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Background A clear understanding of the macro-level contexts in which education impacts health is integral to improving national health administration and policy. In this research, we use a visual analytic approach to explore the association between education and health over a 20-year period for countries around the world. Method Using empirical data from the OECD and the World Bank for 26 OECD countries for the years 1995–2015, we identify patterns/associations between education and health indicators. By incorporating pre- and post-educational attainment indicators, we highlight the dual role of education as both a driver of opportunity as well as of inequality. Results Adults with higher educational attainment have better health and lifespans compared to their less-educated peers. We highlight that tertiary education, particularly, is critical in influencing infant mortality, life expectancy, child vaccination, and enrollment rates. In addition, an economy needs to consider potential years of life lost (premature mortality) as a measure of health quality. Conclusions We bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health. Our country-level findings on NEET (Not in Employment, Education or Training) rates offer implications for economies to address a broad array of vulnerabilities ranging from unemployment, school life expectancy, and labor market discouragement. The health effects of education are at the grass roots-creating better overall self-awareness on personal health and making healthcare more accessible.

The escalating cost of civil litigation is leaving many defendants and plaintiffs unable to meet legal expenses such as attorney fees, court charges and others. This significantly impacts their ability to sue or defend themselves effectively. Related to this phenomenon is the ethics discussion around access to justice and crowdfunding. This article explores the dimensions that explain the phenomenon of litigation crowdfunding. Using data from CrowdJustice, a popular Internet fundraising platform used to assist in turning legal cases into publicly funded social cases, we study litigation crowdfunding through the lenses of the number of pledges, goal achievement, target amount, length of description, country, case category, and others. Overall, we see a higher number of cases seeking funding in the categories of human rights, environment, and judicial review. Meanwhile, the platform offers access to funding for other less prominent categories, such as voting rights, personal injury, intellectual property, and data & privacy. At the same time, donors are willing to donate more to cases related to health, politics, and public services. Also noteworthy is that while donors are willing to donate to education, animal welfare, data & privacy, and inquest-related cases, they are not willing to donate large sums to these causes. In terms of lawyer/law firm status, donors are more willing to donate to cases assisted by experienced lawyers. Furthermore, we also note that the higher the number of successful cases an attorney presents, the greater the amount raised. We analyzed valence, arousal, and dominance in case description and found they have a positive relationship with funds raised. Also, when a case description is updated on a crowdsourcing site, it ends up being more successful in funding—at least in the categories of health, immigration, and judicial review. This is not the case, however, for categories such as public service, human rights, and environment. Our research addresses whether litigation crowdfunding, in particular, levels the playing field in terms of opening up financing opportunities for those individuals who cannot afford the costs of litigation. While it may support social justice, ethical concerns with regards to the kinds of campaigns must also be addressed. Most of the ethical concerns center around issues relating to both the fundraisers and donors. Our findings have ethical and social justice implications for crowdfunding platform design.

In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Five main categories of variables are studied, namely chronic disease conditions, behavioral health, mental health, demographics, and overarching conditions. These are analyzed in the context of regions and states within the U.S. to discover possible correlations between variables in several categories. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is fundamental to developing insights that will help in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, health providers, and society as a whole, address these adverse effects in a timely and efficient manner.

In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Five main categories of variables are studied, namely chronic disease conditions, behavioral health, mental health, demographics, and overarching conditions. These are analyzed in the context of regions and states within the U.S. to discover possible correlations between variables in several categories. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is fundamental to developing insights that will help in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, health providers, and society as a whole, address these adverse effects in a timely and efficient manner.

For this study, we use signaling theory to explain how startups effectively signal to receive funding. Based on a sample of 1,554 startups collected from the websites of Y Combinator, Crunchbase, and LinkedIn we demonstrate that a startup signifies its potential risk level through cues, such as the industry risk level, founders’ educational background, startup location, and accelerator affiliation. We examine how investors perceive risk differently based on their risk-seeking or risk-averse tendencies. Specifically, we found that, with investors’ risk-seeking attitudes, there is a positive association between the investment risk level of the startup industry and the total funding amount received by a startup. Such positive association is stronger for startups that are located far from the accelerator. Conversely, due to investors’ risk-averse attitudes, there is a positive association between the founder’s education level (which theoretically mitigates perceived risk) and the total funding amount. This association is stronger for startups launched by accelerators other than Y Combinator. In summary, our findings suggest that investors’ interpretation of signals from startups varies based on whether they exhibit risk-seeking or risk-averse tendencies. This variance significantly influences the funding amounts secured by these startups. This paper, therefore, sheds light on the opaque process of startup funding and emphasizes risk interpretation by investors. It presents guidelines for startups to either highlight or mitigate different aspects based on the preferences of various investors. It also advises investors to be mindful of their own risk propensities when selecting startups for investment.

We examine the state-of-the-art of the business analytics field by identifying and describing the four types of analytics and the three pillars of modeling. Further, we offer a framework of the interplay between the types of analytics and those pillars of modeling. The article describes the architectural framework and outlines an analytics methodology life cycle. Additionally, key contemporary design issues and challenges are highlighted. In this paper, we offer researchers and practitioners a contemporary overview of business analytics. As business analytics has emerged as a distinct discipline with the key objective to gain insight to make informed decisions, this state-of-the art survey sheds light on recent developments in the business analytics discipline.

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.

In light of the pervasiveness of collaborative decision making and technology in organizations, this research examines computer mediated collaboration using the lens of social capital. We draw from the Distributed Cognition Theory and others for our socio-technical investigation that incorporates the technical component as the artifact of electronic memory (group memory), and the social component as the history of interactions between members (group history). We study these under the mediating influence of the relational social capital dimensions of relational closeness and relational trust. Our study shows how social capital adds an important flavor to collaborative performance. We highlight how the traditional premise of memory being an unequivocal enhancer of group performance gets altered if one fails to consider the influence of relational social capital in the equation. We propose to practitioners that collaborative technology that offers memory cannot be viewed independently, but as a vehicle that impacts performance through the dynamics of social capital of the group. Most importantly, our research reinforces the need for a novel perspective to collaborative development. With the diaspora of technological advancement, there needs to be a transition in perceiving collaborative development as a concurrent, rather than a sequential, process of discussion and development of group activity.

Countries can enhance endogenous innovation using multifaceted incentives for science and technology indicators. We explore country-level innovation using OECD data for research and development (R&D), patents, and exports. We deploy a dual methodology of descriptive visualization and panel regression analysis. Our results highlight industry variances in R&D spending. As a nation develops, governmental expenditure on R&D decreases, and businesses take on an increasing role to fill the gap, increasing local innovation. Our portfolio of local versus foreign resident ownership of patents highlights implications for taxation/innovation policies. Countries with high foreign ownership of patents have low tax revenues due to the lack of associated costs with, and mobility of income from, patents. We call on these countries to devise targeted policies encouraging local patent ownership. Policy makers should also recognize factors influencing high-technology exports for innovation. Lastly, we call on countries to reinstate horizontal and vertical policies, and design national innovation ecosystems that integrate disparate policies in an effort to drive economic growth through innovation.

Sharing economy online labor platforms play a critical role in bringing together freelancers and potential employers. This research is one of the few studies to address how freelancers' characteristics impact the likelihood of being hired by employers using the theory of person-environment fit as a broad framework. Using Freelancer data, this research investigates if country of residence (of a freelancer and the employer), amount earned, and time since registered on the platform, are associated with the employment decision. The results indicate that country of residence does matter. Freelancers who tend to be from the same country as the employers are more likely to be hired. Likewise, high-income freelancers are less likely to be hired. Further, being longer on the platform influences the association between income level and likelihood of being hired. Greater efforts should be made to eliminate the asymmetric information between freelancers and employers and to provide more opportunities for both parties. The operators of online labor platforms should be encouraged to display information about freelancers that relates to country of origin, along with reviews, ratings, and rates earned in the same skill category, which would have strategic implications for freelance entrepreneurs on how to leverage themselves on a shared-economy-based online labor platform.