Customer-Centric Science: Reporting Significant Research Results With Rigor, Relevance, and Practical Impact in Mind

Abstract

In response to the ongoing concern regarding a science-practice gap, we propose a customer-centric approach to reporting significant research results that involves a sequence of three interdependent steps. The first step involves setting an alpha level (i.e., a priori Type I error rate) that considers the relative seriousness of falsely rejecting a null hypothesis of no effect or relationship (i.e., Type I error) relative to not detecting an existing effect or relationship (i.e., Type II error) and reporting the actual observed p value (i.e., probability that the data would be obtained if the null hypothesis is true). The second step involves reporting estimates of the size of the effect or relationship, which indicate the extent to which an outcome is explained or predicted. The third step includes reporting results of a qualitative study to gather evidence regarding the practical significance of the effect or relationship. Our proposal to report research results with rigor, relevance, and practical impact involves important changes in how we report research results with the goal to bridge the science-practice gap.

Adapting Nonprofit Resources to New Social Demands: The Food Banks in Spain

“…The combined research which is summarized in this article assumes a hybrid qualitative-quantitative approach [36]. The first phase, a case study with an exploratory character and tentatively supported by the above theoretical model, was complemented with a survey addressed to all the Spanish food banks aimed at confirming, broadening and generalizing both the model and the results from the case.…”

Ch-Ch-Ch-changes: How Action Phase Functional Leadership, Team Human Capital, and Interim vs. Permanent Leader Status Impact Post-transition Team Performance

“…In such circumstances where statistical power is attenuated, strict efforts to minimize type I error rates may have the unintended consequence of increasing type II error rates (Aguinis et al 2010). In fact, Aguinis et al (2010) have proposed that organizational researchers carefully take into account concerns over both type I and type II errors in specifying statistical significance cut-off levels and do so on an a priori basis. Therefore, given our focus on testing interactive effects in hypotheses 2 and 3, we use p < .10 (two-tailed) as the criteria for determining statistical significance and support for our hypotheses.…”

Publication Bias in the Organizational Sciences

“…According to Orlitzky (2012), such an approach may be pivotal in advancing the methodological rigor in the organizational sciences. Furthermore, this approach is aligned with customer-centric reporting of results as both researchers and practitioners benefit from understanding the robustness of a meta-analytic estimate (Aguinis et al, 2010).…”