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Despite to useful relevance to better understand how group-level identity develops, few studies have explored the identity theory in the esports field and, in particular, considering the impact of a fan's role identity. The current study aims to explore esports fan role-identity vis-à-vis the relationship with the sponsor and the sponsee so as to understand the effects on their behavioral intentions.

Using a sample of 356 esports fans who attended the 2021 FPF eFootball Open Challenge, a Confirmatory Factor Analysis (CFA) analyzed the psychometric properties of the constructs and a subsequent Structural Equation Modeling (SEM) examined the effects of fan identity on two types of behavioral intentions and sponsor–sponsee relationship.

Results indicate that fans who highly identify with esports have the highest attachment to the event and tend toward having a positive word-of-mouth intention. Esports fans who have a higher brand identification reported a positive attitude toward the event's sponsor brand and tend to purchase its products. Moreover, the study findings also provide evidence of the bidirectional interaction between the way in which fans attach with the esports event and its sponsor brand, leading to greater reciprocity in their identity formation.

This study helps to understand how the fan identity process can enhance its fate and develop mutually, building role overlapping identity in the esports sponsor–sponsee relationship. Complementarily, it supports of how the marketeers and managers must analyze the importance of being a fan to the individual in order to understand how its self-identity can shape the future behavior.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Digital communication technologies have become ubiquitous for various firm processes related to international business (IB) and global strategy. However, IB and strategy scholars lack an encompassing and theory-based typology of these technologies that facilitates analysis and discussion of their uses and effects. Likewise, managers have a large choice of technologies at their disposal making it difficult to determine what technology to use in different IB areas. This paper aims to develop a typology of digital communication technologies based on the synchronicity and interactivity of these technologies and capture their fundamental social and temporal dimensions. This results in four ideal types: broadcasting, corresponding, aggregating and collaborating technologies.

This is a conceptual paper incorporating theoretical perspectives to theorize about four ideal types of digital communication technologies. A subsequent empirical test of this typology has been provided in the appendix.

The authors discuss how the typology might be applied in IB decisions and some of the contingencies that impact this choice. Building on that, the authors develop directions for future research to increase their understanding of the use of digital communication technologies to help improve IB functions. Overall, the authors suggest future research explores contingencies about where and when different types of digital communication technologies should be used. Finally, the authors provide implication of having a unified typology for both academics and managers.

The authors offer a robust framework for thinking about and capturing different types of digital communication technologies that can be applied by researchers and used by managers when making decisions related to IB. The authors also provide some initial testing of the typology with a three-country study design helping to determine its validity.

While the three lines model (TLM) provides an organizational structure to execute risk and control duties, research and practice show limitations in the model's implementation. These limitations result in governance issues. Such issues, together with control weaknesses, could be addressed by leveraging properties of distribution, transparency, and immutability of blockchain technology. To this end, in this paper the authors propose a conceptual control framework based on blockchain technology to augment control practice.

The design of the resulting blockchain-based control framework (BBCF) and its prototype, based on the design science research methodology (DSRM), is presented and discussed in terms of the potential impact in the context of the identified problems within the TLM.

One potential outcome of BBCF could be to redefine the scope and boundaries of some of the activities in audit and control practices from a more static to a more dynamic and prospective role. In a larger context of improving governance practices, the BBCF could set the path for a more inclusive and participatory interaction between the different governance actors of an organization.

However, this assumes that blockchain is more widely adopted despite its complexity and rigidity.

BBCF covering both a conceptual model design and a reference implementation provides an innovation in audit and control. BBCF could include all relevant stakeholders who have an interest in corporate governance and control activities, including the regulators.

The contribution intends to serve both as a starting point for discussing the evolution of audit and control practice based on blockchain technology, as well as an initial actionable prototype for experimentation and further development.

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.

Understanding the interplay between transparency, accountability and e-procurement and their collective contribution to anti-corruption efforts in public procurement is crucial for developing effective strategies and policies. This research seeks to investigate whether e-procurement plays a significant role in enhancing transparency and accountability and subsequently reducing corruption risks in the public pharmaceutical procurement system.

The study employed a cross-sectional questionnaire survey to gather data from 274 procurement personnel and pharmacists working in 28 government-owned hospitals in the Southern Highlands of Tanzania. The collected data were then analysed using confirmatory factor analysis (CFA) and the Hayes PROCESS macro to test the study hypotheses.

The study findings revealed a negative and significant relationship between transparency and procurement corruption (ß = −0.117, p < 0.008). Moreover, accountability negatively and significantly affects procurement corruption (ß = −0.162, p = 0.006). Furthermore, the findings indicate that, at a high degree of e-procurement system implementation, transparency and accountability have a stronger impact on procurement anti-corruption measures.

Policymakers and decision-makers should implement robust mechanisms that enhance transparency, accountability and anti-corruption efforts. These may include providing clear and accessible information on procurement processes, efficient mechanisms for monitoring and reporting procurement irregularities and continuous improvement of e-procurement systems. By incorporating these measures and nurturing collaboration amongst procurement stakeholders, it becomes possible to foster a procurement environment characterised by integrity, fairness, accountability and reduced corruption.

Whilst previous studies delved into exploring the effect of transparency and accountability on procurement anti-corruption, the novelty of this study is the inclusion of e-procurement as a moderating variable in the relationship between transparency, accountability and anti-corruption. By so doing, this study adds to the existing body of knowledge regarding anti-corruption measures and offers valuable practical insights for policymakers and professionals aiming to enhance transparency, accountability and ethical conduct within the public pharmaceutical procurement system.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

This study examines the roles of cross-sectional dependence, asymmetric structure and country-to-country policy variations in the inflation-poverty reduction causal nexus in selected sub-Saharan African (SSA) countries from 1981 to 2019.

To account for cross-sectional dependence, heterogeneity and policy variations across countries in the inflation-poverty reduction causal nexus, this study uses robust Hatemi-J data decomposition procedures and a battery of second-generation techniques. These techniques include cross-sectional dependency tests, panel unit root tests, slope homogeneity tests and the Dumitrescu-Hurlin panel Granger non-causality approach.

Unlike existing studies, the panel and country-specific findings exhibit several dimensions of asymmetric causality in the inflation-poverty nexus. Positive inflationary shocks Granger-causes poverty reduction through investment and employment opportunities that benefit the impoverished in SSA. These findings align with country-specific analyses of Botswana, Cameroon, Gabon, Mauritania, South Africa and Togo. Also, a decline in poverty causes inflation to increase in the Congo Republic, Madagascar, Nigeria, Senegal and Togo. All panel and country-specific analyses reveal at least one dimension of asymmetric causality or another.

All stakeholders and policymakers must pay adequate attention to issues of asymmetric structures, nonlinearities and country-to-country policy variations to address country-specific issues and the socioeconomic problems in the probable causal nexus between the high incidence of extreme poverty and double-digit inflation rates in most SSA countries.

Studies on the inflation-poverty nexus are not uncommon in economic literature. Most existing studies focus on inflation’s effect on poverty. Existing studies that examine the inflation-poverty causal relationship covertly assume no asymmetric structure and nonlinearity. Also, the issues of cross-sectional dependence and heterogeneity are unexplored in the causal link in existing studies. All panel studies covertly impose homogeneous policies on countries in the causality. This study relaxes this supposition by allowing policies to vary across countries in the panel framework. Thus, this study makes three-dimensional contributions to increasing understanding of the inflation-poverty nexus.