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The purpose of this study was to compare four different consumer pathway models based on identity theory, attitude/loyalty theory, lifestyle theory and hierarchy of effects theory, each with associated instruments measuring connection to the team.

The authors did a two-study analysis, first collecting data from people aware of an NFL team (N = 218) and then an MLS team (N = 209) to determine which connection item performed better.

The authors found that the Consumer Pathway for Sport Fandom based on the hierarchy of effects theory and its associated interest measurement item performed better than the other three frameworks and items. The Interest item shared the most variance with games attended, games intending to attend, games watched via media and games intending to watch via media.

The Consumer Pathway for Sport Fandom represents the entire consumer spectrum from non-aware consumers all the way up to die-hard sports fans. This pathway will allow sport marketers to track their consumers from initial awareness of the product or service all the way through the brand relationship to ultimate loyalty.

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.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

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.

The purpose of this study is to examine the behaviour of retail investors while making an investment decision and how it gets affected by the behavioural biases of the investors using a moderated-mediation framework.

A mixed method approach has been used to fulfil the objectives of the study. In the first study, a qualitative analysis of the interviews with 15 retail investors was conducted. As part of the quantitative study, a total of 201 responses from Indian retail investors were collected using systematic sampling and analysed using structural equation modelling and Process Macro.

The results indicate that anchoring bias, availability bias, herding bias, switching cost, sunk cost, regret avoidance and perceived threat have a significant effect on retail investors’ investing intention. The attitude of the investors towards investing decisions mediates the effects of behavioural bias and the status quo on investment intention. The results of the moderated-mediation analysis indicate that mediating effect of attitude varied at the low and high-risk aversion of investors.

The findings of this study will help regulators and retail investors to understand the critical behavioural biases which affect the investors’ investing intention.

The paper contributes to the literature on investors’ behaviour, status quo bias theory (SQB) and behavioural bias. This study uniquely proposes a moderated-mediation framework to understand the effects of biases on retail investors’ investment intention.

This study aimed to understand better what makes esports fans engage with streamers' live-streaming of esports gameplay. This study used the Theory of Planned Behavior (TPB) and additionally adopted streamer identification and esports game identification as moderating variables.

Data were collected from streamers' esports content streaming viewers over 18 years of age using an online survey in Amazon M-Turk (N = 307). Based on past esports live-streaming weekly watching hours, which range from 1 to 45 h, the participants were divided into lower (n = 152) and higher (n = 155) frequency groups. PLS-SEM and bootstrapping techniques were used to test the moderated mediation relationships among the constructs.

This study found a negative moderating effect of past watching experience on the relationship between attitudes and behavioral intention, and it positively moderated the path between perceived behavioral control and behavioral intention. Also, it was found statistically significant direct impacts of streamer identification (STI) and esports game identification (EGI) on attitude and subjective norms. While the indirect impact of STI on behavioral intention through attitude was statistically significant, there were no significant indirect impacts of EGI on attitude and behavioral intention through subjective norms.

Theoretically, this study extends the TPB model by exploring the two identifications (i.e. streamers and esports games) as antecedents of the focal TPB factors (i.e. attitudes, subjective norms and perceived behavioral control) and the moderating effect of prior experience based on high/low weekly watching frequencies. Practically, content creators of esports live-streaming and live-streaming platform managers can use the study’s findings to develop strategies to nurture their current and future viewership.

The purpose of this commentary is to discuss a framework (the Revised Model of Sport Consumer Behavior – R-MSCB; Trail, 2019) to accomplish the following objectives. (1) Provide a framework for sport marketers and sport organizations to use in their own market research. (2) Designate the processes needed to generate results and knowledge that sport organizations, sport marketers and associated corporate partners can use to improve marketing campaigns. (3) Provide suggestions for analytic techniques and measurement constructs/instruments that will facilitate market research within the sport industry.

The author created a 140-item questionnaire for the pre-survey and a 50-item questionnaire for the post-survey. The pre-survey was given at the beginning of the season, and the post-survey was given at the end of the season. The research group has generated a sequence of 13 studies and has collected pre-survey responses from over 7,800 people across 4 continents, 8 sports and 13 teams (data collections), with more teams still planned and over 2,000 post-season survey responses with data collection continuing.

This project, with its 13 studies, will provide sport organizations with the theories/frameworks, knowledge and processes to do their own market research in a more effective and efficient manner and will show sport organizations how to understand the complexities of sport consumer behavior, the motivations behind it and how it varies extensively across individuals. The author will show sports organizations how to collect data from their consumers using valid and reliable measures and constructs that will allow them to segment their consumers, how to effectively market to those segments and how to establish long-lasting relationships that make a satisfied and loyal customer.

This paper provides the framework and guidelines so that sport marketers and researchers can identify specific variables, constructs and relationships that will further their own objectives when attempting to explain sport consumer behavior. Using their own market research, they can implement the results/output in association with the Communications Strategy Model to create more effective and efficient marketing and communications campaigns and consequently drive key performance indicators (KPIs) and improve revenue streams.

This study examines the effects of financial literacy and financial risk tolerance on investor behavior by introducing social stigma as a mediator and emotional intelligence as a moderating factor.

Data is collected from 761 financially independent individual investors, with a minimum age of 25 years, a minimum of five years of stock market experience and residing in five selected major Indian cities. The collected data is subsequently analyzed using SmartPLS. Homogeneous purposive sampling followed by snowball sampling was employed.

The findings of the study demonstrate a strong and noteworthy impact of financial literacy on investor behavior. The research reveals that social stigma acts as a partial mediator and emotional intelligence plays a significant moderator with direct effects and indirect effects between financial literacy, financial risk tolerance, social stigma and investor behavior.

Exploring emotional intelligence in financial decisions enriches academic programs by integrating it into financial education. Collaboration between academia and financial institutions yields practical tools, infusing emotional intelligence into services. This prompts systemic shifts, reshaping education and societal discourse, fostering inclusive, emotionally intelligent financial landscapes, aiming to redefine both academic teachings and real-world financial practices.

Integrating emotional intelligence into government-led financial literacy programs can transform societal perspectives on financial decision-making. Customized services, destigmatizing workshops and collaborative efforts with academia foster an emotionally intelligent financial landscape, reshaping traditional paradigms.

Promoting open societal discussions about finances combats stigma, fostering a supportive space for risk-taking. Emphasizing emotional intelligence in awareness campaigns cultivates inclusivity and confidence. Normalizing financial talks empowers individuals, enhancing their well-being. Elevating both financial literacy and emotional intelligence enhances overall financial health, nurturing a community adept at navigating financial journeys.

This study marks a notable contribution to behavioral finance and social stigma theory by examining their intersection with emotional intelligence. It uniquely introduces social stigma as a mediator and emotional intelligence as a moderator, unexplored in this context. This novelty underscores the research’s significance, offering practical insights into financial well-being.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2023-0626

Plastic pollution is a major issue that plagues modern society. Scholars are interested in comprehending consumers’ behavioural actions to address plastic pollution. This study aims to delve into the determinants of consumers’ engagement with social media as a medium to address plastic pollution.

A conceptual model is developed that extends the behavioural reasoning theory (BRT). Using a quantitative approach, 476 responses underwent structural equation modelling analysis.

Results indicate that “reasons for” positively correlate with attitude and intention towards socially responsible engagement. Contrarily, “Reasons against” demonstrated a positive association with socially responsible engagement intention. Attitudes favouring socially responsible engagement correlate positively with the underlying intention. The moderation analysis underscores the positive relation of social return on social media with consumers’ attitude and their “reasons for” leaning towards socially responsible engagement intention. Notably, a positive connection was established between socially responsible engagement intention and the trifecta of consumption, contribution and content creation behaviours.

By enhancing the BRT, this research sheds light on novel perspectives regarding consumers’ engagement on social media platforms. Distinctively, it is among the handful of studies probing the influence of behavioural intention across diverse behavioural outcomes. The insights gained from this study, grounded in empirical evidence from an emerging market, are poised to guide policymakers, governmental agencies and industry practitioners in formulating effective strategies to combat plastic pollution. Additionally, the study can assist in achieving the UN sustainable development goals (SDGs), specifically SGD 12, SGD 13, SDG 14 and SGD 17.

Achieving swift and even flow of cargo through the border, the ultimate objective of cross-border logistics (CBL) requires the close coordination and collaboration of a multitude of stakeholders, as well as optimally configured systems. To achieve and sustain competitiveness in a dynamic international trade environment, CBL processes must undergo periodic analysis, improvement and optimization. This study aims to develop a modelling framework to capture CBL processes for analysis and improvement.

Relying on the extant literature, a meta-model is developed incorporating significant perspectives required to model CBL processes. Popular process modelling notations are evaluated against the meta-model and their ease of comprehension is also evaluated. The selected notation through evalution is augmented with addendums for a comprehensive depiction of CBL processes.

The capacity of role activity diagrams (RADs) to depict all perspectives, including interactions in a single diagram, makes them particularly suitable for modelling CBL processes. RADs have been complemented with physical flow diagrams and methods to capture temporal dimension, enabling a comprehensive view of CBL processes laying the foundation for insightful analysis.

The meta-model developed in this paper paves the way to develop an analysis framework which requires further research.

The lack of well-accepted modelling notations for studying CBL processes prompts researchers to search and adapt different formalisms. This study has filled this gap by proposing a comprehensive modelling framework able to capture CBL processes at different granularities in rich detail. Not only does the developed meta-model aid in selecting the notation, it is also useful in analysing the constituent elements of CBL processes.