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While the extant literature has examined causes for buyer–supplier relationship dissolution, the restoration of severed buyer–supplier relationships has been overlooked. Drawing on organizational justice theory, our research develops and tests a model of relationship restoration. We examine how the supplier's restoration tactics—acknowledgment, compensation, and operational transparency, influence the interactional, distributive, and procedural fairness perception, respectively, of the buyer, resulting in relationship restoration. The results are based on a 2 (Acknowledgment – Yes/No) × 2 (Compensation – Yes/No) × 2 (Operational Transparency – Yes/No) vignette‐based study with 390 experienced practitioners. The analysis shows that compensating the buyer and providing transparent procedures for dealing with similar situations in the future, lead to higher distributive fairness and procedural fairness, respectively, resulting in restored relationships. Compensation makes up for past supplier malperformance, whereas operational transparency mitigates future concerns. We also find that restoration tactics based on interactional justice are less effective than those based on procedural and distributive justice. There is only marginal support for the indirect positive effect of acknowledgment on restoration intentions (p < 0.10). These results point to the importance of knowing how to approach a buyer to initiate relationship restoration. Managers must understand and evaluate the specific needs of each buyer when proposing a compensatory design that appeals to the buyer. Additionally, establishing procedures that are appealing to all buyers can be a challenge for a supplier, due to the differing benefits to the supplier provided by each buyer.

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Motivated by a lack of scales for measuring business undergraduates' grading assessment learning perceptions (GALP), this research created two three-item GALP scales, closed and open. Two separate samples of senior business undergraduates (fall, 2015, n = 220 and spring, 2016, n = 690) were used. Closed GALP and open GALP were identified via exploratory and confirmatory factor analyses. Subsequent stepwise regression analyses consistently showed that satisfaction/reputation had a positive impact and accounted for the most variance in these two GALP scales across both samples. Research limitations and future research issues are discussed.

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The burning number of a graph models the rate at which a disease, information, or other externality can propagate across a network. The burning number is known to be NP-hard even for a tree. Herein, we define a relative of the burning number that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the burning number as it only counts first time infections (i.e., constrains a node from getting the same disease/same variant/same alarm more than once). This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a method to solve for the contagion number of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis) and then employs simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are used to model human social networks generated through a preferential attachment mechanism. With CN differing across network structures and almost identical to ACN, our findings advance disease spread understanding and reveal the importance of network structure. In a borderless world without replete resources, understanding disease spread can do much to inform public policy and managerial decision makers’ allocation decisions. Furthermore, our direct interactions with supply chain executives at two COVID-19 vaccine developers provided practical grounding on what the results suggest for achieving social welfare objectives.