Impact of Corporate Social Responsibility on Financial Performance of Organizations - Literature review Example

Evaluate the challenges of conducting quantitative research.  Synthesize your findings and determine an appropriate quantitative research strategy for a study on corporate social responsibility and its impact on financial performance of business organizations in Ghana, West Africa. Analyze the issues associated with sampling, validity, reliability, and bias within the context of this study. Introduction Researchers use quantitative research methods to collect data that will provide answers to observations or hypotheses. According to Creswell (2008), quantitative research can be used in any research study that is conducted in a systematic empirical manner to understand social or scientific phenomena. The most pronounced characteristic of almost all quantitative research methods is the use of mathematical, statistical or numerical data or computational techniques that allows the researcher to draw conclusions about the topic under investigation (Given, 2008). Researchers consider the function of the mathematical, statistical, and computational techniques to be empirical in nature, and the practical nature of the approach is a globally accepted language that provides consistent interpretations world-wide. The use of the quantitative research method eliminates any manipulation of results in a subjective manner that might serve the interest of the researcher. Types of Quantitative Research Approaches Quantitative research methods have the general characteristic of being orderly and systematic but there are different techniques that the researcher might use to conduct quantitative research studies. Those various techniques include: descriptive, correlational, cause-comparative, and experimental. Descriptive Quantitative Research Approach Sapsford and Jupp (1996) posited that descriptive quantitative research approach was used to provide a vivid account of research variables. The researcher seeks to expose to the outside world, characteristics and features of the variables that sets them apart from each other. As Saunders, Lewis, and Thornhill (2003) proposed, the significance of descriptive research approaches is that they seek to answer the question of “what” rather than how, when and why (p. 13). An example that employs descriptive quantitative research approach is in the context of defining the population of a research setting by identifying a location where all the variables might be represented. The descriptive quantitative research approach allows the researcher to explore the characteristics of the population and describe those features in a manner to highlight similarities and differences. Correlational Quantitative Approach A correlational quantitative approach seeks to draw the relationship between two variables using mathematical, statistical, or computational techniques (Robson, 2002). The approach involves statistical correlation that uses a correlation coefficient to measure the strength and range of correlation (Remenyi, Williams, Money & Swartz, 1998). The range that is considered is from -1.00 to +1.00, and provides information about the strength of the correlation. In all cases, it is anticipated that the information will produce one of three possible outcomes—positive correlation, negative correlation, or no correlation between and among the variables. As suggested by Williams (2007), a positive correlation when the two variables being compared increase or decrease at the same time. For example, if increase in rate of water intake increases with life expectancy, it will be said that there is positive correlation between water intake and life expectancy. There is negative correlation when one variable increases whiles the other variable decreases. No correlation exists if there is no relationship between the variables and the correlation coefficient is 0. Cause-comparative Quantitative Approach A cause-comparative quantitative research approach allows the researcher to determine the cause and effect of a given social or scientific phenomenon (Hussey and Hussey, 1997). In some contexts, cause-comparative quantitative research approaches involve the combination of descriptive and correlational research techniques (Adams & Schvaneveldt, 2011). The significance of cause-comparative research approach is to determine the relationship that exists between the variables and how one of the variable acts as the cause of the other. As a result, the variables involved in the cause-comparative quantitative research approach are referred to as the independent and dependent variables. Williams (2007) emphasized that notwithstanding, cause-comparative research was most functional and ideal when independent variables cannot be examined by the use of a controlled experiment. Experimental Quantitative Approach Experimental quantitative research approach allows the research to investigate treatment and response. The researchers uses the approach to apply a treatment on a subject to observe or recording the response that the treatment will produce on the subjects (Saunders et al., 2003). In conducting an experimental quantitative research, it is very important that the researcher maintain the fundamental provisions of quantitative research, which entails the use of mathematical, statistical, and computational techniques. Although the experimental research approach receive credit for the validity of results, the absence of such techniques may affect the empirical outcomes of the experimental study. According to Creswell (2008), to do this effectively, the researcher might choose to conduct a true experiment in which there is random selection of participants in the study and the data collection is systematic. Alternatively, the researcher might choose to conduct a quasi-experimental approach because it is not possible to have random selection of the participants. In both instances, the researcher might face issues with the validity of the research study. Challenges of Conducting Quantitative Research Many researchers select quantitative research because of its empirical nature in providing very accurate results. However, there are key challenges that act as limitations in producing appropriate findings. These challenges do not necessarily mean the weaknesses of quantitative research but give an outline of issues that researchers must try to address if they want to achieve high outcomes with their quantitative researches. Some of the challenges that might impact the quantitative research approach include: a) design and plan of the study, b) identifying and defining key variables, c) determining sample size, d) financial constraints, e) interpretation of data, f)generalizability of findings, g) reliability, and h)validity, and i) researcher’s bias. Design and Plan of Research Study Several factor must be considered when the researcher begins to design and plan the study. Such issues include: locating the relevant population, selecting the appropriate sample size, determining if the sampling process will be probability or non-probability sampling. When a researcher uses probability sampling, all members within the population have an equal chance of becoming part of the sample size and selection may be done randomly. In non-probability sampling, not all individuals have the same chance of becoming part of the sample size (Creswell, 2008). To overcome the challenge with wrongful design and plan of the study, it is important for the researcher to understand the context of the proposed study and recognize reason for selecting a specific sampling procedure. Robinson (2002) provided guidance by suggesting that where all member might provide valid data for the variables being selected, then probability sampling was the preferred choice. In contrast, where people with specific variables and inclusion criteria were needed, the research would select non-probability sampling. Defining Key Variables Variables are the factors that the researcher desires to investigate when conducting the study. Depending on the role of the variable in the investigation, the researcher classifies these elements as independent or dependent. The independent variable will be feature that the researcher controls, while the dependent variable is measured by the researcher (Creswell, 2008). However, the approach that the researcher uses to define the key variables in the study will determine the accuracy of the outcomes of the study in quantitative research studies. Misplacing independent variables with dependent variables might result in a wrong cause and effect in cause-comparative study or produce inaccurate correlations in correlational studies (Peat, 2002). It is therefore important to ensure that the dependent variable represents the output or effect every time. The independent variable must on the other hand represent the cause or what is being tested (Hakim, 2000). Once key variables are properly identified, all forms of experimentations that are needed to be performed on variables could be completed with optimal expected outcomes. Appropriate Study Sample The sample refers to the participants who will provide direct or indirect data for the study (Adams & Schvaneveldt, 2011). In determining the correct sample size, the researcher should employ the technique that provides a reasonable representation of the target population. The fairness of the representation of the population relates to the number of people in the sample size as well as the variables possessed by the people within the sample size. The three requirements for determining sample size are the confidence level, the confidence interval, and the standard deviation. Creswell (2008) suggested a confidence level of 95%, a standard deviation of .5 and a confidence interval of +/-.5%. At ever point in time, there must be a sizeable percentage of the population in the sample so that the results might be generalized to the population under consideration. Financial Challenges One major challenge for conducting quantitative research study is the issue of finances that is most often overlooked until the study is in progress. As early as 1998, Knapp noted that as with other forms of research methods, quantitative research requires extensive pre-planning. There are unique features that the quantitative researcher must keep in the forefront when during the planning phase, all of which require financial input. The collection of data from various sources, the testing different variables, constructing data collection instruments, travelling, and compensation for participants are components that must be determined before the researcher begins the study. However, the potential financial challenges must never be reason that the researcher completes substandard research. The researcher can avoid such likely issues by seeking financial sponsorship from organizations or individuals that might benefit from the findings of the research study. Interpreting Results Colleges and universities provide students with the knowledge and skills to undertake analysis of collected data. Most researchers complete basic and advanced study in quantitative data analysis. The anticipation is that once the data are collected, the researcher having chosen the appropriate data analysis technique, will be competent with interpreting the results. In most cases, the challenge at the data analysis phase does not come with the analysis of the data as this is easily done by available software, including the Statistical Package for the Social Sciences (SPSS). The major obstacle that the researcher faces is to explain what the data shows—interpreting the data. Since the data are reported in the context of tables, graphs, mathematical or statistical formulas, the researcher must seek to make the task one which than can be easily understood. The researcher must find the skill to explain what the data is telling about the variables under consideration. For this reason, researchers ought to have competence over the interpretation of results. As Easterby-Smith, Thorpe, and Lowe (2002) cautioned, the conclusions should be presented as the data informs and not from review of other studies. Generalizability of Findings The generalization of research findings from quantitative research is one aspect of quantitative research that lends credibility to the research approach. In addition, to replication of the research process, the aim of the study should be to produce findings that might be generalized. However, it is important for the researcher to recognize that approaches that involve convenience sampling might produce findings that are interesting but might not be generalizable since it would be difficult to determine the population that the sample represents (Bryman & Bell, 2011). Generalizability of findings is achieved if after getting the results, the same results can be produced in another setting where relatively same variables are maintained, such as the demographics of the respondents (Ghauri and Gronhaung, 2002). Given (2008) indicated that where the researcher is able to be fair with the sampling and conduction of the research such that all forms of biases are avoided, then the same results can be expected, even when different researchers repeat the research. Researcher’s Bias Most researchers tend to assume that the objective nature of quantitative research approach prevent any incidents of researcher’s bias. However that is a false perception that might actually lead to researcher’s bias. In quantitative research, Ghauri and Gronhaung (2002) noted that there are ways that might result in researcher’s bias. One precaution would be for the researcher to pay particular attention to the selection of the sample size, especially where non-probability sampling to ensure that there are no preferences for study participants. Other instances where researcher’s bias might impact the credibility of the study involve the failure of the researcher to control the variables, asking the wrong questions on a survey instrument, or failure to report bias (Experiment-resources.com., 2012). Although many biases might be minimized through mathematical and statistical computations, if there are biases before the analysis phase, the study could be as much inaccurate as if there was no mathematical and statistical computations (Hussey and Hussey, 1997). It is the duty of the researcher to include analysis of all possible biases to ensure that the completed study is valid and meets with the stated objectives. Validity Validity comes in many different forms and contexts and involves internal validity (cause and effect or good measurement) or external validity (generalization of finding) (Bernard, 2013). Both types of validity involve an understanding of the connections of the relationship of the variables to the hypotheses of the study (Creswell, 2008). However, it may be generalized to represent the extent to which the research instrument is able to measure what it is supposed to measure and perform as it is designed to perform (Trochim & Donnelly, 2008). Validity places the emphasis on the research instrument that the researcher uses to collect the data for the research study. Carter and Porter, (2000) noted that there are several types of validity challenges, including criterion validity, content validity and construct validity. To overcome the challenge with validity and ensure that the research instrument measures what it is supposed to measure, the researcher may resort to the use of various validity strategies including pre-data collection, whereby there may be a mock sample size on which the research instrument is first tried or tested. Reliability Reliability of a study deals purposely with the issue of consistency in the test that is being conducted by the researcher. Normally, a researcher will undertake a quantitative study with the use of one form of test or the other. The test may be a questionnaire that is distributed or a set of questions that is scored. Whatever the format of the, it is expected that once the study is repeated in another location with almost all other variables remaining the same, the same outcomes will result (Carter and Porter, 2000). One of the most common ways to approach reliability is to use test and re-test, where the test is applied more than once to measure its consistency. According to Creswell (2008), the researcher should recognized that the reliability coefficient that measures test error might never achieve perfection. Appropriate Quantitative Research Strategy for Study The proposed quantitative research study will be to investigate the relationship between corporate social responsibility (CSR) activities and financial performance in the Republic of Ghana, Africa. The most appropriate research approach would be a correlational quantitative research strategy. The plan would allow the researcher to identify two variables—CSR activities (dependent variable) and financial performance (independent variable). Using correlational quantitative research strategy would assist the researcher with drawing conclusions about the relationship that exists between the variables. Should the analyses of the data show a positive correlation between CSR activities and financial performance, it would be appropriate to conclude that the more organisations engage in CSR activities, the more successful would be the financial performance. In contrast, with negative correlation, the researcher can conclude that the more organisations engage in CSR activities, the less they will experience financial growth. In terms of the no relationship, then CSR activities and financial performance have no statistically significant relationship. Research Issues associated with Sampling, Validity, Reliability, and Bias To test the impact of corporate social responsibility (CSR) activities on financial performance, the researcher would seek research participants who are corporate affairs managers of identified multinational companies because these individuals have knowledge of CSR as well as the companies, stakeholders, and the public. In terms of sampling technique, the researcher would use a non-probability sampling technique, and more specifically, the purposive sampling technique that would enable the researcher to identify the individuals with the appropriate knowledge. As noted by Creswell (2008), “an advantage with purposive sampling is that it avoids the risk of having a sample size that does not reflect the population’s behaviours, traits, symptoms and beliefs” (p.76). It must be emphasised that the research setting would be the corporate business environment of the Republic of Ghana, Africa and the focus would be on the organisations that engage in CSR activities. The researcher will complete a content analysis of the document analysis form and a questionnaire as research instruments. The validity of the study will be analysed to ensure that the research instrument will be able to collect data that the study is required to collect. The focus of the validity of the study will be on content validity and to safeguard that the outcome of the study reflects the situation in the corporate environment of the Republic of Ghana, Africa regarding the impact of CSR activities on the financial performance of companies. One way to ensure the validity of the research shall be the use of two different research instruments at the same time. The two tools will be complementary and could reveal any inconsistencies between the variables, hypotheses, and objectives of the study. During the data collection process, the researcher should ensure that there will be accuracy and consistency so that the data of the study will be valid and conform to the fundamental goal of the study (Carter &Porter, 2000). There are a number of ways that the researcher shall ensure that in measuring the relationship between CSR activities and financial performance, reliability will be ensured. First, there will be a column in the research report that clearly defines the scope and delimitation of the study. Second, based on the delimitation and scope, the aim of the study will be set, and subsequently the specific objectives of the study. Third, the specific objectives of the study will then be the basis of the research questions and the hypotheses to be tested. The process will determine if internal consistency exists in investigating the relationship between the variables—CSR activities and financial performance. As Given (2008) stated, once the researcher is able to ensure the highest level of fairness by avoiding biases, both the validity and reliability of the study will be enhanced. The researcher will develop appropriate steps to ensure that all forms of biases are avoided or minimized. Although there will be purposive sampling, the researcher will ensure that there is no bias associated with the selection of respondents by attempting to ensure that the participants are from companies in all corporate sectors of the Republic of Ghana, Africa. This way, there is no way the researcher can manipulate the outcomes that should hold for the different backgrounds of the companies. What is more, the researcher shall be objective rather than subjective. This will be done by ensuring that the researcher only acts as a facilitator who guides respondents to provide individual responses rather than influencing the outcome. Conclusion There are various challenges associated with conducting quantitative research, including this one that focuses on the relationship between CSR activities and financial performance in the Republic of Ghana, Africa. As the discussion illustrates, the researcher has the available tools and strategies to overcome the challenges, and many challenges might become opportunities to ensure that the researcher completes a study that is valid and reliable. The essay provided evidence that quantitative research method relies on the researcher and the attitude of the researcher towards data collection. 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