Relationship between intellectual capital and firm performance: evidence from the Indian sugar mill industry

Abstract

Purpose

The aim of the study is to examine the influence of Intellectual Capital (IC) and its components on the financial performance of Indian sugar mill companies.

Design/methodology/approach

The present study follows the quantitative research, and uses data from Indian sugar mill companies over the period of recent 10 years. The Modified Value- Added Intellectual Capital (MVAIC) method is employed to evaluate IC. Authors construct panel regression models to test the hypotheses where Return on Equity (RoE) and Return on Asset (RoA) were considered as a representation of financial performance (dependent variable) and IC has been considered as the independent variable along with control variables.

Findings

The findings reveal that IC components show greater explanatory power than aggregate IC and MVAIC has a positive relationship with firm performance. It is evident that Capital Employed Efficiency (CEE) and Relational Capital Efficiency (RCE) have a positive effect on the RoA, while Human Capital Efficiency (HCE) and CEE have a positive impact on RoE. CEE is found to be a highly significant component to explain the financial performance of Indian sugar mill firms.

Practical implications

The study has practical implications for the policymakers for effective utilization of IC resources for worth enhancement which is essential for the improvement of financial performance.

Originality/value

The research extends the literature of IC by linking it to the financial performance of Indian sugar mill industry.

Citation

Sharma, D., Verma, R., Patil, C. and Nayak, J.K. (2024), "Relationship between intellectual capital and firm performance: evidence from the Indian sugar mill industry", IIMT Journal of Management, Vol. 1 No. 1, pp. 98-111. https://doi.org/10.1108/IIMTJM-11-2023-0054

1. Introduction

The value of disclosure practices, personnel management and efficient use of intelligence for decision-making is debatable for the researchers during the last decade. As a result, the notion of Intellectual Capital (IC) has become more significant (Ozevren, 2010). Information, knowledge, experience and intellectual property are the examples of IC, which can generate wealth (Saime, 1999). Skandia defines IC as “ownership of knowledge, practical involvement, administrative technology, consumer relationships and specialized skills” that condensed to the idea that IC equals the summation of structural as well as human capital. The term “Intellectual Capital” also denotes important information-based assets, which is partially represented in the annual report but accurately captures the company’s true worth.

As corporate complexity has increased during the last decade, a paradigm change in intellectual requirements has occurred, with human intellect serving as the primary engine of economic growth (Bontis, 1998). As a result, businesses have a different impact on their intangible assets, such as customer relationships and knowledge assets, in addition to their material assets (Cheikh and Noubbigh, 2019). Intangible capital comprises initiation of new venture via investment in R&D capital and human capital (Yano and Shiraishi, 2019). IC recently became a crucial resource for the businesses to expand and endure during a changing environment. IC has been defined as the efficient application of knowledge inside an organization; it comprises both organizational and human capital (Youndt et al., 2004).

The sugar mill industry is an agro-based industry, contributing a vital role in the development of Indian economy. The industry continuously provides a platform for employment and income generation, resource mobilization and social infrastructure development (Pandey, 2007). India, a foremost sugar producer in the South Asian region, recently saw plentiful production of sugar. It provides an opportunity for strong sugar industry supported by an advanced R&D infrastructure. The sugar industry is integrally extensive for crop occupying area consisting around 5 million hectares, which accounted for 2.5% of cropped area (gross); backup with the farmer strength of 7 million and 550 sugar mills entrepreneurs throughout the country. Sugar is a vital element, featuring a mass demand of 27 million tons per annum within the country (Solomon and Swapna, 2022). India is one of the major consumers (15% share in global sugar consumption) and second biggest producer of sugar around the world (20% share in global sugar production). These Indian sugar-related trends touch global market abundantly. The leading position of India makes it the most appropriate nation to lead the International Sugar Organization (ISO is the highest global body on sugar consisting about 90 member nations) (Press Information Bureau, 2023).

The researchers specially in the field of management, have conducted the researches on Intellectual Capital in various sectors of India such as banking. pharma, manufacturing, haelthcare, higher education, business process outsourcing, electrician, mining asset financing and services (Gaur and Gupta, 2023; Gupta et al., 2023; Rani and Sharma, 2023; Dharni and Jameel, 2021; Tiwari, 2022; Chatterji and Kiran, 2021; Raushan and Khan, 2017; Narwal and Yadav, 2017), but Indian sugar mill sector is an unexplored area in the existing literature. The contributions of this paper may be summarized as it is the first analytical research that evaluates the impact of IC on business performance of sugar mill companies in India. The present research attempts to focus on the research question, viz. “Does IC affect the financial performance of sugar mill industry?”

1.1 Theoretical motivation of the study

The present study is supported by agency and signaling theories. The agency model describes linkage between principal (management) and the agent. The management, generally accountable for finishing the key assignment, is related to the stakeholder (Keter et al., 2024; Fama and Jensen, 1983). Agency theory contends that the value of the firm can be enhanced by suitable incentives or monitoring to confine them from consuming their personal choices to enhance their own monetary rewards. Consequently, agency problem is the outcome of information irregularity and the management pursues to condense asymmetry of information to lower the agency costs (Donnelly and Mulcahy, 2008). Firms are motivated to reveal IC to assure shareholders that they are reacting rationally on behalf of them, and it further reduces the agency cost (Keter et al., 2024; Vitolla et al., 2020; Jensen and Meckling, 1976). The signaling theory was developed by Michael Spence in 1978 with a motive to establish a signal framework in the working culture relation between an employer and employees. Signaling theory explicates the significance of disclosure of information to deliver progressive signals. The value of the firms is replicated in the form of market prices, known as financial outcome of firms’ activities. Signaling theory postulates that stockholders trust on the information provided by firms (Keter et al., 2024; Vanini and Rieg, 2019; Abhayawansa and Abeysekera, 2009). The application of signaling theory related to the remedial measure for information asymmetry where externals do not permit for the access of inside core information of the company that is known to the internal managers. These theories provide the basic foundation and motivation to conduct research to gauge the association between IC and business performance.

The organization of the paper is as follows: The next part deals with the literature review followed by research methods, analysis and conclusion of the paper in the last section.

2. Review of literature

The review of existing literature presented in Table 1 shows that IC and its components are the main factors to estimate the firm performance in various industries of diverse countries. The similar results were witnessed in the research studies conducted in the Indian context. Although a rich literature is available on the consequence of IC on financial performance, there is dearth of research pertaining to India and mainly to the Indian sugar mill industry. The present study makes an endeavor to fill the cavity in literature while focusing on the same and using the extended model of IC, i.e. MVAIC. In the light of research gaps, the present research aims to inspect the influence of IC and its constituents on the business performance of Indian sugar mill firms. The hypothesis of the research is as follows:

H0.

There is no substantial impact of IC on the financial performance of Indian sugar mill companies.

Ha.

There is a substantial impact of IC on the financial performance of Indian sugar mill companies.

3. Research methodology

3.1 Sample profile

A list of sugar mill companies in India is obtained from the Ministry of Consumer Affairs, Food and Public Distribution. There were 521 sugar mill companies working in India at the end of year 2022–23. In the first step, we identified and consider 33 sugar mill companies which were continuously in operation during the entire study period of 10 years. During the second step, 14 out of 33 companies were eliminated due to unavailability of data. Finally, 19 sugar mill companies are considered as a sample of the study, and details of these companies are given in Table 2.

3.2 Sources of data and study period

The secondary data used in the study specifically related to computation of human, structural and relational capital which have been gathered from financial reports of the respective sugar mill firm. The database, namely Prowess of CMIE, has also been used to collect the other financial data. The study period of the research is the most recent 10 years related to implementation of Companies Act, 2013, ranging from financial year 2012–13 to 2021–22.

3.3 Variable descriptions

The study used RoA and RoE as the proxy of financial performance of Indian sugar mill firms and IC and its components as predictors. MVAIC method is adopted to gauge the IC. Efficiency of IC is the total of structural, human and relational capital efficiency, while MVAIC is the total of ICE and CEE. The descriptions of the all variables used are given in Table 3.

The difference between revenue and expenses is termed as Value Added (VA) where expense includes employee expenditures. Employees’ salaries and wages are Human Capital (HC); VA minus HC is structural capital; selling and advertising expenses and marketing expenses are Relational Capital (RC); and total assets minus total liabilities is known as Capital Employed (CE).

In order to analyze the data effectively, the study employed panel regression analysis to inspect the influence of independent variables on the dependent variable. The models used in the study are as follows:

(1)$ROA={\beta }_0+{\beta }_1\hspace{0.17em}{MVAIC}_{it}+{\beta }_2\hspace{0.17em}{Lev}_{it}+{\beta }_3\hspace{0.17em}Ln\hspace{0.17em}{Size}_{it}+{\beta }_4\hspace{0.17em}Ln\hspace{0.17em}{Age}_{it}+{\epsilon }_{it}$

(2)$ROA={\beta }_0+{\beta }_{1}HCE+{\beta }_2{SCE}_{it}+{\beta }_3{RCE}_{it}+{\beta }_4{CCE}_{it}+{\beta }_5{Lev}_{it}+{\beta }_{6}Ln\hspace{0.17em}{Size}_{it}+{\beta }_{7}Ln\hspace{0.17em}{Age}_{it}+{\epsilon }_{it}$

(3)$ROE={\beta }_0+{\beta }_1\hspace{0.17em}{MVAIC}_{it}+{\beta }_2\hspace{0.17em}{Lev}_{it}+{\beta }_3\hspace{0.17em}Ln\hspace{0.17em}{Size}_{it}+{\beta }_4\hspace{0.17em}Ln\hspace{0.17em}{Age}_{it}+{\epsilon }_{it}$

(4)$ROE={\beta }_0+{\beta }_{1}HCE+{\beta }_2{SCE}_{it}+{\beta }_3{RCE}_{it}+{\beta }_4{CCE}_{it}+{\beta }_5{Lev}_{it}+{\beta }_{6}Ln\hspace{0.17em}{Size}_{it}+{\beta }_{7}Ln\hspace{0.17em}{Age}_{it}+{\epsilon }_{it}$

where

• ROA = Return on Assets

• ROE = Return on Equity

• MVAIC = Modified Value Added Intellectual Capital

• HCE = Human Capital Efficiency

• SCE = Structural Capital Efficiency

• RCE = Relational Capital Efficiency

• CEE = Capital Employed Efficiency

• Lev = Leverage

• Ln Size = Log of firm size

• Ln Age = Log of firm age

4. Results and discussions

This section deals with analysis and interpretation, consisting of results of descriptive statistics, correlation matrix and panel regression.

Table 4 shows descriptive information of all variables. It can be observed from the table that mean of RoA is 5.3%, which shows that Indian sugar mill companies were generating the profit during the study period. The MVAIC indicates value creation efficiency. The negative mean value of MVAIC shows that Indian sugar mill firms failed to create the value during the study period, and it implies that investment cost in IC was more than earnings. Among its components, SCE is the only positive contributing component with a mean of 1.187, while the mean value of HCE was −3.312, RCE −0.112 and CEE −0.080. It shows that Indian sugar mill firms were able to create value from their structure capital only and struggling to add value from their human, relational and financial capital. As the MVAIC mean was negative, HCE had the highest negative mean as compared to the other components; the same is constant with the previous studies (Xu and Li, 2019, 2022; Xu and Liu, 2020; Xu and Wang, 2018, 2019; Phusavat et al., 2011).

Table 5 shows the Karl Pearson coefficient of correlation for all variables. The results of correlation analysis also help in detection of the occurrence of multicollinearity among independent variables. The correlation coefficient exceeds 0.8, which shows the issue of multicollinearity, and it must be taken into consideration as a serious concern (Kennedy, 1985; Scafarto et al., 2016). The correlation coefficients range between −0.702 and 0.729, which depicts that there was no multicollinearity among explanatory variables.

Table 6 depicts the regression Models (1-4) results. The values of R² in Model 1 and 3 are greater than those in Models 2 and 4, which confirms that IC components demonstrate higher prediction power than summative IC (Xu and Li, 2022). The results of Model 2 and Model 4 show that MVAIC is positively associated to profitability of firms. The findings reveal that RoE is expected to increase by 0.011 units if firm generates MVAIC for a single unit. Results are consistent with Xu and Li (2022), Xu and Li (2019), Xu and Wang (2019) and Smriti and Das (2018). Sardo and Serrasqueiro (2018) indicated that IC may enhance financial performance of the firm and create significant wealth in emerging nations. The results support signaling theory which says if a company has higher performance and profitability, it might be an indication to evaluate the efficacy of IC, which further enhances earnings of firms (Xu and Li, 2022). In view of IC components, results show that CEE and RCE have a positive effect on the RoA while HCE and CEE have a positive influence on the RoE. These results are consistent with the results of Soewarno and Tjahjadi (2020) and Smriti and Das (2018) and inconsistent with those of Bataineh et al. (2022) and Sardo and Serrasqueiro (2017). The CEE is significant and positive as well (5% level of significance), which is found to be a dominant contributor to financial performance of Indian sugar mill firms. HCE shows a positive influence on the RoE but negative influence on RoA, whereas the SCE has a negative impact on both indicators of profitability. Among control variables, age was found to have positive association, which is also significant with RoA, and size has a negative and significant effect on the RoA. In contrast, size is found to be positive and significant in case of RoE. The value of Durbin–Watson test shows no problem of autocorrelation of data, while F-statistics shows the model is best fit to explore association of IC with firm performance.

5. Conclusion

Nowadays, IC is a much debatable topic among researchers. The knowledge-based IC, comprising goodwill and intangible assets, shall be considered as an energetic force for the economy growth and development (Ni et al., 2021). It provides the motivation to conduct research, which can deal with association of IC with business performance with special reference to Indian sugar mill companies. This study observes the consequences of IC on the business performance of sugar mill firms in India, considering a sample of 19 sugar mill companies over 10 years from 2012–13 to 2021–22. The study used the MVAIC extended model comprising HCE, SCE, RCE and CEE as independent constructs ; leverage, size and age as control variables and RoA and RoE (dependent variable) as a proxy of profitability.

The main findings of the research include that the MVAIC is positively associated to profitability of firms. It may be observed that RoE is expected to increase by 0.011 units, if firm generates MVAIC in a single unit. The results are consistent with those of Xu and Li (2022), Xu and Wang (2019), Xu and Li (2019), Smriti and Das (2018) and Sardo and Serrasqueiro (2018). The CEE is found to be positive and significant which is the most prominent contributor to business performance of Indian sugar mill companies. These results are in-line with the results of Soewarno and Tjahjadi (2020) and Smriti and Das (2018) and inconsistent with Bataineh et al. (2022) and Sardo and Serrasqueiro (2017). As an extension of the existing literature, measuring the association between IC and the business performance with reference to sugar mill companies in India is the main academic contribution of this study. The present study has the practical implication for the policymakers for effective utilization of IC resources for value creation which is essential for improvement of business performance. The implication of the research is also for the researchers as they will have the relevant input to conduct the study in the field of IC and business performance. The limitation of the research is it is confined to a sample of 19 sugar mill Indian companies based on the obtainability of data for the study period; however, other future studies may be conducted comprising the sugar industry of various emerging economies.

Table 1

Review of literature of selected studies

Author (year)	Industry and sample size	Methods	Representation of firm performance	Results
				Human capital efficiency (HCE)	Structural capital efficiency (SCE)	Relational capital efficiency (RCE)
Tong and Saladrigues (2023)	Spanish new firms industry; 3 years data from 2008 to 2010	Ordinary least squares regression model	RoA	Positive and significant	Positive and significant
Liu et al. (2022)	Chinese manufacturing SMEs; 588 (2015–2020)	Descriptive statistics, correlation analysis and panel data regression model	RoA and RoE	Positive and significant	Positive	Positive
Mohapatra et al. (2019)	Indian banks; 40 (2011–2015)	Descriptive statistics, Pearson correlation statistics	RoA and profitability ratio (revenue to expenses ratio)	Positive and significant	Negative and significant
Xu and Wang (2019)	Textile industry of China and South Korea; 66 (29 and 37; 2012–2017)	Pooled OLS regression model, descriptive statistics, correlation analysis and regression analysis	RoA, RoE and ATO	Negative and significant	Positive and significant	Positive and significant
Lu et al. (2021)	Venture capital syndication background in China (2014–2018)	Descriptive statistics, correlation analysis	RoA and RoE	Positive and significant	Positive and significant	Positive and significant
Desoky and Mousa (2020)	Bahrain; 43 sampled companies (2013-17)	Canonical correlation analysis	RoA and RoE	Positive and significant
Mehri et al. (2013)	Intangible intensive firms of Malaysia; 92 firms (2006–2010)	Descriptive statistics, correlation analysis and regression analysis	M/B value, RoA, RoE and ATO	Positive and significant	Positive and significant
Guo et al. (2012)	Biotech companies of the USA; 279 (1994–2005)	Descriptive statistics	RoA and RoE	Positive and significant
Xu et al. (2022)	Chinese automotive firms; 117 (2013–2018)	Factor analysis, descriptive statistics, normality test and correlation analysis	RoA and RoE		Positive
Lee and Mohammed (2014)	Agricultural firms in Malaysia; 28 (2003–2009)	Descriptive statistics, correlation analysis and regression analysis	RoA, ATO and OI/S	Positive	Positive and significant

Source(s): Authors’ compilation

Table 2

List of sample sugar mill companies

Sr. No	Company name	Establishment year
1	The Andhra Sugars Limited	1947
2	Mawana Sugars Limited	2003
3	Triveni Engineering and Industries Limited	1961
4	Simbhaoli Sugars Limited	1933
5	Rajshree Sugars And Chemicals Limited	1985
6	Piccadily Sugar And Allied Industries Limited	1994
7	Oswal Agro Mills Limited	1979
8	Modi Industries Limited	1932
9	Krebs Biochemicals And Industries Limited	1991
10	EID Parry India Limited	1788
11	Uttam Sugar Mills Limited	1960
12	Kesar Enterprises Limited	1932
13	Shree Renuka Sugars	1995
14	Piccadily Agro Industries Limited	1994
15	Oswal Overseas Limited	1984
16	Nahar Industrial Enterprises Limited	1993
17	Khaitan India Limited	1936
18	Gayatri Sugars Limited	1995
19	Bannari Amman Sugars Limited	1983

Source(s): Authors’ compilation

Table 3

Variables and their description

Variables	Description	Expected relationship	Variable used in the earlier studies
Dependent variable
Return on Assets (ROA)	Earnings after Taxes (EAT)/average total assets		Xu and Liu (2020), Ge and Xu (2021), Xu et al. (2022), Zhang et al. (2021a, b), Al-Musali and Ismail (2016)
Return on Equity (ROE)	(EAT-preference dividend)/average shareholder’s funds		Xu and Liu (2020), Ge and Xu (2021), Nawaz and Haniffa (2017), Li et al. (2021), Weqar et al. (2021)
Independent variable
Human Capital Efficiency (HCE)	VA/HC	+	Xu and Liu (2020), Ge and Xu (2021), Liu et al. (2021), Zhang et al. (2021a, b)
Structural Capital Efficiency (SCE)	SC/VA	+
Relational Capital Efficiency (RCE)	VA/RC	+
Capital Employed Efficiency (CCE)	VA/CE	+
Modified Value-added Intellectual Coefficient (MVAIC)	HCE + SCE + RCE + CEE	+
Control variable
Leverage	Total debt/total assets	–	Xu and Liu (2020), Xu et al. (2021), Al-Musali and Ismail (2016), Dženopoljac et al. (2016), Nawaz and Haniffa (2017), Ozkan et al. (2017), Li et al. (2021), Weqar et al. (2021)
Size	Log of total assets	+
Age	Log of age of the company since its year of incorporation	+/–

Source(s): Authors’ compilation

Table 4

Descriptive statistics

	ROA	ROE	HCE	SCE	RCE	CEE	MVAIC	Leverage	SIZE	Age
Mean	0.053	5.218	−3.312	1.187	−0.112	−0.080	−2.316	0.531	4.885	1.630
Median	0.041	0.943	−0.496	1.159	−0.003	−0.015	0.609	0.336	4.930	1.556
Maximum	1.701	120.018	53.492	51.572	3.918	1.054	53.552	36.438	6.204	2.369
Minimum	−0.206	−11.803	−80.879	−54.076	−5.873	−1.329	−80.844	0.000	3.615	1.000
Std. Dev	0.143	14.255	12.646	5.992	0.783	0.261	13.898	2.636	0.659	0.296
Skewness	8.106	4.864	−2.649	−1.104	−3.551	−2.009	−2.129	13.411	0.014	0.544
Kurtosis	94.544	32.354	17.036	65.579	34.590	10.944	13.728	183.100	2.403	2.888

Source(s): Authors’ compilation

Table 5

Correlation matrix

	ROA	ROE	HCE	SCE	RCE	CEE	Mavic	Leverage	SIZE	Age
ROA	1.000
ROE	0.163	1.000
HCE	−0.033	−0.008	1.000
SCE	−0.057	−0.071	−0.011	1.000
RCE	0.033	0.080	−0.015	−0.702	1.000
CEE	−0.025	0.075	0.690	−0.019	−0.017	1.000
MVAIC	−0.054	−0.032	0.719	0.381	−0.260	0.729	1.000
LEVERAGE	0.002	0.013	0.012	−0.019	0.022	0.019	0.005	1.000
SIZE	−0.007	0.514	−0.279	0.004	0.112	−0.151	−0.249	0.059	1.000
AGE	−0.173	0.439	0.249	−0.048	−0.059	0.319	0.208	−0.021	0.307	1.000

Source(s): Authors’ compilation

Table 6

Results of panel regression

	Model – 1 (dependent variable ROA)	Model – 2 (dependent variable ROA)	Model – 3 (dependent variable ROE)	Model – 4 (dependent variable ROE)
Constant	2.284*** (0.0000)	2.572*** (0.0000)	−62.540*** (0.000)	−61.615*** (0.000)
HCE	−0.001 (0.766)		0.023 (0.841)
SCE	−0.001 (0.700)		−0.122 (0.4910)
RCE	0.004 (0.838)		−0.228 (0.869)
CEE	0.176** (0.011)		5.094 (0.291)
MVAIC		0.000 (0.942)		0.011 (0.879)
SIZE	−0.603*** (0.000)	−0.614*** (0.000)	10.394*** (0.002)	9.683*** (0.002)
LEVERAGE	−0.001 (0.953)	−0.001 (0.955)	−0.015 (0.956)	−0.017 (0.947)
AGE	0.447* (0.052)	0.294 (0.191)	10.790 (0.144)	12.003* (0.082)
R2	0.392	0.363	0.097	0.093
Hausman test	0.000 (Fixed)	0.000 (Fixed)	0.985 (Random)	0.914 (Random)
Durbin–Watson stat	1.632	1.544	0.357	0.632
F-Statistics (p-value)	4.230*** (0.000)	4.318*** (0.000)	2.777*** (0.009)	4.692*** (0.001)

Note(s): ***, ** and * denote statistical significance at 1%, 5% and 10% levels, respectively

Source(s): Authors’ compilation