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Interventions as experiments: Connecting the dots in forecasting and overcoming pandemics, global warming, corruption, civil rights violations, misogyny, income inequality, and guns

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  • Woodside, Arch G.

Abstract

This essay applies the “ultimate broadening of the concept of marketing” for designing and implementing interventions in public laws and policy, national and local regulations, and everyday lives of individuals. The ultimate broadening of the concept of marketing: Marketing is any activity, message, emotion, or behavior by someone, firm, organization, government, community, or brand executed consciously or nonconsciously that may stimulate an observable or non-observable activity, emotion, attitude, belief, or thought by someone else, group, organization, firm or community. The broadening definition applies to the current interventions by national and state/provincial governments as well as healthcare facilities, medical science facilities, firms, and individuals to mitigate and eliminate the impact of the COVID-19 pandemic. Framing interventions as experiments is helpful in improving the quality of their designs, implementing them successfully, and validly interpreting their effectiveness. In January and February 2020, a few nations were exemplars for accurately forecasting the coming disaster of COVID-19 as a cause of illness and death and in designing/implementing effective mitigating strategies: Denmark, Finland, Republic of Korea, New Zealand, Norway, and Vietnam. While the COVID-19 prevention intervention tests now being run for several promising vaccines are true experiments, the researchers analyzing the data from these interventions may need prompting to examine the efficacy of each vaccine tested by modeling demographic subgroups for the members in the treatment and placebo groups in the randomized control trials.

Suggested Citation

  • Woodside, Arch G., 2020. "Interventions as experiments: Connecting the dots in forecasting and overcoming pandemics, global warming, corruption, civil rights violations, misogyny, income inequality, and guns," Journal of Business Research, Elsevier, vol. 117(C), pages 212-218.
    • Handle: RePEc:eee:jbrese:v:117:y:2020:i:c:p:212-218
    DOI: 10.1016/j.jbusres.2020.05.027
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    References listed on IDEAS

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    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Lin, Vera Shanshan & Goodwin, Paul & Song, Haiyan, 2014. "Accuracy and bias of experts’ adjusted forecasts," Annals of Tourism Research, Elsevier, vol. 48(C), pages 156-174.
    3. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    4. Armstrong, J. Scott, 2007. "Statistical significance tests are unnecessary even when properly done and properly interpreted: Reply to commentaries," International Journal of Forecasting, Elsevier, vol. 23(2), pages 335-336.
    5. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    6. J. Scott Armstrong & Kesten C. Green, 2018. "Forecasting methods and principles: Evidence-based checklists," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 28(2), pages 103-159, April.
    7. Belvedere, Valeria & Goodwin, Paul, 2017. "The influence of product involvement and emotion on short-term product demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 652-661.
    8. Robert Fildes & Paul Goodwin, 2007. "Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting," Interfaces, INFORMS, vol. 37(6), pages 570-576, December.
    9. Edouard NOVATOROV, 2016. "Tracing the Emergence of Broadening Marketing Proposition: A Thorny Path," Journal of Economics Bibliography, KSP Journals, vol. 3(3), pages 450-458, September.
    10. Arch G. Woodside, 2017. "Releasing the death-grip of null hypothesis statistical testing ( < .05): Applying complexity theory and somewhat precise outcome testing (SPOT)," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 27(1), pages 1-15, January.
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    6. Lorenzo Pratici & Phillip McMinn Singer, 2021. "COVID-19 Vaccination: What Do We Expect for the Future? A Systematic Literature Review of Social Science Publications in the First Year of the Pandemic (2020–2021)," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    7. Serravalle, Francesca & Pantano, Eleonora, 2023. "Mastering care management strategies to improve retailing: Mechanisms, capabilities, impacts and emerging opportunities," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    8. Saleh Al-Omoush, Khaled & Orero-Blat, Maria & Ribeiro-Soriano, Domingo, 2021. "The role of sense of community in harnessing the wisdom of crowds and creating collaborative knowledge during the COVID-19 pandemic," Journal of Business Research, Elsevier, vol. 132(C), pages 765-774.
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    10. Verma, Surabhi & Gustafsson, Anders, 2020. "Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach," Journal of Business Research, Elsevier, vol. 118(C), pages 253-261.

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