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Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Different age groups display different shopping patterns in terms of how and where consumers buy products. During times of crisis, such behavioural differences become even more striking yet remain under-researched with respect to elderly consumers. This paper investigates the impact of age on retail-related behavioural changes and behavioural stability of elderly shoppers (in comparison to younger consumers) during a crisis.

The authors surveyed 643 Austrian consumers to assess the impact of perceived threat on behavioural change and the moderating effect of age groups. Based on findings from this survey, they subsequently conducted 51 semi-structured interviews to understand the causes of behavioural change and behavioural stability during a crisis.

Elderly shoppers display more stable shopping behaviour during a crisis compared to younger consumers, which is influenced by perceived threat related to the crisis. Such findings indicate that elderly shoppers reinforce their learnt and embedded shopping patterns. The causes of change and stability in behaviour include environmental and inter-personal factors.

Through the lens of social cognitive theory, protection motivation theory and dual process theory, this research contributes to an improved understanding of changes in shopping behaviour of elderly consumers, its antecedents and consequences during a time of crisis. The authors reveal reasons that lead to behavioural stability, hence the absence of change, in terms of shopping during a crisis. They further outline implications for retailers that might wish to better respond to shopping behaviours of the elderly.

This paper aims to deepen the current knowledge of seasonality by investigating visitors’ intentional and behavioural patterns during peak and off-peak seasons. It compares the variation in several key behavioural factors, namely, duration of stay, party size, revisit intention, spending and breakdown of spending in different sectors in hospitality and tourism including entertainment, restaurant, accommodation and transportation. Moreover, this research expands the understanding by examining the effectiveness of two innovative strategies of offering a digital app and organising a unique event to tackle seasonal imbalances through stimulating visitors’ intention to change their timing of visit from peak to off-peak periods.

The author initially used a Delphi approach to gather experts’ opinion on the two scenario settings: event organisation and a trip planner app. The scenarios aimed to potentially encourage visitors to change their visit time to off-peak seasons. Then, using a quantitative survey, the travel habits and spending behaviours of 310 participants were captured. Furthermore, the survey assessed their intention to travel during off-peak seasons in response to the implementation of the two innovative strategies.

The results revealed that although the number of visitors who travel in off-peak seasons may be lower, their daily spending is higher than peak season visitors. In addition to total spending per day, the duration of stay, part size, quality of accommodation and re-visit intention of visitors indicated significant variation between peak and off-peak seasons. According to the statistical analysis’ results, organising events (including festivals) proves more effective in encouraging visitors to travel during off-peak seasons compared to digital innovation (i.e. a trip planner app). This finding is in line with the tenets of the Jobs-to-be-Done Theory of innovation.

This study contributes by conceptualising the mechanism of seasonality and its impacts on subsectors of tourism and hospitality. To the best of the author’s knowledge, this is one of the few empirical research that compares the behavioural patterns of visitors including their average spending per day between peak and off-peak seasons. Previous studies focused on specific regions or sectors, whereas this research investigates visitors’ behaviour on a broader scale to provide more comprehensive view. Furthermore, this study is novel due to practising an outside-in approach through investigating the effectiveness of the two innovative strategies aimed at addressing seasonality in the hospitality and tourism industry from visitors’ point of view.

It has been established that values, beliefs and norms are good predictors of pro-environmental behaviour; however, it is less explored how these constructs can be influenced. The purpose of this paper is to assess whether the COVID-19 pandemic, as a shock event, was a “game changer” or it had limited impacts on deep-rooted travel patterns.

To understand whether the pandemic has influenced people’s value orientations with regards to sustainable travel, the authors have made use of the value–belief–norm (VBN)theory of environmentalism and conducted a large-scale survey in Germany, Italy, France, The Netherlands and UK (N = 1545).

The findings showed that the pandemic caused only temporary change in deep-rooted travel patterns as (self-reported) pre-pandemic behaviour of respondents align with their value orientations and proved to be a good predictor of projected travel behaviour. Based on projected travel behaviour and value orientations, four market segments were identified – Frontrunners, Laggards, Comfortable Crowd and Entitled Stewards. While all segments showed willingness to adapt, the authors found variations in the extent of adaption and in the phases of the customer journey where travellers are likely to make changes.

According to our knowledge, using the VBN theory of environmentalism to understand the impact of shock events on deep-rooted travel patterns represents a new perspective. Moreover, to the best of authors’ knowledge, this study is one of the first, written in the midst of the COVID-19 pandemic, which looks at the impact of the health crisis on deep-rooted travel patterns, instead of the commonly studied risk perception.

This study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.

324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.

LPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.

This study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

Contextual to the recognition of the complex interplay between health and behavioral aspects, integrated behavioral health (IBH) has emerged. Although this model is becoming popular in the Western world, its presence in the global context is not promising. This paper aims to explore the need for IBH in India and address its barriers to implementation and possible solutions.

We analyzed the case of IBH and its potential implications for India using the current evidence base, authors' reflections and experience of implementing similar programs.

This paper identifies contextual factors, including increased instances of non-communicable diseases and psychosocial and cultural determinants of health, that necessitate the implementation of IBH programs in India. The key features of different IBH models and their applicability are outlined. The current status of IBH and potential challenges in implementation in India in terms of human resources and other factors are delineated. We also discuss the potential models for implementing IBH in India.

Integrating behavioral health in primary care is considered an effective and sustainable model to promote health and well-being across various target populations. Towards this end, this paper is the first to discuss the contextual factors of IBH in India. It is a significant addition to the knowledge base on IBH and its possible implementation barriers and strategies in low- and middle-income countries.

The psychological foundations of consumers’ reasons for product choices are analyzed in the field of marketing. The purpose of this research is to identify the implicit reasons for white meat consumption in the UK and Turkey.

In the scope of the means-end chain theory, in-depth interviews were conducted with individuals, and the reasons for consumers’ product preferences were revealed by moving from concrete to abstract.

It has been determined that the white meat consumption of Muslims in the UK is primarily shaped by their religious approach. In Turkey, on the contrary, both consumption patterns and reasons for preference are changing. It has been found that white meat consumption is associated with values such as security needs, satisfaction with life, self-fulfillment and happiness.

This research has contributed to the marketing literature by examining consumers’ implicit consumption reasons for white meat in the context of religion and culture.

Marketing strategies should focus on building trust in halal certification, particularly in the UK. Brands should associate their promotion strategies with feelings of security and happiness, which are associated in the minds of consumers.

This study is a new study in terms of revealing the connotations of consumers about consuming chicken and fish and showing the implicit needs that the brands can emotionally associate with.

This paper aims to conduct a systematic literature review on the fintech services and financial inclusion of the developing nations that particularly focuses on lower middle-income group nations (LMIGN) and upper middle-income group nations (UMIGN) to highlight the research areas that have not received attention and present opportunities for future research.

This paper adopts a systematic approach to examine 65 research articles published from 2016 to 2021, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

The study identifies research gaps in two key themes: backward and outward linkages. In backward linkages, the literature on UMIGN should pay attention to the behavioural patterns associated with lending, investment and market provision-related fintech services. Further research is needed to understand the relationship between fintech services on the usage and quality dimension of financial inclusion in both LMIGN and UMIGN. For outward linkages, future research work should explore the role of fintech and financial inclusion in the development of LMIGN. This study provides valuable insights and guides future research directions by comprehensively mapping the existing studies.

This study does not use quantitative tools, such as meta and bibliometric analysis, to validate the findings.

This research paper offers new perspectives that introduce a novel framework for analysing literature on fintech, financial inclusion and its impact on the overall development of UMIGN and LMIGN.