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This paper aims to uncover patterns of Mexican craft beer consumers and guide companies’ decisions in the creation of new products, marketing strategies, advertising and promotion to increase craft beer sales and contribute to faster growth.

This is a conjoint analysis with a selection of attributes for new or renewed products, marginal disposition to pay for particular characteristics through brand-specific choice-based design, and market simulation.

This paper clearly demonstrates consumers’ preferences and willingness to pay in Mexico, with a cutting-edge market research technique combining the prioritization of preferred craft beer characteristics, and the price consumers are willing to pay for such product characteristics.

The study's sample size of 501 responses is relatively small compared to the total number of craft beer consumers in Mexico. To enhance the validity and reliability of the findings, future studies should aim to obtain larger samples and compare their results with those of this study.

This study has important implications for craft beer producers, allowing them to develop targeted craft beers with appealing attributes for Mexican consumers, such as color, aroma intensity, alcohol degree intensity, bitterness, foam level and price.

This study's market forecasting simulation technique is based on assumptions of consumer behavior and market dynamics. Although relevant variables were considered, unanticipated external factors or market changes could impact the forecasts' accuracy. This will allow for a more comprehensive understanding of craft beer consumer preferences in different markets and enhance the reliability of forecasting techniques.

This paper informs craft beer producers by providing valuable knowledge on customers’ preferences and willingness to pay to enhance craft beer companies’ product development processes.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.

This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.

The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.

While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.

The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.

This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.

Purveyors of fake news perpetuate information that can harm society, including businesses. Social media's reach quickly amplifies distortions of fake news. Research has not yet fully explored the mechanisms of such adversarial behavior or the adversarial techniques of machine learning that might be deployed to detect fake news. Debiasing techniques are also explored to combat against the generation of fake news using adversarial data. The purpose of this paper is to present the challenges and opportunities in fake news detection.

First, this paper provides an overview of adversarial behaviors and current machine learning techniques. Next, it describes the use of long short-term memory (LSTM) to identify fake news in a corpus of articles. Finally, it presents the novel adversarial behavior approach to protect targeted business datasets from attacks.

This research highlights the need for a corpus of fake news that can be used to evaluate classification methods. Adversarial debiasing using IBM's Artificial Intelligence Fairness 360 (AIF360) toolkit can improve the disparate impact of unfavorable characteristics of a dataset. Debiasing also demonstrates significant potential to reduce fake news generation based on the inherent bias in the data. These findings provide avenues for further research on adversarial collaboration and robust information systems.

Adversarial debiasing of datasets demonstrates that by reducing bias related to protected attributes, such as sex, race and age, businesses can reduce the potential of exploitation to generate fake news through adversarial data.

In recent years, the global consumption of craft beer witnesses remarkable growth. This growth is attributed to the evolving demographics of beer consumers, particularly the emergence of a new generation known as Digitarians or Generation Z. This study aims to analyze the key determinants influencing craft beer consumption among Digitarians.

An online questionnaire is administered, and a total of 296 completed responses are included in the statistical analysis. The methodology uses logistic regressions combined with a backward selection process and variance inflation factor analysis to address multicollinearity. The logistic regressions are conducted in three steps to delve into the research objective and gain insights into the behavior of young consumers. The stepwise backward selection aids in obtaining robust coefficients as a variable selection tool.

The results shed light on how Digitarians’ preferences for craft beer are influenced by various factors, including self-perceived knowledge, alcohol content, gender, food pairings, environment and companionship.

To the best of the authors’ knowledge, this paper contributes novel insights by being the first study to explore the significance of craft beer choices among Digitarians, identifying the role of several predictors in their consumption patterns.

This paper aims to explore the risk and benefit perceptions influencing beer patrons’ purchase and consumption decisions in India. It delves into the drivers behind consumers’ consumption patterns and attitudes towards beer, which subsequently impact their behavioural intentions, including word-of-mouth recommendations and repurchase intentions.

The study uses a structured questionnaire to collect data from 306 beer patrons in Delhi and the National Capital Region. Through exploratory factor analysis and structural equation modelling, the research examines the risk (5) and benefit (3) factors associated with beer consumption using a factor model consisting of 29 constructs. A conceptual framework illustrates the interrelationships between variables, which are subsequently validated empirically.

The findings reveal that patrons’ attitudes towards beer are influenced by their perceptions of risk and benefit. Specifically, health and socio-psychological risks are identified as the most significant and negatively impacting factors on patrons’ attitudes. Factorial analysis demonstrates that patrons’ attitudes towards beer are positively influenced by their perception of benefits such as value for money, sensory appeal and convenience. Furthermore, the study highlights that an increase in benefit perception or a decrease in risk perception leads to a favourable shift in patrons’ attitudes towards beer.

To the best of the authors’ knowledge, this study will be the first to investigate how beer consumers’ consumption patterns and purchase decisions are impacted by assessing consumer risk and benefit perceptions. This study will also aid stakeholders in tailoring their beer offerings better to meet the desires and requirements of their customers.

Social commerce (SC) is a new genre in electronic commerce (e-commerce) that has great potential. This study proposes a new research framework to address deficiencies in existing social commerce research frameworks (e.g. the information model).

In the era of Industrial Revolution 4.0 technologies and new social commerce (s-commerce) models, the authors believe that there is an immediate need for a new research framework. The authors analysed the progress of the s-commerce paradigm between 2003 and 2023 by applying longitudinal science mapping. The authors then developed a research framework based on the themes in the strategic diagrams and evolution map.

From 2003 to 2010, studies on s-commerce mainly focused on social networking sites, virtual communities, social shopping and analytic approaches. From 2011 to 2015, it shifted to s-commerce, consumer behaviour, Web 2.0, artificial intelligence, social technologies, online shopping, user studies, data gathering methods, applications, service-based social commerce constructs, e-commerce and cognitive factors. Social commerce remained the primary research paradigm from 2017 to 2023.

The SC framework may be analogous to popular research frameworks such as technology-organisation-environment (T-O-E) and stimulus-organism-response (S-O-R). Based on this SC framework, researchers may gain a better understanding by determining the factors of the social, commercial, technological and behavioural dimensions.

The authors redefined s-commerce and developed an SC framework. Practical guidelines for the SC framework and an exemplary research model are presented. Overall, this study offers a new research agenda for the extant understanding of s-commerce, with the SC framework as the next frontier of the theoretical advancements and applications of s-commerce.