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Although situational crisis communication theory (SCCT) is centered on rationality and cognitive information processing, it ignores that people are also driven by irrationality and non-cognitive information processing. The purpose of this study aims to fill this gap by examining how gender stereotypes, based on perceived spokesperson sex influence the public’s perceptions of crisis response messages.

A 2 (industry type: automotive vs daycare industry) × 2 (spokesperson’s sex: male vs female) × 2 (crisis response appeal: rational vs emotional) between-subject online experiment was conducted to examine the effect of gender stereotype in crisis communication.

Results showed that either matching spokesperson sex with sex differed industry or matching sex differed industry with appropriate crisis response appeal can generate a more positive evaluation of the spokesperson and the organization. The results also revealed under which circumstances, the attractiveness of different sex of the spokesperson can either promote or mitigate people’s perceptions of the organization. Furthermore, when people are aware of a spokesperson’s sex, in a female-associated industry, a mismatching effect of a positive violation of a male-related stereotype overrides a matching effect of a female-related stereotype in crisis communication.

This study is among the first to identify how the gender of a spokesperson and industry type affect publics’ crisis response.

This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-quality rank lists. By strategically selecting a subset of base rankers, the algorithm enhances the quality of the aggregated ranking while using only a subset of base rankers.

The proposed algorithm leverages a graph-based model to represent the interrelationships between base rankers. By applying Spectral clustering, the algorithm identifies a subset of top-performing base rankers based on their retrieval effectiveness. These selected rankers are then integrated into a sequential deep-learning model to estimate relevance labels for query-document pairs.

Empirical evaluation on the MQ2007-agg and MQ2008-agg data sets demonstrates the substantial performance gains achieved by the proposed algorithm compared to baseline methods, with an average improvement of 8.7% in MAP and 11.9% in NDCG@1. The algorithm’s effectiveness can be attributed to its ability to effectively integrate diverse perspectives from base rankers and capture complex relationships within the data.

This research presents a novel approach to rank aggregation that integrates graph theory and deep-learning. The author proposes a graph-based model to select the most effective subset for metasearch applications by constructing a similarity graph of base rankers. This innovative method addresses the challenges posed by inconsistent and low-quality rank lists, offering a unique solution to the problem.

Although emotional dynamics (EDs) during the entrepreneurial learning (EL) process are acknowledged to promote the growth of an entrepreneurial mindset (EM), while having social causes, empirical research on learning mainly looks at emotions as socially isolated concepts. This study aims to investigate how socially induced and regulated emotions during the EL process affect EM development.

We applied a qualitative, inductive approach related to interpretative phenomenological analysis to get deeply involved into individuals’ experienced emotions. We conducted semi-structured interviews with participants of two EL programs. Open-ended questions stimulated free narratives and detailed descriptions of experiences that were analyzed following a five-stage process.

There is a correlation between socially induced and regulated emotions and the development of EM elements. We suggest a framework for the EL process based on EDs, which triggers four main mechanisms that help individuals develop an EM: “re-assessment of individual emotions through EDs”, “EDs affected by facilitator intervention,” “sharing and co-creation of emotions,” and “sensemaking of experiences and emotions.”

This study adds to the knowledge on EDs during the EL process and contributes to the literature on EL and emotions in learning. Therewith, it helps to sensitize practitioners toward the complexity of emotions in the entrepreneurial process, allows to moderate individual emotional reactions and social Eds, and improves existing EE programs. Future research could investigate the interplay of specific personality traits, learning environments, and socioemotional team dynamics in EL.