|
| Digital Applications of Organizational Behavior Management (OBM) |
| Sunday, May 28, 2023 |
| 5:00 PM–6:50 PM |
| Hyatt Regency, Mineral Hall D-G |
| Area: OBM |
| Chair: Donald A. Hantula (Temple University) |
| |
| The Future of Work is Digital: AI, Help Seeking, Neurodiversity, Robotics, and Behavior |
| Domain: Applied Research |
| DONALD A. HANTULA (Temple University), Elizabeth Garrison (Temple University), Slobodan Vucetic (Temple University), Matthew Tincani (Temple University), Lindsay Oulette (Temple University), Drew Kronstadt (Temple University), Madelyn Upthegrove (Temple University), John T Nosek (Guiding Technologies) |
| |
| Abstract: Digital technology is rapidly changing the way we work. This paper describes a series of related studies funded by the National Science Foundation’s Future of Work program and National Institute of Health integrating these technologies with insights from behavior science. Examples include AI performance assistants for individuals working in clinical settings and vocational rehabilitation, performance of neurodiverse and neurotypical workers in data annotation jobs, seeking help for computer problems from humans and bots, and human interaction with autonomous robots. There is a dual focus on how AI can be used as a behavioral prosthetic to extend human capabilities and competence, and how behavior science can be used to adapt these technologies to work with humans. As sophisticated and bedazzling as many digital technologies appear, they are destined to fail without behavioral insights; but at the same time behavioral applications in the workplace that do not integrate digital technologies are destined to become irrelevant. The presentation concludes with some reflections and recommendations for incorporating OBM into technology development and digital technology into OBM the challenges and rewards of working with a multidisciplinary team to accomplish this kind of work. |
| |
| Increasing Practical Verbal Behavior in Esports Players |
| Domain: Applied Research |
| VICTORIA GERMAN (Florida Institute of Technology), Kaitlynn Gokey (Florida Institute of Technology), Rachael Ferguson (Kalamazoo Valley Community College), Alyson Intihar (Florida Institute of Technology) |
| |
| Abstract: Verbal behavior, sports, and organizational performance have all enjoyed extensive behavior analytic research, particularly in more ‘traditional’ areas such as schools, clinics, businesses, and field sports. However, no research to date has targeted performance in Esports as an organization, nor has verbal behavior in organizational settings been studied as extensively as it has been with children, particularly those on the autism spectrum. Esports is a large, growing industry of competitive and professional videogaming, featuring various games, many of which are team-based and require significant coordination and cooperation between players. Thus, in the present study, the behavior analysts targeted various verbal responses, including mands and tacts, in a team of collegiate Esports players. Drawing from the organizational behavior management literature, the behavior analysts implemented task clarification, goal setting, and individualized graphic and verbal feedback. The results showed visible increases in verbal behaviors such as descriptive statements and requests for help across the team, and high social validity scores. |
| |
Applications of Behavior Analysis in Tech: A Constructional Approach of Construction |
| Domain: Applied Research |
| EDDIE BRANDON AMEZQUITA (John Deere) |
| |
| Abstract: Behavior-analytic research methods have a lot to offer the fields of industrial manufacturing and technology; however, the research design and data analysis may not be easily consumable by these audiences. This presentation will describe methods and outcomes of user experience research for product design at John Deere, an international manufacturing company in the United States. Behavior analysis provides a comprehensive account of the variables responsible for behavior change. This research sought to understand the effects of emerging technology used in manufacturing machine operator behavior, specifically safe reversing. Participants were operators of heavy construction vehicles of various ages, races, and genders. The effects of assistive technology were evaluated using 1) verbal reports through usability and feedback surveys, 2) physiological responses through eye-tracking, Galvanic skin response, and heart-rate variability, and 3) operator behavior and machine outcomes. Data showed increased usage of assistive technology during back-ups, increased productivity, and change in verbal reports of “trust”. The physiological data was mixed where eye-tracking supported an understanding of stimulus control of operator behavior change, but Galvanic Skin response and heart rate need further investigation. Operator behavior can be influenced by assistive technology if designed for the environmental and consequential needs for safe reversing. |
| |
| Organizational Behavior Management in the Age of Data Analytics |
| Domain: Service Delivery |
| CHRISTOPHER HEBEIN (Behaviorship LLC) |
| |
| Abstract: Organizational Behavior Management (OBM) has historically applied a unique set of tools anchored in behavior analytic principles to improve performance and solve practical problems in organizations. Concurrently, data science developed in an effort to squeeze meaningful insights from existing raw datasets. Using statistically-based data visualizations and incorporating advances in predictive modeling algorithms and machine learning applications the field is growing with data-platforms projected to represent a market size of over $19 billion by the end of the decade. As work (and leisure) move ever more thoroughly into digital spaces, data scientists have access to increasingly useful troves of data to aid them in their mission. We ask the questions: How represented is OBM in this space and how are OBM professionals helping to develop tools used by data scientists? This presentation highlights key features of both OBM and data science, offers suggestions on areas of potential cooperation between OBM practitioners and modern software analytics platform users and examines some of the current structural barriers between these two fields within data collection and interpretation procedures. |
| |
|
| |