Thank you for visiting my research page. As a doctoral candidate at Temple University's Fox School of Business, I'm excited to share my work on artificial labor (AL) in healthcare with you.
In this space, you'll find my research materials, including my dissertation proposal, surveys, and collected data. I invite you to explore these resources and join me in examining the future of healthcare operations.
Julio Avael III is a senior executive with over 20 years of experience in healthcare, education, and media industries. He currently serves as a key executive at Leava Healthcare and the Florida Disceel Spinal Center, overseeing business development and multistate management.
Growth and change management, crisis management, business development, finance, and operations management
Doctoral candidate at Temple University's Fox School of Business, MS in Management from Barry University, and BA from Florida International University
Artificial labor (AL) in healthcare, including AI-powered diagnostics and robotic assistants to enhance operational efficiency and patient care
Avael has completed advanced certifications from Harvard University and Florida International University in Performance-Based Management, Corporate Governance, and Strategic Management.
As a thought leader, he regularly speaks on telehealth, mental health in school systems, and the future of employee training. His focus includes adapting learning programs to incorporate artificial intelligence.
His community service has earned recognition including the "Lifetime Volunteer Service Award" from former President Obama. He serves on the boards of the Rotary of Miami and the Non-Violence Foundation.
Investigating how AI transforms healthcare delivery systems, benefiting patients, providers, and communities.
Analyzing healthcare workers' viewpoints to ensure ethical AI implementation in medical settings.
Forecasting AI solutions for aging populations and chronic disease management.
Studying hospital managers' AI adoption strategies and organizational change management.
Evaluating AI applications from diagnostics to administration, measuring efficiency and care quality improvements.
How Technology Adoption, Human Behavior, and Cost Efficiency Influence Hospital Task Allocation: A Resource-Based View and Technological Unemployment Perspective
This study examines the factors influencing hospital management's preference for allocating tasks to technology rather than human labor, focusing on three critical drivers:
Analysis of economic factors driving technological implementation
Assessment of behavioral challenges in healthcare settings
Evaluation of implementation trends and acceptance factors
Julio Avael III
Dr. C Anthony Di Benedetto
Department of Marketing
Dr. Min-Seok Pang
Department of Management Information Systems & Mentor
Dr. Sunil Wattal
Associate Dean - Research and Doctoral Programs
Download the proposal outlining the research project.
Access the full dissertation document.
Survey One: Manager Preference of AI for Task Allocation
Download the survey instruments used in the research.
Welcome to my dissertation defense proposal session on technology adoption and task allocation in healthcare settings. This presentation will explore how hospitals make decisions about implementing new technologies and managing their workforce.
Overview of healthcare technology adoption challenges and research motivation based on 20+ years of industry experience.
Examination of factors influencing hospital management's technology adoption decisions, followed by Q&A session.
Analysis of technological unemployment implications in healthcare settings, followed by interactive discussion.
Each segment will include time for questions and collaborative discussion. Your insights and feedback are valuable to this research.
Welcome to my research portal. As a healthcare operations management professional with over two decades of experience, I'm investigating the intersection of technology and healthcare operations.
With more than 20 years in healthcare operations management, I've held key leadership positions across various healthcare sectors including:
My current research examines the technological transformation in healthcare operations, specifically investigating:
Addressing critical issues including staff absenteeism, asset management, malpractice risk, and human error reduction through technological solutions.
Analyzing the impact of managed care, Medicare requirements, cost containment measures, and risk-sharing agreements on healthcare operations.
Investigating how healthcare organizations are adapting to technological change and making strategic decisions about task allocation.
This research investigates the evolving relationship between technological solutions and human labor in modern healthcare organizations, focusing on task allocation and organizational preferences.
To investigate healthcare organizations' preferences in task allocation between technological solutions and human labor, analyzing:
To examine the permanence of technological transitions in healthcare settings, specifically:
This research aims to provide valuable insights into the strategic resource management of healthcare organizations and the role of artificial labor in maintaining organizational competitiveness.
A comprehensive exploration of the relationship between management, technology, and healthcare tasks in modern medical environments.
In healthcare organizations, management serves as the critical gatekeeper of productivity and efficiency. Their primary responsibilities include:
Healthcare tasks represent the fundamental work units across various medical functions (Autor, 2013), including:
Research by Onnasch et al. (2014) and Parasuraman & Riley (1997) reveals the complex relationship between technology and healthcare delivery:
According to Acemoglu and Restrepo (2018), modern healthcare labor is categorized into two distinct types:
A comprehensive analysis of management delegation practices and the growing trust in technological solutions.
Recent research has revealed several key factors that influence how managers approach task delegation in the workplace. Our analysis shows that management delegation decisions are deeply rooted in psychological and organizational factors.
Our research indicates a growing preference for technological solutions in task management, supported by several key findings:
This analysis is based on comprehensive studies by leading researchers in management and organizational behavior:
Our comprehensive analysis reveals significant AI integration across multiple healthcare domains, demonstrating enhanced efficiency and accuracy in previously human-operated tasks.
Key Finding: AI-powered diagnostic tools, particularly Convolutional Neural Networks (CNNs), have revolutionized radiology by enabling:
Sources: Alyami, 2024; Kouser & Aggarwal, 2023; Javanmard, 2024; Ghayvat et al., 2023; Zhang, Joshi, & Hadi, 2024
Key Finding: Machine learning algorithms have transformed cardiovascular risk prediction through:
Sources: Gala et al., 2024; Triantafyllidis et al., 2022; Li et al., 2020; Shah et al., 2015; Narula et al., 2016
Administrative Improvements:
Sources: Kaswan et al., 2021; Tong et al., 2023; Gellert et al., 2024; Kaur et al., 2023
Surgical Planning Advancements:
Sources: Williams et al., 2021; Tariq et al., 2023; Larrain et al., 2024
Our research investigates how artificial intelligence and automation technologies tend to permanently retain ownership of tasks once they acquire them. This phenomenon has significant implications for workforce development and organizational planning.
Research demonstrates that when organizations transition tasks from human workers to technological solutions, these changes typically become permanent. This pattern has been observed across various industries and task types, suggesting a broader trend in technological adoption.
Black and Boal (1994) found that technological implementation gradually diminishes human roles, making it increasingly difficult for human workers to re-engage with these tasks.
W. Brian Arthur's (1983) research shows that early technology adoption creates a dominant position that persists even when potentially superior alternatives emerge later.
These findings suggest organizations need to carefully consider the long-term implications of task automation, as these decisions often become irreversible.
Understanding this permanency is crucial for strategic planning and workforce development in an increasingly automated business environment.
Our research identifies three major gaps in current understanding of artificial labor (AL) adoption in organizations:
Current research lacks comprehensive analysis of why and how management teams choose to assign organizational tasks to technology over human workers. This gap is particularly significant as organizations increasingly face decisions about task automation.
There's limited understanding of the key factors that influence management's preference for artificial labor over human workers. Our research aims to identify and analyze these critical factors:
We need to better understand the permanence of technology adoption in task management. Key questions include:
This research initiative aims to address these crucial gaps in our understanding of artificial labor adoption and its implications for organizational management in healthcare and beyond.
This study examines organizations' preferences between artificial labor (AL) and human labor in healthcare settings, investigating both initial adoption patterns and long-term commitment.
Primary Hypothesis: When organizations deploy Artificial Labor for tasks, their preference for using human labor decreases.
Theory: Resource-Based View Theory - strategic resources must maintain value, rarity, and be difficult to replicate (Penrose, 1959; Barney, 1991).
Healthcare Examples: Analytics platforms for diagnostics, patented AI-driven treatments, robotic surgery
Secondary Hypothesis: Organizations that adopt artificial labor practices are unlikely to revert to human labor.
Theory: Based on technological unemployment theory (Keynes, 2016) and the "Lock-In Effect" (Arthur, 1983).
Applications: Decision Support Systems, Service Robots, AI Diagnostics, Automated EHR
This research explores how healthcare organizations prioritize technology over human labor, focusing on long-term implications and decision-making patterns.
A comprehensive quantitative research study examining AI adoption in U.S. healthcare, involving 258 participants from managerial and executive positions.
Quantitative methodology utilizing structured survey data collection through Qualtrics platform, with quality assurance through Centiment's Audience Paneling services. Pre-testing conducted with 20 participants to optimize survey reliability and validity.
Healthcare industry executives and managers across the U.S., classified using NAICS standards. Comprehensive demographic data collection including age, gender, education level, industry experience, company size, and operational scope.
Multi-dimensional assessment using nominal categorical variables, ordinal scales for frequency and change measurement, and Likert agreement scales. Enhanced data validity through strategic attention and encouragement checks.
Integration strategies, operational changes, and implementation commitment
Efficiency benefits, cost implications, and productivity metrics
Workforce dynamics, task transformation, and cultural adaptation
Sample size of 258 participants with Qualtrics data collection and Centiment's Audience Paneling services for quality assurance
Soft launch with 20 participants, comprehensive demographic data collection, and mixed methodology using nominal categorical variables and ordinal scales
AI adoption, implementation strategies, manager perceptions, and organizational culture impact assessment
Favor AI implementation
Part of AI decision-making teams
Primary decision-makers for AI
• 82.5% are 35+ years old • Largest age group: 45-54 (30.7%) • Gender distribution: 76.1% female, 23.9% male
• 33.7% hold Bachelor's degrees • 23.0% have Master's degrees • 65.7% have 11+ years industry experience • 35.0% with 20+ years expertise
• 34.3% are large enterprises (1000+ employees) • 57.3% operate in single state • 5.8% have nationwide presence • 4.5% operate internationally
• Hospitals: 27.0% • Specialty Care: 18.1% • Primary Care: 14.1% • Nursing Care: 8.9% • Home Healthcare: 6.3%
This comprehensive demographic study represents a highly experienced and well-educated group of healthcare professionals, primarily from large enterprises. The strong female representation and extensive industry experience provide valuable insights into the current state of healthcare management and AI adoption attitudes.
A comprehensive analysis of artificial labor adoption patterns in healthcare organizations
Initial factor analysis evolved into weighted least squares regression due to complex cross-loadings. Prioritized responses from hospital managers and supervisors for real-world applicability.
Model 1 (Adoption Pattern): R-Value: 0.331 | R²: 11.0% | p-value: 0.019 Model 2 (Reversion): R-Value: 0.316 | R²: 10.0% | p-value < 0.025
I. Decreased Human Labor Preference • F-value = 5.912 • β = 0.331 (p=0.019)
II. Reduced Reversion Likelihood • F(1, 48) = 5.329 • β = 0.421 (p < 0.001)
Analysis suggests a declining strategic value of Human Labor as Artificial Labor adoption increases, challenging traditional resource management perspectives.
Findings demonstrate a significant "lock-in" effect, where organizations show strong preference for maintaining technological solutions over reverting to human labor.
Our comprehensive analysis reveals significant insights into the adoption of Artificial Labor (AL) in workplace environments. The study focused on two primary aspects: behavioral challenges and cost efficiency considerations.
Behavioral issues account for 6% of variance in AL adoption (β = -0.237, p < .001)
Cost efficiency shows strong correlation (β = .415, p < .001)
AL reliability explains 60% of adoption variance
"The artificial labor attempt is to save money at all costs" - Survey Respondent
"Loss of hours from employees and constant tardiness caused AL adoption" - Survey Respondent
"Could not find enough people to do specific repetitive jobs" - Survey Respondent
Time consideration and fatigue are significant factors
Distractions and phone usage affecting work completion
Increasing demand for flexible work arrangements
Strategic value in reducing errors and labor costs
Building on Study I findings, this research investigates how cost efficiencies and human behavior impact managerial task allocation preferences in hospitals, specifically examining the shift toward Artificial Labor (AL) over Human Labor (HL).
• What negative human behaviors drive managerial preference for Artificial Labor over Human Labor in hospital settings?
• How does cost efficiency influence hospitals' decision to allocate tasks to artificial labor rather than human labor?
This study employs the Resource-Based View (RBV) theory, examining AL as a strategic resource that must possess value, rarity, and be difficult to replicate or substitute (Penrose, 1959; Barney, 1991).
Negative human behaviors drive managerial task allocation preference for Artificial Labor over Human Labor in hospital settings.
Cost efficiency is a key antecedent driving the preference for allocating tasks to artificial labor over human labor in hospital settings.
A comprehensive quantitative research examining Artificial Labor (AL) adoption patterns in U.S. healthcare institutions, focusing on behavioral impacts and strategic implementation.
Comprehensive evaluation of 29 workplace behaviors influencing AL adoption, measured on a specialized 4-point scale for precise data collection
Open-ended response collection to identify additional behavioral factors affecting AL implementation in healthcare settings
Strategic prioritization of the five most impactful behaviors driving AL adoption decisions in healthcare environments
This research provides crucial insights into AL integration in healthcare settings, specifically addressing cost efficiencies and behavioral challenges faced by modern healthcare institutions.
The study aligns AL adoption with the Resource-Based View (RBV) framework, demonstrating its long-term strategic value as a sustainable resource in healthcare operations management.
Our research combines Technological Unemployment Theory and Resource-Based View (RBV) Theory to explain AL's role in task allocation, competitive advantage, and human labor displacement effects.
AL demonstrates superior performance in repetitive or error-prone tasks
Organizations show sustained reliance on AL post-implementation
Reduced dependence on human labor in decision-making processes
AL emerges as a valuable, rare, inimitable, and non-substitutable resource
Reduces labor costs, mitigates HL challenges, streamlines operations, and enables more effective resource allocation.
Enables faster, more accurate, and consistent task execution, improving diagnostic precision and patient outcomes.
Reduces future labor costs, improves operational margins, and enhances organizational sustainability.
AL adoption affects 1.4M unionized healthcare workers (SEIU, NNU), significantly impacting negotiation scope.
Automation reduces reliance on unionized roles, leading to workforce reductions or redefined jobs. Productivity gains historically bypass the bottom 80% of workers (Economic Policy Institute).
Unions are actively advocating for reskilling programs, severance provisions, and fair treatment policies to address technological transitions (American Progress, n.d.).
Our findings primarily derive from hospital environments, which operate under unique conditions and regulations.
Results may not directly apply to industries such as:
Our research relies heavily on numerical data and statistical analysis, potentially missing valuable qualitative insights.
The study lacks insights from:
The study does not address crucial labor elements including:
Critical ethical considerations remain unaddressed:
These limitations present opportunities for future research to expand our understanding of AL adoption across different contexts and perspectives.
Have questions about my research? I welcome your inquiries and discussion.
I'm happy to discuss methodology, findings, or potential collaborations.
Looking forward to engaging with colleagues and continuing this research journey.
Welcome Message from Julio Avael