Effect of Products and Services Alliances on the Performance of Microfinance Institutions in Rwanda

In today’s world of cutthroat competition, considerable attention has been devoted to the formation of strategic alliances. Different organizations form alliance with other partners with different motivation. Some of the cited motivations for alliances include pooling resources, increasing capabilities; improve product development and service delivery, among others. Due to these varying alliances, there are various forms of alliances that organizations can engage in. However, research has shown that not all alliances end up with gaining the intended benefits. This research investigated the effect of product and service alliance on firm performance in microfinance institutions. Primary data was obtained using structured questionnaire distributed to a sample of 220 managers/CEO selected from a total of 419 MFIs. Data was analyzed using STATA 16.1 and Structural Equation Modeling (SEM) used to test the research hypothesis. The results showed that the model was significant at 10% ( = 21.576, p<0.1). It also revealed that product and service alliance is positively and significantly related to firm performance with a coefficient of 0.072 (p<0.05). This being the first study of the kind, here in Rwanda, among the MFIs, it is recommended that MFIs should embrace alliance as way of improving their resource base and their product innovation and service delivery as well as their financial performance.

This research focuses particularly on product and service alliance in MFI sector. Use of product and service alliance as a strategy may play an important role on the development of MFIs in developing countries including African countries. However, the scarce literature on the subject creates a lack of information to make this strategy more popular among MFIs in these countries. Furthermore, in the field of strategic alliances, the service business alliances remain understudied, especially in the financial service industry (Geleta, 2016). Salimi, Zarea and Khajeheian (2012) note that most of the new products fail to launch or reach to market due to poor product design or lack of marketing capabilities. In addition, there is need for innovation of new products that are tailored to customer's needs. Such difficulties are experienced both by small, medium and even large enterprises. Product and services alliance can provide avenue for firms to deal with these shortcomings and improve their product and service performance, consequently improving their financial performance.
Product and service alliances exist among firms seeking to develop their products or service to increase their customer base. Partners seeking this kind of alliance may be motivated by the existence of particular resource, knowledge or skill in another party that they intend to benefit from. As such, a company with no enough resources for R&D and innovation may seek product and service alliance with another partner to gain a way of improving and innovating their products and services. Product and service alliance is no longer the exclusive domain of product and service companies (Bettencourt & Brown 2019).
In addition, products and services alliance are the key factors to consider for a company to develop a sustainable competitive advantage (Shelbum, 2018). Introduction of new attractive product or service alliance enables a firm to remain successful in the short run as competitors catch up. To remain successful for long period of time, companies should possess the capacity to consistently design and deliver good products and/or services together with good quality in order to meet the required standards.
This study contributes to the theory because it serves as an empirical evidence to see whether product and service alliances contribute positively or negatively to the organization performance in Rwanda's financial service industry.Despite the fact that the microfinance sector in Rwanda is still young, it has been growing at a fast pace in the last years and its outreach surpasses that of the traditional banking industry (Eularie, 2018). Therefore, this research was guided by this hypothesis:  H0: There is no significant influence of products and services alliances on the Microfinance Institutions' performance.

Theoretical Review
The author paid attention to the following: The transaction cost theory, strategic behavior theory, and Resource based theory to guide the conceptions and methodological approaches in the current study.

Transaction Cost Theory
The transaction cost theory was proposed by Ronald Coase in 1937. According to transaction cost theory, firm's decision on mode of transacting is influenced by the minimization of the sum of production and transaction costs (Huda, et al., 2019).The transaction cost theory is considered as the most dominating theory in regards to alliances. (Albers, 2019). This theory is applicable in alliances because its main focus is how firms should gain from all the transactions while minimizing the overall costs involved. In alliances, partners come together to cooperate in order to improve their overall performance and reduce inefficiencies. To better understand transaction cost theory in regards to alliance formation, it is important to understand the occurrence of transaction costs in environments that could favor alliance formation. Narrow markets, in which firms must rely on individual suppliers for specialized products, can force actors to show high commitment due to high switching costs (Penney &Combs, 2019;Koo, Yamanoi & Sakano, 2020).

Strategic Behavioral Theory
Strategic behavioral theory can be traced back to Cyert and March in their work a behavioral theory of firm written in 1963. It refers to actions taken by firms which aim to influence the market environment in which they compete. In reference to this definition, strategic behavior involves primarily long-run actions and decisions like production capacity, research and development (R&D), investment, location, advertising, product differentiation (Ketprapakorn & Kantabutra, 2019).
The theory has two categories: Non-cooperative behavior occurs when a firm tries to improve its position relative to its rivals by seeking to prevent them from entering a market, driving them out of business or notably reducing their profits as well. Cooperative behavior occurs when firms in a market seek to coordinate their actions and thus limit their competitive responses. Companies are expected to form cooperative agreements if they believe that the arrangements will better enable them to meet their strategic objectives, with the focus being on maximizing profits (Vogus, 2018).

Resource-Based Theory
Resource based theory is a theory directed to achieving competitive advantage that emerged in 1980s and 1990s, after the significant works published by Wernerfelt, B. (The Resource-Based View of the Firm), Prahalad and Hamel (The Core Competence of The Corporation), Barney, (Firm resources and sustained competitive advantage) and others. The supporters of this view argue that firms should look inside the institution to seek out the sources of competitive advantage rather than watching competitive environment for it (Arslan, 2018).The RBV establishes the existence of key firm resources that can be used to achieve superior performance (March, Waquet & Martinet, 2017). However, when constraint by resources, an organization, and specifically microfinance institutions, can seek alliance with other firms in order to cover for these short comings (Dyer, et al., 2020).

Empirical Literature Review
The inception of microfinance institutions was convenient in the growth of small and medium enterprises since large banks usually transact with large and middle companies due to reduced risks associated with them. Small and Medium Enterprises are also associated with poor creditworthiness which most commercial banks tend to shy away from. These institutions are, therefore, a major form of funding for small and medium enterprises in the entire of Africa and other both developed and developing countries (see for example, Mutua, 2017;Mohamed & Al-Shaigi, 2017;Prah, 2016;Awuah & Addaney, 2016;Mulungi & Kwagala, 2015). Islam, Hossain and Mia (2018) in their research note that small firms like microfinance institutions don't need to bear the whole burden of developing new technologies, finding new markets, training skilled workers to supply quality products and services. Many of the prices of specialization are shared by or embedded during a dense of alliances among MFIs.
With respect to product alliance, empirical studies typically find positive effects on financial performance outcomes (Hult et al. 2014), and moderate effect (Danneels and Kleinschmidt 2018). For example, product alliance relates positively to revenue growth (De Faria and Mendonça 2017) and profitability (Cozza et al. 2016). In support of this, several meta-analyses confirm the positive impact of product innovation on firm performance (Bowen et al. 2015;Calantone et al. 2015). Lambe et al. (2009) point that product alliance is important in improving organization product innovation, development, designing and distribution.
When it comes to service alliance empirical controversy remains whether they can directly impact the bottom line and thus truly differentiate an organization in the marketplace (Bettencourt & Brown 2019). Researchers and practitioners have expressed positive performance effects (Aas and Pedersen 2017; Kindstroem 2017), following the assumption that they exert indirect effects on company performance through improved customer satisfaction and loyalty (Baines et al. 2018).
Further, amongst others, Thakur and Hale (2015) confirm a positive effect on loyalty of existing customers as well as the attraction of new customers to the firm. However, other studies document that the development of new services alliance can also directly impact the financial outcomes such as revenue growth (e.g., Cainelli et al. 2016), profitability (e.g. Cheng & Krumwiede 2018) and ultimately firm value (Dotzel et al. 2015).
To date, most of the empirical studies available focus on companies operating exclusively in the service sector, such as when Colm, Ordanini and Parasuraman (2017) analyze the financial outcomes of product and service alliance in the hotel industry, or when Cainelli et al. (2018) consider various service sectors, ranging from software development to hotels to financial services. Other empirical studies pay particular attention to the financial service sector (e.g. Papastathopoulou & Hultink 2017).
Yet the overall results remain mixed, Aas and Pedersen (2017) find no effect of service alliance on company profitability and profitability growth, and Mansury and Love (2018) observe an insignificant relationship between service alliance and economic value added per alliance.
Due to the mixed empirical results, open questions remain concerning the impact of product and service alliance on the performance of Microfinance industry. Particularly, practitioners need to understand the consequences of strategic impact of product and service alliance to inform management decisions (Barczak & Kahn, 2018). To shed more light on this important area of strategic alliance research, we investigate the following research questions: what is the effect of product and service alliance on the performance of MFIs in Rwanda?

Figure 1: Conceptual Framework
Source: Researcher 2019 The conceptual framework described in this study clarifies ways in which products and services alliance is influenced and how products and services alliance is positively affected organizational performance and vice versa basing on resource-based theory. Owners of microfinance institutions can better facilitate organizational performance between workers and the organizations they work for, upon product and services alliance which is measured by design, jobs, quality, standards, purchasing and capital. Firm performance is measured by the return on assets (ROA), profitability, and annual sales.

Study Population and Sampling
The study conducted a survey on microfinance institutions in Rwanda. The research consideredall of the five provinces of Rwanda. As it is indicated in the report of the Ministry of Finance and Economic Planning of August 2013 1 , 416 SACCOs (microfinance at 3 level) and 75 MFIs were registered, and this makes a total of 491 which wasthe research population of this study.Slovene's sampling formula (Rosenstein, 2019) was used to determine the sample size = 1 + = 419 1 + 419 * 0.05 ≈ 220 Where: n = Sample size N = total population = 491 e = Level of confidence or error margin = 0.05 The researcher used random sampling method to give equal opportunity to all MFIs in the population.

Variables Measurement Procedures
Primary data was obtained using a structured questionnaire made of seven-point Likert scale where 1=strongly disagree, 2=disagree, 3 more/less disagree, 4= undecided, 5=more/less agree, 6=agree and 7=strongly disagree.This was used to measure the independent variable product and services alliances. Firm performance is measured as the average of the Return on Assets (ROA), Return on Sales (ROS) and profitability. The respondents who included the managers/CEOs of the selected MFIs helped in providing these ratios.

Data Processing and Analysis
The study used quantitative method and Structural Equation Modeling for data analysis. I have preferred to use SEM because of it is a popular method across discipline and increasingly is a 'must' for researchers in the social sciences (Ling & Ling, 2019) and many other studies in strategic alliances have also used SEM, Plazibat and Davor (2013), Jabar, Othman, and Idris (2011). It is a combination of factor analysis and regression or path analysis (Boateng, 2018). The popularity of SEM is based on its role in testing complete theories and concepts.
However, before building the model, preliminary model analyses were conducted to allow the data and the model to meet the assumptions for SEM. The basic assumptions for a standard SEM model include, sample size, interval scale for data, multivariate normality distribution, model identification, and uncorrelated error terms. The procedure involved four basic steps that were followed.
Step one involved building the path diagram in the SEM builder interface. In this, the measurement model and the structural model were drawn as a path diagram connecting latent variables and the observed variables.
Step two involved testing the reliability and validity of the measurement model. The reliability of the model was tested using Cronbach's alpha test for reliability.
The third step involved model estimation. The Maximum Likelihood (ML) method was used because there were no missing variables in the data. The last step involved examining the results and drawing up conclusion. Different statistical measures were used to help draw the conclusions on model fit and on the hypothesis. According to Figure 2 shows that 47.27% of the respondents were female and 53.73% were male. Although, there is a little difference of 6% percentage, generally these results indicate that the sample was reasonably represented in terms of gender.

Figure 3: Working Experience of Respondents
Source: Researcher, 2020 Figure 3 shows that 24.09% of respondents had less than 5years of work experience, 37.27% had between 6 and 10 years of experience, 35% had experience that ranged between 11 and 15 years and 3.64 % had above of 15 years of work experience.

Education level
Frequency ( Table 1 shows that 41.54% of respondents were of secondary/high school level, followed by bachelor level 45.64% and master's level 12.82%. These results suggest the majority of respondents, who were CEOs and managers of microfinance and SACCOs, had attained the university level.  Table 2 shows that 15% of visited MFI were under five old. 46% were between six and ten years old, 30% were 7 between sixteen and twenty years and 1 % were above 20 years old. As outlined above the initiative of establishing microfinance started in 2000s. This is the reason behind having the majority of institutional between six and ten years old.

Number of Employees
Frequency ( The institution size is based by the number of employees. Table 3 shows the majority of visited MFI (35 %) had between 101 and 150 number of employees. The table also shows that only 4% of visited institutions had under 50 percentage of employees.

Descriptive Statistics
Before presenting and analyzing the results related to the objectives of the study, the researcher firs presented the descriptive statistics necessary for this research. This included the mean, standard deviation (sd), variance, skewness and kurtosis. The descriptive statistics for all the constructs are shown in Table 4. According to Paolella (2018), the acceptable range for skewness is between -3 and +3 while for skewness is between -10 and +10 whenever using the SEM technique. This showed that the values obtained in this research were within the acceptable threshold in terms of skewness and kurtosis.

Model Analysis
This research used the structural equation modeling technique to achieve the research objectives. However, before building the model, preliminary model analyses were conducted to allow the data and the model to meet the assumptions for SEM. The basic assumptions for a standard SEM model include, sample size, interval scale for data, multivariate normality distribution, model identification, and uncorrelated error terms. The assumption on sample size holds that the sample should be large enough, at least more than 200, for structural equation modeling.
This research was able to meet this requirement with a sample size of 220 observations. Similarly, the data is required to have interval scale. According to Jackson (2015), if Likert scale is symmetric and equidistance, it can sufficiently approximate an interval scale. The use of Likert scale with seven items enabled the researcher to meet this requirement. Similarly, the errors terms are assumed to be uncorrelated since the data is not time series nor related to time. The test for multivariate normality was conducted using Doornik-Hansen test which revealed significance ( = 150.431, < 0.05).
According to Biswas, Giri & Srivastava, (2006), for a model to achieve identification, the number of distinct sample moments should be greater than or equal to the number of distinct parameters to be estimated (which gives the degrees of freedom, d.f.). If they are equal, the model is considered to be just identified. If the number of distinct sample moments is less than the number of distinct parameters to be estimated, then the model is unidentified. However, if greater than, then it is over identified. In this case, looking at the output of the estimation, the model was over identified since d.f. were computed in each estimation.
According to Schermelleh-Engel, Moosbrugger and Müller (2003) estimations in SEM can only be done if the model is just identified or over identified. Further, note that there exists no specific statistics to ensure that a structural equation model fit. They specifically recommend that the researcher should keep checking results against the results which provide the level of significance and an idea of whether the model fits the empirical data. In addition, other goodness of fit statistics exists that can be used. With this knowledge, the researcher was able to proceed with the analysis.

Measurement Model: Validity and Reliability
As recommended by Hooper, Coughlan and Mullen (2008), before any structural equation modeling is conducted, the measurement model should be tested for reliability of the observable constructs. This was done using the Cronbach alpha reliability test presented in Table 4.7. The cut point for reliability is an alpha equal to or greater than 0.7 (Esposito, et al., 2010). According to Table 4.1, the lowest alpha is 0.675 which is close to the cutoff point and therefore retained. In fact,the overall test scale was 0.697 which approximate to the cut point provided enough reliability to retain the measurement scale and all the items.

Findings on the Research Objective
The research objective sought to examine the effect of products and services alliances on the Microfinance Institutions' performance. The null hypothesis for this objective was formulated as follows:  HO: There is significant influence of products and services alliances on the Microfinance Institutions' performance. As shown in Figure 4, the path diagram presents the model used in this research to test the hypothesis. The model is composed of two composite models, namely the measurement model and the structural model. In STATA, a SEM path diagram is drawn with observable variables/constructs presented as rectangles and latent variables presented as oval as shown in Figure 3. As presented in the figure, the latent variable product and services alliance as measured through the observed variables ProServ1, ProServ2, ProServ3, ProServ4, ProServ5 and ProServ6 was used to determine whether it has an effect on the performance of MFIs. Performance was treated as an observable variable and was computed as average of ROA, ROS and profitability. Table 6 provides more detailed information for testing the hypothesis. Further, Table 7 shows the results obtained for the goodness-of-fit tests.  As shown in the table and Figure, the path coefficient from product and service alliance to performance is 0.072 and positive and significant at 5% (p<0.05). This shows that one percent unit change in product and service alliance results in to 7.2% change in firm performance. Similarly, the results in Table 7 shows the model is significant at 10% (p<0.1).

Conclusion
Bettencourt and Brown (2019) in their research found that product-driven alliances are not meant exclusively for companies dealing with products. Companies that also offer services like microfinance companies can also engage in product and services alliance. The benefits of these alliances include product and service innovation and ability for an organization to customize services to the customers' needs. In addition, Neely (2016) found out that product and service alliance increases the competitive edge of a company. Nguli (2017) further added that product and services alliances can be adopted along various strategies like cost reduction, differentiation and/or focus strategy. Companies entering into these alliances can decide to focus on a given market niche that requires a different product or service.
The shift of focus happens due to the available opportunities for growth for the MFIs through mechanisms brought about by alliances. In this sense, MFIs can be able to embrace new resources even if they are themselves constraints whenever they engage in alliances with other suitable partners. The access to these, alliances provide mechanisms for the microfinance institutions to be more competitive on the market.
In general, MFIs are considered a source of innovation, flexibility and economic growth. However, the insufficiency of technologies, lack of marketing skills, quality products and services and the strong competition in the markets in which they operate generate volatility of their operations. The research findings, in consistence to the findings by Kalra and Mathur (2018), have shown that MFIs can improve their performance through alliance. In particular, product and service alliance positively influence MFI performance. For these and other reasons, it is recommended that MFIs should rigorously follow the right procedures and channels to ensure that the alliance is a successful one. The study recommends that business competitors embrace strategies that can enhance market efficiency, rather than bringing market frictions and imperfections. One of such strategies is through healthy strategic alliances.
The results showed that in general the product and service alliance contribute positively to the microfinance's performance compared to the organizations without alliance. This being the first study of the kind, here in Rwanda, among the MFIs, it is recommended that MFIs should embrace alliance as way of improving their resource base and their product innovation and service delivery as well as their financial performance.