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Drawing upon social exchange theory, this study investigates how witnessed incivility is related to psychological distress for employees. In addition, scholars dug deep into the potential moderating effect of self-esteem that links witnessed incivility, employee silence and psychological distress.

In data were obtained from 292 bankers at family-owned banks. In this work, data analysis was performed using Smart-PLS covariance-based SEM version 4.

The study results indicate that employee silence mediates witnessed incivility and psychological distress. Findings also suggest that high self-esteem can mitigate the harmful effects of witnessed incivility, indirectly causing silence and psychological distress among employees.

Family-owned bank management should encourage employees to speak up, demonstrate self-esteem and share their concerns. Thus, reducing witnessed incivility increases well-being, stress, and mental health in Pakistani family-owned enterprises which operate in diverse industries.

In the context of family-owned banks, our study adds context and theory to the existing body of knowledge by illuminating the underlying process that relates incivility with psychological distress By exploring the use of social exchange theory.

This paper aims to managerial decision styles, following the General Decision-Making Style Inventory, as potential predictors of individual bias awareness and bias blind spots, with a focus on the rational decision style.

The research is based on a survey of 50°C-1 level managers within Forbes 2000 companies. It explores their decision styles and their assessments of their own and others’ decision behavior.

The results show that the awareness of one’s own susceptibility to biases and bias blind spots is highly dependent on an individual’s (self-declared) decision style and type of cognitive bias; decision-makers with a strong tendency toward a rational or spontaneous decision style see themselves as less vulnerable to cognitive biases but also show a much stronger bias blind spot than those with a tendency toward other decision styles. Meanwhile, decision-makers with a strong tendency toward an intuitive decision style tend to recognize their own vulnerability to cognitive biases and even show a negative blind spot, thus seeing themselves as more affected by cognitive biases than others.

To date, decision styles have not been used as a lens through which to view susceptibility to cognitive biases and bias blind spots in managerial decision-making. As demonstrated in this article, decision styles can serve as predictors of individual awareness and susceptibility to cognitive biases and bias blind spots for managers.

The study aims to investigate consumer preferences across 25 attributes of plant-based milk (PBM) products and examine the key predictors and underlying mechanisms of consumer purchase decisions of PBM alternatives.

This study employed a multidimensional approach to investigate consumer preferences and the determinants of PBM purchasing decisions. Drawing on data from 819 online surveys conducted in the Jing-Jin-Ji region of China in 2021, we measured consumer preferences across 25 specific attributes and other individual characteristics. Purchasing decisions were framed as a two-stage process – the decision to purchase (frequency) and the decision on how much to pay (WTP). The Least Absolute Shrinkage and Selection Operator (LASSO) model was utilized to examine these dimensions separately, and the selected predictors were incorporated into OLS linear and Heckman’s two-stage regression analyses to establish the underlying mechanisms.

The findings indicate that consumers exhibit a strong preference for freshness and the absence of spoilage, followed by taste experiences such as taste and aroma. Preferences for milk preservation significantly increase the purchase frequency of PBM, while preference for calorie content has a negative and significant impact. Preferences for milk preservation, aroma and processing methods can also significantly increase WTP. Preferences vary across PBM categories. Social influence, knowledge and advertising exposure positively impact purchase frequency and WTP. Consumers with low food neophobia tend to be more responsive to product-related factors, such as freshness, calorie content and processing methods, in their purchase decisions.

This study contributes to the extant literature by comprehensively examining the determinants of consumer purchase decisions for PBM alternatives. The findings provide practical implications for marketers and policymakers, highlighting the strategic product attributes, consumer segments and marketing levers that can effectively target and cater to consumer preferences for PBM alternatives.

This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.