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\begin{document}

\title{Asset Structure relevance and Corporate Firm performance: Evidence from
Nigerian Manufacturing firms}



\author[1]{Emeka Emengini}%
\author[2]{Chika A. Anisiuba}%
\author[2]{Jacob Chukwuemeka}%
\affil[1]{University of Nigeria, Enugu Campus, Enugu ,Nigeria}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
This paper examines asset structure relevance for corporate performance
of Levered Firms in Nigeria. The data were collected from annual reports
of firms listed on Nigerian Exchange Group (NGX) from 2011 to 2020.
Descriptive statistics, correlation, pooled OLS regression analysis
including multicollinearity and heteroscedasticity tests were employed.
The study findings reveal: that contrary to low levered firms, fixed
asset ratio and customer asset ratio have passive influence on internal
and external corporate performance of high levered firms; unlike the low
levered firms, current asset ratio has significant impact on internal
corporate performance of high levered firms but tends to decrease in
external performance; Non-current asset ratio and ~intangible asset
ratio have passive influence on internal and external corporate
performance of both Low and high levered firms. These findings are
pointers to policymakers and investors on the possible effect of an
additional increase in asset mix. Beyond this, it also reveals how
assets are encumbered in debt financing, which is of serious concern to
managers. An implication of this, is that an understanding of a
company's asset structure improves understanding of the rigorous process
assets investment managers undergo daily, especially as it portends to
assets acquisition patterns or the components of the equity or debt.%
\end{abstract}%



\sloppy


\textbf{1. INTRODUCTION}

The growth of a business entity depends on a lot of factors and one of
such factors is the ability of the firm to use its assets to generate
income and meet its obligations as at when due in relation to their
assets or capital (Chue and Xu 2022). Globalisation and steep
competitive nature of business environment nowadays have necessitated
the effective utilisation of assets as a veritable instrument for
sustainability and management of companies; through the determination of
the degree of funds allocated for each asset component as companies'
investment structure continues to differ However, investment patterns in
Fixed, current and non-current assets are quite prevalent and varies
greatly within firms and industries due to differences in business
environments, for instance, capital-intensive businesses like
construction, oil and gas firms are heavier in non-current assets when
compared to technology or service businesses (Peninah, Dom and Omondi,
2021, Thum-Thysen, Voigt, Bilbao-Osorio, Maier, and Ognyanova, 2019,
Balakrishnan and Fox 2019).

Most often, assets are used as collateral, especially tangible fixed
assets, this implies that a company which has a high level of fixed
assets tend to be rated higher by a lender of fund. That type of
arrangement of high level of fixed assets may impact on the cost of the
funds, as there is a high chance that lenders will tend to reduce
interest due to the high confidence, they have in the long run survival
of the company. Due to these possibilities, credit ratings and cost of
debts differ as asset structure differs (Ali 2011). A high levered firm
is one that has more debt instrument than assets or equity and `Lower
debt instruments than assets or equity' is always implied when
expressing a low levered firm (Ariyani, Pangestuti, and Raharjo 2018).
Thus, asset's structure differs in low and high-levered firms, hence it
has different pulling effects. Thus, there is a logical argument that
asset structure investment affects the structural relationship between
firms' capacities by formation of capital stock, although, there are
mixed results among researchers on how it affects firm performance
(Sirmon \emph{et al} , 2007, Peninah, Dom and Omondi, 2021, Ariyani,
Pangestuti, and Raharjo 2018). Asset structure has attracted much
attention from scholars and investors, yet most of these academic works
were found to emanate from developed economies such as U.S, U.K, China
and few from other developing economies (Song and Liu, 2013, Olmo and
Herrero, 2017, Ahmad, Hunjra and Taskin, 2021, Peninah, Dom and Omondi,
2021, Strebulaev and Yang 2013, Ferdaous and Rahman 2019). Hence, there
exist paucity of empirical literature on relevance of asset structure in
influencing accounting -- based performance of low or high levered firms
in Nigeria.

Thus, the study distinguishes itself by evaluating the impact and
relevance of asset structure on firm performance from the perspective of
both internal and external accounting - based performances of return on
asset and market -- based value (Tobin's Q) respectively. The study
therefore fills the gap in empirical literature by contributing to
accounting practice in the following ways: first, it advances the
current understanding of the critical role of asset structure on low and
high-levered manufacturing companies. Second, by exploring asset
structure impact on internal and external corporate firm performance of
high and low-levered manufacturing firms which provide basic theoretical
support for improving investment efficiency and optimising firms'
structure. From the foregoing, the entire work is arranged in the
following order; Section one: Introduction; Section two: Review of
related literature and Hypotheses development; Section three: Materials
and methods; Section four: Results and discussion of findings; Section
five: Conclusion and Implications of the study.

\textbf{Review of Related Literature and Hypotheses Development}

Many different scholars have viewed asset structure from different
perspectives based on the area of their interest; for instance,
Koralun-Bereznicka (2013) defines asset structure as the combination of
the various asset components which are identified as financial fixed
assets; tangible fixed assets; current assets; current investments and
cash in hand and at bank. Asset structure components include; non --
current assets, fixed assets, intangible assets, current assets, Account
Receivables, Cash holdings and customer asset intensity (Peninah, Dom
and Omondi, 2021). Asset structure also means the ratio of the
relationship between assets in the company and the proportion of total
assets as well.

A high levered firm is one that has more debt instrument than assets or
equity. Lower debt instruments than assets or equity is always implied
when expressing a low levered firm (Ariyani, Pangestuti, and Raharjo
2018). Leverage as an investment strategy is implied when borrowing
money, or specifically, on the use of various financial instruments or
borrowed capital in order to increase the potential return of an
investment (Ahmad, Hunjra and Taskin, 2021, Ramlia, Latanb and Solovdab,
2018, Abdullah and Tursoy, 2021, Chappell and Jaffe 2018). A high
levered firm is one that has more debt instrument than equity or asset
unlike a low levered firm which has more equity/assets than debt
instrument. According to Lamont (1997), highly levered firms display a
great risk and the likelihood of default or bankruptcy. However, low
debt-to-asset ratios does not always imply that a firm is optimizing the
deployment of its assets but it can also mean that the firm is not
taking advantage of the increased profits that financial leverage may
bring (Ahmad, Hunjra, and Taskin, 2021).

2.2 Theoretical review

We have many theories concerning the firm and its performance but we
highlighted two of such theories here that have more relational
foundation to our study;

\emph{2.2.1 The Firm Performance Theory}

Unlike pecking order theory which specifies a hierarchical order in
which firms should source their funds (internal, debt or equity
funding), firm performance theory posits that firms must obtain cash
equilibrium to remain solvent. But certain events can cause a firm to
lose cash equilibrium. The firm performance theory suggests that losing
cash equilibrium creates financial stress on the firm. The ability to
pay obligations as they come due, how much firm performance had the
company maintained, how the company obtains cash and how it spends cash,
can indicate whether a company remains financially healthy or has become
distressed. But at same time, strategic manager's perception can affect
different types of firms' performances (operating, investing,
financing). Information from the firm performance statement may indicate
what stage of financial performance a firm is in, and provide
information about management actions to regain cash equilibrium and
achieve its organisational responsibilities. These propositions are
corroborated by Dao and Ta (2020), in their study on meta-analysis:
capital structure and firm performance.

\emph{Resource-Based Theory}

Although many studies have interpreted firms' performance from different
theoretical lenses like competency-based perspective, Prahalad, and
Hamel (1990), other studies as Poudel, Carter and Lonial (2019), opted
for a different theory to measure firms' performance. As a result, this
study invokes resource-based view (RBV), which argues that tangible and
intangible resources, including the capability to coordinate those
assets or inputs of production in a strategically successful way forms
the basis for competitiveness in the dynamic business environment,
(Helfat, and Peteraf, 2003). However, studies of Tapis, Templeton and
Niu (2020) focus on different properties of firm assets and the
contribution of the assets in managerial decision making that scale up
firm's competitive advantage. Amongst the value and competitive
advantages derived from assets, there are key characteristics RBV
identified: scarcity, tradability and transferability (Amit, and
Schoemaker, 1993, Grant, 1991), on one hand, competitive superiority,
appropriability, complementarity, durability, non-imitability,
replicability and non-substitutability (Amit, and Schoemaker, 1993,
Collis, and Montgomery. 1995, Grant, 1991) on the other hand. The
central tenet of RBV is that idiosyncratic firms' resources are the key
factors for sustained competitive advantage, and firms' orientation is
one of these unique resources. Above all these, RBV explains that a
manager's role is crucial to firm-level competitiveness since their
decision-making of the environmental circumstances dictate the selection
of resources to be exploited, developed, and protected (Ruan, (2020,
Fahy, 2002). In addition, in structuring the firm-level resource
portfolio, managers are able to make successful decisions on strategic
resource divestments (Sirmon, Hitt, and Ireland, 2007, Chappell and
Jaffe, 2018), hence this study is contextualised on RBV theory.

\emph{2.1 Hypotheses Development}

The role of company managers is crucial to firm-level competitiveness
since their perceptions of the environmental circumstances dictate the
selection of resources to be exploited, developed, and protected (Fahy,
2002). Understanding asset structure and the optimum mix or deployment
of these assets is a critical issue for sustainable performance in
manufacturing firms. Different authors examined asset structure from
different perspectives based on the direction of their study ((Ahmad,
Hunjra and Taskin, 2021, Peninah, Dom and Omondi, 2021). Some view it
from the perspective of its relationship with ownership concentration,
human capital, structural capital or corporate governance (Peizhi and
Ramzan, 2020, Ariyani, Pangestuti, and Raharjo, 2018). Few others
believe in structuring the firm- level portfolio so that managers will
be able to make optimal decisions on strategic divestments (Sirmon et
al, 2007, Peninah, Dom and Omondi, 2021). But this study concentrates on
how assets structure level has impacted on accounting based firm
performance of high or low leverage firms. Specifically, the hypotheses
for this study are discussed in relation to the following variables of
asset structure: intangible assets, fixed assets, current assets, non --
current assets and customer asset intensity.

\emph{A. Non -- current assets}

Non--current assets are assets not used up within one accounting period.
Coste, Tudor and Pali-Pista, (2014) classified it into tangible
non-current assets and intangible non-current assets. The former which
is largely referred to as fixed assets have a physical existence, and
include property, plant and equipment, trucks, furniture and fittings,
and automobiles among others. The latter include Trademarks, Patents,
Goodwill, long term investments among others. To be more analytical and
direct to our study interest, we examined fixed and intangible assets
separately from non-current assets. The reason was not necessary to
comply with international financial reporting standard (IFRS) 5
requirements or other similar standards but to show the extent to which
non--current assets ratio generally impacts the performance of high or
low levered firms as different from fixed assets or intangible assets.
Given the above propositions, the following hypothesis is stated thus:

\emph{Hypothesis 1} \textbf{-} Non - current asset structure has
significant effect on the performance of high

and low levered manufacturing firms.

\emph{B. Fixed Asset Structure}

Fixed assets are assets that cannot be used up within one accounting
period and cannot easily or ordinarily be converted into cash within a
short period unlike current assets, all things being equal. They
constitute a major portion of the total assets of manufacturing firms.
The quality of it can help determine the quality of product and long-run
survival plan of a firm. Investment in tangible non-current assets helps
build up a firm's statement of financial position and it can be a
veritable source of finance to a firm when all other sources failed.
Thum-Thysen, \emph{et al} , (2019), examined investment in intangible
and fixed assets and their drivers and barriers; they found out that
tangible and intangible assets are affected differently when tested with
key determinants. They were of the opinion that barriers to investment
which is relevant to one asset type may indirectly impede investment in
other assets because there may be synergies among different asset types.
Dong, Leung and Cai (2012), carried out a study on over-investment in
fixed assets in Chinese firms and found out that certain level of fixed
asset holdings and overhead expenses which are covered by preferential
tax policies are found to be associated with lower risk-adjusted
performance. This therefore encourages investment in fixed assets, but
can as well lower liquidity position of a firm. Lyandres and Palazzo
(2016) carried a study on certain strategic considerations which are
important in shaping cash policies of innovative firms and found out
that firms with low asset tangibility (fixed assets) generally face
costly external financing and are prompted to build up precautionary
savings. Moreso, they posit that firm's equilibrium cash holdings are
related to the expected intensity of the competition. In the light of
the above, the following hypothesis is as stated below;

\emph{Hypothesis 2} \textbf{-} Fixed asset structure has significant
effect in the performance of high

and low levered manufacturing firms.

\emph{C. Intangible Asset Structure}

Intangible assets are non-physical and potential sources of future
economic gain; to a certain degree, they can be retainable and tradable
(Lev \& Daum 2004). Intangible asset structure includes research and
development (R\&D) which can be expensed or capitalized, for instance,
patents, trademarks, human resources and capabilities and organization's
competencies, database and technology, and relational capital (customer
and supplier networks, organizational design and process) (OECD 2006,
Jozef, Silvia, Teksty, and Publikacji 2018). According to Ciprian,
Valentin, Madalina, and Lucia (2012), the value of a company is largely
determined by its intangible assets. Intellectual capital is considered
the key factor of competitiveness, existing from the moment a seller
sets a good relationship with a customer. In like manner, Richard
(2022), posits that social capital can alter financing relationship and
helps to explain the preference of firms for equity financing, he
maintains that equity financing is favoured by firms that use large
amounts of social capital as it increases their value. Haji and Mohd
Ghazali (2018), investigated the role of intangible assets and
liabilities in performance of firms in Malaysia and found out that
intangible assets have a contrasting positive impact on firm performance
when compared to underperforming intangible liability of same firm. From
the above perspectives, we state the following hypothesis;

\emph{Hypothesis 3} \textbf{-} Intangible asset structure has
significant effect in the performance of high and low levered
manufacturing firms.

\emph{D. Current Asset Structure}

Unlike pecking order theory which specifies a hierarchical order in
which firms should source their funds (internal, debt or equity
funding), firm performance theory posits that firms must obtain cash
equilibrium to remain solvent. But certain events can cause a firm to
lose cash equilibrium. The firm performance theory suggests that losing
cash equilibrium creates financial stress on the firm. The ability to
pay obligations as they come due, how much firm performance had the
company maintained, how the company obtains cash and how it spends cash,
can indicate whether a company remains financially healthy or has become
distressed (Dao and Ta, 2020). There has been an inexhaustible discourse
by different authors on appropriate level of this class of assets for
optimum firm performance (Ahmad, Sulaiman and Mohammad, 2021, Lyandres
and Palazzo, 2016). Hitherto, Current asset management has gained
increased attention as firms begin to be aware of the difference between
profitability and liquidity (Ardina and Isnalita, 2018, Rehman, Liu, Wu
and Li, 2022). A firm can avoid purchasing capital assets like land, but
a business can't run without the provision of adequate current assets.
This is particularly true as the major role of the financial manager of
a firm is to spend greater number of his time managing current assets
and current liabilities. Ahmad, Sulaiman and Mohammad (2021) examine
Liquidity and Solvency management and its impact on firm's financial
performance of Jordanian manufacturing companies listed in Amman stock
exchange from 2010 - 2019. The results show that liquidity has negative
and non - significant impact on firm's financial performance while
solvency reveals a significant negative impact on firms' performance.
The findings of this study did not corroborate with the findings of our
study in regards to current assets ratio, although, it did not
specifically focus on high and low levered firms. Mathuva (2009) also
argues that the lack of efficient current asset management practices can
expose a firm to financial distress. In line with these propositions,
the following hypothesis is stated:

\emph{Hypothesis 4} - Current asset structure has significant impact on
the performance of high

and low levered manufacturing firms.

\emph{E. Customer Asset Structure}

Customer lifetime value is a fundamental and quantitative measure of the
financial consequences of the relationship which a firm has with its
customers because it provides a useful metric for judging both firm
actions and financial market valuation (Gupta and Lehmann, 2003). Gupta
and Lehman (2003), noted also that brand names, customer lists,
trademarks and patents are part of such intangible assets that investors
want to know about and are interested in it. Also, Rehman, Liu, Wu and
Li (2022), examine the relationship between customer concentration and
capital structure adjustment speed using a sample of US listed firms
from 1977 to 2022. The findings show that customer-concentrated firms
have a lower speed of leverage adjustment. And this affects leverage
adjustment speed mainly through increased cash flow volatility and asset
specificity. The findings also show negative association is more
pronounced in firms with high relationship specific investments and low
switching costs for their customers. In line with the above
perspectives, we state the following hypothesis;

\emph{Hypothesis 7} \textbf{.} Customer Asset structure has significant
effect in the performance of

high and low levered manufacturing firms.

\textbf{3. MATERIALS AND METHODS}

The study used data collected from annual financial statements of all
manufacturing firms listed on the Nigerian stock Exchange (Nigerian
Exchange Group) from 2011-2020 as sourced from MachameRatios Database.
As such, sixty (60) firms were sampled with 596 firm -- year
observations. Asset structure is the independent variable and is
measured in terms of non-current asset ratio (NASS), fixed asset ratio
(FASS), intangible asset ratio (IASS), current asset ratio (CUAS), and
customer asset intensity (CUAI) The internal firm performance is
measured by return on asset (RETA) and external performance of the firm
by market - based value of Tobin's Q (Wu \emph{et al} 2012).
Furthermore, we control the model's goodness of fit by using firm size
as a control variable. Leverage ratio is measured in terms of total
debt/total assets. The criteria for selecting high or low leverage firms
are based on its formula in finance literature of leverage ratio (total
debts/total assets) expressed in percentage as is in this study. In this
case, if leverage is 1 or less than 1 (that is 100\%), it is adjudged as
low levered whereas leverage ratio of above 1 is adjudged high. Details
on this can be found in supplementary material attached.

\emph{3.1 Variable Description}

Dependent variable is Firm performance which has its proxy as return on
asset (RETA) and Tobino s Q while the proxies for the independent
variables (Asset structure) are Non -- current assets, Intangible asset,
Fixed asset, current asset but Firm size was applied as control
variables. These were operationalised as shown in Table I.

Table I: Data and variables Description\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
CODES & MEANING & DATA PURPOSE & COMPUTATION\tabularnewline
\midrule
\endhead
LEVERAGE (LEVE) & Debt to Asset Ratio & Asset structure & Total
Debt/Total Asset\tabularnewline
RETA & Return on Asset & profitability & Return on Asset based on net
income is measured as profit after tax divided by total assets.
(PAT/total asset)\tabularnewline
TOBQ & Tobin's Q & firm value & Firm value or Tobin's Q in number is
computed as market capitalization + total liabilities - cash flow
divided by total asset.\tabularnewline
NASS & Non-current asset ratio & Asset structure & Non-current Assets
divided by total asset (\%)\tabularnewline
FASS & Fixed Asset ratio & Asset structure & Non - current assets minus
goodwill and intangible assets divided by total asset
(\%)\tabularnewline
IASS & Intangible asset ratio & Asset structure & Goodwill and
intangible asset divided by total asset (\%)\tabularnewline
CUAS & Current Asset Ratio & Asset structure & Current assets divided by
total asset (\%)\tabularnewline
CUAI & Customer Asset intensity & Asset structure & inventories and
receivables divided by total asset (\%)\tabularnewline
FSIZE & Firm size & Firm size & Firm size or log of total asset in
thousands is computed as the natural logarithm of total asset. This
measure is best for comparing companies with the same
currencies.\tabularnewline
\bottomrule
\end{longtable}

Source: Researcher's (2023).

\emph{3.2. Model Specification}

The study adapted the model specified by Mwaniki and Job (2017) which
was modified for the purposes of establishing the relationship between
the dependent variables and the linear combinations of several
determining variables captured in the study. Succinctly, the econometric
form of our models is expressed as:

\(\text{RETA}_{\text{it}}=\beta_{0}+\beta_{1}\text{NASS}_{\text{it}}+\ \beta_{2}\text{FASS}_{\text{it}}+\ \beta_{3}\text{IASS}_{\text{it}}+\ \beta_{4}\text{CUAI}_{\text{it}}\ +\beta_{5}\text{CUAS}_{\text{it}}\ +\ \beta_{6}\text{FSIZE}_{\text{it}}+\mu_{\text{it}}\)\emph{\ldots{} (Equation 1)}

\(\text{TOBQ}_{\text{it}}=\beta_{0}+\beta_{1}\text{NASS}_{\text{it}}+\ \beta_{2}\text{FASS}_{\text{it}}+\ \beta_{3}\text{IASS}_{\text{it}}+\ \beta_{4}\text{CUAI}_{\text{it}}\ +\beta_{5}\text{CUAS}_{\text{it}}\ +\ \beta_{6}\text{FSIZE}_{\text{it}}+\mu_{\text{it}}\)\emph{\ldots{} (Equation .2)}

\textbf{Where:}

RETA = Profitability measure of corporate performance

TOBQ = Market value measure of corporate performance

NASS = non-current asset ratio

FASS = Fixed asset ratio

IASS = Intangible asset ratio

CUAI = Customer asset intensity ratio

CUAS = Current asset ratio

FSIZE = Firm size (Control Variable)

\selectlanguage{greek}β\selectlanguage{english}\textsubscript{0} = Constant

\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}- \selectlanguage{greek}β\selectlanguage{english}\textsubscript{6} = Slope Coefficient

\(\mu\) = Stochastic disturbance

i = i\textsuperscript{th} companies

t = time period

\emph{3.2 Descriptive Analysis}

Each variable in the study is examined based on the mean, standard
deviation, maximum and minimum. Table II displays the descriptive
statistics for the study.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
VARIABLES & MEAN & SD & MIN & MAX & NO OBS\tabularnewline
\midrule
\endhead
TOBQ & 1.47 & 1.99 & -16.06 & 11.30 & 594\tabularnewline
RETA & 2.78 & 19.28 & -195.89 & 176.27 & 595\tabularnewline
NASS & 53.79 & 23.99 & 0 & 99.83 & 596\tabularnewline
FASS & 42.29 & 23.39 & 0 & 98.82 & 596\tabularnewline
IASS & 1.94 & 6.02 & 0 & 41.04 & 596\tabularnewline
CUAI & 33.30 & 19.73 & 0.07 & 94.43 & 596\tabularnewline
CUAS & 46.21 & 23.99 & 0.17 & 100 & 596\tabularnewline
FSIZE & 7.21 & 0.86 & 5.24 & 9.31 & 596\tabularnewline
LEVE & 62.69 & 26.71 & 6.34 & 247.85 & 596\tabularnewline
\bottomrule
\end{longtable}

Source: Author (2023)

The result from Table II shows that the mean of corporate performance
when proxied in terms of the market -- based value (Tobin's Q) is 1.47
with a standard deviation of 1.99 whereas return on asset (RETA) has a
mean of 2.78 with a standard deviation of 19.28. In the case of the
independent variables, Table II shows that the mean of non-current asset
ratio (NASS) is 53.79 with a standard deviation of 23.99. In the same
vein, fixed asset ratio (FASS) has a mean of 42.29 and a standard
deviation of 23.39. Intangible asset ratio (IASS) has a mean of 1.94 and
a standard deviation of 6.02. Customer asset intensity (CUAI) has a mean
of 33.30 with a standard deviation of 19.73. Moreover, current asset
ratio (CUAS) has a mean of 46.21 with a standard deviation of 23.99. In
summary, these results reveal the level of dispersions of the variables
from the mean. Also, firm size (FSIZE), has a mean value of 7.21 and the
standard deviation is 0.86. Leverage has a mean value of 62.69 with a
standard deviation of 26.71.

\emph{3.3 Correlation Analysis}

In examining the association among the variables, we employed the
Spearman Rank Correlation Coefficient (correlation matrix), and the
results are as presented in Table III

Table III: Correlation analysis\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& TOBQ & RETA & NASS & FASS & IASS & CUAI & CUAS & FSIZE &
LEVE\tabularnewline
\midrule
\endhead
TOBQ & 1.00 & & & & & & & &\tabularnewline
RETA & 0.38 & 1.00 & & & & & & &\tabularnewline
NASS & 0.08 & -0.15 & 1.00 & & & & & &\tabularnewline
FASS & 0.18 & -0.12 & 0.74 & 1.00 & & & & &\tabularnewline
IASS & 0.07 & 0.13 & 0.03 & -0.11 & 1.00 & & & &\tabularnewline
CUAI & -0.08 & 0.02 & -0.82 & -0.54 & -0.04 & 1.00 & & &\tabularnewline
CUAS & -0.08 & 0.15 & -1.00 & -0.74 & -0.03 & 0.82 & 1.00 &
&\tabularnewline
FSIZE & 0.08 & 0.24 & 0.06 & -0.04 & 0.43 & -0.05 & -0.06 & 1.00
&\tabularnewline
LEVE & 0.13 & -0.40 & -0.04 & -0.04 & -0.02 & 0.13 & 0.04 & 0.05 &
1.00\tabularnewline
\bottomrule
\end{longtable}

Author's computation (2023)

The results in Table III show that non-current asset ratio, fixed asset
ratio and intangible asset ratio has a positive association with the
dependent variables of corporate performance when measured by market
value (Tobin's Q) of 0.08, 0.18 and 0.07 respectively. However, there is
a negative association between customer asset intensity ratio, current
asset ratio and the dependent variables of corporate performance when
measured by market value (Tobin's Q) of -0.08 and -0.08 respectively.
For the control variable, the result shows that there is a positive
association between firm size and the dependent variable of corporate
performance when measured by market value (Tobin's Q) (0.08). There is a
positive association between leverage and the dependent variable of
corporate performance when measured by market value (Tobin's Q) (0.13).

On the other hand, the result shows that non-current asset ratio, fixed
asset ratio and leverage has negative association with the dependent
variable when measured by profitability (RETA) which is shown as -0.15,
- 0.12 and -0.40 respectively. We also find that there is positive
association between intangible asset ratio, customer asset intensity,
current asset ratio with the dependent variable of corporate performance
when measured by profitability (RETA) of 0.13, 0.02, 0.15 respectively.
For the control variable, the result shows that there is a positive
association between firm size and the dependent variable of corporate
performance when measured by profitability (RETA) (0.24). However, all
the associations are seen to be weak, hence there is no need to suspect
the presence of multicollinearity in the model. Nonetheless, a
regression analysis was carried out since correlation test does not
capture cause-effect relationship.

\textbf{4.0: RESULTS}

The study made use of regression analysis and the OLS pooled results and
the Robust OLS regression results from STATA\_15.0x64 are as presented
in Table IV and discussed.

Table IV: Regression Result\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
LOW LEVERAGE FIRMS\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
LOW LEVERAGE FIRMS\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
HIGH LEVERAGE FIRMS\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
HIGH LEVERAGE FIRMS\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Variables\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
RETA Model (Pooled OLS)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
RETA Model (Robust Regression)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
TOBQ Model (Pooled OLS)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
TOBQ Model (Robust Regression)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
RETA Model (Pooled OLS)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
RETA Model (Robust Regression)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
TOBQ Model (Pooled OLS)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
TOBQ Model (Robust Regression)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
CONS.\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-6.931 \{0.559\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-13.738 \{0.008\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-1.571 \{0.259\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.060 \{0.871\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-145.927 \{0.261\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-30.875 \{0.352\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
18.904 \{0.003\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
20.188 \{0.003\} **\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
NASS\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.215 \{0.010\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.022 \{0.550\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.021 \{0.030\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.001 \{0.831\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.554 \{0.483\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.306 \{0.110\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.036 \{0.316\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.044 \{0.254\}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
FASS\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.017 \{0.728\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.003 \{0.897\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.013 \{0.024\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.005 \{0.001\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.025 \{0.963\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.074 \{0.568\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.003 \{0.913\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.002 \{0.928\}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
IASS\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.002 \{0.985\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.044 \{0.426\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.017 \{0.257\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.006 \{0.140\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
2.232 \{0.384\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.997 \{0.112\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.008 \{0.942\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.000 \{0.998\}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
CUAI\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.274 \{0.000\} ***\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.114 \{0.000\} ***\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.018 \{0.010\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.002 \{0.247\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-2.495 \{0.010\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-0.100 \{0.732\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.037 \{0.373\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.035 \{0.431\}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
CUAS\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
3.253 \{0.101\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
2.972 \{0.001\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.332 \{0.154\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.019 \{0.759\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
35.814 \{0.086\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
16.056 \{0.006\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-2.335 \{0.018\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-2.644 \{0.013\} **\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
FSIZE\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
2.764 \{0.002\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1.724 \{0.000\} ***\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.402 \{0.000\} ***\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.090 \{0.001\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
4.625 \{0.690\}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-6.727 \{0.026\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-1.180 \{0.032\} **\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
-1.137 \{0.054\} **\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
F-Statistics\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.04 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.94 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.79 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.75 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.63 (0.0384)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.63 (0.0007)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.97 (0.0060)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.71 (0.0085)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
R- Squared\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0891\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0891\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0590\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0590\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.3693\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.3693\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.4781\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.4781\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
VIF Test\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.42\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.40\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Het. Test\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
35.41 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
35.41 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
23.88 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
23.88 (0.0000)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.01 (0.0252)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.01 (0.0252)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.60 (0.0011)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.60 (0.0011)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Author's computation (2023)

Note: (1) bracket \{\} are p-values

(2) **, ***, implies statistical significance at 5\% and 1\% levels
respectively

In terms of the low levered firms in Table IV; the result of the Pool
OLS regression shows an R-square value of 0.0891 for the return on asset
model and 0.0590 for the Tobin Q model. This indicates that
approximately 9\% and 6\% of the systematic variations in corporate
performance (when measured in terms of internal and external performance
(ROA and Tobin Q) respectively) are jointly explained by the independent
and control variables in the model during the period under study. This
implies that variations in corporate performance of listed firms in
Nigeria cannot be 100 percent explained by the asset structure proxies
employed in this study. However, the unexplained part of corporate
performance is attributed to the exclusion of other independent
variables that are not within the scope of our study but have been
captured as error term. Furthermore, the F-statistic value of 9.04 for
the return on asset model and 5.79 for the Tobin Q model and their
associated P-value of 0.0000 and 0.0000 respectively indicates that both
models of the Pool OLS regression are statistically significant at the
1\% level. This means that both models of the Pool OLS regression are
valid and can be used for statistical analysis.

In contrast, in terms of the high levered firms, results from the Pool
OLS regression shows an R-square value of 0.3693 for the return on asset
model and 0.4781 for the Tobin Q model. This indicates that
approximately 37\% and 48\% of the systematic variations in corporate
performance (when measured in terms of internal and external performance
respectively) are jointly explained by the independent and control
variables in the model during the period under study. This implies that
variations in corporate performance of listed firms in Nigeria cannot be
100 percent explained by the asset structure proxies employed in this
study. However, the unexplained part of corporate performance is
attributed to the exclusion of other independent variables that are not
within the scope of our study but have been captured as error term.
Furthermore, the F-statistic value of 2.63 for the return on asset model
and 3.97 for the Tobin Q model and their associated P-value of 0.0384
and 0.0060 respectively indicates that both models of the Pool OLS
regression are statistically significant at the 5\% level. This means
that both models of the Pool OLS regression are valid and can be used
for statistical analysis.

To further validate the estimate of the pool OLS regression results in
Table IV, we carried out some basic diagnostic tests. These regression
diagnostics tests include test for multicollinearity and test for
heteroscedasticity. We employed the variance inflation factor (VIF)
technique to determine the presence or absence of multicollinearity in
this study. A cut-off VIF value of 10 is used to determine whether a VIF
is high. This is in line with Gujarati's (2004) recommendations that the
mean VIF should be less than ten. In terms of the low levered firms,
Table IV shows a mean VIF value of 3.28 for the return on asset model
and 3.28 for the Tobin Q model. In the same vein, in terms of the high
levered firms, Table IV shows a mean VIF value of 5.42 for the return on
asset model and 5.40 for the Tobin Q model. This result implies that the
mean VIF across all the models are within the benchmark of 10 as
recommended by Gujarati's (2004). Hence, there is no room to suspect of
multicollinearity in the models under study. For the test for
homoscedasticity assumption, the result obtained from the test as shown
in Table IV reveals a significant P-value of the Chi2 at the 1\% level
for the low levered firms and 5\% level for the high levered firms.
These results indicates that the assumption of homoscedasticity has been
violated due to very low P-values. This suggest that the estimate of the
OLS regression cannot be relied upon for policy recommendation. We,
therefore, employed the robust regression technique to control for the
violation of the homoscedasticity assumption of the OLS regression as
shown above.

\emph{4.1 Discussions of Findings}

For the low leverage firms, the results obtained from the robust
regression model revealed that non-current asset ratio has negative and
non - significant effect on corporate performance of low levered firms
in Nigeria. Specifically, in terms of the return on asset model, this is
shown as (Coef. = -0.022; P -value = 0.550) and in terms of Tobin's Q,
it is shown as (Coef. = 0.001; P -value = 0.831). This result indicates
that increase in non-current asset has negative and passive effect on
corporate performance when measured in terms of profitability (return on
asset) and market value (Tobin's Q) of low levered firms in Nigeria
during the period under study. Similarly, the result from Table IV also
shows that non-current asset ratio has a non - significant effect on
corporate performance of high levered firms in Nigeria. Specifically, in
terms of the return on asset model, this is shown as (Coef. = 0.306; P
-value = 0.110) and in terms of Tobin's Q, it is shown as (Coef. =
-0.044; P -value = 0.254). This result indicates that similar to the low
levered firms, non-current asset ratio has passive effect on corporate
performance when measured in terms of profitability (return on asset)
and the market value (Tobin's Q) of high levered firms in Nigeria during
the period under study. This result implies also that the hypothesis
that `non-current asset structure has a significant effect on corporate
performance of high and low leverage firms' is rejected. This result
corroborates the findings of those of Coste, Tudor and Pali-Pista,
(2014) and Thum-Thysen, \emph{et al} , (2019).

Also, the results obtained from the robust regression model of Table IV
reveal that fixed asset ratio has a non - significant effect on
corporate performance of low levered firms in Nigeria when measured in
terms of profitability {[}shown as (Coef. = -0.003; P -value = 0.897){]}
but has a positive and significant effect on corporate performance when
measured in terms of their market -based value {[}shown as (Coef. =
0.005; P -value = 0.001){]}. This result signifies that fixed assets do
not significantly influence profitability performance (return on asset)
of low levered firms in Nigeria but significantly improves their market
- based value of their performance (Tobin Q) during the same period
under study. On the other hand, the results in Table IV reveal that
fixed asset ratio has a non - significant effect on corporate
performance of high levered firms in Nigeria. Specifically, in terms of
the return on asset model, this is shown as (Coef. = -0.074; P -value =
0.568) and in terms of Tobin Q, it is shown as (Coef. = -0.002; P -value
= 0.928). This result implies that unlike in the low levered firms,
fixed assets have passive influence on corporate performance of high
levered firms in Nigeria when measured in terms of profitability (return
on asset) and market value (Tobin Q). The result implies that the
hypothesis that `fixed asset structure has a significant effect on
corporate performance of high and low leverage firms' is accepted only
for low levered firms. The findings of this study corroborate with
findings of Dong, Leung and Cai (2012) and Thum-Thysen, \emph{et
al}(2019), Jozef et al. (2018) and against those of Koralun and
Bereznicka (2013).

However, the results obtained from the robust regression model revealed
that intangible asset ratio has a non - significant effect on corporate
performance of low levered firms in Nigeria. Specifically, in terms of
the return on asset model, this is shown as (Coef. = -0.044; P -value =
0.426) and in terms of Tobin Q, it is shown as (Coef. = -0.006; P -value
= 0.140). The result implies that intangible asset ratio has a passive
influence on corporate performance when measured in terms of
profitability (return on asset) and market value (Tobin Q) of low
levered firms. Similarly, the result from Table IV above also shows that
intangible asset ratio has a non - significant effect on corporate
performance of high levered firms in Nigeria. Specifically, in terms of
the return on asset model (Coef. = 0.997; P -value = 0.112) and in terms
of Tobin Q, (Coef. = -0.000; P -value = 0.998). This result also
signifies that similar to the low levered firms, intangible asset ratio
has a passive influence on corporate firm performance when measured in
terms of profitability (return on asset) and market value (Tobin Q) of
high levered firms. This result implies that the hypothesis that
`intangible asset structure has a significant effect on corporate firm
performance of high and low leverage firms' is rejected. The result is
in line with those of Haji and Mohd Ghazali 2018, Bates, Kahle, and
Stulz, 2009; Lyandres and Palazzo, 2016.

We find that the results obtained from the robust regression model
revealed that current asset ratio has a significant and positive effect
on corporate performance of low levered firms in Nigeria when measured
in terms of profitability (RETA) {[}shown as (Coef. = 2.972; P -value =
0.001){]} but has a positive and non - significant effect on corporate
performance when measured in terms of market -- based value {[}shown as
(Coef. = 0.019; P -value = 0.759){]}. The result implies that current
asset ratio significantly improves profitability performance (return on
asset) of low levered firms in Nigeria but passively improves their
market- based value performance (Tobin Q) during the same period under
study. In contrast, our result shows that current asset ratio has a
significant and positive effect on corporate performance of high levered
firms in Nigeria when measured in terms of profitability (RETA) {[}shown
as (Coef. = 16.056; P -value = 0.006){]} and has a negative and
significant effect on corporate performance when measured in terms of
market value {[}shown as (Coef. = -2.644; P -value = 0.013){]}. The
result implies that unlike the low levered firms, current asset ratio
tends to significantly improve the profitability performance (return on
asset) of high levered firms but on a decreasing rate in terms of their
market value performance (Tobin Q. The result implies that the
hypothesis that `current asset structure has a significant effect on
corporate performance of high and low leverage firms\textbf{'} is
accepted. This result corroborates with the findings of Dong, Leung and
Cai 2012, Rehman, Liu, Wu and Li, 2022, Ahmad, Sulaiman, and Mohammad,
2021.

Finally, we show that the results obtained from the robust regression
model reveal that customer asset intensity has a negative and
significant effect on corporate performance of low levered firms in
Nigeria when measured in terms of profitability (RETA) {[}shown as
(Coef. = -0.114; P -value = 0.000){]} but has a positive and passive
effect on corporate performance when measured in terms of market --
based value {[}shown as (Coef. = 0.002; P -value = 0.247){]}. However,
the results in Table IV indicate also that customer asset ratio has a
negative and passive influence on the profitability performance (RETA)
shown as {[}Coef. = -0.100; P -value = 0.732{]} of high levered firms in
Nigeria whereas it has a positive and non - significant effect on
market- based value performance (Tobin Q) {[}shown as Coef. = 0.035; P
-value = 0.431{]}. This signifies that customer asset intensity ratio
has a passive effect on corporate performance of high levered firms in
Nigeria. The result implies that in the low levered firms, a 1\%
increase in customer asset intensity will lead to 11.4\% negative
increase but with a significant influence on RETA while it leads to an
0.2\% positive but with passive influence on market -- based value
(Tobin Q). On the other hand, in high levered firms, a 1\% increase in
customer asset intensity will lead to 10\% negative but with passive
increase in firm performance (RETA) while it will lead to 3.5\% increase
in market -- based value (TOBQ) but with a non - significant impact.
This result implies that the hypothesis that `customer asset intensity
ratio has a significant effect on corporate performance of high and low
leverage firms\textbf{'} is accepted with respect to Low leverage firms
whereas, it is not accepted for High leverage firms. This also implies
that the higher the available customer assets the better the anticipated
revenue from those customers (Gupta and Lehmann, 2003). This result
corroborates the findings of Gupta and Lehmann, (2003), Rehman, Liu, Wu
and Li (2022).

\textbf{5.1 Conclusion}

This study examines asset structure relevance and impact on corporate
performance of listed manufacturing firms on the Nigerian Exchange Group
(NGX) by drawing samples from high and low leveraged firms from
2011-2020. We conclude as follows: that similar to the low levered
firms, non-current asset ratio has no significant effect on corporate
performance of high levered firms in Nigeria during the period under
study; Second, that unlike the low levered firms, fixed asset ratio does
not significantly influence corporate performance of high levered firms
in Nigeria (that is, on the other hand, Fixed asset ratio has
significant impact on corporate performance of low levered firms);
Third, that similar to the low levered firms, intangible asset ratio has
no significant effect on corporate performance of high levered firms in
Nigeria; Fourth, that unlike the low levered firms, current asset ratio
has significant impact on the profitability performance (return on
asset) of high levered firms but tends to decrease their market - based
value performance (Tobin Q). Finally, contrary to the low levered firms,
customer asset intensity ratio does not significantly impact on
corporate performance of high levered firms in Nigeria.

\emph{5.2: Implications of the Study}

The first concern here is that understanding a company's asset structure
improves understanding of the rigorous process of asset investment
managers undergo daily, especially as it portends to assets acquisition
patterns or the components of the equity or debt. Beyond this, it also
reveals the level assets are encumbered in debt financing, which is of
serious concern to managers. Moreover, the outcome of this study will be
a pointer for policymakers and investors on the possible effect of an
additional increase in non--current assets, fixed assets, intangible
assets, current assets and customer assets intensity for sustainable
firm performance. Second, the study reveals that increase in customer
asset intensity has capacity to improve on investors' interest in a firm
since high customer asset intensity has the potential for generation of
future assets. Assets mix of high and low levered firms with regards to
fixed assets and other forms of asset structure are core issues for
sustainable business performance which this study has brought to
limelight.

\textbf{5.3 Suggestions for further study}

This very study concerns only manufacturing firms in Nigeria; therefore,
we suggest as follows;

\begin{enumerate}
\tightlist
\item
  That similar studies be carried in the financial and service sectors.
\item
  That such studies be carried out comparatively within different
  countries.
\item
  That effect of capital structure on corporate performance of high or
  low levered firms be carried out as well.
\end{enumerate}

\textbf{6.1. Author information}

\begin{enumerate}
\tightlist
\item
  Emengini Emeka Steve; University of Nigeria, Enugu Campus, Enugu,
  Nigeria Email: emeka,emengini@unn,edu.ng Corresponding Author
\item
  Jacob Chukwuemeka, University of Nigeria, Enugu Campus, Enugu, Nigeria
  Email: chukwuemekajacob@gmail.com
\item
  Mrs Anisiuba Chika Anastesia; University of Nigeria, Enugu Campus,
  Enugu, Nigeria Email: chika.anisiuba@unn.edu.ng
\end{enumerate}

\textbf{6.2. Disclosure Statements}

Competing Interest Statement:

The authors report that there are no relevant financial or non -
financial competing interest to declare.

Funding Statement

The authors hereby declare that they did not receive any funding from
any agency or corporate bodies. The authors therefore, affirm that they
personally sponsored or funded this research work from personal savings.

Data availability statement: The authors confirm that the data
supporting the findings of this study are available within the article
and can still supplied upon due request.

( d) Acknowledgements

The authors will not fail to recognise or appreciate the invaluable
assistance from the editor, MachameRatios Database company and the blind
reviewers. We are grateful to many prior authors referenced in our work
because that has greatly enriched our work.

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\textbf{Hosted file}

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