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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.

The present study investigates human behavioral and emotional experiences based on human-built environment interaction with a specific interest in urban graffiti displaying fear and pleasure-inducing facial expressions. Regarding human behavioral and emotional experience, two questions are asked for the outcome of human responses and two hypotheses are formulated. H1 is based on the behavioral experience and posits that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified behavioral fear and pleasure responses. H2 is based on emotional experience and states that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified emotional fear and pleasure responses.

The research design is developed as a multi-method approach, applying a lab-based experimental strategy (N:39). The research equipment includes a mobile electroencephalogram (EEG) and a Virtual Reality (VR) headset. The behavioral and emotional human responses concerning the representational features of urban graffiti are assessed objectively by measuring physiological variables, EEG signals and subjectively by behavioral variables, systematic behavioral observation and self-report variables, Self-assessment Manikin (SAM) questionnaire. Additionally, correlational analyses between behavioral and emotional results are performed.

The findings of behavioral and emotional evaluations and correlational results show that specialized fear and pleasure response patterns occur due to the affective characteristics of the urban graffiti's representational features, supporting our hypotheses. As a result, the characteristics of behavioral fear and pleasure response and emotional fear and pleasure response are identified.

The present paper contributes to the literature on human-built environment interactions by using physiological, behavioral and self-report measurements as indicators of human behavioral and emotional experiences. Additionally, the literature on urban graffiti is expanded by studying the representational features of urban graffiti as a parameter of investigating human experience in the built environment.

The purpose of this study was to investigate work-related adaptive performance from a longitudinal process perspective. This paper clustered specific behavioral patterns following the introduction of a change and related them to retentivity as an individual cognitive ability. In addition, this paper investigated whether the occurrence of adaptation errors varied depending on the type of change content.

Data from 35 participants collected in the simulated manufacturing environment of a Research and Application Center Industry 4.0 (RACI) were analyzed. The participants were required to learn and train a manufacturing process in the RACI and through an online training program. At a second measurement point in the RACI, specific manufacturing steps were subject to change and participants had to adapt their task execution. Adaptive performance was evaluated by counting the adaptation errors.

The participants showed one of the following behavioral patterns: (1) no adaptation errors, (2) few adaptation errors, (3) repeated adaptation errors regarding the same actions, or (4) many adaptation errors distributed over many different actions. The latter ones had a very low retentivity compared to the other groups. Most of the adaptation errors were made when new actions were added to the manufacturing process.

Our study adds empirical research on adaptive performance and its underlying processes. It contributes to a detailed understanding of different behaviors in change situations and derives implications for organizational change management.

This study aims to build on the well-documented case of the Olympus scandal to dissect how social networks and corporate culture enabled corporate elites to commit fraud across multiple generations of leaders.

A flexible pattern matching approach was used to identify matches and mismatches between behavioural theory in corporate governance and the patterns observed in data from diverse sources.

The study applies the behavioural theory of corporate governance from different perspectives. Social networks and relationships were essential for the execution of the fraud and keeping it secret. The group of corporate elites actively created opportunities for committing misappropriation. This research presents individuals committing embezzlement because the opportunity already exists, and they can enrich themselves. The group of insiders who committed the fraud elaborated the rationalizations to others and asked outside associates to help rationalise the activities, while usually individuals provide rationalizations to themselves only.

The social processes among actors described in this case can inform the design of mechanisms to detect these behaviours in similar contexts.

This study provides both perspectives on the fraud scandal: the one of the whistle-blowers, and the opposing side of the transgressors and their associates. The extant case studies on Olympus presented the timeframe of the scandal right after the exposure. The current study dissects the events during the fraud execution and presents the case in a neutral or a negative light.

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 research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.

The study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.

Empirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.

The findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.

This research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.

This paper aims to explore how the establishment modes used by emerging economy multinational corporations (EE-MNCs) influence their subsequent experiences of liability of origin (LOO) in developed economies based on the causal-model theory of categorization.

Taking Chinese listed firms' direct investments in developed economies as the sample, this paper utilizes Heckman (1979)'s self-selection model to examine the effect of establishment modes. Besides, when checking the robustness, subsample analyses and 2SLS regressions are used to rule out the alternative explanation associated with LOO mitigation.

EE-MNCs that enter a developed economy by greenfield investment experience heightened LOO while entries using M&A are associated with the mitigated liability. When EEMNCs enter a more institutionally distant developed country, the establishment modes will be more determinant of their subsequent experiences of this liability. Moreover, the effect of establishment modes can recede when EE-MNCs have established their presence in a developed country for a longer time.

This paper utilizes the causal-model theory of categorization to articulate the underlying mechanisms through which the country-of-origin cue is weakened by the cue transmitted by M&A. It further considers the context-saliency of the cue of M&A and clarifies boundary conditions for the effectiveness of this establishment mode to mitigate LOO.

The purpose of this study is to identify the factors that influence the willingness of Saudi Arabian students from four universities in Saudi Arabia, to adopt learning management systems (LMSs). This will be accomplished by using two popular technology acceptance models unified theory of acceptance and use of technology (UTAUT) and theory of planned behavior (TPB).

In total, 445 undergraduates from four Saudi educational institutions participate in filling out the study questionnaire. To investigate the correlations between the variables, the study used structural equation modeling for data analysis.

The results of the study show that effort expectancy (EE), subjective norm (SN), attitude toward behavior (ATB) and perceived behavioral control (PBC) are found to be substantially connected with their intentions to use (ITU) LMSs. The findings also show that there is a strong relationship between students’ intentions and their actual use of LMSs.

Like many studies, this research has some limitations. The primary limitation is that the findings of the study cannot be extrapolated to other settings since the report’s analysis and investigation were limited to four Saudi universities. Therefore, to generalize the study’s findings, similar research needs to be conducted in other Gulf and similar cultural universities.

The integrated model identifies key factors that influence the intent of Saudi Arabian students to use LMS, including EEs, social influence, ATB and PBC. This model can help develop solutions for the obstacles that prevent students from using LMS. The findings can be used to provide assistance to increase the likelihood of LMS acceptance as part of the educational experience. The model may also inspire further research on this topic in the Gulf nations, particularly in Saudi Arabia.

As none of the relevant studies conducted previously in Saudi Arabia has integrated the two models to study the students’ ITU LMSs, this study combines two major theories, TPB and UTAUT, in the context of Saudi Arabia, contributing to the field of technology use in education by expanding empirical research and providing a thorough understanding of the challenges associated with the use of LMS in Saudi universities. This study should be viewed as filling a crucial gap in the field. Moreover, this integrated model, using more than one theoretical perspective, brings a thorough comprehension of the barriers that hinder students’ adoption of LMSs in the academic context in Saudi Arabia and thus assists in making effective decisions and reaching viable solutions.

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.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.