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The study investigates factors like media, tribal, religious and party politics' influence on Nigerias’ 2023 presidential election choice. It confirms dominant social media platforms and examines their influence on election polls, e-participation and political candidate choice. The main objectives of this study are to: investigate if tribal, religious and party politics affect the respondent’s choice of a presidential candidate, ascertain the respondent's most used social media platform for political engagement and determine how social media platforms influenced the election polls during the 2023 Nigerian presidential election.

A sample size of 384 registered voters was used to survey three states in Southeast Nigeria hinged on the technological acceptance model, the instrumentalist theory of ethnicity and the theory of reasoned action.

The study found that tribal politics did not influence political candidates during the 2023 Nigerian presidential election. However, religious and party politics influenced their choices as well as X (Twitter), found as the most used and most influential social media platform vital for enhancing participatory democracy and informing people at real-time.

The researchers experienced challenges such as ensuring that the respondents filled the questions appropriately to reduce the number of void questionnaires and a funding problem since they had yet to receive any grant to enhance the study.

The study commends improved Internet connectivity and accessibility among the citizens for increased political engagement on social media. It also recommends that the Nigerian government enforce the rule of law in politics to enable diverse tribes and religions to experience democratic e-participation and development without marginalisation or subjugation by incumbent power. The findings affirm that social media is apt in political communication during the 2023 presidential elections in Nigeria. The study is a contribution to knowledge, timely and original.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

In the face of increasing political polarization worldwide, this study explores whether people create biased perceptions of political knowledge and how this affects their selection and evaluation of political content on YouTube.

For this study, an online experiment was conducted with 441 panels of South Korean respondents. In the first phase, participants answered 10 questions designed to capture their level of objective political knowledge, and for each question, they indicated whether they had responded to that question correctly as a means of measuring their subjective political knowledge. In the second phase, two types of YouTube thumbnails were presented to represent progressive and conservative claims on two controversial political issues, and participants rated and selected the content they would like to see.

Participants with low political knowledge perceived their knowledge as more than it really was. In contrast, participants with high political knowledge perceived their political knowledge as less than it really was. This biased perception of political knowledge influences respondents' choice and evaluation of political YouTube channel videos.

At a time when political polarization is increasing around the world, this study sought to explore how perceptions of political knowledge differ from actual political knowledge by applying the Dunning-Kruger effect. The authors also used political YouTube channels, whose role in forming public opinion and political influence is rapidly growing, to study the behavior and attitudes of a group of Korean respondents in the media according to their actual and perceived level of political literacy.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

This article explores the applicability of participatory action research (PAR) on two levels: on the one hand, as a participatory evaluation method for community engagement and community development; on the other hand, as a tool to link ex ante and ex-post evaluation that situated at various stages of the policy cycle.

Through a practice-based case study of the community engagement process of Valletta Design Cluster, this paper aims to illustrate how PAR can offer collaborative and continuous evaluation by facilitating social action through a practical, situative, context-bounded, responsive and transformative framework.

The study explores how PAR can contribute to cultural sustainability by linking community development with participatory evaluation, and it offers new perspectives on the applicability of PAR as a tool to link ex ante and ex-post evaluation, situated at various stages of the policy cycle.

Although based on a single case, the paper demonstrates that the method has the potential to be applied in various contexts, as it helps to foster local ownership and to develop future cultural strategies, thus providing a base for cultural sustainability.

The novelty of this study is to link evaluation – a majorly top-down and ex ante approach – with participatory planning. PAR-E offers a continuous participatory framework for the whole European Capitals of Culture (ECoC) cycle, as well as serving as a tool for empowerment and community development.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Korea’s Institute for Basic Science (IBS), the first research institute dedicated to basic science in Korea, started ten years ago as part of a science policy called the Science Belt. It is noteworthy that Korea, with a short history of basic science, established such a research institute exclusively for basic science within a short period of time and made it one of the representative institutions of basic science in Korea. This paper aims to uncover the impetuses and constraints surrounding the policy of Science Belt, centering on the IBS.

Kingdon’s stream theory is used to clarify the factors that acted as impetuses or constraints for the Science Belt. For the analysis, in-depth interviews with the active policy participants were conducted in addition to the thorough literature review. The interviews enabled an in-depth understanding of the underlying factors for the Science Belt and the actual procedures of the policy decision.

This study found that the most powerful impetus in the Science Belt policymaking process was the President and a small group composed of a few scientists who played a leading role in the political stream. The constraint of the Science Belt was that the participation of scientist experts and governmental officials, the so-called invisible participants of Kingdon, was insignificant. In particular, there was no system in place to select policy alternatives for basic science through discussion between scientists and governmental officials.

The temporal scope of this study was limited to policy formation, that is, until the establishment of IBS. Therefore, future studies shall conduct a research on the implementation of the actual policy, IBS’s achievements and IBS’s impact of Korea’s basic science community.

This study applied both a theoretical framework and in-depth interviews along with the literature overview to understand a policymaking process from various angles.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.