IMPACT OF INVESTMENT IN INTELLECTUAL CAPITAL ON …...Methodology / Sample – Panel data analyses are applied to examine the effect of intellectual capital on non-financial firm’s

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International Journal of Management (IJM) Volume 11, Issue 7, July 2020, pp. 1622-1637, Article ID: IJM_11_07_146

Available online at http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7

ISSN Print: 0976-6502 and ISSN Online: 0976-6510

DOI: 10.34218/IJM.11.7.2020.146

© IAEME Publication Scopus Indexed

IMPACT OF INVESTMENT IN INTELLECTUAL

CAPITAL ON FINANCIAL PERFORMANCE OF

PSX LISTED NON-FINANCIAL FIRMS; A

PANEL DATA ANALYSIS INVESTIGATION

Muhammad Kashif Khurshid

Lecturer, Department of Management Sciences, National University of Modern Languages

(NUML) Faisalabad Campus, Pakistan.

Prof. Dr. Hazoor Muhammad Sabir*

Dean Faculty of Economics and Management Sciences Govt.

College University Faisalabad, Pakistan.

Muhammad Imran

Master of Business Administration, National University of Modern Languages (NUML)

Islamabad, Pakistan.

Muhammad Kashif

Lecturer, Govt. Degree College Khurrianwala Faisalabad, Pakistan; MS Business

Administration (NUML) Islamabad, Pakistan.

Muhammad Sajid

Assistant Professor Govt. Post Graduate College Toba Tek Singh, Pakistan; MS Finance

(NUML) Islamabad, Pakistan.

*Corresponding Author

ABSTRACT

Purpose – This study investigates the impact of investment in intellectual capital

on financial performance of listed Pakistan Stock Exchange (PSX) non-financial firms

during the period of 2011 to 2015.

Methodology / Sample – Panel data analyses are applied to examine the effect of

intellectual capital on non-financial firm’s financial performance. Simple random

sampling is used to choose the sample for the study. Panel data of 86 firms is collected

for the period of five yeas i.e. 2011-2015. So, there were 430 observations for the

collected data.

Findings – “Results of the study, give the conclusion in a fashion that value added

intellectual capital (VAIC) and its components i.e. capital employed efficiency (CEE),

Impact of Investment in Intellectual Capital on Financial Performance of PSX Listed Non-

Financial Firms; A Panel Data Analysis Investigation


human capital efficiency (HCE) and structural capital efficiency (SCE) are positively

correlated with four measures of financial performance i.e. return on assets (ROA),

return of equity (ROE), return on capital employed (ROCE) and earnings per share

(EPS). It is also found structural capital efficiency gives higher impact on non-

financial firm’s financial performance as compared to other two components of IC i.e.

capital employed efficiency and human capital efficiency.”

Practical Implications – “Corporate managers, board of directors, shareholders

and all other stakeholders can use this study to focus not only on the tangible assets of

the firms but also the intangible assets of the firms. They can also capture the

importance of intellectual capital efficiency, and its impact on financial performance.

Furthermore, these stakeholders can focus on the most important component of IC i.e.

capital employed efficiency (CEE).”

Key words: Intellectual capital (IC); Human capital efficiency (HCE); Structural

capital efficiency (SCE); Capital employed efficiency (CEE); Financial performance

(FP)

Cite this Article: Muhammad Kashif Khurshid, Hazoor Muhammad Sabir,

Muhammad Imran, Muhammad Kashif and Muhammad Sajid, Impact of Investment

in Intellectual Capital on Financial Performance of PSX Listed Non-Financial Firms;

A Panel Data Analysis Investigation, International Journal of Management, 11(7),

2020, pp. 1622-1637.

http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7

1. INTRODUCTION

Intellectual capital has been considered by many of researchers since the 1990s until now.

Intellectual capital is a key factor to maintain the company’s position and improves its

performance in both dimensions i.e. financial and non-financial (Cheng, Lin, Hsiao, & Lin,

2010). Intellectual capital has vital importance especially in the knowledge-based

organizations. Most of researchers believe that difference of market and book value depends

upon the successful utilization of intellectual human resources capital. Another issue that must

be considered in intellectual capital is essential awareness of this investment by managers of

companies. Without awareness of this valuable asset, managers cannot make the right

decision and thereby perhaps be causing wasting this precious asset. Managers should be

aware of the company's intellectual capital in order to manage these assets properly and make

necessary plan for better use of their assets. Suherman (2017) also stated that financial

performance of a firm can be enhanced if intellectual capital is used effectively.

1.1. Definitions and Components of Intellectual Capital

From 1960’s till now, different definitions have been proposed for intellectual capital by

different authors. Brooking (1997) considered the intellectual capital as “increase in the book

value of the firm in the form of market value of the firm”. IC is defined by Edvinsson &

Malone (1997) as “the knowledge oriented process and information that include applied

experiences, organizational technologies, customer relationship and professional skills which

increase the competitive capabilities and future profits of the company”. Stewart (2007)

expressed that “intellectual capital is the intangible value created in company by its human

resources through knowledge, skills, experience and staff's motivation and company's

resources including software, information technology and other resources”.

Intellectual capital as comprised of two primary modules i.e. human and capital

efficiencies (Edvinsson & Malone, 1997). Sullivan (1998) said IC is the knowledge or

Muhammad Kashif Khurshid, Hazoor Muhammad Sabir, Muhammad Imran, Muhammad Kashif

and Muhammad Sajid


information that can be changed into earnings. Pew Tan, Plowman, and Hancock (2007)

defined IC as a collection of knowledge assets belonging to the organization that increase

value of company and improve its performance. According to these definitions; intellectual

capital is assumed as talents, knowledge and skills that can create wealth or valuable outputs

for company.

With regards to different definitions of intellectual capital, IC can be classified in three

different groups i.e. Human, Structural and Social/Customer capital.

1.2. Human Capital (HC)

Human Capital (HC) is considered as a fundamental resource for any organization which is

comprised of professional knowledge of its employees and staff. Human capital includes the

abilities, skills, experience, specialties of an individual in the organization and it becomes the

prime source of the innovation (Brooking, 1997; Edvinsson & Malone, 1997). For an

organization, HC is like a pool of knowledge which is reflected by its staff. Human capital is a

blend of individual and organizational capabilities and capacities which creates business value

and solves business problems in an organization (Hudson, 1993).

1.3. Social or Customer Capital

Social capital also called customer capital includes the both individual and organizational

level relations with the society or other stakeholders (Edvinsson & Malone, 1997; Stewart,

2007). Social capital comprises on marketing knowledge and social channels. Bontis (1998)

presented the relationships with suppliers, customers, competitors and other market players,

those play a vibrant role for the future progress and success of the firms.

1.4. Structural Capital

Structural capital is includes other than human knowledge in the organization like databases,

strategies, guidelines, processes and any other thing that have more value than its valor

(Bontis, 1998). Structural capital’s components are systems, processes, structures and any

other business owned intangibles but those are not shown in the balance sheet of the firm

(Brooking, 1997; Edvinsson & Malone, 1997; Stewart, 2007). Structural capital plays caring

role for the human capital in organizations to convert the individual know how to the group

property. Structural capital supports to the employees to get the optimum intellectual

performance which leads to the success of the firms. Structural capital is remained within the

company even employees leave the firms.

1.5. Financial Performance (FP)

Prime objective of the corporate firms is to maximize the stockholder’s wealth (Gitman,

Juchau, Flanagan, Pearson, & Clemens, 1998) which can be achieved by generating profits.

Some of these profits are distributed to shareholders as dividends while remaining are

reinvested or plowed back or kept in the form of retained earnings. These reinvested profits

become the cause of the growths, which increases the future profits and yields. Traditionally

financial performance can be captured through different profitability ratios (Iswati & Anshori,

2007).

In developing economy of Pakistan investments by the firms in intellectual capital can

contribute in enhancing economic growth and development. Human capital can also play its

role in boosting social and economic developments of the economy. Human capital can be

channelized to develop individual and business sectors.




1.6. Research Objectives

This study focuses on the following objectives:

To detect the impact of investments in IC on the FP of the PSX listed non-financial

firms covering the period of 2011 to 2015.

To find the impact of investments in HC on the FP of the PSX listed non-financial

firms covering the period of 2011 to 2015.

To detect the impact of investments in social capital on the FP of the PSX listed non-

financial firms covering the period of 2011 to 2015.

To detect the impact of investments in structural capital on the FP of the PSX listed

non-financial firms during the time period of 2011 to 2015.

2. LITERATURE REVIEW

2.1. Worldwide Perspective

It is observed that the market value of big firm such as Apple, Amazon, Google and

Microsoft, has increased substantially from their book values (Li & Zhao, 2017). The major

reason behind this large gap is intellectual capital. In the past, firm’s market value was

computed through some conventional methods such as physical capital which included cash,

land and building etc. (Li & Zhao, 2017). According to (Li & Zhao, 2017) with the changing

of business environment dynamics, now firms use intellectual capital to compute the firm

value. Moreover, intellectual capital is begin calculated with the help of its three main

components. Li and Zhao (2017) conducted a study on 1850 Chinese firms and panel data was

collected from 2003 to 2015 for analysis purpose. The researchers applied IV and GMM

method for data analysis and concluded that the relationship between human capital and firm

value was positive only in case of firms with high-intensive capital. The study also found that

the relationship between organizational capital and firm value was significant in all types of

firms. The results showed the inverse relationship between organizational capital and firm

value current year while it was direct in subsequent years.

Anuonye (2016) investigated the impact of IC on ROA of Nigerian insurance firms. The

study used primary as well as secondary data. The study targeted the population of 150

workers from marketing, accounts and human resources departments of eighteen Nigerian

insurance companies.

Initially, 150 questionnaires were distributed to the workers of different departments and

finally got 74% response rate. The results indicated that structural has significant impact on

ROA while relational and human capitals had not played and significant influence on ROA of

the insurance firms in Nigeria.

Pratama (2016) investigated the relationship between IC (predicting variable) and FP

(outcome variable). Study population included high-tech firms which were listed on

Indonesian stock exchange. Sample was selected by applying purposive sampling technique.

Only those firms were included in the sample which contained data for all study variables.

Panel data analysis was used and a positive as well as significant relationship was found

between IC and FP. In order to increase the financial performance, (Pratama, 2016) suggested

optimum usage of IC.

Ozkan, Cakan, and Kayacan (2017) also inspected the relationship between IC (predicting

variable) and FP (predicted variable) in banking sector of Turkey. The researchers divided

Turkey’s banking sector into three categories for the purpose of data analysis. These

categories included deposit banks, participation banks, and development & investment banks.

10 year’s panel data 2005 - 2014 was taken for analysis purpose. ROA was taken as the


and Muhammad Sajid


measure of FP while IC was taken as independent. The analysis showed that both HC and CE

had positive effect on Turkish Banking Industry while, HC was more influential component

of IC in terms of FP as compared to other two components of IC.

Gogan, Artene, Sarca, and Draghici (2016) applied correlation analysis to examine the

relationship between IC and firm performance. In this study they took sample of 4 drinking

water distribution firms from Romania’s South-West region. For the purpose of data

collection structured questionnaire was used. The results were analyzed with the help of MS

Excel and simple graphs. The results of this study showed the positive relationship between

predicting i.e. IC and outcome variable i.e. firm performance. Although, very simple data

analysis technique was used in this study but still the results were in accordance with most of

the other researches on the same issue.

Intellectual capital also plays a vital role in banking sector. To examine the relationship

between IC, firm value and firm performance in banking sector a study was conducted by

(Lotfi, Elkabbouri, & Ifleh, 2016). They analyzed the Morocco banking sector and panel data

was collected for the years 2009 to 2014. In this study SCE, CEE & HCE (3 components of

IC) were taken as indigenous variable while ROA, ROE and ROI were taken as the proxies of

financial performance and, M/B ratio was taken as a measure of firm value. Simple and

multiple regression models were tested. They concluded that the relationship between IC and

FP were found to be positive as well as significant in case of all three components of IC while

no relationship was found between IC and FV.

Bhatia and Agarwal (2015) analyzed the impact of IC on FP through panel data in Indian

software industry. They took the panel data for eleven years from 2001 to 2011 from the

accounting records of the selected firms. They applied VAIC model developed by (Pulic,

1998) to capture the intellectual capital. Panel data results showed positive relation with

intellectual capital and financial performance, however they found physical capital as most

significant factor affecting the financial performance of software industry.

Kawamorita Kesim, Salamzadeh, and Jafari Moghadam (2013) examined the association

of IC and FP of the Iranian firms. Time series data, unit root tests and smooth transition

regression models were applied to study the relationships. A positive as well as significant

results was concluded in this study between IC and FP.

Khalique, Shaari, Abdul, Isa, and Samad (2013) reviewed the link between IC and

organizational performance of the Islamic banks in Malaysia. Results were captured through

correlation and regression analysis. They also found significant and positive relationship of IC

with performance of the Islamic banking system of Malaysia.

Joshi, Cahill, Sidhu, and Kansal (2013) inspected the relationship of IC with FP in the

financial firms of the Australia for the period of 2006-2008. Various component of IC i.e. HC,

CE and SC were used to investigate IC’s relation with financial performance. They found IC

is significantly related with HC and value addition of Australian banks. They found that HC

proficiency is higher than CE and SC proficiencies in Australian banks.

2.2. Pakistan’s Perspective

Amin, Aslam, Makki, and Abdul (2014) observed that the production economy has been

transforming into knowledge-based economy in 21st century. Amin et al. (2014) investigated

the connection between intellectual capital and firm performance. The focus of this study was

Pharmaceutical sector of Pakistan. Panel data was collected from annual reports of

pharmaceutical firms listed on PSX. The data was collected for the period 2009 to 2013.

Partial least square (PLS) technique, a SEM technique, was followed in this study for data

analysis purpose. Intellectual capital was measured with the help of VAIC model while ROA,




ROE and EPS were taken as the proxies for firm performance. It was concluded that a

positive as well as significant relationship exists between IC and FP in pharmaceutical sector

of Pakistan.

Aslam and Amin (2015) stated that IC has been regarded as a strategic asset which helps

firms create value and sustain the competitive advantage over its competitors. In this study,

the researchers have investigated the impact of IC on financial vulnerability. This study has

also been conducted on pharmaceutical sector of Pakistan. Financial vulnerability was

measured with the help of model developed by (Tuckman & Chang, 1991) which was

consisted on four components i.e. equity, revenue concentration, administrative cost ratios,

and negative operating income. While, IC was computed with the help of VAIC model. The

panel data was collected for the period of 2009 to 2013 and PLS technique was applied and it

was concluded that IC affects the financial vulnerability significantly and positively.

Arslan and Zaman (2015) stated that IC has been accepted as a major contributing factor

in firm’s value creation as well as financial performance. According to Arslan and Zaman

(2015) this statement holds true in Oil and Gas sector of Pakistan as well. To test this

hypothesis, Arslan and Zaman (2015) collected panel data of Oil and Gas sector of firms

listed on KSE over the period of 2007 to 2011. Pulic (1998) model was used for measurement

of IC and its three components i.e. HCE, SCE, and CEE while firm’s performance was

measured through ROA, ROE and EPS. Based on data analysis, it was concluded that HCE

and SCE have significant and positive relationship with firm’s financial performance.

Iqbal, Ahmad, and Javaid (2013) studied the impact of IC (independent variable) on FP

(dependent variable). The study was conducted on three different sectors of PSX which

included oil and gas sectors, banking sector and manufacturing sector. The researchers used

VAIC, VACA, VAHU and STVA as independent while ROA was used as dependent

variable. As statistical tool; they applied correlation to capture the relationship. Their results

depicted a positive relation between IC and FP.

Latif, Malik, and Aslam (2012) studied the IC and its relationship with the FP based on

productivity, profitability, and market value of Pakistani Islamic banks in comparison with

conventional (Non-Islamic) banks. Correlation and multiple regressions were used for the

purpose of analysis and it was concluded that HC had a significant as well as positive

relationship with all measures of financial performance i.e. market valuation, profitability and

performance in case of Islamic banks whereas, all components of performance were also

found to be related by CE significantly.

Khan and Khalique (2014) conducted a study on the strategic planning, intellectual capital

and SME’s financial performance in Pakistan. They mentioned that the elements of strategic

planning like mission of the company, vision of the company, market orientation, competitor

orientation and customer orientations should be explored with the performance of the small

scale companies. Furthermore, intellectual capital efficiency and its six components have an

impact on firm’s FP and has a positive impact on firm’s strategic performance.

Makki and Lodhi (2008) captured the impact of IC efficiency on profitability on 25 listed

Lahore Stock Exchange companies. They used year wise data of top 25 companies of LSE

during the period of 2002 to 2006. They also applied VAIC model as proxy of intellectual

capital and net profit as profitability of the firms. Structural equations model is applied by

using the PLS software. The study captured year wise positive impact of IC on profitability.


and Muhammad Sajid


2.3. Research Hypotheses

The following hypotheses are presented based on theoretical basics and research background:

H1: “Intellectual Capital has significant impact on the firm’s financial performance.”

H2: “Customer Capital has significant impact on the firm’s financial performance.”

H3: “Human Capital has significant impact on the firm’s financial performance.”

H4: “Structural Capital has significant impact on the firm’s financial performance.”

3. RESEARCH METHODOLOGY

3.1. Sources of Data and Sample

In non-financial sector of Pakistan under the BSA of SBP, 399 firm’s data were available in

2011 while 384 firm’s data were available in 2015. Finally, 86 firms were selected out of 384

based on simple random sampling technique. The reasons in population size reduction were;

some firms delisted while some new firms were listed. Published financial statements and

BSA published by statistical department of the SBP were used to capture the required data.

Data were collected for the period of 5 consecutive years i.e. 2011-2015.

3.2. Independent Variables

In this study “intellectual capital” is used as an independent variable. For which the procedure

of (Pulic, 1998) is used. According to (Makki & Lodhi, 2008); the procedure to calculate

VAIC falls into numerous steps. To measure VAIC following steps were taken on the

collected of sampled firms.

Step 1. V A = P + C + D + A

Where: VA indicates value added, P indicates operating profits, C indicates employee’s

salaries, D indicated depreciation and A indicates the amortization.

Step 2. SC = VA – HC

Where HC is total amount spent on salaries including wages and cost of employee benefits.

SC is indicated as structural capital and computed by deducting HC from VA.

Step 3. “CEE” = “VA/CE

CEE indicates capital employed efficiency, CE indicates capital employed and is captured by

net book value of the firm’s total assets.”

Step 4. “HCE = VA/HC

HCE indicates human capital efficiency, VA indicates value added, HC is total amount spent

on salaries including wages and cost of employee benefits.”

Step 5. “SCE = SC / VA

SCE indicates structural capital efficiency, SC = VA –HC, VA = P+C+D+A”

Step 6. “VAIC = HCE + SCE + CEE”

3.3. Dependent Variables

Return on Assets (ROA), Return on Equity (ROE), Return on Capital Employed (ROCE) and

Earnings per Share (EPS) are used as dependent variables to capture the financial

performance of the selected non-financial firm listed on PSX.




3.4. Conceptual Framework

Figure 1 Conceptual framework

3.5. Statistical Research Models

Following research models were formulated to capture the results

Model 1 -----> it

Model 2 -----> it

Model 3 -----> it

Model 4 -----> it

Model 5 -----> it

Model 6 -----> it

Model 7 -----> it

Model 8 -----> it

Where; βO is the constant, β1, β2 and β3 are coefficients for independent variables and ε is

the error term.

3.6. Data Analysis Techniques

Appropriate data analysis techniques such as descriptive statistics, correlation analysis,

regression analysis, panel data analysis have been applied with the help of Eviews 9.0 and

results are presented and discussed in section 4. Data representation into tabular or graphical

form is obtained with the help of descriptive statistics. The researchers have calculated

minimum, maximum, range, mean, median and standard deviation with the help of descriptive

statistics.

Measure of direction and strength of association between two quantitative variables is

determined with the help of correlation analysis. Correlation matrix has been used to find the

association between all types of variables i.e. dependent and independent.

Firm Performance (FP)

"Return on Assets

(ROA)"

"Return on Equity

(ROE)"

"Return on Capital Employed (ROCE)"

"Earnings per Share (EPS)"

Intellectual Capital (VAIC)

"Capital Employed

Efficiency (CEE)"

"Human Capital

Efficiency (HCE)"

"Structural_Capital

Efficiency (SCE)"


and Muhammad Sajid


Regression analysis is used to find the association between dependent and independent

variable. In this study multiple regression model is used because number of independent

variables are greater than 1.

3.7. Panel Data Analysis

A data set containing multiple observations against each sampling unit is referred as panel

data (Lavrakas, 2008). Panel data analysis requires appropriate data analysis tools. Most

widely data analysis tools for panel data include: common effects model, fixed effects model

and random effects model. The similar models are used in studies of (Ozkan et al., 2017;

Pratama, 2016). The selection of suitable models among three stated models is based on the

statistical results of two tests which are known as “likelihood ratio test” and “Hausman test”.

If the result of likelihood test is statistically significant then common effect model is

appropriate for panel data analysis. In case of insignificant results, further Hausman test is

applied to select appropriate model between fixed effects and random effects model. If the

result of Hausman test is statistically significant then fixed effects model is appropriate. In

case of insignificant results random effects model is selected.

4. RESULTS AND FINDINGS

Table 1 Descriptive Statistics

Min. Max. Range Mean Median St. Dev. N

ROA -13.7200 31.4300 45.1500 7.1794 6.5600 7.2326 430

ROE -55.9600 96.4500 152.4100 16.6354 15.2100 19.4512 430

ROCE -31.7800 53.0500 84.8300 12.3719 11.5200 13.4021 430

NP -15.8900 26.7500 42.6400 4.9905 4.5400 5.7393 430

EPS -72.8600 117.1200 189.9800 10.5115 5.0600 18.1832 430

VAIC -1.5084 9.0344 10.5428 2.6049 2.4378 1.7215 430

CEE -0.1129 0.4716 0.5845 0.1531 0.1417 0.0874 430

HCE -2.7581 7.9880 10.7461 2.0942 1.8175 1.3930 430

SCE -1.8717 2.0075 3.8792 0.3575 0.4571 0.4734 430

Table 1 describes the min, max, mean and median values of the selected independent and

dependent variables with their range and standard deviations. All of performance measures

are showing minimum values as negative while the maximum values are positive. Mean and

median values are close to each other in all the financial performance indicators, which shows

that the behavior of data is normal. Only one performance measure i.e. EPS has distant mean

and median values, with high standard deviation, showing the less normality of the data.

Variables related to intellectual capital are also showing minimum values as negative

while maximum values as positive with some range. Closer mean and median values of all

intellectual capital indicators showing the normality of the data.

4.1. Correlation Matrix

Table 2 gives the correlation among all the variables. All performance variables are showing

high correlation i.e. ROA, ROE, ROCE are highly correlated while NP, EPS are showing

mediocre correlation. The correlation among all the independent variables is not very high,

which gives the indicator that there is no problem of multicollinearity. The mediocre

correlation between dependent variable and all the independent variable suggests to pass the

data from regression analysis.




Table 2 Correlation Matrix

“ROA” “ROE” ROCE NP “EPS” VAIC CEE HCE SCE

ROA 1

ROE 0.84156 1

ROCE 0.91428 0.92742 1

NP 0.74869 0.57398 0.61707 1

EPS 0.58488 0.51263 0.59540 0.47110 1

VAIC 0.41777 0.41201 0.46480 0.36671 0.35833 1

CEE 0.53832 0.55066 0.58682 0.23779 0.32879 0.37869 1

HCE 0.38377 0.37155 0.43816 0.34625 0.33499 0.76004 0.32359 1

SCE 0.29056 0.30326 0.29256 0.27078 0.25661 0.71219 0.24026 0.52524 1

4.2. Regression Analysis

Eight regression equations are formulated. Because the data has both dimensions i.e. time

series and cross sections, so panel data analyses were applied.

4.3. Panel Data Method Selection

Three models are used in panel data analysis i.e.

Common effects model (Pooled OLS)”

“Fixed effects model and”

“Random effects Model”

Based on intercept’s assumptions, all the three models are differentiated. If intercept is

assumed constant in time series or cross sections; then common effect method is used. If

intercept is not assumed fixed; then random effects method used. Otherwise fixed effects

method is used. Redundant fixed effect likelihood ratio is used to choose between common

and fixed effects models. Hausman test is used to choose between fixed effects and random

effects models.

5. RESULTS OF REGRESSION ANALYSIS

Results are gathered on the basis of eight regression models. Results are presented in the

appendix. Hausman test is applied to decide; which panel data model is appropriate to capture

the results. Table 3 gives the selection between two effect models i.e. fixed effect and random

effect. The insignificant P Value (0.8002) suggests that random effects model is best fit the

data set.

Table 3 “Hausman Test”

“Test’s Summary” “Chi - Sq. Statistic” “Chi - Sq.’s d.f.” P

“Cross section random” 24.039315 5 0.8002

Table 4 shows the impact of (CEE), (HCE) and (SCE) on return on assets (ROA). The

value of Adjusted R2 shows that there is 58% change in ROA due to selected variables of

intellectual capital. The significant F value suggest overall fitness of the model. The

coefficient values of CEE, HCE and SCE predicts positive change in ROA with their

respective coefficient values. While individual p values of CEE, HCE and SCE gives

significance of their coefficients. The results are in accordance to previous studies of (Arslan

& Zaman, 2015; M. Bhatia & Agarwal, 2015; Latif et al., 2012; Ozkan et al., 2017). In Table

5 results of Model 2 shows that with one unit’s increase in investment in VAIC gives 2.96


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times increase in ROA at 1% level of significance. The p value of F statistics shows the

overall model is fit. The Adjusted R2 indicates that there is 64% change in ROA is caused by

intellectual capital. Moreover, the results are in accordance with the studies of (Amin et al.,

2014; Aslam & Amin, 2015; Iqbal et al., 2013; Khan & Khalique, 2014; Pratama, 2016).

Table 4 Regression Analysis Model 1

it

Variable Coeff. S.E. t-Statistic Prob.

C -4.177767 0.717309 -5.824220 0.0000

CEE 60.03001 3.437524 17.46315 0.0000

HCE 0.846975 0.229869 3.684600 0.0003

SCE 1.087718 0.527454 2.062206 0.0398

R2 0.585449 “F-Statistic” 200.5393

Adj.R2 0.582530 “Prob. (F-Statistic)” 0.000000


it

Variable Coeff. S. E. t-Statistic Prob.

C -0.535628 0.579827 -0.923771 0.3563

VAIC 2.961930 0.207886 14.24787 0.0000

R2 0.717464 “F-Statistic” 10.12796

Adj. R2 0.646624 “Prob. (F-Statistic)” 0.000000

Table 6 shows the impact of (CEE), (HCE) and (SCE) on return on equity (ROE). The

value of Adjusted R2 shows that there is 52% change in ROE due to selected variables of

intellectual capital. The significant F value suggest overall fitness of the model. The

coefficient values of CEE, HCE and SCE predicts positive change in ROE with respective

coefficient values. While individual p values of CEE, HCE and SCE gives significance of

coefficients. The results are in accordance to previous studies of (Arslan & Zaman, 2015; M.

Bhatia & Agarwal, 2015; Latif et al., 2012; Ozkan et al., 2017). In Table 7 results of Model 4

shows that with one unit’s increase in investment in VAIC gives 8.74 times increase in ROE

at 1% level of significance. The p value of F statistics shows the overall model is fit. The

Adjusted R2 indicates that there is 57% change in ROE is caused by intellectual capital.

Moreover, the results are in accordance with the studies of (Amin et al., 2014; Aslam &

Amin, 2015; Iqbal et al., 2013; Khan & Khalique, 2014; Pratama, 2016).


it


C -14.01653 1.950165 -7.187358 0.0000

CEE 153.8908 10.09858 15.23885 0.0000

HCE 2.603793 0.682423 3.815512 0.0002

SCE 4.553193 1.620691 2.809415 0.0052

R2 0.525402 “F-Statistic” 157.2003





Table 7 Panel Data Analysis Model 4

it


C -6.139789 1.716022 -3.577920 0.0004

VAIC 8.743732 0.615246 14.21176 0.0000

R2 0.657848 “F-Statistic” 7.668350



it


C -9.667971 1.288334 -7.504244 0.0000

CEE 109.0273 6.604409 16.50826 0.0000

HCE 2.465899 0.445419 5.536140 0.0000

SCE 0.493815 1.051019 0.469844 0.6387

R2 0.562560 “F-Statistic” 182.6159



it


C -2.741071 1.143380 -2.397340 0.0170

VAIC 5.802097 0.409937 14.15364 0.0000

R2 0.680038 “F-Statistic” 8.476755



it


C -8.435873 2.026416 -4.162952 0.0000

CEE 89.28847 14.87979 6.000654 0.0000

HCE 2.317388 0.975064 2.376651 0.0180

SCE 1.169930 2.090292 0.559697 0.5761

R2 0.649063 “F-Statistic” 7.166879



it


C -2.409363 1.699570 -1.417631 0.1572

VAIC 4.960525 0.609348 8.140714 0.0000

R2 0.615938 “F-Statistic” 6.396334


Table 8 shows the impact of (CEE), (HCE) and (SCE) on (ROCE). The value of Adjusted

R2 shows that there is 55% change in ROCE due to selected variables of intellectual capital.

The significant F value suggests that overall model is fit. The coefficient values of CEE, HCE

and SCE predicts positive change in ROCE with respective coefficient values. While


and Muhammad Sajid


individual p values of CEE, HCE and SCE gives significance of coefficients. The results are

in accordance to previous studies of (Arslan & Zaman, 2015; M. Bhatia & Agarwal, 2015;

Latif et al., 2012; Ozkan et al., 2017). In Table 9 results of Model 6 shows that with one units

increase in investment in VAIC gives 5.80 times increase in ROCE at 1% level of

significance. The p value of F statistics shows the overall model is fit. The Adjusted R2

indicates that there is 64% change in ROCE is caused by intellectual capital. Moreover, the

results are in accordance with the studies of (Amin et al., 2014; Aslam & Amin, 2015; Iqbal et

al., 2013; Khan & Khalique, 2014; Pratama, 2016).

Table 10 shows the impact of (CEE), (HCE) and (SCE) on earnings per share (EPS). The

value of Adjusted R square shows that there is 55% change in EPS due to selected variables

of intellectual capital. The significant F value suggests overall fitness of the model. The

coefficient values of CEE, HCE and SCE predicts positive change in EPS with respective

coefficient values. While individual p values of CEE, HCE and SCE gives significance of the

coefficients. The results are in accordance to previous studies of (Arslan & Zaman, 2015; A.

Bhatia & Aggarwal, 2015; Latif et al., 2012; Ozkan et al., 2017). In Table 11 results of Model

8 shows that with one units increase in investment in VAIC gives 4.96 times increase in EPS

at 1% level of significance. The p value of F statistics shows the overall model is fit. The

Adjusted R2 indicates that there is 51% change in ROA is caused by intellectual capital.

Moreover, the results are in accordance with the studies of (Amin et al. (2014); Arslan &

Zaman, 2015; Iqbal et al., 2013; Khan & Khalique, 2014; Pratama, 2016).

6. CONCLUSION AND RECOMMENDATIONS

6.1. Conclusion

The study is conducted to check the impact of investments in firm’s IC on FP. IC is captured

by using the (Pulic, 1998) model having three components i.e. CEE, HCE and SCE. To

account the FP, four most commonly used financial ratios were used i.e. ROA, ROE, ROCE

and EPS. To check the relationship between IC efficiency and firm’s FP, Pearson’s

correlation is applied. It is found that there exists positive and significant correlation between

VAIC and all measures of FP. The individual components of IC like CEE, HCE and SCE are

also positively correlated with FP ratios but with different magnitude.

Panel data analysis were applied to investigate the impact of IC on FP. Hausman test’s

insignificant P Value indicated to use Random Effects Model for the collected data rather than

Fixed Effects Model. Eight regression equation were used to check the impact of independent

variables on dependent variables. The results of the study revealed that firm’s having

investment in intellectual capital shows better financial performance i.e. VAIC has positive

and significant impact on ROA, ROE, ROCE and EPS. Those firms which are investing not

only in tangible assets but also in intangible assets i.e. intellectual capital have greater

inclination to financial performance. The results of the study are in line with the past studies

like (Edvinsson & Malone, 1997; Khalique et al., 2013; Makki & Lodhi, 2008).

6.2. Recommendations

The positive impact of IC on firm’s FP reveals that the top management and boards of

director must focus on the investments in IC. IC should be treated as investment rather than

expense. Following are some overall recommendations.

Companies tend to treat IC as expense while, it is strongly recommended to treat it as

investment due to the immerse benefits for the organizations.

Both tangible and intangible assets contribute in profits and financial output. So firms

should divert their focus on intangible assets also.




Investment in capital employed efficiency is the most influential component of IC,

which impacts on the firm’s financial performance.

Human capital which includes wages, salaries and employees’ benefits are expensed

but in long run they contribute to the profits of the business and becomes the assets of

the business.

Good relations with the public and customers creates the social capital efficiency for

the firms and in future leads to the profitability and financial performance.

7. LIMITATIONS AND FUTURE RESEARCH

Every study includes some limitations and major limitations of this study are listed below.

5-years study period i.e. 2011 to 2015, has been used in this study. In future data may

be collected for more years, which can help researchers to generalize the results more

accurately.

ROA, ROE, ROCE and EPS are used as proxy for financial performance in this study.

There are many other ratios which can also be used as a proxy for financial

performance in addition to the five proxies used in this study.

Only those ratios are used for the proxy of financial performance, which are related to

balance sheet. In future some market based ratios or performance measure many be

used i.e. M/B ratio and Tobin’s Q etc.

The study is only based on secondary data, so in future research, primary data can also

be gathered through questionnaires. A multi method data analysis technique may be

applied to analyze the data.

The study covers the population overall in non-financial sector of Pakistan but in

future a sector wise or comparative study among different sector of the economy can

be done.

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IMPACT OF INVESTMENT IN INTELLECTUAL CAPITAL ON …...Methodology / Sample – Panel data analyses are applied to examine the effect of intellectual capital on non-financial firm’s