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Abstract

Small and medium enterprise (SME) development is essential for India’s economic expansion. SME is an important participant in the development of accessories that supports major industries. The current study is focused on the variables impacting performance measurement in SMEs, despite the fact that many small and medium businesses are experiencing significant losses and closures as a result of poor performance. The next goal is to determine the connection between small and medium business performance management and financial management practices. This study uses SME performance measurement as a dependent variable. Risk management, working capital management and credit management are three independent factors that can be used to assess SME financial management practices. The questionnaire that was used for each item was based on the earlier research. A five-point Likert scale was used to score each item scale used in this investigation. Working people in a few districts in South Tamil Nadu, India and used online structured questionnaires to gather data. The present sample size of the study is 157. The statistical data analysis has been carried out employing jamovi 1.8. For factor loading, reliability, sample size, model fit, regression analysis for hypothesis testing and structural education modelling were used. Policymakers and SME entrepreneurs who are familiar with performance measurement of many elements can benefit from the study’s findings. The study found a relationship between SMEs’ performance measurement and financial management practices.

Keywords

Working capital management credit management risk management performance measurement

Introduction

The small and medium enterprises (SMEs) sector reported the largest share in the mass of industrial units, yield in manufacturing, service sector and exports in the state. Besides being a major employment provider next only to agriculture, the need for stimulating this sector assumes greater importance.

Over the past 50 years, the micro, small and medium-sized enterprises (MSMEs) sector has grown to be a very active and dynamic area of the Indian economy. MSMEs not only play an integral part in creating many job opportunities at capital costs that are substantially less than those of large industries, but they also aid in the industrialisation of areas that are underdeveloped or rural, thereby reducing regional imbalances and ensuring a more equitable distribution of income and wealth throughout the country.

Armstrong (2001) states financial management predicts the overall financial performance of SME and gives alerts on how to manage funds, when it will be required and how much will be needed. Service quality and customer satisfaction positively influence customer loyalty in the financial sector (Venkatesan, 2018). The association between enterprise risk management strategies and competitive advantage is strongly moderated by financial literacy (Yang et al., 2018).

Problem Identification

The current researcher has adopted financial management practices, such as working capital management, risk management, credit management and performance measurement, to improve the growth for the survival and sustainability of SMEs in the context of globalisation. Small businesses find it difficult to compete with big businesses due to limited and timely finance. The lack of economical raw materials appears to be the key barrier. According to numerous studies, fund management is a substantial challenge for SMEs in India and other nations. For creating a model for a study proposal along with a research design that will ascertain the financial management practices of SMEs, the previously mentioned study gaps can be used as a guide.

Need of the Study

MSMEs frequently face major financial challenges due to a lack of financial management comprehension and business unpredictability. The present examination of financial management practices and performance measurement in SMEs is critical to the growth and success of any small or medium-sized organisation.

Research Gap

The majority of the research focuses solely on assessing financial performance. There is a gap in the literature that researchers can fill by assessing performance with financial management methods in small and medium-sized businesses. The current study’s intent is to identify and address concerns concerning SME financial management practices that affect either general or non-financial performance measurement.

Research Question

For this study, two research questions were formulated in light of the research analysis.

What elements affect managers’ and owners’ financial management practices in SMEs?

How does financial performance affect SMEs?

Objectives of Study

The primary objective of the study was to find out the relationship between financial management practices and the performance management of SMEs. The next objective is to analyse the factors influencing financial management practices. The third aim is to evaluate the factors influencing performance measurement in SMEs. The final objective is to make the necessary recommendations to improve the overall performance of SMEs.

Literature Review

Financial Management Practices of SME

Gawali and Gadekar (2017) revealed that the success or failure of MSMEs was strongly determined by the owner-managers’ attitudes, level of involvement in financial aspects of the business, and understanding of accounting and finance. Working capital was the final factor, contributing barely 10 per cent to the corporate sector (Hunjra et al., 2011). SMEs were, to some extent, prevented from utilising the proper performance measures due to a lack of knowledge, skilled staff, top management backing and necessary resources like computers (Maduekwe & Kamala, 2016). According to Venkatesan (2021), a number of conflict factors are cumulative every day, both in the family and at work, which poorly affects the performance of businesses.

Nkundabanyanga et al. (2017) results indicate that competitive advantage fully mediates the relationship between financial management practices and loan performance, indicating that the relationship between financial management practices and loan performance at microfinance institutions (MFIs) is solely a result of their competitive advantage. There is a need for a recommendation for financial risk management system for SMEs due to the dangers that exist in SMEs along with how they differ from those in larger businesses as well as the lack of suitable risk-management proposals in theory (Napp, 2011).

Pham et al. (2012) suggest that highly materialistic people with poor financial management practices are particularly prone to compulsive buying issues, and they encourage the inclusion of financial management components in existing psychosocial therapies. The environmental, social and governance (ESG) scores might not accurately represent a company’s genuine non-financial practices (Siew et al., 2013). Due to their lower default risk, financially sound SMEs may borrow more money from banks at cheaper interest rates, but SMEs in poor financial health would be compelled to pay higher interest rates and have a lower borrowing ceiling (Yoshino & Taghizadeh-Hesary, 2015).

Performance measurement of SME

Performance of SME measures through the satisfaction of customer, employee, organisation and society. Organisations must prioritise providing excellent customer service, measuring the level of client satisfaction, and gaining long-term loyalty (Venkatesan, 2018). As a result, SME managers must use effective cash management techniques as a tactic to boost their financial performance and thrive in unstable business environment (Hamza et al., 2015).

Siew et al. (2013) observed that, despite the weak association between financial performance and ESG scores, construction companies that provide non-financial reports do significantly better than those that do not in a number of chosen financial parameters. Potential perks, including a reduction in the over-management of risks and organisational alignment with the SME’s task, may be realised by implementing a structured approach to enterprise risk management within SMEs (Smit & Watkins, 2012). Ahinful et al. (2023) results demonstrate that the influence of industry and firm-specific factors varies across the financial performance sub-components in the manufacturing and service sectors. The financial performance of SMEs is positively and significantly impacted by management accounting services (Gyamera et al., 2023).

Methods and Procedures

Hypotheses Development

Based on the literature review and conceptual framework, the author put forth the following hypotheses:

H₁: Risk management positively influences financial management practices in SMEs.

H₂: Working capital management positively influences financial management practices in SMEs.

H₃: Credit management positively influences financial management practices in SMEs.

H₄: Financial management practices positively influence performance management in SMEs.

Survey Design

A self-administered printed and online survey was used to test the conceptual model depicted in Figure 1. This study’s item scales were all scored using a five-point Likert scale (Likert, 1932).

Figure 1.

Conceptual Model of Performance Measurements.

Questionnaire and Variables’ Measurement

SME performance measurement is a dependent variable used in this study. The remaining variables are independent. The independent variables are financial management practices. The questionnaire of each item was employed based on the studies previously used. Measure the SME financial management practices using independent variables risk management, working capital management and credit management. The questionnaire of each item was employed based on the studies previously used. Five items measure risk management and three items working capital management (Hunjra et al., 2011; Nkundabanyanga et al., 2017); five items credit management were adopted from Parrotta and Johnson (1998) to measure the SME financial management practices. To measure the performance of SMEs, 11 items were adopted from Maduekwe and Kamala (2016).

Procedures

Data were collected by the working people through structured questionnaires online at selected districts in South Tamil Nadu, India.

Participants

All participants were requested to fill out an anonymous online questionnaire that measured financial management practices and performance measurement of SMEs. The present research covers five districts. These districts have many SMEs like organic farming, leather industry, food processing, etc.

Sample Size

Respondents were given surveys via paper copies (200) and online via Google Forms (175). The total response received was 225 (including 130 online and 95 offline). Due to ambiguity, more missing responses, double entries, and other reasons, etc., 68 questionnaires were not able to be used. The present sample size is 157. The total response is 42 per cent.

Data Analysis

Statistical data analysis was conducted using jamovi version 1.8 (Project, 2021). Exploratory factor and confirmatory factor analysis were done for factor loading, reliability, sampling adequacy and model fit.

Ethical Consideration

While answering the offline and online questionnaire served as confirmation of participants’ informed consent to participate in the study, participants were free to withdraw their consent at any point if they felt the online and offline filling processes were not satisfactory. The respondents’ self-interest led to their completely voluntary participation in the study. Respondents were fully reassured by researchers that the data they had collected would only be used for the current study and would not be shared with anyone else.

Convergent Validity

Convergent validity is the correlation of items within the construct. Convergent validity is sometimes claimed if the correlation coefficient is above 0.50, although it is usually recommended at above 0.70. The present research correlation values are above 0.70 and are significant at the 0.01 level (two-tailed).

Reliability

The assessment of survey instruments reliability-internal consistency was performed using values of Cronbach’s alpha (Cronbach, 1951). It is the most important reliability index and it is based on the number of variables/items of the questionnaire. In the present research, all variables of Cronbach’s alpha reliability values are more than 0.9 and accepted level.

Around 60 per cent respondents are male and remaining are others, Most of the respondents locations are Tiruvannamalai (43.9%), the detailed demographic analysis are presented in the Table 1.

Table 1.

Demographic Profile (N = 157).

Frequency		%	Frequency		%
Category of the respondent			Gender
Proprietor	60	38.2	Male	94	59.9
Manager	51	32.5	Female	39	24.8
Supervisor	23	14.6	Prefer not to say	24	15.3
Accountant	23	14.6	Age of the respondent
Educational qualification			Below 30	18	11.5
School education	45	28.7	31–40	51	32.5
Undergraduate	66	42.0	41–50	44	28.0
Postgraduate	23	14.6	Above 50	44	28.0
Research degree	23	14.6	Location of respondent (years)
Working area specialisation			Tiruvannamalai	69	43.9
General management	45	28.7	Vellore	23	14.6
Operational	24	15.3	Ranipet	23	14.6
Marketing and sales	23	14.6	Tirupattur	21	13.4
Human resource	44	28.0	Vaniyambadi	21	13.4
Financial management	21	13.4

Source: Primary data.

The behavioural characteristics of the respondents included types of business, duration of business operating, and kind of business. The detailed responses of behavioural characteristics are depicting in Table 2.

Table 2.

Behavioural Characteristics of the Respondents.

Behavioural Characteristics (N = 157)
Frequency		%	Frequency		%
Type of business			Kind of business
Manufacturing	66	42.0	Small enterprises	89	56.7
Trading	45	28.7	Medium enterprises	68	43.3
Services	23	14.6	Small and medium enterprise (SME) sectors
Others	23	14.6	Health care	18	11.5
How long business operating			Textile	27	17.2
Less than a year	66	42.0	Information technology	24	15.3
1–3 years	45	28.7	Foods and beverage	44	28.0
4–10 years	23	14.6	Hotel	23	14.6
More than 10 years	23	14.6	Other sectors	21	13.4

Source: Primary data.

Results and Discussion

Demographic Profile and Behavioural Characteristics

Most of the respondents are male (around 60 per cent), 60 per cent respondents are proprietor and educational qualification is undergraduate (42 per cent). Geographical areas of respondents are Tiruvannamalai (44 per cent), followed by Vellore and Ranipet (15 per cent).

Most of the respondents are manufacturing industries (42 per cent) followed by trading (29 per cent) and foods and beverage (28 per cent) business.

Descriptive Statistics

Table 3 shows that all variable mean values are more than 0.5. Correlation values of all variables are more than 0.5 and positively correlated.

Table 3.

Mean Standard Deviation and Zero Order Value Correlations for the Variables.

Variables	Mean	Std. Deviation	1	2	3
RM	3.879	0.701
WM	3.679	1.164	0.806
CM	3.566	0.838	0.747	0.799
PM	3.527	0.792	0.866	0.863	0.729

Source: Primary data.

Note: RM - risk management; WM - working capital management; CM - credit management; PM - performance measurement.

Reliability Test

Table 4 shows that all variables’ reliability values are more than 0.9 and accepted.

Table 4.

Reliability Results of Variables.

Variables	Cronbach’s Alpha
RM	0.9450
WM	0.9120
CM	0.9240
PM	0.9650

Source: Primary data.

Note: RM - risk management; WM - working capital management; CM - credit management; PM - performance measurement.

Sampling Adequacy Method

Table 5 shows that Kaiser–Meyer–Olkin (KMO) value is more than 0.5 and research sampling size is adequate.

Table 5.

Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test.

KMO measure of sampling adequacy		0.890
Bartlett’s test of sphericity	Approx. chi-square	2,451.635
	df	276
	Sig.	0.000

Source: Primary data.

The KMO measure of sampling adequacy (MSA) is used to assess the sampling adequacy. The exploratory factor analysis (EFA) is effective if the KMO index is high (1), while the EFA is irrelevant if the KMO index is low (0). Amounts between 0.5 and 0.7 are deemed adequate, 0.7–0.9 are deemed good, and 0.9–1.0 are deemed exceptional. The KMO score for the current study indicates that all values are wonderful and greater than 0.8 (Kaiser, 1974). Table 6 shows that the KMO sampling adequacy of each item indicates more than the accepted threshold value.

Table 6.

Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy (MSA).

	MSA		MSA
Overall	0.890	CM5	0.816
RM1	0.918	PM1	0.88
RM2	0.875	PM2	0.689
RM3	0.902	PM3	0.954
RM4	0.870	PM4	0.865
RM5	0.874	PM5	0.919
WM1	0.936	PM6	0.899
WM2	0.908	PM7	0.932
WM3	0.923	PM8	0.845
CM1	0.939	PM9	0.834
CM2	0.872	PM10	0.902
CM3	0.802	PM11	0.896
CM4	0.882

Source: Primary data.

Note: RM - risk management; WM - working capital management; CM - credit management; PM - performance measurement.

Exploratory Factor Analysis

Table 7 shows the items and their standardised loads. According to Hair et al. (2010), loadings must have values greater than 0.50. In Table 7, it is seen that most of the loads of the indicators exceeded this value, thereby ensuring reliability and validity of the measurement instruments. Uniqueness is the percentage of variance that is unique to the variable and unanswered by factors. Greater uniqueness indicates lower contribution or relevance of the variable in the factor model. Present research uniqueness values are less than 0.4.

Table 7.

Factor Loadings and Uniqueness.

	Factor				Uniqueness
	1	2	3	4	Uniqueness
RM1	0.565				0.38973
RM2	0.557				0.01034
RM3	0.760				0.35749
RM4	0.667				0.14041
RM5	0.650				0.13823
WM1		0.735			0.29545
WM2		0.718			0.11444
WM3		0.605			0.28632
CM1			0.561		0.40002
CM2			0.698		0.33831
CM3			0.804		0.38414
CM4			0.582		0.25011
CM5			0.568		0.13622
PM1				0.777	0.03328
PM2				0.831	0.25906
PM3				0.895	0.39343
PM4				0.936	0.28795
PM5				0.619	0.38252
PM6				0.615	0.08895
PM7				0.624	0.21707
PM8				0.855	0.00498
PM9				0.950	0.17352
PM10				0.881	0.33536
PM11				0.553	0.21195

Source: Primary data.

Notes: ‘Maximum likelihood’ extraction method was used in combination with a ‘varimax’ rotation. RM - risk management; WM - working capital management; CM - credit management; PM - performance measurement.

Confirmatory Factor Analysis

In Table 8, the Z test results (i.e., they are not zero) and p value of significance for each of these constraints indicate building a logical contribution to the model, so it is not necessary to remove any variable-factor paths or correlations from the model.

Table 8.

Variables and Items Estimates, Standard Error (SE) and Z Score.

Factor	Indicator	Estimate	SE	Z	p
Risk management	RM1	0.563	0.0764	7.37	<.001
	RM2	0.626	0.0960	6.52	<.001
	RM3	0.613	0.0629	9.75	<.001
	RM4	0.331	0.0801	4.13	<.001
	RM5	0.752	0.0753	10.00	<.001
Working capital management	WM1	1.013	0.0822	12.31	<.001
	WM2	1.681	0.1067	15.75	<.001
	WM3	0.543	0.0703	7.72	<.001
Credit management	CM1	1.041	0.0984	10.58	<.001
	CM2	0.881	0.0989	8.91	<.001
	CM3	0.432	0.0781	5.54	<.001
	CM4	0.528	0.0957	5.52	<.001
	CM5	0.665	0.0790	8.42	<.001
Performance measurement	PM1	0.309	0.0772	4.00	<.001
	PM2	0.431	0.0899	4.79	<.001
	PM3	0.820	0.0742	11.06	<.001
	PM4	0.504	0.0926	5.44	<.001
	PM5	0.893	0.0846	10.55	<.001
	PM6	1.536	0.0990	15.51	<.001
	PM7	0.806	0.0960	8.39	<.001
	PM8	1.137	0.0827	13.76	<.001
	PM9	0.246	0.0866	2.84	.004
	PM10	0.538	0.0976	5.51	<.001
	PM11	0.894	0.0970	9.22	<.001

Source: Primary data.

The factor that estimates the covariance of risk management, working capital management and credit management with performance measurement is significant because p values of all items are less than .001.

The factor covariances of each variable with others are displayed in Table 9, along with the estimates, standard errors, Z c the p-values are less than 0.001 and are significant.

Table 9.

Factor Covariances.

		Estimate	SE	Z	p
Risk management	Risk management	1.000^a
	Working capital management	1.070	0.0264	40.5	<.001
	Credit management	0.818	0.0680	12.0	<.001
	Performance measurement	0.995	0.0361	27.5	<.001
Working capital management	Working capital management	1.000^a
	Credit management	0.804	0.0471	17.1	<.001
	Performance measurement	0.940	0.0195	48.1	<.001
Credit management	Credit management	1.000^a
Credit management	Performance measurement	0.962	0.0285	33.8	<.001
Performance measurement	Performance measurement	1.000^a

Source: Primary data.

Note: ᵃFixed parameter.

Structural Equation Model

Table 10 shows the structural equation model (SEM) information; estimation method is maximum likelihood, number of observation, error standard, number of free parameters (76), number of iterations (61) and list of variables with item details.

Table 10.

Structural Equation Model (SEM) Information.

Estimation method	ML
Optimisation method	NLMINB
Number of observations	157
Free parameters	76
Standard errors	Standard
Scaled test	None
Converged	TRUE
Iterations	61
Model	CM=~CM1+CM2+CM3+CM4+CM5
	WCM=~WM1+WM2+WM3
	RM=~RM1+RM2+RM3+RM4+RM5
	PM=~PM1+PM2+PM3+PM4+PM5+PM6+PM7+PM8+PM9+PM10+PM11
	FMP=~CM+WCM+RM
	PM~FMP

Source: Primary data.

Overall Tests

Table 11 shows the chi-square value, degree of freedom and significance of p value of user and baseline model. To test model fit, use the χ² statistic, which, if small, shows that the model has a good match to the data. The model fit of χ² statistic assessment is based on sample size, with a large number of observations supporting a good model fit and a significant (p < .05) χ² value of 877.

Table 11.

Model Test.

Label	χ²	df	p
User model	877	248	<.001
Baseline model	2615	276	<.001

Source: Primary data.

Fit Indices

Table 12 shows the user model versus baseline model fit index values of the present study and recommended values. Bollen’s incremental fit index (IFI) value is closer to the accepted value, and remaining all index values are above the recommended values. Model fit is evaluated using the comparative fit index (CFI), Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the 90 per cent confidence interval (CI) for the RMSEA. CFI > 0.9, TLI > 0.9 and RMSEA of roughly 0.05–0.08 imply an acceptable match. CFI > 0.95, TLI > 0.95 and RMSEA and upper CI for RMSEA 0.05 indicate an excellent match. Table 10 displays the results of the current model fit; the CFI is 0.957, the TLI is 0.918, and the RMSEA is 0.013 with a 90 per cent CI and an upper CI of 0.046 for the RMSEA. Good fit is indicated by the model. The current study’s standardised root mean square residual (SRMR) result is 0.0490, which is within the acceptable range for the SRMR index of 0–0.08 (Hu & Bentler, 1998).

Table 12.

User Model Versus Baseline Model.

Fit Indices	Model (Value in This Study)	Recommended Value
Comparative fit index (CFI)	0.931	≥0.90 (Hu & Bentler, 1998)
Tucker–Lewis index (TLI)	0.901	≥0.90 (Tucker & Lewis, 1973)
Bentler–Bonett non-normed fit index (NNFI)	0.901	≥0.95 (Bollen, 1989)
Bentler–Bonett normed fit index (NFI)	0.965	≥0.90 (Hu & Bentler, 1998)
Parsimony normed fit index (PNFI)	0.597	≥0.50
Bollen’s relative fit index (RFI)	0.927	≥0.90
Bollen’s incremental fit index (IFI)	0.834	≥0.90
Relative non-centrality index (RNI)	0.981	≥0.95 (McDonald  & Marsh, 1990)
Standardised root mean square residual (SRMR)	0.049	0–0.08
Root mean square error of approximation (RMSEA)	0.013	0.05–0.08

Source: Primary data.

Hoelter (1983) noted that if the outcome exceeded the critical sample size (CN), the derived χ² would become significant at the predetermined threshold a, rejecting the model’s fit. For the evaluation of SEM sample size, Hoelter (1983) established the critical N (CN) statistic, where CN 200 was regarded as sufficient. Given that the current sample produces a CN of 52.153, it is improper to use the minimum fit function χ² to determine the model’s fitness. Therefore, while calculating the approximation error, it is necessary to compensate for both the sample size and the number of parameters. Goodness of fit index (GFI) values greater than 0.95 scaled between 0 and 1, with higher values indicating better model fit. In the present study, GFI indicates 0.949 and is accepted.

Table 13 shows the values of additional fit indices, including the log likelihood and unrestricted log likelihood. The GFI value is greater than the accepted threshold of 0.9, indicating a better model fit.

Table 13.

Additional Fit Indices.

	Model
Log likelihood	–5,081.134
Unrestricted log likelihood	–4,642.643
Hoelter critical N (CN), a = 0.05	52.153
Hoelter critical N (CN), a = 0.01	55.196
Goodness of fit index (GFI)	0.949

Source: Primary data.

R-square

The R² values of 0.25, 0.50 and 0.75 represent the explanatory power of the latent variables as low, medium and significant, respectively. Table 14 indicates each item’s R-square values. CM5 and RM3 indicate low, CM1, CM2, CM5, WM1, RM2, RM5 and PM10 R-square values are medium, and the remaining items are statistically significant.

Table 14.

R-square Value.

Variable	R²	Variable	R²
CM1	0.5639	RM5	0.7111
CM2	0.7364	PM1	0.8962
CM3	0.8056	PM2	0.8483
CM4	0.8107	PM3	0.8708
CM5	0.3981	PM4	0.9846
WM1	0.6471	PM5	0.9342
WM2	0.9195	PM6	0.8762
WM3	0.9185	PM7	0.9893
RM1	0.7997	PM8	0.8637
RM2	0.7396	PM9	0.8562
RM3	0.3465	PM10	0.6965
RM4	0.8089	PM11	0.9427

Source: Primary data.

Note: CM - credit management; WM - working capital management; RM - risk management; PM - performance measurement.

Table 15 shows the latent variables like CM, WCM, RM, FMP and PM, with observed items of each latent variable to corresponding estimates, standard error, beta, Z score and p value. The standard error calculates how closely each particular sample’s mean corresponds to the population’s actual mean. A positive coefficient means that the dependent variable’s (performance measurement) mean tends to rise when the independent variable’s (credit, risk and working capital management) value rises. A negative coefficient indicates that the dependent variable tends to drop as the independent variable rises. The closer Z test result is to 0, the closer value is to the mean. The further away Z test result is from zero, and the further away value is from mean.

Table 15.

Latent and Observed Variables Estimate, Standard Error (SE), Beta, z and p Values in Measurement Model.

95% Confidence Intervals
Latent	Observed	Estimate	SE	Lower	Upper	β	z	p
CM	CM1	1.000	0.0000	1.000	1.000	0.751
	CM2	0.843	0.1044	0.639	1.048	0.661	8.08	<.001
	CM3	0.424	0.0779	0.272	0.577	0.453	5.45	<.001
	CM4	0.518	0.0940	0.334	0.703	0.459	5.52	<.001
	CM5	0.643	0.0835	0.479	0.807	0.631	7.7	<.001
WCM	WM1	1.000	0.0000	1.000	1.000	0.804
	WM2	1.702	0.1160	1.475	1.930	0.959	14.67	<.001
	WM3	0.534	0.0717	0.393	0.674	0.564	7.44	<.001
RM	RM1	1.000	0.0000	1.000	1.000	0.632
	RM2	0.899	0.1484	0.608	1.189	0.490	6.06	<.001
	RM3	0.799	0.1125	0.579	1.020	0.589	7.11	<.001
	RM4	0.707	0.1241	0.464	0.950	0.457	5.70	<.001
	RM5	1.046	0.1371	0.777	1.315	0.641	7.63	<.001
PM	PM1	1.000	0.0000	1.000	1.000	0.310
	PM2	1.457	0.4607	0.554	2.360	0.385	3.16	.002
	PM3	2.715	0.7030	1.337	4.093	0.756	3.86	<.001
	PM4	1.690	0.5107	0.690	2.691	0.430	3.31	<.001
	PM5	2.954	0.7694	1.446	4.462	0.731	3.84	<.001
	PM6	5.065	1.2716	2.572	7.557	0.936	3.98	<.001
	PM7	2.701	0.7269	1.276	4.126	0.624	3.72	< .001
	PM8	3.738	0.9467	1.883	5.594	0.874	3.95	< .001
	PM9	0.842	0.3501	0.156	1.528	0.237	2.40	.016
	PM10	1.845	0.5506	0.766	2.924	0.443	3.35	<.001
	PM11	2.980	0.7906	1.430	4.529	0.665	3.77	<.001
FMP	CM	1.000	0.0000	1.000	1.000	0.927
	WCM	0.977	0.1062	0.768	1.185	0.939	9.19	<.001
	RM	0.718	0.0880	0.546	0.891	1.060	8.16	<.001

Source: Primary data.

Note: CM - credit management; WCM - working capital management; RM risk management; PM - performance measurement; FMP - financial management practices.

Path Diagram

Table t-Statistics of Path Coefficients

The critical values of t for a two-tailed test, according to Henseler et al. (2009), are 1.65 (significance level = 0.10 or 10 per cent), 1.96 (significance level = 0.05 or 5 per cent) and 2.58 (significance level = 0.01 or 1 per cent). The acknowledged values and model are significant, as seen in Table 16 values of t for the structural model.

Table 16.

Model Coefficients.

Hypothesis	Predictor	Estimate	SE	t	p	Results
H ₁	TOT_RM ↓ TOT_FMP	0.320	0.049	6.54	<.001	Accepted
H ₂	TOT_WM ↓ TOT_FMP	0.384	0.026	14.69	<.001	Accepted
H ₃	TOT_CM ↓ TOT_FMP	0.353	0.031	11.50	<.001	Accepted
H ₄	TOT_FMP ↓ TOT_PM	0.792	0.041	19.13	<.001	Accepted

Source: Primary data.

Managerial Implications

This study provides a number of useful recommendations for senior management, accounts officers and small company organisation owners on how to pay special attention to financial management procedures and performance evaluation in order to get a competitive advantage and make a profitable business. The findings indicate that the variables of risk management, credit management, working capital management and performance measurement are crucial for capturing the essential traits of small to medium-sized businesses. Risk and credit management should be maintained in a proper manner; otherwise, SME performance will be badly affected. Financial management practices are positively significant for performance measurement in SME. Financial and non-financial performances are essential to measuring the performance of SMEs.

Figure 2.

Path Diagrams of Financial Management Practices and Performance Measurement.

Concluding Remarks

Small and medium-sized businesses are essential for the emerging economy of India. SME are in favour of supplying major industries with accessories. The variables in the current study (risk, credit and working capital management) have significance in determining the efficiency of financial management strategies. The author suggests using cutting-edge technology (such as artificial intelligence, big data analysis and blockchain technology) to boost SME performance. Collaborations and research across borders would help to broaden the concept of SMEs and the digital transformation process beyond nations and industries (Melo et al., 2023).

Appendix

Questionnaire

Financial management practices (Hunjra et al., 2011; Nkundabanyanga et al., 2017)

Five-point Likert scale 1—highly disagree to 5—highly agree

1. Risk management

We’re always in touch with our customers about their businesses

We always assess our customers before giving them a credit

We constantly update our customer’s record

Proper loan assessment is done before approval

We usually acquire enough knowledge before assessing loan applicants

2. Working capital management

We usually have alternative cash sources if we forecast a deficit

There is enough liquidity to meet our obligation to disburse loans to our borrowers

The credit manager establishes all credit limits

Financial management practices (Parrotta & Johnson, 1998)

Five-point Likert scale (‘1’ = not typical, ‘5’ = very typical)

3. Credit management

We often spend more money than I have

We obtain cash advances in order to pay other credit balances

We rarely pay finance charges

We pay bills as due

We make payments on large debts as scheduled

Performance measurement (Maduekwe & Kamala, 2016)

Scale: 1 = never; 2 = rarely; 3 = sometimes; 4 = always; 5 = very frequently

For monitoring the business

For measuring performance

For future planning

For control purposes

For improving decision-making

For business process improvement

For problem identification

For optimising the use of resources

For developing tactical strategies

For improving communication

For motivating employees

Potential Limitation and Future Scope of the Study

Like many other studies, this one has some limitations on data collecting, sampling and generalisation of the results. The study’s sample size might not be sufficient to simplify the findings. The results of a comparable study could be different since other major cities might have different demographic characteristics. Selected districts from Tamil Nadu state served as the respondents in this study. The investigation should be expanded to include rural areas as well. Future research, however, might be able to get around these restrictions by using data from other cities or nations, representative samples and further variables.

Footnotes

Acknowledgement

The author has no support to report.

Author Contributions

The author contributed to the frame conceptual model, credit management, risk management, working capital management to financial management practices, financial management practices to performance measurement; methodology, software, analysis and interpretation, implication and recommendation of future studies.

Availability of Data and Materials

All data analysed in this study are included in the published article

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

ORCID iD

Ranganathan Venkatesan

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Impact of Financial Management Practices on Performance Measurement of Small and Medium Enterprises in Tamil Nadu

Abstract

Keywords

Introduction

Problem Identification

Need of the Study

Research Gap

Research Question

Objectives of Study

Literature Review

Financial Management Practices of SME

Performance measurement of SME

Methods and Procedures

Hypotheses Development

Survey Design

Conceptual Model of Performance Measurements.

Questionnaire and Variables’ Measurement

Procedures

Participants

Sample Size

Data Analysis

Ethical Consideration

Convergent Validity

Reliability

Demographic Profile (N = 157).

Behavioural Characteristics of the Respondents.

Results and Discussion

Demographic Profile and Behavioural Characteristics

Descriptive Statistics

Mean Standard Deviation and Zero Order Value Correlations for the Variables.

Reliability Test

Reliability Results of Variables.

Sampling Adequacy Method

Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test.

Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy (MSA).

Exploratory Factor Analysis

Factor Loadings and Uniqueness.

Confirmatory Factor Analysis

Variables and Items Estimates, Standard Error (SE) and Z Score.

Factor Covariances.

Structural Equation Model

Structural Equation Model (SEM) Information.

Overall Tests

Model Test.

Fit Indices

User Model Versus Baseline Model.

Additional Fit Indices.

R-square

R-square Value.

Latent and Observed Variables Estimate, Standard Error (SE), Beta, z and p Values in Measurement Model.

Path Diagram

Table t-Statistics of Path Coefficients

Model Coefficients.

Managerial Implications

Path Diagrams of Financial Management Practices and Performance Measurement.

Concluding Remarks

Appendix

Potential Limitation and Future Scope of the Study

Footnotes

Acknowledgement

Author Contributions

Availability of Data and Materials

Declaration of Conflicting Interests

Funding

Informed Consent

ORCID iD

References