CHAPTER III – RESEARCH METHODOLOGY
3.1 Introduction
The research methodology provides an illustration of the research activity and a description of the methods used to achieve the goals. The research technique serves as a guide and makes it easier to validate the data that has been gathered. The chosen research methodology could also be seen as the establishment of a theoretical framework, ensuring that data collecting, and already-existing data are evaluated and used appropriately to ultimately achieve the goal of authenticating new data (Al-Wattar et al., 2019).
The methods and research design used in this study are described in this section. The current chapter is primarily concerned with the methodological aspects of carrying out the current study, including the research design, procedures, data collection, and data analysis plan. The study area, sampling strategy, development of measurements, data collection process, and statistical analysis techniques are all further described in the research methods section. The chapter concludes with a discussion of the ethical issues surrounding the research (Al-ahdal et al., 2020).
3.2 Research Design
The contemporary design utilized for the purpose of conducting research is referred to as research design. The type of study that is intended to assess and analyze the financial performance of the information technology industry with a basis in India is the focus of the research design. It demonstrates the relationships and differences between the Indian IT market and the global market. Explanatory research, descriptive research, and exploratory research are the three types of study that will be categorized according to their purposes (Khalil et al., 2022)
Explanatory research is the investigation of a recent phenomenon. Exploratory research is characterized by its adaptability. Even though the problem is significant and poorly defined, the researchers used exploratory research as a first step. (Exploratory research will be useful for understanding what might occur, discovering new insights, posing questions, and evaluating phenomena in a new light. Exploratory research aims to formulate the issues precisely, to make concepts clear, to collect explanations, to gain insight, to get rid of unrealistic assumptions, and to produce hypotheses (Prasada et al., 2020).
Exploratory investigations will typically be conducted using case studies, focus groups, surveys, and literature research. Although it won't attempt to test it, exploratory research will aid in the formation of hypotheses. Exploratory research design aids in the comprehension and identification of specific phenomena within the classification of research questions so that the study's findings are understood clearly (Khaghaany et al., 2019).
3.3 Research Philosophy
Understanding how to gather, analyze, and use primary data requires having a clear research philosophy. Understanding research philosophies helps in planning and developing a strategy for conducting the research. Therefore, before beginning any kind of research, scholars should understand these fundamental ideas. The study philosophy has been separated into ontology, epistemology, and axiology, three distinct areas. There are four basic philosophical strands in each of these branches: interpretivism, positivism, realism, and pragmatism (Nair & Bhattacharyya, 2019).
Positivism: According to Shrestha & Bhattarai (2021) a positivist, the idea and concept of the objective can be used to determine reality's constant and stable aspects. The positivists believe that segregating the phenomena and repeating the analysis is the best strategy for identifying the phenomena. Regarding this methodology, altering an independent variable to assess the dependencies and correlations between the distinct fundamentals present in the social environment allows for the manipulation of reality.
Interpretivism: When it comes to the research environment, this kind of philosophy is utilized. Additionally, this method cannot compare the variables, and it does not produce a particular model of study. Therefore, this philosophy is unable to create a framework for politics or analysis.
Realism: Realism was also known as post-positivism, according to Lashgari Tafreshi (2019) , as well as (Wang et al., 2019). According to this theory, similar circumstances might be applied, and reality's existence is independent of thoughts and beliefs. In general, it is believed that this method can be compared to and combined with the previous two methods.
Pragmatism: The creation of useful applications for the research findings is what gives pragmatism research its significance. They believe that multiple realities may need to be considered for any given research challenge and that a single viewpoint cannot provide the full picture. A pragmatist researcher cannot, therefore, constantly use analytical strategies from various data collection techniques. Furthermore, Diagne et al (2020) argues that the research design is more concerned with encouraging the collection of pertinent, accurate, and reliable data so that, eventually, actual action can be taken (Fraisl et al., 2020).
3.4 Research Approach
According to Chang et al (2020), "The research approach reveals whether the use of theory is evident within the research design." Loncar-Turukalo et al (2019) made the same point in the same context, stating that a research strategy should bolster the claim with a theory. In the future, a research approach could be either inductive or deductive.
3.4.1 Deductive approach
With this method, the answers to the study questions are discovered through confirming various hypotheses. It begins with philosophical assertions and ends with concrete recommendations. Based on the context of the investigation, older literary works are examined to test various theories. This is consistent with how the theory is put forth, which serves as the framework for the hypotheses. To draw a conclusion, the defined research theory is then authenticated, in accordance with (Groenland & Dana, 2019). Consequently, this is also known as testing hypotheses.
3.4.2 Inductive research approach
The inductive research approach is in opposition to the deductive method, according to Saunders. This strategy helps the research specialist gain a thorough understanding of the issue by compiling information from a variety of sources. The inductive technique typically begins with an evaluation of the material that is already available, which eventually guides the construction of the research theory (Fleischmann & Ivens, 2019). Based on the developed theory, the hypothesis is then developed. As a result, depending on the previously established experimental data, this method formulates conceptions and hypotheses. It is also known as developing hypotheses (Schmiedel et al., 2019).
The deduction method uses a research approach that was especially created for the purpose of the study and draws on existing literature to discover concepts and theories. This kind of methodology begins with a hypothesis or theory and ultimately offers either a change or confirmation of the assumed theory. A novel hypothesis is developed using the researcher's data analysis in the inductive method rather than deductive reasoning (Beerbaum Dr., 2021). Beginning with the study's goals before presenting a theory is the type of technique used. Abduction, which combines deduction and induction, is the third sort of research strategy since it begins and finishes with the two previously described strategies, induction, and deduction (Shashi et al., 2019).
3.5 Research Strategy
In the current study, it is thought to be crucial in selecting a pertinent research approach to meet the research objectives. The researcher is intrigued by this and examines the many study techniques available. They have a hybrid, qualitative, and quantitative approach.
Qualitative approach: The constructivism paradigm, which is most frequently employed by several research professionals worldwide, forms the foundation of the link between the qualitative approach and this methodology. Many academics and data explorers who are interested in a variety of socially relevant issues use the specific research strategy, according to (Beerbaum Dr., 2021). The use of predetermined theories is not communicated when researchers employ this tactic. Instead, they'll put more of an emphasis on coming up with a solution to the current problem, which is driven by a few fictitious elements. With the use of the method, information gathering and information analysis through interpretation take a very long period (Pejić Bach et al., 2019).
Quantitative Approach: This method uses survey and statistical approaches to collect data that is particular to the domain under consideration. In this method, graphs are created using the data analysis process and information gathered via questionnaires. The quantitative method is built on statistical analysis (Asongu et al., 2019). To provide results that are pertinent to the research study, statistical professionals analyze the data that is collected through questionnaires and interview methods and is then further transformed into numerical form. Before the study is finished, several theories and methods need be considered to produce a result (Bilan et al., 2019).
The empirical methodology used in this study will be statistically quantified following the gathering of secondary data. Additionally, the methodology is meritorious for explanatory research because it does not already address the study issues that are suggested. Aside from that, the outcomes of the quantitative approach to the study of primary data. The methodology and procedure overview that will be used to conduct the scientific analysis is what the study design's main goal is to include (Gazzola et al., 2020). For this investigation, a quantitative design was employed. In most cases, it entails gathering primary information from a lot of people with the goal of extrapolating the findings to a larger population. A quantitative approach was judged appropriate because the goal of the study was to generalize about the impulsivity of shoppers based on the representative sample. Furthermore, to obtain data that were broadly representative, the results were subjected to several mathematical and statistical adjustments (Lashitew et al., 2019).
3.6 Sampling Methods and sample data
The NSE or BSE listed companies on the Indian Stock Exchange are the sources of the sample data used in this study. The following factors are considered in this design of the sample data:
They should have the appropriate data and a listing history of at least 10 years, and they should be listed on either the BSE or the NSE (2011 to 2022)
During the time of the study, they should have had an average market capitalization of more than Rs. 500 crores.
Over the course of the research period, they shouldn't have had any negative results for their leverage net worth or total assets.
Therefore, it is thought that the sample data should be chosen from relatively successful, large IT organizations. The companies with low or negative value were not chosen in this case since they frequently experience bankruptcy and are subject to the bankruptcy processes. Out of a total of 1000 enterprises, the researcher found a total of 50 that met the criteria in the CMIE-PROWESS and ACE-Equity (Accord financing) databases. 35 IT companies were chosen as the sample group.
3.6.1 Designs and Data Sources
In this study, secondary data from those companies' annual reports from 2010 to 2020 span the previous ten years. Other notable sources include IT bulletins, books, periodicals, newsletters, magazines, conference theses, government reports, business reports, and publications relating to the IT sector. The two main databases with financial statements, balance sheets, profit & loss accounts, and all other data from annual reports, other regulatory reports (from stock market filings), and press releases from thousands of Indian companies, as well as daily stock prices for companies, are Accord Finance and CMIE.
3.7 Data Collection Methods
Almost exclusively secondary data were used in this investigation. To study the effects of financial factors like CFM (Cash Flow Measure), ROA (Return on Asset), ROCE (Return on Capital Employed), and RONW (Return on Net Worth) on the profitability of the IT firm, a sample of 34 medium and large Indian listed and non-listed IT enterprises is chosen. Regression analysis, correlation analysis, and root test analysis form the foundation of this quantitative methodology. To make a sound judgement based on the financial performance of the Indian IT industry, additional financial statement and capital market analysis is conducted. Here, a number of factors are taken into account to assess this industry's financial performance. The value of shareholders, accounting profitability, capital ratio, profitability ratios, financial leverage, and risk analysis for those chosen organizations are some of these dimensions. Furthermore, it is a given that both domestic and foreign markets hold a sizable amount of investment potential for the IT sector. The following factors led to the growth drivers:
- The return of superior IT services from developed nations like the US or UK
- Consumer attention is shifting away from embracing new technology, and the government is taking steps to develop various laws to boost the need for IT services globally.
- Foreign investments may occur automatically in a number of IT-related industries, such as software development, business processing, and market research.
3.8 Data Analysis Tools and Techniques
Because it makes it easier to anticipate, control, and explain the phenomena of interest, the quantitative method is employed to acquire numerical data. Additionally, empirical research that incorporates numerical data is also demonstrated (George, 2019). On the other hand, the researcher (Hair et al., 2020) defined quantitative research as "an inquiry into a human or social issues, depends upon testing a theory which consists of variables, examined with numbers, as well as using statistical process, so as to assess the extent to which the predictive generalizations of the theory can be hold true."
Thus, it appears that quantitative research is based on both statistical analysis and numerical data. Based on data from either secondary or primary sources, statistical methods that relate to populations and samples, and the analysis is carried out. The entire process comprises decision-making and data manipulation, which are guided by combining the knowledgeable insights, thoughts, and expertise of the researcher with the complexity or other characteristics of the information itself.
3.9 Validity and Reliability
The degree to which one can rely on the data that is to be collected is reliability in this context. In this context, reliable data is information that can be depended upon, meaning it is dependable, trustworthy, authentic, and also comes from a recognized source. The data's accuracy is thought to be of the utmost importance in this type of finance and economics research. The timeline of the data collected, and the objectivity of the records are the factors for gauging the reliability in this regard. The annual reports of those companies from the past ten years, from 2010 to 2020, were used as the source of secondary data for this study.
Other notable sources for information about the IT sector include IT bulletins, books, periodicals, newsletters, magazines, conference theses, government papers, and company reports (Benitez et al., 2020). The two main databases for financial statements, balance sheets, profit & loss accounts, and other data culled from annual reports, other regulatory reports (from stock market filings), and press releases from thousands of Indian companies, as well as daily stock prices for companies, are Accord Finance and CMIE. As a result, the data in this case does meet the requirements for reliability (Shrestha et al., 2021).
To assess the data's trustworthiness and ensure that the research is sufficiently supported, validity and reliability are examined. The relevance of the data to the stated research question is referred to as validity of the data in this context (Jakobsen & Mehmetoglu, 2022). It also refers to the data's suitability in terms of the degree of correlation with the concepts under consideration. In the context of the current study, data samples are chosen from NSE, or BSE listed companies on the Indian Stock Exchange. Given that there are numerous variables within which this financial performance may be measured, the data in this case may be regarded as being reliable (Kurdi et al., 2019).
3.10 Summary
This section gives a general summary of the methodology utilized in this study to achieve the goals and objectives, as well as the reasoning behind the selection of various research-related elements. Along with the numerous procedures used for data gathering, statistical techniques, and tests run for data analysis, this methodology also includes selecting an appropriate study design. This study depended on secondary data sources, and SPSS software was used to evaluate the data that was gathered.
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