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ABSTRACTThis article empirically examines the occurrence of price‐oriented maverick buying (MB) during supplier selection, in a direct purchasing process context. Drawing on agency theory, maverick buying, and total cost of ownership (TCO) literature, the statistically significant existence of price‐oriented MB is investigated and the purchasing manager (PM)‐related factors that influence such noncompliant behavior are determined. A discrete choice experiment is designed to simulate a TCO‐based supplier selection process in which an established purchasing framework agreement stipulates PMs not necessarily be price‐oriented (i.e., select suppliers primarily based on lowest price), and then models PM choice behavior in the supplier selection process (SSP), utilizing a conditional logit model (CLM) to determine PM compliance to the established purchasing framework agreement and identify if price‐oriented MB exists. Statistical tests utilizing comprehensive primary and secondary data are then conducted to determine if correlational relationships exist between PM‐related factors and PM price‐orientation. Results indicate that three PM‐related factors bear a significant correlational relationship to PM price‐orientation.

AbstractThe wholesale changes brought about by the COVID‐19 pandemic to men and women's paid work arrangements and work–family balance provide a natural experiment for testing the common elements of two theories, needs exposure (Schafer et al. Canadian Review of Sociology/Revue Canadienne De Sociologie, 57(4);2020:523–549) and parental proximity (Sullivan et al. Family Theory & Review, 2018;10(1):263–279) against a third theory also suggested by Schafer et al. (2020), and labelled in this article, entrenchment/exacerbation of gender inequality. Both needs exposure and parental proximity suggest that by being home because of the pandemic, in proximity to their children, fathers are exposed to new and enduring family needs, which may move them toward more equal sharing in childcare and other domestic responsibilities. By contrast to studies that have tested such theories using retrospective, self‐report survey data over a 2‐year period, we analyse more than a decade of time‐use diary data from the American Time Use Survey (ATUS) that covers the first 2 years of the pandemic. We model the secular and quarterly trends to predict what would have occurred in the absence of the pandemic, contrasting this to what indeed happened. Our analyses consider aggregate and individual impacts, using methods of sequence analysis, clustering, and matching. Among our results, we find that the division of childcare responsibilities did not become more equitable during the pandemic. Suggestions for future research are provided as are suggestions for the implementation of social policies that could influence greater gender equity in unpaid work and childcare.

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.