The impact of Intellectual
Capital on Pharmaceutical and
Food Industry company’s
By using Data Envelopment Analysis in
Tehran Stock Exchange
Author: Ali Esmaili, Soolmaz Assadi and
Haseeb Akram Khan
Supervisor: Paul Scarbrough Examiner: Åsa Gustavsson
The Intellectual Capital (IC) term was introduced by Sveiby and his group in Sweden 1986 (Sullivan, 1998). The main point in all organizational analyses is to evaluate performance, and therefore an organization’s success is not imaginable without a performance evaluation system. Performance assessment is a process that measures, evaluates, and judges the performance over time. Performance assessment in organizations is usually established for assuring the effectiveness of activities.
Purpose of the research is to identify and emphasize the relationship and impact of Intellectual Capital (IC) on the manufacturing companies by analyzing the level of their financial performance in the Tehran Stock Exchange (TSE).
What is the impact of the Pulic (1998) form of intellectual capital efficiency on the financial performance of manufacturing companies on the Tehran Stock Exchange?
H1: The value-added intellectual coefficient (VAIC) has relation with the financial performance, measured by DEA, of Pharmaceutical and food companies in the Tehran Stock Exchange.
H2: The human capital efficiency (HCE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
H3: The structural capital efficiency (SCE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
H4: The capital employed efficiency (CEE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
The methodology used is Quantitative Method with deductive, positivism approach. Collection of statistical data is gathered regarding Pharmaceutical & Food manufacturing industries and evaluated IC through Pulic 1998 VAIC model along with observed FP through DEA method. After getting the VAIC and FP regression method is used to test our Hypothesis
Except H2 with pharmaceutical and H3 with food industries all other hypothesizes are accepted
Intellectual Capital, Financial Performance, VAIC, DEA, Regression, Pharmaceutical and Food Manufacturing Industries Write your key words here
BV = Book Value CC = Customer Capital CE = Capital Employed
CEE = Capital Employed Efficiency DEA = Data Envelopment Analysis DMU = Decision Making Units
FASB = Financial Accounting Standard Board FP =Financial Performance
FS = Financial Statements
GAAP = Generally Accepted Accounting Principles HC = Human Capital
HCE = Human Capital Efficiency HR = Human Resource
IA = Identifiable Assets IC = Intellectual Capital
ICE = Intellectual Capital Efficiency MV = Market Value
OCF = Operating Cash Flows OE = Operating Expenses OP= Operating Profit
R&D = Research and Development ROA = Return on Asset
ROE = Return on Equity ROS = Return on Sales RQs = Research Questions S = Sales
SC = Structural Capital
SCE = Structural Capital Efficiency VA = Value Added
VAIC = Value Added Intellectual Coefficient VAIS = Value Added Intellectual Statements TSE = Tehran Stock Exchange
Firstly, we thank God for making us eligible to initiate, pursue and complete our Degree Project in a best possible way. We are thankful to our tutor Professor Dr. Paul Scarbrough and examiner Dr. Åsa Gustavsson for guiding us and redirecting our journey in correct direction as well as their suggestions made this project worth wile. Beside them we thank our parents and our whole families along with friends who have helped us suggesting some directions as well as encouraged us to complete this project efficiently in time.
We thank our oppositions (Malin, Emma, Rida and Dipto) who guided us with their knowledge and experience regarding any discrepancies and suggesting ways to eliminate it in our project.
We also thank our classmates for encouraging us to for doing this project efficiently. Lastly we thank and appreciate each other for being best collaborator and pushing each other for this achievement as well as tolerating each other deficiencies and completing this project in a best manner
Thanks Ali Esmaili Soolmaaz Assadi Haseeb Akram Khan
Table of contentsIntroduction 1 1.1 Background 1 1.2 Problem Discussion 6 1.3 Research Question 8 1.4 Purpose 8 Theoretical framework 9
2.1 Intellectual Capital (IC) 9
2.1.1 Research Area of Intellectual Capital (IC) 10
2.2 The Value-added Intellectual Coefficient (VAIC) 11
2.2.1 Strength and Weakness of VAIC 13
2.2.2 2.2.2 VAIC and Financial Performance 14
2.3 Capital Employed Efficiency (CEE) 15
2.3.1 Capital Employed and Financial Performance 15
2.4 Human Capital Efficiency (HCE) 15
2.4.1 Human Capital and Financial Performance 16
2.5 Structural Capital Efficiency (SCE) 16
2.5.1 Structural Capital and Financial Performance 17
2.6 Organizational Performance Measure Through DEA 17
2.6.1 Non Parametric (DEA) and Parametric (SFA) Measures 17
2.7 Benefits of this Study 18
3.1 Scientific Perspective 20
3.1.1 Positivism and Hermeneutics 20
3.2 Research Strategy 21
3.2.1 Qualitative and Quantitative 21
3.3 Scientific Research Approach 22
3.3.1 Inductive, Deductive and Abductive Research 23
3.4 Research Method 24
3.5 Sampling Method 25
3.6 Data Collection Methods 26
3.7 Data Analysis Method 27
3.7.1 Independent variables: VAIC and components 27
3.7.2 Dependent variable: Financial Performance measured by DEA 28
3.7.3 Control Variable: Size 30
3.8 Scientific Credibility 31 3.8.1 Validity 31 3.8.2 Content Validity 32 3.8.3 Construct Validity 32 3.8.4 Criterion Validity 33 3.8.5 Reliability 35 3.8.6 3.8.6 Homogeneity 35 3.8.7 Stability 35 3.8.8 Equivalence 36 3.9 Ethical Considerations 36
Analyzing and Empirical Data 37
4.1 Introduction and Summary of Pervious Chapters 37
4.2 Descriptive Statistics 38
4.2.1 Comparison between pharmaceutical and food industries 39
4.3 Examining the Normality and Stationarity of Data 39
4.3.1 Statistical assumption of Jarque-Bera (JB) 40
4.3.2 Levin, Lane, and Chou (LLC) static tests 41
4.4 Inferential Statistics and Research Econometric 42
4.4.1 Examining and Selecting the Best Effect Model 42
4.4.2 Heteroscedasticity Test 43
4.5 Testing the Hypothesis of Pharmaceutical Companies 44
4.5.1 Analyzing Main Hypothesis of Pharmaceutical Companies (VAIC
4.5.2 Analyzing the Sub-hypothesis of Pharmaceutical Companies
(Components of VAIC Model) 46
4.6 Testing the Hypothesis of Food Companies 47
4.6.1 Analyzing the Hypothesis of Food companies (VAIC Model) 47
4.3.1 4.6.2 Analyzing the Sub-hypothesis of Food Companies
(Components of VAIC Model) 49
4.7 Summary of the Chapter 50
Conclusion and Recommendation 52
5.1 Introduction 52
5.2 Evaluating the Test Results of the Pharmaceutical Industry 52
5.2.1 The Results of Main Hypothesis 52
5.2.2 The Result of the First Sub-Hypothesis 53
5.2.3 The Result of the Second Sub-Hypothesis 53
5.2.4 The Result of the Third Sub-Hypothesis 53
5.3 Evaluating the Test Results of the Food Industry 54
5.3.1 The Results of Main Hypothesis 54
5.3.2 The Result of the First Sub-Hypothesis 54
5.3.3 The Result of the Second Sub-hypothesis 55
5.3.4 The Result of the Third Sub-Hypothesis 55
5.4 General Conclusion of the Research 55
5.5 Suggestions Based on Research Findings 55
5.6 Research Limitations 56 References 57 Appendix 1 1 Appendix 2 1 Appendix 3 3 Appendix 4 13
List of Tables
Table 1-1The Summary of Some Previous Similar Studies (Created by Researchers)
Table 3-1 The Target Population of This Thesis and Other Component((Created by Researchers) ... 25
Table 3-2 Variable Table(Created by Researchers) ... 31
Table 3-3 Described by (Heale and Twycross, 2015, p. 66) ... 31
Table 3-4 The variables of Pulic’s model in other tools (made by researchers)... 33
Table 3-5 types of reliability in quantitative research (Heale and Twycross, 2015, p. 66) ... 35
Table 4-1 Descriptive Statistics for Pharmaceutical Industry Companies ... 38
Table 4-2 Descriptive Statistics for Food Industry Companies ... 39
Table 4-3 Examining the Normality and Stationarity of Pharmaceutical Industry Companies ... 41
Table 4-4 Examining the Normality and Stationarity of Food Industry Companies 41 Table 4-5 The Results of Hausman and F-Limmer Tests ... 43
Table 4-6 The results of ARCH test ... 44
Table 4-7 Results of Regression model of first hypothesis ... 45
Table 4-8 The Results of the Estimation Regression Model for Variable ... 46
Table 4-9 Results of Regression model of first hypothesis of food companies (VAIC Model) ... 48
Table 4-10 The results of the regression model of sub-hypotheses of food industry companies ... 49
Table 4-11 Comparison Between Expected Results and Achieved Results in Pharmaceutical Industry ... 51
Table 4-12 Comparison Between Expected Results and Achieved Results in Food Industry Companies ... 51
Conventional financial accounting method not calculate the value of companies, but rather, measure the balance sheet and tangible assets based on Generally Accepted Accounting Principles (GAAP). In the nineteen eighties, with the development of new approaches in the field of economics, analysts started to pay a little less attention to tangible resources. Organizations increasingly started to consider all “assets”, including financial, physical, and intangible resources and intellectual (invisible) capital (Lev, 2005).
In the late nineteen-seventies and early eighties, most of strategic management was fascinated by the trading of organizations at more than the book value (Sullivan, 1998). According to Lev (2005), in the 1980s there was a perpetual growth in the trend of higher market value (MV) then book value (BV). As is evident from the case of Microsoft in 1985, the shares of this software company were purchased at the rate of 10 times more than the rate of book value (Sveiby, 1997). Therefore, at that time the managers of companies felt the unseen aspect of intelligence and thought that by collecting knowledge elements such as innovation and attentiveness to intellectual capital, they could obtain more added value (Venugopal, D., Nambi, Lakshmanan M., 2018)
The Intellectual Capital (IC) term was introduced by Sveiby and his group in Sweden 1986 (Sullivan, 1998). According to Investopedia (2019), IC can be defined simply as all kinds of proprietary information that is able to make a competitive advantage for a corporation or organization. It can be included the value of a firm or the knowledge of personnel of a company, the expertise, the business training, organizational procedures, and any other intangibles asset.
The main point in all organizational analyses is to evaluate performance, and therefore an organization’s success is not imaginable without a performance evaluation system. Financial performance assessment is a process that measures, evaluates, and judges the financial and economic health of the organization over a period of time (Investopedia, 2020). Performance assessment in organizations is usually established for assuring the effectiveness of activities. In today's economy, IC generates value for the organization, and recently, the achievement of any organization relies upon the capability to manage these intangible and intellectual assets. In the recent business environment, it is observed that IC is related to the growth of companies and is a useful instrument for increasing competitiveness and efficiency of companies (Rahimi, et al., 2016). Although, there does not still exist a standard method for measuring IC, and measuring criteria differ across companies(Investopedia,2019).
According to Mention (2012), the concept of IC is tough describing because of variety in views that are included. IC is related to management accounting and field in many
ways. Management Accounting consists of the knowledge of management, accounting, reporting problems, financial performance, management control systems, and Research and Development (R&D).
In fact, IC has a multidimensional meaning which is a mixture of human, organizational, structural, and relational sources of the company (Meritum, 2002). The book named "The Knowledge Company" is said to be the primary manuscript in the world which includes some concepts of management and IC. This Swedish book is written by Sveiby. Later in 1995 Skandia was the first company that released its financial statements that accompanied the IC report (Abdul Majid Makki, and Aziz Lodhi 2008).
IC plays crucial character in generating organizational performance and it is one of the reasons for distinguishing between companies which leads to success or failure of them (Pulic, 1988). IC is a platform of awareness and skills which belong to all stakeholders related to organization and produce value for that company (Abdul Majid Makki, and Aziz Lodhi 2008). By starting the new age of the economy which highly relies on information and knowledge, the old bases that are more dependent on the conventional competitive advantage which emphasizes tangible assets for value creation are going to be ruined. In addition to this, financial capitals and facilities of manufacturing are not anymore considered the only factors that can bring sustainable competitive advantages. Whereas, intangible assets, particularly expertise, are attaining more superiority regarding survival and competitive edge over the other companies in the strategic competition (Tefera, 2018).
During the past 10 years, many research papers have been written in prestigious academic journals about IC and its relation to financial performance. Researchers always have tried to use various methods both for the evaluation of IC and calculating financial performance. They also have done their studies in different samples, countries, and evidence in order to discover more in this area. Although, generally, a considerable number of them have used Pulic's model for calculation (Tan, et al., 2007; Ting & Lean (2009) Matinfard and Khavari,2015;) or researched on pharmaceutical, bank, insurance, food, hotel and technology industries which are more dependent on IC (Shiu ,2006; Mohiuddin M., et al ,2006; Rudez and Mihalic ,2007; Laing et al ,2010; Mehralian G et. al, 2012; Ekwe, 2012; Gholamipour and Arabani,2014; Chizari et al, 2015; Isanzua, 2015 Shafi’u et al. ,2017; Smriti and Das, 2017; Tefera,2018).
For instance, Tan, et al. (2007) examines relationships among the IC of companies with their financial efficiency and concluded with existence of a progressive correlation among IC and financial outcomes of present and future companies and secondly, that there is significant influence of IC on corporate financial performance (FP) regarding multiple industries. Isanzo (2015) investigated the connection between IC and FP of Tanzania banks. In this study, pulic's model and ROA were used as the
main methods and the finding indicated that Tanzanian banks will gain more profit if they invest more in IC because statistical operation approved that human capital and capital employed as the components of IC had a positive relationship with financial performance. A summary of some of the similar studies with different findings is presented in table (1-1).
Since this thesis concentrates on the pharmaceutical and food industries, it should be mentioned that the pharmaceutical industry is known as an appropriate source of IC because the nature of that is dependent on studies, innovation, human knowledge (Daum,2005; Hermans, 2004). Furthermore, both of these industries have a pivotal role in human life, and these days by the risk of COVID-19 the importance of both is increasing more. The food industry also faces many challenges, for example, safety, nutrition, handling of packaging and etc. In addition, food sustainability is a controversial topic these days, and people, organizations, and media have heeded on the problems of the lack of business training and poor situation of the workplace especially in developing countries (Tsai and Mutuc,2020).
Pharmacy and medicine are accounted for as the oldest knowledge of Iranian, and Mohammad-ibn-e Zakaria Razi and Avicenna are two well-known Persian scientists in this field (Najmabadi, 1987). According to Siamak-Nejad (1989) In 1979, many local, international, and private firms worked in Iran's pharmaceutical industry. At that time, the pharmaceutical section had become a market that had an annual cash flow of about 300 million dollars. The national pharmaceutical industry has been targeted at the brand-generic systems in recent years, which makes a suitable chance for the competition in this area. In addition, this industry has not yet improved sufficiently to its full potential and there exist still many opportunities for progress in the future (Mehralian et al, 2012).
According to Bakhshani (2015), which did a research about the connection of IC and FP in Iranian food industry companies, one of the causes behind the suspension of Iranian firms that are involved in the food industry is the lack of expertise and skill of employees. She believes that firms must grow human capital to be able to develop their productivity and FP.
Furthermore, IC has also been the topic of many studies in the developed countries; with an emphasis on particular industries. On the other hand, few studies have concentrated on developing countries like India, Nigeria, Pakistan, and Iran (Ekwe,2012). The concept of IC in Iran newly arrives, and research on Intellectual Capital in Iranian companies is low in quantity or in limited quantity. The reasons for limited research in this area are Iran is a developing country, Iranians have little knowledge about IC, Iranians face many foreign restrictions, Iranians lack political support and resources. Private and government sectors release less funding in developing countries like Iran, which makes it almost impossible to investigate this
area on intangible assets since they focus more on tangible assets research (Malhotra, 2003).
The Summary of Some Relevant Studies
Title Author & Year Positive Negative Mixed
Intellectual capital and traditional measures of corporate
Firer & Williams
Measuring intellectual capital: a
new model and empirical study Chen et al. (2005) Yes The application of the value added
intellectual coefficient to measure corporate performance: evidence
from technological firms
Shiu (2006) Yes
An exploratory study on IC performance Commercial Banks of
Mohiuddin M., et al
Intellectual capital in the hotel industry: A case study from
Rudez and Mihalic (2007)
Intellectual capital and financial
returns of companies Tan, et al. (2007)
The impact of intellectual capital on investors’ capital gains on shares: An empirical investigation
of Thai banking
Impact of Intellectual Capital Efficiency on Profitability (A Case
Study of LSE25 Companies)
Abdul Majid Makki, and Aziz
Impact of Intellectual Capital on organizational performance. The
Intellectual capital performance of
financial institutions in Malaysia Ting & Lean (2009) Yes
Applying VAIC to Australian Hotel Laing et al (2010) Yes
The relationship between intellectual capital (human,
structural, consumer) and performance Insurance Industry
Mojtahedzadeh et al
Analyzing value-added as an indicator of intellectual capital and
its consequences on company performance
The impact of intellectual capital on firms’ market value and
Maditinos et al.
The Value Relevance of Intellectual Capital on the Firm’s
Market Value: An Empirical Survey on the Italian Listed Firms
Ferraro and Veltri
The Impact of Intellectual Capital Efficiency on Market Value: An
Empirical Study from Iranian Pharmaceutical Companies
Mehralian G et. al,
The relationship between intellectual capital and financial
performance in the Nigerian Banking Sector
Intellectual capital and firm performance of high intangible
intensive industries: Malaysia evidence
Mehri et al (2013) Yes
The impact of intellectual capital on firm financial performance by moderating dynamic capability
Sofian S (2014). Yes
The relationship between Intellectual capital and the performance of food industry
1.2 Problem Discussion
Today, the majority of companies are moving toward using IC and the economy based on knowledge in order to find more effective ways to increase performance, while among economists and academicians still there are controversial, endless discussions about the effect of IC on efficiency and the methods of measuring that (Lipunga,
companied of Tehran Stock Exchange
The influence of intellectual capital components towards the company
performance Lina (2014) Yes
The impact of intellectual capitals of pharmaceutical companies listed
in Tehran Stock Exchange on their market performance
Chizari et al. (2015) Yes
Impact of Intellectual Capital on Financial Performance of Banks in
Tanzania. Isanzua, (2015) Yes The impact of intellectual capital
on firm performance: Evidence from Tehran Stock Exchange
Matinfard and Khavari (2015)
The impact of intellectual capital on the financial performance of
listed Nigerian food products companies
Shafi’u et al.(2017) Yes
Impact of Intellectual Capital on Business Performance: Evidence from Indian Pharmaceutical
Effect of intellectual capital efficiency on financial performance: Evidence from Ethiopian commercial banks
Tefera (2018) Yes
2014). In fact, many previous studies mentioned before in the background have used different methods for measuring IC and FP, and used different samples and data, also have got different results. This is the reason behind the requirement of more investigations in this field. Table (1-1) demonstrates a summary of some papers that could find a positive relation between IC and FP or could not or even were not able to get a clear conclusion. Furthermore, the extended variety of methods for measuring IC and FP in order to achieve better results is another worthwhile and attractive point to do more research in this area. For instance, the majority of studies have measured FP by only ratios and regression as analysis methods, while in this study is used Data Envelopment Analysis (DEA) method for mearing FP. The problem with ratios methods is that it provides overall analysis by predicting efficient or inefficient business units. But, DEA indicates a single objective score and ranking along with providing targets to improve (Halkos, G.E. & Salamouris, 2004). In addition, regression considers the average equation regarding DMUs rather, DEA evaluates a single DMU and points the area of improvement (Charnes et al., 1994). In the following paragraphs some papers, their different methods and paradoxical results, and gaps in IC research will be examined.
Appuhami (2007) based and evaluated research on the significance of IC in the companies operating in Thailand whereas Chen et al. in (2005) focus and summarizes the importance of IC in developing product value and FP of organization. On the other hand, Shiu (2006) insisted the weaker aspect in the research regarding the association factors among VAIC and organizational performance however Firer & Williams (2003) has concluded their thesis on finding that owners and shareholders emphasize highly on the prominence of tangible assets rather considering IC an asset and Chan (2009) also agrees with this research outputs. As viewing these results one can think about the variation in the outputs and focus more on defining the performance and IC relationship.
Furthermore, in the context of evaluating FP with the IC, Lina (2014) emphasizes this relation by taking companies from ISE (Indonesian Stock Exchange) for (2009 - 2011) and resulted in high influence of CE and no effect of HC along with SC on the financial performance.
Unlikely, Mehri et. al, (2013) insisted strong affirmative connection among IC (HC based) and FP in the Malaysian Industries with the other identical result research done by Dadashinasab & Sofian (2014) with data analyzed for the period 2000-2011. Likewise, research commenced by Laing et. al, (2010) as well as Maditinos et. al, (2011) also evaluates the strong and positive relation of HC based IC with financial performance in Australia and Athens respectively. However, the study initiated by Mohiuddin M., et al (2006) contradicts a similar area by taking data of 22 Bangladeshi Banks which were registered in the Dhaka Stock Exchange (DSE), study concludes with mixed reviews.
According to table (1-1) and examining previous papers, there is still a requirement for more studies in this subject to strengthen the view or a more certain answer about the impact of IC in the efficiency of firms. These investigations are essential especially in developing countries like Iran where there has not been much research in this field and overall Iran is encountering a number of economic and financial instabilities recently. Few investigations were done in this field in Iran such as, Mojtahedzadeh et. al, (2010) Mehnralian, et.al, (2012), and Matinfard and Khavari (2015) are some of them. Mehralian G et. al, (2012) states IC efficiency on market value (performance) relationship on companies of Iran concluded that it varies market to market (country) and depends upon investor’s awareness to foresee this aspect in the specific market. Mehralian G et. al, (2012) further concluded that these types of "VAIC studies" might not stand with the positive relationship in these types of developing countries. But on the other hand, agreed and insisted on the idea of Katsanis (2006) that employee's training and development leads to an exclusive performance of an organization.
Mojtahedzadeh et. al, (2010) commenced the study regarding relationship of IC with HC, CC and SC ended with finding a positive relationship.
As a result, in order to do more research about Iranian companies and economic situation of today of Iran, also to improve the knowledge of IC generally, which is still accompanied by many challenges, gaps, and questions especially in its relationship with FP, this thesis is done. Furthermore, it has emphasized on DEA approach that is very less used to evaluate the relation between IC and FP with the hope to find new outcomes that will be developed the knowledge of business administration more than before.
1.3 Research Question
The research focuses on the valuation of the efficiency of the firms. We intend to observe the relation between IC and FP in a company by assessing statistics. Therefore, we consider one basic research question for this thesis:
• What is the impact of the Pulic (1998) form of intellectual capital efficiency
on the financial performance of manufacturing companies on the Tehran Stock Exchange?
Purpose of the research is to identify and emphasize the relationship and impact of Intellectual Capital (IC) on the manufacturing companies by analyzing the level of their financial performance in the Tehran Stock Exchange (TSE).
2.1 Intellectual Capital (IC)
IC is relatively a contemporary perception of business studies and different researchers have interpreted this perception in accordance with the scenarios they were into and their definitions created were reflecting their own specific perspectives. John Kenneth Galbraith primarily published this perception which named as Intellectual Capital (IC) in 1969 (Feiwel, 1975) thought that IC is more than just calling it as “pure intelligence” rather than it is the amalgamation and the process between the intelligence and the intelligent actions. Edvinsson & Malone (1997) states that the IC consists of HC as well as SC, these are enveloped with systems, customers, processes, brands and so on. However, Al-Hamadeen & Suwaidan, (2014) describes IC as a combination of three components by adding one more component Relational Capital (RC) besides human and structural capitals.
IC is playing its part sufficiently in providing organizations with competitive advantages which leads them to sustainability in the long run (Kaplan and Norton, 2004). In the organization's success intangible assets have an exclusive part and IC has now been more enlighten as the most powerful intangible asset the organization can ever have (Latif et al., 2012) and, due to this evidence companies are investing more in skill development as it leads to their sustainability (Bontis, 2001). The assets which get more importance than material and production in the sense of sustainability is IC (Chen et.al, 2005). Also Cohen & Kaimenakis, (2007) insisted that IC should become the priority for the organizations as it creates the value of products, increase the overall performance level (especially financial) for a sustainable business in the recent competitive business environment. As far as managerial perspective is concerned IC can be evaluated as theoretical knowledge converted into applied knowledge for creating professional skills and with the support of new technology and relationships which leads to performance of the organization reaching new heights (Jay Chatzkel, 2002).
Despite having huge advantages of IC it is still in the beginning stage, and as it is not consumed properly by some organizations, so it will trigger the problems in the form of losing lots of money on yearly basis for them (Sudarsanam, Sorwar, & Marr, 2006). In order to get the extensive results of value addition of IC, it should be evaluated in a new way of Corporate Intellectual Ability which is also named as Value Added Intellectual Coefficient abbreviated as VAIC and this coefficient is being used in an increasing trend (Pulic, 1998, 2000). Firer and Williams (2003) stated that VAIC is also used in different researches by the researchers. Unlike developed countries, developing countries have focused less and provided less resources to IC research and practices so it is a very new concept in such countries (Al-Hamadeen & Suwaidan, 2014).
2.1.1 Research Area of Intellectual Capital (IC)
A number of studies on IC is mainly emphasized on (as discussed before) providing the awareness about IC as well as creating and providing different measures to calculate the IC of the specific or group of organizations (Roos et al., 1997, Stewart, 1997, Brooking et al., 1996). Roos et al., (1997) further suggested the three measures of IC (a) Variance among the values in book and in the market (b) identify and measure the hidden intellectual assets (IA) (c) the last one is to use IC index to evaluate the performance of IA, initiating by identifying the fundamental measures of success of a firm. IC related researchers (Sveiby, 1997; Mouritsen et al., 2001; Bontis et al., 2000; Lev, 2001; Meritum, 2002) have moved one step forward by turning around their basic theme of research to only calculate, rather emphasized on development of frameworks or models which can measure IC. Sveiby (1997) has developed a model which is known as Content Analysis Framework with the focus on classifying IC as capital like other capitals e.g. HC, internal capital or external capital etc. Abeysekera and Guthrie (2005) managed data based on two years (1998-1999) from the annual reports of thirty different companies and identified some IC items, and these companies were listed in (CSE) Colombo Stock Exchange.
In the South African recorded organizations, Firer & Mitchell Williams (2003) perceived a meaningful impact regarding IC on the organization achievement and affirmative force of CEE on organization MV. Identical model is initiated by Pal and Soriya (2012) managed comparable investigation on the pharmaceutical along with textile manufacturers and pointed that FP of those enterprises has positive association with IC and no meaningful relations of intellectual capital between productivity as well as market valuation have been recognized. (Smriti & Das. 2018).
Research handled by Yalama & Coskun (2007) in order to investigate IC influence with their subparts on the success of Istanbul Banks and recognized a meaningful performance regarding IC contrasted to capital employed efficiency (CEE). Mavridis (2004) applied a similar method to assume the human capital holds the most potent grade of relation with the performance symbol regarding the banks operating in Japan. In the research on impact of VAIC on Financial performance commenced by Ismail & Karem (2011) observed vital relationships between CEE and HCE with banks achievement located in Bahrain. However, their analysis missed noting any important correlation between structural capital efficiency (SCE) with the corporate performance. (Smriti & Das. 2018).
Kamath (2008) observed that HC has a significantly influences only ROA in the Indian Pharmaceutical Sector, concluded with having no significant relation of IC with productivity of the company and MV. Vishnu & Gupta (2014) observed identical data in an investigation regarding Indian Pharmaceutical Industry, and revealed that all parts of the VAIC strongly related to CEE and has a positive influence on the
corporate performance as estimated with ROA. These evaluations contribute into the Indian intellectual areas of pharmaceuticals, where related people acknowledge the importance of IC in the firm. (Smriti & Das. 2018).
However, the latest trend in the IC research has shifted its focus towards the evaluation of performance in the organization by using the VAIC tool to evaluate IC in the businesses, launched by Pulic A. in 2000 and he applied this tool on a number of different banks to generate their IC output. In this research VAIC tool would be used on a number of manufacturing companies based on food and pharmaceutical sector which are registered in Tehran Stock Exchange TSE and comparison would be made in order to estimate their financial weightage specifically in IC viewpoint.
2.2 The Value
added Intellectual Coefficient (VAIC)
Ante Pulic (1988), founder of the IC Research Centre brought up the VAIC which is an analytical instrument shaped in order to calculate productivity and output of IC within a firm. This tool enables stakeholders to control and evaluate the oval resources attained by a company. VAIC comes up with a new attitude about how the firms measure and track value creation efficiency by using accounting-based figures (Rahim et al.,2017).
Pulic argued that traditional accounting is founded on financial aspects and costs, while in today's competitive business environment, the main emphasis is on value creation. In fact, the traditional components of business success, for instance, profit, market share, revenue, and cash flow are not able to give accurate information about value creation. Thus, these days, companies require a long term perspective to generate and control value, and the key area of investments for them is frequent investments in intellectual resources (Fijałkowska, 2014).
In other words, these days the tangible results of the value creation procedures rely on intangible aspects of value creation, such as increased speed and communication capacity, improved consumer relationships, ability to develop and sustain a good reputation, and paying attention to human resources. Pulic defined VAIC as an index that depends on the value-added concept for measuring performance relative to IC can respond to the needs of the modern business world. Ståhle et. al., (2011), states that VAIC measures total efficiency of a firm and ICE efficiency. Furthermore, is founded on dual key expectations: primarily, generation of added value is based on usage of physical capital along with IC, with additionally, the VA has a relation with total output. Ståhle et. al., (2011) also explained that VAIC measurements are relying on the following factors, and it is measured by adding main efficiencies observed, that are accounted as ratios:
· Human Capital (HC): It is generally defined as personnel expenses. The
‘‘human capital’’ indicates the abilities, the capabilities, knowledge regarding human resources. (costa, 2012). It is generally identified as personnel expenses.
Human Capital Efficiency (HCE): human capital efficiency points out the
value-added efficiency of the human capital. It means: HCE= VA/HC
· Structural Capital (SC): It is change among generated VA and HC, which
means SC equals VA minus HC. The ‘‘structural capital’’ describes the organizational knowledge, necessarily included in business processes, procedures, and systems.
Structural Capital efficiency (SCE): SCE assesses the value-added efficiency of SC
and mentioned below:
SCE= SC / VA
· Capital Employed (CE): It is the financial property that means the book value
(BV). It is tangible assets that are shown by book value. CE calculated by total assets minus intangible assets.
Capital Employed Efficiency (CEE): capital employed efficiency evaluates the
participation of CE is the representative for the VA efficiency of CE, is described below:
CEE = VA / CE
Intellectual Capital Efficiency(ICE): ICE is obtained by adding HCE and SCE:
Value-added Intellectual Coefficient(VAIC): VAIC equals the sum of ICE and
CEE or the sum of HCE, SCE, and CEE:
VAIC= CEE+ HCE+SCE or
VAIC = CEE + ICE
Exemple text: Cras varius eu nisi vitae convallis. Nam dignissim nunc vel semper faucibus. Mauris semper diam quis felis vestibulum, vitae accumsan nisi gravida.
Figure 1: VAIC model (Ståhle et. al, 2011, 6)
2.2.1 Strength and Weakness of VAIC
Iazzolino & Laise (2013), Pulic’s model is definitely innovative, both in terms of meaning and methodology. The most considered advantage of that is the development of a link among IC studies and performance measurement of companies which leads to increased discussion about the drivers of value creation by a pivotal contribution. Pulic strongly has claimed that the Value Added Income Statement (VAIS) to be used in a knowledge company to calculate value creation. Fijałkowska (2014) wrote a paper about VAIC and explained about many advantages of VAIC which other researchers have also admitted. She stated that the VAIC uses traditional financial statements data for measuring financial performance, which means the data are accessible publicly. This is the reason this approach is an effective method for evaluating IC. In addition, she mentioned that the Pulic's model in the opposite of many performance evaluation approaches uses quantitative data that is also admitted by independent auditors. Thus, these data have high credibility, and the judgments involved in qualitative data do not have a role in them. Another advantage of VAIC is its simple way of gathering data because the complex gathering data leads to the greater risk that data gathering and procedures become ends in themselves (Fijałkowska, 2014).
Overall, VAIC which is the consequence of procedure simplification enables the comparison among firms and provides an opportunity to identify the firms which have
an appropriate potential for value creation in the analyzed sample (Fijałkowska ,2014). However, the fundamental disadvantage regarding Pulic's method is his perspective on performance measurement of companies which means the mono-criterial view of the performance measurement of companies (Iazzolino & Laise, 2013). Pulic believed that the measures which are based on VA must be replaced with the measures which are based on Earnings Before Interest Taxes (EBIT) because these traditional methods cannot provide useful information about value creation for stakeholders (Iazzolino & Laise, 2013). Basically Pulic's belief there exist a mono-criterial view about business performance measurement. In fact, the problem is the assumption which considers that ROA and HCE evaluates identical results, which leads to choosing only one of them. However, they they evaluate performance in different dimension, so no requirement to select one of them. If a multi-criterial measure vision is taken, for example, the balanced scorecard (BSC), both ROA and HCE can be kept as complementary, and the issue for choosing the best tool for measuring the performance of companies would not even occur (Iazzolino & Laise, 2013). Furthermore, Ståhle et. al., (2011) wrote a critical paper about Pulic's idea which strongly rejects his model and formula. They believed that there are two basic problems in his work. First, they believed that the paradoxical findings of VAIC are related to value-added volatility. Since value-added is a core factor in Pulic's model thus renders it unstable. Because of that, the time interval of VAIC measurement has an impact on the conclusions. Second, they state that Pulic's formula is confusing and vague. Pulic's definitions of VIAC components add nothing to standard performance measurement.
However, even in a multi-criterial method, every criterion can take only one feature of a multidimensional fact, and all methods have some merits and demerits. In fact, there does not exist a perfect criterion that would be superior to others (Iazzolino & Laise, 2013).
2.2.2 2.2.2 VAIC and Financial Performance
In spite of much continuous controversy regarding which VAIC and its parts tends to hype the FP of companies, there is an extended range of research examining the effect of IC on the FP of companies as calculated through VAIC model. Some of them have admitted this positive relationship (Chen et al., 2004; Rudez and Mihalic, 2007; Tan et al., 2007), while others have not been able to prove this relationship (Ferraro and Veltri, 2011; Mehralian, et.al., 2012). As a result, rely on the previous results of research, the hypotheses of this thesis is made:
The value-added intellectual coefficient (VAIC) has relation with the financial performance, measured by DEA, of Pharmaceutical and food companies in the Tehran Stock Exchange.
2.3 Capital Employed Efficiency (CEE)
Tefera (2018) states Capital Employed (CE) might be explained as entire capital utilized in present and fixed assets of an organization that reflects the potential of that company. Furthermore, CE is described as it relates to value stakeholders which are categorized in the forms of brand equity and customer loyalty. The knowledge and information which is behind these kinds of relationships are vital for the survival of a company in today's business competitive environment.
It should be considered that, although CE is a term that is used continuously, it is hard to describe due to there exist many contexts in which it can be used. Overall, all descriptions of CE mention capital investment which is vital for the business's operation. In other words, CE may mirror a picture of how firms or organizations are investing their money (Investopedia.com, 2019).
According to previous explanations, CEE is an index that represents the level of VA is made by every unit of money financed in CE, or how much a firm generates by investing ﬁnancial and physical capital (Xu and Wang, 2019).
CEE = VA / CE 2.3.1 Capital Employed and Financial Performance
In spite of some papers which have could not find a meaningful relationship between CEE and financial performance of companies and some of them only have got to misleading and mixed findings, besides many researcher’s state that there is a positive relationship between CEE as a component of VAIC and financial performance of companies (Chen et al. 2005; Abdul Majid Makki. and Aziz Lodhi,2008; Ekwe (2012); Appuhami and Bhuyan,2015; Ahmad et al.,2016). There exist some studies which have argued that CEE is the most effective component of VAIC (Ting & Lean, 2009; Joshi et al., 2013; Al-Musalli & Ku Ismail, 2014). According to these studies it has been assumed that:
The capital employed efficiency (CEE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
2.4 Human Capital Efficiency (HCE)
Gary Becker, who received the 1992 Nobel Prize regarding Economic Science, acknowledged significance of HC in the 1960s. He stated that expenses done on any individual’s training, education, health is not the financial as well as physical capitals to be considered in books (Becker, 1964, p 16). During the past years, many
researchers have offered various definitions of HC, although the basic meaning in all descriptions consists of the fact that HC is the affirmation on expertise and experience of personnel rather than on the physical assets of a firm (Rahim et al. 2017).
According to Roose et al (1998), HC is moveable and it is not the property of a particular company due to employees that are the real owners of that. Bontis (1999) claimed that the importance of HC is because of its role as a resource of strategic innovation for companies. Fincham and Roslender (2003) believed that the only value-generating asset is human capital. HC includes the wider human source like essentials of labor, special needs of personal skills in way of employee experience, multiple skills and abilities (Mcgregor et al., 2004).
Nielson, Bukh, Johansen, Gormsen (2006) stated that HC is the key part of IC concepts which consist of specialized personnel, innovation, and management insight. The performance of a firm has been impressive. HCE which is the VAIC element can calculate the added value through HR of a company (Rahima, Atan and Kamaluddin, 2016). In addition, it is calculated as the relation of VA (specifically human assets) to human costs (including wages and employee benefits for staff expenses) (Danjuma and Ajike, 2016).
HCE = VA / HC 2.4.1 Human Capital and Financial Performance
In the previous investigations, it is claimed that existence of a considerable meaningful connection among HCE and performance of the firm which leads to getting a competitive advantage. (Belkaoui, 2003; Chen et al., 2005; Goh, 2005; Abdul Majid Makki, and Aziz Lodhi ,2008; Gan and Saleh, 2008; Ting and Lean, 2009; Ghosh and Mondal, 2009; Plink and Barning, 2010; Ekwe,2012; Al-Musali and Ismail, 2014). Some of these researches have introduced HCE as the most critical component of VAIC which creates positive effect on FP (Goh, 2005; Mondal and Ghosh, 2012). Also, Plink and Barning (2010) stated that HC has an affirmative outcome on the efficiency of firms because of ability to produce an eye-catching value for the firm and bring them a stable competitive advantage. As a result, rely on the previous results of research, the hypotheses of this thesis is made:
The human capital efficiency (HCE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
2.5 Structural Capital Efficiency (SCE)
The structural capital (SC) is a framework that protects human capital and consists of organizational procedures, technologies, databases, training materials, and
intellectual property rights. In fact, SC is the thing that continues to exist in a company when the personnel leave the company (Gogan, et al., 2015). Bontis et. al., (2000), states that his framework is presented by a company to complete intangible achievements, for instance, teammate spirit, transparency, and faith between personnel. Therefore, a company that has a powerful SC can have an encouraging value that triggers the personnel to attain contemporary knowledge successfully. Bontis et al. (2000) also emphasized that the companies contain SC, whose MV is more significant than financial value (FV). According to previous definitions, SCE can be explained as one of the components of VAIC and the rate of SCE, it is an index which demonstrates the portion of SC in value creation.
SCE = SC / VA 2.5.1 Structural Capital and Financial Performance
Although there exist some researches which rejected the correlation between SCE and financial performance, many papers state that this relationship is positive and strong (Appuhami, 2007; Muhammad & Ismail, 2009; Maditinos et al., 2010). Furthermore, in newest thesis which we could find in this field, Tefera (2018), the finding of the researcher concludes that there is an affirmative correlation among SCE and FP of Ethiopian Commercial Banks. According to these articles, the third hypothesis of this thesis has been made:
The structural capital efficiency (SCE) has relation with the financial performance, measured by DEA, of pharmaceutical and food companies in the Tehran Stock Exchange.
2.6 Organizational Performance Measure Through DEA
To calculate the performance of the company there are two areas from where the performance can be determined: Efficiency and Productivity of that specific company (Pontus M., 2018), the theme of the ongoing study is to emphasize on assessing the efficiency level of the company. There are companies with a ratio between 67% to 82% which conduct the performance evaluation (Pontus M., 2018) by different techniques like ROE, ROA, ROI, SFA and DEA. In the recent time DEA and SFA has become a regular technique to judge the competence of a firm on essentials of non-parametric and parametric measure respectively (Pontus M., 2018). Below are described the non-parametric and parametric measures.
2.6.1 Non Parametric (DEA) and Parametric (SFA) Measures
Non parametric arithmetic in which the information is not in normal distributions (Investopedia, 2019). To measure these types of nonparametric statistics the first
model was introduced in (1937) by von Neuman and then it was made general and comprehensive by Kemeny, Morgenstern, and Thompson (1956). Latest technique used in measuring or testing the non-parametric data is DEA which was initiated by Charnes, Cooper, and Rhodes (1978) DEA was abbreviated as CCR before based on their first names. This measure is consisting of both multiples input and output (Charnes et al., 1978). DEA is being introduced and developed by the process management oriented researchers with the purpose of generating the arithmetical as well as accurate evaluation of non-parametric numbers to aid the researchers and economists (Bogetoft, 2012; Färe, Grosskopf and Lovell, 2013). It uses Decision Making Units (DMU) to analyze and evaluate the results. In latest business research, an extended number of papers are using DEA measures to evaluate non-parametric data (Emrouznejad and Yang, 2018). DEA is based on distance function or metrics, which means the distance between a couple of features (Shephard, 1953, 1970). DEA is used in input direction as described in Shephard, (1953) or output direction in Shephard, (1970) or in mixed directional distance function (Luenberger, 1992, 1995). This can be exemplified in the way that if it is input direction method the output is measured or dependent according to input level whereas in output direction method the input is measured or dependent according to output or to decrease the input to increase the output in directional distance function (IGI Global, 2020).
As far as DEA is concern there is an issue in its usage sometimes the results are not perfect and there is a biases in the results for suggesting a change and to eradicate this situation the method of Bootstrapping is normally done, this will help in generating non bias results for efficient decision making (Efron, 1979; Simar and Wilson, 2000). However, to measure the parametric data the measure efficiency best frequently practiced is Stochastic Frontier Analysis (SFA), it was developed by a number of researchers like Aigner, Lovell, and Schmidt (1977) and Meeusen & van Den Broeck (1977). According to Pitt and Lee (1981) the efficiency was evaluated and changed in units but there is no time variation. After more than a decade Kumbhakar (1990) developed the times aspect into the evaluation process. Efficiency was then categorized into sub parts of Persistent Efficiency, Noise and Time variance (Kumbhakar and Heshmati, 1995). In 2014 by Colombi et. al., (2014); Tsionas and Kumbhakar (2014); and Kumbhakar, Lien, and Hardaker (2014) further categorized the efficiency in four instead of three like Persistent Efficiency, Firm Heterogeneity, Random Noise and Time Variance.
2.7 Benefits of this Study
This study will allow the readers and the strategic management in multiple companies particularly those who are commencing their business in Iran, significance of the intellectual capital for businesses as tangible assets and its beneficial role for enhancing their performance which leads to sustainable business recent challenging
business environment. Besides Iran this study will be helpful in spreading the IC significance to readers and businesses operating elsewhere especially in developing countries. This research will be beneficial for Iranian companies to understand the purpose of IC in business and make them to invest more on the employee development as well as on updating working facilities and environment to generate and retain quality intellectual capital for their organizations respectively. However, this research will also aid other researchers to pursue more research in Iran on this and relevant topics.
3.1 Scientific Perspective
In business research, the scientific perspective aids the researcher in the form of commencing and articulating as well as making an analysis of specific research (Hair et. al., 2013). To produce convincing study, the scholar should have the ability to judge that already created philosophies have a tangible empirical output (Hair et al, 2013). So researchers have identified and mostly in practice are two traditions positivism and hermeneutics (Prasad, 2005; Bryman & Bell, 2011).
3.1.1 Positivism and Hermeneutics
Positivism is an approach that emphasizes the philosophy and art of knowledge and focuses on the assumption of objective reality which can be observed as well as computable (Trochim & Donnelly, 2001). Positivism leads to attaining the extended knowledge of aspects of the research by authentic sources and observation is the basic trait of quantitative technique (Trochim & Donnelly, 2001). In positivism, the collection of data is observed through surveys, experiments & tests and the statistical data in the literature of the research (Collins, 2010; Trochim & Donnelly, 2001; Neuman, 2003). The essential data collected regarding the research is then analyzed to agree or disagree with pre observed hypotheses, as well as positivism, includes deductive approach rather than inductive approach (Crowther & Lancaster, 2008). Positivism is also defined by Collins (2010, p. 38) as it is the viewpoint of the observer and translated according to his experience and “it has an atomistic, ontological view of the world as comprising discrete, observable elements and events that interact in an observable, determined and regular manner."
Hermeneutics is defined as “a form of textual interpretation, concerned mainly with the methodical analysis of different forms of texts and special effort is taken to discover the real meanings of texts, which due to complexity, personal world-view or social location of the original authors, are often not immediately recognizable” (Prasad, 2005, p. 30). Unlike positivism which includes the measurable aspects, hermeneutic is a traditional approach in which negative attitude or interpretation to any aspect can be expected (Gadamer, 1960, cited by Prasad, 2005). Prasad (2005) further insisted that the hermeneutic writings are measured by the hermeneutic circle, the essence of this is it squeezes the difference of obvious meaning of writing with the hidden meaning of writing (Prasad, 2005). Researchers through extensive analysis in this field identify the complex combination of writing context versus a writer's individual element (Arnold & Fisher, 1994, cited by Prasad, 2005).
In this study, Pulic’s model (VAIC), is used and IC value of registered manufacturing companies in the TSE for the period of 7 years from 2012 to 2018 will be evaluated. Then, there will be a regression analysis done to check relation among IC and elements of IC of companies with FP to satisfy the hypothesis. For the extracting company’s FP, DEA method through software will be considered. Hypotheses are developed in the light of existing theories and literature. In this study, the statistics method will be used, and data analysis will conduct multiple regression and correlation coefficient. The theoretical basis and research history will be collected by the library, article, and internet, and the practical information will gather from the Tehran Stock Exchange. Thus, this thesis has a positivistic view and deductive approach is observed and data gathered is statistical nature of established organizations. The aim of information gathering is testing of hypotheses which are developed already under the light of theories and previous almost similar studies in order to verify or reject them according to specific market of Iran based on pharmaceutical and food manufacturing industries. And to prove a real fact, which is measurable and deals with the statistical operation.
3.2 Research Strategy
According to the researcher's research strategy indicates the direction and way of conducting business research. A research strategy is based on two strategies which are commonly interpreted as Qualitative and Quantitative Research strategies and these strategies are a way of interpreting the way and commencing of business research (Bryman & Bell, 2011). Different research strategies are used according to different scenarios and circumstances and every research strategy has its own pros and cons and no strategy has an upper hand (Saunders et al., 2014). To determine what strategy is going to be implemented, research question(s), and purpose, the research approach plays a vital role (Denzin and Lincoln, 2005).
3.2.1 Qualitative and Quantitative
The qualitative technique is focused on social aspects and their sources by observing their attitudes, behaviors, and responses (Krishnaswami & Satyaprasad, 2010). Unlike the quantitative technique which is more into the numerical aspect, the qualitative technique is naturally descriptive in the form of words, sentences, pictures as well as videos (Meyers, 1997; Bryman & Bell, 2007; Saunders, Lewis & Thornhill, 2012). In this technique the data is collected by conducting the interviews, surveys, observations and case study as well as this method is based on the non-arithmetic techniques (Ghauri & Grønhaug, 2010; Bryman & Bell, 2011; Saunders, Lewis & Thornhill, 2012). Bryman & Bell (2011) states that in a qualitative method the creation of a relationship between the research subject with a theory is an inductive approach. This affirms that qualitative research triggers with observation related to
any specific point and further lead to generalization (Creswell, 2009). This leads to the ability of the researcher in order to transfer the gathered data into new theory by personal traits (Ghauri & Grønhaug, 2005; Creswell, 2009) and in a way of achieving the desired results researcher should emphasize more on that specific point (Bryman & Bell, 2011).
Burns and burns (2008) has emphasized on the implementation of the quantitative technique in order to generate common philosophies under severe precise trails. Quantitative means that numerical data, as a matter of fact, the data gathered as well as used for making analysis will be based on arithmetical variants (Burns & Burns, 2008). Bryman and Bell (2007) indicate that the quantitative technique emphasizes on investigating the themes which have already been created in order to judge their hypothetical impact. A quantitative analysis is based on a deductive methodology and this approach aids the researcher to compare the ongoing study with the prevailing studies (Bryman & Bell, 2007). In quantitative technique the data is gathered by conducting surveys, performing experiments and statistical data available on a physical basis or online provided by different organizations (Bryman & Bell, 2007; Shuttleworth, 2008). Saunders Lewis & Thornhill (2012) insisted on the possibility of the quantitative technique of formulating structured interviews that have the collection of close-ended questions. The quantitative technique while working is highly dependable on challenging as well as authenticating the existing philosophies thus advances towards generalization and objectivity along with this approach follow the footsteps of positivism as well (Ghauri & Grønhaug, 2010; Bryman & Bell, 2007). As far as this research is concerned this will be based on the quantitative strategy, the reason for this selection is that the statistical data is considered regarding manufacturing organizations for this research. Furthermore, this statistical data will be used to evaluate the results and correlate these results with the hypothesis. The reasons of taking quantitative study and statistical data of the already established companies is beware Iranian market as well as identify the significance of the IC which is already being playing it role in the companies selected. The quantitative research is more scientific than qualitative research as it is least biased and is more in control. As it is more controlled it is more focused than qualitative study means there is a clear vision where the researcher is heading. It deals in large samples comparatively and obtaining of consistent, reliable information to attain unbiased results and it is highly repeatable type of research to obtain most reliable results. As this is relatively more reliable and credible this is mostly used by the influential people in different sectors so highly beneficial for decision making at the higher level.
3.3 Scientific Research Approach
To help the researchers in the quest of approaches to be used in their research, three types of research approaches are indicated and further elaborated, and these are the
Inductive Approach, Deductive Approach and Abductive Approach (Saunders et al., 2014).
3.3.1 Inductive, Deductive and Abductive Research
The Abductive research approach is an area of approach which is powered by the amazing and unforeseen facts (Saunders et al., 2014). The abduction approach is further described as the amalgamation of the inductive and deductive approaches as well as there are many reappearances in this approach (Suddaby, 2006). The abductive approach is mostly emphasized on social issues and there is always a repetitive drive among the real and exemplary scenarios (Alvesson and Skoldberg, 2009; Dubois & Gadde, 2002, p. 554). The intention of the abductive approach is to realize the understandings and purpose of the social life as well as its performers and their circumstances and their responses to those circumstances (Blaikie, 2010, p. 84,89).
However, the deductive type is aimed to unveil the link among the diverse variables (Saunders et al., 2009, p. 125) in order to check their accuracy and precision rather than developing the new concepts (Kovács & Spens, 2005). Bryman & Bell (2011, p. 11) indicates that the deductive approach includes a chain of sequences like concept structuring, hypothesis development, data gathering, outcomes and adjusting hypotheses and theories according to those outcomes.
However, the Inductive Approach is triggered by structuring the theory on some specific case studies investigations and developing an overview of those investigations by (Dubois & Gadde, 2002). The difference between the Inductive and Deductive approach is that the inductive approach is considered when there is a gap among the theories developed, results gathered and their conclusions as per observations, however, the deductive approach emphasizes the conclusion based on multiple theories (Ketokivi and Mantere, 2010). The difference in accordance with Bryman & Bell (2011) which is more clear and precise is that the deductive approach initiates with theory and finalizes at data and observation versus inductive approach begins with observations and concludes on theory thus acting reciprocally to each other.
As this research is based on a quantitative strategy for this strategy the deductive approach will be used. The rationales of using deductive approach is the there is already a theory developed in the sense of hypothesis which are already mentioned above. So the statistical data is collected regarding the sample or full population of specific market like Iranian manufacturing companies, in order to identify that these hypotheses are accepted or rejected in the Iranian market which is considered as developing country. There are 34 manufacturing companies taken are registered in TSE belongs to two sector out of which 19 companies belong to pharmaceutical
industry and 15 are from food industry. Pulic VAIC method is used to measure IC of the company and DEA method will be used to evaluate FP. The two types of industries are also identifying if there is some variation the hypothetical results or there is complete similarity in the Iranian business approaches. The benefit of selecting DEA is that it can accommodate multiple inputs and outputs as compared to regression analysis (Bowlin, 1998). There are different methods of evaluating IC in the companies but the most reputable model to evaluate IC is 1998 Ante Pulic Model of VAIC. Finally, there will be a regression analysis to evaluate the correlation among the VAIC and FP of the company. And to check the validation of any singular method both methodologies (regression and DEA) can be used as combination and this will allow to rectify the methodology error of biasness mentioned by Charnes, Cooper and Sueyoshi in 1986 (Bowlin, 1998).
3.4 Research Method
According to Håkansson (2013), The research methods are a collection of methodologies or guidelines, that assist researchers to implement the investigation. These methodologies consist of categorizing, planning, design, and conduct of the study. It should be considered that choosing the method of research is dependent as aim of the research is so and also the research strategy (Qualitative or Quantitative). The most frequent methodologies for quantitative investigations can be seen in the below list:
• Experimental Method: This method works through verifies or rejecting
hypotheses. In addition, it leads to correlations among dependent and independent variables. It can be said that it makes reveals cause-and-effect relationships among variables. Because of usually small volume of raw data this method commonly analyzed by statistics.
• Ex Post Facto Method: This method is similar to the experimental one,
although it is not manipulating or controlling the independent variables because it is done after information has been gathered. This method, despite being able to verify or reject hypotheses and also providing cause-and-effect relationships among variables, provides no guarantee to be efficient in strong inferences.
• Surveys Method: This method can be cross-sectional and longitudinal. Cross-sectional surveys gather data about a population, on one point of time, while Longitudinal surveys gather information throughout the interval. Surveys method evaluates a wide variety of attitudes and characteristics of subjects and also is known as a descriptive research method, which assesses repetitions besides relations among variables and defines a occurrence which cannot be seen directly.