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Based on a formalization of the 1981 constitutional negotiation in Canada, this article analyses the impact of procedural constraints on collective decisions. Four procedural constraints are considered: voting procedures, voting rules, decision rules, and the order of presentation of options to the vote. Sincere voting (voting according to a voter's preference scale), complete information, and free communication are assumed in the first part of the analysis. The assumption of sincere voting is relaxed in the second part where strategic voting is considered The analysis shows that (1) a collective decision is determined by some interaction of voters' preferences, procedural constraints, and voting strategies, and (2) procedural constraints can be ordered in terms of their relative impact on the collective decision (in decreasing order: decision rules, voting rules, order of presentation, voting procedures). In the conclusion, a general model of the determination of collective decisions is presented.

This paper aims to explain voter perceptions and voting behaviour in the 2010 UK General Election on the basis of this theory, by evaluating the differential impact of government performance on key political issues defined as hierarchical voter satisfaction factor types. The validity of the three-factor theory of satisfaction in explaining consumer decision-making for products and services is well-established.

British Election Survey (2010) data are used to test the relative influence of hierarchical voter satisfaction factor types in predicting the perceived overall performance of the former Labour government and actual voting behaviour. Sequential and multinomial logistic regression models are used in the perceived overall performance of the former Labour government and actual voting behaviour, respectively.

“Basic” factors explain more of the variance in perceived overall government performance and voting behaviour than “performance” factors. There are significant positive main and interaction effects on Conservative and Liberal Democrat party votes from Labour’s underperformance on the “basic” factors. The results have important implications for political marketing and voting behaviour research.

The study establishes the relevance of the three-factor theory of satisfaction within a political marketing context. It demonstrates that, controlling for party loyalty, perceived government performance on the hierarchical voter satisfaction factors explains voter perceptions and voting behaviour to a significant degree. In particular, it highlights the criticality for voting behaviour of both the direct and indirect impacts of “basic” factor underperformance.

Drawing on cognitive theories of information and democracy, this paper argues that carefully designed voting advice applications (VAAs) strengthen voter competence by matching voters’ preferences on important policy issues with parties' relevant positions on those issues.

To examine the causal effect of information acquisition on voting willingness, we employed a lab experiment on the pioneering VAA program in Taiwan: iVoter. Our participants consisted of 120 undergraduates who were randomly assigned to be treated (two groups) or controlled (one group). Each participant of the respective treatment groups was provided with full or partial information. The purpose was to identify the ideological distance between the participant and the respective political parties. Individuals in the control group did not receive the respective information prior to their decision on whether to vote or not.

Those who received full relevant information were most willing to vote in the forthcoming elections. We furthermore found VAA utilization to be positively associated with other aspects of democracy, such as political enthusiasm, civic duty and political efficacy.

This paper aims to contribute to the development of VAAs and to democracy as a whole by confirming a causal link between receiving relevant information and voting willingness.

The purpose of this paper is to explore how hubs’ social influence on social network decisions can cause the behavior of information cascades in a market.

The authors establish understanding of the fundamental mechanism of information cascades through a computational simulation approach.

Eigenvector centrality, betweenness centrality, and PageRank are statistically correlated with the occurrence of information cascades among agents; the hubs’ incorrect decisions in the early diffusion stage can significantly cause misled shift cascades; and the bridge role of hubs is more influential than their pivotal position role in the process of misled shift cascades.

This implication can be extendable in the field of marketing, sequential voting, and technology, or innovation adoption.

Juries are a decision making peer group composed of citizens who did not volunteer for the task, who do not know one another, and who are not connected to the people and events in the trial on which they will render a verdict. This chapter illuminates the communication events during deliberations, from selecting a foreperson, deciding when and how to vote, participation and turn-taking, the emergence of conflict, and rule-breaking. Deadlock juries, storytelling jurors, and juror misconduct are described during the group's task. Sources for scholars to gain access to jury data, partner with organizations in the judicial system, and available recent recordings of jury deliberations are shared. Knowledge gaps are pointed out in understanding how group verdicts emerge from the unregulated talk of jurors, as well as new challenges for the judicial system as the 2020 COVID-19 pandemic suddenly rendered jury service an unhealthy task for citizens.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

This study investigates how individuals' self-awareness (specifically, private and public self-awareness) and environment-awareness (perceived expertise, similarity and familiarity) shape herd behavior, encompassing discounting one’s information and imitating others. Drawing from latent state-trait theory, this research aims to discern the impact of these factors on purchase intention and behavior.

Longitudinal data from 231 users in Xiaohongshu, China’s leading social commerce platform, were collected to test the proposed model and hypotheses.

The findings from this study show that private self-awareness negatively influences discounting one’s own information and imitating others. Public self-awareness positively affects imitating others, while it does not affect discounting one’s own information. Perceived expertise diminishes discounting one’s own information but does not significantly affect imitating others. Perceived similarity and perceived familiarity are positively related to discounting one’s own information and imitating others. The results confirm different interaction effects between self-awareness and environment-awareness on herd behavior.

First, this contributes back to the latent state-trait theory by expanding the applicability of this theory to explain the phenomenon of herd behavior. Second, this study takes an important step toward theoretical advancement in the extant literature by qualifying that both self- and environment-awareness should be considered to trigger additional effects on herd behavior. Third, this study provides a more enlightened understanding of herd behavior by highlighting the significance of considering the interplay between self- and environment-awareness on herd behavior. Finally, this study also empirically confirms the validity of classifying self-awareness into private and public aspects.