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There is a perception in the United States that campaign contributions equate with vote buying. Outright vote buying is illegal, but many citizens believe that loopholes in campaign contribution laws allow some to buy votes while perpetuating a façade of legitimacy. Both federal and state laws attempt to regulate campaign contributions, but many of those have been limited by the Supreme Court’s ruling that campaign spending is considered free speech (Buckley vs. Valeo, 1976). Without the ability to limit campaign spending, the amount of money it takes to run a campaign, particularly a presidential campaign, has increased substantially. This had led to an increase in the use of bundling by presidential campaigns, with the winners often rewarding their bundlers. It has also led to an increase in outside independent organizations, known as Super PACs, with an unlimited ability to raise and spend money. This creates an additional problem as a small percentage of wealthy individuals constitute the vast majority of campaign contributors, leading to the perception that politicians cater to the elite. Whether a politician is affected by these factors or not is hard to prove, but it still leaves a perception by voters that their votes are less influential than large campaign contributors and there is always a risk that a vote has been bought.

This mixed methods multiple case study examines the knowledge, understanding, and awareness of 25 health board/facility oversight managers and 20 health professional association directors about privacy and security issues important to achieving health information exchange (HIE) in the state of Nebraska. Within case analyses revealed that health board/facility oversight managers were unaware of key elements of the federal agenda; their concerns about privacy encompassed broad definitions both of what constituted a “health record” and “regulations centeredness.” Alternatively, health professional association leaders were keenly aware of national initiatives. Despite concerns about HIE, they supported information exchange believing that patient care quality and safety would improve. Cross-case analyses revealed a perceptual disconnect between board/facility oversight managers and professional association leaders; however, both favored HIE. Understanding state-level stakeholder perceptions helps us further understand our progress toward achieving the national health information interoperability goal. There is an ongoing need to assure adequate patient privacy protection. Licensure and facility boards at the state level are likely to have a major role in the assurance of patient protections through facility oversight and provider behavior. The need for these boards to take an active role in oversight of patient rights and protections is imminent. Similarly, professional associations are the major vehicles for post-graduate education of practicing health professionals. Their engagement is essential to maintaining health professions knowledge. States will need to understand and engage both of these key stakeholders to make substantial progress in moving the HIE agenda forward.

Results of a previous study showed that use of health information technology (HIT) significantly reduced potential medication prescribing errors. However, the results also revealed a less than 100% rate of HIT adoption by primary care physicians. The current study reports on personal interviews with participating physicians that explored the barriers they faced when attempting to fully adopt a particular HIT. Content analysis of qualitative interviews revealed three barrier themes: time, technology, and environment. Interviews also revealed two other areas of concern; specifically, the compatibility of the HIT with the physician's patient mix and the physician's own attitude toward the use of HIT. A theoretical model of technology acceptance and use is used to discuss and further explain the data derived from the physician interviews. With a better understanding of these issues, health care administrators can develop successful strategies for adoption of HIT across their health care organizations.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

While the concept of legal culture has been receiving a growing attention from scholars, this research often overemphasizes the similarity of the opinions held by different segments of population. Furthermore, the relationship of migration and the change of legal-cultural attitudes has not received particular attention. Drawing on 70 in-depth interviews with the immigrants of the early 1990s from the former Soviet Union to Israel and the secular Israeli Jews, this chapter provides a comprehensive account of the various aspects of legal culture of these groups. The second important finding is the persistence of the legal-cultural attitudes and perceptions over time.

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.