Purpose Due to the growing dominance of the millennials in the secondhand clothing (SHC) market, it is crucial to understand the dynamics of their SHC buying behavior. Despite such significance, it has yet to be explored in the current literature. To address such a gap, this paper aims to explore the antecedents of the SHC buying behavior of millennials. Design/methodology/approach A purposive survey is conducted to establish relationships between the antecedents. As such, the interrelationships of the antecedents are modeled using the interpretative structural modeling (ISM) approach. Findings Results reveal that SHC antecedents exhibit several characteristics depending upon their characterization of being driving, dependence, linkage and autonomous variables. Originality/value This work pioneers the identification of SHC buying behavior antecedents specifically for the millennial market, as well as in the provision of a holistic analysis of the complex contextual relationships of these antecedents. The findings of this work provide insights that are crucial to the extant literature in developing theoretical frameworks and paradigms that help in understanding the dynamics of the SHC buying behavior. Moreover, such results are beneficial to marketing managers and practitioners in innovating their strategies to capture the millennial market better.

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Despite the rigid public safety protocols of the restaurant sector amid the COVID-19 pandemic in an effort to restart economic activities, customers do not feel secure eating at a sit-in restaurant, which is associated with prolonged restrictions on movement. As a mitigating initiative, holistically evaluating customers’ perceived degree of exposure to COVID-19 in restaurants is deemed relevant in the design of mitigation measures. Such an agenda is associated with multiple attributes under decision-making uncertainty within the framework of multiple criteria sorting (MCS). Thus, this work addresses this problem domain by proposing an intuitionistic fuzzy set extension of the previously developed TOPSIS-Sort (i.e., IF TOPSIS-Sort). As a case demonstration, 40 restaurants are evaluated under six attributes that define exposure to COVID-19. With 250 survey participants, the IF TOPSIS-Sort assigns 10, 13, and 17 restaurants to low, moderate, and high exposure classes, respectively. With this classification, crucial insights are offered to the restaurant industry for planning and policy formulation. To determine its effectiveness, a comparative analysis was carried with other distance-based MCS methods. Findings reveal that the proposed method is pessimistic and that other methods tend to underestimate the assignments, which may be counterintuitive, especially in applications related to public health. These sorting differences may be associated with addressing the vagueness and uncertainty in decision-making within the IF TOPSIS-Sort platform. The proposed novel IF TOPSIS-Sort is sufficiently generic for other domain sorting applications and contributes to the MCS literature.

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