Search Results (33)

The pursuit of mergers and acquisitions (M&A) is often an acclaimed strategy for firm growth, resource sharing, and extended reach into new market segments. However, in the healthcare marketplace, there are two very different perspectives related to M&A. On the one hand, the American Hospital Association commends M&A activity as a tool to reduce healthcare costs, drive quality, and serve rural markets. On the other hand, a recent United States’ Presidential executive order suggests that M&A in the healthcare space is harmful to healthcare due to its restrictions on competition and adverse impacts on patients. These conflicting perspectives reflect differing M&A views in mainstream management research, as well. The purpose of the current study is twofold. First, we aim to explore these two seemingly paradoxical perspectives by examining the degree of hospital network geographic dispersion that results from M&A activity. Second, we contribute to the broader M&A literature by drawing attention to the importance of considering geographic influences on M&A performance. Using a spatial analysis of 147 nationwide hospital networks comprising 1713 hospitals, we propose and find support for the notion that the degree of network dispersion, as measured by actual driving distances in healthcare networks, are correlated with patient experiences. Using ordinary least squares (OLS) regression to examine relationships between patient experiences and overall hospital network geographic dispersion, we found support for the hypothesis that more spatially dispersed healthcare networks are associated with lower overall performance outcomes, as measured by customer (patient) satisfaction. The implications of these findings suggest that growth strategies that involve M&A activity should carefully consider the spatial influences on M&A entity selection. Our exploratory findings also provide a foundation for future research to bridge the gap between industry and governmental perspectives on healthcare M&A practices. Full article

This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on 180,000 screenings) with an AI-LLM neuroscience-based marketing assistant (CoPilot), with 67,429 areas of interest (AOIs). The original flyer, from an Al Jazeera article, served as the baseline. Professional graphic designers created three redesigned versions, and one was done using recommendations from CoPilot. Metrics including total attention, engagement, start attention, end attention, and percentage seen were evaluated across 13–14 areas of interest (AOIs) for each design. Results indicated that human-enhanced Design 1 with AI eye-tracking achieved superior overall performance across multiple metrics. While the AI-enhanced Design 3 demonstrated strengths in optimising specific AOIs, it did not consistently outperform human-touched designs, particularly in text-heavy areas. The study underscores the complex interplay between neuroscience AI algorithms and human-centred design in political campaign branding, offering valuable insights for future research in neuromarketing and design communication strategies. Python, Pandas, Matplotlib, Seaborn, Spearman correlation, and the Kruskal–Wallis H-test were employed for data analysis and visualisation. Full article

This study measured the individual and conjoint effects of Argentina’s primaries and first- and second-voting presidential election results, as well as their post-election comparative effects, on the stock market performance of its most relevant economic sectors. Within four different estimation methods, the state-space specification outperformed the rest. The findings suggest that investors can under/overreact compared to post-election sectors performance, the public services sector being the exception. Therefore, those investors who anticipated the election results by liquidating positions in companies in the materials sector and investing more in companies in the energy and other industrial sectors achieved a superior performance. Full article

A logical presentation of the Mean-Median Compromise Method (MMCM) is provided in this paper. The objective is to show that the method is a generalization of majority judgment, where each tie-break step is $L^p$ deepest voting. Therefore, in its tie-breaking procedures, the proposed method returns scores that range from the median to the mean. Among the established characteristics that it satisfies are universality, neutrality, independence of irrelevant alternatives, unanimity, and monotonicity. Additionally covered are robustness, reaching consensus, controlling extremes, responding to single-peakedness, and the impact of outliers. Through computer simulations, it is shown that the MMCM score does not vary by more than 12% even for up to 50% of strategic voters, ensuring the method’s robustness. The 1976 Paris wine taste along with the French presidential poll organized by OpinionWay in 2012 were well-known and highly regarded situations in the area of social choice to which the MMCM was used. The outcomes of MMCM have shown remarkable consistency. On the basis of the democratic standards that are most frequently discussed in the literature, other comparisons were performed. With 19 of the 25 criteria satisfied, the MMCM is in the top ranking. Supporting theorems have shown that MMCM does not necessarily require an absolute majority to pass an opinion for which a minority expresses a strong preference while the majority is only marginally opposed. Full article

This study seeks to understand the political discourse of Javier Milei and to determine which concept of populism best captures his approach. Although perceived by many as a populist, Milei is unusual in that he sees himself as a liberal libertarian and defender of the West against collectivist policies. To this end, this study analyzes selected speeches by Milei from three different periods during and after the 2024 presidential election campaign and applies a deductive coding scheme designed to identify ideational populism, populist discursive framing, populism as strategy, and populism as crisis performance. The analysis confirms that Milei is at best a partial populist, as he fails to define the core populist concept of “the people”. It concludes that the concept of crisis performance emerges as the most apt theoretical framework to classify Milei’s type of populism. By rhetorically transforming the crisis not only into an existential economic issue but also into a moral tale of corruption and failure at the highest levels, he can appeal for radical change and offer himself as the national political savior. Milei’s discourse also illustrates that, unlike ideological populism or discursive populist framing, in the performative turn, the victims of the crisis, the people, often remain a vague signifier defined by their suffering at the hands of elites. Full article

Cluster randomized experiments and estimator comparisons are well-documented topics. In this paper, using the datasets of the popular vote in the presidential elections of the United States of America (2012, 2016, 2020), we evaluate the properties (SE, MSE) of three cluster sampling estimators: Ratio estimator, Horvitz–Thompson estimator and the linear regression estimator. While both the Ratio and Horvitz–Thompson estimators are widely used in cluster analysis, we propose a linear regression estimator defined for unequal cluster sizes, which, in many scenarios, performs better than the other two. The main objective of this paper is twofold. Firstly, to indicate which estimator is most suited for predicting the outcome of the popular vote in the United States of America. We do so by applying the single-stage cluster sampling technique to our data. In the first partition, we use the 50 states plus the District of Columbia as primary sampling units, whereas in the second one, we use 3112 counties instead. Secondly, based on the results of the aforementioned procedure, we estimate the number of clusters in a sample for a set standard error while also considering the diminishing returns from increasing the number of clusters in the sample. The linear regression estimator is best in the majority of the examined cases. This type of comparison can also be used for the estimation of any other country’s elections if prior voting results are available. Full article

In the USA, for the first time in the 1960s, and in a very systematic manner from 1976 onwards, pre-election debates (televised presidential debates) have become a fundamental and integral method of communication for political parties, as well as an institution of American democracy that contributes significantly to shaping a culture of public political dialogue at a relatively high level, through which citizens accumulate knowledge about political figures and their parties’ positions within a very short period of time before the elections. In Greece, on the contrary, these television programs have not sparked significant interest to date. The subject of this study is the television debates in Greece, evaluated through a brief historical overview and commentary on their structure, culminating in the two televised confrontations that took place within a five-month period during two electoral contests in 2023. Firstly, the reactions to and reception of the two televised debates by citizens on platform X and, secondly, the commentary on the two debates by journalists, columnists, and renowned analysts, reveal the differing interests of both sides. The research results confirm that, in addition to the performance of politicians, citizens are also interested in the conditions and form in which these pre-election televised debates are staged. Full article

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios. Full article

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments. Full article

Managing the logistics and safety of an election system, from delivering voting machines to the right locations at the right time to ensuring that voting lines remain reasonable in length is a complex problem due to the scarcity of resources, especially human poll workers, and the impact of human behavior and disrupting events on the performance of this critical system. These complexities grew with the need for physical distancing during the COVID-19 pandemic coinciding with multiple key national elections, including the 2020 general presidential election in the USA. In this paper, we propose a digital clone platform leveraging agent-based simulation to model and experiment with resource allocation decisions and voter turnout fluctuations and facilitate “what-if” scenario testing of any election. As a use case, we consider three different concurrent polling location problems, namely, resource allocation, polling layout, and management. The main aim is to reduce voter waiting time and provide visibility of different scenarios for polling and state-level managers. We explain the proposed simulation platform based on Fulton County for the 2020 presidential US election. Fulton County had 238 polling locations in 2020, which provided publicly available voter turnout data. The developed platform realistically models at the county level and at specific locations, suggesting the possible allocation of finite resources among locations in the county and the configuration of each location, accounting for physical, legal, and technical constraints. Multiple realistic scenarios were developed and embedded into the simulation platform to evaluate and verify the different systems. The system performance and key attributes of the election system, such as waiting time, resource utilization, and layout safety, were tested and validated. Full article

In this paper, we present a two-component Weibull mixture model. An important property is that this new model accommodates bimodality, which can appear in data representing phenomena in some heterogeneous populations. We provide statistical properties, such as the quantile function and moments. Additionally, the expectation-maximization (EM) algorithm is used to find maximum-likelihood estimates of the model parameters. Further, a Monte Carlo study is carried out to evaluate the performance of the estimators on finite samples. The new model’s relevance is shown with an application referring to the vote proportion for the Brazilian presidential elections runoff in 2018. The proportion of votes is an important measure in analyzing electoral data. Since it is a variable limited to the unitary interval, unit distributions should be considered to analyze its probabilistic behavior. Thus, the introduced model is suitable for describing the characteristics detected in these data, such as the asymmetric behavior, bimodality, and the unit interval as support. In the application, the superiority of the proposed model is verified when comparing the fit with the two-component beta mixture models. Full article

Populism has become one of the main features of political action worldwide. This research aims to characterize the populist discourse in the tweets of presidential candidates in the Andean Community in recent elections (2020–2022). Accordingly, we analyze the characteristics of their social network profiles, as well as the content and latent discourse of their tweets. We demonstrate that the differences and similarities of their discourse go beyond their right and left association. The differences result from how they construct their identity and establish their relationship with their electorate. Our analysis reveals that this type of discourse is ideological as well as performative. It is ideological because, in the candidates’ discourse, they recontextualize the actual meanings of “us” and “them”. It is performative because it is carried out by a charismatic leader who acts in a specific way to define himself or herself as the embodiment of “the people” and “the good”. Full article

Understanding the factors that influence voter turnout is a fundamentally important question in public policy and political science research. Bayesian logistic regression models are useful for incorporating individual level heterogeneity to answer these and many other questions. When these questions involve incorporating individual level heterogeneity for large data sets that include many demographic and ethnic subgroups, however, standard Markov Chain Monte Carlo (MCMC) sampling methods to estimate such models can be quite slow and impractical to perform in a reasonable amount of time. We present an innovative closed form Empirical Bayesian approach that is significantly faster than MCMC methods, thus enabling the estimation of voter turnout models that had previously been considered computationally infeasible. Our results shed light on factors impacting voter turnout data in the 2000, 2004, and 2008 presidential elections. We conclude with a discussion of these factors and the associated policy implications. We emphasize, however, that although our application is to the social sciences, our approach is fully generalizable to the myriads of other fields involving statistical models with binary dependent variables and high-dimensional parameter spaces as well. Full article

This article presents the results of reviewing the predictive capacity of Google Trends for national elections in Chile. The electoral results of the elections between Michelle Bachelet and Sebastián Piñera in 2006, Sebastián Piñera and Eduardo Frei in 2010, Michelle Bachelet and Evelyn Matthei in 2013, Sebastián Piñera and Alejandro Guillier in 2017, and Gabriel Boric and José Antonio Kast in 2021 were reviewed. The time series analyzed were organized on the basis of relative searches between the candidacies, assisted by R software, mainly with the gtrendsR and forecast libraries. With the series constructed, forecasts were made using the Auto Regressive Integrated Moving Average (ARIMA) technique to check the weight of one presidential option over the other. The ARIMA analyses were performed on 3 ways of organizing the data: the linear series, the series transformed by moving average, and the series transformed by Hodrick–Prescott. The results indicate that the method offers the optimal predictive ability. Full article

This paper presents the application of the Logarithmic Mean Divisia Index Decomposition Analysis (LMDI) to the aggregate carbon intensity (ACI) of the power sector in Colombia in the period 1990–2020, with the aim of identifying the main drivers influencing the ACI change. The analysis performed identifies the main drivers among: carbon intensity, generation efficiency, and contribution of fossil generation at the specific and total level of electricity production. The analysis is performed at the aggregate and disaggregated level of fossil fuels. Due to the highly variable behavior of the ACI, a multi-temporal decomposition is performed in the eight presidential administrations in the period of analysis. For each period, the main drivers are identified and the energy policy implications and their effects on the operation and management of the power sector are analyzed. The results show that the main driver is the fossil share of total energy production. Important effects on thermal generation efficiency and fossil energy mix were also identified in some analysis periods. The need for effective long-term policies and regulation in relation to the factors influencing the ACI was identified. It is recommended to accelerate the diversification of the energy mix of the power sector and the permanent monitoring of the behavior of the drivers. Full article