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Challenges and Opportunities for Humanitarian Researchers: Dreadful Biases and Heavenly Combinations of Mixed Methods

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The Palgrave Handbook of Humanitarian Logistics and Supply Chain Management

Abstract

This chapter offers much-needed complementary methodological choices that emphasize the heavenly combinations of mixed methods (case studies coupled with analytic hierarchy/network processes; surveys combined with in-depth interviews). It particularly focuses on the linkages between these combinations and humanitarian operations. The chapter also draws attention to the dreadful challenges of biases (e.g., network and citation biases) and statistical complications (e.g., data quality measures and endogeneity issues), which could lead towards invalid findings and biased publications if they are not dealt systematically. Additionally, how contemporary research methods and techniques (e.g., network analysis and machine learning techniques) detect biased publications and highlight the corporate-academic corruption is illustrated with a case taken from the special issue published by the Journal of Business Research in 2016. The chapter finally suggests some pragmatic actions to maintain the quality of our future research.

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Notes

  1. 1.

    The paper was originally titled “The Exploring the Role of Business Analytics Capabilities in Creating Business Value in Healthcare Industries: A Resource-Based View Perspective” – published with a changed title “Exploring the Path to Big Data Analytics Success in Healthcare”

References

  • Abdallah, W., Goergen, M., & O’Sullivan, N. (2015). Endogeneity: How failure to correct for it can cause wrong inferences and some remedies. British Journal of Management, 26(4), 791–804.

    Article  Google Scholar 

  • Agarwal, A., Shankar, R., & Tiwari, M. (2006). Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research, 173(1), 211–225.

    Article  Google Scholar 

  • Akhtar, P., Marr, N., & Garnevska, E. (2012). Coordination in humanitarian relief chains: Chain coordinators. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 85–103.

    Article  Google Scholar 

  • Antonakis, J., & Dietz, J. (2011). More on testing for validity instead of looking for it. Personality and Individual Differences, 50(3), 418–421.

    Article  Google Scholar 

  • Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120.

    Article  Google Scholar 

  • Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014). Causality and endogeneity: Problems and solutions. In D. V. Day (Ed.), The Oxford Handbook of Leadership and Organizations (93–117). New York: Oxford University Press.

    Google Scholar 

  • Bayazit, O., & Karpak, B. (2007). An analytical network process-based framework for successful total quality management (TQM): An assessment of Turkish manufacturing industry readiness. International Journal of Production Economics, 105(1), 79–96.

    Article  Google Scholar 

  • Benitez, J., Henseler, J., & Roldán, J. (2016). How to address endogeneity in partial least squares path modeling, http://aisel.aisnet.org/amcis2016/AsiaPac/Presentations/6/ .

  • Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117.

    Article  Google Scholar 

  • Bias, N. (2016). Special issue on big data and analytics in technology and organizational resource management. Available at: http://mailman11.u.washington.edu/pipermail/egov-list/2015/000577.html. Assessed 30 August 2016.

  • Bollen, K. A. (1989). Structural equations with latent variables. NY, USA: John Wiley & Sons.

    Google Scholar 

  • Boring, E. G. (1953). The role of theory in experimental psychology. The American Journal of Psychology, 66(2), 169–184.

    Article  Google Scholar 

  • Bowblis, J. R., & McHone, H. S. (2013). An instrumental variables approach to post-acute care nursing home quality: Is there a dime’s worth of evidence that continuing care retirement communities provide higher quality? Journal of Health Economics, 32(5), 980–996.

    Article  Google Scholar 

  • Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. NY, USA: Routledge.

    Google Scholar 

  • Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81.

    Article  Google Scholar 

  • Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. NJ, USA: Ravenio Books.

    Google Scholar 

  • Cortina, J. M. (2002). Big things have small beginnings: An assortment of “minor” methodological misunderstandings. Journal of Management, 28(3), 339–362.

    Article  Google Scholar 

  • Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. London, UK: Sage Publications.

    Google Scholar 

  • Danneman, N., & Heimann, R. (2014). Social media mining with R. Birmingham, UK: Packt Publishing Ltd.

    Google Scholar 

  • Denzin, N. K. (1973). The research act: A theoretical introduction to sociological methods. Transaction Publishers.

    Google Scholar 

  • DeShon, R. P. (1998). A cautionary note on measurement error corrections in structural equation models. Psychological Methods, 3(4), 412–423.

    Article  Google Scholar 

  • Dou, Y., Zhu, Q., & Sarkis, J. (2014). Evaluating green supplier development programs with a grey-analytical network process-based methodology. European Journal of Operational Research, 233(2), 420–431.

    Article  Google Scholar 

  • Easterbrook, P. J., Gopalan, R., Berlin, J., & Matthews, D. R. (1991). Publication bias in clinical research. The Lancet, 337(8746), 867–872.

    Article  Google Scholar 

  • Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.

    Google Scholar 

  • Ellram, L. M. (1996). The use of the case study method in logistics research. Journal of Business Logistics, 17(2), 93.

    Google Scholar 

  • Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative content analysis. Sage Open, 4(1), 2158244014522633.

    Article  Google Scholar 

  • Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science, 345(6203), 1502–1505.

    Article  Google Scholar 

  • Frone, M. R., Russell, M., & Cooper, M. L. (1994). Relationship between job and family satisfaction: Causal or noncausal covariation? Journal of Management, 20(3), 565–579.

    Article  Google Scholar 

  • Graneheim, U. H., & Lundman, B. (2004). Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–112.

    Article  Google Scholar 

  • Hamilton, B. H., & Nickerson, J. A. (2003). Correcting for endogeneity in strategic management research. Strategic Organization, 1(1), 51–78.

    Article  Google Scholar 

  • Hashemi, S. H., Karimi, A., & Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. International Journal of Production Economics, 159(2015), 178–191.

    Article  Google Scholar 

  • Hunt, M. R. (2009). Moral experience of Canadian healthcare professionals in humanitarian work. Prehospital and Disaster Medicine, 24(06), 518–524.

    Article  Google Scholar 

  • Irani, Z. (2016). Available at: http://www.brunel.ac.uk/people/zahir-irani. Assessed 30 August 2016.

  • JBRSI. (2016). Available at: http://www.sciencedirect.com/science/journal/aip/01482963. Retrieved 30 August 2016.

  • Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: An analytic network process (ANP) approach. Omega, 35(3), 274–289.

    Article  Google Scholar 

  • Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112–133.

    Article  Google Scholar 

  • Kabra, G., & Ramesh, A. (2016). Exploring the challenges in implementation of information technology in humanitarian Relief Organisations in India: A qualitative study. In Managing humanitarian logistics (pp. 105–113), Springer Proceedings in Business and Economics.

    Google Scholar 

  • Kaewkitipong, L., Chen, C. C., & Ractham, P. (2016). A community-based approach to sharing knowledge before, during, and after crisis events: A case study from Thailand. Computers in Human Behavior, 54, 653–666.

    Article  Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling. NY, USA: Guilford Publications.

    Google Scholar 

  • Lance, C. E. (2011). More statistical and methodological myths and urban legends. Organizational Research Methods, 14(2), 279–286.

    Article  Google Scholar 

  • Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria what did they really say? Organizational Research Methods, 9(2), 202–220.

    Article  Google Scholar 

  • Lantz, B. (2015). Machine learning with R. Packt Publishing Ltd.

    Google Scholar 

  • Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52.

    Article  Google Scholar 

  • Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.

    Article  Google Scholar 

  • Love, P. (2016). Available at: http://oasisapps.curtin.edu.au/staff/profile/view/P.Love. Retrieved30 August 2016.

  • Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.

    Article  Google Scholar 

  • Mann, S. (2016). Research interviews: Modes and types. In The research interview (pp. 86–113), Palgrave Macmillan, UK.

    Google Scholar 

  • Martens, M. P., & Haase, R. F. (2006). Advanced applications of structural equation modeling in counseling psychology research. The Counseling Psychologist, 34(6), 878–911.

    Article  Google Scholar 

  • Mayring, P. (2000). Qualitative content analysis [28 paragraphs]. Forum Qualitative Sozialforshung. Paper presented at the Forum: Qualitative Social Research [On-line Journal].

    Google Scholar 

  • McLeod, S. (2008). Case study method. Retrieved from www.simplypsychology.org/case-study.html.

  • Meade, L., & Sarkis, J. (1998). Strategic analysis of logistics and supply chain management systems using the analytical network process. Transportation Research Part E: Logistics and Transportation Review, 34(3), 201–215.

    Article  Google Scholar 

  • Meade, L., & Sarkis, J. (1999). Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach. International Journal of Production Research, 37(2), 241–261.

    Article  Google Scholar 

  • Mogre, R., & D’Amico, F. (2016). A Decision Framework to Mitigate Supply Chain Risks: An Application in the Offshore-Wind Industry. IEEE Transactions on Engineering Management, 63(3), 316–325.

    Google Scholar 

  • Montgomery, D. C. (2013). Design and analysis of experiments. North Carolina, USA: Wiley.

    Google Scholar 

  • Neuman, W. L. (2005). Social research methods: Quantitative and qualitative approaches (Vol. 13). Allyn and Bacon Boston.

    Google Scholar 

  • O’Halloran, K., Tan, S., Pham, S., Bateman, J., & Vande Moere, A. (2016). A digital mixed methods research design: Integrating multimodal analysis with data mining and information visualization for big data analytics, 1–20, DOI: 10.1177/1558689816651015.

  • Ohm, J. (2016). Multimedia content analysis, Berlin, Germany: Springer.

    Google Scholar 

  • Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544.

    Article  Google Scholar 

  • Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2016). The role of big data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142(2), 1108–1118.

    Google Scholar 

  • Pham, P. N., Vinck, P., Marchesi, B., Johnson, D., Dixon, P. J., & Sikkink, K. (2016). Evaluating transitional justice: The role of multi-level mixed methods datasets and the Colombia reparation program for War Victims. Transitional Justice Review, 1(4), 60–94.

    Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

    Article  Google Scholar 

  • Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12(4), 762–800.

    Google Scholar 

  • Riffe, D., Lacy, S., & Fico, F. G. (2005). Analyzing media messages: Using quantitative content analysis in research. Mahwah, NJ: Lawrence Earlbaum Associates Inc.

    Google Scholar 

  • Riff, D., Lacy, S., & Fico, F. (2014). Analyzing media messages: Using quantitative content analysis in research. NY, USA: Routledge.

    Google Scholar 

  • Robson, Colin, and Kieran McCartan. Real world research. London, UK: John Wiley & Sons, 2016.

    Google Scholar 

  • Rourke, L., & Anderson, T. (2004). Validity in quantitative content analysis. Educational Technology Research and Development, 52(1), 5–18.

    Article  Google Scholar 

  • Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3), 161–176.

    Article  Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (2013). Decision making with the analytic network process: Economic, political, social and technological applications with benefits, opportunities, costs and risks (Vol. 195). Springer Science & Business Media. NY, USA: Springer.

    Google Scholar 

  • Saunders, M. N. (2011). Research methods for business students, 5/e. England: Pearson Education Ltd.

    Google Scholar 

  • Sekaran, U., & Bougie, R. (2010). Research method for business, a skill building approach. Singapore: John Wiley & Sons Inc.

    Google Scholar 

  • Sharma, R., Yetton, P., & Crawford, J. (2009). Estimating the effect of common method variance: The method—Method pair technique with an Illustration from TAM Research. MIS Quarterly, 33(3), 473–490.

    Google Scholar 

  • Siegle, D. (2016). Purposive sampling. Available at: http://researchbasics.education.uconn.edu/purposive-sampling/. Assessed 25 August 2016.

  • Tashakkori, A., & Teddlie, C. (2006). Validity issues in mixed methods research: Calling for an integrative framework. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.

    Google Scholar 

  • Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures: Nonreactive research in the social sciences (Vol. 111). USA: Rand McNally Chicago.

    Google Scholar 

  • Williams, L. J., & Brown, B. K. (1994). Method variance in organizational behavior and human resources research: Effects on correlations, path coefficients, and hypothesis testing. Organizational Behavior and Human Decision Processes, 57(2), 185–209.

    Article  Google Scholar 

  • Yin, R. K. (2013). Case study research: Design and methods. USA: Sage Publications.

    Google Scholar 

  • Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2012). Business research methods. USA: Cengage Learning.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Management Systems, Faculty of Business, Law and Politics, Hull, United Kingdom

    Pervaiz Akhtar

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  1. Pervaiz Akhtar
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Correspondence to Pervaiz Akhtar .

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  1. Hanken School of Economics , Helsinki, Finland

    Gyöngyi Kovács

  2. Hanken School of Economics , Helsinki, Finland

    Karen Spens

  3. Hanken School of Economics , Helsinki, Finland

    Mohammad Moshtari

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Akhtar, P. (2018). Challenges and Opportunities for Humanitarian Researchers: Dreadful Biases and Heavenly Combinations of Mixed Methods. In: Kovács, G., Spens, K., Moshtari, M. (eds) The Palgrave Handbook of Humanitarian Logistics and Supply Chain Management. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59099-2_4

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