Hasil untuk "Public relations. Industrial publicity"

R. Jackall

Giulio Mattioli, Cameron Roberts, J. Steinberger et al.

Research on car dependence exposes the difficulty of moving away from a car-dominated, high-carbon transport system, but neglects the political-economic factors underpinning car-dependent societies. Yet these factors are key constraints to attempts to ‘decouple' human well-being from energy use and climate change emissions. In this critical review paper, we identify some of the main political-economic factors behind car dependence, drawing together research from several fields. Five key constituent elements of what we call the ‘car-dependent transport system’ are identified: i) the automotive industry; ii) the provision of car infrastructure; iii) the political economy of urban sprawl; iv) the provision of public transport; v) cultures of car consumption. Using the ‘systems of provision’ approach within political economy, we locate the part played by each element within the key dynamic processes of the system as a whole. Such processes encompass industrial structure, political-economic relations, the built environment, and cultural feedback loops. We argue that linkages between these processes are crucial to maintaining car dependence and thus create carbon lock-in. In developing our argument we discuss several important characteristics of car-dependent transport systems: the role of integrated socio-technical aspects of provision, the opportunistic use of contradictory economic arguments serving industrial agendas, the creation of an apolitical facade around pro-car decision-making, and the ‘capture’ of the state within the car-dependent transport system. Through uncovering the constituents, processes and characteristics of car-dependent transport systems, we show that moving past the automobile age will require an overt and historically aware political program of research and action.

R. Pond

J. Carlos Martínez Mori, Alejandro Toriello

We consider the cooperative elements that arise in the design of public goods, such as transportation policies and infrastructure. These involve a variety of stakeholders: governments, businesses, advocates, and users. Their eventual deployment depends on the decision maker's ability to garner sufficient support from each of these groups; we formalize these strategic requirements from the perspective of cooperative game theory. Specifically, we introduce non-transferable utility, linear production (NTU LP) games, which combine the game-theoretic tensions inherent in public decision-making with the modeling flexibility of linear programming. We derive structural properties regarding the non-emptiness, representability and complexity of the core, a solution concept that models the viability of cooperation. In particular, we provide fairly general sufficient conditions under which the core of an NTU LP game is guaranteed to be non-empty, prove that determining membership in the core is co-NP-complete, and develop a cutting plane algorithm to optimize various social welfare objectives subject to core membership. Lastly, we apply these results in a data-driven case study on service plan optimization for the Chicago bus system. As our study illustrates, cooperation is necessary for the successful deployment of transportation service plans and similar public goods, but it may also have adverse or counterintuitive distributive implications.

Timo Kuosmanen, Xun Zhou

This paper critically investigates standard total factor productivity (TFP) measurement in the public sector, where output information is often incomplete or distorted. The analysis reveals fundamental paradoxes under three common output measurement conventions. When cost-based value added is used as the aggregate output, measured TFP may paradoxically decline as a result of genuine productivity-enhancing changes such as technical progress and improved allocative and scale efficiencies, as well as reductions in real input prices. We show that the same problems carry over to the situation where the aggregate output is constructed as the cost-share weighted index of outputs. In the case of distorted output prices, measured TFP may move independently of any productivity changes and instead reflect shifts in pricing mechanisms. Using empirical illustrations from the United Kingdom and Finland, we demonstrate that such distortions are not merely theoretical but are embedded in widely used public productivity statistics. We argue that public sector TFP measurement requires a shift away from cost-based aggregation of outputs and toward non-market valuation methods grounded in economic theory.

Georgy Lukyanov, Konstantin Shamruk, Tong Su et al.

This paper develops a model in which a sender strategically communicates with a group of receivers whose payoffs depend on the sender's information. It is shown that aggregate payoff externalities create an endogenous conflict of interests between the sender and the receivers, rendering full information revelation, in general infeasible. We demonstrate that an exogenous bias in the sender's preferences can improve public information provision and raise welfare. Two applications of the setup are discussed.

Akshay Irudayaraj, Nathan Ye, Yash Chainani

Within the field of media framing, homelessness has been a historically under-researched topic. Framing theory states that the media's method of presenting information plays a pivotal role in controlling public sentiment toward a topic. The sentiment held towards homeless individuals influences their ability to access jobs, housing, and resources as a result of discrimination. This study analyzes the topic and sentiment trends in related media articles to validate framing theory within the scope of homelessness. It correlates these shifts in media reporting with public sentiment. We examine state-level trends in California, Florida, Washington, Oregon, and New York from 2015 to 2023. We utilize the GDELT 2.0 Global Knowledge Graph (GKG) database to gather article data and use X to measure public sentiment towards homeless individuals. Additionally, to identify if there is a correlation between media reporting and public policy, we examine the media's impact on state-level legislation. Our research uses Granger-causality tests and vector autoregressive (VAR) models to establish a correlation between media framing and public sentiment. We also use latent Dirichlet allocation (LDA) and GPT-3.5 (LLM-as-annotator paradigm) for topic modeling and sentiment analysis. Our findings demonstrate a statistically significant correlation between media framing and public sentiment, especially in states with high homelessness rates. We found no significant correlation between media framing and legislation, suggesting a possible disconnect between public opinion and policy-making. These findings reveal the broader impact of the media's framing decisions and delineate its ability to affect society.

S. Bana, Erik Brynjolfsson, Wang Jin et al.

Given the rise in the frequency and cost of data security threats, it is critical to understand whether and how companies strategically adapt their operational workforce in response to data breaches. We study hiring in the aftermath of data breaches by combining information on data breach events with detailed firm-level job posting data. Using a staggered Difference-in-Differences approach, we show that breached firms significantly increase their demand for cybersecurity workers. Furthermore, firms' responses to data breaches extend to promptly recruiting public relations personnel — an act aimed at managing trust and alleviating negative publicity — often ahead of cybersecurity hires. Following a breach, the likelihood that firms post a cybersecurity job rises by approximately two percentage points, which translates to an average willingness to spend an additional $61,961 in annual wages on cybersecurity, public relations, and legal workers. While these hiring adjustments are small for affected firms, they represent a large potential impact of over $300 million on the overall economy. Our findings underscore the vital role of human capital investments in shaping firms’ cyber defenses and provide a valuable roadmap for managers and firms navigating cyberthreats in an increasingly digital age.

Saif Ur Rahman, Shurong Zhao

The literature on social media (SM) use in government organizations primarily focuses on service delivery, publicity, and public relations. But, how a public sector security organization’s use of social media is related to electoral politics in a country is least understood. This paper conceptualizes the Pakistani military’s use of digital media, drawing on the theory of organizational impression management (IM). It further explores the impact of military-related social media activists (SMAs) on electoral politics in Pakistan. Structural equation modeling was used to test the conceptual model, demonstrating that the citizens’ connectedness with military-related SMAs is significant and positively associated with their voting realignments during the 2018 elections in Pakistan. The results revealed that the citizens’ greater online political participation increases their likelihood of connecting with military-related SMAs. On the contrary, their engagement with the offline political process reduces the chances of consuming the military’s impression management content over social media.

Mgs Nazarudin, Siti khodijah, Ibrahim Ibrahim

This research aims to identify the public relations strategies used to build a positive image of MTs Negeri 2 Palembang. The method used in this study is qualitative research with a descriptive qualitative approach. Data collection techniques include observation, interviews, and documentation. Additionally, the data analysis techniques used in this research involve data reduction, data presentation, and verification through triangulation to check the validity of the data. The results of this study indicate that the public relations strategies to maintain the school's image at MTs Negeri 2 Palembang have been effective. These strategies include the Strategy of Publicity, where public relations disseminate information by collaborating with mass media; the Strategy of Persuasion, where public relations persuade the public to change their opinions through various activities; the Strategy of Argumentation, where public relations anticipate negative news by organizing activities; and the Strategy of Image, where public relations create publicity by demonstrating environmental awareness

Behnoosh Zamanlooy, Mario Diaz, Shahab Asoodeh

Local differential privacy (LDP) is increasingly employed in privacy-preserving machine learning to protect user data before sharing it with an untrusted aggregator. Most LDP methods assume that users possess only a single data record, which is a significant limitation since users often gather extensive datasets (e.g., images, text, time-series data) and frequently have access to public datasets. To address this limitation, we propose a locally private sampling framework that leverages both the private and public datasets of each user. Specifically, we assume each user has two distributions: $p$ and $q$ that represent their private dataset and the public dataset, respectively. The objective is to design a mechanism that generates a private sample approximating $p$ while simultaneously preserving $q$. We frame this objective as a minimax optimization problem using $f$-divergence as the utility measure. We fully characterize the minimax optimal mechanisms for general $f$-divergences provided that $p$ and $q$ are discrete distributions. Remarkably, we demonstrate that this optimal mechanism is universal across all $f$-divergences. Experiments validate the effectiveness of our minimax optimal sampler compared to the state-of-the-art locally private sampler.

Ramit Debnath, Pengyu Zhang, Tianzhu Qin et al.

As the conversation around using geoengineering to combat climate change intensifies, it is imperative to engage the public and deeply understand their perspectives on geoengineering research, development, and potential deployment. Through a comprehensive data-driven investigation, this paper explores the types of news that captivate public interest in geoengineering. We delved into 30,773 English-language news articles from the BBC and the New York Times, combined with Google Trends data spanning 2018 to 2022, to explore how public interest in geoengineering fluctuates in response to news coverage of broader climate issues. Using BERT-based topic modeling, sentiment analysis, and time-series regression models, we found that positive sentiment in energy-related news serves as a good predictor of heightened public interest in geoengineering, a trend that persists over time. Our findings suggest that public engagement with geoengineering and climate action is not uniform, with some topics being more potent in shaping interest over time, such as climate news related to energy, disasters, and politics. Understanding these patterns is crucial for scientists, policymakers, and educators aiming to craft effective strategies for engaging with the public and fostering dialogue around emerging climate technologies.

María Pereda

The problem of free-riding arises when individuals benefit from a shared resource, service, or public good without contributing proportionately to its provision. This conduct often leads to a collective action problem, as individuals pursue personal gains while relying on the contributions of others. In this study, we present a Bayesian inference model to elucidate the behaviour of participants in a Public Goods Game, a conceptual framework that captures the essence of the free-riding problem. Here, individuals possess information on the distribution of group donations to the public good. Our model is grounded in the premise that individuals strive to harmonise their actions with the group's donation patterns. Our model is able to replicate behavioural patterns that resemble those observed in experiments with midsized groups (100 people), but fails to replicate those for larger scales (1000 people). Our results suggest that, in these scenarios, humans prefer imitation and convergence behaviours over profit optimisation. These insights contribute to understanding how cooperation is achieved through alignment with group behaviour.

Mithinti Srikanth, G. Ramakrishna

Public transport administrators rely on efficient algorithms for various problems that arise in public transport networks. In particular, our study focused on designing linear-time algorithms for two fundamental path problems: the earliest arrival time (\textsc{eat}) and the fastest path duration (\textsc{fpd}) on public transportation data. We conduct a comparative analysis with state-of-the-art algorithms. The results are quite promising, indicating substantial efficiency improvements. Specifically, the fastest path problem shows a remarkable 34-fold speedup, while the earliest arrival time problem exhibits an even more impressive 183-fold speedup. These findings highlight the effectiveness of our algorithms to solve \textsc{eat} and \textsc{fpd} problems in public transport, and eventually help public administrators to enrich the urban transport experience.

Sulistyo Budi Utomo, Jefri Putri Nugraha, Endang Sri wahyuningsih et al.

This research aims to analyze the stages of implementing an integrated marketing communications strategy for MSMEs to build brand awareness through Instagram social media. The paradigm used is a constructivist paradigm. The research approach used is a qualitative-descriptive approach. The instruments for this research are observation, interviews, and documentation studies. Data processing is carried out by recording the results of observations, interviews and documentation studies, as well as identifying problems and planning. The data analysis technique used by researchers is a qualitative data analysis technique, referring to the technique proposed by Miles and Huberman, namely the interactive model. This technique consists of the following three components: data reduction, data presentation, and drawing and verifying conclusions. It can be concluded that in public relations and publicity activities, MSMEs do not have a public relations division. Therefore, all control is still held by the marketing division. As for publicity, MSMEs use Instagram as their main means of conveying information. Judging from the conclusions that the researchers have presented, there is a match between the concept or theory regarding integrated marketing communication strategy and the marketing communication strategies that SMEs implement in the field. However, there is one element that is not implemented, namely the public relations element, because it is still part of the marketing division, but regarding publicity, SMEs use Instagram as their publicity medium.

Christos Anagnostopoulos, Georgios Mylonas, Apostolos P. Fournaris et al.

Virtual and augmented reality are currently enjoying a great deal of attention from the research community and the industry towards their adoption within industrial spaces and processes. However, the current design and implementation landscape is still very fluid, while the community as a whole has not yet consolidated into concrete design directions, other than basic patterns. Other open issues include the choice over a cloud or edge-based architecture when designing such systems. Within this work, we present our approach for a monitoring intervention inside a factory space utilizing both VR and AR, based primarily on edge computing, while also utilizing the cloud. We discuss its main design directions, as well as a basic ontology to aid in simple description of factory assets. In order to highlight the design aspects of our approach, we present a prototype implementation, based on a use case scenario in a factory site, within the context of the ENERMAN H2020 project.

Diomidis Spinellis

Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications. Its results end up in a paper's "background" section or in standalone articles, which include meta-analyses and systematic literature reviews. The required research is aided through the use of online scientific publication databases and search engines, such as Web of Science, Scopus, and Google Scholar. However, use of online databases suffers from a lack of repeatability and transparency, as well as from technical restrictions. Thankfully, open data, powerful personal computers, and open source software now make it possible to run sophisticated publication studies on the desktop in a self-contained environment that peers can readily reproduce. Here we report a Python software package and an associated command-line tool that can populate embedded relational databases with slices from the complete set of Crossref publication metadata, ORCID author records, and other open data sets, for in-depth processing through performant queries. We demonstrate the software's utility by analyzing the underlying dataset's contents, by visualizing the evolution of publications in diverse scientific fields and relationships among them, by outlining scientometric facts associated with COVID-19 research, and by replicating commonly-used bibliometric measures of productivity, impact, and disruption.

Andrew Lowy, Zeman Li, Tianjian Huang et al.

Differential privacy (DP) ensures that training a machine learning model does not leak private data. In practice, we may have access to auxiliary public data that is free of privacy concerns. In this work, we assume access to a given amount of public data and settle the following fundamental open questions: 1. What is the optimal (worst-case) error of a DP model trained over a private data set while having access to side public data? 2. How can we harness public data to improve DP model training in practice? We consider these questions in both the local and central models of pure and approximate DP. To answer the first question, we prove tight (up to log factors) lower and upper bounds that characterize the optimal error rates of three fundamental problems: mean estimation, empirical risk minimization, and stochastic convex optimization. We show that the optimal error rates can be attained (up to log factors) by either discarding private data and training a public model, or treating public data like it is private and using an optimal DP algorithm. To address the second question, we develop novel algorithms that are "even more optimal" (i.e. better constants) than the asymptotically optimal approaches described above. For local DP mean estimation, our algorithm is optimal including constants. Empirically, our algorithms show benefits over the state-of-the-art.

Brianna M White, Chad A Melton, Parya Zareie et al.

The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper we examine the role of social messaging shared by Persons in the Public Eye (i.e. athletes, politicians, news personnel) in determining overall public discourse direction. We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first two years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light.

Nirmalya Thakur, Cat Luong, Chia Y. Han

Over the last decade, exoskeletons have had an extensive impact on different disciplines and application domains such as assisted living, military, healthcare, firefighting, and industries, on account of their diverse and dynamic functionalities to augment human abilities, stamina, potential, and performance in a multitude of ways. In view of this wide-scale applicability and use-cases of exoskeletons, it is crucial to investigate and analyze the public opinion, views, and perspectives towards exoskeletons which would help to interpret the effectiveness of the underlining human-robot, human-machine, and human-technology interactions. The Internet of Everything era of today's living, characterized by people spending more time on the internet than ever before, holds the potential for the investigation of the same by mining and analyzing relevant web behavior, specifically from social media, that can be interpreted to understand public opinion, views, and perspectives towards a topic or set of topics. Therefore, this paper aims to address this research challenge related to exoskeletons by utilizing the potential of web behavior-based Big Data mining in the modern-day Internet of Everything era. As Twitter is one of the most popular social media platforms on a global scale - characterized by both the number of users and the amount of time spent by its users on the platform - this work focused on investigating web behavior on Twitter to interpret the public opinion, views, and perspectives towards exoskeleton technology. A total of approximately 20,000 tweets related to exoskeletons were used to evaluate the effectiveness of the proposed approach. The results presented and discussed uphold the efficacy of the proposed approach to interpret and analyze the public opinion, views, and perspectives towards exoskeletons from the associated tweets.