Wizardry in Qualitative Marketing Analysis: A Toolbox for Teaching

Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

The teaching of qualitative marketing research requires a systematic and robust toolbox. This toolbox should offer a comprehensive and systematic means of substantiating the insights of qualitative marketing analysis. This will only be possible when the respondent data, the propositions and the theoretical corpus can be analysed for logical consistency. To do so, the semantic format of respondent data needs to be converted into a formal system of representation. Thus, this study will develop a rigorous system for truth verifiability, via the rules and principles of symbolic logic and critical reasoning. This study will be novel and unique as extant studies of qualitative marketing research do not offer a systematic means of training students to analyse and substantiate the findings of qualitative marketing research. It will do so by verifying the categories, themes and propositions of qualitative marketing research. This verification will elevate the data into premises and conclusions. This will ensure that the retrospective and prospective verification of the themes and propositions of the study. Thus, our study will be invaluable to teachers, students and practitioners.

Keywords

Qualitative marketing research Teaching toolbox Symbolic logic Critical reasoning 

References

  1. Bench-Capon, T. (2003). Persuasion in practical argument using value-based argumenta-tion frameworks. Journal of Logic and Computation, 13, 429–448.CrossRefGoogle Scholar
  2. Bouajjani, A., Emmi, M., Enea, C., Hamza, J. (2013). Verifying concurrent programs against sequential specifications. In Proc. 22nd European Symposium on Programming (ESOP ‘13), volume 7792 of LNCS, (pp. 290–309) Springer Berlin, Germany.Google Scholar
  3. Diggelen, J. v., Beun, R., Dignum, F., Eijk, v. R, Meyer J. C. (2006). Anemone: An effective minimal ontology negotiation environment. In Proceedings of the V International Conference on Autonomous Agents and Multi-Agent Systems (pp. 899–906), New York, NY, USA.Google Scholar
  4. Giunchiglia, F., Shvaiko, P., Yatskevich, M. (2004). S-match: An algorithm and an implementation of semantic matching. In First European Semantic Web Symposium, 2004, Heraklion, Crete.Google Scholar
  5. Glaser, B., & Strauss, A. (1966). The purpose and credibility of Qualitative research. Nursing Research, 15(1), 56–61.CrossRefGoogle Scholar
  6. Jones, K. (2004). Mission drift in qualitative research, or moving toward a systematic review of qualitative studies, moving back to a more systematic narrative review. The Qualitative Report, 9(1), 94–111.Google Scholar
  7. Kirsch, C. M., Lippautz, M. Payer, H. (2013). Fast and scalable, lock-free k-FIFO queues. In Proceedings of 12th International Conference on Parallel Computing Technologies (PaCT 13), volume 7979 of LNCS (pp. 208–223). Springer, Cham, Switzerland.Google Scholar
  8. Khattak, A. M., Latif, K., Lee, S. Y., Lee, Y. K., Rasheed, T. (2009). Building an integrated framework for ontology evolution management, in Proceedings of the 12 th Conference on Creating Global Economies through Innovation and Knowledge Management (pp. 55–60). New York, NY, USA.Google Scholar
  9. Laera, L. T. V., Euzenat, J., Bench-Capon, T., Payne, T. R. (2006). Reaching agreement over ontology alignments. In Proceedings of 5th International Semantic Web Conference (ISWC 2006). Berlin, Germany.Google Scholar
  10. Paula, C. C., Padoin, S. M. M., Terra, M. G., Souza, I. E. O., & Cabral, I. E. (2014). Driving modes of the interview in phenomenological research: Experience report. Rev Bras Enferm [Internet], 67(3), 468–472. [cited 2015 Feb 02]. Available from: http://www.scielo.br/pdf/reben/v67n3/0034-7167-reben-67-03-0468.pdfPortuguese.
  11. Singh, K. D. (2015). Creating your own qualitative research approach: Selecting, integrating and operationalizing philosophy, methodology and methods. Vision: The Journal Of Business Perspective, 19(2), 132–146.CrossRefGoogle Scholar
  12. Vander Nat, A. (2010). In 1st ed (Ed.), Simple formal logic. New York: Routledge. Print.Google Scholar
  13. Von Plato, J. (2014). From axiomatic logic to natural deduction. Studia Logica, 102(6), 1167–1184.CrossRefGoogle Scholar
  14. Zablith, F. (2009). Ontology evolution: A practical approach, poster, in Proceedings of Workshop on Matching and Meaning at Artificial Intelligence and Simulation of Behavior.Google Scholar
  15. Zhang, L., Chattopadhyay, A., Wan, C. (2013). Round-up: Runtime checking quasi linearizability of concurrent data structures. In 28th IEEE/ACM International Conference on Automated Software Engineering (ASE ‘13) (pp. 4–14). IEEE, 2013. Piscataway, NJ, USAGoogle Scholar

Copyright information

© Academy of Marketing Science 2018

Authors and Affiliations

  • Varsha Jain
    • 1
  • Philip J. Kitchen
    • 2
    • 3
  • B. E. Ganesh
    • 1
  1. 1.MICAAhmedabadIndia
  2. 2.Salford University Business SchoolSalfordUK
  3. 3.ESC Rennes School of BusinessRennesFrance

Personalised recommendations