RESEARCH METHODOLOGY

3.0 Introduction

This chapter described about the research methodologies that were used in the study, like how they were done. It was divided into seven parts. It covered research design, data collection, sample design, research tools, construct measurement, data processing, and data analysis, as well as the other parts.

3.1 Research Design

To evaluate whether CG has an influence on business performance among family- controlled companies listed on Bursa Malaysia from 2013 to 2020. An analysis of board quality and family-owned company performance using ROA, ROA, and Tobin's Q. Preparation for a research project is characterised as the research approach. Consequently, quantitative research measures and analyses variables to draw conclusions. It involves utilising and analysing numerical data to answer questions like who, how much, what, where, when, and how many (Apuke, Quantitative Research Methods a Synopsis Approach, 2017). The analysis examined data from 2013 to 2020 from family-owned firms and Bloomberg. The panel data analysis requires all eight years of data. The relationship between DVs and IVs was examined cross-sectionally during an eight-year period.

3.2 Data Collection Method

3.2.1 Secondary Data

This research used secondary data, mainly from annual reports. From 2013 to 2020, annual reports of family-controlled businesses listed on Bursa Malaysia were

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acquired from official websites and Bloomberg. The statistics for ROA, ROE, and Tobin's Q came from annual reports and Bloomberg.

The statistics in the annual reports were reliable and accurate since they were all verified by Malaysian authorities. It was straightforward and affordable compared to the original data. Secondary data sources included Google Scholar, e-databases at Universiti Tunku Abdul Rahman, Sage, ResearchGate, and Science Direct.

3.3 Sampling Design

This part included the target population, followed by the sample frame, sampling element, sampling technique, and sampling sizes.

3.3.1 Target Population

This is decided by whether or not individuals of the general population meet the selection criteria. Once a member meets these requirements, he or she is accepted.

Because the capacity to reply is of little or no value, a few selection criteria may be used (Asiamah, Mensah, & Mensah, 2017). The population for this study was chosen from all of the family-controlled firms registered on the Bursa Malaysia, for which annual reports and financial data were available. As a result, the overall population of this study includes 27 Bursa Malaysia and 13 Singapore listed family- controlled companies.

3.3.2 Sampling Frame

The sampling frame identifies and positions sample units within a population (West, 2016). This study's sample frame included all family-controlled enterprises listed on Bursa Malaysia. "Family Controlled Companies in Bursa Malaysia" provided this list of family-controlled companies by Tan (2016).

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Table 3.3.2(a): List of Top 25 Selected Family Controlled Business in Malaysia

Malaysia

1. AMMB Holdings Berhad 15. Oriental Holdings Berhad

2. Batu Kawan Berhad 16. PPB Group

3. Berjaya Group 17. Press Metal Berhad

4. Genting Berhad 18. QL Resources Berhad

5. Glodis Berhad 19. Sarawak Oil Palm Berhad

6. Hong Leong Bank 20. Scientex Berhad

7. Hup Seng Industries Berhad 21. Sunway Berhad

8. IOI Group 22. Tan Chong Motors

9. Kossan Rubber Industries Bhd 23.Top Glove Corporation Berhad 10. Mah Sing Group Bhd 24. Tropicana Corporation Berhad 11. Malayan Flour Mills Berhad 25. Unisem (M) Berhad

12. Media Chinese International 26. V.S Industry Berhad 13. Muda Holdings Berhad 27. YTL Corporation Berhad 14. Muhibbah Engineering (M)

Berhad

Table 3.3.2(b): List of Top 25 Selected Family Controlled Business in Singapore

Singapore

1. CDL Hospitality Trusts 8. Lippo Malls Indonesia Retail Trust

2. Frasers Centrepoint Trust 9. OUE Ltd 3. Golden Agri-Resources 10. QAF Limited

4. Haw Par Corp 11. Thai Beverage

5. Hong Leong Asia 12. UOL Group

6. Hong Kong Land 13. Wilmar Intl

7. Indofood Agri Resources

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3.3.3 Sampling Element

Not all the family-controlled corporations included in Tan (2016)'s book "Family Controlled Companies in Bursa Malaysia" were used in this study. Some firms were omitted from the population because there was very little and insufficient information and data available. As a result, a sample size of 40 family-controlled businesses was chosen for this study.

3.3.4 Sampling Technique

Probability and non-probability sampling methods exist. This ensures that each instance in the population has an equal probability of getting picked. (Andrea E.

Berndt, 2020).

3.3.5 Sampling Size

The effect magnitude, projected measurement variability, required statistical power, significance requirements, and whether a one- or two-tailed statistical analysis is planned are all factors to consider. (Oribhabor, 2019).

3.4 Research Instruments

The information was gathered from financial statements and annual reports spanning the years 2013 to 2018. To acquire all of the numbers and data for IVs, all of the annual reports were downloaded from the different firms' official websites.

Microsoft Excel was used to calculate the ROA, ROE, and Tobin's Q.

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3.5 Construct Measurement

3.5.1 Origin of Construct

There were 3 DVs and 7 IVs; a table of DVs shown in Table 3.5.1a, although a table of IVs was shown in Table 3.4.1b.

Table 3.5.1(a): Table of Dependent Variables

DVs Formulas Adopted From

ROA 𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

(Rosikah, Prananingrum, Muthalib, IrfandyAzis, &

Rohansyah, 2018).

(Jewell & Mankin, 2011) (Supriyadi, 2021)

ROE 𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝐸𝑞𝑢𝑖𝑡𝑦

(AHSAN, 2012)

Tobin’s Q 𝑇𝑜𝑡𝑎𝑙 𝑚𝑎𝑟𝑘𝑒𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑐𝑜𝑚𝑝𝑎𝑛𝑦 + 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 𝑣𝑎𝑙𝑒 + 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠

(Kaijser, 2014)

Source: Developed for the research

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Table 3.5.1(b): Table of Independent Variables

IVs Formulas Adopted From

BS Total number of directors (Kalsie & Shrivastav, 2016)

%ID 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑑𝑖𝑟𝑒𝑐𝑡𝑜𝑟𝑠 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑏𝑜𝑎𝑟𝑑 𝑠𝑖𝑧𝑒𝑠

(Fuzi, Halim, & M.K., 2016)

NOWD Total number of female directors on board (Phua & Ho, 2017)

NOFD Total number of foreign directors (Alshirah, Rahman, &

Mustapa, 2019)

NOFQ Total number of foreign qualifications (AGUILERA, MARANO,

& HAXHI, 2019)

NOBM Total of number of board meeting (Al-Daoud, Saidin, &

Abidin, 2016) NOFDAC Total number of foreign directors audit

committee

(Al-Baidhani, 2014)

Source: Developed for the research

3.5.2 Scale of Measurement

All the measurements in science were conducted using four types of scales that he named as "nominal", "ordinal", "interval" and "ratio". There were interval (ROA, ROE and Tobin’s Q) and ratio (BS, %ID, NOWD, NOFD, NOFQ, NOFDAC, and NOBM) scale of measurement in this research

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3.6 Data Processing

Figure 3.6: Data Processing

According to the diagram shown, there were five steps of data processing in this study. The first stage was to identify the dependent and independent variables that were relevant to this study based on past research. Microsoft Excel was used for data input; each row represents the name of a company from each country, and each column provides the DVs and IVs. Furthermore, several related formulae were discovered in order to gather data for each variable.

The second phase was gathering information from secondary sources such as annual reports, financial statements, and Bloomberg. The data entry procedure then began by obtaining and entering all essential data into various files, DVs, and IVs. The entire collection of data was then integrated into a single Excel file.

Following that, all of the data was migrated into EViews version 12. The findings were then generated by both pieces of software. Finally, the data was evaluated and interpreted. All data (ROA, ROE, Tobin's Q, BS, %ID, NOWD, NOFD, NOFQ, NOFDAC, and NOBM) were gathered from Bloomberg and yearly reports from 2013 to 2020.

1. Select Variables based on past studies

2. Select data from secondary sources

3. Data entry/ coding

4. Transfer data into Eviews Version 12

5. Analyze results and interpret findings

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3.7 Data Analysis

3.7.1 Descriptive Analysis

Descriptive analysis uses statistics to discover what works in education. A good description provides information on capabilities, needs, techniques, practises, policies, people, and settings related to a study or policy issue. a project and a need.

In the absence of high-quality causal analysis, data alone do not constitute descriptive research. These are helpful for certain applications, but they do not fit the requirements for descriptive analysis (Loeb, Dynarski, McFarland, Morris, &

Sean Reardon, 2017). Describing the study's findings The lowest, maximum, averages, and standard deviations of DVs and IVs were described in a descriptive table.

3.7.2 Inferential Analysis

Inferential statistics are another vast group of approaches that go beyond just summarising a data set. Inferential statistics can assist researchers in drawing inferences from a sample to a population. Inferential statistics may be used to investigate disparities between groups as well as the relationships between variables (Guetterman, 2019).

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3.7.3 Panel Data Analysis

Panel data analysis is more statistically reliable than cross-sectional data analysis in policy and programme assessment. Panel data analysis has grown in popularity among academics in recent years (Fitrianto & Musakkal, 2016).

Because the data in this study included both cross-sectional and time dimensions, which were the specific characteristics of family-controlled firms in Bursa Malaysia presented in different variables, as well as time-series information for a 6-year period, the changes within the studied companies over time were reflected in the data. It was thus decided to use the panel regression model in order to demonstrate the association between the DVs and IVs.

Fixed Effects Models (FEM) is becoming increasingly popular in economics and political science, owing to its standing as the default "gold standard." This approach presupposes that individual variance in intercept may be addressed. To estimate FEM panel data using dummy variables to describe changes in work, management, and incentive cultures (Rizka Zulfikar, 2019). Random Effects Models (REM), also known as multilevel models, hierarchical linear models, and mixed models, have grown in popularity in political science and are now widely utilised in fields such as education, epidemiology, geography, and biomedical sciences. Both methodologies may be used to answer difficult research problems, such as those involving place-based hierarchies (Bell & Jones, 2015). This model estimates panel data using temporal and individual interference factors. In the REM, the error terms of each business account for the intercept difference. Using the REM eliminates heteroscedasticity. (Rizka Zulfikar, 2019)

The Hausman test must be carried out before determining which effect model to apply. According to (Nguyen & Nguyen, 2020), stated that Hausman test is where to choose between FEM or REM model, since the P value is smaller than 0.05, therefore FEM model was used.

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