Optimization of product category allocation to minimize online customer order splitting

We study the problem of allocating product categories to warehouses to minimize online order splitting.  An order can be split and fulfilled through multiple shipments when the order includes products stored in different warehouses.  Minimizing order splitting is crucial in reducing shipping costs, improving customer satisfaction, and reducing pollution.  This study presents a column generation-based algorithm to explicitly minimize the number of order splits.  The algorithm generates improved solutions over the state-of-the-art approach and a commercial optimization solver.

Sponsor: Oklahoma State University

PI/PD: Ali Amiri

 

The Obnoxious Facility Closure Problem

We introduce an important problem to the field of obnoxious facility location.  The problem is relevant to optimize an existing obnoxious facility system by deciding which facilities to close to minimize the negative impact of the remaining open facilities on the clients.  We propose algorithms to solve the problem effectively. 

Sponsor: Oklahoma State University

PI/PD: Ali Amiri

 

Drivers of Value in Healthcare: Sourcing of Electronic Health Records and Practice Integration

Should hospitals source electronic health records (EHR) systems from a single vendor or multiple vendors to deliver high-value care? We study hospitals’ EHR sourcing strategies based on their fit with hospital-physician practice integration and their role in driving value in healthcare. Our results indicate that it is important to consider the type of practice integration model when develop EHR sourcing strategies to improve patient health data sharing and deliver high-value care. As the industry moves toward value-based healthcare, our findings provide a useful roadmap to practitioners and policy makers to improve the performance of hospitals and healthcare providers.

Sponsors: Oklahoma State University, University of Texas, Temple University

PI/PDs: Chenzhang Bao

University of Texas: Indranil Bardhan

Temple University: Sezgin Ayabakan

 

To Share or Not to Share? Electronic Health Record Spillovers and Sustainable Cooperation Among Providers

We model spillover effects of ambulatory electronic health records (EHR) adoption on the inpatient cost of neighboring hospitals. Leveraging on a nationwide sample of 3,483 US hospitals across 13 years, matched with approximately 30,000 ambulatory care entities, we find that focal hospital's inpatient cost per discharge decreases as EHR adoption by neighboring ambulatory entities increases. Most importantly, we find that hospitals can save more on inpatient costs when they share health information with ambulatory entities outside of their health system. Our empirical evidence on the business value of information exchange encourages the culture of cooperation among competing healthcare providers.

Sponsor: Oklahoma State University

PI/PDs: Ankita Srivastava, Chenzhang Bao, Dursun Delen

 

Investigating Uneven Distribution of Health IT Vendor Products

While there is an increasing trend of adopting systems from several dominating vendors, health IT market remains competitive and fragmented. This study investigates the distribution of different vendor products and how hospitals adopt health ITs compared to other neighboring peers in the local healthcare market. We focus on the longitudinal trajectories of different applications across years.

Sponsor: Oklahoma State University

PI/PD: Chenzhang Bao

 

Evolution of EMRs and the Impact on Performance

Health IT applications have been criticized for the lack of interoperability across vendor products. We investigate the difference in vendor selection of EMR applications within a hospital referral region. We cluster the longitudinal patterns of this evolution in vendor difference/similarity and examine its impact on hospital performance.

Sponsors: Oklahoma State University, Temple University

PI/PDs: Chenzhang Bao

Temple University: Sezgin Ayabakan

 

Health Information Exchange: Hype or Hope?

In recent years, healthcare providers increasingly adopt health IT systems primarily from a single supplier in order to maximize their capability to share patient health data. However, little is known about how much this merit in health information exchange explains the variations in hospital performance. In this study, we observe that while single-sourcing improves hospital performance, the level of information sharing has negligible explanation on relationship. Compared to information sharing, the benefit of adopting single-sourced IT system is more likely rooted on its capability to facilitate clinical workflow.

Sponsor: Oklahoma State University

PI/PD: Chenzhang Bao

 

Health IT Spillover Effect in the Era of Value-Based Care

The past literature has documented the productivity paradox of health IT by showing its spillover effect. The past decade has witnessed the prevalence of value-based care initiatives, spearheaded by the Affordable Care Act. Through various financial incentives, these programs significantly change the way health care is practiced and impose requirements on how health IT systems are used. As a result, we conjecture that these nuanced reforms in healthcare field might fundamentally change the spillover effect of health IT. In this study, we intend to reveal this phenomenon via replicating the prior spillover study using more recent research data.

Sponsor: Oklahoma State University

PI/PDs: Ankita Srivastava, Chenzhang Bao, Dursun Delen

 

Healthcare Productivity and Value in the Context of Value-Based Care

In this study, we attempt to differentiate the two critical outcome concepts that have been frequently referred in healthcare articles – productivity and value. We examine the longitudinal trend in these two measures with a special focus on the impact of value-based care reform. Surprisingly, our analyses criticized the effectiveness of pay-for-performance programs that they improve healthcare productivity but have marginal effect on value of care. Furthermore, we also address the question regarding who performed well in value-based care by investigating the impact of hospital characteristics.

Sponsors: Oklahoma State University, University of Texas

PI/PDs: Chenzhang Bao

University of Texas: Indranil Bardhan

 

The Unintended Consequence of Integration: Scale Efficiency in Value-based Care

The past decade has witnessed an acceleration of integration in healthcare, partially stimulated by value-based care. As of now, more than 50% of physicians are employed by large hospital systems with only a few staying in solo practices. However, physicians are not cheap: to breakeven the cost of hiring a doctor, the hospital requires that employed doctor to refer more patients to the hospital setting. We show that this often result in reduced scale efficiency in hospitals. This reveals the unintended consequence of healthcare integration that may increase the congestion and workflow of hospitals, leading to potential clinician burnout.

Sponsors: Oklahoma State University, University of Texas

PI/PDs: Chenzhang Bao

University of Texas: Indranil Bardhan

 

The Effect of Patient Updates on Medical Crowdfunding

Past literature has suggested medical crowdfunding platform as an effective avenue to raise healthcare expense. Drawing on marketing theory, this study intends to examine the effect of updates of patient information on fund donation. As expected, we observe that posting patient latest information increases the likelihood of donation. However, it is surprising that too frequent posts would backfire and reduce the number and amount of donation. Further analysis indicates an inverse-U shape distribution to solicit funding in the platform.

Sponsors: Oklahoma State University, Baylor University

PI/PDs: Chenzhang Bao

Baylor University: Wen Zhang, Min Kyung Lee

 

Entrepreneurial Organizational Culture, IT-business Alignment, and Firm Agility: A Moderated Mediation

IT-business strategic alignment is one of the most long-standing managerial challenges. Research differs concerning its impact on firm agility. To better understand this relationship, we study the impact of both intellectual and social alignment on firm agility in the context of entrepreneurial organizational culture (EOC) to account for a firm's level of entrepreneurial intensity.  In a field study of 100 CIOs, we find that EOC positively impacts firm agility through mediated and moderated effects of both social and intellectual alignment, respectively. This paper provides a theory-driven explanation of the dynamics of social and intellectual alignment's impact on firm agility. 

Sponsor: Oklahoma State University

PI/PDs: Corey Baham, Andy Luse, Ramesh Sharda

University of Tennessee: Randy Bradley

Virginia Military Institute: Jennifer Gerow

 

Bridging the Acceptance-Routinization Gap in Agile Software Development Assimilation: An Exploratory Cross Case Analysis

Agile software development methods represent a departure from the strong document-driven procedures of plan-driven approaches. As organizations continue to adopt agile methods, understanding how to sustain agile methods is a growing concern. In recent years, researchers have focused their attention on the issues of sustained agile use in order to extend our knowledge on agile assimilation. However, little research has been conducted to expose the assimilation gaps that occur as organizations seek to increase the extent and intensity of their agile use. Following prior literature, we investigate the role of organizational factors in the continuance of agile methods.

Sponsor: n/a

PI/PDs: Corey Baham

Louisiana State University: Rudy Hirschheim

Georgia State University: Likeobe Maruping

 

Quantum Information Systems: Harnessing Individual and Group Energies

In this working paper, we propose that an organization is a living organism that generates energy to achieve certain outcomes. We propose that the relationship between the inputs (individual and group use of information systems) and the outputs (strategic alignment and competitive advantage) of a system depends on the basic principles of quantum mechanics. Specifically, we connect the neuroscience research that addresses qualia (individual) and quale (group) to the Management Information Systems (MIS) research. In this paper, we proffer our research objective, discuss our constructs, and present our interview process and survey items that we plan to conduct and administer.

Sponsor: Oklahoma State University

PI/PDs: Corey Baham

Virginia Military Institute: Jennifer Gerow

Indiana University of Pennsylvania: James Rodgers

 

Is Technostress Forcing Physicians to Leave Their Careers? An Exploration of EHR-Related Physician Burnout?

The growing evidence on physician burnout is gaining paramount attention among practitioners, researchers and policy-makers. IS literature has theorized this phenomenon as technostress and there is considerable causal evidence explaining the effect of use of ICT’s on manifestations of strain and reduced productivity. We argue that the existing literature on technostress fails to generalize in healthcare and thus needs to be extended. We propose that an understanding of how EHR’s contribute to physician burnout is needed. A qualitative study using interviews will be deployed at all three levels of care delivery- primary, secondary and tertiary for a holistic exploration.

Sponsor: Oklahoma State University

PI/PDs: Ankita Srivastava, Corey Baham, Dursun Delen

 

Signals and Mechanisms for Unintended Consequences in AI: A Grounded Theory Approach

Artificial Intelligence (AI) technologies such as including machine learning, deep learning, computer vision, and natural language processing, are becoming general-purpose technologies that significantly impact the economic and social structure of organizations and society. However, that impact has not been entirely positive. There have already been many cases where undesirable or negative consequences of AI tools have harmed their respective organizations in social, financial, and legal spheres. This research seeks to uncover common signals and mechanisms that lead to unintended consequences in AI. Using a grounded theory approach, we propose a unifying theoretical framework for unintended consequences in AI projects.

Sponsor: Oklahoma State University

PI/PDs: Madhav Sharma, David Biros, Corey Baham

Industry: Jacob Biros

 

The Moderating Effect of Ambiguity on Fake News and Sensemaking

Fakes news on the Internet has emerged as an issue with far-reaching consequences. Given fake news’ breadth of influence, depth of consequences, and perpetuity, we extend this line of inquiry to a less studied area - uncertainty-reducing behaviors in fake news online. In this study, we examine ambiguity in fake news and its relationship to information seeking and sensemaking. Our results yield strong theoretical and practical implications for public policy and future research.

Sponsor: Louisiana State University

PI/PDs: Reginald Tucker

Oklahoma State University: Corey Baham

 

Data is the Disaster: Data Issues in Disaster Management Scenarios

The 21st Century has been termed “the century of disasters” (Achenbach, 2011) due to several notorious forms of disasters (e.g., geophysical, hydrological, climatological). Among these, the recent biological disaster of the COVID-19 disease epidemic has seen global impacts. The disastrous effects of COVID-19 have exacted a devastating toll on civil and technological infrastructure and society as a whole (e.g., loss of human life, social and economic disturbances, and industry interruptions and shutdowns).

Sponsor: Oklahoma State University

PI/PDs: Corey Baham, Andy Luse, Ramesh Sharda, Jared Taylor

 

Classification of Malicious Insiders and the Association of the Forms of Attacks

Malicious insiders continue to pose a great threat to organizations. With their knowledge about organizational security countermeasures as well as valuable organizational resources, malicious insiders can launch an attack towards the organization easier than an outsider could and with more devastating consequences. Many studies have attempted to identify the characteristics of malicious insiders in order to deter and prevent attacks. We argue that the current studies confuse the fact that malicious attacks belong to two different categories: those that launch instrumental attacks and expressive attacks. This current study paves the way for future research about the heterogeneity of malicious insiders.

Sponsor: Oklahoma State University

PI/PDs: David Biros, Fletcher Glancy, Andy Luse

Louisiana State University: Nan Peter Liang

 

A Qualitative Approach to Understand Non-malicious Unintentional Information Security Misbehaviors

Insiders within organizations threaten information security unconsciously/accidentally through non-harmful intentions and they put organizations in the risk of security threats. Drawing on in-depth qualitative approach, we explore possible organizational and human factors causing unintentional information security misbehaviors. We employ a case study of a business in the energy industry along with interviewing employees in different job.  We aim first to identify individual-related factors or work environment-related factors that influence employees’ unintentional security-related behaviors. Second, applying causal mapping methodology, we explain in what degree each of these influencers are associated with unintentional security misbehaviors which in turn endanger institutions’ security.

Sponsor:  Oklahoma State University

PI/PDs: David Biros, Forough Nasirpouri Shadbad, Corey Baham

 

Uncovering commonalities in causes of unintended consequences of AI

Artificial intelligence (AI) and machine learning (ML) offer promising technologies in areas such as healthcare, transportation, and finance. However, the undesirable or negative consequences of AI-tools have harmed their respective organizations in social, financial, and legal spheres. This research seeks to uncover common causes in design and implementation of AI tools that can lead to unintended consequences. Using text mining and natural language processing technique over a large set of news articles, we conduct a textual comparison to investigate the following questions: (1) what are commonalities and differences between successful and unsuccessful AI projects? (2) What are common causes of AI failure?

Sponsor:  Oklahoma State University

PI/PDs: David Biros, Madhav Sharma

 

What went wrong? A Grounded Theory Study of Signals and Mechanisms for Unintended Consequences in AI

Technologies we have come to know as Artificial Intelligence (AI), including machine learning, deep learning, computer vision, and natural language processing, are becoming general-purpose technologies that significantly impact the economic and social structure of organizations and society.  That impact has not been entirely positive. There have been many cases where undesirable or negative consequences of AI tools have harmed their respective organizations in social, financial, and legal spheres. This research seeks to uncover common signals and mechanisms that lead to unintended consequences in AI. Using a grounded theory approach, we propose a unifying theoretical framework for unintended consequences in AI projects.

Sponsor:  Oklahoma State University

PI/PDs: David Biros, Madhav Sharma, Corey Baham

Exawizards, Tokyo Japan: Jacob Biros

 

Agents and Stewards of Information Security

This study investigates information security behaviors disruptions that resulted in increased work from home. We examine and compare the effects of two organization controls: agency and stewardship theories in explaining information security behaviors. While the literature suggests that the continuum of both controls is better suited to explain organizational behaviors, and specifically, information systems behaviors, we find that constructs consistent with agency theory are more likely to explain decreases in security policy violations, than stewardship theory. Further, by combining agency and stewardship perspectives, this study’s results supplement our understanding of security policy violations in work from home contexts. 

Sponsor:  Oklahoma State University

PI/PDs: David Biros

Washington State University:  Rob Crossler

University of North Texas: Obi Ogbanufe

 

An Empirical Comparison of Malicious Insiders and Benign Insiders

Malicious insiders continue to pose significant threats to organizations. With their knowledge, privilege, and access to organizational resources, malicious insiders can attack the organization easier than outsiders and even bypass security measures. However, current research about malicious insiders’ traits is often based on a limited number of cases and is lacking empirical validation. With few exceptions, most research focuses on the effects of individual traits without investigation of their interactions. Interaction effects of some traits indicate that, although they are not significant at the unary level, their co-occurrence differentiates the images of malicious insiders from benign insiders as portrayed in the media.

Sponsor: Oklahoma State University

PI/PDs: David Biros, Fletcher Glancy, Andy Luse

Louisiana State University: Nan Peter Liang

 

How Effective are SETA Programs Anyway? Learning and Forgetting in Security Awareness Training. 

Prevalent security threats caused by human errors necessitate security education, training, and awareness (SETA) programs in organizations. Despite strong theoretical foundations in behavioral cybersecurity, field evidence on the effectiveness of SETA programs in mitigating actual threats is scarce. In a baseline experiment, we establish that SETA programs reduce phishing susceptibility by 50%, whereas the training intensity does not affect the rate. In a follow-up experiment, we find that SETA programs can increase employees’ cybersecurity knowledge by 12-17%, but the increment wears off within a month. Furthermore, technical-level knowledge decays faster than application-level knowledge.

Sponsor:  Oklahoma State University

PI/PDs: David Biros, Fletcher Glancy

City University of Hong Kong: Tianjian Zhang

 

A Study of Signals and Mechanism for Unintended Consequences in AI

The market value of AI-related technology will reach over $3.9 trillion by the end 2022. Pervasive trends such as autonomous vehicles, predictive algorithms for customer-relationship management and recommendations, and facial recognition are powered by combinations of analytics and automation-based technologies such as machine learning, deep learning, and computer vision. The use of AI tools signals organizations’ intent to add value by increasing efficiency in their processes Yet, there have been numerous cases that have shown AI can have unintended consequences. This study develops a model of signals and mechanisms and their impact on the unintended consequences of AI.

Sponsor:  Oklahoma State University

PI/PDs: David Biros, Corey Baham

Kansas State University:  Madhav Sharma

 

Developing a Privacy Enhanced, Federated API Infrastructure for Access to Social Determinants of Health Data

The goal of our project is to support computational research and applications in this space by making data about social determinants of health (SDoH) findable, accessible, interoperable, and reusable, following the FAIR Guiding Principles for scientific data management and stewardship. Our proposed infrastructure will allow researchers to access and integrate data from a variety of health, social, and policy domains. It will support comprehensive and accurate analysis of social policy issues as well as the development of new and innovative models and algorithms that can be used to advance policymaking.

Sponsor: A Research Proposal Submitted to NSF for funding consideration (total proposed budged: 2M)

PI/PDs: Dursun Delen

University of Tulsa: Kazim Topuz

University of Oklahoma: David Kendrick

Asemio, Tulsa, OK: Aaron Bean

 

Social capital and organizational performance: The mediating role of innovation activities and intellectual capital

While the positive influence of intellectual capital on innovation is well-established in the extant literature, research on how innovation activities affect intellectual capital is relatively scarce. This study aims to contribute to the body of knowledge by investigating the influence of innovation activities on the depth of intellectual capital and the role they play in the relationship of social capital and organizational performance, using Turkish public hospitals as an exemplary application case. We argue that the activities carried out in these institutions during the innovation implementation process contribute to intellectual capital internally, with positive impacts on organizational performance.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

Marmara University: Ayse H Ozgun, Mehves Tarim

Istanbul Zaim University: Selim Zaim

 

An explanatory analytics framework for early detection of chronic risk factors in pandemics

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors.

Sponsor: Oklahoma State University & Center for Health Systems Innovation

PI/PDs: Dursun Delen

University of Wisconsin-Whitewater: Behrooz Davazdahemami

University of Dayton: Hamed M Zolbanin

 

A deep learning approach for predicting early bounce-backs to the emergency departments

Reviewing patients who return to the emergency department (ED) within 72 h (i.e., bounce-back) is a standard quality assurance procedure used to identify correctable system- and clinician-level causes for earlier-than-expected return to the ED and ultimately ensure patients’ safety. This study proposes a deep learning (DL) framework to automatically extract features from structured and unstructured Electronic Health Records (EHR) data of ED visits and predict patients who are likely to bounce-back. Data from 120,000+ visits to the ED of four major hospitals in New York city over 4+ years are used to validate the proposed framework.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

University of Wisconsin-Whitewater: Behrooz Davazdahemami

Mount Sinai Hospitals: Paul Peng

 

An explanatory analytics model for identifying factors indicative of long- versus short-term survival after lung transplantation

Due to the shortage of available organs compared to the number of patients on waitlists, the organ allocation process has always been challenging and calls for an equitable and optimized allocation system. This system demands minimizing the waitlist mortality and improving transplantation benefits (e.g., survival time and quality of life). According to prior research, lung recipients’ long-term survival time is lower than other solid organs recipients. This study proposes an explanatory analytics framework to study the most prominent factors contributing to long-term survival after lung transplantation using a large data along with the latest machine learning techniques.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen, Mostafa Amini, Ali Bagheri

 

The Interrelationships between the length of stay, readmission, and post-acute care referral in cardiac surgery patients

Prolonged hospital stays, and readmission contribute to substantial healthcare cost. Hence, an assessment of the optimal inpatient length of stay (LOS) associated with lower readmission rate is important for healthcare providers. Post-acute care (PAC) facilities have promising potential to shorten the LOS; however, currently their influence on overall patient outcomes is not well understood. The primary goal of this study is to highlight the interrelated risk factors of LOS and readmission for cardiac patients. The study also examines the influence of PAC referral on LOS and readmission.

Sponsor: Oklahoma State University & Center for Health Systems Innovation

PI/PDs: Dursun Delen

Texas A&M University: Ineen Sultana, Madhav Erraguntla, Hye-Chung Kum, Mark Lawley

 

A text-mining based cyber-risk assessment and mitigation framework for critical analysis of online hacker forums

Online hacker communities are meeting spots for aspiring and seasoned cybercriminals where they can engage in technical discussions, and share exploits and relevant hacking tools to be used in launching cyber-attacks on business organizations. Sometimes, the affected organizations can detect these attacks in advance, with the help of cyber-threat intelligence derived from the explicit and implicit features of hacker communication in these forums. In this research, we develop a novel text-mining based cyber-risk assessment and mitigation framework, which performs the cyber-risk assessment using explicit and implicit features applying various machine learning algorithms, sentiment analysis, and topic detection methods.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

Dublin City University: Baidyanath Biswas

Indian Institute of Management: Arunabha Mukhopadhyay

University of Connecticut: Sudip Bhattacharjee

EMLYON Business School, France: Ajay Kumar

 

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Compared to time-demanding traditional methods, advanced data analytics techniques can be used to speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory-predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner.

PI/PDs: Dursun Delen

University of Wisconsin-Whitewater: Behrooz Davazdahemami

University of Dayton: Hamed M Zolbanin

 

A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival

Understanding breast cancer survival has proven to be a challenging problem for practitioners and researchers. Identifying the factors affecting cancer progression, their interrelationships, and their influence on patients’ long-term survival helps make timely treatment decisions. This study aims to address this problem by proposing a Tree-Augmented Bayesian Belief Network (TAN)-based analytics methodology comprising of four steps: data acquisition and preprocessing, variable selection via Genetic Algorithm (GA), data balancing with synthetic over-sampling and random under-sampling methods, and finally the development of the TAN model to determine the probabilistic inter-conditional dependency structure among breast cancer-related variables along with the posterior survival probabilities.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

Creighton University: Asli Z Dag

Stevens Institute of Technology: Zumrut Akcam

Montclair State University: Eyyub Kibis, Serhat Simsek

 

A critical assessment of consumer reviews: A hybrid NLP-based methodology

Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

Dublin City University: Baidyanath Biswas

Indian Institute of Management: Pooja Sengupta

EMLYON Business School, France: Ajay Kumar

NEOMA Business School, France: Shivam Gupta

 

Relationship between electronic health records strategy and user satisfaction: a longitudinal study using clinicians’ online reviews

In this study we investigated how the electronic health records (EHRs) strategies concerning EHR sourcing and vendor switching impact user satisfaction over time. Using a novel longitudinal dataset created by scraping clinicians’ Glassdoor.com reviews on 109 US health systems from 2012 to 2017 and combining it with the Healthcare Information and Management Systems Society (HIMSS) database, we performed sentiment analysis of clinician reviews to construct our main dependent variable, user satisfaction. The results showed that as health systems gain more experience with EHR, a single vendor sourcing strategy was associated with higher user satisfaction.

Sponsors: Oklahoma State University & Center for Health Systems Innovation

PI/PDs: Dursun Delen, Ankita Srivastava, Surya Ayyalasomayajula, Chenzhang Bao

Temple University: Sezgin Ayabakan

 

An interactive decision support system for real-time ambulance relocation with priority guidelines

Changes in demand patterns and unexpected events are the two primary sources of delays in healthcare emergency operations. To mitigate such delays, this study proposes the movement of idle ambulances between emergency bases as one of the effective ways to improve the areal coverage of future demands. Accordingly, we have developed a model-driven decision support system that simultaneously seeks to maximize demand coverage while minimizing travel time by optimally relocating emergency response vehicles. The findings show that the average workload added to each ambulance due to relocations has significantly improved the response time and coverage ratio.

Sponsor: Oklahoma State University

PI/PDs: Dursun Delen

Iran University of Science and Technology: Mahdi Hajiali, Ebrahim Teimoury

University of Oregon: Meysam Rabiee

 

Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression

Detection and characterization of comorbidity is an invaluable decision aid and a prominent challenge in healthcare research and practice. This study aims to design a novel visual analytics system that can support efficient pattern detection and intuitive visualization of comorbidity progression modeled via temporal disease networks (TDNs). In the underlying system, we proposed two new clustering technologies—temporal clustering and disease clustering to detect the time of notable progression changes and simplify the visualization of TDNs. Through two case studies, we demonstrate that the proposed system is able to provide evidence-based insights regarding comorbidity progression for clinical decision support.

Sponsor: Oklahoma State University, OSU Center for Health Systems Innovation

PI/PDs:

Jacksonville State University: Yajun Lu

Oklahoma State University: Suhao Chen, Zhuqi Miao, Dursun Delen

Oklahoma State University CHSI: Andrew Gin

 

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94, 127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications, and laboratory results.

Sponsors: Oklahoma State University, OSU Center for Health Systems Innovation

PI/PDs:

Ru Wang, Zhuqi Miao, Tieming Liu, Mei Liu, Kristine Grdinovac, Xing Song, Ye Liang, Dursun Delen, William Paiva

 

ICT4D and the Capability Approach: Understanding How Conversion Factors Affect Opportunity and Process Freedoms at the Country-Level

Prior macro-level research on ICT4Ds has measured country-level development using resource- or utilitarian-based approaches. We argue for a people-centered lens using opportunity and process freedoms. Four conversion factors of ICTs are identified as enablers/restrictors of opportunity or freedoms. Using archival data and a 2SLS model, we test ICT-cost and ICT-infrastructure, and the interaction of e-participation and freedoms on ICTs to predict a country’s human development (HD). Results suggest that cost and infrastructure significantly affect HD, e-participation interacts with freedoms on ICTs such that freedom is only effective when accompanied by high levels of e-participation within a country.

Sponsor: State of Oklahoma

PI/PDs: Gabriel Bahr, Bryan Hammer, Andy Luse

 

International Technology Diffusion, Development, and Trading Partner Spillovers

The purpose of this paper is to expand ICT4D literature by investigating the associations between international trade of technology merchandise and development across countries. Using a spatial autoregression model and data on 45 upper-middle and high income countries from 2009 to 2018, we examine the effects of imports and exports of technology driven trade on two measures of development (GDP and Human Development Index). Additionally, we define spatial borders through a trade partner network and discover spillover effects of trade on development through the associated trading partner countries.

Sponsor: State of Oklahoma

PI/PDs: Gabriel Bahr, Bryan Hammer, Andy Luse

 

The Role of Technological Progress in Vertical Specialization and Economic Growth

Previous research has studied how ICT adoption impacts trade on economic and human growth. Countries contribute to the global supply chain (GSC) with various levels of intermediate to final production. This research investigates to what extent countries with a higher intermediate to final product trade ratio and a higher IT skills/capabilities see faster levels of growth (GDP & HDI) than countries with lower IT skills/capabilities over time. Using 2SLS and data on 82 countries spanning 2005 to 2015, we examine the moderating impact of IT Skills/Capabilities with vertical specialization on economic growth and human development.

Sponsor: State of Oklahoma

PI/PDs: Gabriel Bahr, Bryan Hammer, Andy Luse

 

Anchoring and adjustment, approach-avoidance disposition, and information sensitivity on information disclosure: A longitudinal experiment using EEG

Previous research on privacy has investigated information sharing as a single instance in time. What is not understood is how multiple instances of information sharing impact one another. Additionally, research has found inconsistent results of the impact information sensitivity has on disclosure. The initial level of information sensitivity acts as an anchoring point for subsequent sharing. Using a longitudinal experimental design, we found that the sharing of high-sensitive information is significantly affected by dispositions toward sharing or not sharing, such that disclosure is negatively impacted by initial exposure to high-sensitive information for those who display avoidance tendencies.

Sponsor: State of Oklahoma

PI/PDs: Bryan Hammer, Andy Luse

 

The Role of Psychological Contract Violations in Social Media Platforms

Online business entities rely on privacy seals and user agreements to facilitate user information sharing. Interactions among users of social media platforms (SMP) do not rely on user agreements. As a form of social exchange, information sharing on SMPs uses a psychological contract (PC; implicit and assumed reciprocal obligations). This study investigates how PC violations (PCVs) affect sharing intentions on SMPs. We find that sharing intention is negatively influenced by interpersonal and institutional PCVs through privacy concern and trust. PCV by another user positively influences the perceived violation by the SMP, suggesting a collateral damage of interpersonal-PCV towards the SMP.

Sponsor: State of Oklahoma

PI/PDs: Bryan Hammer

Oregon State University: Forough Nasirpouri Shadbad

 

Privacy as a Multidirectional Problem: A Social Relations Model of The Reciprocation of Privacy and Trust Using Network Analysis

Previous research on privacy (IS, Marketing, Management, Psychology, etc.) models information exchange in privacy salient situations as unidirectional, often from the perspective of a single user. As a social construct, privacy operates in social exchanges in which information moves between and among groups of individuals. Using network analysis, we model privacy and trust within a social relation model. Data come from an organization that uses a social media platform as its primary communication tool. We determine that trust is a reciprocating mechanism within relationships; however, privacy concerns are not reciprocated, especially between management and subordinate relationships.

Sponsors: State of Oklahoma, Spears School of Business

PI/PDs: Bryan Hammer, Andy Luse, Caleb Krieger, QinHui Wang

Virginia Tech University: Paul Lowry

 

Information Sharing as a Multidimensional, Dyadic Phenomenon: A Multilevel Study of Multiplex Relationships, Privacy, and Trust

Research on privacy and trust often model relationships (interpersonal, business-consumer, etc.) as unidimensional, lacking depth. Prior research indicates multidimensional relationships enhance trust, resource sharing, and satisfaction. This research proposes that multidimensional (i.e. multiplex) relationships increase trust, decrease privacy concerns, and increase information sharing in social media platforms (SMP). Data was gathered from an organization utilizing Facebook as their primary communication platform. Results suggest that more multiplex relationships lead to information exchange, especially when information sensitivity increases.

Sponsors: State of Oklahoma, Spears School of Business

PI/PDs: Bryan Hammer, Andy Luse

Texas Tech University: Fred Davis

 

The Fear of Social Exclusion and Our Need to Belong: A Study of Interpersonal Privacy and Trust in Social Media Platforms

All individuals have an innate desire to belong to society in order to reduce costs of living as well as experience a reciprocation of welfare. Social media platforms (SMP) provide a vehicle that delivers a means to experience this. This research proposes that individuals’ fear of social exclusion and goal-directed enjoyment leads to information sharing in SMP While enjoyment influences all types of information (low, medium, high), social exclusion only influences sharing of low-sensitive information. Interpersonal trust leads to high-sensitive information sharing, but not medium- or low-sensitive; interpersonal privacy concern decreases medium- and low-sensitive information sharing, but not high.

Sponsor: State of Oklahoma

PI/PDs: Bryan Hammer, Andy Luse, Gabriel Bahr, Caleb Krieger, QinHui Wang

 

The Antecedents of Habit on IS Continuance

This study theorizes the antecedents of the habit construct in hedonic IS usage. Studies of habit in IS investigated habit as a construct in both utilitarian and hedonic contexts, but the link between habit and hedonic IS usage isn't fully understood. Investigating the antecedents of habit should help establish a clearer picture. We investigate social network site and online gaming users through MTurk anonymous surveys to better understand the link between habit and antecedents from the literature.

Sponsor: State of Oklahoma

PI/PDs: Jerome Kirtley, Bryan Hammer, Andy Luse

 

Interruptions and Information Recall: Differences Between Virtual and Face-to-Face Learning

This study investigates the link between sensory interruptions and information recall in computer-mediated learning. There is a dearth of literature concerning sensory interruptions in IS literature and with the rising prevalence of virtual learning in the current environment, investigation of this phenomenon is critical. We will conduct an experiment combined with a survey to assess the effect of visual and audio interruptions on information recall.

Sponsor: State of Oklahoma

PI/PDs: Jerome Kirtley, Bryan Hammer, Andy Luse

 

Feelings of Inclusion and Empowerment: How Technology Supports Individuals with Intellectual and Developmental Disabilities and Their Caregivers

Technology is often seen to enable independence and a sense of self for those with intellectual and developmental disabilities (IDD). Various solutions have been implemented in both face-to-face and online environments. Yet, despite these improvements, much work is still needed to help individuals with IDD feel socially included and develop digital skills. Additionally, caregivers of those with IDD often feel burnout, depression, or anxiety around the use of technology. The aim of this project is to understand 1) how the relationship with a caregiver impacts technology use and 2) how interpersonal and intrapersonal factors may contribute to motivation.

Sponsors: State of Oklahoma, Spears School of Business, Institute for Developmental Disabilities at OSU

PI/PDs: Bryan Hammer, Gabriel Bahr

College of Education and Human Sciences: Kami Gallus, Aubrey Hammer

 

Does the InfoSec-Employee Relationship Matter?

Soft skills have been constantly identified as one of the most sought-after skill sets by employers for various positions. Information security is not an exception. However, how do infosec staff’s soft skills translate into business values has not been explained well. This research focuses on the end-users’ positive perception of their infosec team within an organization and identifies the mechanism where such perception can exert a positive effect on infosec performance. The research also investigates various techniques, along with the necessary soft skills, to develop and maintain a positive perception of an infosec team.

Sponsor: Oklahoma State University

PI/PD: JinKyu Lee

 

Privacy Protection Expectation

This research focuses on individuals’ value of privacy and identifies the determinants of the level of privacy protection expectation. With the advance of big data analytics tools, companies that can collect private information from a large number of customers can take advantage of the data more easily. However, such advantage may diminish if their customers do not value privacy and give out their private information to just about anyone. Therefore, maintaining privacy concerns at a healthy level is very important to businesses that seeks to leverage voluntarily provided private data for a competitive advantage.

Sponsor: Oklahoma State University

PI/PD: JinKyu Lee

 

Inducing Supports for Large-Scale Systems Implementation Projects
This research investigates major factors that can influence the attitudes of IT professionals toward a large-scale systems implementation project. Strong support from all stakeholders is a critical necessity for any successful project.  Unfortunately, large-scale IT projects often involve high uncertainty and start without full support from their stakeholders. This study examines a case where a large organization attempts to implement major changes in its information security architecture. The results will provide insights into the different levels of support from project team members and offer some techniques to induce stronger supports.

Sponsor: Oklahoma State University

PI/PD: JinKyu Lee

 

The Sense of Responsibility and Attributability for Information Security
Security training has long been used as the primary tool to promote secure IT use by improving the awareness and understanding of security threats. This research extends previous research by examining the effects of responsibility and attributability on non-IT employees’ secure IT use. As more organizations adopt cloud-based systems, the ownership and responsibility for business data fall closer to the non-IT employees who use the data every day. This study also develops mechanisms that managers can use to improve the sense of responsibility and attributability.

Sponsor: Oklahoma State University

PI/PD: JinKyu Lee

 

Does Technology Thwart Gender Stereotypes: An Impression Formation-based Examination of the Differential Influence of Technology across Gender and Message

This research examines the relationship between gender, message bias, and technology use on the way that observers form impressions of others. Building on impression formation and gender stereotype research and theory, we develop a two-study research methodology for examining how impressions are formed of technology users. The results of our two studies indicate that technology use is an important component in impression formation, significantly inhibiting the effects of gender stereotyping, such that women and men are not evaluated differently based upon their apparent competency in using technology nor on the content of their messaging.

Sponsors: Oklahoma State University, Iowa State University

PI/PDs: Andy Luse

Iowa State University: Anthony Townsend

 

Learned Helplessness Attributional Scale (LHAS): Development and Validation of an Attributional Style Measure

In answer to the call to increase the use of attribution theory, we look to both the theory of learned helplessness and Weiner’s attribution theory to create a new set of scales to provide a stable, parsimonious instrument for measuring attributions.  Twelve sections of four courses across ten semesters were used to develop the scales and test them across groups and time.  The final result is a new measurement tool, the Learned Helplessness Attribution Scale (LHAS), that demonstrates solid psychometric properties.

Sponsor: Oklahoma State University

PI/PDs: Andy Luse, Jim Burkman

 

Who Turned the Lights Out? Using Home Automation to Teach IoT

Enactive mastery provides the greatest educational improvement to individual self-efficacy, yet not all enactive experiences are the same, certainly not when individuals have no means of accessing materials physically. Using home automation IoT technology and concerted efforts to maintain similarity between sections (remote, traditional), we experimentally evaluate hands-on instruction in IoT in remote settings and compare them to traditional face-to-face environments. Results indicate that this laboratory exercise was successful in offering online students the means to perform hands-on IoT automation remotely, achieving comparable performance metrics in the remote setting, even while perceptions are notably lower in this remote setting.

Sponsor: Oklahoma State University

PI/PDs: Andy Luse, Caleb Krieger

 

Utilizing a Virtual Internet Testbed and Private Cloud to Teach Organizational Cloud Integration

We posit a method to enhance the ability of IS professionals to perform successfully in post academic environments that utilize cloud technologies via practical, hands-on cloud integration training. To minimize organizational overhead, we use a private cloud, existing within the Internet-Scale Event and Attack Generation Environment (ISEAGE) testbed, to mimic real-world processes required for deployment of these services. Comparing pre and posttest surveys across two studies (traditional treatment, cloud treatment), students reported a higher perception of task specific self-efficacy with the cloud treatment and completed the assignment at a much higher rate than the traditional treatment.

Sponsor: Oklahoma State University

PI/PDs: Andy Luse, Caleb Krieger, Corey Baham

 

Best of Both Worlds: The Inclusion of Gamified Elements in Virtual Lab Environments to Increase Educational Value

This research explores the idea of investigating both contexts within one unified platform. We examine whether using gamified elements within virtual labs is effective in enhancing learners’ educational performance. Particularly, we employ leaderboards as a motivational gamification mechanism for more engagement and participation that can result in higher learning outcomes. Using a sample of students, our results show that utilization of gamification within a virtual lab environment causes students to exhibit higher performance in terms of more task accomplishments (specifically those tasks that are more complex in nature) and higher self-efficacy.

Sponsors: Oklahoma State University, Oregon State University

PI/PDs: Andy Luse, Gabe Bhar, Bryan Hammer

Oregon State University: Forough Shadbad

 

Hackalytics: Using Computer Hacking to Engage Students in Analytics

This teaching brief describes a novel approach to teaching analytics through computer hacking. Students are exposed to the entire data lifecycle by first collecting intrusion detection data through the hacking of other student machines and then utilizing simple analytics procedures to analyze this data. Quantitative and qualitative results show that the students enjoy the activity both in terms of the fun of hacking their fellow classmates as well as analyzing this data in an area less utilized in analytics instruction – security analytics. Three levels of the exercise are provided as well as how-to materials for students to run the exercise.

Sponsors: Oklahoma State University, Oregon State University

PI/PDs: Andy Luse

Oregon State University: Forough Shadbad

 

Hot or Not: The Impact of Self-Perceived Facial Attractiveness on Webcam Use During Virtual Meetings

This research investigates the impact of facial attractiveness in the decision of an individual to display their webcam during a videoconference. Results show that while men are driven by self-views of their own facial attractiveness, women are instead driven by their beliefs about what others think of their facial attractiveness. This provides important information for those who wish to create a richer interaction for the widespread use of videoconferencing tools.

Sponsor: Oklahoma State University

PI/PDs: Andy Luse, Jim Burkman, Erin Stewart

 

ICT4D and the Capability Approach: Understanding How Conversion Factors Affect Opportunity and Process Freedoms at the Country-Level

Using publicly available archival data and a 2SLS model with instrumental variables, we test ICT cost, ICT infrastructure, and the interaction effect between e-participation and freedom of expression on ICTs to predict a country’s human development. Results suggest that both ICT cost and infrastructure significantly affect human development and that e-participation interacts with freedom of expression on ICTs in a way that freedom of expression is only effective when accompanied by high levels of e-participation within a country.

Sponsor: Oklahoma State University

PI/PDs: Gabe Bahr, Bryan Hammer, Andy Luse

 

Anchoring and Adjustment of Approach-Avoidance on Information Sensitivity and Information Disclosure: A Longitudinal Experiment using EEG

We theorize that an overarching approach-avoidance mechanism drives information disclosure in which privacy- and trust-related concepts are driven by avoidance tendencies and approach tendencies, respectively. We posit blocking effects and approach-avoidance dispositions explain the inconsistencies. Using a longitudinal experimental design with EEG, we found that while sharing behavior of low sensitive information is not affected by either the approach-avoidance tendencies of the individual or initial information exposure, the sharing of highly sensitive information is significantly affected by both, such that disclosure is negatively impacted by initial exposure to highly sensitive information for those who display avoidance tendencies.

Sponsor: Oklahoma State University

PI/PDs: Bryan Hammer, Andy Luse

 

Information Sharing as a Multidimensional, Dyadic Problem: A Multilevel Study of Multiplex Relationships, Privacy, and Trust

Using network theory, we theorize that privacy concerns, trust, and information sharing occur at two levels: relational (i.e., dyadic) and individual. Relationships characterized as multiplex, or more multidimensional, are richer and experience greater trust while reducing privacy concerns. Utilizing data collected from an organization using Facebook as their communication and organization platform, we analyze our data using a Bayesian multilevel model approach. Our results indicate that privacy concerns operate mainly through the individual level while trust operates at the relational level. Our findings suggest that privacy mechanisms are more complex than previously modeled and that they depend on interpersonal relationships.

Sponsor: Oklahoma State University

PI/PDs: Bryan Hammer, Andy Luse

 

Strong vs. Weak Theory:  An Evaluative Mechanism for Theoretical Development

The goal of many theoreticians is to develop sound theory that will be utilized within their field both by researchers and practitioners.  Yet, scholars have not arrived at a consensus concerning what constitutes appropriate theoretical structure. In this paper, we offer an approach to theory design and analysis based on a categorization of strong and weak theory structure.  We first offer a concrete definition of the meaning of strong and weak theory that is based on a variety of literature. Second, we apply this evaluative framework to a prominent stream of theory development and discuss the nature of theory evolution.

Sponsors: Oklahoma State University, Iowa State University

PI/PDs: Andy Luse, Bryan Edwards

Iowa State University: Anthony Townsend

 

Predictors of Success in Information Security Policy Compliance

This research builds on the ISP compliance literature by creating a prediction model that includes four predictor variables, namely, leadership, supportive organizational culture, engagement, and role values with one dependent variable— ISP compliance.  An instrument with five constructs was developed to administer to a diverse set of employees in the U.S.A. ranging in work experience from new hires to CEOs from numerous industries with the goal of finding out which of the predictor variables are most influential in predicting ISP compliance.

Sponsor:  Oklahoma State University

PI/PDs: Jeretta Horn Nord, Carole Sargent, Alex Koohang, Angelica Marotta

 

Critical Components in Organizational Performance

Organizational performance is one of the most important factors leading to a company’s success.  Recognizing the vital role of competencies, knowledge-oriented leadership, and innovation in organizations, there is a need to study how these variables affect organizational performance. This research builds a model with four constructs, i.e., competencies, knowledge-oriented leadership, innovation, and performance within manufacturing companies to find out, through path modeling, 1) the effect of competencies on innovation, 2) the effect of knowledge-oriented leadership on innovation, and 3) the effect of innovation on performance. The survey instrument included four constructs – Competencies, Knowledge-oriented Leadership, Innovation, and Performance. 

Sponsor:  Oklahoma State University

PI/PDs: Jeretta Horn Nord, Alex Koohang, Joanna Paliszkiewicz, Marcin Soniewicki

 

Data Analytics in Organizations: Leadership, Management, Talent, and Performance

The success of today’s organizations depends on data analytics—obtaining data, analyzing it, and using the results to make informed decisions. Although the significance of analytics is recognized more than ever by those in businesses, many lack the leadership and talent to optimize the transition from data analytics to data-driven decision making. This research investigates the state of data analytics in organizations through an investigation of leadership, management, talent, and performance.

Sponsor: Oklahoma State University

PI/PD: Jeretta Horn Nord

 

The Internet of Things (IoTs)

Those in the emerging digital world have recently witnessed the proliferation and impact of IoT-enabled devices. The Internet of Things (IoTs) has provided new opportunities in the technology arena while bringing security, privacy, and trust challenges to an increased level of concern. This research investigates the usage, benefits, and challenges of IoTs in organizations. The research has both practical and theoretical impetus since IoT is still in its infancy, yet is considered by many as the most important technology initiative of today.

Sponsor: Oklahoma State University

PI/PD: Jeretta Horn Nord

 

Using the Gaussian Copula to Generate the Predictive Distribution in Monotonic Nonlinear Models: An Efficient Resampling Approach

We present a resampling approach for generating the predictive distribution of a dependent variable that has monotonic nonlinear relationships with its predictors. The procedure provides an empirical estimate of the predictive (conditional) distribution in terms of the original variables without requiring analyst intervention to identify appropriate transformations (and back-transformations) of variables. This allows predictions based on the estimated conditional expectation, and prediction intervals based on the estimate of conditional variance. It employs the well-known Gaussian copula, is easily implementable and is computationally efficient.

Sponsors: Oklahoma State University, University of Oklahoma

PI/PDs: Rathindra Sarathy

University of Oklahoma: Krish Muralidhar

 

When will I get out of the hospital? Modeling Length of Stay using Comorbidity Networks

Using the EMR hosted by CHSI, we build models for predicting hospital length of stay by incorporating historical and probable comorbidities that a patient is likely to face during their hospital stay. The results show significant improvement in predictive performance.

Sponsors: Oklahoma State University, Auburn University

PI/PDs: Ramesh Sharda

Auburn University: Pankush Kalgotra

 

Pandemic Information Support Lifecycle: Evidence from the Evolution of Mobile Apps during COVID-19

We propose a pandemic information support lifecycle (PISL) consisting of five phases: awareness, preventive care, active information, confidence building and evaluation. We validated this PISL using analysis of the mobile apps developed worldwide.

Sponsors: Oklahoma State University, Auburn University

PI/PDs: Ramesh Sharda

Auburn University: Pankush Kalgotra, Ashish Gupta

 

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records

Using the EMR hosted through CHSI, we study health differences by analyzing multi-morbidities among seven population groups based on race. Our multimorbidity network analysis identifies specific differences in diagnoses among different population groups, and presents questions for biological, behavioral, clinical, social science, and policy research.

Sponsors: Oklahoma State University, Auburn University

PI/PDs: Ramesh Sharda, Julie Croff

Auburn University: Pankush Kalgotra

 

What should I believe? Exploring information validity on social network platforms

We develop a theoretical framework to explore the accuracy and objectivity of social networks content by employing social capital theory. The proposed and validated measures can help assign an accuracy and objectivity score to a conversation taking place in social media.

Sponsors: Oklahoma State University, Wright State University

PI/PDs: Ramesh Sharda

Wright State University: Daniel Asamoah

 

How Can Our Tweets Go Viral? Point-Process Modelling of Brand Content

We develop and test stochastic models based on Hawke’s process to be able to predict which tweets are likely to go viral.

Sponsors: Oklahoma State University, Wright State University

PI/PDs: Ramesh Sharda

Wright State University: Amir Hasan Zadeh

 

Analytics/Data Science Decision Support for Management of Oklahoma COVID pandemic

We assist the State of Oklahoma with data accuracy analyses and positivity rate changes to better understand and mitigate the Covid-19 pandemic.

Sponsor: Oklahoma State Department of Health

PI/PDs: Ramesh Sharda, Andy Luse

 

COVID increase drives decrease in travel risk perception

Comparative analysis of perceived international travel risk immediately prior to COVID outbreak and in the months following most airlines returning to near full capacity.

Sponsor: Oklahoma State University

PI/PDs: Mark Weiser, Andy Luse

 

Task-based self-efficacy and perception changes from short-term study abroad experiences

This study evaluates the practical value of short-term faculty-led study abroad experiences to increase individual's' efficacy in tasks specifically related to traveling and interacting abroad, and in comfort and security perceptions about the country of travel and other countries.

Sponsor: Oklahoma State University

PI/PDs: Mark Weiser, Andy Luse

 

Close to Home: A Survey of Municipal Policies for Short-term Home Rentals

Surveys municipal legislation from small cities in which a Division 1 FBS school is located to categorize approaches in developing home-sharing markets.

Sponsor: Oklahoma State University

PI/PD: Mark Weiser

 

Apportionment Methods Revisited: Common Sense vs. Mathematical Purity

Apportionment methods for assigning seats to legislative bodies is a much-studied problem, and it has significant social and political impact every 10 years in the U.S. (most recently the 2020 Census).  The present method used to allocate Senate seats by population is favored by mathematicians, even though it violates “common sense” criteria.   This study quantifies the ‘allocation errors’ that competing methods provide and illustrates the trade-off between mathematical purity and practical use.

Sponsor: Oklahoma State University

PI/PD: Rick L. Wilson

 

Clinical Vascular System Diagnosis Synthesis

Past diagnosis, existing research and cardiologist knowledge are synthesized into a rule-based approach to improve clinical vascular system treatment.  The proposed novel approach has ground-breaking potential in the vascular medicine area.

Sponsors: Oklahoma State University, Creighton University

PI/PDs: Rick L. Wilson, Scott E. Fletcher, MD

 

GOATs: An Analytic Hierarchy Process (AHP) Approach

A popular topic in many sports: who is the ‘greatest of all time’ (GOAT) player/team?  A decision-making framework such as the multi-objective and flexible AHP process is shown to be useful in helping decision makers and/or fans to help frame their debate, leading to a much more rational process even if it does not lead to a singular conclusion. Generalized practical implications of the process will also be highlighted. 

Sponsor: Oklahoma State University

PI/PD: Rick L. Wilson

 

A Critical Analysis of the RPI/NET Team Assessment Systems in Collegiate Sports

Components of the RPI/NET system are used in NCAA basketball (and other sports) to assist the tournament Selection Committee in determining participants and seedings. Past research in sports ranking tools is used to examine these systems, identify major flaws, and inject new mathematically sound constructs to improve the assessment process.  

Sponsor: Oklahoma State University

PI/PD: Rick L. Wilson

 

Systematic Analysis of TOPSIS vs. AHP: A Comparative Simulation Study

TOPSIS and AHP are two MCDM processes that result in alternative ‘choice’ rankings.  This research will work to find a unifying analysis of how the two processes are similar and different from result outcome, and through simulated decisions, develop guidelines for use.

Sponsor: Oklahoma State University

PI/PD: Rick L. Wilson