2023 DCADS Research Fellowships

Exploring algorithms and data-intensive business practices that increase equity and promote truthfulness in business and in society. 
 

The University of Virginia's Darden-Data Science Collaboratory for Applied Data Science in Business (DCADS) is sponsoring a research fellowship to explore the challenges posed by bias and misinformation to business and society and consider the opportunities afforded by application of data and technology to assist in understanding, identifying, and mitigating its harmful impacts. 

This topic is timely given recent controversies over covid-19, fake news and election interference, and the growing debate over how and to what extent businesses should play a more active role in addressing the sources and accelerants at work in the information domain.

The use of data and technology to identify biased or inaccurate information, prevent its creation, limit its propagation, and ameliorate its harms at a meaningfully large scale, in the modern business context, affords unique benefits for individuals, businesses, and society.  In this context, the use of data and technology can enable:

• individuals to recognize biased or inaccurate information and react appropriately to minimize its spread and mitigate its negative consequences,

• businesses to operate in ways that minimize the production and dissemination of biased or inaccurate information through management policy, process and practice,

• society to sustain the open, productive, and peaceful exchange of ideas and information necessary to nourish and advance a free, just and equitable society.

The Fellowship in Bias and Misinformation in Business is intended to support multidisciplinary research efforts by scholars and/or practitioners from a variety of disciplines, including data science, business, journalism, communication, law, and media, that addresses the problem of bias and misinformation. 

The work may cover topics such as data science methods for detecting misinformation, the impact of bias and misinformation on business and the economy, and ways to educate people about avoiding false information. Several representative examples of topics that are of interest are shown below.  These examples may be incorporated in DCADS Fellowship proposals, but they are listed here primarily to serve as guidance.  They are not intended to limit the scope or focus of proposed research in any way.  Examples include:

1. Interventions to counter misinformation: Explore how we can best measure the relative benefits and consequences of interventions to counter misinformation or provide access to authoritative content.  
   

2. Information processing on social media platforms: Explore the social, psychological, and cognitive variables involved in the consumption of “grey area” content experiences – sensational, provocative, divisive, hateful, misleading, polarizing, or biased information – received and produced on social media platforms. 

3. Violence and incitement, hateful and/or graphic content: Examine how people and organizations are leveraging social media to organize and potentially influence intergroup relations in their constituencies.

4. Misinformation across formats: Investigate the role of non-textual media (images, videos, audio, etc.) on the effectiveness of and people's engagement with misinformation. This area includes basic multimedia like infographics, memes, and audio, compared to more-complex video and emerging technological advances.

5. Trust, legitimacy, and information quality: Examine social media users’ exposure to, interaction with, and understanding of qualities of information, especially their attitudes and interpretations of information quality, trust, and bias. 

6. Coordinated harm and inauthentic behavior: Inspect information practices and flows across multiple communication technologies or mediums. 

7. Digital literacy, demographics, and misinformation: Explore the relation between digital literacy and vulnerability to misinformation in communication technologies.

    

Managing and leading analytical individuals, high-performing teams, and distinctive organizations in the face of an explosion of data and the near ubiquity of technologies that enable leaders to use or misuse it.

The University of Virginia's Darden-Data Science Collaboratory for Applied Data Science in Business (DCADS) is sponsoring a research fellowship to advance our understanding of business leadership in a future where data and technology are pervasive and analytical expertise is necessary for individual and organizational success.  

The explosive growth  of data and technology presents challenges and opportunities to leaders in all fields.  In medicine, physician leaders must master new sources such as genomic data and integrate disparate information from electronic health records in the exam room to deliver evidence-based care.  In government, civic leaders are faced with mountains of information from inside and outside government on every substantive issue and subjected to perceptions that spread with lightning speed among constituents, regardless of their accuracy.  In law, legal leaders apply new tools which increase fairness but have unintended consequences that are poorly understood and navigate laws and regulations from another era that are wholly inadequate in today’s data and technology-intensive world.  Similarly in business, corporate leaders must deal with myriad impacts  of this heretofore unchecked explosion.  

In business, the explosion of data and technology has changed nearly every major activity, from marketing and selling to customers, sourcing materials and producing products, managing investment and financial resources, and hiring, developing and evaluating employees, to the very fundamental actions of communicating, managing, and making decisions every day.  In many ways, the very nature of business leadership may be changing as a result of the volume of data produced by, or available to, leaders in business and the near ubiquitous access to the technology tools needed to use or misuse it.

The DCADS Fellowship in Analytical Leadership is designed to foster exploration of the leadership challenges and opportunities that result from these dramatic changes with an emphasis in two areas - leading analytics and leading analytically.  

Leading analytics should explore the leadership of analytical functions and departments in a corporate setting.  This responsibility is increasingly in the hands of a designated executive, often titled Chief Analytics Officer or Chief Data Officer, which is a defined role at slightly more than half  of Fortune 1000 companies.  Understanding the unique leadership challenges of this new role and the essential characteristics and behaviors of the leaders who fill it successfully is an important focus.

Leading analytically should examine the changing nature of business leadership, in its many and varied forms and levels, in a world where data and technology are ubiquitous and essential to business success.  In taking a broader look at business leadership as a discipline, the impact of data and technology should be considered in the context of established approaches to understanding the topic of leadership and through new avenues of inquiry that are uniquely suited to address the dramatic changes that are taking place.

The Fellowship in Analytical Leadership is intended to support multidisciplinary research efforts by scholars and/or practitioners from a variety of disciplines, including data science, leadership, management, economics, psychology, public policy, and business, focused on the problems and opportunities of analytical leadership. 

Several representative examples of topics that are of interest are shown below.  These examples may be incorporated in DCADS Fellowship proposals, but they are listed here primarily to serve as guidance.  They are not intended to limit the scope or focus of proposed research in any way.  Examples include:

1. CAO/CDO Role, Organization, and Relationships: Examine contemporary role design, organization structure, and formal and informal relationships and their association with success of analytical individuals, teams and organizations.
    
2. Profile and Preparation of Successful Analytical Leaders: Explore the characteristics of successful analytical leaders, including personal attributes, education, experience, and others, and identify drivers of and pathways to success.
    
3. Existing Theories of Leadership or New Approaches Needed: Assess existing theories of leadership to determine how and to what extent they consider the impact of data and technology competencies on leader behavior and success.  Suggest novel new approaches that may better explain the path to successful analytical leadership.
    
4. Becoming Analytical: Understanding the journey to embrace analytical competencies and transform the way leaders think, work, and make decisions, at multiple levels including the individual, team and enterprise. 
    

1 Taylor, P. (2022). Total data volume worldwide 2010-2025. Statista.
2 Brown, S. (2020). How to build a data analytics dream team. MIT Management Sloan School.
3 NewVantage Partners (2021). Big Data and AI Executive Survey 2021

Understanding and influencing consumer and healthcare professional behavior through interventions, experiments, and analysis using data and technology, with the objective of improving health and better managing care.

The University of Virginia's Darden-Data Science Collaboratory for Applied Data Science in Business (DCADS) is sponsoring a research fellowship to advance our understanding of why and how people in the United States make decisions related to their health, and how we can effectively, ethically, and safely influence those decisions in ways that benefit the individual, the healthcare system, and the nation.  

The healthcare system in the United States is expensive, with spending averaging more than $11,500 per capita and totaling 16.8% of GDP in 2019, exceeding the OECD averages of $4,087 and 8.8% respectively. In addition, investment in medical and health research and development exceeded $194 billion in 2019, or about 1% of US GDP. Despite spending and investment that far exceed the rest of the world, life expectancy in the United States is 78.7 years at birth and ranks 26th of 37 OECD countries. While some Americans lack access to the care they need to be their healthiest, many have most or all of the tools that are required, including some of the best nurses, doctors, tests and treatments in the world.  The same is true of many other determinants of health.  Many, but not all, Americans also have access to clean water and air, healthy food, sufficient shelter and protection from harm, and the opportunity to be active and social.  

So, why aren’t Americans the healthiest and longest lived in the world? And why isn’t the system of healthcare in the United States the model for all others?  Among the factors that contribute to this underperformance is one that is both simple and complex; at once, easy to grasp and nearly impossible to understand. Americans are not the healthiest people in part for a very different reason – because we choose not to be.  All too often, we choose to behave in ways that diminish our health or not to behave in ways that would improve it. These seemingly irrational behaviors are the focus of the DCADS Fellowship in Healthy Choices. 

By understanding why individuals make these choices, how those decisions spread, and the ways we can influence them at a meaningfully large scale, this initiative will help to improve individual health and increase the business performance of the healthcare system, benefitting UVA, the Commonwealth and the Nation. 
Understanding the social, economic, and community determinants of health-related choices may allow prediction, detection and targeted intervention to treat the kinds of acute emergencies or chronic health crises and disparities that threaten the well-being of individuals, the vibrancy of our communities, and the viability of our healthcare system.

The Fellowship in Healthy Choices is intended to support multidisciplinary research efforts by scholars and/or practitioners from a variety of disciplines, including data science, business, economics, psychology, public policy, and medicine focused on the problems and opportunities of decision-making in healthcare. 

The work may cover topics such as data science methods for modeling health-related choice behavior, the diffusion of such decisions through families, communities, workplaces and other social networks, and informational and other interventions enabled by contemporary digital technologies.  

Several representative examples of topics that are of interest are shown below.  These examples may be incorporated in DCADS Fellowship proposals, but they are listed here primarily to serve as guidance.  They are not intended to limit the scope or focus of proposed research in any way.  Examples include:

1. Health-related decisions at the individual level: Explore how people make decisions about their health, well-being, and care, using contemporary choice modelling approaches that apply new data science methods and extend our ability to understand and predict choice among individuals. 
    
2. The diffusion of health-related decisions through networks: Examine the network effects by which individual choices influence the decisions of others in families, communities, workplaces and other social networks, and result in aggregate outcomes at a scale that matters to the healthcare system. 
    
3. Interventions that influence health-related behavior: Design, test and evaluate informational and other individual interventions in the field or in virtual and simulated environments to demonstrate and measure the positive health and financial impact these efforts can have on individuals, groups, and on the healthcare system overall.  

Footnotes:

1 OECD (2022), Health spending (indicator). doi: 10.1787/8643de7e-en 
2 ResearchAmerica (Fall 2019). U.S. Investments in Medical and Health Research and Development, 2013-2018. 
3 United Health Foundation (2020). International Comparison. America’s Health Rankings.