Intellectual Capital, Firm Performance, and Sustainable Growth: A Study on DSE-Listed Nonfinancial Companies in Bangladesh

Abstract

Intellectual Capital (IC) stimulates corporate competitive advantages that can enhance firm performance and sustainable growth (SG). This study investigates IC’s affinity with and impact on accounting and market performance and SG of listed nonfinancial companies in Bangladesh. Data were collected from 69 nonfinancial companies for five years during 2017–2021, forming 345 observations. Modified Value Added Intellectual Coefficient (MVAIC) and steady-state models consecutively measured IC efficiency and Sustainable Growth Rate (SGR). The Robust fixed effect model was applied to confirm the highest reliable results. Results of MVAIC, Intellectual Capital Efficiency (ICE), and Human Capital Efficiency (HCE) showed a positive affinity with and impact on firm performance and SGR. Structural Capital Efficiency (SCE) showed an insignificant association with and impact on explained variables, whereas Relational Capital Efficiency (RCE) showed a significant negative relationship with and impact on the same. Tangible capital employed efficiency (CEE) enhanced firm performance but failed to confirm sustainable growth. The findings help business executives, government, and policymakers formulate strategic plans for sustainable resource utilization and can create value, competitive edges, and survival for firms. The study recommends that corporate entities should strive to enhance their efficiency in internal structural resources and relational activities to achieve better firm performance and sustainable growth.

1. Introduction

The shift from traditional industrial backgrounds to knowledge-based economic systems for augmenting business excellence has recently been a hot issue of discussion. According to Guthrie et al. [1], the world economy shifted rapidly from an industrial to a knowledge-based economy in the 1980s. In this situation, executives would learn about the most effective managerial game plan to maximize their financial and strategic objectives. In today’s changing global system, knowledge capital has become the most recognized factor in the performance of the organization’s tangible assets. Nevado-Pena et al. [2] assert that IC has replaced an organization’s financial and physical resources. Organizations that better collect, disseminate and exploit their IC and knowledge possessions are more successful in their business [3,4]. The 2020 valuation of S&P 500 companies shows that 90 percent of their total assets are intangible assets, up from one-sixth of total assets in 1975 [5]. In this perspective, IC is the actual driver for corporate performance and sustainable business growth. It leads organizations to survive in the long run by protecting and utilizing knowledge resources. Knowledge can differentiate the value between knowledge-based and traditional companies. Thus, IC is the knowledge that adds more value to the organization [6,7]. However, based on the Resource Based View, tangible and intangible resources are essential for organizational success [8].

In 2015, Bangladesh became classified as a lower middle-income country by United Nations (UN), now on track to leave the UN list of Least Developed Countries (LDCs) in 2026 [9]. The long-term goals of Bangladesh are to move into the upper middle-income country (UMIC) category by 2031 and to enter the high-income category by 2041 [10]. So, currently, trade, tariff, and quota-free market facilities as international privileges enjoyed under the Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement, including patent protection, will be vastly reduced.

As such, swift industrialization is needed to meet these challenges, which are the initial condition for higher growth. Not just for high growth, national progress is possible through rapid industrialization. Following this, the Bangladesh government approved the National Industrial Policy—2022 on 11 August 2022 [11]. In light of the policy, Bangladesh’s economy could be developed by augmenting sector-wise productivity and excellence in the quality of products and services in line with the technological advantages of the fourth industrial revolution and labor-intensive industrialization using domestic raw materials and resources.

According to Bangladesh Economic Survey—2022 [12], the contribution of industry to the country’s GDP is 37.07 percent. The government is boosting this sector’s contribution to 40 percent of GDP by 2027 through improving human resources productivity [11]. The Resource Based View confirms that tangible and intangible resources should be used to the highest level for better performance. In light of the Resource Based Theory, we need to see how efficiently Bangladeshi companies utilize their tangible and intangible assets.

The core objective of using these assets depends on up-to-date skilled human resources. That would make an organization’s internal and external resources more effective by optimally utilizing knowledge-based resources. Bangladeshi business firms must appropriately use their visible and intellectual resources to accelerate sustainable growth and corporate success. Questions naturally arise in this context: are nonfinancial companies in Bangladesh using their intangible assets productively to enhance their accounting and market performance? Can these assets have any impact on the sustainable development of the company? Responding to the research questions, the research objective is to find the link between IC and its elements with firm performance and SG. Further, this study explores the effects of IC and its distinct components on the performance and SG of DSE- listed nonfinancial companies in Bangladesh.

The prior literature on the impact of IC efficiency on firm performance and SG is reviewed and stated as under:

A positive association between VAIC and ROA is found in many studies [13,14]. However, a negative impact was evident in some studies [15,16]. Higher HCE enhances the ROA and ROE of firms, which are evident in some other studies [14,15]. Nevertheless, several other studies [13,17] documented that HCE negatively impacted ROA and ROE. SCE positively impacted and associated with ROA, and ROE was evident in several studies [13,18]. In another study, Xu and Wang [14] found a positive impact of RCE on ROA and ROE, whereas Vishnu and Kumar Gupta [19] found a reverse result. The literature also shows that IC performance enhances business sustainability [20,21,22]. However, a study by Smriti and Das [23] found the opposite effect. Nadeem et al. [24] asserted that HCE and SCE enhance business sustainability, but Hasan and Miah [18] found a negative impact of SCE on SG.

Our research is distinct from other studies in respect of several motives. First, we measured the effects of IC and its associated indicators on firms’ accounting performance (ROE and ROA), market performance (Tobin’s Q), and sustainable growth rate. Accounting performance indicates only the prior historical performance of firms, whereas Tobin’s Q demonstrates future performance though it is ascertained through traditional data. Additionally, it is a remarkable performance indicator from the perspective of investors’ value creation. Second, the dependent variables reflect tangible and intellectual capital effects separately. Third, our research mainly measured IC efficiency by the MVAIC method, modified by Ulum et al. in 2014 [25], instead of the original VAIC method developed by Ante Pulic in 2000 [26]. VAIC method describes only HCE, SCE, and CEE. In contrast, the MVAIC method describes HCE, SCE, RCE, and CEE. Here, the latter method can reflect Relational Capital Efficiency additionally. Fourth, this study deployed data from nonfinancial companies in Bangladesh, which is different from prior studies that used only manufacturing or financial companies such as banking, textile, and pharmaceutical companies in Bangladesh. Fifth, it employed a robust fixed effect regression method to reflect the highest reliable result. Finally, it is the first study on Bangladesh, an emerging market in Southeast Asia.

This paper consists of six sections. Section 1 offers the introduction, such as the background and research context. Section 2 deals with the review of the prior literature, Section 3 explains the methods and materials, followed by results and discussions in Section 4. Lastly, the conclusion and references are included successively in Section 5 and last section.

2. Review of Literature

2.1. Conceptualization of Intellectual Capital and Its Components

2.1.1. Intellectual Capital

Intellectual capital is the transformation of knowledge into value [27]. In 1969, Kenneth Galbraith, an American scholar, innovated the IC concept for the first time. After that, the IC efficacy was extended in the Skandia model by Leif Edvinson in 1997 [27]. Under this model, IC comprises four key elements; Customer capital, Innovation capital, Human capital, and Process capital. Several scholars identified IC into various segments. Pedro et al. [28] wrote a review paper on 777 preeminent research articles and found that IC comprises 25 main dimensions. Out of those, 81.3 percent of studies mentioned Human capital, 75.2 percent used Structural capital, 57.7 percent utilized Relational capital, and 12.7 percent used Employed capital sequentially [28]. Therefore, we inferred that the IC entails four major components: Structural, Human, Relational, and Employed capital. Ulum et al. [25] also used these four elements to measure IC efficiency by expanding the Pulic [26] original VAIC model.

2.1.2. Human Capital

Human capital (HC) is the knowledge, talents, expertise, competencies, and other invisible attributes of the working force that can create value for the organization. According to HC theory, companies may derive value from employees’ skills, experience, and knowledge [29]. With this statement, Kannan and Aulbur [30] stated that the value of an organization’s human capital is measured by how much it invests in its employees’ education, skills, and potential for future advancement. According to Luthy [31], HC refers to an organization’s ability to solve business challenges via the united efforts of its employees. In addition, it is the sum total of an individual’s knowledge, skills, and abilities [32]. Individuals have all of their knowledge assets (tacit and explicit) is referred to as human capital [33].

2.1.3. Structural Capital

Structural capital (SC) covers both intangible and tangible components. What remains of a business after all of its human resources have gone home is called SC [6]. The firm’s information technology, customer databases, commercial and industrial practices, and strategic plans are examples of intangible aspects. When it comes to business acumen, structural capital is all about a company’s structure and information systems [34]. Manzaneque et al. [35] assert that SC includes all internal processes for disseminating, communicating, and managing scientific and technical knowledge. Soewarno and Tjahjadi [36] stated that SC comprises the company’s databases, strategies, organizational procedures, and any other activities with a value higher than just tangible ones.

2.1.4. Relational Capital

Relational capital (RC) is the current and potential future value of an organization’s relationships with customers and other parties outside the business [37]. It consists of the total value of a business’s relationships with suppliers, customers, industry groups, and markets. Customer capital (CC) also includes things such as understanding and trust, along with the loyalty and strength of customer relationships [30]. RC is what a company knows about people outside of it, such as customers [36]. Based on Lee’s [38] statement, RC is the total of all the assets that a company uses to set up and manage its relationships with the outside world.

2.2. IC and Companies’ Performance

The resource-based theory describes that the achievement of the organizations is contingent on the proper utilization of all resources (intangible and tangible). MVAIC is the combination of both the efficiency of assets of an organization. So, it is assumed that MVAIC assists in improving companies’ performance. However, such studies exhibit mixed results. The association and impact of VAIC on ROA and ROE are shown positive by [13,14,24,39,40] studies and with TQ [17]. Buallay et al. [41] found a negative influence on ROA studied after 59 banks in GCC countries during 2012–2016. The same results were found by Shahwan and Fathalla [42] in Egypt from 81 nonfinancial firms during 2014–2018. Further studies by Ozkan et al. [43] revealed an insignificant impact on ROA and ROE. From this literature, we assumed that higher values of VAIC positively influenced company performance.

H1. 

MVAIC positively impacts the company’s performance.

H2. 

ICE positively impacts the company’s performance.

2.2.1. Capital Employed Efficiency (CEE) and Companies’ Performance

CEE is the efficiency of tangible capital invested by a company. Firms augment their performance by effective deployment of these resources. CE helps develop more revenue, accounting, and market performances. Several studies were evident that the effective utilization of tangible resources uplifts organizational accounting performance (ROA and ROE) [24,40,41,44,45] and market performance (TQ) [46,47]. However, some other studies showed the reverse results. Moreover, the CEE declined the firm’s ROA [13,48,49] and ROE [13,50] and market performance (TQ) [51]. The influence of CEE on corporate performance is evident in various directions. Based on the literature, we assumed the following hypothesis:

H3. 

CEE positively impacts a company’s performance.

2.2.2. Human Capital Efficiency (HCE) and Companies’ Performance

HCE is the relationship of value added to employee expenses. Human resources activate all other resources of an organization. Without the proper use of human efforts, no firm can run effectively. Different studies established an optimistic affinity between HCE and firm performance to a great extent [40,48,52,53]. An enterprise’s accounting performance (ROE and ROA) correlate positively [14,44]. Moreover, HCE enhanced market performance (TQ) positively [46,47]. However, such studies revealed that HCE negatively influenced a firm’s accounting performance [13,45] and market performance [17,54]. Likewise, a very negligible impact of HCE on ROA was disclosed by Ousama and Fatima [44] after studying 16 banking companies in Malaysia during 2008–2010. Based on the above discussion, we assumed the following hypothesis:

H4. 

HCE positively impacts a company’s performance.

2.2.3. Structural Capital Efficiency (SCE) and Companies’ Performance

SCE defines the ratio of structural expenses to the company’s value added. Structural capital consists of infrastructural components such as the company’s management philosophy, process, database, technological advantages, innovation capabilities, R & D, and intellectual properties. The efficient exploitation of these resources assists in enhancing organizational performance. SCE showed a positive relationship and impact on the firm’s accounting and market performance. Various studies found this type of result. Haris et al. [39] find that SCE improves the firm’s accounting performance (ROE and ROA) after a study on 26 banking companies in Pakistan using ten years database. Similar results were also demonstrated by several studies [13,24,45]. Another study asserts that more SCE help uplifts an organization’s market performance (TQ) [47]. Some other studies found that higher SCE reduced a firm’s ROA and ROE [48,49,55]. Hejazi et al. [46] studied 100 manufacturing firms in Iran using the data from 2000–2006 and revealed that higher SCE leads to a decline in companies’ market performance. From this review, we cannot claim the fixed impact of SCE on a firm’s performance. Results vary due to the time, place, and context of studies. However, most studies indicate that the higher SCE, the more enriched an organization’s performance. So, we may draw the following hypothesis:

H5. 

SCE positively impacts a company’s performance.

2.2.4. Relational Capital Efficiency (RCE) and Companies’ Performance

RCE is the ratio of the company’s relational expenses, i.e., marketing expenses to value added. The positive affinity between RCE and company performance is one of the influential invisible assets of an organization. Building resilient collaboration with external parties helps a firm survive in the long run. It also allows the firm to attain competitive advantages. RC is the amount of money invested by the company for marketing expenses, including advertising, selling, distribution, and other costs associated with external parties. Prior studies revealed that higher RCE enhances a firm’s accounting (ROA and ROE) and market performance (TQ) [14,41,47]. However, different result were found by Vishnu and Kumar Gupta [19] while studying 22 pharmaceutical firms in India. In this context, we draw the following hypothesis.

H6. 

RCE positively impacts a company’s performance.

2.3. Corporate Sustainable Growth

Sustainable growth is a term employed for multi-purposes, such as environmental, social, and financial. From an economic context, SG aims to enhance profitability with long-term future survival. When explicitly used, “sustainable development” describes sustainable corporate development, which is persistently pursuing company objectives and preserving fundamental competitiveness. The business must guarantee a sustained competitive edge and create consistent and uninterrupted earnings in the professional arena [21]. The term “Sustainable Growth Rate” (SGR) was innovated first by a well-known researcher Higgins in 1977. In this context, Van Horne (2000) proposed a dynamic non-equilibrium and steady-state equilibrium SG model [56].

2.4. Intellectual Capital and Companies’ Sustainable Growth

IC confirms a firm’s profitability with the proper deployment of tangible resources to boost a firm’s competitive advantages for a more extended period [57,58]. Alipour [59] argued that IC provides the cornerstone for long-term growth and a competitive edge. Zhang and Wang [21] asserted that higher IC performance tends to increase a company’s sustainable expansion and investors’ morale after studying the listed manufacturing companies in China during 2015–2020. The fixed effect regression results support that IC positively influenced companies SGR. Applying the same model by Xu and Wang [14] found a substantial positive effect of VAIC on SGR. Kasoga [13] also found similar results from 22 manufacturing firms in Tanzania using data for ten years, from 2010–2019. However, a negative affinity between VAIC and SG and its small influence on SG was explored by Smriti and Das [23]. The results from prior research exhibit mixed effects of IC on SG. Nevertheless, most of the studies showed positive impacts in this respect. So, we assumed the following hypothesis.

H7. 

MVAIC positively impacts a company’s Sustainable growth.

H8. 

ICE positively impacts a company’s Sustainable growth.

2.4.1. CEE and Companies’ Sustainable Growth

For sustainable growth of the firm, it is required to utilize both tangible and intellectual resources efficiently. Smriti and Das [23] studied 15 Indian banking industries during 2001–2016 by applying system GMM methods and concluded that higher CEE enhances corporate SG. Similar results were found in different studies in this respect [14,24,45]. Nevertheless, reverse results were found by Kasoga [13] and Hasan and Miah [18]. From this above discussion, our hypothesis is as follows:

H9. 

CEE positively impacts a company’s Sustainable growth.

2.4.2. HCE and Companies’ Sustainable Growth

HC is the furthermost influential IC resource, dominating overall IC efficiency [60]. HCE and VAIC behave in parallel directions. Most of the studies’ correlation matrices proved this statement [61,62]. Xu and Wang [14] studied 390 manufacturing firms in Korea, taking data from 2012–2016. Using a fixed-effect model, they concluded that HCE influenced the company’s SG positively and significantly. Identical results were found by Lu et al. [53] in the studies performed in Pakistan and China. However, Kasoga [13] found a negative impact on SG from panel data evidence in Tanzanian manufacturing firms. Moreover, such an insignificant influence was found in the studies conducted by Smriti and Das [23] using the system GMM method consisting of 15 industries in India. Thus, we draw the following hypothesis.

H10. 

HCE positively influences a company’s Sustainable growth.

2.4.3. SCE and Companies’ Sustainable Growth

Structural Capital (SC) is a unique resource for an organization which varies from institution to institution. Each organization has its culture, philosophy, database, process, etc. The management should consider these unique resources very carefully to attain long-run business goals, i.e., sustainable growth. In this respect, several research studies evidence that the optimum utilization of structural resources enhances corporate SG [13,24,53]. However, Hasan and Miah [18] found that higher SCE negatively influenced a firm’s SG. Based on RB theory and the literature as mentioned earlier, we draw the following hypothesis:

H11. 

SCE positively impacts a company’s Sustainable growth..

2.4.4. RCE and Companies’ Sustainable Growth

Relational capital (RC) pertains to organizational affinities with suppliers and consumers that help acquire and sell products and services efficiently [63]. To be more explicit, it symbolizes a firm’s capacity to converse with possible stakeholders. According to Januškaitė and Užienė [64], RC is a firm’s capacity to interact favorably with individuals within the business society to inspire the potential for wealth creation through boosting organizational and human capital. Xu and Wang [14] studied to explore the influence of IC on SG from Korean manufacturing firms and concluded that more RCE enhanced companies’ sustainable growth. Similar results were found by [65]. From the above review, our hypothesis is as follows:

H12. 

RCE positively impacts a company’s Sustainable growth.

3. Materials and Methods

3.1. Sample

This study strives to identify the effects of IC and its components on company performance and the sustainable evolution of publicly listed nonfinancial firms in Bangladesh. The study employed data from all the listed nonfinancial companies in Bangladesh to attain its objective. Presently, 241 nonfinancial companies are listed on Dhaka Stock Exchange Ltd. [66]. Due to the non-availability of required data to measure IC efficiency, we excluded 172 companies. So, our study contains 69 nonfinancial companies from 11 sectors, such as Cement 6, Ceramics Sector 4, Engineering 9, Food and Allied 6, Fuel and Power 13, IT Sector 4, Pharmaceuticals and Chemicals 10, Services and Real Estate 3, Tannery Industries 2, Telecommunication 1, and Textile 11 companies, whose data are available for the last five years from 2017 to 2021. Hence, the final observation for this study is 345 and arranged a strongly balanced panel data.

3.2. Variables Used

The study employed four dependent, six independent, and two control variables. The measurement indicators of these variables are shown in

Table 1. Indicators for calculating all deployed variables.

Variables	Measurement Indicators
Dependent Variables
Return on Assets (ROA)	Net Earnings/Total Assets
Return on Equity (ROE)	Stockholder’s Equity/Total Assets
Tobin’s Q	(Market value of equity capital + Book value of Debt capital)/(Book value of equity capital + Book value of Debt capital)
Sustainable Growth Rate (SGR)	$SGR=\frac{b\left(\frac{NP}{S}\right)\left(1+\frac{D}{Eq}\right)}{\left(\frac{A}{S}\right)-\left[b\left(\frac{NP}{S}\right)\left(1+\frac{D}{Eq}\right)\right]}$ where Eq = Beginning equity capital, D = Beginning debt capital, S = Sales, b = Retention ratio (1 − dividend payout ratio), NP/S = Net profit ratio, D/Eq = Leverage ratio, A/S = Assets to sales
Independent Variables
MVAIC	ICE + CEE
ICE	HCE + SCE + RCE
HCE	Value Added/Human Capital
SCE	Structural Capital/Value added
RCE	Relational Capital/Value added
CEE	Value added/Capital Employed
Control Variables
TA	Natural log of Total Assets
LEV	Debt Capital/Equity Capital

.

3.2.1. Dependent Variables

The study attempts to find IC’s influence on corporate performance and sustainable growth. The accounting (ROA and ROE) and market (Tobin’s Q) measures specify a firm’s performance. The prior literature reveals that ROA and ROE are the best measures of firm performance [13,14,45]. Between the two, ROA is the key indicator of a firm’s efficiency for asset deployment. It is the ratio of the company’s net earnings to average assets used. ROE suggests the efficiency of a company’s equity provided by its owner. Empirically, it is the company’s net earnings to the average owner’s equity. As stated earlier, the study used Tobin’s Q ratio for gauging market performance, as adopted by several studies [13,23,41,46]. Tobin’s Q is the market value to the company’s book value ratio.

The study employed Van Horne and Wachowicz’s [56] Sustainable Growth Rate, a steady-state model, to measure SGR. The SGR rate indicates that a firm may accomplish its growth by using just its internal finances instead of loans from banks or other financial collaborations. The reasons for deploying this model are to derive benefits for long-term growth planning, asset acquisitions, cash flow forecasting, and financing strategies [14]. Further, this model employed all indicators calculated from the company’s audited financial statements that ensured reliable information. Furthermore, various prior studies guided us to use this method for calculating SGR [14,67,68,69]. The model is as follows:

$$SGR=\frac{b\left(\frac{NP}{S}\right)\left(1+\frac{D}{Eq}\right)}{\left(\frac{A}{S}\right)-\left[b\left(\frac{NP}{S}\right)\left(1+\frac{D}{Eq}\right)\right]}$$

where SGR = Sustainable Growth Rate, Eq = Beginning equity capital, D = Beginning debt capital, S = Sales, b = Retention ratio (1 − dividend payout ratio), NP/S = Net profit ratio, D/Eq = Leverage ratio, A/S = Assets to sales.

3.2.2. Independent Variables

The study adopted Modified-Value Added Intellectual Coefficient (MVAIC) model originated by Ante Pulic [26] as VAIC model and modified by Ulum et al. [25] to measure IC efficiency. Based on the model guide, MVAIC and its four mechanisms, SCE, HCE, RCE, and CEE, are cast off as independent variables. Numerous studies used the MVAIC method as the independent variable [14,53,70,71,72,73]. MVAIC model is composed of six steps.

Step one is related to calculating Value Added (VA). VA combines a firm’s Operating profit, employee expenses, depreciation, and amortization. So, VA = OP + E+D + A. Where OP = Operating profit, E = Employee expenses (HC), D = Depreciation, and A = Amortization.

Step two measured HCE, the ratio of value added to employee expenses (human capital), i.e., HCE = VA/HC.

Step three measured SCE by dividing structural capital by value-added, where SC is the difference between value-added and human capital and relational capital. Hence, SCE = SC/VA.

Step four determines the RCE by dividing relational capital into value-added. RC is the marketing and other expenses related to external parties. Mathematically, RCE = RC/VA.

Step five relates to measuring Capital Employed Efficiency (CEE), the ratio of value added to capital employed (CE). CE is the excess of total assets over current liabilities. Therefore, CEE = VA/CE.

Step six measured the MVAIC value comprising all IC components. So, MVAIC = HCE + CEE + RCE + SCE.

3.2.3. Control Variables

The study used two control variables, Total Assets (TA) and Leverage (LEV). Numerous studies applied these two variables as control variables [74,75,76,77,78]. TA is the natural logarithm of the volume of all assets, and LEV is the debt-to-equity capital ratio.

3.3. IC Measurement Model

IC measurement is a big challenge for the present world. Property that cannot see or touch but can feel is difficult to measure. Various IC measuring methods have been created throughout the last three decades to keep intellectual capital functioning [79]. However, a universally accepted methodology for measuring IC must be made available. Sveiby [80] categorizes IC measurement models into four approaches named as Market Capitalization approach (MCM), the Return on Assets (ROA) approach, the Direct Intellectual Capital (DIC) approach, and the Scorecard method (SC) approach. These IC measurement models are divided into two segments—nonmonetary and monetary. SC used nonmonetary measurements, and the rest used monetary measurements. Moreover, both DIC and SC approaches measured IC with the component-by-component approach, and another two (MCM and ROA) measured IC in financial terms without considering individual components, such as the whole organization.

The ROA is the popular technique for measuring IC with financial terms for the whole enterprise. VAIC is the most widely used method to measure IC efficiency [81]. Pulic [26] proposed this model to evaluate IC efficiency, modified by Ulum et al. [25]. We applied the MVAIC method to measure IC efficiency for several reasons. This method engages the intangible and tangible possessions for value creation, which supports the resources-based theory [82]; it assists in measuring RCE separately; it estimates IC efficiency from a firm’s audited data [83]; it disseminates information necessary for all stakeholders rather than shareholders [84]; and it does not violate the core accounting principles [85]. It provides fair excesses for researchers and academicians for cross-sectional evaluation.

Although the VAIC method has numerous benefits, it agonizes from several drawbacks. Ståhle et al. [81] contended that the VAIC approach flops in assessing businesses’ IC; moderately, it dealings the business organization’s labor and employed capital efficiency. They also argued that SCE measurement is not permissible under this model. Due to historical data from companies’ financial statements, this model only measured IC for the past period(s) and did not calculate IC for the future [49]. The VAIC framework disregards the potential for enhanced returns from combining divergent resources [86]. Despite these underlying restrictions and strengths, VAIC was employed in an extensive number of IC research tasks performed earlier [39,40,47,87].

3.4. Econometric Models

This study uses three models to fulfill its research objectives. Model 1 reflects the influences of inclusive IC on companies accounting and financial performance along with sustainable growth. Model 2 measures IC and CE efficiency separately, and model 3 explores the influences of all the elements of IC on companies’ performances and sustainability. We applied the panel data regression model in three methods, specifically, (i) Pooled OLS, (ii) Random effects model, and (iii) Random effects model. The best method was selected based on some diagnostic tests (discussed in Section 4.3).

Specification of the pooled OLS regression model is as follows:

Model 1: $Y_{}=\alpha +{\beta }_{1}MVAIC+{\beta }_{2}ASSET{S}_{}+{\beta }_{3}LE{V}_{}+{\epsilon }_{}$

Model 2: $Y_{}=\alpha +{\beta }_{1}IC{E}_{}+{\beta }_{2}CE{E}_{}+{\beta }_{3}ASSETS+{\beta }_{4}LEV+{\epsilon }_{}$

Model 3: $Y_{}=\alpha +{\beta }_{1}HCE+{\beta }_{2}SCE+{\beta }_{3}RCE+{\beta }_{4}CEE+{\beta }_{5}ASSETS+{\beta }_{6}LEV+\epsilon$ where Y denotes the dependent variable as ROA and ROE for accounting performance, Tobin’s Q for market performance, and SGR for sustainable growth rate; α = intercept; β = beta coefficient; ε = error

Specification of the FE regression model is given below:

Model 1: $Y_{it}=\alpha +{\beta }_{1}MVAI{C}_{it}+{\beta }_{2}ASSET{S}_{it}+{\beta }_{3}LE{V}_{it}+{\epsilon }_{it}$

Model 2: $Y_{it}=\alpha +{\beta }_{1}IC{E}_{it}+{\beta }_{2}CE{E}_{it}+{\beta }_{3}ASSET{S}_{it}+{\beta }_{4}LE{V}_{it}+{\epsilon }_{it}$

Model 3: $Y_{it}=\alpha +{\beta }_{1}HC{E}_{it}+{\beta }_{2}SC{E}_{it}+{\beta }_{3}RC{E}_{it}+{\beta }_{4}CE{E}_{it}+{\beta }_{5}ASSET{S}_{it}+{\beta }_{6}LE{V}_{it}+{\epsilon }_{it}$ where i = 1, 2, …, n and t = 1, 2, …, n year represents firm and year, respectively.

Specification of the RE regression model is given below:

Model 1: $Y_{it}=\alpha +{\beta }_{1}MVAI{C}_{it}+{\beta }_{2}ASSET{S}_{it}+{\beta }_{3}LE{V}_{it}+u_{it}+{\epsilon }_{it}$

Model 2: $Y_{it}=\alpha +{\beta }_{1}IC{E}_{it}+{\beta }_{2}CE{E}_{it}+{\beta }_{3}ASSET{S}_{it}+{\beta }_{4}LE{V}_{it}+u_{it}+{\epsilon }_{it}$

Model 3: $Y_{it}=\alpha +{\beta }_{1}HC{E}_{it}+{\beta }_{2}SC{E}_{it}+{\beta }_{3}RC{E}_{it}+{\beta }_{4}CE{E}_{it}+{\beta }_{5}ASSET{S}_{it}+{\beta }_{6}LE{V}_{it}+u_{it}+{\epsilon }_{it}$ where u = individual effects of firm, which is immeasurable variable.

4. Results and Discussion

4.1. Descriptive Statistics

Descriptive statistics showed the nature and general overview of data. This section explored the maximum, minimum, standard deviation, and average values of all the entire variables used in this study. After 1st para of this section.

Table 2. Descriptive Statistics.

Variable	N	Mean	Std. Dev.	Minimum	Maximum
ROA	345	0.053622	0.089735	−0.63419	0.532819
ROE	345	0.108948	0.274463	−2.69531	1.90634
TQ	345	1.785349	1.640101	0.485098	12.74387
SGR	345	0.097113	0.176401	−0.846378	0.882906
HCE	345	6.720099	9.562619	−1.49167	86.38947
SCE	345	0.560828	0.509572	−0.87	6.109682
RCE	345	0.169239	0.350071	−1.33426	5.666667
CEE	345	0.306351	0.392978	−0.0544	2.897926
ICE	345	7.433755	9.666588	−0.85333	87.3779
MVAIC	345	7.740106	9.653992	−0.83737	87.88168
ASSETS	345	25,923.31	44,308.89	451	359,803
LEV	345	1.770829	2.747648	0.008225	24.34002

shows that the average profitability on total assets is 0.053622, with a range of −0.63419 to 0.532819. However, a high SD indicates that ROA is volatile. The range also supported volatility characteristics. The mean ROE of 10.8948 percent specifies that companies earned above 10 percent profit, but it fluctuates over time and year with a high margin (0.274463). Tobin’s Q ratio revealed that the company’s market value is 1.78 times higher than the book value, which turned into more than 12 times, indicating companies have higher market value. SGR also holds about 10 percent (0.097113), which specifies companies able to ensure their growth sufficiently. HCE expressed that employees efficiently increased the firm’s VA more than six times.

Nevertheless, some companies have negative HCE that fails to utilize employees’ efficiency. HCE also directs the most influential factor for ICE and MVAIC. More than half and one-sixth of the VA spends by the company for SC and RC purposes, respectively. About one-third (0.306351) of the value added earned from employed capital (tangible capital), which was enhanced near to three times, specifies outstanding performance for the entire organization(s). ICE and MVAIC values showed almost the same due to the influential factor of HCE. The average assets held by Bangladeshi companies is 25,923.31 million, with a high SD (44,308.89), indicating higher volatility from company to company. The average leverage of the companies is 1.77, demonstrating that the equity capital is lower than its debt capital, nearly half. Some companies used more than 24 times their debt capital than their equity capital.

4.2. Correlation Analysis

Table 3. Correlation Matrix.

Variables	ROA	ROE	TQ	SGR	HCE	SCE	RCE	CEE	ICE	MVAIC	ASSETS	LEV
ROA	1
ROE	0.892 ***	1
TQ	0.698 ***	0.560 ***	1
SGR	0.3937 ***	0.2838 ***	0.0232	1
HCE	0.268 ***	0.130 **	0.057	0.2752 ***	1
SCE	0.088	0.098 *	−0.096 *	0.0454	0.268 ***	1
RCE	−0.295 ***	−0.418 ***	0.146 ***	−0.1797 ***	−0.145 ***	−0.565 ***	1
CEE	0.570 ***	0.555 ***	0.727 ***	−0.038	−0.047	−0.177 ***	0.053	1
ICE	0.272 ***	0.136 **	0.055	0.2761 ***	0.999 ***	0.302 ***	−0.164 ***	−0.052	1
MVAIC	0.296 ***	0.159 ***	0.085	0.2749 ***	0.999 ***	0.295 ***	−0.162 ***	−0.012	0.999 ***	1
ASSETS	0.091	0.188 ***	−0.055	0.3258 ***	−0.002	0.003	−0.038	0.006	−0.002	−0.001	1
LEV	−0.150 ***	0.032	−0.065	0.0486	−0.092*	−0.096 *	0.046	0.064	−0.095 *	−0.093 *	0.675 ***	1

Note: *, **, and, *** indicate 10%, 5%, and 1% significance levels, correspondingly.

exhibits the relationship between dependent, independent, and control variables. Results showed that HCE, ICE, and MVAIC are positively correlated with companies’ accounting profitability (ROA and ROE) and sustainable growth (SG) substantially. The market performance also behaved in the same direction, although insignificantly. From these results, it is apparent that HCE eventually adapts to ICE and then MVAIC. SCE showed a significant parallel relationship with ROE and SGR but something else with market performance (TQ). RCE correlates negatively with both the measures of accounting performance and SGR. However, its higher performance helps improve market performance. CEE shows a close strong association with all dependent variables at p < 0.01 except for SG. More tangible assets can elevate ROE and SGR, and leverage negatively correlates only with ROA.

4.3. Diagnostic Test

The study used a purposive sampling method due to the availability of essential data. Our dataset arranged a strongly balanced panel. To choose an appropriate econometric model, we applied several tests. Firstly, we performed a panel unit root test. Here, we used Fisher-type unit-root test based on Phillips–Perron (PP) tests. The results from

Table 4. Fisher-type unit-root test based on Phillips–Perron (PP) tests.

Variables	Statistic	p Value
ROA	466.2433	0.0000
ROE	513.9435	0.0000
TQ	643.9966	0.0000
SGR	690.2026	0.0000
HCE	542.5436	0.0000
SCE	737.6978	0.0000
RCE	861.7664	0.0000
CEE	868.9690	0.0000
ICE	576.7422	0.0000
MVAIC	555.2467	0.0000
ASSETS	578.7146	0.0000
LEV	486.8486	0.0000

assured that all the employed variables failed to have unit roots (because all the variables were significant at a 1 percent significant level), meaning that have contained stationary at least one panel.

Secondly, we tested multicollinearity issues by applying the VIF test. Results from

Table 5. Variance Inflation Factor (VIF).

Variables	VIF	1/VIF
LEV	1.89	0.529101
ASSETS	1.87	0.534759
SCE	1.60	0.625000
RCE	1.48	0.675676
HCE	1.09	0.917431
CEE	1.04	0.961538
MVAIC	1.02	0.980392
ICE	1.02	0.980392

confirm that no multicollinearity exists in our dataset because VIF values range from 1.02 to 1.89. Gujarati and Porter [88] evident that a VIF score within 1 to 10 creates no multicollinearity problems. Numerous researchers performed these two types of tests [40,73,89] before advancing further analysis.

Table 3 also shows that HCE, ICE, MVAIC, and ROA with ROE and SGR have positive associations exceeding 0.80 at a 1% significant level. However, it will not create any multicollinearity problem because these variables have not been used in the same model. According to Kennedy [90], multicollinearity will be an issue if the independent variables correlate more than 0.80.

Thirdly, we performed Pooled OLS and Random Effect regression methods. The method was chosen based on Breusch and Pagan Lagrangian multiplier (LM) test. The LM test’s significant result (p < 0.05) guided the choice of RE as the best regression method over Pooled OLS. Fourthly, the Fixed Effect regression model was executed, and then, the estimation was compared with the random effect model by performing the DWH (Hausman) test. The systematic difference in coefficients (p < 0.05) of the Hausman test prescribed using FE as the best model. Otherwise, the RE model was chosen. Fifthly, Durbin-Watson (DW) test was conducted, and results (from Table 6, Table 7, Table 8 and Table 9) showed that the DW value is less than 2 for all the models. That indicates severe autocorrelation problems. Sixthly, we executed the Modified Wald test for identifying the groupwise heteroskedasticity problem. This test’s significant results (p < 0.05) confirm the existence of the heteroskedastic problem. Lastly, we applied the Robust FE regression model (where necessary) to stun these two issues (autocorrelation and heteroskedasticity) [73].

4.4. Regression Results and Discussions

The empirical evidence from regression analysis showed the impact of overall IC efficiency and the individual component on the company’s accounting, market performance, and sustainable growth performance. Table 6, Table 7, Table 8 and Table 9 present the comparative results of different regression models, which allows the evaluation of the entire hypothesis of the study. Results from all the tables showed that the F-statistics is significant at a 5 percent level or less (p < 0.05) for all the models, indicating that all models are good fits for decision. Results of the Breusch and Pagan (LM) test confirm that all the models rejected the pooled OLS regression model due to the existence of either fixed or random effects. Model 2 from Table 6 and model 1 from Table 8 failed to reject the null hypothesis, i.e., the difference in standard error coefficients is not systematic (probability of Hausman test chi square <0.05). In these cases, we used RE as the best regression model. Modified Wald (MW) test chi-square results were also significant (p < 0.01), indicating that all the data suffered from heteroskedastic problems. Thus, a robust fixed-effect regression model is used.

Model 1 from Table 6, Table 7 and Table 8 was used to evaluate Hypothesis 1. The robust FE model was treated as the best regression model to measure the effects of MVAIC on ROA and ROE, and the RE model was utilized for measuring the same on market performance. The overall R² score ranged from 0.0032 to 0.1349, indicating that the changes in MVAIC and its control variables have little power to improve firms’ performances. However, the study revealed that overall IC performance (MVAIC) could significantly enhance firm accounting (t value of ROA is 4.35 and ROE is 2.58) and market performance (p < 0.01, t = 2.96) simultaneously. These results support our first hypothesis (H1), which is consistent with the prior studies [13,14,39,44,91] and varies with the finding of Smriti and Das [23]. Bangladeshi nonfinancial companies could handle their aggregate tangible and intangible efficiency to improve their performance.

Model 2 from Table 6, Table 7 and Table 8 showed the effects of ICE and tangible capital efficiency on the firm’s accounting and market performance separately. Different diagnostic tests for model 2 confirm that the RE regression model provides the best and unbiased result for the influence of ICE and CEE on ROA. In contrast, robust FE regression results offer the most reliable output for the same explanatory variables on ROE and TQ. The overall R² from the RE model showed the highest score (0.5048) compared to the FE model (0.1875 and 30.89), indicating that the RE model was able to influence ICE and CEE on firm performance more. Regression results revealed that ICE strongly influenced the firm’s accounting performance, especially on ROA at a 1% significance level with a substantial t value (6.96). Additionally, it impacted ROE and market performance (TQ) at a 10 percent significant level (p < 0.1) with a t value of 1.91 and 1.69, respectively. These results support our hypothesis two (H2). Results imply that Bangladeshi nonfinancial companies utilized their intellectual resources to enhance accounting performance (ROA) more and contribute comparatively little to market performance.

Table 6. Regression results of IC and its components on ROA (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2			Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0351 (5.70) ***	0.0274 (2.53) **	0.02295 (2.73) ***	0.023 (2.03) **	−0.00269 −(0.48)	−0.00573 −(0.66)	−0.00869 −(1.00)	0.0221 (2.88) ***	0.0309 (3.25) ***	0.0403 (4.73) ***	0.0403 (1.79) *
MVAIC	0.00246 (5.28) ***	0.0042 (7.33) ***	0.0053 (7.58) ***	0.0053 (4.35) ***
ICE					0.002467 (6.96) ***	0.00331 (6.96) ***	0.00434 (6.69) ***
CEE					0.1389 (16.02) ***	0.1385 (11.53) ***	0.13585 (7.91) ***	0.1384 (16.91) ***	0.10214 (9.25) ***	0.0664 (4.63) ***	0.0664 (1.22)
HCE								0.00223 (6.5) ***	0.00321 (7.56) ***	0.00409 (7.99) ***	0.00409 (3.47) ***
SCE								−0.01399 −(1.79) *	−0.01255 −(1.85) *	−0.0119 −(1.68) *	−0.01188 −(0.62)
RCE								−0.07852 −(7.17) ***	−0.0882 −(12.28) ***	−0.0949 −(12.91) ***	−0.0949 −(7.83) ***
ASSETS	6.57 × 10−7 (4.80) ***	5.48 × 10−7 (2.69) ***	4.30 × 10−7 (1.43)	4.30 × 10−7 (0.87)	7.39 × 10−7 (7.09) ***	7.96 × 10−7 (4.93) ***	8.86 × 10−7 (3.16) ***	6.88 × 10−7 (7.08) ***	6.25 × 10−7 (4.19) ***	4.23 × 10−7 (1.92) *	4.23 × 10−7 (1.02)
LEV	−0.1125 −(5.08) ***	−0.0116 −(4.07) ***	−0.0122 −(3.37) ***	−0.1217 −(1.66)	−0.13392 −(7.92) ***	−0.01597 −(6.78) ***	−0.01949 −(5.69) ***	−0.1274 −(8.07) ***	−0.0138 −(6.56) ***	−0.0128 −(4.74) ***	−0.01283 −(1.79) *
R2 (Overall)	0.1521	0.1478	0.1349	0.1349	0.5083	0.5048	0.4746	0.5750	0.5356	0.4129	0.4129
F-statistics	21.58 ***	72.16 ***	22.95 ***	7.6 ***	35.27 ***	219.79 ***	35.27 ***	78.55 ***	465.47 ***	69.6 ***	2539.39 ***
Observations	345	345	345	345	345	345	345	345	345	345	345
DWH Test	8.74 **				7.11			21.77 ***
LM Test	322.49 ***				215.53 ***			301.14 ***
DW Test	0.7773783				0.7840227			0.749061
Wald Test (χ2)	2.4 × 106 ***				Not Applicable			3.5 × 105 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed significance level, respectively. ROA = Earnings on Total Assets; CONST = Constant; MVAIC = Extended VAIC; CEE = Efficiency of Employed Capital; HCE = Human Capital Efficiency; RCE = Relational Capital Efficiency; SCE = Structural Capital Efficiency; ICE = Intellectual Capital Efficiency; LEV = Leverage; Wald = Modified Wald; LM = Breusch and Pagan Lagrangian Multiplier; DW = Durbin–Watson; Coef. = Coefficient.

Table 6. Regression results of IC and its components on ROA (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2			Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0351 (5.70) ***	0.0274 (2.53) **	0.02295 (2.73) ***	0.023 (2.03) **	−0.00269 −(0.48)	−0.00573 −(0.66)	−0.00869 −(1.00)	0.0221 (2.88) ***	0.0309 (3.25) ***	0.0403 (4.73) ***	0.0403 (1.79) *
MVAIC	0.00246 (5.28) ***	0.0042 (7.33) ***	0.0053 (7.58) ***	0.0053 (4.35) ***
ICE					0.002467 (6.96) ***	0.00331 (6.96) ***	0.00434 (6.69) ***
CEE					0.1389 (16.02) ***	0.1385 (11.53) ***	0.13585 (7.91) ***	0.1384 (16.91) ***	0.10214 (9.25) ***	0.0664 (4.63) ***	0.0664 (1.22)
HCE								0.00223 (6.5) ***	0.00321 (7.56) ***	0.00409 (7.99) ***	0.00409 (3.47) ***
SCE								−0.01399 −(1.79) *	−0.01255 −(1.85) *	−0.0119 −(1.68) *	−0.01188 −(0.62)
RCE								−0.07852 −(7.17) ***	−0.0882 −(12.28) ***	−0.0949 −(12.91) ***	−0.0949 −(7.83) ***
ASSETS	6.57 × 10−7 (4.80) ***	5.48 × 10−7 (2.69) ***	4.30 × 10−7 (1.43)	4.30 × 10−7 (0.87)	7.39 × 10−7 (7.09) ***	7.96 × 10−7 (4.93) ***	8.86 × 10−7 (3.16) ***	6.88 × 10−7 (7.08) ***	6.25 × 10−7 (4.19) ***	4.23 × 10−7 (1.92) *	4.23 × 10−7 (1.02)
LEV	−0.1125 −(5.08) ***	−0.0116 −(4.07) ***	−0.0122 −(3.37) ***	−0.1217 −(1.66)	−0.13392 −(7.92) ***	−0.01597 −(6.78) ***	−0.01949 −(5.69) ***	−0.1274 −(8.07) ***	−0.0138 −(6.56) ***	−0.0128 −(4.74) ***	−0.01283 −(1.79) *
R2 (Overall)	0.1521	0.1478	0.1349	0.1349	0.5083	0.5048	0.4746	0.5750	0.5356	0.4129	0.4129
F-statistics	21.58 ***	72.16 ***	22.95 ***	7.6 ***	35.27 ***	219.79 ***	35.27 ***	78.55 ***	465.47 ***	69.6 ***	2539.39 ***
Observations	345	345	345	345	345	345	345	345	345	345	345
DWH Test	8.74 **				7.11			21.77 ***
LM Test	322.49 ***				215.53 ***			301.14 ***
DW Test	0.7773783				0.7840227			0.749061
Wald Test (χ2)	2.4 × 106 ***				Not Applicable			3.5 × 105 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed significance level, respectively. ROA = Earnings on Total Assets; CONST = Constant; MVAIC = Extended VAIC; CEE = Efficiency of Employed Capital; HCE = Human Capital Efficiency; RCE = Relational Capital Efficiency; SCE = Structural Capital Efficiency; ICE = Intellectual Capital Efficiency; LEV = Leverage; Wald = Modified Wald; LM = Breusch and Pagan Lagrangian Multiplier; DW = Durbin–Watson; Coef. = Coefficient.

Table 7. Regression results of IC and its components on ROE (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0570661 (2.7) ***	0.0501825 (1.46)	0.0339922 (1.12)	0.0339922 (1.1)	−0.059958 −(3.12) ***	−0.0547691 −(1.87) *	−0.07553 −(2.39) **	−0.07553 −(2.03) **	0.0512306 (2.15) **	0.0965039 (3.24) ***	0.1240319 (4.48) ***	0.1240319 (1.4)
MVAIC	0.0041278 (2.76) ***	0.0069616 (3.57) ***	0.0101489 (4.04) ***	0.0101489 (2.58) **
ICE					0.0041551 (3.43) ***	0.0049354 (2.98) ***	0.0068152 (2.9) ***	0.4614463 (4.3) ***
CEE					0.401189 (13.54) ***	0.4112953 (9.87) ***	0.4614463 (7.42) ***	0.0068152 (1.91) *	0.4009257 (15.78) ***	0.2789568 (7.95) ***	0.1753941 (3.77) ***	0.1753941 (0.64)
HCE									0.0029447 (2.76) ***	0.0038115 (2.8) ***	0.0058229 (3.5) ***	0.0058229 (2.5) ***
SCE									−0.0634829 −(2.61) ***	−0.0597291 −(2.7) ***	−0.0599263 −(2.6) ***	−0.0599263 −(1.13)
RCE									−0.3771422 −(11.09) ***	−0.3760466 −(15.96) ***	−0.3906461 −(16.35) ***	−0.3906461 −(7.74) ***
ASSETS	1.79 × 10−6 (4.08) ***	2.61 × 10−6 (3.88) ***	3.90 × 10−6 (3.61) ***	3.90 × 10−6 (1.5)	2.03 × 10−6 (5.7) ***	3.00 × 10−6 (5.45) ***	5.48 × 10−6 (5.39) ***	5.48 × 10−6 (1.44)	1.78 × 10−6 (5.89) ***	2.72 × 10−6 (5.81) ***	3.58 × 10−6 (5.00) ***	3.58 × 10−6 (2.04) **
LEV	−0.0149594 −(2.1) **	−0.03543 −(3.69) ***	−0.0591682 −(4.55) ***	−0.0591682 −(1.4)	−0.0211798 −(3.66) ***	−0.0432658 −(5.3) ***	−0.0844781 −(6.8) ***	−0.0844781 −(1.35)	−0.0178126 −(3.63) ***	−0.0406823 −(6.09) ***	−0.057066 −(6.49) ***	−0.057066 −(1.82) ***
R2 (Overall)	0.0644	0.0641	0.0594	0.0594	0.3864	0.3732	0.3089	0.3089	0.5622	0.5026	0.3636	0.3636
F-statistics	8.89 ***	30.98 ***	11.97 ***	3.39 **	55.16 ***	131.35 ***	23.85 ***	8.74 ***	74.63 ***	502.72 ***	81.8 ***	2817.52 ***
Observations	345	345	345	345	345	345	345	345	345	345	345	345
DWH Test	10.23 **				20.46 ***				37.18 ***
LM Test	253.37 ***				156.67 ***				248.08 ***
DW Test	0.7561961				0.7525604				0.7951258
Wald Test (χ2)	2.0 × 107 ***				4.2 × 107 ***				4.2 × 106 ***

Note(s): ***, **^, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed respectively.

Table 7. Regression results of IC and its components on ROE (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0570661 (2.7) ***	0.0501825 (1.46)	0.0339922 (1.12)	0.0339922 (1.1)	−0.059958 −(3.12) ***	−0.0547691 −(1.87) *	−0.07553 −(2.39) **	−0.07553 −(2.03) **	0.0512306 (2.15) **	0.0965039 (3.24) ***	0.1240319 (4.48) ***	0.1240319 (1.4)
MVAIC	0.0041278 (2.76) ***	0.0069616 (3.57) ***	0.0101489 (4.04) ***	0.0101489 (2.58) **
ICE					0.0041551 (3.43) ***	0.0049354 (2.98) ***	0.0068152 (2.9) ***	0.4614463 (4.3) ***
CEE					0.401189 (13.54) ***	0.4112953 (9.87) ***	0.4614463 (7.42) ***	0.0068152 (1.91) *	0.4009257 (15.78) ***	0.2789568 (7.95) ***	0.1753941 (3.77) ***	0.1753941 (0.64)
HCE									0.0029447 (2.76) ***	0.0038115 (2.8) ***	0.0058229 (3.5) ***	0.0058229 (2.5) ***
SCE									−0.0634829 −(2.61) ***	−0.0597291 −(2.7) ***	−0.0599263 −(2.6) ***	−0.0599263 −(1.13)
RCE									−0.3771422 −(11.09) ***	−0.3760466 −(15.96) ***	−0.3906461 −(16.35) ***	−0.3906461 −(7.74) ***
ASSETS	1.79 × 10−6 (4.08) ***	2.61 × 10−6 (3.88) ***	3.90 × 10−6 (3.61) ***	3.90 × 10−6 (1.5)	2.03 × 10−6 (5.7) ***	3.00 × 10−6 (5.45) ***	5.48 × 10−6 (5.39) ***	5.48 × 10−6 (1.44)	1.78 × 10−6 (5.89) ***	2.72 × 10−6 (5.81) ***	3.58 × 10−6 (5.00) ***	3.58 × 10−6 (2.04) **
LEV	−0.0149594 −(2.1) **	−0.03543 −(3.69) ***	−0.0591682 −(4.55) ***	−0.0591682 −(1.4)	−0.0211798 −(3.66) ***	−0.0432658 −(5.3) ***	−0.0844781 −(6.8) ***	−0.0844781 −(1.35)	−0.0178126 −(3.63) ***	−0.0406823 −(6.09) ***	−0.057066 −(6.49) ***	−0.057066 −(1.82) ***
R2 (Overall)	0.0644	0.0641	0.0594	0.0594	0.3864	0.3732	0.3089	0.3089	0.5622	0.5026	0.3636	0.3636
F-statistics	8.89 ***	30.98 ***	11.97 ***	3.39 **	55.16 ***	131.35 ***	23.85 ***	8.74 ***	74.63 ***	502.72 ***	81.8 ***	2817.52 ***
Observations	345	345	345	345	345	345	345	345	345	345	345	345
DWH Test	10.23 **				20.46 ***				37.18 ***
LM Test	253.37 ***				156.67 ***				248.08 ***
DW Test	0.7561961				0.7525604				0.7951258
Wald Test (χ2)	2.0 × 107 ***				4.2 × 107 ***				4.2 × 106 ***

Note(s): ***, **^, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed respectively.

Table 8. Regression results of IC and its components on Tobin’s Q (bold results denote statistically accepted).

Independent and Control Variables	Model-1			Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	1.745967 (13.39) ***	1.682516 (7.99) ***	1.703338 (18.54) ***	0.8413863 (8.49) ***	1.310703 (8.4) ***	1.537694 (14.98) ***	1.537694 (10.45) ***	0.4971401 (3.5) ***	1.118647 (6.56) ***	1.400504 (10.99) ***	1.400504 (6.3) ***
MVAIC	0.0138256 (1.5)	0.022322 (3.1) ***	0.022637 (2.96) ***
ICE				0.0140365 (2.25) **	0.0133248 (1.89) *	0.017595 (2.3) **	0.017595 (1.69) *
CEE				3.083057 (20.18) ***	1.48605 (8.17) ***	0.7051904 (3.49) ***	0.7051904 (1.71) *	3.121081 (20.59) ***	1.710344 (9.2) ***	0.9414071 (4.39) ***	0.9414071 (1.54)
HCE								0.0135228 (2.12) **	0.0153789 (2.19) **	0.0191032 (2.49) **	0.0191032 (1.87) *
SCE								0.3479353 (2.4) **	0.0703141 (0.66)	0.0206345 (0.19)	0.0206345 (0.29)
RCE								0.8712525 (4.29) ***	0.4864238 (4.35) ***	0.3196176 (2.9) ***	0.3196176 (2.96) ***
ASSETS	−1.10 × 10−6 −(0.41)	−0.0000114 −(3.97) ***	−0.0000144 −(4.38) ***	7.44 × 10−7 (0.41)	−5.41 × 10−6 −(2.05) **	−0.000012 −(3.64) ***	−0.000012 −(1.84) *	1.17 × 10−6 (0.65)	−4.13 × 10−6 −(1.59)	−0.0000103 −(3.11) ***	−0.0000103 −(1.95) *
LEV	−0.0220388 −(0.5)	0.1281178 (3.49) ***	0.1580444 (4.00) ***	−0.0701223 −(2.35) **	0.0342243 (0.96)	0.1197649 (2.96) ***	0.1197649 (1.44)	−0.0743231 −(2.54) **	0.0139269 (0.4)	0.0951984 (2.35) ***	0.951984 (1.67) *
R2 (Overall)	0.0022	0.0032	0.0026	0.5428	0.4951	0.1875	0.1875	0.5636	0.5367	0.3099	0.3099
F-statistics	1.26	27.29 ***	10.47 ***	103.09 ***	86.64 ***	11 ***	3.07 **	75.05 ***	111.96 ***	9.32 ***	47.19 ***
Observations	345	345	345	345	345	345	345	345	345	345	345
DWH Test	4.65			84.87 ***				32.36 ***
LM Test	561.49 ***			350.13 ***				369.28 ***
DW Test	0.4639			0.6833721				0.6091197
Wald Test (χ2)	Not Applicable			1.6 × 105 ***				2.6 × 105 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed, respectively.

Table 8. Regression results of IC and its components on Tobin’s Q (bold results denote statistically accepted).

Independent and Control Variables	Model-1			Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	1.745967 (13.39) ***	1.682516 (7.99) ***	1.703338 (18.54) ***	0.8413863 (8.49) ***	1.310703 (8.4) ***	1.537694 (14.98) ***	1.537694 (10.45) ***	0.4971401 (3.5) ***	1.118647 (6.56) ***	1.400504 (10.99) ***	1.400504 (6.3) ***
MVAIC	0.0138256 (1.5)	0.022322 (3.1) ***	0.022637 (2.96) ***
ICE				0.0140365 (2.25) **	0.0133248 (1.89) *	0.017595 (2.3) **	0.017595 (1.69) *
CEE				3.083057 (20.18) ***	1.48605 (8.17) ***	0.7051904 (3.49) ***	0.7051904 (1.71) *	3.121081 (20.59) ***	1.710344 (9.2) ***	0.9414071 (4.39) ***	0.9414071 (1.54)
HCE								0.0135228 (2.12) **	0.0153789 (2.19) **	0.0191032 (2.49) **	0.0191032 (1.87) *
SCE								0.3479353 (2.4) **	0.0703141 (0.66)	0.0206345 (0.19)	0.0206345 (0.29)
RCE								0.8712525 (4.29) ***	0.4864238 (4.35) ***	0.3196176 (2.9) ***	0.3196176 (2.96) ***
ASSETS	−1.10 × 10−6 −(0.41)	−0.0000114 −(3.97) ***	−0.0000144 −(4.38) ***	7.44 × 10−7 (0.41)	−5.41 × 10−6 −(2.05) **	−0.000012 −(3.64) ***	−0.000012 −(1.84) *	1.17 × 10−6 (0.65)	−4.13 × 10−6 −(1.59)	−0.0000103 −(3.11) ***	−0.0000103 −(1.95) *
LEV	−0.0220388 −(0.5)	0.1281178 (3.49) ***	0.1580444 (4.00) ***	−0.0701223 −(2.35) **	0.0342243 (0.96)	0.1197649 (2.96) ***	0.1197649 (1.44)	−0.0743231 −(2.54) **	0.0139269 (0.4)	0.0951984 (2.35) ***	0.951984 (1.67) *
R2 (Overall)	0.0022	0.0032	0.0026	0.5428	0.4951	0.1875	0.1875	0.5636	0.5367	0.3099	0.3099
F-statistics	1.26	27.29 ***	10.47 ***	103.09 ***	86.64 ***	11 ***	3.07 **	75.05 ***	111.96 ***	9.32 ***	47.19 ***
Observations	345	345	345	345	345	345	345	345	345	345	345
DWH Test	4.65			84.87 ***				32.36 ***
LM Test	561.49 ***			350.13 ***				369.28 ***
DW Test	0.4639			0.6833721				0.6091197
Wald Test (χ2)	Not Applicable			1.6 × 105 ***				2.6 × 105 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed, respectively.

Table 9. Regression results of IC and its components on SGR (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0400075 (3.22) ***	0.0277383 (1.38)	0.0162747 (0.88)	0.0162747 (0.78)	0.0427672 (3.06) ***	0.0252122 (1.17)	0.0104131 (0.49) ***	0.0104131 (0.26) ***	0.0994013 (4.93) ***	0.0748979 (2.95) ***	0.0679197 (2.65) ***	0.0679197 (1.23)
MVAIC	0.0045779 (5.2) ***	0.0073841 (6.33) ***	0.0105 (6.81) ***	0.0105 (5.55) ***
ICE					0.0045773 (5.19) ***	0.0073646 (6.26) ***	0.0103216 (6.56) ***	0.0103216 (5.32) ***
CEE					−0.0047856 −(0.22)	0.0168623 (0.57)	0.0346535 (0.83)	0.0346535 (0.27)	−0.0141915 −(0.66)	−0.0190274 −(0.63)	−0.0437545 −(1.02)	−0.0437545 −(0.32)
HCE									0.0049233 (5.45) ***	0.0072571 (6.15) ***	0.0100405 (6.53) ***	0.0100405 (5.07) ***
SCE									−0.0611595 −(2.97) ***	−0.0261977 −(1.34)	−0.0103904 −(0.49)	−0.0103904 −(0.26)
RCE									−0.1046962 −(3.64) ***	−0.1060078 −(5.03) ***	−0.1074354 −(4.86) ***	−0.1074354 −(3.77) ***
ASSETS	2.03 × 10−6 (7.85) ***	2.13 × 10−6 (5.36) ***	2.28 × 10−6 (3.43) ***	2.28 × 10−6 (2.17) **	2.03 × 10−6 (7.81) ***	2.15 × 10−6 (5.34) ***	2.36 × 10−6 (3.47) ***	2.36 × 10−6 (1.98) *	2.00 × 10−6 (7.83) ***	1.99 × 10−6 (5.00) ***	1.85 × 10−6 (2.79) ***	1.85 × 10−6 (2.11) **
LEV	−0.0175048 −(4.18) ***	−0.0242569 −(4.23) ***	−0.0335945 −(4.21) ***	−0.0335945 −(1.89) ***	−0.0173581 −(4.12) ***	−0.0246316 −(4.22) ***	−0.0349491 −(4.2) ***	−0.0349491 −(1.74) *	−0.0174267 −(4.2) ***	−0.0223781 −(3.89) ***	−0.027504 −(3.38) ***	−0.027504 −(1.87) *
R2 (Overall)	0.2149	0.2054	0.1748	0.1748	0.2131	0.2045	0.1748	0.1748	0.2411	0.2276	0.1815	0.1815
F-statistics	32.4 ***	71.62 ***	20.73 ***	10.29 ***	24.29 ***	71.51 ***	15.59 ***	7.95 ***	19.21 ***	101.75 ***	15.52 ***	235.13 ***
Observations	345	345	345	345	345	345	345	345	345	345	345	345
DWH Test	12.82 ***				11.35 ***				14.09 **
LM Test	222.1 ***				221.31 ***				231.47 ***
DW Test	0.9549852				0.9626458				0.9654752
Wald Test (χ2)	2.8 × 106 ***				2.9 × 106 ***				5.0 × 106 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed, respectively.

Table 9. Regression results of IC and its components on SGR (bold results denote statistically accepted).

Independent and Control Variables	Model-1				Model-2				Model-3
	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)	Pooled OLS Coef.(t)	RE Coef.(z)	FE Coef.(t)	Robust FE Coef.(t)
CONST	0.0400075 (3.22) ***	0.0277383 (1.38)	0.0162747 (0.88)	0.0162747 (0.78)	0.0427672 (3.06) ***	0.0252122 (1.17)	0.0104131 (0.49) ***	0.0104131 (0.26) ***	0.0994013 (4.93) ***	0.0748979 (2.95) ***	0.0679197 (2.65) ***	0.0679197 (1.23)
MVAIC	0.0045779 (5.2) ***	0.0073841 (6.33) ***	0.0105 (6.81) ***	0.0105 (5.55) ***
ICE					0.0045773 (5.19) ***	0.0073646 (6.26) ***	0.0103216 (6.56) ***	0.0103216 (5.32) ***
CEE					−0.0047856 −(0.22)	0.0168623 (0.57)	0.0346535 (0.83)	0.0346535 (0.27)	−0.0141915 −(0.66)	−0.0190274 −(0.63)	−0.0437545 −(1.02)	−0.0437545 −(0.32)
HCE									0.0049233 (5.45) ***	0.0072571 (6.15) ***	0.0100405 (6.53) ***	0.0100405 (5.07) ***
SCE									−0.0611595 −(2.97) ***	−0.0261977 −(1.34)	−0.0103904 −(0.49)	−0.0103904 −(0.26)
RCE									−0.1046962 −(3.64) ***	−0.1060078 −(5.03) ***	−0.1074354 −(4.86) ***	−0.1074354 −(3.77) ***
ASSETS	2.03 × 10−6 (7.85) ***	2.13 × 10−6 (5.36) ***	2.28 × 10−6 (3.43) ***	2.28 × 10−6 (2.17) **	2.03 × 10−6 (7.81) ***	2.15 × 10−6 (5.34) ***	2.36 × 10−6 (3.47) ***	2.36 × 10−6 (1.98) *	2.00 × 10−6 (7.83) ***	1.99 × 10−6 (5.00) ***	1.85 × 10−6 (2.79) ***	1.85 × 10−6 (2.11) **
LEV	−0.0175048 −(4.18) ***	−0.0242569 −(4.23) ***	−0.0335945 −(4.21) ***	−0.0335945 −(1.89) ***	−0.0173581 −(4.12) ***	−0.0246316 −(4.22) ***	−0.0349491 −(4.2) ***	−0.0349491 −(1.74) *	−0.0174267 −(4.2) ***	−0.0223781 −(3.89) ***	−0.027504 −(3.38) ***	−0.027504 −(1.87) *
R2 (Overall)	0.2149	0.2054	0.1748	0.1748	0.2131	0.2045	0.1748	0.1748	0.2411	0.2276	0.1815	0.1815
F-statistics	32.4 ***	71.62 ***	20.73 ***	10.29 ***	24.29 ***	71.51 ***	15.59 ***	7.95 ***	19.21 ***	101.75 ***	15.52 ***	235.13 ***
Observations	345	345	345	345	345	345	345	345	345	345	345	345
DWH Test	12.82 ***				11.35 ***				14.09 **
LM Test	222.1 ***				221.31 ***				231.47 ***
DW Test	0.9549852				0.9626458				0.9654752
Wald Test (χ2)	2.8 × 106 ***				2.9 × 106 ***				5.0 × 106 ***

Note(s): ***, **, and * indicates p < 0.01, p < 0.05, and p < 0.10 two-tailed, respectively.

The same model and tables revealed that the CEE affects the accounting performance (ROA and ROE) at a 1% significant level with a substantial t value (11.53 and 4.3, respectively). However, it affects market performance poorly (p < 0.10, t = 1.71). Results suggest that CEE positively enhanced the firm’s accounting and market performance. It supports the third hypothesis (H3). The results of hypothesis three are unswerving with other studies [14,36,91,92] but differ from some studies [13,18,52].

Model 3 from Table 6, Table 7 and Table 8 was engaged for answering hypotheses four to six, which cover the individual effects of IC elements on the company’s accounting and market performance. Different diagnostic test results suggest that a robust FE regression model provides the optimum decision-making results. The overall R² score ranged from 0.3099 to 0.4129 for model 3, indicating that individual components of IC give a better explanation of the firm’s accounting and market performance than that of overall IC efficiency. The separation of IC and tangible capital can also better explain a firm’s performance. Regression results indicated that only HCE positively enhanced the company’s performance in both market and accounting. It strongly enhanced the company’s ROA (t value = 3.47, p < 0.01) rather than ROE (t value = 2.5, p < 0.05) and Tobin’s Q (t value = 1.87, p < 0.10). Results support hypothesis four (H4), which is a coincidence with the preceding studies [14,24,41,93] and opposed to some other earlier studies [13,36]. More investment in employees enhanced the organizational performance of Bangladeshi nonfinancial companies.

SCE showed an insignificant negative influence on the company’s accounting performance (t value of ROA is −0.62 and t value of ROE is −1.13) and a positive effect on market performance (t value of Tobin’s Q is 0.29, p > 0.10). Moreover, the expenses incurred for structural assets such as process, database, intellectual properties, and R&D, declined the firm’s accounting return but increased market return insignificantly. Our findings rejected hypothesis five (H5), which is parallel with several studies [41,44,52,94] and controversial with [13,18,36,53] studies.

The effects of RCE stalwartly dropped the firm’s accounting performance significantly with a high t value (−7.83, p < 0.01 for ROA and −7.74, p < 0.01 for ROE). However, RC efficiency enhanced market performance strongly with a t value of 2.96 (p < 0.01). The result specifies that more expenses for marketing, advertising, distribution, and external purposes lead to decreased accounting profitability and vice-versa with market performance. So, our hypothesis six (H6) was rejected.

Table 9 exhibits the regression results between IC and its distinct components on sustainable growth. The F-statistics showed significance (p < 0.01), establishing that the econometric model is fit for regression analysis. LM test for all three models indicated that Pooled OLS biased the regression results. Furthermore, the Hausman test confirms that the FE model provides the most reliable results over the RE regression model. Modified Wald test results reject the null hypothesis significantly (p < 0.01) and indicate heteroskedastic problems for every model.

Further, DW (Durbin–Watson) test results provide evidence of an autocorrelation problem for all the models. Consequently, we used a robust fixed-effect regression model to mitigate these heteroskedastic and autocorrelation issues. Model 1 and 2 demonstrates that overall R² is 0.1748, whereas model 3 showed 0.1815. It is evident that separate IC elements predicted more solid sustainable performance than aggregate IC performance.

Model 1 from Table 9 showed that MVAIC significantly uplifts sustainable growth (p < 0.01). MVAIC performance ensures business sustainability firmly (t value is 5.55). This result supported our hypothesis seven (H7), which is consistent with the results of [13,14,95] studies.

Model 2 depicts the evidence of ICE ensuring SG with a t value of 5.32 (p < 0.01), i.e., more IC efficiency fosters SG. Thus, our hypothesis eight (H8) is accepted. The same model and table show that the CEE cannot impact achieving sustainable growth (t value is 0.27, p > 0.10). The result implies that the investment in tangible capital (physical and financial) failed to enhance companies’ sustainable performance for the future. This result rejects our hypothesis nine (H9), which is consistent with the [13,96] studies and opposes the [14,18,23,97] studies.

Model 3 from the same table answered our hypotheses nine to eleven. Regression results revealed that HCE is a significant factor in achieving sustainable growth due to its significant impact (t value 5.07, p < 0.01) on SG. More investment involvement in human capital ensures SG for nonfinancial firms in Bangladesh. This outcome settles our hypothesis ten (H10), which conforms to various studies [14,18,53,98]. However, it differs from the study of Kasoga [13].

The same model also revealed that the firm’s SCE plays a negative insignificant (with t value of −0.26, p > 0.10) role on SG. More investment in SC, i.e., technology, process, database, and intellectual properties, declined SG performance insignificantly. This evidence suggests rejecting our hypothesis eleven (H11). The result is supported by the study of Hasan and Miah [18] and inconsistent with [13,14,53] results. A similar but significant impact was revealed regarding RCE with a t value of −3.77 and p < 0.01. More investment in RC, i.e., expenses related to third parties, declined the SG performance of the nonfinancial firms in Bangladesh. This result rejects our hypothesis twelve (H12). (

Table 10. Summary of Results.

Predictors	Expected Sign	Company’s Performance				Sustainable Growth
		ROA	ROE	TQ	Decision	SGR	Decision
MVAIC	+	+	+	+	H1 Accepted	+	H7 Accepted
ICE	+	+	+	+	H2 Accepted	+	H8 Accepted
CEE	+	+	+	+	H3 Accepted	Insig.	H9 Rejected
HCE	+	+	+	+	H4 Accepted	+	H10 Accepted
SCE	+	-Insig.	-Insig.	Insig.	H5 Rejected	-Insig.	H11 Rejected
RCE	+	-	-	+	H6 Rejected	-	H12 Rejected

).

5. Conclusions

IC is the most crucial resource that generates value for the company through better performance and sustainable competitive advantages. The study wishes to find out the effects of IC and its distinct components on the company’s performance and sustainable growth of nonfinancial companies in Bangladesh. The main finding of our research revealed that Intellectual and tangible capital efficiency positively enhances the company’s performance. IC promotes sustainable growth of nonfinancial companies in Bangladesh, whereas CEE cannot. HCE is the most influential factor for promoting business performance and SG. SCE and RCE impacted negatively on both the business performance and achieving SG. It means Bangladeshi nonfinancial companies did not properly utilize their Structural resources (process, database, technology, organizational culture, and philosophy) and external relational resources (customers, suppliers, banks, and other external parties).

5.1. Theoretical contributions

Our research contributes to the resource-based theory based on evidence that physical and intellectual capital contribute to business success and lead to survival with sustainable competitive advantages. This study also extends the prevailing literature by contributing to the relationship between IC, SG, and the company’s performance and its effects. This study synthesizes the effects of IC and its associated indicators on firms’ accounting, market, and sustainable performance.

5.2. Managerial Implications

Practically, this study offers knowledge to corporate executives about various IC components and their utilization patterns that can assist in rearranging the present policy. Bangladeshi companies need to adequately utilize their internal resources like a technological advantage, database, organizational culture, and intellectual properties. Relational activities with customers, suppliers, banks, and other potential customers are not satisfactory, which leads to the company’s declining performance (both financial and sustainable). To cope with the attainment of long-term survival opportunities, the nonfinancial companies in Bangladesh should use these structural and relational properties to beat complications nicely. Firms should carefully utilize tangible resources (physical and financial) to foster sustainable growth and attain the long-run mark. We noticed that the human resource utilization is satisfactory, and in such a conducive situation, firms should keep up with regular training to create and share knowledge within the organization, and taking steps to enhance employees’ morale and assertiveness for sustainable growth.

5.3. Future research directions

Although our research provides insights into the impact of IC and tangible resources on nonfinancial companies’ profitability and sustainability in Bangladesh, further study may be conducted in many ways. First, it may include more samples covering nonfinancial firms and extend the study period. Second, it may use all financial and nonfinancial sectors. Third, it may perform a comparison between financial and nonfinancial sectors. Fourth, it may conduct inter-country or inter-economy comparisons. Finally, it may use another model from the ROA approach like Economic Value Added (EVA), Knowledge Capital Earnings (KCE), or Calculated Intangible Value (CIV) to measure intellectual capital.