In this chapter, the research methodology used in this dissertation will be articulated. Firstly, the research philosophy employed in this dissertation will be discussed. The rationales of the selection of employing quantitative research (precisely, factor analysis) in this dissertation will be presented. The relevant assumptions used in the research design will also be outlined. Later, a detail discussion on the quantitative method used, namely factor analysis will also be articulated. The concepts behind such quantitative methods will be discussed in order to facilitate the analysis process in the next chapter. Then, in the subsequent section, the discussion will be focusing on the assumptions, methods and techniques used in questionnaires design in this dissertation. Next, the sampling techniques used will be briefed. Lastly, limitations of this research design will be outlined.
Quantitative Research method will be used. This is because such research is less subjective to personal interpretations of biases from the researchers. Besides, such research methods are common and widely employed by other academicians or researchers in conducting studies in the context of business management and business ethics. Thus, more references are available for the preparation and execution of this research. For example, the research questions employed in this study will be adopted from the questionnaires design developed by Sharma and Sharma (2011). The list of questions developed by Sharma and Sharma (2011) is assumed to be proper, relevant and accurate for the study to be conducted in this dissertation because those questionnaires design used by the authored are published in peer-reviewed journals. Furthermore, these questions are relevant to this study to be conducted in this dissertation because the nature of the research conducted by Sharma and Sharma (2011) is similar to the research nature of this dissertation.
In order to conduct the research, questionnaires (as attached in the Appendix section) will be distributed to selected research participants. As mentioned before, all the research participants should come from the pool of management students studying in Royal Holloway, University of London. A total of 200 samples will be collected. The many students studying in Royal Holloway, University of London will be selected randomly to participate in the research. Specifically, the students randomly selected to participate in this research study are those students enrolled in the following course: (1) Master of Arts in Asia Pacific Business; (2) Master of Science in Business Information Systems; (3) Master of Science in Entrepreneurship; (4) Master of Science in International Accounting; (5) Master of Science in International Human Resource Management; (6) Master of Arts in Marketing; (7) Master of Business Administration in International Management; (8) Master of Science in International Management; and lastly (9) Master of Science in Sustainability and Management. In order to collect the data fast, a total of 5 research assistants are selected. Each of the research assistant will be given 30 set of questionnaires. They will then assist in finding target research participants to complete the questionnaires, according to the course they are assign to survey. Then, the author will take the responsibilities to find relevant research participants for survey purposes. The author interviewed a total of 50 students. In the process, each of the people responsible in interviewing the students for their perceptions is well-aware of the possibilities of redundant respondent. This is solved by assigning the different research assistant to target management student from different courses (as outlined above). Overall, the interviewing process will resulted in a total of 200 set of completed questionnaires.
In the interview process, the research participants will be brief on the purpose of this study, the methods used in the research study and the methods to answer the research questionnaires. They are also briefed properly whenever they have doubts on the questions presented in the list of questions presented for them. Efforts have been spent to ensure that they can understand the contents of the questionnaires and they are able to make objective judgment before answering the questions. Overall, the research participants should not face any significant problems in answering the questionnaires because these questions designed are simple and straightforward. It is estimated only 5 minutes will be required from the research participants to answer these questions. Thus, research participants should not suffer from any sign of fatigue in answering the questions, which could possibly affect the accuracy of their answers. Apart from that, the research participants are also being informed that their answers will solely be used for this research purposes. Their names will not be collected. This is to encourage them to answer the questions in an objective manner, and to prevent them from faking the answers to a politically correct or socially acceptable one.
Once the data has been collected, the answers from the students will be tabulated and compiled into Excel spreadsheet. In order to analyze the data, SPSS version 17.0 will be used. As to be further discussed in the following topic, factor analysis will be used to ferret out the minimum factors possibly useful to characterize the management student attitudes towards CSR in Royal Holloway, University of London. Once these minimum numbers of factors can be identified, the student attitudes towards CSR can be understood better. Besides, by focusing on these factors in the formulation of CSR plan in any companies, any business may be able to make better impression on the students that the CSR efforts carried out by the corporations are valid and delivering true value to the society.
The 7-scale Likert style questionnaires will be used to investigate the attitudes of students on concepts and philosophies of CSR in Royal Holloway, University of London. The questionnaires will be adopted from Sharma and Sharma (2011). Sharma and Sharma (2011) have performed a study on the attitudes of the youth on Corporate Social Responsibilities in India, and the questionnaires used for the study are available in the journal published by the authors. As mentioned before, Sharma and Sharma (2011) had developed a comprehensive set of questionnaires to investigate the youth attitudes towards corporate social responsibilities in India. In the research, CSR is characterized by a set of 46 queries relevant in characterizing the adoption of CSR by a company. By employing quantitative methods of factor analysis, the many queries relevant to describing CSR adoption by companies were summarized into a minimum number of factors (that is perceived by the youth that should be incorporated into the business daily activities by corporations). As the questionnaires designed by the authors are readily available on the journal, the same questions can be used to investigate the attitudes of students on concepts and philosophies of CSR in Royal Holloway, University of London. Under their study, a total of 46 queries that are believed to be able to describe the various dimensions of corporate social responsibilities are used to design the questionnaires used to interview the research participants. The various questions are presented in Appendix. These questionnaires are designed as the 7-scale Likert type questionnaires ranging from 1 (strongly disagree) to 7 (strongly agree).
Factor analysis is quite a popular quantitative tool used by researchers in the context of business management, economics and social sciences as a data reduction technique. Under such method, the many variables related to a particular topic or context can be summarized into a few crucial and related factors in a parsimonious manner. Technically speaking, factor analysis is the technique used to pinpoint factors that can explain both the statistically significant variation and co-variation among measures. The number of factors generated from factor analysis will be smaller than the number of measures, so that the several few factors can be used to represent the set of measures in succinctly manner. Under such method, the many overlapping variables will be reduced into several different dimensions. The different dimensions can be perceived as the broader conceptual system, i.e., as the few factors that can reasonably good to be used to represent or correspond to a particular construct or issue. Factor analysis is a useful technique to understand behaviors, by generalizing the many measures or variables into fewer set of factors that can reasonably used to explain or describe the behaviors. In this dissertation, factor analysis is particularly useful to summarize the many different perceptions of students towards Corporate Social Responsibilities from a questionnaire that have a total of 46 items. In order to enhance researchers’ understandings on the student perceptions in a more parsimonious manner and to investigate the few dimensions that can be possibly used to explain student perceptions on the issue of Corporate Social Responsibilities, factor analysis is the appropriate method to be employed to summarize the many items into a few succinct factors in this study.
In order to conduct factor analysis to analyze a particular issue, it is important to determine the appropriate choice of measures to be included in the questionnaires designed to elicit research participants’ perceptions on an issue. According to Green and Salkind (2008), it is the ideal case to choose at least four or more measures (i.e., items or research questions) to represent each construct of interest. If, however, the several construct of interests are not predetermined, factor analysis can still be used, but the analysis of the results obtained from the method may be hard to be interpreted. This is the case with the study conducted in this dissertation. Nonetheless, even the results could be hard to be interpreted; the validity and appropriateness of factor analysis to investigate the various management student perceptions towards Corporate Social Responsibility in ‘Royal Holloway, University of London’ can be reasonably assured. There are several reasons for this. Firstly, the measures used in this study are adopted from Sharma and Sharma (2011). The researchers had studied a large sum of literature related to the context of Corporate Social responsibilities before designing a comprehensive set of 46 items that can be used as the research questions to investigate people perceptions towards CSR. Secondly, it can be rationally deduced that the various measures used in the questionnaires can be classified or categorized into several larger dimensions. The use of factor analysis in this case will be to provide further empirical or statistical evidences that the measures can be categorized into the few dimensions.
As discussed by Hair, Black, Babin, Anderson & Tatham (2006), factor analysis has two general objectives, either for (a) data summarization, or (b) data reduction. The two objectives will be discussed as follow.
Data summarization is about to deriving the fewer underlying dimensions that can be used to describe and characterize the many original measures or variables. In order to achieve this objective, factor analysis will identify the relevant factors that can be formed to explain the entire variable set as much as possible. Thus, in the process of data summarization, the objective is accomplished when a smaller amount of factors can be reasonably used to characterize the original set of variables. Ultimately, the usage of factor analysis in this context is to define the structure in a set of variables based on the inter-relatedness of the variables through specifying a smaller number of dimensions (Hair et. al., 2006).
The second objective, which is about data reduction, is similar to data summarization. Under data reduction, the data summarization process will be extended, in order to derive an empirical value (which, technically is also known as the factor score) for each dimensions (i.e., the factors extracted from the data set); so that the factor score can be used to substitute the original values of each of the variables. There are two possible ways in which this can be achieved. Firstly, factor analysis can be used to identify several representative variables from the much larger set of variables (to be used in subsequent analysis). Secondly, factor analysis can also be employed to create an entirely new set of variables, to completely replacing the original much larger set of variables (to be used in subsequent analysis). In both cases, the ideal situation is to maintain the characteristics of the original variables, but to reduce the variables to a much smaller set of dimensions for ease of subsequent analysis (Hair et. al., 2006). In this dissertation, factor analysis will be used for data reduction purposes. From a total of 46 variables (which may or may not directly related to the concept of Corporate Social Responsibilities), this study should be able to derive the several most important few variables that can be reasonably used to characterize the attitudes and perceptions of management students towards Corporate Social Responsibilities in a parsimonious manner.
Generally, factor analysis can only be completed through at least two different stages. According to Green and Salkind (2008), the two main stages under factor analysis are: (a) factor extraction and (b) factor rotation. Generally speaking, the first stage is to decide or determine the number of factors that can be reasonably used to explain the set of many measures (i.e., items or research questions) in a particular study. Then, in the second stage, the objectives of factor rotation are twofold, namely: (a) to rotate the factors in order to ensure the different factors are more easily interpretable, and (b) to finally decide the number of underlying factors to be used to explain the many measures in a parsimonious manner.
In the first stage, the minimum number of factors can be extracted from a correlation matrix. There are various types of methods under factor analysis that can be used to achieve this objective. One of the commonly used methods for this purpose is through Principle component analysis. Such method will also be employed in this study. Under Principal component analysis, the first factors will be extracted if that factor able to account for the largest amount of variability among the measured variables. Then, the second factor that will be extracted will be the factor that is found to be able to account for the second largest of variability among the other measured variables. The process will go on until all of the measured variables are explained or represented through a factor. However, the number of factors to be elicited form such procedure is highly dependent on the purpose and preferences of the researchers. Nonetheless, there are statistical criteria or suggestion to assist researchers in determining the number of factors to be elicited from such procedures. The first statistical criterion is to rely on the absolute magnitude of the eigen-values of factors. Technically speaking, eigen-value can be defined as the variability of a factor. Under such method, any factor that has an eigen-value greater than one should be accepted as one of the factors used to explain the set of measures. The second statistical criterion is about using a scree test. Under the scree test, the relative magnitude of the eigen-values will be used to determine the amount of factors to be extracted. Apart from statistical criterion, researchers may also choose the amount of factor based on their concept understandings and beliefs on the underlying dimensions or factors that should be able to explain the set of measures investigated.
In the second stage, factor rotation is necessary to make the factors more interpretable and meaningful. There are various factor rotation methods available. One of the famous factor rotation methods includes VARIMAX rotation method. In this dissertation, VARIMAX factor rotation method will be employed. In this stage, the researcher can choose to rotate the factor solutions with different amount of factors. The financial decision about the number of factors to be selected will be the total number of factor that is most interpretable. After the factors are determined, each of the factors elicited from factor analysis can be interpreted by examining the largest values linking the factor to the measured variables under the rotated factor matrix (Green and Salkind, 2008).
However, it should be acknowledged that there are many details to be discussed in performing factor analysis for data reduction purposes. This is because by nature of factor analysis, subjective judgment, based primarily on the objectives or the researcher is often necessary. As such, some of the details will be articulated in Chapter 4, whereby the process of factor analysis will be discussed in greater details.