Supply chain inclusion in base of the pyramid markets: A cluster analysis and implications for global supply chains

Inclusion of marginalized actors in MNE-led global supply chains

Although topics of inclusion and local participation have gained momentum in both academia and practice, only a few empirical studies adopt the SCM perspective and inclusion of impoverished actors. Building on these and other key studies from international business and strategy literature, several important findings can be outlined concerning the inclusion of BOP actors in MNE-led global supply chains. First, MNEs face significant challenges when operating in BOP markets and including BOP in their supply chains. MNEs often lack the skills and capabilities needed in order to embed and co-create locally relevant value propositions and to build on local structures (Venn and Berg, 2013). They also face difficulties related to the lack of global standards enforcement (Prahalad, 2012). Additionally, they need to develop “an active assistance approach” in order to enable the adoption of global standards at the BOP level (Perez-Aleman and Sandilands, 2008). The lack of commitment of top management of MNEs toward BOP inclusion due to long return horizons also presents challenges (Hart et al., 2016; Olsen and Boxenbaum, 2009). Additionally, existing power and knowledge asymmetries between the local BOP actors and MNEs present barriers for knowledge diffusion between impoverished actors and other supply chain members (Hall and Matos, 2010).

In spite of all the challenges faced by MNEs to engage local BOP in their value creation activities, their efforts may be essential to drive sustainable development and poverty alleviation efforts in BOP markets (Nahi, 2016). Critics of the initial BOP proposition argue that the only way to eradicate poverty is to provide steady employment and reasonable wages, to help increase productivity levels and to strengthen capabilities and freedoms along social, cultural and political dimensions (Karnani, 2007). Inclusion of marginalized BOP in supply chain activities can provide such opportunities. Supply chain inclusion mechanisms can enable the creation of social impact in global supply chains. Social impact is as the positive effects of different activities on sellers (local distributors or producers), buyers (local consumers or agents) and communities (London, 2009). These effects can refer to potential changes in economics (e.g. income, productivity), capabilities (e.g. skills, knowledge, self-esteem) and relationships (e.g. social status, access to networks) (London, 2009).

Second, local non-business partners can play an important role by contributing local knowledge and capabilities (Parmigiani and Rivera-Santos, 2015). In particular, non-business partners frequently undertake activities that require close connection with the local context (Hahn and Gold, 2014). These activities include access to distribution networks, demand studies, social marketing and education campaigns, facilitating partnerships and value chain activities. Typical non-business partners are NGOs and government agencies who can use their local knowledge to re-conceptualize supply chains and fit them to the constraints of BOP markets (Gold et al., 2013). Yet, partnerships between for-profit MNEs and non-profit organizations can be challenging because a common ground of resources, values and processes is needed and different organizational identities may result in uneven expectations and goals (Le Ber and Branzei, 2010).

Third, MNEs tend to include BOP actors in either upstream processes (BOP as suppliers) or downstream processes (BOP as distributors), but rarely both. Case studies from literature illustrate examples of supply chain inclusion of BOP actors as either producers/suppliers of raw materials or as distributors through women-led door-to-door distribution models (Parmigiani and Rivera-Santos, 2015; Gold et al., 2013; Hall and Matos, 2010). In particular, distribution models based on local networks of micro-entrepreneurs (frequently women) are often presented as key mechanisms to address the lack of infrastructure and to mobilize the strength of social ties in informal markets (Bendul et al., 2016; Prahalad, 2012; Seelos and Mair, 2007).

Altogether, the inclusion of local BOP actors in global supply chains presents significant barriers for MNEs in regards to institutional voids, informal market characteristics, power and knowledge asymmetries and top management commitment. While non-business partners often mediate the relationship between MNEs and local BOP actors, different organizational mindsets and goals between for-profit and non-profit organizations can create further tensions. Supply chain inclusion of local BOP actors can serve as mechanisms to overcome local barriers, but more importantly can enable the creation of social impact in BOP communities.

Inclusion of marginalized actors in MSME-led local supply chains

Recent empirical work suggests that key organizations for supply chain inclusion and local development in BOP markets are MSMEs (see, for a review, Rosca et al., 2017; Kolk et al., 2014). Various aspects of local supply chains of MSMEs (e.g. distribution, sourcing from smallholder farmers, partnerships and social impact) have received more attention from social entrepreneurship and sustainable business research streams and less attention in the SCM literature. Insights from these research streams indicate a large diversity in terms of organizational models (e.g. for-profit initiatives, social enterprises). MSMEs in BOP markets have real potential to enable local supply chains through access to credit, market information and market access, and to achieve higher productivity through provision of new technologies and equipment (Sodhi and Tang, 2011). Additionally, due to their small size and flexible organizational structure, MSMEs are in a better position to engage with local BOP actors. Yet, their own survival is at risk due to challenges related to lack of capital and capabilities for scaling.

When exploring supply chain inclusion mechanisms employed by MSMEs in BOP markets, several analytical constructs from the literature emerge (see Table I). First, marginalized actors are central to supply chain inclusion. A core tenet of the inclusivity narrative relates to the inclusion of actors from BOP communities. Some scholars argue that, by definition, inclusion in BOP markets should engage low-income consumers (Kolk et al., 2014). Building on insights from marketing and service science and aligned with the evolution of BOP first- and second-order generation strategies, a distinction can be made between behavioral inclusion and value chain inclusion. On one hand, when a firm aims to provide products and services to BOP consumers, it may engage in traditional information-sharing activities, where customers provide/share information, make suggestions and are involved in the decision making (Chan et al., 2010). On the other hand, supply chain inclusion refers to the actual integration of BOP customers at different stages of the value chain (Lau et al., 2010). A focus on behavioral inclusion can identify enterprises which address inclusion concerns through the provision of products and services to BOP consumers, while value chain integration of BOP consumers can address challenges related to empowering, rapport-building and quality (Nahi, 2016). The differentiation between behavioral inclusion and value chain integration is valuable in order to place inclusion activities on a continuum. Since consumer integration into value chain activities requires behavioral participation, it is more likely that some firms will engage only in behavioral inclusion, while others will take it further and engage BOP consumers as suppliers, producers, distributors and service providers (Simanis and Hart, 2008).

Second, MSMEs can engage in partnerships with MNEs who have resources, capabilities and knowledge for scaling, business development and international expansion and can help MSMEs grow their economic and social impact (Seelos and Mair, 2007). Third, there are cross-sector partners, such as local NGOs, community associations and knowledge institutions, which are known to have knowledge of local systems and structures and to have legitimacy (Rosca and Bendul, 2018). MSMEs often engage in partnerships with local cross-sector partners and leverage their resources and capabilities in order to acquire local knowledge, legitimacy and connections with BOP consumers (London et al., 2010).

Current studies also suggest that supply chain inclusion mechanisms may take different forms, depending on the industries involved (Nahi, 2016) and whether BOP is a market for selling goods (a consumer approach) or a producer to be potentially sourced from (a supplier approach) (London et al., 2010).

Research gaps and objectives

While the role of MSMEs has gained importance in BOP contexts, the literature review outlines the challenges that MNEs face when attempting to integrate marginalized actors into their supply chains. The increasing role of MSMEs is supported not only by recent studies (Rosca et al., 2017; Kolk et al., 2014) but also by numerous awards, incubators and accelerator programs targeting market-based initiatives in BOP markets. Therefore, the goal of this paper is to investigate supply chain inclusion mechanisms in BOP markets and to inform MNEs about how they can facilitate the inclusion of BOP actors by building on the structures already developed by local MSMEs. Thereby, the paper contributes a novel perspective on local supply chains of MSMEs and links the debates on local and global supply chains by proposing partnership trajectories between MNEs and MSMEs, thereby discussing the implications and trade-offs for social impact creation.

Stage 1: data collection

The units of analysis for this study are MSMEs that operate in BOP markets. An online questionnaire was designed for this target group of respondents. The selection of respondent MSMEs employs a purposive sampling approach, where subjects are selected based on pre-specified criteria (Black, 1999). These criteria are as follows: First, the enterprises can have up to 250 employees[1] and can address BOP issues through consumer or supplier models, as described in the literature review. Second, these enterprises can be local or foreign with headquarters in industrialized countries but with small-scale production facilities or local branches in BOP settings. Third, the targeted enterprises need to have a profit orientation, or at least the intention of being profitable and achieving self-financing growth through retained earnings.

In order to find MSMEs that fit these criteria, databases with case studies and online platforms showcasing BOP enterprises were screened[2]. This resulted in a total sample of 1089 enterprises operating in BOP markets across various geographical regions and industries. The survey was administered between August and November 2016, mainly in two stages, and it recorded responses from 175 BOP ventures. The questionnaire was tested in several rounds with both practitioners and academics before the application. In the first stage, the online questionnaire was sent via e-mail to the respondents together with a text explaining the background of the study and promising them the results of the study. Due to a low response rate during this stage (6 percent, 36/623), in the second stage, personal emails were sent out, addressing the founders/directors of the enterprises. This stage resulted in a higher response rate of 38 percent (139/367). After excluding incomplete and insufficient quality responses, the final sample used for this study consists of 134 enterprises. It presents a representative distribution across various characteristics (see Appendix 1). The sample also presents a focus on Africa and South Asia, with young, small and mostly stand-alone enterprises.

In order to evaluate the representativeness of the sample, the sample characteristics of this study were compared with samples from similar studies. This sample is very comparable with the study conducted by De Beule and Verwaal (2014) in terms of the enterprises’ age, size, sectors and geographical focus. The characteristics of the respondents along with the small size of enterprises suggest that respondents had in-depth knowledge of the enterprises and its related activities. Owners, managing directors, founders, executive directors and chairpersons filled out 59 percent of the questionnaires. In total, 31 percent of the questionnaires were completed by internal managers responsible for specific areas in the companies, such as IT, marketing or technical issues, or regional project managers. The remaining 10 percent of the questionnaires were returned by other actors with intimate knowledge of the firms (e.g. impact advisors, researchers, lawyers). In order to check for potential non-response biases in the sample, the extrapolation approach was employed (Armstrong and Overton, 1977). Several χ² group tests between early and late respondents did not reveal any differences (all p >0.05). Therefore, one can conclude that there is not enough evidence to suggest the existence of non-response bias.

Measures

This study employs data from the questionnaire related to information-sharing activities, value chain inclusion and partnerships with business and non-business actors. Moreover, data were collected on the type of BOP model employed and the industry. The type of data used for all variables can be found in Table I.

In order to measure value chain inclusion, items from existing studies on consumer integration were adapted based on the testing rounds of the questionnaire with academics and practitioners. Behavioral inclusion indicators are adapted from Chan et al. (2010), while value chain integration items are inspired from Lau et al. (2010)[3]. The final items used for value chain inclusion along with the reliability indicators can be found in Appendix 2. Descriptive statistics for all data before scaling can be found in Table II.

To evaluate partnerships with business actors, respondents were asked if their organization collaborates with MNEs. While for partnerships with non-business partners were asked to select the cross-sector partner with whom they work most closely from NGOs, community associations, governments and knowledge institutions. Measuring the external variables “sectors” and “BOP approach” proceeded as follows. In the questionnaire, respondents were asked to describe the main product/service that they provide and the mission of the enterprise. Based on these two categories, the approach of (Mair, Battilana and Cardenas, 2012) was followed and enterprises classified into six themes, namely, sustainable solutions, education, food, employment, technology and health. Several enterprises were coded within more than one theme, since this was often the case. For example, a technological venture addressing healthcare issues was coded as both technology and healthcare. Lastly, a question was included to differentiate between consumer and producer models in BOP markets. However, during the testing of the questionnaire, an additional option was added, namely, that of intermediaries, since it became clear that numerous enterprises in BOP markets offer products and services for BOP consumers, but due to affordability constraints, the actual customers are intermediaries (NGOs, governments, etc.).

Stage 2: cluster analysis – determining the number of clusters

As the first step to conduct cluster analysis, the variables were standardized using Z-scores (Brusco et al., 2017) and a correlation analysis conducted in order to evaluate preliminary relationships between the main variables (Möllering, 2003). To determine the proximity (similarity or dissimilarity) between the observations, each observation is represented by its scaled vector of constructs. The dissimilarity (distance) between two observations is calculated as the Euclidean distance (L2 norm) between the observations (Lattin et al., 2003). A partial distance matrix of 15 observations is presented in Table AI; a full version is available upon request.

In order to determine the number of clusters, the two-stage procedure recommended by Brusco et al. (2017) was followed. The number of clusters is determined in the first stage using hierarchical clustering. Then, the final clusters are determined with K-means clustering with known number of clusters. For the hierarchical clustering, Ward’s method is employed to select an appropriate number of clusters (Lattin et al., 2003). Ward’s method is chosen as it has been known to be superior compared to other methods such as single, average and complete linkage in several simulation studies (Ferreira and Hitchcock, 2009).

Two stopping rules are used to identify the number of clusters – the index of Caliński and Harabasz (1974) and the elbow method for the intra-cluster variation (Hennig, 2007). The approach of Caliński and Harabasz (1974) employs a ratio of total variance between the clusters and total variance within clusters, and a large value for the ratio shows more distinct clusters better (Mair, Martí and Ventresca, 2012; Mair, Battilana, and Cardenas, 2012). Both approaches employed for stopping rules suggest a three-cluster solution. The three clusters from the hierarchical clustering can be visualized in the rooted dendrogram illustrated in Figure 1. Using multiple approaches for determining the number of clusters increases the confidence in the solution and overcomes the limitations of using a single method (Ketchen and Shook, 1996). Actual clusters are determined via the K-means clustering method, with the centroids determined by the hierarchical cluster analysis with 5,000 re-starts (Brusco et al., 2017). As a result of this procedure, the 134 observations are classified into three clusters of MSMEs.

Stage 3: internal consistency

In order to evaluate the internal consistency of the three-cluster solution, discriminant analysis and ANOVA are performed as additional multivariate techniques (Mair, Battilana and Cardenas, 2012; Hambrick, 1983). Using the discriminant analysis with 50 percent randomly sampled training data and the complementary testing data, 90 percent of the observations were classified accurately into the cluster they belonged to in the K-means clusters. Furthermore, the internal consistency of the cluster solution was investigated by measuring the internal stability of the clusters. The original data set was bootstrapped 100 times with replacement, and the K-means clustering was performed with 5,000 re-starts each time. The bootstrapped means of the Jaccard similarity index for the three clusters are 0.78, 0.83 and 0.75, respectively. Therefore, the three-cluster solution approach is robust and reliable. The high Jaccard similarity index[4] (Hennig, 2007) of the clusters determined from the bootstrap compared to the original clusters indicates that each of the clusters is valid and stable. The ANOVA test of variables across the clusters shows that only one variable is not significantly different across clusters (collaboration with MNEs).

Stage 4: external validity – profiling the clusters

Additional analyses were conducted in order to explore statistically significant differences between the three clusters. Multiple pair-wise comparisons based on post hoc tests are employed for this purpose (Gabriel’s[5] or Games–Howell[6] tests). The selection of post hoc tests is determined by the result of the Levene’s test of homogeneity of variances[7], namely, the assumption of equal variances (Field, 2013).

In order to enhance the external validity of the cluster solution, additional tests were conducted with external variables not used in defining the clusters. This approach reduces researcher’s judgments and biases and is regarded as superior (Brusco et al., 2017; Ketchen and Shook, 1996). In this sense, the three clusters were cross-tabulated with external variables “BOP approach” and “themes,” and Cramér’s V[8] index is employed to assess the strength of association between different variables (see Table IV). These analyses help to gain a more nuanced picture of the individual clusters. Furthermore, several discussions with academics were conducted in order to evaluate the external validity of the clusters.

Cluster 1 – direct inclusion network (DIN)

Cluster 1, DIN, differentiates itself through very high levels of both behavioral and value chain inclusion and a wide and diverse network of non-business partners supporting the inclusion. Typical offerings include organic fertilizers, briquettes, clean cooking fuel and stoves, solar lamps, heating and rural electrification. These enterprises offer products and services directly to BOP consumers and address various aspects related to education, namely health awareness, ecological education and agronomic advice. Several of these enterprises operate in the agriculture/agri-business sector. They support farmers with information, technologies, input, advice, training, materials and related information to increase farmers’ productivity and/or to educate them on agri-business issues. These MSMEs are very difficult to be classified into given industries or sectors because they provide a wide range of solutions for BOP customers. A typical example of the organizations in this cluster is an enterprise located in South East Asia, called Agrotrain (pseudonym). Its main aim is to train smallholder farmers and increase their incomes and productivity. This social enterprise offers a wide range of services for farmers including irrigation products, farm advisory services, financial services, rural energy, infrastructure and economic research.

Cluster 2 – indirect inclusion network (IIN)

Cluster 2, IIN, differentiates itself through low levels of both behavioral and value chain inclusion and a wide network of non-business partners supporting the inclusion. This cluster includes MSMEs which follow the supplier- or intermediaries-based models, where products and services are either sourced from the BOP producers and commercialized to higher income markets or they are sold to intermediaries (NGOs, governments) and then distributed/sold at very low price to BOP consumers. In the case of the producer-type of enterprises, their aim is to offer education training and employment opportunities for local farmers and artisans while opening access to new markets for their own products/services. Their network of non-business partners is also very wide and includes a variety of partners who handle specific issues. They often target rural farmers, artisans, women and other actors from marginalized groups, and sourced products include cocoa, tea, coffee, garments and peanut butter. They are known to engage closely with NGOs and other local partners. A typical example would be Solarity (pseudonym), incubated by a large foundation and aiming to make energy available at affordable prices in remote areas in Africa. It works together with several international and local NGOs, and employs local entrepreneurs as distributors for its products in remote areas.

Cluster 3 – direct NGO collaboration (DNC)

Cluster 3, DNC, distinguishes itself through high levels of behavioral and value chain inclusion and a single type of partner – NGOs with various competencies and networks. This cluster includes MSMEs which offer basic needs services, including nutritious food products, water, waste services and sanitation, last mile healthcare, sanitary pads, sanitation, water treatment systems, electricity, solar pump systems – services and products which traditionally have been in the repertoire of NGOs. The relationship of these enterprises with NGOs can be traced back to different scenarios: enterprises founded together with an NGO or organizations that transitioned from an NGO model to a social business. A typical example of an enterprise in this cluster is Puraqua (pseudonym) in Central America, which is a social company committed to providing drinking water to rural BOP. Its customers are people in remote areas with no access to clean drinking water, and its offering includes a water filter used to remove contaminants from water. It works together with local NGOs to select and train women entrepreneurs as sales agents.

Direct vs indirect supply chain inclusion

This study suggests that there are two main approaches to value chain inclusion. It is distinguished between direct and indirect inclusion based on the chain of responsibility for the inclusion. Direct inclusion refers to situations in which focal companies directly engage in inclusion activities with BOP and are only supported by local partners for different activities (DIN and DNC). In the case of indirect inclusion, the focal firms do not engage in inclusion themselves but rather “outsource” the inclusion to local partners (IIN). The indirect inclusion can take up different forms, whereby MSMEs can distribute products to BOP consumers through local micro-entrepreneurial distribution networks managed by NGOs or can create new markets for agricultural products of smallholder farmers. Both direct and indirect mechanisms of inclusion represent two potential ways to solve the supply chain inclusion in BOP markets, and each entails benefits and challenges for both the firm and the social impact created in local communities.

One interesting aspect highlighted by DIN cluster, which directly engages in inclusion activities, is the idea of “inclusion as an end or inclusion as a means to an end.” MSMEs in DIN cluster tend to be inclusive across all value chain stages rather than being inclusive in a single stage (e.g. distribution). One example of this typology of enterprises can also be the inclusive value chain example cited by Bendul et al. (2016): Toyola, a local enterprise from Ghana which produces cooking stoves. Its truly inclusive value chain illustrates how local BOP are engaged at every stage: stoves are made, sold, used, managed and maintained by local, independent, self-employed artisans. It is very likely that these types of MSMEs perceive BOP inclusion as their main value proposition and the products/services offered are only the means through which inclusion can be materialized. With this inclusion, MSMEs aim to drive social impact through empowerment and local capacity building. The indirect form of inclusion, where MSMEs “outsource” the function of integrating BOP across the supply chain, raises important aspects, namely, the attribution of social impact and lack of control over extended supply chain networks, while the fact that some MSMEs create employment and opportunities for the BOP indirectly through partners does not necessarily imply they are not inclusive or the legitimacy of any social impact claims can be questioned.

Diversity issues in inclusive supply chains

The findings highlight several tactics that MSMEs employ to address complexities caused by diverse organizational logics and modus operandi. The clusters reveal the issue of maintaining relationships with single or multiple types of organizational logics which can be observed between DIN and DNC clusters. In order to source the local capabilities needed to enable supply chain inclusion, some MSMEs tend to engage extensively with a wide range of partners (DIN), while others rely on key NGOs who can handle all the local issues by leveraging on their extensive local network (DNC). The aspect of collaborating with multiple partners is also valid for IIN cluster where in order to ‘outsource” the inclusion, MSMEs still need to collaborate with a variety of partners. Between IIN and DNC, there are two approaches to partnerships – multiple partners who handle specific issues, or a single large partner with diverse competencies (Parmigiani and Rivera-Santos, 2015).

On one hand, firms in BOP markets do sometimes choose only one single large organization as a partner instead of working with multiple partners since this can be more cost-effective than identifying and developing partnerships with multiple organizations (Parmigiani and Rivera-Santos, 2015). This is particularly relevant if there are large NGOs like BRAC or Grameen in Bangladesh who have extensive knowledge of local structures, systems and even connections in the government. An example of this type of value chain inclusion includes the case of Vision Spring, a social enterprise from India, who scaled up its distribution network quickly and very efficiently by collaborating with one of the world’s largest NGO – BRAC. BRAC helped Vision Spring sell their eyeglasses by leveraging their wide network in rural Bangladesh (Sodhi and Tang, 2011).

On the other hand, working with multiple partners, MSMEs can leverage on a wider range of resources and capabilities. Yet, dealing with multiple type of partners with different goals, demands and interests can result in issues of “stakeholder ambiguity” (Matos and Hall, 2007). Furthermore, additional costs may incur due to resources and capabilities needed to identify, select and retain multiple partners for different stages of the supply chain.