CHAPTER 3: RESEARCH METHODOLOGY
3.1 Research Design
The research design is a procedural plan adopted by the researcher to answer research questions objectively, accurately, validly and economically. In general, the research design plays two key roles. First, it aids in the identification and development of logical arrangements and procedures required to undertake a study. Second, the design lays emphasis on the quality (objectivity, validity and accuracy) of the underlying procedures. When conducting research, it is vital to note that the research design is distinct from the method through which data are collected.
In general, there are a number of ways in which research designs can be classified. First, research design can either be descriptive, correlational, experimental, semi-experimental, review and meta-analytic. Moreover, the research design can be grouped into quantitative and qualitative research designs. In this study, the resign design will primarily be descriptive, particular a survey. The rationale for ignoring other designs such as experimental is due to resource constraints. In addition, unlike other designs, descriptive research design enables the researcher to utilize both qualitative and quantitative data in finding out answers to research questions. This is primarily because; the descriptive research design allows various data sources to be taken into consideration. There are extra benefits also extra benefits associated with the use of descriptive research design, particularly in data collections (Hall 2008). For instance, it enables the researcher to make of use research surveys.
In general, the research design utilized in this study consisted of five main phases (see figure 3.1). The first phase entailed the identification of the research problem. Provided in the second phase was a review of the literature on the Literature review on the effect of consumer innovativeness on adoption of smartphones. Thereafter, the researcher embarked on the gathering of empirical data, systematic analysis and discussion the collected data, and drawing of a conclusion based on the empirical data collected and analyzed.
Figure 3.1: General Research Design
In conducting descriptive research design, the researcher found it crucial to make use of survey method. Survey methodology provides a study of a sample of individuals from the target population through the use of data collection techniques such as questionnaire design (survey questions), with the aim of making statistical inferences regarding the population. Most surveys often take the form of census, government surveys, market research surveys, public health surveys, and public opinions. In this study, the researcher conducted a single survey that focused on the effect of Consumer Innovativeness on Adoption of Smartphones. Since a survey research is merely based on a population sample, the success of the research significantly depends on the representativeness of the population sample with respect to the target population. Despite its ease of use, the survey methodology is coupled with a number of challenges. These include making decisions on how to evaluate and test questions, supervise and train interviewers, spot and select sample participants, check data files for internal consistency and accuracy, select the approach for posturing questions and collecting responses, and adjust survey approximations to correct errors.
3.2 Research Approach
Owing to the nature of the research, the researcher largely used of a quantitative research approach to collect quantitative data from the research participants. Quantitative research is a systematic investigation of social phenomena through mathematic, statistical or computational techniques. The key objective of quantitative research is to provide fundamental connection between quantitative data (Mahoney, and Goertz 2006). The key essence of quantitative research approach is to reduce collected data into numerical format such as rates and percentages. In quantitative research, the researcher usually knows in advance the elements he/she needs to focus on. Thus, all aspects of the study are cautiously designed prior to data collection. In using quantitative research, the researcher made use of surveys for collecting primary data and secondary data sources. The rationale for employing this approach was based on the fact that it produces highly reliable and quantifiable data, which can easily be generalized. Moreover, this approach permits researchers to test specific hypotheses, which is more exploratory (Mahoney and Goertz 2006). In spite of its associated strengths, the approach has been criticized of decontextualizing human behavior in a manner that removes that shifts an event from its real world setting.
In overcoming the limitations attributable to quantitative research, however, the researcher used some aspects of qualitative research. A qualitative research is a method of inquiry that aims at gathering an in-depth understanding of human behaviors and the key drivers that govern such behaviors. Frequently, qualitative method investigates the how and why aspects of decisions making, and not just where, what and when. In qualitative research, the researcher was required to ask broad questions on the research topic in collecting word data from participants (Mahoney and Goertz 2006). Here, the researcher collected some qualitative formation regarding the personal views and feelings of consumers by looking for relevant themes, describe the information provided in themes and patterns restricted to that set of participants.
3.3 Sample Characteristics
In this study, the target population mainly consisted of both potential and current users of smartphones in the country. Ideally, the sample comprised of students, in University of Southampton, who were pursuing Accounting & Finance at the time when the research was conducted. The rationale for utilizing the students at the University of Southampton was primarily based on their personal accessibility. The study largely targeted the first and second years due to their high level of curiosity to try new mobile devices than third and fourth year students.
Prior to selecting a population sample, the researcher took necessary steps to include only those respondents with a considerable experience with smartphones. For strong theoretical and practical reasons, it was appropriate for the researcher to use probability sampling. This increased the chance of every member in the team to be included in the sample. As opposed to probability sampling, non-probability sampling target specific individuals (Levy, Lemeshow and Wiley InterScience 2008). The key feature of non-probability is that the samples selected are based on the researcher’s subjective judgment, as opposed to a random selection. Non-probability sampling includes methods like volunteer sampling, purposive sampling, quota sampling and Haphazard sampling (Levy, Lemeshow and Wiley InterScience 2008). In spite of the availability of a number of probability sampling methods, the research utilized a simple random sampling technique in selecting the smartphone consumers that were included in the study sample. The key rationale for employing simple random sampling was largely attributed to the nature of the study.
The sample size for the study was decided by factoring in Creswell and Plano Clark's suggestion regarding a typical sample size. The total sample size consisted of 100 respondents out of which 60% were those that had used the phones for over 6 months. Besides, 70 percent of the students sample were first and second year students in the programme. The main reason for using such a large sample of respondents (100) was to improve the degree of exactitude of the research findings. Besides, this sample size was considered as crucial in relation to the researcher’s easy of generalization of the study results.
3.4 Design Instruments
In this study, the researcher utilized data from both primary and secondary data sources. Primary data was collected through a primary research. In most cases, the primary research is undertaken after the researcher has acquired some imminent into the research issue. Though considered very expensive compared to secondary research, the use of primary research was to enable the researcher focus on both quantitative and qualitative issues that were critical in this study (Hall 2008). In addition, using the primary research was to enable the researcher to control the research design in order to fit the research needs. Despite the existence of various methods that can be used to collect primary data, the primary data collection techniques consisted of questionnaires, personal interviews and observations. The first phase of the study’s data collection entailed the use of questionnaire that had both open and closed-ended questions (Mertens 2009). The questionnaires were distributed to the research respondents via a number of ways. These included hand delivery, postal addresses and emails. They were delivered in timely manner to enable the respondents prepare in answering them effectively. In the second phase, the researcher carried out in-depth quantitative semi-structured personal interviews with the research respondents (Hall 2008). The use of in-depth interviews was to spearhead micro-level explanation and exploration of an under-researched area. Moreover, during the interview, the researcher gathered some empirical data by means of observation. Each interview was completed in approximately one and half hours.
In order to supplement primary data sources, the research found it appropriate to gather data from secondary sources (Hall 2008). The secondary data sources utilized in this study were mainly attributed to organizational databases, scholarly journal articles, trusted web sites, conference papers and reference books that the researcher considered relevant to the research topic. The key argument in favor of using many secondary data sources was to increase the level of precision or accurate of the data collected, since the use of few sources were considered to be potentially biased.
3.5 The techniques for data analysis
Data analysis was be based the concept of hypothesis testing. A statistical hypothesis test refers to a technique of statistical inference utilizing data derived from the scientific study. Since data analysis was based on confirmatory data analysis, the following steps were applied. The first entailed stating the relevant null and alternative hypotheses. Mann and Lacke (2010) argue that the null hypothesis should be chosen in a way that will easily allow the researcher to conclude whether it will be appropriate to accept the alternative hypothesis or remain undecided as it was prior to the test. The second step entailed considering the statistical assumptions that will be made concerning the sample in doing the test. In the third step, the researcher decided on the appropriate test, and stated the test statistic (T) that he/she deem to be relevant. At the next step, the researcher derived the distribution of the test statistic, from the assumptions, under the null hypothesis (Woodbury 2002). Thereafter, the researcher selected the significance level (α) to be used. This is a probability threshold below which the researcher will reject the null hypothesis. In this study, the significance level of 1% (99% confidence level) was utilized. The next step involved determining the critical region and the region that is not critical. The probability of the critical region was denoted as α (Ruppert 2004). At the subsequent step, the researcher computed the observed value of tobs of the test statistic T from the observations. Finally, using the value of the of tobs of the test statistic, the researcher decided to either accept or reject the null hypothesis in favor of the alternative hypothesis (Woodbury 2002). In making the above judgment, the researcher was compelled to follow an existing decision rule whether to will reject H0 if tobs falls on the critical region, otherwise fall the reject it. The key hypotheses tested in this study have been stated below.
H2: Increases in open-processing innovativeness will result in an increase in consumer adoption of a smartphone.
H3: Increases in domain-specific innovativeness will result in an increase in consumer adoption of a smartphone.
H4: The relationship between mobile phone usage and adoption of smartphone will be moderated by open-processing innovativeness and domain-specific innovativeness.
The hypotheses were stated and then an appropriate tool was used test in order to determine it was statistically rational whether to accept or reject them. The first stated hypothesis was often a Null Hypothesis, which is the hypothesis of no difference or no effect between the populations of interest (Shi and Tao 2008). The Null Hypothesis was be denoted using the symbol H0. The second hypothesis, Alternative Hypothesis or Study Hypothesis, was equally stated. This is the hypothesis that the researcher would prefer to be true. The alternative hypothesis, in this case, will be denoted using the symbol H1 or HA (Lehmann and Romano 2005).
Given that, in this study, the data analysis was based on t-test, the researcher employed the SPSS software package to evaluate the relationship between consumer innovativeness and adoption of smartphones (Antonius 2003). An SPSS is arguably one of the most widely applied programs for statistical analysis. This program can extra data from any file and utilize it to generate tabulated charts, charts, trends and plots of distributions, and descriptive statistics (Healey 2012). The researcher availed the SPSS program from the UNIX systems platform.The results (outcomes) of from the SPSS analysis were carry out t-testing that were aimed at testing the following study hypotheses (stated earlier).
3.6 Research Ethics
Before carrying out this study, every study participant was obligated to read and understand the provision of market research ethics. This helped to minimize the violation of rights of the respondents during the study (Mertens and Ginsberg 2009). Moreover, each research participant was required to sign a compulsory form declaring that they have agreed to operate within the scope of the research ethics, which lays emphasis on research integrity, friendly researcher-participant relationship, and non-violation of the institutional rights.
3.7 Validity and Reliability
Quality is a crucial element in any work. The quality of this study was be earned with the use of qualitative research itself, as opposed to quantitative research (Klenke 2008). In general, the quality of a piece of research work can be appraised through measures such as study validity and reliability.
According to Klenke (2008), research validity takes two forms: internal and external validity. Internal validity is concerned with the degree to which the study results correspond to reality. It tends to establish a fundamental relationship between the actual and observed data, as opposed to imitation relationships (Johnson and Christensen 2012). However, not of fundamental in nature, qualitative studies must be accompanied by a considerable amount of internal validity. In this work, internal validity of the study results has been achieved by severally reviewing the information collected and recorded during the interviews. Conversely, external validity entails the extent to which the study results can be generalized. Yin (2003) asserts that the study results can easily be generalized where several situations have been considered during the study, and similar results found. In this study, the external research validity was achieved through the use of the large sample size of 100 respondents and number wide range of secondary data sources.
Reliability refers to the degree to which research can be repeated and obtain the same study results. Since it is not practical for a qualitative study to be conducted in more than once, the reliability of this study has been realized by establishing trustworthiness (Johnson and Christensen 2012). In this study, the collected data has been made auditable by frequently checking that the interpretations are transferable, credible, dependable and confirmable. In addition, the researcher has tightened the reliability of the study by providing an in-depth explanation on the underlying theories and perspectives of the study, and details regarding interviewees’ selection and background.
References List
Hall, R., 2008. Applied social research: Planning, designing and conducting real-world research. South Yarra, Vic: Palgrave Macmillan.
Healey, J. F., 2012. Statistics: A tool for social research. Belmont, CA: Wadsworth, Cengage Learning.
Johnson, B. and Christensen, L. B., 2012. Educational research: Quantitative, qualitative, and mixed approaches. Thousand Oaks, Calif: SAGE Publications.
Klenke, K., 2008. Qualitative research in the study of leadership. Bingley, UK: Emerald Group Pub.
Lee, C. F., Lee, A. C. and Lee, J. C., 2000. Statistics for business and financial economics. Singapore: World Scientific Publ. Co.
Lehmann, E. L. and Romano, J. P., 2005. Testing statistical hypotheses. New York: Springer.
Levy, P. S., Lemeshow, S. and Wiley InterScience (Online service)., 2008. Sampling of populations: Methods and applications. Hoboken, N.J: Wiley.
Mahoney, J. and Goertz, G., 2006. A Tale of Two Cultures: Contrasting Quantitative and Qualitative Research, Political Analysis, 14, p. 227–249.
Mann, P. S. and Lacke, C. J., 2010. Introductory statistics. Hoboken, NJ: John Wiley & Sons.
Mertens, D. M., 2009. Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Los Angeles: Sage.
Mertens, D. M. and Ginsberg, P. E., 2009. The handbook of social research ethics. Thousand Oaks, CA: Sage Publications.
Ruppert, D., 2004. Statistics and finance: An introduction. New York, NY: Springer.
Shi, N.-Z. and Tao, J., 2008. Statistical hypothesis testing: Theory and methods. Singapore: World Scientific Pub.
Woodbury, G., 2002. An introduction to statistics. Pacific Grove, Calif: Duxbury.
