The Closeness of Adventure Branding to Field Operators - Research Proposal Example

Table of Contents 1.0.Research Problem 2 2.0.Theoretical Framework 2 3.0.Research Hypotheses 4 4.0.Research Variables 5 5.0.Measures for the Variables 6 6.0.Types of data 8 7.0.Data collection 9 8.0.Sample selection process 10 9.0.Data coding/merging 11 9.1.Preparation of the data codebook or dictionary 11 10.0.Data analysis 13 11.0.Conclusion 14 1.0. Research Problem Analysis and evaluation of the relationship between adventure branding and closeness of adventure branding to field operators forms the research problem. The research problem will assist the research conceptualise the company’s strategy of embarking in a new market segments. Since preliminary information has shown that penetrating new market segments will require Resource Tyre Company to be innovative and design brands such as rugged and yet stylish 4-wheel drive tyre, the research problem refines and explains statistical results by exploring participants’ views in more depth. Understanding the relationship between the variables will therefore ascertain the value of the economic contributions of the firms in emerging markets. The research problem also augurs well with plans the research intends to take in order to deal with practical problem or ethical issues that may be encountered. It further helps in having research plan, and shaping the questions that will be asked during data collection thus linking the secondary research problem. A point to note regarding the statement problem is that it shows that the research proposal will be looking for actionable information and knowledge from quantitative and qualitative research. 2.0. Theoretical Framework The theoretical framework deals with marketing and cognitive theories of which are further achieved through succinct research problems that not only encompass the sections suggested above but also captures the tenets of dependent and independent variables. Secondly, integrating the two levels of theories (marketing and cognitive) gives a better overview of the theoretical framework, as adventure branding and closeness are strongly related. The first theory that the study captures is the brand familiarity theory as suggested by Hair et al. (2008). According to this theory customers are ready to buy and use brands they are familiar with compared to those they are not familiar with. Connecting this to the research problem as stated, the relationship between adventure branding and closeness of adventure branding to field operators are pegged on the level of familiarity customers will have with innovations Resource Tyre Company will have on their brand. According to Creswell (2013) brand familiarity has established much concentration in marketing literature thus being conceptualized in various ways. One understanding of brand familiarity is the extent to which customers will be familiar with rugged and yet stylish 4-wheel drive tyre. Another theoretical framework is what Neumeier (2012) suggested as commitment trust theory which provides significant relationship between marketing and the strategy the company used in capturing the data through the questionnaire. One of the critical problems that the research problem has identified is uncertainty especially with the model Trish McGregor, James and Nadia will be using in establishing trust, relationship commitment and cooperation customers have with the company’s products. As uncertainty is growing predictability, this theory becomes vital for the company in comparing quantitative and qualitative data from respondents with a view to integrating such with research problem. Additionally, the product system life cycle the company intends to introduce is the appropriate decision framework for Trish McGregor, James and Nadia. It therefore means that this theory can function as a tool to fulfil the demand of a company’s customers in a responsible manner to sustain, in a long term, the future of the company. Thirdly, attitudinal loyalty of the brand is a theoretical framework that provides the link between marketing research strategies and attitudes targeted customers have on a brand (Winston & Mintu-Wimsatt, 2013). Underpinning this theory within the context of Resource Tyre Company, data cluster sampling technique has shown that response rate was excellent with close to 80% of the questionnaires filled out. This data is an indication that with regard to attitudinal brand loyalty, the company should build its marketing strategy based on the fact that there is already commitment unto their brand. 3.0. Research Hypotheses Hypothesis 1: Adventure branding will have a correlation with filed operators for Resource Tyre Company. The first hypothesis is directional where the research attempts to predict the expected outcome with regard to data collection and analysis tools that have been used. Secondly, the hypothesis has been adopted to test the relationship between the two variables. There is need to gain better comprehension of marketing environment where Resource Tyre Company will be introducing new product. The hypothesis is therefore meant to decipher final answers or decisions and provide an overview of a given marketing phenomenon, such as introducing new brands of tyres against rugged and yet stylish 4-wheel drive tyre. This view has been supported by Punch (2013) who argued that adoption of directional hypothesis in a study helps the research to decipher or discover ideas and insights since the variables are based on causal relationships. Hypothesis 2: There gap in the company’s advertisement frameworks that deals with investigation and measurement of the two variables; adventure branding’ and ‘closeness to field operators. The second hypothesis is directional and tests research validity. Validity in this case, particularly in data collection means that the findings will truly represent the scenario the research is purporting to measure. In other words, it looks towards developing the relationship between research objectives and questions so that findings, data gathering and analysis can be interpreted in a specific paradigm. Hypothesis 3: identification and dissemination of critical information is important in the company’s strategy of embarking in a new market strategy. The third hypothesis helps in designing a research method. According to Bellenger et al. (2011) hypothesis helps in designing research methods which in turn, is an inquiry that combines or associates both qualitative and quantitative forms. The third hypothesis thus suggests philosophical assumption, the use of qualitative and quantitative approaches, and the mixing of both approaches in understanding the dynamics of market where the company intends to introduce new models of tyres. Thus, it is more than simply collecting and analysing both kinds of data; it involves the use of both approaches in tandem so that the overall strength of a study is greater than either qualitative or quantitative research. 4.0. Research Variables Dependent variable: Closeness to field operators The research will manipulate this variable to see if it makes the dependent variable change. In so doing, the dependent variable becomes the main focus of this proposal. On the other hand, this factor brings the independent variable which is the causal factor that tends to influence the problem of the research. Independent variable: Adventure branding Independent variables as stated include approaches that will be used as interventions and subject characteristics that directly influence dependent variables. Therefore data collection sub-section also describes how each variable will be measured. Control variables: New market segments, marketing strategy and company name They will form factors that are eliminated or fixed so as to succinctly develop the relationship between dependent and independent variable. 5.0. Measures for the Variables The research adopts a rule for assigning labels to properties of different variables so that indicators of constructs are measured. In as much as studies such as Collis & Hussey (2013) have suggested that four different levels of measurements should be used in a given study, the proposal has chosen two levels as they will be assessed below. These levels of measurement have been chosen because they determine the types of statistical package to be used (SPSS, ANOVA, Cronbach’s Alpha and Pearson Correlations). The first level of measurement the study uses is nominal level of measurement. The study will use symbols in the classification of observation. These observations will create mutually exclusive as well as exhaustive categories of information gathered. Silverman (2013) defines exhaustive as a case whereby variables have sufficient categories so that observations made fall into the same category. Contextualisation this level of measurement, there will be a survey on the relationship between adventure branding and closeness to field operators. Therefore observations that will be gained from these variables will be sorted into two distinct but mutually exhaustive and exclusive categories where these observations are labelled in terms of elements such as Agree and Disagree or numbers such as 0 and 1. However, these categories will have to be defined so that all observations can be harmonised or fit into one category but no more than one at a given time (1=Absolutely agreed, 2= Agreed, 3=Neutral, 4=Not agreed). Finally, this case will employ numbers to be used as names just like it has been presented. However, these numbers have to factor the following aspects: 1. Basic empirical operations such as Determination of equality 2. Examples such as Number of field operators Assignment or model of the tyre 3. Permissible statistics such as Contingency correlation Mode Number of cases The second level of measurement that has been adopted is the ordinal which uses symbols to classify observations from the variables into samples or categories that are not only mutually exhaustive and exclusive but correlate with each other. The reason for adopting this level is to provide reliability and validity for the first level of measurement discussed. The questionnaire is likely to pose questions on Likert type items. That is, it may pose questions on the satisfaction of the new tyres. These kinds of questions bring the aspect of ordinal scale of measurement needed to justify the nominal scale. For instance, it will help in the assessment of permissible statistics such as median, rank order correlation or percentiles. 6.0. Types of data In as much as the data can be categorised as process based data, outcome data, purpose data, logical data model, these fall under the category of either qualitative and quantitative. Beginning with quantitative, the research will obtain measures of counts or values which are expressed as numbers. Silverman (2013) defines quantitative data as the numerical values regarding a certain variables such as the total number of field operators linking new tyres. Contextualising this definition within the premise of this company, it is a set of data that improves the reliability of research in the sense that it helps in measuring the degree to which the research instrument yield consistent results after a repeated administration. Quantitative data provides an opportunity to quantify the information which have been collected and generalise the results from the sample to a whole population. This thereby tends to provide more information regarding issues for which there are different views regarding the research topic and offers a direction through which the overall effectiveness of the research will be increased (Shank & Lyberger, 2014). Qualitative data is concerned with non-numeric data in regard to phenomenological aspects such as people’s perceptions on tyres. Qualitative research design and approach evolves as the research continues and is not succinctly clarified at the start (Shank & Lyberger, 2014). In this study, issues such as the perception of the impact of metacognitive strategies of introducing new models of tyres are deciphered through a qualitative rather than a quantitative approach. This is because perception needs the researcher to have in-depth interviews and reliable information in the qualitative data to extract drivers, tourists or passengers’ level of perception of the importance of tyres from the company. Shank & Lyberger (2014) suggest that qualitative data entails a naturalistic and interpretative approach. The outcome of a piece of qualitative data will enhance development of an initial understanding of an identified problem which, in the study, is the determination of the impact adventure branding on filed operators for Resource Tyre Company. 7.0. Data collection Based on the nature of the study and targeted respondents, data will be collected from field operators, drivers, tourists and passengers. Data from these respondents will ensure the research in valid and reliability. Validity and reliability of the study will play a fundamental role in the acceptability of the findings, conclusions and recommendations emerging from the study. The research uses mixed method of data collection as it enhances reliability of the data collected and the conclusions or deductions made therefore. Three data collection procedures used are: observation, structured interviews and structured questionnaires. Xxxx argues that the most common qualitative and quantitative data collection instruments range from interviews, observations and document reviews to survey questionnaires. That is, the adopted methodology of data collection ensures that relevant information for the research is obtained. According to Silverman (2013), there are four main methods of collecting data or information: participation in the setting, direct observation, in-depth interviews, and a review of documentary evidence. A mix of data collection instruments was applied due to the mixed approach to the study. Additionally, these methods were intended to help the researcher answer the research questions by deciphering the respondents’ perception and awareness of the use of new products or tyres from the company. The process also identifies the respondents or field operators’ self-assessment of their views and experience or the new products. It must be borne in mind that extensive reliance on these methods of enquiry may challenge the reliability of a study. As a researcher, I intend to evaluate the progress made and challenges faced respondents are these methods are applied in the process of data collection. 8.0. Sample selection process Based on the nature of the research and the targeted respondents, samples will be selected using stratified random sampling process. According to Silverman (2013) this is a probability sampling method that samples respondents based on possibilities. This method has been preferred ahead of other process such as systematic random sample and simple random sample because the research will be targeting a given strata (unique group) within the population as the company is not interested in the whole population. With stratified random process, there will be an equal probability or chance of selecting every unit from within a given group or stratum of the population when creating the sample. The research is interested in understanding the relationship between the variables earlier identified and the conceptualisation of the research hypothesis. Taking hypothetical figure, the total population of region expected to be studied can be denoted as N therefore in order to select a sample of field operators within the N the study can denote the sample as n of N. But since the study is interested in a particular group or strata within N the stratified random samples mean dividing the total population into different samples or strata. Putting this practically, there can be 2000 N and of this, 60% (per cent) is the targeted group. The study will ensure that the unit selected as sample is proportionate to the number not selected or the total population. This can be done by dividing the desired sample by the proportion of units in each stratum. This process provides an opportunity to validate the data and ensure that the sample which was selected was representative. Using this process helps to strengthen the overall study and provide a framework through which corrective methods and procedures could be used for the research. 9.0. Data coding/merging 9.1. Preparation of the data codebook or dictionary The data obtained from the tools aforementioned are going to be entered in a computer program in form of data base, spreadsheet and statistical program. As a result, they will be entered in the same manner for every field operator, state, questionnaire or unit of analysis. The study will therefore have variable names that are assigned to the data which will in turn reflect the nominal definitions of these variables (field operators, advertisement etc). This study uses lower case letter to reflect data entered in the dictionary. This according to Silverman (2013) is efficient when typing variable names and later when the study will be commanding the software package on which variable to analyse. The diagram below represent a sample structure of coding that will be adopted for data merging/coding. On scale of attitude about 4-wheel drive tyres On scale of attitude from field operators (Are you field operators? No=0 Yes=1) On scale of attitude about adventure branding and ‘closeness to field operators 1=Very Dissatisfied 1=Very Dissatisfied 1=Absolutely agreed 2=Dissatisfied  2=Dissatisfied  2= Agreed 3=Neutral  3=Neutral  3=Neutral  4=Satisfied  4=Satisfied  4=Not agreed 5=Very satisfied  5=Very satisfied  5=Not sure  From the figure above, the research may adopt a zero (0) when coding or merging variables which may have binary responses and in such cases, these may be an example from this study: Are you field operator? No=0 Yes=1 Are you at the field or in headquarters? Headquarters=0 Field=1 Sex: Female=1 Male=0 In such cases the research will ensure that number zero (0) but not letter O is entered in the statistical analysis software. This will be the same case with 1 but not letter L as this confusion may arise in data coding and analysis respectively. Silverman (2013) advices that when doing data coding, it is essential for researchers to carry out pilot testing of the instrument as well as dry run the data collection process. 10.0. Data analysis According to Silverman (2013), data analysis is a mechanism for reducing and organising data to produce findings that require interpretation by the researcher. The scores of the tests were processed through Statistical Package for the Social Sciences (SPSS) software and used in the quantitative analysis. The qualitative analysis of the responses involved interpreting each aspect of the rubric according to the respondents’ view. Additionally, descriptive statistics (means and frequencies) were utilised in the analysis. Along with various references such as SPSS textbooks, a statistician was engaged to ensure accurate entering of data and correct test usage. Pearson’s Correlation analysis was employed to decipher the relationship between different variables. The use of SPSS to carry out the quantitative analysis could strengthen the validity and reliability of the research. This is primarily due to the fact that it provides an opportunity through which, when the same data sets are entered, the same results will be achieved. This helps to increase the reliability and validity of the research. SPSS will also ensure that the results achieved can be verified and will contribute towards strengthening the overall issues and highlight the manner in which the research was carried out. Since data collected will be from different sources, the regression analysis such as ANOVA will be carried out to determine the extent to which these variables correlate. In this study, reliability of the instrument will be tested using Cronbach’s Alpha test in order to find out if they achieve a satisfactory level of acceptance. The Cronbanch is a method of measuring internal consistency when there is regression analysis. In this case, reliability coefficient of α = 0.7 if adopted, can denote an acceptable level of internal reliability since variables under investigation are between 2 and 5. 11.0. Conclusion The the justification for using qualitative and quantitative approach for the research has been discussed. The study’s philosophical assumptions as well as the research design and the various methodological steps taken to achieve the research objective were examined. The rationale for this approach is to enhance the reliability of the study. Furthermore, the research location and method of data collection was explained. The proposal also examined issues relating to sampling method, selection and sampling process. It also provided detailed explanation of how themes are developed, thematic analysis and data coding. Data analysis software has been identified to assist with the reliability and validity of the research. References Bellenger, D. N., Bellenger, D. N., Goldstucker, K. L. B. J. L., Bernhardt, K. L., & Goldstucker, J. L. (2011). Qualitative research in marketing. Marketing Classics Press. Collis, J., & Hussey, R. (2013). Business research: A practical guide for undergraduate and postgraduate students. Palgrave macmillan. Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. Hair, J. F., Wolfinbarger, M. F., Ortinau, D. J., & Bush, R. P. (2008). Essentials of marketing research. McGraw-Hill/Higher Education. Neumeier, S. (2012). Why do social innovations in rural development matter and should they be considered more seriously in rural development research?–proposal for a stronger focus on social innovations in rural development research. Sociologia ruralis, 52(1), 48-69. Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. Sage. Shank, M. D., & Lyberger, M. R. (2014). Sports marketing: A strategic perspective. Sociologia ruralis, 52(1), 48-69. Routledge. Silverman, D. (2013). Doing qualitative research: A practical handbook. SAGE Publications Limited. Winston, W., & Mintu-Wimsatt, A. T. (2013). Environmental marketing: strategies, practice, theory, and research. Routledge. Read More