Effect of Resource Fluidity on Operational Performance of SMEs in Nairobi City County, Kenya

Keywords: Resource Fluidity; Operational Performance; Resource Reallocation Flexibility; Resource Adaptation; Resource-Based View; Small and Medium-Sized Enterprises; Nairobi City County; Kenya.

Paper Type: Research Article

Recommended citation: Alvil, C., Lelei, J., & Mbaraka, R. (2026). Effect of Resource Fluidity on Operational Performance of SMEs in Nairobi City County, Kenya. Journal of Economics, Management Sciences and Procurement, 5(1), pp. 226–240.

1. Introduction

Small and medium-sized enterprises (SMEs) are of paramount importance for employment generation, income generation, innovation and economic development at a global level. But not only their survival but also efficiency and adaptability to new business contexts are important for their role in economic development. Operational performance is the efficiency a business uses its resources in relation to quality, waste, delays, productivity and customer and market needs. As the markets grow more dynamic and competitive, SMEs need to be capable of adapting the deployment of their resources in a reactive way, in order to meet the evolving needs for their operations. The capacity to reallocate, redeploy and recombine financial, human, technological and other organizational resources in a flexible manner when business conditions change is one such capability. Resource fluidity allows companies to reallocate resources from less productive activities to new priorities, deal with interruptions in operations, and meet the evolving needs of customers. The study by Uddin et al. (2023) identified that the moving and reconfiguration of resources can be crucial for organizational adaptation, as dynamic digital capabilities can increase resource flexibility and strategic agility among SMEs. Likewise, Khan et al. (2022) showed that technological capability and networking capability improves strategic agility of SMEs in emerging economies. This implies that SMEs can be flexible with regard to their resources to operate efficiently in the face of environmental influences. The need for resource fluidity is especially pronounced in developing economies where SMEs frequently are constrained by financial, technological and infrastructural factors, as well as by fluctuating market conditions. SMEs in Sub-Saharan Africa often lack sufficient resources, expensive operating expenses, experienced supply-chain disruptions and access to suitable technologies, all of which can limit their ability to run and operate efficiently. In such cases, the ability to quickly mobilise existing resources could help businesses reduce the impact on their operations, increase process efficiency and maintain service or quality of their products. Yet, the extent to which SMEs can convert the resource flexibility into better operational efficiency could be related to the managerial and technological skills of the firm.

SMEs are a significant part of the Kenyan economy and employment base, but still many SMEs are facing challenges in operations, due to inefficient processes, low technology uptake, uneven product quality, high operational costs, and challenges adapting to customer needs and expectations. The challenges are especially pertinent in Nairobi City County, which constitutes Kenya's main commercial and business hub, where SMEs are in a very competitive and changing environment. The density of businesses, technological advances, changing customer needs and fierce competition mean that businesses must use their scarce resources effectively, and be ready to redirect them if their current requirements change. SMEs not able to adjust the allocation of human resource, investment, technology and other resources can find themselves facing delays, losses of resources, reduced productivity and loss of operational efficiency. While there has been some empirical research on the relationships between organizational capabilities, strategic agility and digital capabilities and firm performance, there is relatively limited research that focuses on resource fluidity as a specific capability that can affect the operational performance of SMEs in Nairobi City County. There is preceding evidence, such as Uddin et al. (2023), that highlight the importance of resource flexibility in terms of dynamic capabilities and strategic agility, and Khan et al. (2022) has concentrated on technological and networking capabilities as antecedents of strategic agility. This leaves an empirical vacuum on the question of whether the capacity of SMEs to reallocate/redeploy is directly linked to better operational performance. Thus, the study aims at finding the impact of fluidity of resources on the operational performance of SMEs in Nairobi City County is the study objective..

2. Theoretical review

The Resource Based View (RBV) theory was developed by Wernerfelt (1984) and Barney (1991) to explain the superior performance that can be obtained by a firm by utilizing valuable, rare, inimitable and non-substitutable resources. The purpose of this study is to examine the role of resource fluidity in the operational performance of SMEs in Nairobi City County as per RBV. Resource fluidity indicates the SME's capacity to reallocate, redeploy and adjust resources between activities, as needed for changing operational needs. An RBV view suggests that the more that SMEs can flex their financial, human, technological and physical resources, the more efficient they are likely to be, the more responsive they are going to be to changes in the market, the less disruption they are going to experience in their operations and the greater their productivity will be, all of which will support their operational performance.

3. Empirical review

To study the relationship between strategic agility and operational performance of advocacy NGOs in Kenya, Aidah, Waiganjo and Njeru (2025) applied a mixed methods research design and cross sectional survey approach. The objective of the study was to respond to questionnaire and the filled checklist to cover a total of 11,176 advocacy NGOs registered in the country and finally 305 questionnaires/ checklist were received. Descriptive statistics, Pearson correlation and multiple regression analysis were used to analyze the data. The findings indicated that strategic agility (strategic sensitivity, leadership unity and resource fluidity) has a positive and powerful influence on the financial sustainability. The study showed that the facility would be more likely to be financially sustainable long-term if they had the flexibility to adapt resources and be quick to respond to changes in the environment.

Oluoch (2023) used descriptive correlational research design to study the effect of firm's resource portfolio on the operational performance of NGOs in Kenya. A total of 413 Chief Executive Officers and Board members of registered NGOs were sampled through stratified random sampling and the data were gathered using structured questionnaires, and analyzed with correlation and ordinal logistic regression methods. The results showed that the use of resources was very flexible, which led to better operational performance through financial resilience, enhanced operational efficiency and helped diversifying revenue sources. The study recommended companies to continuously develop their ability of flexible utilization of resources to reach their long-term financial sustainability.

In the research work on the linking role of technology and resource fluidity in strategic agility and business performance of SMEs carried out in Nigeria Adewuyi (2026) adopted the quantitative research design using the Structural Equation Modelling (SEM). The data were collected from the SMEs in Lagos State and analyzed using the techniques of the structural equation modelling (SEM). The findings demonstrated that, interestingly, the resource fluidity had a great impact on the business performance as firms were able to redirect the resources quickly to meet the changes in the environment. Furthermore, the findings revealed that the association between the resource fluidity and the organizational performance was mediated by technology adoption, suggesting that resource fluidity helps to improve the financial performance through operational efficiency and innovation by utilizing technology optimally.

In the conceptual and empirical study conducted by Ahammad, Glaister and others (2021), they used the dynamic capabilities approach as their methodology for studying strategic agility, dynamic relational capability and trust among SMEs in transitional economies in India. The study was based on strategic agility dimensions of strategic sensitivity, leadership unity and resource fluidity, and their explanations of the organizational competitiveness and firm outcomes. The results indicated that the resource fluidity helps to reallocate financially, human and technologically resources rapidly, which increases the adaptability of the organizations, the competitive advantage and the sustainable organizational and operational performance in the situation of uncertainty of the environment.

H1: Resource fluidity has significant effect on operational performance of SMEs in Nairobi City, County

Figure1: Research Model (Resource fluidity → Operational performance; H01)

4. Research methodology

The study was an explanatory research design where the researcher was able to investigate the relationships between strategic agility, digital capability and operational performance of SMEs in Nairobi City County, Kenya. It was appropriate because it was a quantitative study which was based on objective measurement and statistical analysis of the relationships between the study variables. The unit of observation was the owner-managers/senior managers of the SMEs and the individual SME was the unit of analysis. The respondents were stratified based on the sector of operation and then a stratified random sampling method was used to select the SMEs for the study.

Sample size and data collection

The target population for the study in Nairobi City County, Kenya was 103,214 registered SMEs in the area. The SMEs included the enterprises in wholesale and retail trade, manufacturing, professional, scientific and business services, accommodation and food services, information and communication technology, transport and logistics, construction and other service enterprises. The study used stratified random sampling technique where the sample size of SMEs is classified in eight sectors to ensure adequate sampling in each SMEs sector. The number of SMEs selected from each stratum was proportionate and then the enterprises were randomly selected. The owner manager and senior manager of each SME selected was chosen due to their knowledge of strategic agility, digital capability and operational performance within the selected SME. A well-structured questionnaire was used to gather quantitative data in the field and the constructs of strategic agility, digital capability and operational performance were measured on a 5-point Likert scale. The sample size was determined using Yamane's (1967) formula to get 399 respondents from a population of 103214 SMEs at 5% margin of error. Before the main study, the research instrument was pre-tested on 40 SME owner-managers or senior managers of about 10% of the sample size of Kiambu County, Kenya. The pilot study aimed to evaluate the validity, clarity and reliability of the questionnaire. Based upon the reliability analysis, the reliability coefficients of the constructs of the study were recommended to be at least 0.70 or higher, which is sufficient for use of the questionnaire in the main data collection and statistical analysis.

Measurement of variables

This section addresses the measurement of the study variables, in which resource fluidity is the independent variable and operational performance is the dependent variable. The study also considers relevant control variables that may influence operational performance, including firm size and firm age. Detailed definitions and measurements of all the research variables are provided in Table 1.

Data analysis and model specification

The study used a strictly quantitative data analysis technique which involved both inferential and descriptive statistical analysis using SPSS version 25. Both inferential and descriptive statistics which included mean, standard deviation, percentages, minimum and maximum were all used to summarize demographics and study variables. Inferential statistics was also used in the study as both correlation and regression analysis was carried out. To test the proposed hypotheses the following regression equation was adopted:

Model one involved the interaction of the control variables

Y = β₀ + C…………………………………………………………… Model 1

Testing the effect of the independent variables on operational performance

Y = β₀ + C + B₁X₁ + B₂X₂ + B₃X₃ + ε………………………………………. Model 2

Where:
Y = Operational performance
β₀ = Constant
C = Control variables
β₁ = Regression coefficient of career concern
X₁ = Resource fluidity
ε = Error term

5. Findings.

The number of questionnaires that were distributed to respondents of the selected SMEs in Nairobi County, Kenya was 399. Of these 303 questionnaires were returned with a response rate of 75.94%. Once the questionnaires were cleaned and screened for completeness, consistency and accuracy, 280 questionnaires were suitable for analysis. This resulted in usable questionnaires of 92.41% of questionnaires returned and 70.18% of questionnaires distributed. The response rate was adequate for statistical analysis because this is generally acceptable for survey research, studies indicate a percentage greater than 70% is considered satisfactory (Mugenda & Mugenda, 2019). The level of response rate achieved was adequate to explore the moderating role of digital capability on the relationship between strategic agility and operational performance of SMEs in Nairobi County, Kenya.

Source: Primary Data (2026)

Firm characteristics

Descriptive statistics were used to examine the control variables: firm age (FA) and firm size (FS) among 280 SMEs in Nairobi City County. All variables recorded complete responses (N=280), with minimum and maximum values of 1 and 5. Firm age had an overall mean of 3.3160 (SD=1.05261), while firm size had a mean of 3.2940 (SD=1.07218). These results indicate that responses were generally centred around the middle categories of the five-point measurement scales, with moderate variability. Firm age was categorized as less than one year, 1–5 years, 6–10 years, 11–20 years, and above 20 years, while firm size was categorized according to the number of employees as 1–10, 11–50, 51–100, 101–250, and 251 employees and above. The results therefore suggest variation in the operating histories and employee capacities of the sampled SMEs. Since both variables were measured using categorical classifications, the mean values should be interpreted as indicators of the central response category rather than exact years of operation or numbers of employees. The inclusion of firm age and firm size as control variables helps account for firm-level differences that may independently influence operational performance when examining the effect of resource fluidity.

Descriptive for operational performance

The operational performance descriptive statistics consist of mean and standard deviation. The mean represents the average consensus of the respondents, and the standard deviation represents the variation of the responses from the average. A five-point Likert scale was used for the study with 1 indicating strongly disagree, 5 indicating strongly agree. The analysis was done with 280 valid answers.

The mean score for OE1 was 3.24 with a standard deviation of 1.287, indicating that the company is constantly generating enough profits to sustain its day-to-day business activities and pay its operating costs, with no major challenges to its finances. The mean has reflected that the respondents were moderately agreeable with the statement which indicates that overall, their businesses were able to generate enough income to cover operating costs and to operate on a day-to-day basis. The standard deviation of 1.287, however, suggests that there is a fairly large range of perception of the financial security of enterprises, reflecting a situation in which some enterprises were sufficiently financially secure, while others lacked financial security to pay their running costs. The business maintains adequate cash flow to meet its short term financial obligations such as paying suppliers, employees and other short term commitments as they fall due, with a mean of 3.31 and a standard deviation of 1.254. The mean shows that there is a moderate level of agreement and most respondents felt their businesses had a reasonably good cash flow to pay their short-term financial demands. A standard deviation of 1.254 indicates that there was a wide range of variation in responses, indicating that enterprises' ability to ensure adequate liquidity and to settle financial obligations differs. The mean score for the OE3 was 3.18 with a standard deviation of 1.302, suggesting that their use of its financial, human and technological resources is efficient, with a view to minimising operating costs and enhancing overall operational efficiency. The mean represents moderate agreement and indicates that overall, the respondents were generally of the opinion that their enterprises used resources to a moderate degree. However, the relatively high standard deviation suggests that there are significant variations in responses, suggesting that some enterprises were more successful than others in managing financial, human and technological resources to control the operating cost. The mean for OE4 was 3.36 with a standard deviation of 1.241; Our enterprise completes its routine business activities efficiently, with minimal delays, wastage and unnecessary use of resources. The mean was the highest for the items related to the operational efficiency of their enterprises, suggesting that the general opinion was that their enterprise was successfully meeting routine activities in a productive way. This indicates a relative improvement in enterprises' performance in reducing delays, wastages and unnecessary use of resources. The standard deviation of 1.241 means that there was variation in perceptions, which means that there was a difference in the level of 'operational efficiency' between enterprises. For the mean, the standard deviation for OE5 was found to be 1.276, and the mean was 3.29 with a satisfactory profitability level that allows the company to operate and maintain its business process and enhance their business initiatives for competitiveness. The mean value represents a moderate level of agreement, indicating that businesses were generally regarded as being profiting at a moderate level which allowed the businesses to stay in operation and be reinvested. But there was a lot of variation in the responses (standard deviation of 1.276), with some enterprises more profitable and financially able to reinvest than other enterprises.

The enterprise is able to withstand unexpected financial shocks without significant deterioration in its operational performance, for which the mean was 3.22 and the standard deviation 1.291. The mean represents moderate agreement, indicating that respondents generally felt that their business had some ability to withstand unexpected financial shocks. There is also significant variability in responses, as reflected by the standard deviation of 1.291, suggesting that the resilience of enterprises to economic downturns, inflation and market disruptions varied significantly. The mean of QP2 was 3.34 with a standard deviation of 1.263, and they stated that “Our business has enough financial reserves and flexible funding options to bounce back quickly from unexpected financial difficulties and remain in operation effectively.” The mean results represents moderate level of agreement, meaning that most people thought that their businesses had moderate financial resources and funding opportunities to deal with financial difficulties. The standard deviation was relatively high at 1.263, reflecting a significant range of responses and suggesting there were differences in financial reserves and flexible funding between enterprises. The mean of the responses for QP3 was 3.16 with a standard deviation of 1.315; the enterprise can successfully adapt its financial strategies and resource allocation in response to changes in the business environment and maintain its financial stability and sustainability. The mean value indicated moderate agreement, indicating that the general perception of respondents is that their enterprises have moderate capacity in adapting financial strategies and allocating resources in response to business conditions. The standard deviation of 1.315, however, was rather high, suggesting some considerable variation in opinions on the capacity of enterprises to remain financially stable and sustainable in times of environmental change.

QP4, with the statement “Our enterprise always produce products and services which conform to the quality standards and customer expectation,” had the mean of 3.28 and a standard deviation of 1.279. The mean value represents moderate level of agreement, indicating that the respondents generally felt that their enterprises provided products or services of a quality level which met the quality standards and the expectations of customers. The standard deviation is 1.279, showing that there is a lot of variability in the responses, which means the quality performance was not consistent among enterprises. QP5 said: “Our enterprise maintains consistent quality in our products or services by minimizing the number of errors, defects and customer complaints.” The mean was 3.30 and the standard deviation was 1.258. The mean represents a moderate level of agreement with the enterprises generally agreeing that their operations were relatively error, defect and complaint free, thereby promoting an acceptable quality of their products and services to their customers. The standard deviation of 1.258 suggests some variation in the responses, suggesting that not all enterprises were as effective as others in keeping quality consistent and minimizing customer complaints. For DR1, it was found that the enterprise consistently had a steady increase in sales revenue, profitability and overall operational performance over a period of time, meaning that it had a long-term sustainability objective, and it was found that the mean was 3.25 with a standard deviation of 1.284. The mean represents a moderate level of agreement, suggesting that respondents had a general agreement that their enterprises generally experienced a moderate amount of steady growth in sales revenue, profitability, and overall operational performance. There is wide variability in responses, given a standard deviation of 1.284, and so the consistency of growth varied between enterprises. For DR2, the mean was 3.33 and the standard deviation was 1.247, as their business regularly invests its financial resources into innovation, technology, market expansion and operational improvements to boost its future growth and competitive advantage. The mean represents the moderate agreement, which suggests that most of the respondents viewed their businesses as investing in reinvestment, innovation, technology, business growth and operation improvements. The standard deviation of 1.247 suggests that the responses varied, suggesting that enterprises' capabilities and willingness to reinvest resources to enhance their future growth and competitiveness varied. DR3 reported mean 3.19 and standard deviation of 1.308 because of sustainable financial growth by expanding their customers, market share and overall financial position in time. The mean value represents moderate agreement, meaning that the majority of respondents felt their businesses had a moderate level of capability to sustain financial growth through expanding customer base, gaining market share and strengthening their finances. The high standard deviation of 1.308 shows that there is a lot of variation in the responses, meaning that the amount of financial growth varied between enterprises. DR4 said: “Our enterprise always provides our customers the products or services on time.” The mean was 3.35 with a standard deviation of 1.259. The mean represents moderate agreement, and most respondents saw that their companies were providing agreed on products or services on time. This means that there is relatively positive perception regarding delivery timeliness and delivery reliability. The dispersion of the responses around the mean is shown by the standard deviation of 1.259; this suggests that there is significant variation between the performance of enterprises in terms of adherence to delivery schedules. DR5, whose slogan is “Our enterprise reliably fulfills the customer's order and service commitments without frequent delays, cancellations and disruption,” had a mean of 3.27 and a standard deviation of 1.286. The mean result points indicated moderate level of agreement, meaning that the respondents generally considered their business to be reasonably reliable to deliver orders and service commitments to customers. The mean of 1.286, however, shows that there is a lot of variation in the responses so the consistency of order fulfilment/service delivery was found to vary between enterprises.

The overall operational performance construct had a mean of 3.2714 and a standard deviation of 1.00012, with a total of 280 items that were valid and could be analyzed. The overall mean shows that the majority of the responding enterprises reported moderate agreement with the statements that quantify operational efficiency, quality performance and delivery and reliability to suggest a moderate level of operational performance for the enterprises surveyed. The average perception of operational performance are fairly consistent overall, as evidenced by the standard deviation of 1.00012. For operational efficiency indicators, the highest mean was for OE4 indicating that enterprises were relatively better in completing routine business activities without unnecessary resource usage, wastages, and delays. Utilization of resource and cost minimization was the comparatively weaker aspect of operational efficiency as this was the lowest mean (3.18) among the three. The highest mean value was found in QP2, indicating fairly positive perceptions about financial reserves and flexibility in financial resources to overcome unexpected financial difficulties for quality performance. The mean of 3.16 was the lowest score in QP3, meaning that business environment changes and financial strategy/ resource allocation were a relatively poorer area of quality performance. With regards to delivery and reliability, DR4 had the largest mean of 3.35, which indicates that enterprises were relatively stronger in delivering products or services within stipulated time. The mean of DR3 was the lowest at 3.19, suggesting sustainable financial improvements via customer expansion, market share growth and financial position [less developed]. Overall, the results show a medium level of performance in the three dimensions of enterprise operations in the enterprises surveyed. In the individual items, however, the standard deviations (from 1.241 to 1.315) show that there was quite a bit of variation in respondents' perceptions. This implies that companies had varying efficiencies, quality management, financial stability and delivery performances. The results, therefore, suggest that enterprises need to enhance the utilization of resources, financial adaptability, sustainable growth and continuity of operational processes to achieve the overall operational performance.

Descriptive for resource fluidity

Descriptive statistics are reported as means and standard deviations to reflect respondents' perceptions about the ability of enterprises to adjust and redeploy their resources to meet changing business environments and situations.

The first resource fluidity item did not assess if enterprises have the ability to quickly redeploy financial, human and technological resources to react to new market opportunities and shifting customer needs without significant operational disruption. The mean for RF1 was 3.26 and the mean score was 1.267. The mean represents a moderate level of agreement between respondents, indicating that there was some ability for SMEs to respond to changes in market conditions and/or customer needs by changing resource allocation. The discovery suggests that there was reallocation of resources in enterprises surveyed but that it was not always fast and smooth in all enterprises. The standard deviation of 1.267 indicates a high level of variability in responses, suggesting that there were variations in how well the enterprises were able to make effective resource adjustments. The 1 and 5 represent the lowest and highest responses, respectively, and show that the respondents gave responses across the entire range of the five-point Likert scale.

The second item studied was how well businesses could reassign staff, technologies, and organizational expertise among projects or business activities, if priorities shifted. The mean of RF2 was 3.34 with a standard deviation of 1.221. The relatively higher mean score for the items related to resource fluidity suggest that respondents were relatively more capable to shift organizational resources in response to emerging organizational strategic needs. It implies that the SMEs were somewhat flexible in transferring employees, technology and skills to activities deemed to be more strategically relevant. However, the mean score is at a level close to the midpoint of the scale suggesting that this flexibility was not consistently high in the enterprises. The standard deviation of 1.221 indicates that there are some differences in respondents' perceptions of the redeployment capability of resources. The scores obtained in response varied from 1 to 5 indicating that the perceptions were spread across the Likert scale.

For the third item, it was assessed whether organizational resources were continuously redistributed to support innovation, operating efficiency and adoption of new strategic initiatives as business conditions changed. The mean for RF3 was 3.18 and the standard deviation was 1.318. The mean value shows the presence of a moderate degree of agreement in the SMEs surveyed with respect to the resource reviews and redistribution that took place. The mean of the other resource fluidity items, however, is slightly higher than this, indicating that the resource reassessment and redistribution was less consistently conducted. This could suggest that a few businesses found it difficult to ensure that resources were allocated to innovation and new strategic directions in a systematic manner. The standard deviation of 1.318 indicates a significant range of responses, indicating that there was a lot of variation in resource management practices between enterprises. The range of minimum and maximum scores of 1 and 5 indicates that the views of respondents were spread throughout the measurement scale.

The fourth item was about the ability of enterprises to shift financial and human resources from non-strategic activities to strategic activities when business conditions change. The mean of RF4 was 3.30 and the standard deviation was 1.252. The mean indicates moderate agreement, indicating that SMEs had some capacity to reprioritize and redirect resources to activities that require more strategic attention. The results show that enterprises could adjust their resource commitments reasonably in response to the changing business environment. However, the moderate mean also indicates that this capability was not developed equally between all the firms. The relatively high standard deviation of 1.252 shows that there are significant variations in the perception of the respondents, which means that enterprises had differential abilities to move resources from one competing priority to another. There was a wide range of responses with the minimum score of 1 and maximum score of 5, indicating that the full range of scores was observed.

The fifth item tested the ability of enterprises to efficiently redirect organizational resources to the development of new products, services, technologies and business opportunities without significant disruption of operations. The mean of RF5 was 3.23 and the standard deviation was 1.281. Moderate level of agreement, indicated by the mean suggests that SMEs were able to shift some of their resources to new opportunities while continuing their existing operations. The discovery suggests the presence of resource fluidity within the analyzed companies, but in some the fluidity was more effective and consistent than in others. The standard deviation of 1.281 shows that there is a significant range of responses to how successful enterprises were in reallocating resources and minimizing disruption to operations, with some being more successful than others. The range of 1 to 5 indicates that the responses covered the entire range of the Likert scale.

Correlation analysis

A correlation analysis was carried out to determine the direction, strength and statistical significance of the relationships between the operational performance (OP), firm age (FAA), firm size (FSS) and resource fluidity (RF) among SMEs in Nairobi City County, Kenya. Based on 280 valid cases, the Pearson product-moment correlation coefficients were computed. It was observed that there was a weak positive and statistically significant relationship between firm age and operational performance (r = .225, p = .000) while there was a weak positive and statistically significant relationship between firm size and operational performance (r = .138, p = .021). Financed by the strong positive and statistically significant correlation between resource fluidity and operational performance (r = .762, p = .000), it is concluded that the ability of SMEs to move financial, human and technological resources quickly and effectively was related to good operational performance. The findings, overall, indicated preliminary evidence of a positive relationship between resource fluidity and operational performance, and also indicated a need for further regression analysis to capture the unique contribution of the resource fluidity to operational performance.

Regression analysis (Hypothesis testing)

Hierarchical regression analysis examined the contribution of resource fluidity to operational performance of SMEs in Nairobi City County, Kenya, beyond firm age and firm size. In Model 1, the control variables were entered first, followed by resource fluidity in Model 2. The regression coefficients indicated that firm age (β = .127, p = .001) and firm size (β = .132, p = .001) had positive and statistically significant effects on operational performance. Resource fluidity (β = .232) also had a positive and statistically significant coefficient (p < .001). These findings demonstrate that resource fluidity significantly improved the explanation of operational performance beyond the control variables. Thus, the findings provide support for the hypothesis that resource fluidity has a significant positive effect on operational performance of SMEs in Nairobi City County, Kenya.

FAA: Firm size, FSS: Firm Age, RFF: Resource fluidity, OP: operational performance.

6. Discussion

The results show that resource fluidity has a positive and significant effect on operational performance of SMEs in Nairobi City County, Kenya. The outcome of the correlation analysis reveals a high positive correlation between resource fluidity and operational performance (r = .762, p < .001), suggesting that the more flexible the SMEs are in using and reallocating resources, the more likely they are to achieve good operational results. The hierarchical regression analysis also revealed that resource fluidity, over and above firm age and firm size, was positively and statistically significant in relation to operational performance (β = .232, p < .001). The positive coefficients of firm age (β = .127, p = .001) and firm size (β = .132, p = .001) also demonstrate that these factors relate to the operational performance. The results highlight the significance of resource fluidity for SMEs to adapt to market fluctuations, make use of the resources and improve its performance.

7. Conclusion

The study finds that resource fluidity is a key positive determinant of SMEs performance in Nairobi City County under the operational aspects in Kenya. The correlation results suggest that there is a strong positive relationship between resource fluidity and operational performance and the results of hierarchical regression analysis show that resource fluidity is a significant predictor of operational performance beyond the influence of firm age and firm size. The consequences conclude that SMEs which have the ability to redirect financial, human and technological resources promptly to meet the new opportunities and changing customer needs, are more likely to be effective. Besides, the age of the firm and its size were also found significant for the operational performance. Therefore, resource fluidity is one of the factors that affects the performance of SMEs in operating the business. Capacity building of SMEs on mobilizing and reallocating and efficient deployment of resources can enhance SMEs operational effectiveness and enable sustained productivity in Nairobi City County.

8. Recommendation of the Study

Policymakers Recommendations

National policies need to be developed and improved to promote the flexibility of resources, the effective use of resources and the productivity of its operations in SMEs. The Ministry of Cooperatives and Micro, Small and Medium Enterprises Development should encourage policies to help SMEs access financial, human and technological resources to be able to respond to changing market conditions. Policies should aim at skills development, the use of digital tools and financial inclusion, as well as the possibility for SMEs to reallocate resources based on new business opportunities. There is also a need for the government and industry partners to do more to improve training and advisory programs focusing on SME owners/managers in terms of resource planning and mobilization. Furthermore, policy makers should offer incentives and support for SMEs investing in the technologies, employee development and flexible operational systems. These measures would improve the ability of SMEs to react to market variations; improve operational performance of SMEs and increase productivity and competitiveness of SMEs in Kenya.

Recommendations to Managers

Nairobi City County SMEs should adopt practices which would enhance the fluidity of resources and operational performance. They should include the flexibility of allocating resources, ongoing assessment of the requirements for resources, and timely reallocation of financial, human and technological resources. The managers need to periodically evaluate new market possibilities and shifts in customer needs to make sure that they have resources invested in activities that add value to the operation. Resource planning and reallocation, too, should be a part of regular operational reviews and strategic decision making. Managers should develop employee skills, invest in digital technologies and systems that enable the agility of resource flow between operational activities. Enabling resource fluidity will help managers to optimize operations, better respond to market dynamics and boost the productivity and competitiveness of SMEs in Nairobi City County.

Theoretical Implications

The findings resonate with the resource view as they illustrate the ability of effective management and utilization of organizational resources to enhance operating performance. Results suggest that having greater resource fluidity is related to better business outcomes for SMEs, suggesting that reallocating and deploying resources to the right places and at the right time to meet business needs is an important capability in SMEs. The findings go beyond resource-based explanations by focusing on the mobilization and use of resources in SMEs. Additionally, there were significant impacts of firm age and firm size, indicating that organizational factors can also impact operational performance differences. Hence, the study underscores the need for resource fluidity as an explanatory construct to understand how SMEs can enhance their operational performance by deploying resources flexibly and effectively.

References