although the insurance product or the mobile phone does not directly contribute to the loan performance of an MFI,² it builds borrower resilience and ability to repay loans, reduces default rates, and improves MFI financial performance.

When SEs engage in MDD, they play the role of an information and capability development intermediary that builds capacity and confidence in the target communities and prepares them to engage in market activities (Khanna & Palepu, 2010). MDD fills market development voids and market participation voids (Mair et al., 2012; Mair & Marti, 2009), that is, it builds demand conditions necessary for markets to operate and removes hurdles that might inhibit certain sections from participating in market activities.

As compared to MFD that builds resilience at the individual consumer level, MDD is a premarket activity that builds capacity at the community level. Furthermore, MDD will involve a substantial financial outlay from the organization to roll out these new products and services for community development (Bhawe & Jha, 2024). Whether and to what extent these pre-market activities contribute toward the SE’s financial growth depends on the competitive dynamics in the particular community. Overall, given that the impact of MDD on the financial growth of the SE is indirect and limited, we hypothesize:

Hypothesis 1a (H1a): Market functioning diversification (MFD) will be associated with a greater impact on the financial growth as compared to market development diversification (MDD).

Social Impact

MFD opens up access to markets for consumers and lowers transaction costs. At the same time, since it expands the choice set for the customers, it exposes them to the risk of falling into a debt trap or a consumption trap, which can have a negative social impact. For instance, market facilitation can increase consumption, which then can increase the indebtedness of borrowers, sending them into a spiral of debt. MFD may also spread the borrower’s limited pool of financial resources across an array of products and services. Given an expanded choice of products and services, customers may be tempted to choose products that provide immediate benefits as opposed to those that create empowerment and stability over the long run. In other words, MFD can pull customers into a consumption trap and reduce the proportion of loans borrowed/deployed for productive, income-generation activities. This can have a negative effect on social impact.³

By contrast, MDD will have a positive effect on social impact by preparing the communities to engage in income-generating activities. The lack of access to basic infrastructure such as healthcare, water, sanitation, and education often leaves no bandwidth for engaging in productive economic activities, and markets for microfinance remain underdeveloped. MDD plugs these market-development and market-participation voids (Mair et al., 2012; Mair & Marti, 2009; Prashantham et al., 2018) and lowers the hurdle faced by communities in leveraging microfinance to engage in productive economic activities.

In addition to expanding on the potential market for microfinance, MDD can encourage collective action via aggregating grievances and problems, enabling communities to build capacity for income generation instead of frittering away time and energy in meeting basic livelihood requirements. Examples would be initiatives to build local roads, water tanks for irrigation, and other such efforts. MDD can also bring about awareness and behavioral change (Santos et al., 2015), enabling communities to benefit from using markets to enhance their well-being. For instance, the lack of financial awareness, especially among women, makes them susceptible to fraud as loans taken in their name may be used for non-income-generation purposes.

In other words, MDD are likely to expand the number of people who might undertake new income-generating activities, as well as empower the community, enabling existing customers to participate more effectively, all of which can lead to greater social impact. Taken together, our arguments suggest:

Hypothesis 1b (H1b): Market development diversification (MDD) will be associated with greater social impact than Market functioning diversification (MFD)

The Moderating Effect of Legal Form

Ideally, SEs are expected to pursue their dual mission of financial and social goals with equal intensity (M. T. Dacin et al., 2011; Deng et al., 2020; Saebi et al., 2019; Santos et al., 2015). But in practice, this varies along a continuum, with some SEs focusing more on social goals while others may give primacy to financial goals. This may occur due to various internal and external factors that may steer the organization in one direction or another. However, a key factor that determines the dominant leaning of a SE is its choice of the legal form of incorporation (Battilana et al., 2012; Haigh et al., 2015). Some SEs are incorporated as for-profits while others are incorporated as non-profits (Battilana et al., 2012, 2015).

This early choice of legal form shapes the innate orientation of an SE (i.e., its culture or “DNA”) and has strategic implications for the organization (Battilana et al., 2015; Haigh et al., 2015). A for-profit SE is driven by a commercial logic and is focused on achieving profitable growth. On the other hand, a non-profit SE is likely to be dominated by a social welfare logic where the organization is primed for greater social impact (Battilana et al., 2012; Doherty et al., 2014). The for-profit SE typically relies on mainstream capital markets and works with stakeholders who expect higher rates of growth and financial return (Battilana et al., 2012). A non-profit SE relies on philanthropic capital and government funding to maximize social impact (Haigh et al., 2015). Their stakeholder relationships, governance structures, and processes are also different (Doherty et al., 2014). Overall, for-profit and non-profit SEs differ in their approach, including choices regarding the type and pace of growth, and prioritization of competing demands (Kannothra et al., 2018).

The legal form of SEs will influence the extent to which diversification will succeed in meeting the financial and social goals of the organization. This occurs through the notion of strategic fit, which indicates the level of alignment between an organization’s resources, capabilities, and culture and the opportunities in its external environment (Aragón-Correa & Sharma, 2003; Scholz, 1987; Zajac et al., 2000). Strategic fit enables organizations to effectively identify, evaluate, and implement growth opportunities through diversification. A for-profit SE is inherently tuned to spotting and capitalizing on new revenue streams. Therefore, it is likely to spot opportunities for MFD. It also has access to financial resources from capital markets (Battilana et al., 2012; Haigh et al., 2015) which enables it to quickly and efficiently work on reducing market frictions. This means, it can pick up the feedback from the customers and rapidly build and deploy new products. Therefore, a for-profit SE has a natural fit with the strategy of MFD and will positively moderate the relationship between MFD and financial growth.

A non-profit SE with a social welfare logic seeks to deeply entrench itself into the lives of the people in the target community and addressing their problems (Battilana et al., 2012; Haigh et al., 2015). It is better equipped to spot issues, identify the underlying causes, and undertake corrective interventions that build capacity and remove hurdles in the way of market development and participation. Given the community focus of these organizations, they are also better networked with other community service providers, which enables them to engage in MDD more effectively. Therefore, a non-profit SE has a natural fit with the strategy of MDD that requires sensitivity to spot ecosystem issues and will positively moderate the relationship between MDD and social impact. Hence, we hypothesize:

Hypothesis 2a (H2a): The effect of market functioning diversification (MFD) on financial growth is greater for for-profit SEs than for non-profit SEs.

Hypothesis 2b (H2b): The effect of market development diversification (MDD) on social impact is greater for non-profit SEs than for for-profit SEs.

Methods

Qualitative Validation

The development of our typology of context-driven diversification draws as much from theory on voids and diversification as our qualitative interviews with CEOs of MFIs operating in India. To validate MDD and MFD in the field, we worked with our data-partner Sa-Dhan to identify and reach out to a set of MFI CEOs. We chose a diverse group of MFIs that have operated over different time horizons, in different regions of the country, and at different scales to have more generalizability for our claims (see Table 1 for an anonymized list, the details on the MFIs and CEOs/principals we interviewed are available from on reasonable request). We started with a list of seven open-ended questions to all our interviewees that focused on their product diversification journey starting from a basic loan product and the rationale for diversifying into different types of products and services (see Appendix).

Table 1.

Details on MFIs Interviewed.

S. no.	MFI name	Legal form	Founding year	No. of states	Avg. loan portfolio(10 million INR) in FY 2019
1	MFI-A	Non-profit	2002	5	1,406.23
2	MFI-B	For-profit	2009	4	567.00
3	MFI-C	For-profit	1999	5	6,679.80
4	MFI-D	Non-profit	2009	3	9.18
5	MFI-E	For-profit	2008	16	2,475.00
6	MFI-F	Non-profit	2007	4	189.69
7	MFI-G	For-profit	1990	22	6,374.00
8	MFI-H	For-profit	2016	22	621.70
9	MFI-I	For-profit	2005	24	9,353.00
10	MFI-J	For-profit	2009	11	4,139.00

We transcribed the interviews using software and then looked for identifying common themes in the way MFIs diversified. Parsing the transcripts for examples of diversification activities and their rationale, we found that they aligned with our typology of the two types of diversification we had developed from our theory—MDD and MFD. MDD is driven by the need to build capacity in the community through financial literacy, skill development, education, health, and nutrition. MFD focuses on products and services that enhance the efficiency of lending such as savings facilitation, pensions, insurance schemes, connectivity, housing, and energy. Below, we draw on some quotes from our interviews to illustrate the relevance of our claims and provide illustrative examples of MFD and MDD in practice.

From our interview data, it was evident that diversification was driven by considerations of their borrowers and locational factors, that is, their context of operations. The following quotes exemplify this.

Some part (of the loan meant for agriculture) was being used for purchasing mobile phones, televisions and fulfilling other needs. This signaled a need gap. As a company, we wanted to be aligned with the needs of our beneficiaries. (MFI-E)

In designing a loan product, we learnt that microcredit is not sufficient. For reducing poverty, we needed to build savings ability through other products and services which were lacking. (MFI-A)

The rationale for diversification for the MFIs was driven less by capability concerns, that is, “what we can do,” but on the necessity of bridging voids in the ecosystem, that is, “what is needed” to make microfinance successful. The motivation and nature of MDD are clear from the following quotes.

What we did was to train before we gave out loans or other products. We need to be responsible. They need to understand the various modules—household budgeting, necessary and unnecessary expenditure. (MFI-E)

We engage in a range of awareness, skill building and support activities that enhance the resilience and overall well-being of the communities in which we operate. (MFI-D)

The MFIs realized that the lack of financial literacy and vocational skills in the community can impede their ability to benefit from micro-finance, and so they offered services that catered to these requirements. MFI-H served an area that had water-borne pollutants and decided to increase awareness and thereby reduce sickness and concomitant loss of productivity. Similarly, MFI-B primarily served migrant communities and offered subsidized health and education services to ensure preventive checkups to reduce the number of sick days in the household. While education, water, sanitation, and health are normally within the purview of local governments, the voids in these areas make successful participation in microfinance much less likely—this is where MDD is required to build these capacities.

We started holding health camps. These camps raise awareness, reduce number of sick days and improve overall well-being and productivity of households [indirectly giving the confidence to engage in market activities]. (MFI-H)

Meeting financial ratios depended on improving the education and health of our borrowers if we were to really bring about a change. (MFI-B)

Our interviews also suggested that MFIs offered products and services that created more efficiencies in lending. For example, MFI-A realized that their lending for small-scale dairy enterprises was not being optimally utilized as borrowers were purchasing more resilient but less-productive livestock. By diversifying into livestock insurance, MFIs ensured that most productive livestock were purchased, leading to increased profits and lower default rates. Other examples include diversifying into products through the aggregator model, thereby making them more affordable and cutting down on borrower’s consumptions that can increase their indebtedness. The following quotes illustrate how diversified products and services can create efficiencies in microfinance markets and serve as exemplars for MFD.

We first started with microcredit and then based on the customers’ demand, i.e., their aspirational need, we entered into an agreement with Samsung and decided to cross-sell consumer durables like mobile phones along with a loan to support it. (MFI-H)

Our household survey revealed that the customers wanted insurance and pension products. We became one of the early aggregators of the national pension scheme. (MFI-J)

We noticed that about half the loan was being used for house repairs. Even after 5-6 years, the family was spending half the money towards the house, and it was never getting completed. So, we came up with a house completion loan. It was large sized loan and we combined it with mason advisory service. (MFI-D)

We give out micro-finance to individuals who buy a cow and use the dairy products to setup their small enterprise. However, there is always the risk of individuals not utilizing the best practices for rearing, leading to livestock distress. To overcome this challenge, we diversified into livestock insurance for our borrowers. (MFI-A)

Quantitative Analyses

The qualitative interviews confirm MFD and MDD as two types of context-led diversification. We then test our hypotheses that link the two types of diversification to MFI performance by analyzing a longitudinal database of MFIs that operate in India. In October 2010, the Reserve Bank of India (RBI) recommended a self-regulatory framework for monitoring MFIs in India. We worked with Sa-Dhan, the industry’s oldest self-regulatory organization to develop detailed data that cover a majority of MFIs operating in India. A data-acquisition sheet was sent out to major MFIs who were members of Sa-Dhan that collected data on basic operations, financial earnings and expenses, loan portfolio attributes, and usage of credit by borrowers.⁴ The self-reported measures were collated and validated with past data and peer-group comparison. The data were collected over a 2-month-long survey conducted annually and compiled by July of the following year by the industry regulator. The MFI-identifying information was confidential, and a unique identifier was used instead. Any discrepancies and missing information from the data sheet were brought to the notice of Sa-Dhan who verified and validated information with the focal MFIs.

Our final sample yields data on 154 MFIs that were in operation during the period 2013–2018, which constitutes more than 90% of MFIs operating in India. There was sample attrition as 44 MFIs ceased operations during the sample period while 29 new MFIs were founded after 2013 and added to the sample. In addition, not all MFIs reported data for all variables used in statistical analyses, so we ended up with a sample of 433 observations when using 1-year lag for empirical analyses. The longitudinal nature of our sample allows us to draw inferences as well as control idiosyncratic firm-level effects that can be sources of unobserved heterogeneity affecting our inferences. In the following sections, we discuss our variables in more detail.

Dependent Variables

MFIs have two main objectives: financial growth and social impact. To measure their financial growth, we used the interest income earned on loans given by the MFI in the focal year, and it was measured in Indian rupees. We do not look at other sources of income that MFIs can earn via their other product lines such as offering insurance and receiving commission for being an aggregator. We kept the measurement in Indian rupees (INR) for consistency without converting it using the prevailing exchange rate for U.S. dollars as this changed over the period of our study. The average earned interest for our sample was INR 454.17 million while the maximum earned interest was for INR 17 billion. Our measure of MFI’s financial growth is a good proxy for the internal growth of the MFI as similar accounting measures have been used in prior studies to measure growth (Mersland & Strøm, 2009). Given that all MFIs in our sample worked within the same institutional environment, they faced relatively similar regulatory constraints, but we did control for the rate of interest for their credit from banks.

Social impact is a broad construct, and measuring it is empirically challenging. Much of the previous work focuses on anecdotal data and individual case studies. Regardless, there has been a consensus that measuring social impact should be contextually relevant and empirically specific to the sector being studied. A review of social impact measurement from an empirical perspective dichotomizes social impact measurement as either activity- or outcome-driven (Rawhouser et al., 2017). There is considerable variability in measuring the social impact of MFIs ranging from codifying mission statements (activity-driven) to looking at quantitative measures like loan amount dispersed (outcome-driven; Casselman et al., 2015; Randøy et al., 2015; Rawhouser et al., 2017). In the case of MFIs, an outcome-driven social impact measure would look at the efficacy of microloans in empowering borrowers and improving their financial condition. This suggests moving beyond gross measures of loans granted to measuring the usage of loans in enhancing borrower’s economic situations. Our interviews revealed that loans were often not used for income-generating, productive activities but were diverted toward meeting other household needs such as medical emergencies or personal consumption. This is exemplified by a quote from one of the industry experts and former CEO of a large MFI we interviewed who said,

When we check the loan utilization, we have noticed that about 15%–20% of the loan are used for other purposes. Many second, third time borrowers had shifted utilization from income generating to consumption.

Our dataset captures the usage of MFI loans, that is, how much of it was used for income-generating activities vis-à-vis other purposes. We measure social impact for an MFI as the amount of loan granted by the MFI that is used in income-generating activities. The survey sent to MFIs asked them to classify the loans that were granted by the way the loan money was used by the borrowers. The loan amount given to borrowers was categorized into (a) agriculture development, (b) animal husbandry, (c) trading, (d) business transport, (e) cottage industry, and (f) arts and handicrafts—these activities were tied to income generation for the borrowers. On the other hand, when loans were used for (a) housing, (b) health/medical expenditure, (c) education, (d) sanitation, and (e) consumption goods, they were classified as non-income-generating loans. This classification into income- and non-income-generation credit usage depends on whether there is a possibility to create income gains for the borrowers and was done by the industry regulator. The average amount loaned by an MFI that was used for income generation was INR 1.77 billion with a maximum of INR 95.26 billion.

It may be argued here that loans used for other purposes such as healthcare or education also contribute to social impact. However, when loans are repurposed for non-income-generating activities, they can push households into over indebtedness, offsetting any positive social impact. Therefore, drawing on previous work that has called for sharper measures to avoid confounding factors (Rawhouser et al., 2017), we rely on a narrow but unambiguous proxy for social impact, that is, loans used toward income-generation activities.

Independent Variables: MFD and MDD

In our sample, detailed data were collected on the diversified product offerings of MFIs. Due to the ubiquitous nature of institutional voids that pervade informal economies in which MFIs in India operate, they often diversify into complementary product lines beyond their core mission of microfinance. In our sample, 68.57% of MFIs diversified beyond micro loan products to offer other products and services that were utilized in their area of operations. These other product lines include among others savings-facilitation services, training initiatives, preventive healthcare services, water and sanitation services, capacity building, financial literacy, livelihood-promotion services, micro-insurance services (health and non-health), and remittance services.

As elaborated in the theory section, the diversified products and services offered by MFIs were parsed into two separate categories: (a) MFD and (b) MDD products. We measured the relative degree of diversification by the focal MFI in each product/service category using the Herfindahl-Hirschman Index (HHI), which measures the concentration of users across the product portfolio for each category. For example, MFD product categories include offering savings facilities, micro-insurance (health and non-health), remittance services, pension plans, small retail banking products, and other investing schemes that facilitate microfinance activities. Similarly, for MDD, these are livelihood development, financial literacy initiatives, training and skill development, general education services, and water and sanitation development, which help build capacity in the ecosystem. Using HHI, product diversification for an MFI was calculated as the sum of squares of portfolio proportions of aggregate users for each product category.

$H H I = \sum_{i = 1}^{n} c_{i}^{2}$ , where c_i is the proportion of users for the product line i

Legal form

According to the RBI regulations, MFIs operating in India can be incorporated as a society, a trust, section 25/8 company,⁵ a bank, and a non-banking finance company. A non-banking finance company MFI is a registered for-profit company that performs banking functions for low-income populations without a bank license. It is engaged in the business of loans and advances and acquisition of shares, stock, bonds, and/or chit-fund business for under-served groups and communities. The choice of legal form captures the innate orientation of the MFI and the strategic levers available at its disposal (Haigh et al., 2015). The legal form will take a value of one for for-profit MFIs (bank and non-banking finance company) and a value of zero for non-profit MFIs (society, trust, or section 25/8 company). In our sample, 44.8% of MFIs have a for-profit legal form while the rest have a non-profit legal form. Given the challenges of changing legal incorporation in India, only two MFIs in our sample changed their legal form of incorporation during our study period.

Control Variables

We control for a number of potentially confounding factors that can affect our hypothesized relationships by considering three categories of control variables—those related to MFI attributes such as size and age, capacity constraints, and those related to contextual factors in which MFIs operate. We control for the size and scale of the MFI by considering the total number of branches the MFI operates. This was reported in the annually conducted survey by the regulatory organization. In our sample, the average number of branches for MFIs in our sample was 74. We control for MFI experience as the number of years elapsed since their founding. Data on MFI founding were separately collected and cross-referenced with the data collected by the MFI-regulating organization to avoid errors. On average, MFIs had 12 years of experience operating in their industry.

In addition to size and scale, we also controlled for capacity constraints that could affect the quantum and direction of loan granted to borrowers. As we discussed earlier in the article, MFIs may also give out loans for other purposes under specific schemes such as home loans or other consumption loans. Their ability to provide micro-finance may be related to the other non-income loans it provides to its borrowers. Unlike micro-finance loans given to start and grow local businesses, non-income loans can reduce MFIs’ ability to fund small businesses due to capacity constraints. We control for these other personal non-income loans in our analyses. The average personal non-income loans in our sample given out by MFIs amounted to INR 183 million, with about 47.11% of the MFIs in our sample issuing non-income personal loans.

We control for two contextual factors. First, we control for differences in the level of economic development across different geographic regions in which MFIs operate. These differences are correlated with the presence and persistence of different institutional voids that can affect MFI financial growth, as well as their social impact. We control for the geographical diversity in which MFIs operate by aggregating the number of different states in which the focal MFI operated for the focal year. In addition, we account for government policy changes that may play a key role in shaping the context of SEs and enabling/inhibiting their growth (Bloom & Chatterji, 2009). On November 8, 2016, the Indian government in a sudden move decided to demonetize higher-denomination currency that amounted to roughly 86% of the total currency in circulation. The policy led to a short-term cash crunch and was aimed at primarily curbing the illicit and illegal non-taxed cash transactions that put a severe dent on the informal economy. We control for this event using the dummy Demo, which is one for the period following November 2016 and zero for the period prior to it. We also control for the bank credit rate that allows non-banking finance companies and cooperatives to borrow money from the banks. In general, the rate of interest charged by the RBI referred to as the repurchase option rate or Repo rate indirectly controls the interest rate charged by MFIs to their borrowers by affecting the liquidity of money in the economy. We control for the Repo rate taking the average by year of any changes in the repurchase option rate, which is adjusted quarterly or semi-quarterly by the RBI.

Results and Analyses

Table 2 shows the zero-order correlations for our data. All correlations above 0.07 are significant at 95% confidence level. We have a panel data structure that allows us to test our hypotheses by drawing inferences on the relationships while controlling for idiosyncratic firm-level variables. We start by checking if there is a systematic difference in observed MFI attributes between MFIs that ceased operations and those in our final sample. We found that there was no difference in geographical diversity of operations (t = 0.848, ns), MFI size, i.e., number of branches (t = 0.981, ns), MFI experience (t = 1.166, ns), personal loans (t = 0.444, ns), and legal form (t = −1.174, ns). In terms of diversification, the MFIs that ceased operations were less diversified in both MDD (t = 3.84***, p < .001) and MFD (t = 2.987***, p < .003). While this does not affect our theory and findings, it could be the result of MFIs winding down their operations as they cease operations or diversification itself being less common in less-successful MFIs.

Table 2.

Zero-Order Correlations.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) MFI Financial Growth INR (000,000)	1.00
(2) MFI Social Impact INR (000,000)	.56	1.00
(3) Geographical Diversity	.75	.64	1.00
(4) Number of States	.68	.48	.76	1.00
(5) Personal Non-Income Loans	.14	.30	.10	.12	1.00
(6) MFI Experience (Years)	.06	.04	.15	.13	.07	1.00
(7) For Profit (=1)	.27	.22	.33	.44	.03	.01	1.00
(8) Mkt. Functioning Diversification (HHI)	.10	.08	.07	.05	.02	−.01	.07	1.00
(9) Mkt. Development Diversification (HHI)	.05	.07	.04	.09	.01	−.01	.06	.12	1.00
(10) Demonetization Dummy	.00	−.02	−.01	.07	.02	−.01	.09	−.11	−.01	1.00
(11) RBI Repo Rate	−.02	−.02	.00	−.10	−.01	.01	−.10	.04	−.05	−.72	1.00

All correlations above .07 are significant at p < .05

Table 3 shows the random-effects panel data models for financial growth and social impact. Models 1 and 2 in Table 3 show the model estimates for MFI’s financial growth while Models 3 and 4 show the estimates for MFI’s social impact. Taking advantage of our panel data structure, we used one-year lagged values of our independent variables. Model 1 in Table 3 validates our claim that MFD products and services are associated with a positive effect on the financial growth of MFIs (β = 1494.46***, p < .001). The effect of MDD products and services on financial growth was not significant (β = −957.64, p < .391). Although the coefficient is not significant, the direction and sign indicate that MDD may reduce financial performance of the MFI. Taken together, our results show support for H1a that MFD is associated with higher financial performance relative to MDD.

Table 3.

Financial Growth and Social Impact of MFIs.

	Model 1	Model 2	Model 3	Model 4
	Fin. GrowthINR (000,000)	Fin. GrowthINR (000,000)	Social ImpactINR (000,000)	Social ImpactINR (000,000)
L1. Number of Branches	16.073^**(6.061)	22.354^***(5.976)	−3.051(18.828)	−0.232(18.930)
L1. Geographical Diversity	3104.672^***(281.885)	2787.495^***(279.210)	8600.111^***(974.219)	8354.034^***(983.226)
L1. Personal Non-Income Loans	0.002(0.025)	0.002(0.025)	0.934^***(0.138)	0.908^***(0.138)
L1. MFI Experience (Years)	−31.618(98.111)	−29.115(94.774)	−282.371(320.740)	−250.207(321.197)
L1. For Profit (=1)	62.282(1768.228)	858.613(1847.369)	1495.456(5212.640)	−6101.249(6088.575)
L1. Mkt. Functioning Diversification	1493.460^***(439.232)	−186.873(531.732)	−2233.234(6687.959)	−6294.882(8711.724)
L1. Mkt. Development Diversification	−957.639(1143.624)	890.347(1571.418)	9322.991^†(4768.851)	−4822.992(9100.888)
Demonetization Dummy	−2739.547(4225.600)	−3953.428(4125.981)	19302.622(24242.440)	20600.847(24124.449)
L1. Bank Interest Rate	−1.02e+04(49558.088)	−2.04e+04(48444.993)	73068.900(4.89e+05)	43955.385(4.87e+05)
L1. For Profit X L1. Mkt. Functioning Diver.		4860.655^***(901.203)		7180.561(12765.224)
L1. For Profit X L1. Mkt. Development Diver.		−3642.609^†(2149.046)		29378.092^**(13055.822)
Year=2015	136.788(387.982)	−78.451(452.761)	14277.535(8816.781)	16144.636^†(8807.651)
Year=2016	195.977(696.654)	−33.643(681.703)	4207.878(6804.708)	5186.281(6784.569)
Year=2017	−513.665(792.300)	−741.268(773.621)	4825.655(6060.610)	5150.212(6031.112)
Constant	−2804.960(4297.909)	−2283.693(4169.188)	−1.64e+04(34029.281)	−1.09e+04(33974.815)
Observations	433	433	433	433
R ²	0.641	0.676	0.313	0.321

Robust Standard errors in parentheses.

†

p < .10; *p < .05; **p < .01; ***p < .001.

Similarly, Model 3 from Table 3 shows marginal support for H1b suggesting that MDD products and services enhance the social impact of MFIs by directing borrowers toward greater wealth creation (β = 9322.92^†, p < .067). The effect of MFD products and services on social impact is not significant (β = −2233.33, p < .744). Overall, our results show strong support for the claim that the type of diversification has an effect on the locus and direction of growth—MFD is associated with greater financial profits for the MFIs as compared to MDD, whereas the MDD enhances social impact relative to MFD. But neither type of diversification by themselves enhance both financial growth and social impact.

Model 2 in Table 3 shows that the effect of MFD on financial growth is enhanced when the MFIs have a for-profit legal form (β = 4860.65***, p < .001). Interestingly, although not hypothesized, a for-profit legal form negatively moderates the relationship between MDD products and MFI’s financial growth (β = −3642.61^†, p < .065). We plot these interactions in Figure 1. From Figure 1, we can see that a for-profit legal form enhances the effect of MFD on financial growth of MFIs when compared with MFIs that have a non-profit legal form. Indulging in greater MFD has a marginal negative effect for non-profit MFIs. These results lend strong support to H2a. Our results from Model 4 in Table 3 are contradictory to our proposed H2b. Instead of the positive moderation of non-profit legal form on the relationship between MDD and social impact, we find the opposite effect. Our results from Model 4 and plotted interaction effects in Figure 2 suggest that contrary to our proposed hypothesis, a for-profit legal form enhances the relationship between MDD and social impact (β = 28378.09*, p < .024). From the interaction plots, it is clear that engaging in more MDD enhances the social impact of for-profit MFIs, and there is a marginal decrease in social impact for non-profit MFIs if they indulge in MDD.

Figure 1.

The Interaction Plots for MFI Financial Growth.

Figure 2.

The Interaction Plots for MFI Social Impact.

This surprising finding could be attributed to two reasons. First, a for-profit SE (as compared to a non-profit) might channel MDD products and services to existing customers, as opposed to non-customers, thereby amplifying the social impact. Second, for-profit MFIs might have better organizational structures and processes in place (compared to non-profit MFIs) to span voids that inhibit market development, thereby enabling customers to take loans mostly for wealth creation instead of dissipating them to meet their basic needs.

Taken together, the results from Model 2 suggest that when it comes to financial performance, strategic fit between the legal form and the selection of diversification type enhances performance. On the other hand, H2b is not supported, which suggests that the notion of strategic fit between the legal form and diversification choices does not amplify social impact.

Robustness and Sensitivity Analyses

We explore and test some alternate explanations and extensions to our findings in this section. There could be potential selection concerns on the nature of relationship between the choice of product diversification and growth. For example, if prior financial performance enhances or constrains MFIs’ extent and direction of product diversification, then our results might potentially be endogenous due to reverse causality. Using the panel structure of our data, we use alternate specifications using lagged variables as instruments for the Arellano-Bond GMM estimator (Blundell & Bond, 1998). While this greatly reduces our sample size as we lose observations due to lagged variables used as instruments, our results still hold giving greater robustness to our overall analyses. In Models 1 and 4 in Table 4, we show that the estimates for the Arellano-Bond models that also use lagged values of financial growth and social impact serve as regressors. While the sample size reduces, we can see from Model 1 in Table 4 that the hypothesized positive moderation of for-profit legal form on the relationship between MFD and financial growth remains significant (β = 663.43*, p < .048). The surprising positive effect of for-profit legal form on the relationship between developmental diversification and social impact is also marginally significant as can be seen from Model 4 in Table 4 (β = 6899.52^†, p < .078). Despite using panel data and lagged effects, an important caveat here is to note that our results are associative in nature given the nature of our data and analyses.

Table 4.

Robustness and Sensitivity Analyses.

	(1)	(2)	(3)	(4)	(5)	(6)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	Fin. GrowthINR (000,000)	Fin. GrowthINR (000,000)	Fin. GrowthINR (000,000)	Social ImpactINR (000,000)	Social ImpactINR (000,000)	Social ImpactINR (000,000)
L.MFI Fin. Growth	0.979^***(0.015)
L.MFI Soc. Impact				0.421^***(0.032)
L1. Number of Branches	7.064^**(2.252)	38.774^***(4.083)	10.008(8.738)	42.639^**(15.554)	23.579(16.727)	−26.448(22.829)
L1. Geographical Diversity	945.151^***(90.688)	1617.204^***(211.490)	3995.104^***(420.068)	1488.376^†(829.158)	3321.708^***(920.532)	11553.876^***(1249.307)
L1. Personal Non-Income Loans	−0.030*(0.013)	0.037(0.028)	−0.111(0.140)	1.093^***(0.056)	0.979^***(0.107)	0.226(0.234)
L1. MFI Experience (Years)	41.978*(17.144)	18.856(74.019)	−88.780(110.911)	−68.825(90.360)	−265.774(271.535)	138.181(473.565)
L1. For Profit (=1)	−379.577(314.162)	1427.907(1423.390)	−1411.381(2456.757)	438.819(1892.802)	−1297.527(5186.741)	−5277.595(8179.407)
L1. Mkt. Functioning Diversification	165.169(214.187)	−297.888(322.562)	−39.394(521.529)	−1187.341(3010.828)	−3480.487(7027.827)	−2980.546(10947.090)
L1. Mkt. Development Diversification	−527.118(416.887)	813.502(1140.475)	131.688(2012.889)	4729.092(4160.452)	−6301.683(7509.670)	−3564.008(12540.112)
Demonetization Dummy	−827.424^***(219.801)	−3617.896(2900.663)		3431.370^†(1942.935)	10779.816(18535.852)
L1. Bank Interest Rate	−1.18e+05*(46430.159)	−1.93e+05(1.60e+05)		46118.019(5.32e+05)	2.51e+05(1.37e+06)
L1. For Profit X L1. Mkt. Functioning Diver.	663.431*(332.919)	1689.624*(660.917)	3682.111^***(956.207)	3595.183(4763.489)	24288.632*(10781.825)	−5299.210(16383.276)
L1. For Profit X L1. Mkt. Development Diver.	−279.433(503.821)	−4805.507^**(1597.608)	−932.121(2739.931)	6899.515^†(3948.006)	27525.792*(10961.532)	48495.977^**(18134.795)
Year=2015	2408.834^**(839.330)	1479.788(6008.568)	1123.341(3239.569)	2806.195(5254.390)	6375.997(8585.715)	11565.203(7642.595)
Year=2016	717.422(510.144)	−1710.666(1257.549)	−364.769(662.235)		5777.448(6559.223)	−941.388(7799.979)
Constant	5711.321*(2863.380)	12474.225(12921.563)	−3986.468^†(2054.868)	−5369.717(33832.346)	−1.93e+04(1.03e+05)	−1.55e+04*(7889.277)
Observations	467	310	224	467	468	358
Adjusted R²		0.721	0.699		0.332	0.401

Robust Standard errors in parentheses

†

p < .10; *p < .05; **p < .01; ***p < .001.

Another potential confound in the results could be due to business-focused MFIs self-selecting “bankable” borrowers instead of serving the truly unbanked. In India, where the MFIs in our sample operate, rural areas are underserved by the lack of infrastructure and development, which hampers access to banking with some studies pointing to the lack of credit access to roughly 50% of the rural population (Karmakar & Mohapatra, 2009). While checking for MFI’s selection of “bankable” borrowers is beyond the scope of our data, we use a proxy for the ratio of loans disbursed in rural areas compared to the number of loans disbursed overall to explore the validity of our results for those MFIs who give out more than 50% of loans in rural⁶ areas. Models 2 and 5 in Table 4 show the estimates for the models, with financial performance and social impact as the dependent variables, respectively. From Model 2, we can see that although the sample size reduces substantially, the positive effect of MFD on financial growth is enhanced for for-profit MFIs, and this effect remains highly significant (β = 1689.63**, p < .011). Similarly, Model 5 in Table 4 shows that the unexpected positive moderation on MDD and social impact also remains significant for for-profit MFIs (β = 24288.63**, p < .012).

During the period of our study, the Indian government in a move targeted at the informal economy decided to demonetize the INR 2000 and INR 500 bank notes which accounted for 86% of the currency in circulation. The effects of the sudden demonetization are not the focus of this article, but there could be potential effects of the policy that could favor for-profit MFIs who may have better access to bank capital than community-focused MFIs. Since we had only one time period clearly following the 2016 November demonetization, we estimated the models for the period before demonetization. In Models 3 and 6, we show the estimates for our sample from the period before the currency demonetization. From Model 3 in Table 4, we find that the positive moderation of for-profit legal form on the relationship between MFD and financial performance is strongly significant predemonetization (β = 3682.11***, p < .001). In addition, from Model 6 in Table 4, we can see that the relationship between MDD and social impact is enhanced by for-profit MFIs, and this interaction effect remains strongly significant (β = 48495.98**, p < .008). In our sample, 37.56% of MFIs diversified into MFD products and services, and 47.62% of MFIs were engaged in MDD products and services. We also assess if MFIs diversify based on their perceived fit with their corresponding legal form, that is, whether for-profit MFIs focus more on MFD and vice versa. We find this to not be the case as we found no significant difference in MFD diversification between for-profit and non-profit MFIs (t = −1.31, ns). Similarly, there is no particular preference for MDD diversification in non-profit MFIs as our analyses revealed no difference between for-profit and non-profit MFIs (t = −0.597, ns). Overall, we find partial support for our hypotheses. These findings are robust to a range of different alternate explanations and conditions as discussed earlier.

Discussion

If SEs can grow financially while addressing social problems of inequity, they can unleash market forces to alleviate disparity in the world. Despite this promise, real-world results of SEs in ensuring social impact while generating financial return have been mixed with some studies hinting at contextual voids as roadblocks to growth (Battilana & Dorado, 2010; Battilana & Lee, 2014; Cornelissen et al., 2021). In this study, we use a longitudinal database of MFIs operating in India to examine how the context influences the diversification strategies of SEs and their impact on SE growth. Drawing on the literature on institutional voids and augmenting with confirmatory interviews, we identify MFD and MDD as two archetypes of diversification strategies that closely depend on the type of voids being bridged—MFD relates to providing products and services that reduce market frictions and facilitate access; MDD relates to products and services that support the development of markets that do not exist or are very rudimentary. From our panel data analyses, we find that MFD increases financial growth while MDD has a positive effect on social impact. This positive effect of MFD on financial growth is enhanced for those SEs that are registered as for-profits, but somewhat surprisingly, for-profit SEs also achieve high social impact when they offer MDD products and services. This finding indicates that for-profit SEs may perform better than non-profit SEs when they adopt a balanced diversification strategy. This supports the recent trend of SEs shifting toward a for-profit legal form (Haigh et al., 2015).

Our understanding of how SEs can grow has mostly come from theoretical or grounded case work, and while it has contributed to our knowledge, a large-sample empirical study like ours can validate theoretical claims and bring out new insights to advance scholarship in this domain. We elaborate on these contributions below. Our main contribution is identifying and recognizing the importance of the context in influencing the diversification strategies of SEs, which often operate in underdeveloped institutional environments. This work addresses concerns among scholars that much of the work on diversification has largely ignored the context, such as the role of institutions and the broader environment in which firms operate (Benito-Osorio et al., 2012; Bruton et al., 2022; Ng, 2007; Saebi et al., 2019). Prior studies have primarily taken a firm-centric view of diversification, drawing on resource-based/capability perspective (Barney, 1991; Wan et al., 2011; Wernerfelt, 1984) and strategic resource relatedness (Farjoun, 1998; Markides & Williamson, 1994). This is not as much a critique of prior diversification research but a phenomenological artifact of work that has emerged in environments where markets function with few frictions. Addressing this gap on the role of context, our study pushes the scholarship in the direction of context-led diversification, which is more germane to the underdeveloped institutional environments in which most SEs operate.

From our inquiry, we found that both informal and formal institutional voids inhibit the ability of MFIs to grow. For example, in the typical contexts where MFIs operate, markets for insurance, pension, or consumer appliances do not function efficiently, which affects the borrowers’ ability to repay loans, which in turn affects the ability of MFIs to grow financially and offer more loans. Diversification into financial literacy, education, livestock insurance, health insurance, and even into consumer goods is not necessarily a function of MFI capability but a localized need driven by the type of institutional void that needs to be bridged for markets to work (Khanna & Palepu, 2010; Mair et al., 2012; Mair & Marti, 2009; Webb et al., 2020). Our study thus departs from the traditional view of diversification which asks “what can we do” and instead presents a diversification perspective driven by the context which asks “what do we need to do?.”

Second, we also contribute to the literature on persistence of business-social tension in SEs and ways to overcome the same (Battilana & Dorado, 2010; Battilana et al., 2015; Cornelissen et al., 2021; Deng et al., 2020; Saebi et al., 2019). It has been suggested that product diversification may be an effective strategy to sidestep this tension in SEs and grow the organization along the twin dimensions of financial growth and social impact (Fosfuri et al., 2015, 2016). Our study lends qualified support to this hypothesis in the context of MFIs—we find that a more granular consideration of diversification strategy shows differential impact on the two growth dimensions. While MFD has a positive impact on the organization’s financial growth, MDD has a positive impact on social impact. This finding also has an important implication for practitioners—that an exclusive focus on either type of diversification can compromise the pursuit of one or the other goal, highlighting the need for a deliberate, balanced approach. It also informs policy makers, who can incentivize MFIs to take a balanced approach to diversification to ensure financial inclusion in a sustainable manner. Furthermore, the impact of diversification strategy on financial growth is accentuated if there is strategic fit between the legal form and type of diversification (Zajac et al., 2000). However, strategic fit does not seem to affect the relationship between diversification strategy and social impact. In fact, contrary to what the strategic fit framework would predict, the relationship between MDD and social impact is enhanced for a for-profit MFI. This raises several interesting questions—What is the role of strategic fit? Is the notion strategic fit as relevant to social impact as it is for financial performance? Why or why not? These questions need more empirical work across multiple sectors especially since some work seems to identify business models as intervening links with hybrid outcomes (Lanzolla & Markides, 2021; Villani et al., 2017).

Third, the empirical contribution of our study is the large sample validation of the effect of diversification on the twin goals of SEs. Studies on SE diversification have so far been conceptual or qualitative in nature (Fosfuri et al., 2016; Jha et al., 2021). Our study adds empirical heft to this line of inquiry. The operationalization of MDD and MFD is contextually grounded to accommodate the specificities of MFIs, a type of SE, but at the same time, draws on the existing body of work on diversification to arrive at the degree of diversification. This operationalization is an important contribution that can be leveraged by future work on SE diversification. We also define and operationalize the dependent variable, SE growth, on two dimensions—financial growth and social impact—which capture the central tension in SEs (Battilana & Lee, 2014; Haigh et al., 2015; Im & Sun, 2015; Santos et al., 2015) and provide a more accurate representation of their performance (André & Pache, 2016; Battilana et al., 2015; Ebrahim et al., 2014). Both financial growth and social impact are important for sustainable growth as doing one at the expense of the other is likely to impede sustainability. The use of multiple measures for SEs has often been advocated by SE scholars as helping to understand the performance of social organizations in a holistic manner (Randøy et al., 2015; Rawhouser et al., 2017). By examining these two dimensions simultaneously, we can better understand the tradeoffs involved in product diversification strategies.

Limitations and Future Work

Our study has a few limitations. First, the study is limited to blending SEs (Santos et al., 2015), where the paying customers are the beneficiaries themselves. There are other types of SEs where the beneficiaries are different from paying clients (Kannothra et al., 2018; Santos et al., 2015). The results of this study may be only partially applicable to other types of SEs and need additional validation. Second, the data in our dataset are partially self-reported. While there is some room for social desirability bias, where the numbers reported could be inflated to show the organization in a positive light, such bias has been minimized by cross-checking the data from secondary sources. Also, the stringent reporting norms laid down by the RBI for MFIs act as a deterrent against inflated reporting. Third, one of our main goals was to bring out the importance of context when studying diversification, and we built on the literature on voids to identify two typologies of diversification, MDD and MFD, and their impact on financial performance and social impact of MFIs. However, we were limited by our sample size and focused on direct and moderating effects. Future research can unpack and explore if “only MFD” or “only MDD” or some combination of these can lead to better financial growth and social impact. It can also explore non-linear effects and find if there are limits/thresholds to the benefits of MDD and MFD. A final limitation is that our proxy for social impact is an imperfect measure of the construct. Impact is best measured at the individual or household level before and after the entry of MFIs. However, this is also problematic because without granular data at the individual or community level, multiple confounding effects of government policies, NGOs, and general economic development may all be responsible for improving social impact. Future research could look to parse these different effects to get a more precise measure of social impact. Nonetheless, we believe that our study has taken an important stride toward exploring the role of institutional context in influencing diversification in SEs.

Conclusion

It is well acknowledged that SEs need to grow so that they can make a dent in addressing some of the most vexing problems of our times. At the same time, it is also a fact that SEs struggle to chalk out a growth path that keeps both their social and financial goals intact. Our study makes an important contribution to furthering this conversation. We suggest that SEs can turn the challenges they face in their operating environment, that is, the institutional voids, into a diversification strategy that can help them grow. A well-balanced diversification strategy can unlock financial growth as well as greater social impact. This opens a fertile ground for future research and practice.

Footnotes

Appendix

Acknowledgements

Special thanks to our data partner, Sa-Dhan and its supportive leadership and staff, who made this research possible. In particular, our gratitude to Mr. Ardhendu Nandi and Mr. Ambrish Gopal of Sa-Dhan for their help in facilitating the interviews.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nachiket Bhawe

Srivardhini K. Jha

Notes

Author Biographies

Nachiket Bhawe is an Associate Professor of Management at the Thomas College of Business and Economics—University of North Carolina-Pembroke. His research focuses on social enterprises, policy, and the use of knowledge in large teams. His research has been published in the Journal of Applied Psychology, Strategic Entrepreneurship Journal, Small Business Economics, Journal of Business Research, and Journal of Business Venturing Insights among others.

Srivardhini K. Jha is a Professor of Entrepreneurship at the Indian Institute of Management Bangalore (IIMB). Her research interest is in innovation and entrepreneurship in the emerging country context. Her papers have appeared in journals such as Annals of the New York Academy of Sciences, Journal of Management Inquiry, MIS Quarterly, MIT Sloan Management Review, and others. She has co-edited a book on the Indian startup ecosystem.

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