Transformative Capabilities: Does it play a Role in the Nexus Between Late Movers’ Strategies and Performance of Microfinance Banks in Kenya?

1. Introduction

Success of any business is largely determined by organizational performance, which entails its ability to attain core objectives that include financial and non-financial goals through prudent use of resources in income generating activities (Fatihudin, 2018). In essence, organizational performance can be considered as efficiency and effectiveness of attaining in meeting client desires which not only gives it a clear orientation, but also a sense of optimizing financial growth (Agarwal & Sinha, 2010). Performance in the context of an organization can either be client or the firm's perspective (Elena-Iuliana & Maria, 2016). The client perspective focuses on the return rates of customers based on the principal invested by the firm (Taouab & Issor, 2019). On the other hand, organizational performance from a firm’s perspective entails financial indicators representing the financial health state of a firm (Ouma & Kilika, 2018). Nonetheless, the definition of organizational performance lacks universality in terms of dimensions, indicators, and measures; as such, use differs from scholars, thereby, firm performance is premised on multidimensional factors.

Notably, firm performance may be driven by technological leapfrogging strategy, pricing strategy, benchmarking strategy, and competitive diffusion strategy (Geroski, 2005 Neuvonen, 2019; Yayboke, 2020; Lankford, 2022). However, Ciplet et al. (2018) and Schroeder (2019) noted that late-mover strategies are not entirely sufficient ingredients to spur organization performance but transformative capabilities like unique skills, core competencies, knowledge creation and development and technical skills play important roles in aligning the firm’s late mover strategies (Larson, 2021). In addition, the impact of late mover strategies on firm performance tends to differ across organizations operating in different business segments.

Markides and Geroski (2005) considers late mover strategy as strategies taken by late entrants in the process of pursuing new business ideas, concepts and innovations. In yet another stance, Besharat et al. (2016) observe that mover strategy as a situation within an organization during which innovation and imitation occur in the process of immense competition. However, Koch (2014) views late-mover strategy as the returns an entrant firm enjoys because there is no first mover's strategy. On his part, Klepper and Sleeper (2005) view it as a process by which firms take advantage of the positive externalities of first-movers and their innovations. Considering the lack of consensus among scholars on whether late movers reap the benefits associated with a new business concept or not, this study sought to uncover this research gap by interrogating an organization's internal factors particularly its transformative capabilities.

The transformative capabilities of an organization and its goals are important factors (Le & Le, 2021; Boateng & Li, 2022). The differentiation of the organization's capabilities includes a dedicated focus on the organization's true sense of existence and core principles while doing the same by continuously evolving and changing the way this is achieved in response to the seemingly dynamic business environment (Boso et al., 2018).Despite the significance of transformative capabilities in the operational performance of organizations, empirical research has not determined how transformative capabilities mediate the linkage between late mover strategy and the performance of MFBs.

The transformative capabilities provide a guide for acquiring specific abilities, and leadership that can be effectively utilized to support a successful transformation in an organization. The fluidity of this idea should be highlighted showing that the evolving transformative capability allows the organization to compete in an ongoing stiff business environment. Nevertheless, the connection of transformative potential to the organizations' performance is still an object of disagreement among scholars. Shroeder (2019) claims that organizational transformative capability has a direct implication on organizational performance. However, Widodo (2015) points out that the effects of transforming capabilities on organization's performance differ across firms. This assertion is supported by the view that the capabilities enhancing transformational capacity can influence organizational performance. The mediating effect transformational capability has been discussed in literature for instance Le and Le (2021) mentioned an intervening role of transformative capability in the association between transformational capabilities and organization performance. Similarly, Boateng and Li (2022) demonstrated the mediating role of transformative capability in the nexus between technological advancement and organization performance.

The declining performance of some Microfinance Banks (MFBs) in Kenya has drawn the attention of investors, MFB management, regulators and policymakers. A significant of MFBs are experiencing low financial growth, as reflected by negative Return on Equity (ROE) and Return on Assets (ROA) (CBK, 2022). The negative ROA has been attributed to a challenging business environment, marked by intense competition from other financial service providers, particularly commercial banks (Maluki, 2021). Additionally, the instability in MFBs’ financial health is linked to their failure to implement business strategies and innovations that align with the evolving needs of the financial market. Ouma and Kilika, (2018) observed that MFBs have generally not been proactive in pursuing innovation strategies, often adopting a passive, wait and see approach toward a new business concept and technological advancements. According to Ouma, Kinyua, and Muchemi (2022), MFB performance is closely tied to innovation, which is crucial for sustained success, business creativity, and competitiveness in the financial sector. According to Ouma, Kinyua, and Muchemi (2022), MFB performance is closely tied to innovation, which is crucial for sustained success, business creativity, and competitiveness in the financial sector.

2. Statement of the Problem

Microfinance Banks (MFBs) in Kenya play a vital role in advancing financial inclusion and supporting socio-economic development. Despite their importance, many MFBs have experienced fluctuating financial performance, marked by losses and stagnating customer deposits. For example, MFBs posted a loss of 2.2 billion in 2020, up from Kes. 0.71 billion in 2019 (CBK, 2020). Although customer deposits have shown intermittent growth, sustainability remains a concern due to deteriorating financial health, stiff competition from commercial banks and SACCOs, and an inability to keep pace with market dynamics. The sector also, faces lack of strategic direction, especially in innovation adoption, with most MFBs taking a reactive wait and see approach rather than proactively embracing new business models (Ouma & Kilika, 2018). Despite supportive policy framework like Kenya’s Vision 2030 and AU agenda 2063 promoting financial inclusion, MFBs continue to struggle with maintaining competiveness and achieving long term viability.

There is also inconclusive debate among scholars and industry practitioners on whether late movers reap the benefits of an innovation or business idea indicating the existence of an empirical gap. A study by Lee, and Zhou (2012) targeted how late mover strategies affect performance and it was established that late mover performance remains inconclusive. Investigation by Jiang, et al. (2017) examined first movers’ strategies and firm performance and found out that late movers have been stifled in the market as a result first mover early entry into market hence diversification was considered as the only viable remedy. Considering the lack of consensus among scholars on whether early movers or late movers reap the benefits associated with a new business concept, it is postulated that either early movers or late movers can strive to be competitive. However, this was dependent on how the organizations integrate other internal. Of particular concern is the internal factor is the organizational transformative capabilities that are related to the organization’s kind of leadership and aspirations (Le & Le, 2021; Boateng & Li, 2022). However, this is yet to be determined via empirical literature in the context of MFBs.

Investigation by Mutie (2018) found out a positive correlation system development and performance in government projects but presented a contextual gap since government structures have different regulation with MFBs. Ombati and Muturi (2018) research adopted descriptive design and this is not the most appropriate hence occasioned methodological gap. Investigation by Ouma and Kilika (2018) revealed that most MFBs are late movers but failed to outline how late movers’ strategies in Kenya resulting conceptual gap. Further, Ouma, muchemi and Kinyua (2022) innovation aspect of late movers influenced performance but failed to highlight other three aspects considered critical by literature and this resulted in conceptual gap. Based on the mentioned gaps, the study sought to establish the significance of late mover strategies in enhancing financial performance of MFBs in Kenya with transformative capabilities as mediator variable.

3. Literature Review

3.1 Dynamic Capability Theory

Teece et al. (1997) introduced the Dynamic Capability Theory as an advancement of the Resource-Based View (RBV) developed by Barney (1991). While the RBV emphasizes the identification and deployment of valuable, rare, inimitable and non-substitutable resources, it has been criticized for overlooking the necessity of reconfiguring these resources in response to environmental changes. The Dynamic Capability Theory addresses this gap by emphasizing the need for organization to continuously reconfigure, integrate and redeploy resources to align with evolving business environment and strategic objectives (Teece, 2014). This theory highlights the that sustainable competitive advantage arises not just from possessing strategic resources, but also from firm’s ability to adapt and utilize these resources effectively in dynamic context (Helfat, 2009; Wang et al., 2015). Through dynamic capabilities, firms are better positioned to respond to uncertainty, innovate, and enhance overall performance.

Despite its strengths, the Dynamic Capability Theory faces criticism for conceptual overlap and definitional inconsistencies when distinguishing dynamic capabilities from other forms of organizational capabilities (Salvato, 2003; Zahra et al., 2006; Schreyogg & Kliesch-Eberl, 2007). Zahra and George (2002) argue that while dynamic capabilities focus on aligning strategies with customer and competitor demand, they may not fully represent a firm’s strength in resource endowment. Nonetheless, the theory remains valuable for this study at it provides a framework for understanding how organization can leverage internal human capabilities such as skills, expertise and strategic leadership alongside financial and technological resources to meet core objectives. By aligning these resources with business strategies, firms can improve adaptability, competitiveness and financial performance in turbulent market environment.

3.2 Balance Score Card Model

The Balanced Scorecard (BSC) model, introduced by Kaplan and Norton in 1992, transforms an organization’s strategy, vision, and mission into specific performance objectives, measures, targets, and initiatives, thereby demystifying the process of continuous performance and improvement. The theory is structured around four models that include financial customer, internal processes, and learning and growth underpinned by a cause-and-effect logic. The strength of the BSC lies in its flexibility and broad applicability across diverse organizational contexts, allowing managers to coordinate resources effectively for market growth and overall prosperity (Butt, 2021). In Today’s dynamic business environment, traditional financial metrics alone are insufficient due to the increasing complexity and volume of financial data (Akbarzadeh, 2012). Organizations need to assess both financial and non-financial factors to gain a comprehensive understanding of their performance in relation to strategic goals (Wieczorek, 2008).  As such, the BSC serves not only as a tool for measuring performance but also as strategic management framework that guides thinking, decision-making, and implementation (Szczupak & Stajniak, 2022). The balanced scorecard predicts the accuracy of the strategy of the organization through various performance indicators based on four prepositions that comprise customers, finance, employee learning and growth, and internal processes. For MFBs in Kenya, the BSC is particularly relevant as it helps align financial, customer, innovation, and internal process strategies with organizational objectives. It serves as a roadmap for enhancing performance by linking strategy to operations and guiding implementation. As a result, MFBs can benchmark their progress against market players like commercial banks and SACCOs, driving competiveness and improve financial performance.

3.3 Theory of Change

The theory of change was developed by Weiss (1995). The theory of change was created by evaluating planning methods like logic frameworks and is often used in intervention planning. Compared to other methods, the theory of change is seen to be more effective in the causal modeling of interventions (Rogers, 2014). This theory is focused on understanding the critical conditions or requirements to be met to achieve a certain long-term endeavor (Mayne, 2023). As indicated by Kail and Lumley (2012), the theory of change documents the process and requirements needed by an organization to make a certain intervention occur. The theory is useful in outlining how an organization can make a change (Riesman et al., 2004).  In addition, it outlines evidence-based interventions and key assumptions to be considered in the pursuit of change.

Thus, the theory of change ought to carry the vision of all the organization participants. This was important for the unified implementation of the idea or concept in the organization. Through the theory of change, the participants can pinpoint key necessary changes that should drive the vision of the organization.  After the deployment of the theory of change, the organization can pursue its core endeavors of achieving the organization's goals.  Thus, the theory of change is relevant in this study in the deployment of late-mover strategies to various changes in how organizations operate. They define the approach that MFBs should take to implement new technologies and processes aimed at enhancing their performance.

3.4 Empirical Literature Review

According to Schroeder (2019), transformative capability has a direct impact on firm performance. A similar assertion is made by Widodo (2015) who establishes a direct impact of transformative capability on firm performance. However, some scholars establish that transformative capability has a mediating effect on organizational performance. According to Homaid (2016), transformative capability mediated the linkage between total quality management and the performance of microfinance banks. In another instance, Le and Le (2021) argue transformative capability mediates the association between transformational leadership and firm performance while Boateng and Li (2022) indicate that transformative capability mediates the nexus between technology innovation deployment and firm performance.

Based on the scholars, there lacks an agreement among studies pertaining the effect of transformative capability on organizational performance. Other scholars provide a direct impact of transformative capability on organizational performance (Widodo, 2015; Schroeder, 2019) while others indirectly with transformative capability having a mediating effect on organizational performance (Qamari, et al., 2020).  Le and Le (2021) indicated that transformative capability mediates the link between transformational leadership and the organizational performance of manufacturing Vietnamese firms. In another study, Rono et al. (2020) indicate that transformative capability mediates the linkage between dynamic capabilities and the competitive advantage of manufacturing firms. However, Para-González, et al. (2018) established that transformative capability does not mediate the nexus between strategic innovation and organizational performance of Spanish industrial firms. This phenomenon may be attributed to operational contextual differences where firms operate.

Para-González et al. (2018) explored the mediating role of transformational capabilities and firm performance. This study focused on 200 manufacturing firms in Spain. Partial Least Squares were employed in testing the relationship. It was noted that transformational capabilities mediate the link between innovation and firm performance. This study sought to determine if transformational capabilities mediate the link between late mover strategy and performance of microfinance banks in Kenya.

Focusing on agricultural organizations in West Azerbaijan Province, Iran, Rezaei and Amin Fanak, (2019) determined the mediating effect of transformative capabilities on the nexus between entrepreneurial orientation and performance of organizations. Semi structured questionnaire was used to collect. Structural equation modelling (SEM) was adopted to test hypotheses. It was noted transformational capabilities mediate the nexus between entrepreneurial orientation and performance of the agricultural organizations. However, it is clear that in the microfinance sector, no amount of meditation has been witnessed regarding transformational capabilities.

 Al-Husban, et al. (2021) determined the digital leadership and organization performance under the mediating role of transformational capabilities. A total of 130 industrial firms in Jordan formed the target population. Structural Equation Modelling (SEM) was used in analyzing data. The research found that transformational capabilities mediate the nexus between digital leadership and performance of industrial firms.

Homaid (2016) applied cross-sectional surveys to determine the mediating effect of transformative capabilities between market orientation, total quality management, and performance of microfinance in Yemen. Dynamic Capability Theory, Complementarity Theory and Resource-Based View guided the study.  Data were collected by use of a questionnaire. Supporting the theoretical base of the study, transformative capabilities meditate the relationship between total quality management, market orientation, and microfinance performances.

Rehman et al. (2019) explored the mediating effect of transformative capabilities on the relationship between control system design and firm performance in Pakistan. Textile companies participated in the research whereby a questionnaire was employed in collecting data. Smart Partial Least Square (PLS) was employed in testing the relationship among the research variables. It was established that transformative capabilities moderate the influence of management control system design (culture, trust, data, technology, and organization effectiveness) and organization performance.

Boateng and Cai (2022) investigated the mediating effect of transformative capabilities on the nexus between technology innovation and firm performance of manufacturing firms in Ghana. Data was gathered from 325 managers across a diverse number of manufacturing companies. Using Hayes process module in SPSS version 25 was employed to establish the direct and indirect relationships between the study variables. Transformative capabilities were found to have a partial mediation in the relationship between financial resource and firm performance, as well as top management support and performance.

Noor, et al. (2021) investigated the mediating effect of transformative capabilities in the nexus between knowledge management, competitive intelligence and business strategy formulation. Data was collected from 331 managers of Multimedia Super Corridor enterprise. It was established that transformative capabilities mediate the relationship between knowledge management, competitive intelligence and business strategy formulation. This current study explored the effect of transformational capabilities as mediating factor on the nexus between late mover strategy and performance of the MFBs in Kenya.

 Markides and Geroski (2005) considers mover strategy as strategies taken by late entrants in the process of pursuing new business ideas, concepts and innovations. In yet another stance, Besharat et al. (2016) observe that mover strategy as a situation within an organization during which innovation and imitation occur in the process of immense competition. However, Koch (2014) views late-mover strategy as the returns an entrant firm enjoys because there is no first mover's strategy. On his part, Klepper and Sleeper (2005) view it as a process by which firms take advantage of the positive externalities of first-movers and their innovations. Considering the lack of consensus among scholars on whether late movers reap the benefits associated with a new business concept or not, this study sought to uncover this research gap by interrogating an organization's internal factors particularly its transformative capabilities.

The transformative abilities of an organization, of its institutional leadership, and the goals of that institution are important factors (Le & Le, 2021; Boateng & Li, 2022). The differentiation of the organization's capabilities includes a dedicated focus on the organization's true sense of existence and core principles while doing the same by continuously evolving and changing the way this is achieved in response to the seemingly dynamic business environment (Boso et al., 2018). Despite the significance of transformative capabilities and regulatory environment in the operational performance of organizations, empirical research has not determined how transformative capabilities mediate the linkage between late mover strategy and the performance of MFBs.

3.5 Conceptual Framework

In Figure 1, late mover strategies as the predictor variables and organization performance as the dependent variable. Transformative capabilities are operationalized as an intervening variable.

Figure 1: Conceptual Framework

3.5 Research Hypothesis

Ho: Transformative capabilities have no significant mediating effect on the relationship between late movers' strategies and the performance of Microfinance Banks in Nairobi City County.

H1: Transformative capabilities have significant mediating effect on the relationship between late movers' strategies and the performance of Microfinance Banks in Nairobi City County.

4. Research Methodology

The positivism research philosophy supports the use of quantitative data to model the existence and nature of relationships between variables in a study (Rehman & Alharthi, 2016). It aligns with structured methodological approaches, such as explanatory research design, systematic sampling procedures, and use of standardized instruments. Positivism is grounded in a deductive approach, emphasizing empirical investigation, hypothesis testing, and scientific rigor (Corry et al., 2019). This philosophy was appropriate for the current study, which was anchored on a theoretical framework and aimed to test a series of hypotheses to answer research objectives. Positivism facilitated the identification of cause-and-effect relationships among study variables and supported drawing empirical inferences from the collected data. Specifically, it was well-suited to establish how late mover strategies influence the performance of Microfinance Banks (MFBs) in Nairobi City County, Kenya, using a quantitative approach.

The explanatory research design was adopted to address the research questions of "how" and "why" certain relationships exist between variables (Baskerville & Pries-Heje, 2010; Blatter & Haverland, 2012). This design enabled the study to describe findings while simultaneously explaining the relationships, including the direction and strength of associations among variables. It was particularly suitable for examining the impact of late mover strategies on the performance of MFBs, under the mediating role of transformative capabilities and moderating effect of the regulatory environment.

The target population consisted of all thirteen (13) MFBs licensed and regulated by the Central Bank of Kenya (CBK) and located in Nairobi City County. These MFBs served as the units of analysis, while the units of observation were the departmental heads or unit managers in key functional areas: Information Technology, Finance, Human Resource Development, Research and Development, Legal, Strategy and Innovation, Marketing, Operations, and Sales. Each unit is overseen by a manager reporting directly to the Chief Executive Officer of the MFB.

To determine the sample size, a stratified random sampling technique was employed, allowing for equal representation across different departments. This approach was combined with simple random sampling within each stratum to ensure unbiased selection. The Yamane formula (Yamane, 1967), which assumes a normal distribution, was used to calculate the sample size at a 95% confidence level and 5% margin of error (0.05). The formula used is:

The Yamane formula is suitable for a homogenous population sharing similar characteristics. As such, for parametric tests like multiple linear regression to be adopted, using the Yamane formula to calculate the sample size is justified. According to Chaokromthong and Sintao (2021), the Yamane formula is suitable for a homogenous population. In this study, the level of precision of 0.05 at a 95% confidence interval was employed. From the target population of 389 respondents, the sample size was calculated as;

In consequence, the sampling proportion adopted in the calculation of samples across the sampling frame is provided as;

Thus, the sampling proportion was adopted to compute the number of participants to be included from each of the microfinance banks categorized as large, medium, and small MFBs.

Table 1: Sample Size Distribution

The study employed a stratified random sampling technique to select unit managers and their assistants from 13 regulated MFBs, with proportionate stratified sampling ensuring equitable representation across different functional units and bank sizes. A sampling proportion of 0.506 was applied, resulting in a sample of 93 respondents from large-sized, 87 from medium sized, and 17 from small-sized MFBs, totaling 197 respondents. These participants were drawn from departments such as Information Technology, Finance, Human Resource Development, Research and Development, Legal, Strategy and Innovation, Marketing, Sales, and Operations, units are critical in implementing late-mover strategies, transformative capabilities, and regulatory practices. Data collection utilized a drop and pick later method using semi-structured questionnaires, allowing respondents ample time to complete them.  Participation was voluntary, and follow-ups were conducted to boost response rate. After collection, the data underwent cleaning and entry for analysis.

To enhance quality of the data collection instrument, pilot testing was conducted on 19 respondents (10% of the sample), who were excluded from the main study. This pilot phase helped assess content validity, which was verified through expert reviews and supervisor feedback, consistent with Kothari (2004). Reliability of the questionnaire was evaluated using Cronbach's alpha (α), a statistical measure of internal consistency. An alpha value of 0.7 and above was accepted as reliable, following Cronbach (1951) and Field (2013), though values above 0.6 were also considered acceptable based on Taherdoost (2016). The tool's high reliability ensured consistency and credibility in measuring responses across different MFB functional units.

Table 2: Reliability Test

The study’s instrument demonstrated a strong reliability, with most Cronbach’s alpha values exceeding 0.7, indicating high internal consistency and making the results acceptable as supported by Cronbach (1951), Field, (2013), and Taherdoost (2016). The researcher properly introduced the study’s purpose to respondents, emphasized the voluntary nature of participation, and ensured anonymity, privacy and confidentiality of the data. All the sources were duly acknowledged, and the collected information was solely for academic purposes, without any intent to promote or discredit participating organizations.

In checking the meditating effect of transformative capabilities on the linkage between late-mover strategies and the performance of regulated microfinance banks, the four-step technique advanced by Baron and Kenny (1986) was employed. The technique by Baron and Kenny is most suitable for predicting the linear or nonlinear effect of the mediator in comparison to the product-coefficient technique advanced by Fiedler and Sivo (2015).  The following steps were followed while testing for mediating effect.

Step 1: Simple linear regression where the explanatory variable predicts the outcome variable.

Y =β₀ + β₁ X + ε……………………………………………………………1

Step 2:  Simple linear regression with explanatory variable predicting the mediator variable

M =β₀ + β₁ X + ε……………………………………………………………2

Step 3: Simple linear regression where the mediator variable predicting the outcome variable

Y =β₀ + β₂ M + ε……………………………………………………………3

Step 4: Multiple linear regression where the explanatory variable and mediator variable predict the outcome variable

Y =β₀ + β₁X + β₂M + ε……………………………………………………………4

Where: 

Y depicts the performance of regulated microfinance banks in Kenya

β₀ denotes the constant value of the model

β₁ and β₂ are the beta coefficients of the explanatory (composite of late mover strategy) and mediator variables respectively.

X is the composite value denoting the late mover strategy

M is the mediator variable, transformative capabilities in the context of this study.

The error term is depicted by ε 

Testing the mediating effect of a variable encompasses establishing if the explanatory variable (X) significantly influences the outcome variable (Y), in this study the performance of regulated MFBs in the presence of the mediator variable (transformative capabilities). The purpose of steps 1-3 when checking the link between the study’s constructs is to ascertain if the conditions are met, proceeding to the fourth step. The influence of the late mover strategies on the performance of MFBs in the presence of transformative capabilities as the mediator was evaluated if the effect is partial or full mediation in stage four. The assertion was made as guided by the postulations of Baron and Kenny (1986) and Wood et al. (2008) when testing the mediating effect of a variable. Thus, transformative capabilities were employed to test its mediating role on the linkage between late mover strategies and the performance of MFBs and whether there exists a partial, full, or no mediating effect.

5. Descriptive Results

5.1 Response Rate

The questionnaires were administered to a total of 197 MFB unit managers and assistants in the 7 of MFBs. Amongst the 197 selected participants, the study managed to collect 164 questionnaires for analysis.  The responses and non-response rates were 83.25% and 16.75% respectively. The proportion of response rate exceed 50% that is viewed acceptable by Sileyew (2019). The return rate was thus sufficient to make inference about this survey population. As indicated by Fincham (2008), a response rate of 60 per cent and above is sufficient while according to Sataloff and Vontela (2021), response rate of 70 per cent and above is very good.

5.2 Descriptive Statistics for Transformative Capabilities

Transformative capabilities were operationalized as activities that depict the ability to use and reconfigure human and financial resources for the benefit of the organization. Table 3 tabulates the means, standard deviations and CVs for transformative capabilities.

Table 3: Descriptive Statistics for Transformative Capabilities

Calculating Overall Variability= CV*100%

The descriptive statistics on transformation capacity indicate that the mean scores ranged from 3.82 to 4.16, suggesting that respondents generally agreed with the presence and importance of transformation capacity practices in the institutions studied. These values hover around 4.00 on the rating scale, showing consistent agreement across different aspects of transformation capacity. The corresponding standard deviations ranged from 0.55 to 1.04, reflecting low dispersion and confirming that responses were closely aligned with their respective means. This suggests that the respondents perceived transformation capacity as essential and actively embedded in organizational practices.

Furthermore, the overall mean score was 4.01, with a standard deviation of 0.79, reinforcing the consistency in the responses across the measured items. The coefficient of variation ranged between 14% (for presence of information meetings) and 27% (for use of IT systems in knowledge dissemination), further demonstrating limited variability among responses. These results indicate that transformation capacity is widely practiced and deemed vital for organizational success, and that the sample mean is a reliable estimator of the broader population’s view.

5.3 Descriptive Statistics for the Performance of Microfinance Banks

The performance of microfinance banks was operationalized as outcomes that include non-performing Loans, market share, employee satisfaction, and profitability. The means, standard deviations and CVs for the performance of microfinance banks are tabulated in Table 4.

Table 4: Descriptive Statistics for Performance of Microfinance Banks

Calculating Overall Variability= CV*100%

The results in Table 4 show that the sample mean responses for various aspects of microfinance bank performance ranged narrowly between 1.49 for market share growth in 2020 and 2.68 for employee satisfaction with working conditions. This narrow spread indicates general agreement among participants that while internal satisfaction among employees was moderate, external performance indicators such as market share and profit margins were relatively low. The low scores for market share and net profit growth (means approximating 2.00) suggest that these performance outcomes were less prominent among the observed microfinance banks.

The standard deviations for all performance indicators were low, ranging from 0.29 to 0.70, with the highest coefficient of variation being 29 percent, indicating that the responses were consistently clustered around their respective means. The overall sample mean for microfinance bank performance was 2.24, with a corresponding standard deviation of 0.47, and an aggregate variability of 21 percent. This confirms that the observed sample mean is a reliable estimator of the overall population performance, reflecting relatively low but consistent performance levels across the microfinance institutions studied.

6. Inferential Statistics

In this study linear regression was used as an approach for modelling the relationship between the set of research variables chosen. The research hypotheses drawn from the independent and dependent variables were modelled on the basis of simple linear regression analysis. As a result, exploitation capacity was regressed on organizational performance. The output of this regression analysis is displayed in Table 5.

Where:

Xi is the ith dimension of late movers’ strategies 

wi is the weight associated with ith dimension of late movers’ strategies

Using the composite index, the causal step technique advanced by Baron and Kenny (1986) was employed to perform mediation tests whereby four regression test were step by step estimated, whereby the significance level of each of the variable in the model was examined at each level.

Step 1: Regressing late movers' strategies and performance of MFBs

Table 5: Regression of Late Movers’ Strategies and Performance of Microfinance Banks

Table 5 shows coefficient of determination results showing that the adjusted R-square is 0.338 demonstrating that late movers' strategies explain 33.8 percent of the changes in the performance of microfinance banks. The output of ANOVA on the statistical significance of the estimated model revealed an F statistic of 84.169 at a 0.000 level of significance. The F-test therefore confirmed that the study model provided the best fit for the observed data as it was statistically significant at 0.05 level of significance. The produced regression model is shown below in equation 6.

Performance of Microfinance Banks = 1.718 + 0.552Late Movers’ Strategies…………. 6

From equation 4.8 above, it is clear that when late movers' strategies are put at a constant value of 0, the performance of microfinance banks will be 1.718. The resultant p-value is 0.000 that is below the 0.05 level of significance for corroborating the statistical significance of the respective variable. As a result, the calculated value of the beta coefficient for the intercept of the estimated model was statistically significant at a 95 percent CI implying the model estimated is statistically significant. The corresponding output of simple linear regression analysis revealed a beta coefficient of 0.552 and a p-value of 0.000 for late movers' strategies.

Step 2: Regressing late movers’ strategies on transformative capabilities

The second step involved a simple linear regression analysis where late movers' strategies were regressed on transformative capabilities. The output of the regression analysis are depicted in Table 6.

Table 6: Regression of Late Movers' Strategies on Transformative Capabilities

The model summary in Table 6 revealed an adjusted R-square of 0.277 demonstrating that late movers' strategies explained 27.7 percent of the changes in the transformative capabilities. The results of ANOVA relating to the statistical significance of the estimated model revealed an F statistic of 63.320 at a 0.000 which is less than 0.05. The F-test results confirmed that the estimated model provided the best fit for the observed data as it was statistically significant at 0.05 level of significance.

The produced regression model is shown below in equation 7;

Transformative Capabilities = 1.644 + 0.565Late Movers’ Strategies ……………. 7

From equation 4.9 above it is clear that when late movers' strategies are put at a constant value of 0, transformative capabilities will be 1.644. The resultant p-value of 0.000 which is below the 0.05 level of significance for confirming the statistical significance of the study variable under study. As a result, the calculated beta coefficient for the intercept was statistically significant at a 95 percent confidence interval thereby necessitating the next step to be taken.

Step 3: Regressing transformative capabilities on the performance of the MFBs

The third step entailed a simple linear regression analysis where transformative capabilities were regressed against performance of the microfinance banks. Table 7 shows the output of the regression test results.

Table 7: Regression of Transformative Capabilities on the Performance of the Microfinance Banks

The results of the model summary showed that the adjusted R-square was 0.629 showing that 62.9 percent of the change in performance of the microfinance banks is explained by transformative capabilities. The results of ANOVA that reflect the statistical significance of the regression model showed an F statistic of 277.262 at a 0.000 level of significance less than 0.05. The F-test confirmed that the regression model provided the optimal model fit for the empirical data as it was statistically significant at a 95 percent level of confidence level. The regression model estimated is shown in equation 8.

Performance of Microfinance Banks = 1.123 + 0.704 Transformative Capabilities…..8

From equation 4.10 it is clear that whenever transformative capabilities are put at constant value of 0, the level of performance of the microfinance banks will be 1.123. The resultant p-value is 0.000 that is less than 0.05 thus corroborating the statistical significance of the study variable under investigation. As a result, the variable under study was statistically significant at 0.05. The results of simple linear regression analysis also reveal a beta coefficient of 0.704 and p p-value of 0.000 for transformative capabilities. The statistical results imply that transformative capabilities positively affects the performance of microfinance banks. A change of 1 unit in transformative capabilities, therefore, triggers an increase of 0.704 in the performance of MFBs. The results of the first three steps in mediation analysis laid the ground for the final step, the causal-step approach as advised by Baron and Kenny (1986).

Step 4: Regressing late movers' strategies and performance of the microfinance banks

In the final step, a multiple linear regression test was conducted with late movers' strategies and transformative capabilities as the predictor variables while the performance of the microfinance banks served is the response variable. The investigation results of this analysis are presented in Table 8.

Table 8: Regression Analysis for Mediation Effect of Transformative Capabilities

The output of the model summary output in Table 8 revealed that the adjusted R-square is 0.664 confirming that 66.4 percent of the change in performance of the microfinance banks can be collectively explained by late movers' strategies and transformative capabilities. The F-test of the estimated model revealed a F statistic of 162.271 and a calculated p-value 0.000 level of significance that is less than 0.05. These results confirmed that the estimated regression model provided an optimal model fit that is statistically significant at a 95 percent CI and at 0.05.  The estimated model thus could be relied on.

The resultant multiple regression model is shown in equation 9;

Performance of Microfinance Banks= 0.737 + 0.215Late Movers’ Strategies + 0.597 Transformative Capabilities………. 9

Equation 9 provides clear evidence that if late movers' strategies and transformative capabilities are made to take the value of 0, then the performance of the MFBs 0.737. The resultant p-value was 0.000 which does not exceed 0.05 affirming statistical significance of the of the study variable under investigation. Consequently, the beta coefficient of the variable under investigation was statistically significant at a 95 percent.

The corresponding output of the linear regression estimate revealed a beta coefficient of 0.215 and p-value of 0.000 for late movers' strategies. These statistical results imply that late movers' strategies positively affect performance of microfinance banks. Therefore, an increase of 1 unit in late movers 'strategies will increase the performance of microfinance banks by 0.215. Similarly, the beta coefficient and p-value for transformative capabilities were 0.597 and 0.000 respectively. These findings imply that transformative capabilities have a positive effect on the performance of microfinance banks. As per results, change in transformative capabilities, will positively affect the performance of the MFBs 0.597 units.

The actual nature of the mediation in this situation was depicted by the statistical significance of late movers' strategies while controlling for transformative capabilities. The statistical evidence drawn from the output of regression analysis in step four of mediation analysis revealed that the beta coefficients for both late movers' strategies and transformative capabilities were significant at a 95 percent confidence interval which indicated the case of partial mediation of transformative capabilities on the effect of late movers' strategies on performance of MFBs. Therefore, there is no sufficient statistical evidence to fail to reject the null hypothesis that transformative capabilities have no significant mediating effect on the linkage between late movers' strategies and the performance of microfinance banks in Kenya.

The results on mediation are in line with the findings of Schroeder (2019) who found transformative capability has a mediating effect on organizational performance. Le and Le (2021) also argued that transformative capability mediates the association between transformational leadership and firm performance while Boateng and Li (2022) indicate that transformative capability mediates the nexus between technology innovation deployment and firm performance. Transformative capabilities improve late movers' strategies which improve the performance of the microfinance banks. Strengthening transformative capabilities, therefore, places the organization in a good position to drive late movers' strategies and ensure that the performance of the microfinance banks is improved (Le & Le, 2021).

The findings are also supported by the theory of change and dynamic capabilities theory. The theory of change is often used for intervention and focuses on critical conditions or interventions that are important to achieve long-term growth. The theory of change documents the process and requirements needed by an organization to make a certain intervention occur and this is appropriate if the organization is to improve on its transformative capabilities (Kail & Lumley (2012). The dynamic capability theory highlights the importance of reconfiguring organisation resources to ne in tandem with the organization goals and endeavors (Teece, 2014). This is important as it enables the organization to have the requirements that are needed to implement strategies that will enable it to improve its performance effectively and efficiently. The dynamic capability theory is appropriate for all organizations irrespective of financial ability as it focuses on using resources prudently in addressing the organizational needs in the ever-changing business environment (Kim et al., 2015).

7. Conclusion of the Study

The study investigated the mediating role of transformative Capabilities on the Relationship between Late Mover Strategy and Performance of Microfinance Banks in Nairobi City.  The output of inferential analysis demonstrated that parameters of late mover strategy, transformative capabilities and the interaction term (of the two variables) are statistically significant. This affirmed that transformed capabilities influenced the relationship between late mover strategy and performance of MFBs. Consequently, the study concludes that transformative capabilities mediate the relationship between late movers' strategies and performance of MFBs.

8. Recommendations

Transformative capabilities mediate the relationship between late movers' strategies and the performance of microfinance banks. Practically, MFBs managers are supposed to continuously invest in capacity building by establishing structured programs for employee development. Training managers should regularly organize in-service training, workshops, and seminars in collaboration with industry experts and regulatory bodies. These initiatives should be tailored to strengthen specific competencies such as customer service innovation, digital finance, risk management, and product development.

Author Contributions: All authors contributed to this research.

Funding: Not applicable.

Conflict of Interest: The authors declare no conflict of interest.

Informed Consent Statement/Ethics Approval: Not applicable.

Declaration of Generative AI and AI-assisted Technologies: This study has not used any generative AI tools or technologies in the preparation of this manuscript.