Journal Of The Academy Science Of Marketing -Myassignmenthelp.Com

Question: Discuss About The Journal Of The Academy Science Of Marketing? Answer: Introduction: Now days, qualitative data analysis has become a major segment of business research. The prosperity of business process, strategy and nature of business are dependent up on business data analysis. As a marketing tool, qualitative data analysis has acted as an important part of business analytics (Burns et al., 2015). The qualitative data analysis is dependent upon nature and sampling. The structured analysis observation and content analysis relies upon secondary analysis and quantitative data analysis (Bryman and Bell 2015). Business research and research methods are embedded with formulation of general principles. Quantitative and qualitative researches constitute different approaches to social investigation (Chen and Zhang 2014). There is a distinction between a research method and a research method. Research method is already discussed in assignment 2 and therefore we are going to discuss research design in this report. The report is based on the various potentiality, major keys and potential threats of business research analysis. The research questions help to analyse and interpret the business data. Effectiveness of Project key experiences: There are three key technical terms for evaluating research such as validity, reliability and replicability. The five key points of the research design are experimental, cross-sectional, longitudinal, case study and comparative. We need experimental research because there are various potential threats to validate non-experimental research. Case study is a single type research design. Key issues with the nature of case study have external validity in general (Punt et al. 2015). Qualitative research does not delineate of a clear set of linear steps. The fundamental principles of qualitative research are to select cases, individuals and forefront sampling consideration. Interview process plays a key role in qualitative research. Secondary analysis of qualitative data is increasingly prominent part in business analytics. Elements and usefulness of Learning Process: The course and program would provide us lots of learning. The future career could be brighten if we transfer our knowledge and learning to the further achievement. The course would help us for better understanding of the coming business planning and management. The knowledge and insights would transfer to success and profit from the view-point of a business organization. Social development planning develops social issues. Evidence-based management is the systematic use of the best available evidence to improve management practice. Business research process makes a choice between deductive and inductive approaches (Van der Aalst, 2014). The business research questions are related about explaining causes and consequences of phenomenon. Sampling, data collection and data analysis are the main three processes of analysis. The research process involves deductivism theory for quantitative and inductivism theory for qualitative research (Ryan and Barnerd 2010). In case of quantitative analysis, explicit hypotheses are to be confirmed or rejected and in case of qualitative analysis, generalized inferences are concluded from the observations. Objectivity of learning of data analysis: Qualitative data collection frequently results in accumulating a large volume of information. It is not governed by confined rules and it has theoretical prominent approach. The key process of strategies in qualitative data analysis is coding. However, problem of fragmentation is a major issue in qualitative data analysis of secondary data. A verified analysed data indicates prosperity of the business improvement. Evaluation of learning: The research learning process was very helpful for our Business research. We learned that every company should analyse qualitative and quantitative data before coming to the business-oriented decision and planning. Profit and man management also relies on the learning and strategy (Booth 2015). The requirement to recognize that learning is not inherently prior to research that employs a single research strategy. In multi strategy, we observe quantitative and qualitative research with separate and incommensurable paradigms. The foci of the nature of learning methods research should be planned. We could provide hypotheses in an unstructured and open-ended approach helpful in generating hunches for testing through experimentation and survey. In this case, quantitative and qualitative research could reveal regularities and social processes (Taylor et al. 2013). Quantitative methods test researchers theories and qualitative methods make participants meanings the centre of attention. Explanation of learning data Analysis: A research design provides a framework for the collection and analysis of data. Cross-sectional study is the most common form of survey research (Here and Lukas 2015). It involves cross-sectional design, longitudinal design and case study design. The different methods of measurement of variability and procedures of testing of hypotheses are the main ways of calculation. Cross-sectional Design: It entails the collection of data on more than one case in order to collect a body of qualitative and quantitative data in connection with two or more variables that expresses association. Survey research comprises a cross-sectional design in relation with questionnaire by structured interview on more than one case (Falaschetti 2014). This design usually connects with quantitative data and qualitative data. Reliability and measurement validity are not connected. Replicability would be high, internal validity would be weak, external validity would be high, ecological validity would be compromised in this design. Longitudinal Design learning: In this design, the same sample on more than one occasion would be surveyed. A panel study and a cohort study are handled in the longitudinal design (Battaglia 2014). The characteristics of this design are very similar to the cross-sectional research design. Case Study Design learning: We analyse intensively with details in case study design. Case is the focus of interest according to the location and background. The cases could be critical, extreme, unique, revelatory and exemplifying. Cases might be longitudinally extended or through a comparative design. Comparative Design learning: By comparative method, we can compare two or more meaningfully contrasting cases. It can be qualitative or quantitative. Often cross-cultural comparisons include multiple case studies. The characteristics are identical as cross-sectional design. Because, the comparative design is essentially two or more cross-sectional studies carried out at the same point in time. Comparing of two or more cases, we can show the cases in which cross sectional studies are carried out. Mixed methods learning: In mixed methods research, qualitative research facilitates quantitative research. This method does not include multi strategy. This model mainly explores arguments against the combination of quantitative and qualitative research. Explanation of application of learning process: The process of learning involves theory, hypothesis, data collection, findings and inferences. This process involves theory-testing research, inductive case research and interpretive research. For the epistemological considerations, positivism and interpretivism are contrasting approaches. This process asserts the sociological phenomena in case of objectivism of business research. Contrastivism and paradigm are the research issues of quantitative analysis. Numerical and statistical data of social variables reviews deductive theory testing. Qualitative research is a research strategy that emphasises on qualification and collection of data. Quantitative and qualitative research constitutes different approaches to social investigation. We can apply learning from business analysis course that qualitative and quantitative data learning both are applicable in different types of study. Qualitative learning: In the case study, we can learn the process of collecting reliable primary data from interview of target population or selected groups. This section mainly focuses on textual analysis on attribute type or categorical data coding the text and talk (Elo et a. 2014). After collecting the relevant data, using software packages, we can present qualitative research and assess the rigor of qualitative research (Silverman 2016). Next, we can conceptualize theoretical work and interpret the findings. Quantitative learning: We learned that quantitative data is actually numeric, dependent on survey questionnaires, scale development and sampling respondents. The data is collected from field-work and analysed with the help of descriptive statistics, probability distributions and exploratory factor analysis (Anderson et al. 2015). We can apply sample survey methods by several following steps such as introducing the strata, estimation and confidence intervals, hypothesis testing, testing of distributions, regression methods and non-linear functions for interpretation and decision-making. This method emphasizes objective measurements and the statistical or numerical analysis of data collected through structured primary or secondary sources (Capozzoli et al. 2016) References: Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D. and Cochran, J.J., 2015.An introduction to management science: quantitative approaches to decision making. Cengage learning. Battaglia, M., Passetti, E., Bianchi, L. and Frey, M., 2016. Managing for integration: a longitudinal analysis of management control for sustainability.Journal of Cleaner Production,136, pp.213-225. Booth, S.A., 2015.Crisis management strategy: Competition and change in modern enterprises. Routledge. Bryman, A. and Bell, E., 2015.Business research methods. Oxford University Press, USA. Burns, A.C., Burns, K.R., Bush, R.F. and Burns, J.M., 2014. A Customized Excel Data Analysis System for Use in Undergraduate Marketing Research.Developments in Business Simulation and Experiential Learning,31. Capozzoli, A., Piscitelli, M.S., Neri, F., Grassi, D. and Serale, G., 2016. A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres.Applied Energy,171, pp.592-607. Chen, Z. and Zhang, H., International Business Machines Corporation, 2017.Business model data management. U.S. Patent 9,741,016. Elo, S., Kriinen, M., Kanste, O., Plkki, T., Utriainen, K. and Kyngs, H., 2014. Qualitative content analysis: A focus on trustworthiness.Sage Open,4(1), p.2158244014522633. Falaschetti, E., Mindell, J., Knott, C. and Poulter, N., 2014. Hypertension management in England: a serial cross-sectional study from 1994 to 2011.The Lancet,383(9932), pp.1912-1919. Hair Jr, J.F. and Lukas, B., 2014.Marketing research(Vol. 2). McGraw-Hill Education Australia. Murray, K.B. and Montanari, J.B., 1986. Strategic management of the socially responsible firm: Integrating management and marketing theory.Academy of Management Review,11(4), pp.815-827. Punt, A.E., Butterworth, D.S., Moor, C.L., De Oliveira, J.A. and Haddon, M., 2016. Management strategy evaluation: best practices.Fish and Fisheries,17(2), pp.303-334. Ryan, G.W. and Bernard, H.R., 2010. Data management and analysis methods. Silverman, D. ed., 2016.Qualitative research. Sage. Taylor, S.A., Kim, M.J., Ishida, C. and Mulligan, J.R., 2014. Augmenting null hypothesis significance testing in marketing research.Journal of Management and Marketing Research,15, pp.1-24. Van Der Aalst, W.M., 2013. Business process management: a comprehensive survey.ISRN Software Engineering,2013. Voorhees, C.M., Brady, M.K., Calantone, R. and Ramirez, E., 2016. Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies.Journal of the Academy of Marketing Science,44(1), pp.119-134. Zikmund, W.G., Babin, B.J., Carr, J.C. and Griffin, M., 2013.Business research methods. Cengage Learning.