Effects of Budget Planning on Financial Performance of Selected SMEs in Eldoret Town, Kenya Moderated by Digital Finance Services

Effects of Budget Planning on Financial Performance of Selected SMEs in Eldoret Town, Kenya Moderated by Digital Finance Services

¹Jepleting Gladys, ²Dr. John Tarus, ³Dr. Zachariah Shitote

¹Department of Business Management, School of Business, Economics and Management Sciences, University of Eldoret, Kenya

²Lecturer, Department of Accounting and Finance, School of Business and Economics, Moi University, Kenya

³Lecturer, Department of Business Management, School of Business, Economics and Management Sciences, University of Eldoret, Kenya

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110057

Received: 21 October 2024; Accepted: 30 October 2024; Published: 02 December 2024

ABSTRACT

The study intention was to investigate effects of budget planning on financial performance of selected SMEs in Eldoret town, Kenya, moderated by digital financial services. Specific objectives of the study were to examine the influence of budget planning on financial performance and effect of digital financial services on financial performance. The study was designed to assess the moderating role of digital financial services on the relationship between budget planning and financial performance of selected SMEs. The research was guided by the Priority-Based Budgeting Theory and Resource Based Theory. The study utilized explanatory research design and descriptive research design. Simple random sampling techniques in collecting data from 302 selected SMEs using structured questionnaires. Cronbach alpha was applied to test reliability while Factor analysis was applied to test validity. Using SPSS version 23 hierarchical regression model was employed in data analysis and testing of the hypotheses. The results reveal that digital finance services had significantly positive relationship between budget planning and financial performance.

Keywords: Budget Planning, Digital Finance Services and Financial Performance

INTRODUCTION

Financial performance refers to the measure of how well a company is using its assets to generate revenue, profit, and value for shareholders. It is typically assessed using financial metrics such as return on assets, return on investment and profitability. Small and medium enterprises are indispensable in all economies, can be describe as a driving force of business growth, innovation, competitiveness and a very important employer. SME’s are business that maintain revenues, assets or number of employees below a certain threshold (Liberto, 2022 a). SME’s play vital part in economies around the world through innovation, flexibility, creativity, efficiency and locally (Liberto, 2022b). SME’s goals help in generation of revenue and creation of employment, enhanced competency of the country on global scale, attracting international companies to the country through improved inter-counter trade growth and improve social amenities like schools and public space (OECD, 2018).

Financial planning and budgeting involve setting goals, allocating resources, and monitoring performance to achieve desired outcomes. Developing comprehensive financial plans and budgets helps SMEs set realistic goals, allocate resources effectively, and monitor performance against targets. Chowdhury et al., (2016) emphasized that SMEs that engage in proactive financial planning and budgeting are better equipped to achieve their financial goals and improve overall financial performance.

Digital finance has become a key tool for reducing social inequality and promoting economic growth (Liu et al., 2022: Ozturk & Ullah 2022). Yang & Zhang (2020) point out that promoting digital financial inclusion and restructuring the financial industry can boost SMEs and macro economy. Hence, was necessary to examined moderating role of digital financial services on the relationship between budget planning on financial performance of SMEs in Eldoret Town, Kenya.

LITERATURE REVIEW

Theoretical Framework

Priority Based budgeting theory and Resource Based theory were used in the study.

Priority-Based Budgeting Theory

Priority-based Budgeting Theory was introduced by scholars such as John E. Beasley and Kavanagh and Fabian (2011) in the field of public administration and budgeting. Priority-based Budgeting Theory is a modification of Zero-Based approach that focused on firm priorities and allocation of resources to enhance savings and growth in budgeting. The Theory emphasizes the importance of allocating financial resources based on the relative importance of various programs, projects, or goals based on priorities and objectives rather than simply making incremental adjustments to past budgets and historical spending patterns to enhance financial performance (Pidgeon,2010) . It suggests that by aligning budget allocations with strategic priorities, firms can enhance financial performance and achieve their goals more effectively.

Resource Based Theory

Resource Based theory was first developed by Edith Penrose (1959), states that unused managerial resources are the primary driver for growth and expansion through diversification of any firms that undertake. Penrose who propose a model as an effective tool for management of resources, firms, diversification strategy and productive opportunity. Penrose gave an insights in the process of resource acquisition, utilization and expansion for competitive advantage (Rugman & Verbeke 2002). The theory suggests that firms possess resources, a subset of which enable them to achieve competitive advantages, and a further subset which leads to sustained competitive advantage. Resource Based theory focus on firms performance and predicts fundamentals of organization performance and competitive advantage. The theory provides an essential framework to explain and predict the fundamentals of a firms performance and competitive advantage ( Utami & Alamanos, 2023).

Concept of SMEs financial performance

SME are life blood of economics driving force to development and social transformations. Kenya Economic survey reports (2017), states that SME’s had been acknowledged and given priority as growth driver for achievement, development blue print and improves the living standards for the citizen through economic and sustainable growth as well as social capacity for international investors. SME’s in Kenya function as live blood to the unfortunate, create employment opportunities, income generation and contribute to economic growth (Mukoma & Masin, 2015). SME’s helps in generation of revenue and creation of employment, enhanced competency of the country on global scale, attracting international companies to the country through improved inter-counter trade growth and improve social amenities like schools and public space. Government has set up ministry of cooperatives and SMEs in effort to try and enhance a sector that is key to Kenyan economy (Juma, 2022). The existence of SME’s projects is a step towards achieving country vision 2030 through job creation, use of technology by both skilled and unskilled labors (Mbura, 2018).

Concept of Budget Planning

Budget planning involves the use of budgets to develop financial forecasts, which include Sales budgets, operational budgets, cash budgets, capital budgets, strategic budgets, and Budgeted financial statements (Bedford, 2015). Budgetary planning is defined as the process of forecasting future events, and how activities should be handled based on predetermined goals set by an organization (Agbenyo et al., 2018).

Budget planning facilitates goal setting and achievement by providing SMEs with a roadmap for financial targets. Hassan et al., (2020), SMEs that engage in comprehensive budget planning are more likely to set realistic financial goals and achieve them, leading to improved financial performance. Budget planning instills financial discipline within SMEs, ensuring that resources are allocated efficiently and expenditures are controlled. Al-Najjar (2019) indicates that disciplined budgeting practices positively influence SMEs’ financial performance by reducing wasteful spending and improving cash flow management. Effective budget planning enables SMEs to identify and mitigate financial risks.

Concept of Digital Financial Services

Digital Financial Services are financial services which rely on digital technology for their delivery and use by consumers. Digital Financial Services comprises a broad range of financial services accessed and delivered through digital channels, including payments, credit, savings, remittance and insurance (Kambale,2016). Further, refers to a new generation of financial services that combine internet and information technology with traditional financial services (Luo, 2023).

“Digital Financial Services” also refers to financial products and services, technology and infrastructure that enable individuals and businesses to access payments, savings, credit, and investment facilities online, without having to interact physically with a bank or other financial service provider (Pazarbasioglu et al., 2020). Digital finance services encompass a range of financial products and services delivered through digital channels, such as mobile phones, the internet or cards, and electronic payment systems, (Manyika et al, 2016).

Effects of Budgetary Planning on financial Performance of SMEs

Gupta and Jain (2016) studied capital budgeting practices in SMEs, a study of selected enterprises in Haryana. The findings of the study revealed that majority of the SMEs did not make much investment in fixed assets after starting the business , other SMEs considered aspects of capital budgeting but did not propose proper capital budgets.

Maduekwe and Kamala (2016) studied the use of budget in Cape Metropolis, South Africa. The study revealed that most SMEs reviewed uses of fixed budgeting due to lack of qualified personnel and support from top management that affects usage of effective budgets by SMEs.

Kiiru et al, (2018) conducted research on effect of budget planning on financial performance of SMEs in Nakuru town central business district. The study revealed that there is a moderate positive and statistically significant correlation between budget planning and financial performance. Chowdhury et al., (2016) emphasized that SMEs that engage in proactive financial planning and budgeting are better equipped to achieve their financial goals and improve overall performance.

Effects of Digital Financial Services on financial Performance of SMEs

Digital financial services pertains delivery of financial services via mobile phones, personal computers, the internet, or cards connected to a secure digital payment system (Ozili, 2018). Digital finance services offer numerous benefits for SMEs, it’s essential to address potential challenges and risks to ensure their effective utilization and positive impact on performance. Luo, (2023) studied coverage breath of digital finance and SMEs financing constraints. The findings shows that development of digital finance can effectively alleviate the financing constraints of SMEs. Development of digital finance complements traditional finance for SMEs. Schonberger (2023) studied digital finance, financial constraints and SME technology innovation- An empirical study based on SMEs and growth enterprise market data. The sample of the study consists of public financial data of Chinese SMEs board and growth enterprise market listed companies from 2012 to 2018. The findings show that digital finance coverage contribute significantly to technological innovation of SMEs through intermediary channel.

Luu et al., (2023), investigates the impact of digitalization on the financial performance of SMEs: Empirical evidence from an emerging economy. The study use a comprehensive firm- level data set of 5000 SMEs in Vietnam during the period from 2005 to 2015. The findings shows that digitalization plays a vital role in facilitating the financial performance of SMEs through improved productivity such as computers, email, e-trading and internet to boosts SMEs.

Kambi and Onyiego (2022) studied effects of digital financial inclusion on financial growth of micro, small and medium, enterprises in Kenya. The study use descriptive survey and inferential statistics. The study results revealed that independent variable affects growth of micro, small and medium, enterprises in Kenya. Keter et al., (2023), investigates financial performance and firm value. The moderating role of going digital among companies listed in Nairobi security exchange. The sample of 39 firms with observations of 390 during the period of 2010 to 2019. The findings revealed that going digital at lower levels enhances the relationship between financial performance and firm value.

Digital finance services helped to fill the financial gap by providing access to alternative sources of funds and enabling new digital products and process automation. The study examined digital finance services, such as adoption of mobile money, online lending platforms and E-wallet among the SMEs.

Figure 1: Conceptual Framework

RESEARCH METHODOLOGY

The study intention was to investigate effects of budget planning on financial performance of selected SMEs in Eldoret town, Kenya moderated by digital financial services. Eldoret town was selected as the primary focus for the study due to the high concentration of registered SMEs and to generate homogeneity among related business firms in urban center. The study utilized explanatory research design was chosen since it explains the cause, elaborate and predict effect of relationship between independent variable, moderator and dependent variable that is based on the study. Descriptive research design was also used to enables researcher to obtain information concerning a current phenomenon exactly the way it is with minimal interference, hence collection of quantitative and qualitative data.

Table 1 Target Population of selected SMEs in Eldoret

Simple random sampling method was utilized to collect the data for the study. Stratified random sampling was employed in picking the sample since it ensures each subgroup within the population receives proper representation within the sample thus reflects the population being studied. The simple random sampling technique used were to pick a sample from every stratum in order to provide all the sampling units equal chance of being selected (Bacon- Shone, 2015).

Table 2 Sample size

The study used structured questionnaire with 7 point Likert-scale in collecting data (Strongly Agree (7) to Strongly Disagree (1)). The first section had demographic information of the respondents: gender, age, firm age, education, firm size and annual turnover. The second section had questionnaire that were carefully designed to cover relevant variables of the study. Budget planning had 9 items adopted from (Awan et al., 2015: Erambo, 2017; Otoo,2024; Gerber, 2024). Financial performance had 10 items, three construct were used, that is return on Assets, return on investment and profitability adopted from (Nunes et al.,2012: Khan et al., 2017; Okumu & Buyinza,2019) and Digital finance services, as moderating variable had construct such as access, quality and usage with 9 items adopted from (Hayworth et al., 2019; Kambale, 2016; financial inclusion data working group, 2019; Ravikumar, 2020). Two covariates was controlled in the study namely firm size grouped into 4 categories and firm age grouped into 5 categories. ( Chelogoi, 2020: Yang et al., 2020; Pervan et al., 2017) found that firm size and firm age have a positive influence on firms performance.

ANALYSIS AND RESULTS

Response rate, demographic and descriptive statistics

Researcher administered 302 questionnaires to the respondents, however 289 questionnaire was collected. The researcher cross check questionnaires on the field in order to reduce the missing values, and after screening 281 were correctly filled representing 93% of the total questionnaires administered while 8 questionnaires were dropped from the study since they were not completely filled representing 3% of the total questionnaires administered. According to Njeru (2013) considered that a response rate of 50% and above is sufficient for data analysis. The study had 93% response rate, hence viable.

The results of the study in Table 3 indicated that respondents were equally distributed since there was a minimal percentage difference between both gender were male were 52% (n=146), while female were 48% (n=135). The findings also revealed that majority of the respondents were within the age group of 26-35 years representing 44.5% (n=125), followed by those with below 25 years representing 36.7% (n=103), third category were those aged 36-45 years representing 12.5% (n=35) and the least category were those above 46 years representing 6.4% (n=18). The study outcome indicates that majority of the respondents are youths below 35 years representing 87.5% (n=228) whom are energetic and active group to run businesses.

The study findings also shows that majority of the SMEs are below 1- 3 years of its operation accounting for 54.1% (n=152) and the least were above 7 years representing 22.4 (n=63). The findings support research showing that most SMEs tend to be relatively young, with a significant portion falling within the first decade of operation (Beck, 2016). The study indicates that there is mortality rate of SMEs, which could be caused by poor financial management practices, high taxation rates and other factors that could influence SMEs operations.

The study results also revealed that majority of the respondents are those whom had a diploma certificates representing 32.4% (n=91), followed by those held a degree certificates representing 31.7% (n=89), third category are those with certificate representing 21% (n=21), 10.7% (n=30) had other qualifications while the least in running businesses are those whom had Masters certificate representing 4.3% (n=12). The study findings based on education statistics revealed that majority of the SMEs are able to understand the questions highlighted in questionnaires and give a viable choices. The study finding indicates that majority of SMEs respondents are those with employees below 10 representing 84.7% (n=238) followed by those with 11-49 number of employees representing 11.4% (n=32) and the least are the medium size business that had 50-99 number of employees representing 3.9% (n=11). This result reflects the common small-scale nature of SMEs, as discussed in the study by Ayyagari, et al (2017), where micro and small enterprises dominate the SME sector globally. The study outcomes revealed that there is high rate of starting businesses but cannot grow to another level of firm size this might be caused by inadequate financial performance.

The study finally revealed that majority of SMEs annual turnover are below Ksh.500,000 representing 66.2% (n=186), followed by Ksh.500,001-5,000,000 and the least are those who are above to obtain annual turnover of above Ksh.5,000,000. The study results revealed that the business might not grow to the next level due to economic factors, financial performance and even the government policies that will hinder the business growth.

Table 3

Descriptive Statistics, Reliability and Correlation for the Study Variables

Descriptive statistics provide a summary of the main characteristics of the study variables, as explained in Table 4. The digital finance services had the highest mean of 5.0735 and standard deviation of 1.34408 followed by budget planning with mean of (5.0308) and standard deviation of (1.30550) and financial performance with mean of 4.7892 and standard deviation of1.18953.
The researcher used Cronbach’s Alpha to examine internal consistency to estimate degree of unreliability. The value of Cronbach’s Alpha ranges from 0 to 1. Cronbach’s Alpha (> 0.90) might suggest redundancy and greater internal consistency among the items and show that the test length should be shortened (Tavakol and Dennick 2011). The Cronbach’s Alpha coefficient equal to 1 and greater than 0.7 was used to test internally consistent of the questionnaire ((Nderitu & Githinji., 2015). Where all the variables meet the coefficient that varies from 0 to 1, thus the value of each variable gives satisfactory consistency reliability, which was within an acceptable range.

The researcher also used Karl Pearson’s coefficient to identify correlation results which indicates that budget planning had a strong positive relationship with financial performance r=0.762 and p- value of 0.000. Digital Finance Services had also strong correlation coefficient of r=0.709 and p=0.000 with financial performance.

Table 4 Descriptive Statistics for the Study Constructs

** Correlation is significant at the 0.01 level (2-tailed)

Factor Analysis

Factor analysis is used to test validity of the construct and to eliminate some problematic items. Factor analysis assumes that relationships among variables are linear. A common rule of thumb is to have at least 5-10 observations for each variable. A sample size of 100 or more is often recommended. For factor analysis to be recommended suitable for the statistics, the Bartlett’s test of Sphericity must be greater than 0.05 (Nderitu & Githinji., 2015). The study had 281 respondents hence was necessary to carry out factor analysis of 19 items using Varimax rotation before testing the hypotheses. The analysis indicates a Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) of 0.897 with Bartlett’s Test of Sphericity (Chi-Square) of 1043.394, df 45 which has significant at p=0.000.

The results explained that ten items measuring financial performance were factor analyzed. After analyzing financial performance two dimension emerged from the data. Component one named return on investment and assets after five items loaded on it. Item seven was eliminated from the study because of cross loading. This component had Eigenvalues of 4.575 explaining approximately 46% of the variance in financial performance. Component two was named profitability after four items loaded on it. This component had an initial Eigenvalues of 1.0 accounting for 10% of the variance in financial performance. In totality the two components (return on investment and asset and profitability) cumulatively explain approximately 56% of the variance in financial performance. In addition, (Table 5). Hence, confirming the appropriateness of factor analysis of the data set.

Table 5 Rotated Components Analysis for Retained Items

Factor Analysis for Budget Planning

9 items were used to analyze budget planning. The outcome revealed that all 9 items loaded in 1 component, hence emerge only in 1 dimension from the data set. Thus, the component cannot be dropped or rotated. The component in had Eigenvalues of 4.818 explaining approximately 54% of the variance in budget planning. In addition, the analysis indicates a Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) of 0.894 with Bartlett’s Test of Sphericity (Chi-Square) of 1165.575, df 36 which has significant at p=0.000 (Table 6). Hence, confirming the appropriateness of factor analysis of the data set were valid.

Table 6 Eigenvalues and Variance of Cash Flow Management

Factor Analysis for Digital Finance Services

Table 7a shows that 9 measuring items of digital finance services these factors analyzed and emerged 2 dimensions from the data set that had respective initial eigenvalues of 5.109 and 1.138, and they are explained by variances of 57% and 13% (Table 7b). Component 1 named usage and quality after 6 items loaded on it, while component 2 named access after 3 items loaded on it. In addition, the analysis indicates a Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) of 0.862 with Bartlett’s Test of Sphericity (Chi-Square) of 1533.487, df 36 which has significant at p=0.000.

Table 7a Rotated Components Analysis for Retained Items

Table 7b Eigenvalues and Variance of Digital Finance Services

Hypotheses Testing

Hierarchical regression model was used to test hypotheses. The researcher formulated hypotheses having a 5% significant level with 95% confidence interval by use of p-value concept to test hypotheses. P-value having ≤.0.05 of tested hypotheses is false thus reject and p value >is greater than 0.05 we fail to reject. The researcher analyzed for model fit before testing hypotheses which had significant at; p≤.01; p≤.05; p≤.001

Direct effect outcome on hypotheses

The findings on (Table 8) Model 1, indicate control variables that firm size (β =0.070 p=0.149) was not to dependent variable. However, firm age (β=0.282 p=0.022), shows positive and statistically significant effect on the outcome variable. The outcome also revealed that control variables had R²of 0.028 and ∆ R²of 0.028. Thus, the model was significant at p≤0.05, F=4.037. This model explains 2.8% of the variance in the dependent variable. Hence, accounting for these control variables.

Model 2 The outcome further revealed that digital finance services had direct effect, with a coefficient of (β = .137, p = .007), closely met the criteria of p ≤ 0.01. Digital finance services were found to significantly and positively impact the dependent variable, financial performance of SMEs. As a result, the model was significant, leading to the rejection of the null hypothesis.

Model 3 statistical model fit of F=61.030 at p=0.000 indicating that the model is highly significant. Interaction between budget planning and digital finance services is significant with financial performance. Statistical interaction indicates β= -0.049 which was significant at p=0.000, since p value is less than 0.001 Therefore, we reject the null hypothesis.

Table 8 Hierarchical Regression Results

Dependent variable: financial performance; p≤0.5. Note: significant *p≤.05; **p≤.01; ***p≤.001.BP= budget planning, DFS= digital finance services

Table 9 Summary Results of Hypotheses Tests

DISCUSSION

The outcome revealed that control variables (firm size and firm age) had R²of 0.028, thus, explains that control variables had 2.8% of the variance in financial performance. Covariates was controlled in this study in order to make the right inferences. The study outcome further revealed budget planning (H02) (β=.364, p=.000) had a direct and significant effect on financial performance of SMEs. The impact of budget planning on financial performance shows significant positive results in several studies, particularly those that emphasize structured budget processes, employee participation, and alignment with strategic goals (Mulani & Yang, 2013; Keng’ara & Makina, 2020; Abongo, 2017; Kiiru et al., 2018). These studies demonstrate that when budget planning is effectively implemented, it enhances resource allocation, risk management, and decision-making, all of which improve financial performance. Overall, while budget planning holds significant potential for improving financial performance, its success depends on the level of involvement, support, and structure within SMEs.

The study also revealed that digital finance services had positive and significant effect on financial performance of (β=.137, p=.007) particularly through improved access to financing and operational efficiencies. Studies such as those by Luo (2023), Schonberger (2023),Luu et al. (2023), and Christanti (2023) Demirgüç-Kunt and Klapper (2019) affirm a significant positive relationship between digital finance services and SME financial performance, suggesting that proper utilization of these services can enhance productivity and growth.

The researcher also examined effect of moderator (Digital finance services) on relationship between budget planning and financial performance. The results of the study states that digital finance services significantly moderate relationship between budget planning and financial performance (H05b) β= -0.049, p=0.000. The findings shows that SMEs prepares financial plans to follow in order to achieve their objectives.

CONCLUSION OF THE STUDY

The study concludes that budget planning is crucial determinants of financial performance for SMEs. This implies that SMEs leveraging digital finance tools in these areas can achieve better financial outcomes, through improved financial planning, resource allocation, and strategic decision-making. Moreover, the study’s incorporation of control variables such as firm size and firm age provided a more nuanced understanding of the factors affecting financial performance. Overall, the study contributes valuable insights for SMEs seeking to improve their financial performance through effective budget planning and the utilization of digital finance services.

Theoretical Implications of the Study

The study contributes to theories of budget planning by highlighting the importance of specific financial practices budget planning in influencing financial performance. Digital finance services offer the tools SMEs need to make more informed, data-driven decisions regarding budget allocations, helping them avoid expenditures that do not align with their strategic priorities. This study aligns with Priority-Based Budgeting (PBB) Theory by showing that the integration of digital finance services enables SMEs to dynamically reallocate resources to address changing priorities and market conditions, thereby optimizing financial performance.

The study also explains that resource-based view (RBV) theory, which suggests that access to valuable, rare, and non-substitutable resources (such as digital financial tools) can provide firms with a competitive advantage.

Policy Implication of the Study

The findings of the study on the relationship between budget planning and financial performance, with digital finance services as a moderator, have several important policy implications for SMEs and the broader economic environment to minimize mortality rate of SMEs. Governments and financial institutions should promote the adoption of digital finance services among SMEs by providing incentives, subsidies, or grants for technology adoption. Develop policies that enhance access to financial resources for SMEs, particularly in adopting digital finance services. Foster collaboration between government agencies, financial institutions, and private sector organizations to support SMEs in implementing digital finance services.

RECOMMENDATIONS FOR FURTHER STUDIES

The researcher recommends that future studies should consider larger and more diverse samples of SMEs across different regions and sectors. Future researchers should also investigate the relationship in specific industries or sectors to understand how budget planning and the impact of digital finance services vary across different sectors (e.g., agriculture, manufacturing, retail). The current study further focused on digital finance services as moderator on the relationship between budget planning and financial performance. The future scholars can introduce other potential moderating variables such as firm size, age, owner-manager characteristics, or access to finance.

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