Science is about corroborating empirical evidence, even in academic business research journals

Crowding in the time of COVID: Effects on rapport and shopping satisfaction

“…Based on this rationale, our work has two objectives, namely, to replicate and extend selected previous findings under the new environment dictated by the pandemic. The replication goal is inspired by the recent calls to corroborate and re-examine findings in the marketing literature in pursuit of higher rigor ( Babin et al, 2020 ; Hubbard, 2015 ; Hubbard and Carriquiry, 2019 ). Specifically, these calls encourage reproduction and replication efforts with “different contextual settings, populations, scale measurements and sampling units” ( Babin et al, 2020 , p.3).…”

“…The replication goal is inspired by the recent calls to corroborate and re-examine findings in the marketing literature in pursuit of higher rigor ( Babin et al, 2020 ; Hubbard, 2015 ; Hubbard and Carriquiry, 2019 ). Specifically, these calls encourage reproduction and replication efforts with “different contextual settings, populations, scale measurements and sampling units” ( Babin et al, 2020 , p.3). Thus, in this study we first aim to re-examine the well-established effects of a social-based environmental construct, retail crowding, and its impact on shopper satisfaction within the context of the new retail landscape defined by the pandemic.…”

Guest editorial: age is a construct, not a characteristic

“…When confronted with age research, reviewers tend to judge such works as being old and not including a new contribution. In line with several calls for corroborating original empirical findings (Babin et al, 2020), comparing and contrasting models across different age concepts is a valuable contribution by itself. It reflects the current age structure of many populations better than focusing on a younger generation, and it tests if assumptions and models are still true when the sample ages.…”

Tutorial-Articles: The Importance of Data and Code Sharing

“…Finally, I must add that I find highly relevant a tutorialarticle written with the primary purpose of discussing data management; this shall be a valuable contribution to our community. P-hacking, HARKing (hypothesizing after the results are known), and positive publication bias A significant set of issues that recent editorials and articles are paying more attention to are those related to p-hacking, and HARKing (hypothesizing after the results are known), which leads to positive publication bias (Aguinis et al, 2017;Babin et al, 2020;Harvey, 2017). Considering a lack of a universal definition, one could say that p-hacking is the practice of changing systematically a model or the variables used in a model to find p-values that are significant at typical thresholds such as the 1% or 5%, "leading" the results to be "significant" (emphasis on the quote-unquote here, since this significance is likely to be found by chance).…”

“…It is almost 2021, and the discussion about open research data, transparency, credibility, and research funding has been extensive in the past few years in the academic research communities of business and management (Babin, Ortinau, Herrmann, & Lopez, 2020;Beugelsdijk, Van Witteloostuijn, & Meyer, 2020). The list of journals debating and increasing practices related to what is known as open science (OS) grows by the minute and, at the moment, includes Management Science, Journal of International Business Studies, Journal of Marketing, Journal of Business Research, Strategic Management Journal, Organization Science, Journal of Management, and many others. Lists like this one are not a novelty anymore.…”