Theses and Dissertations

Date of Award

2025

Document Type

Dissertation

Degree Name

Doctor of Education (EdD)

Department

Abraham S. Fischler College of Education and School of Criminal Justice

Advisor

Michael Simonson

Committee Member

Elizabeth Swann

Committee Member

Kimberly Durham

Keywords

instructional design and technology, laggards, late adopters, diffusion of innovations theory, instructional technology, artificial intelligence, generative artificial intelligence

Abstract

Laggards are often thought of as aloof and uninformed late adopters, as traditionalists living in the past (Rogers,2003). Laggards represent the last 16% of a population to adopt an innovation, and, as a result, they are often depicted for their resistance to adopt an innovation much later than the rest of the population, if ever (Jahanmir et al., 2020; Rogers, 2003). The present study attempted to expand the research on laggards, specifically laggard Instructional Design and Technology (IDT) professionals.

IDT professionals were the target group because of their perceived innovativeness as a result of their high exposure to instructional technology (IT). Indeed, IDT professionals are typically innovative individuals with strong IT backgrounds (AECT, n.d.). Yet some IDT professionals are slower to adopt IT than their peers.

This qualitative phenomenological study was conducted to better understand the behaviors, personal barriers, and organizational barriers that laggard IDT professionals face in adopting IT. Out of 34 volunteer participants, 10 with the lowest scores on the Innovativeness Scale (IS; Hurt et al., 1977) were interviewed in a one-on-one, semistructured format via Zoom videoconferencing software. These 10 were operationally defined as laggards. Remarkable findings included the importance of leading with instructional design (ID), the human loop, and the notion of a misunderstood laggard. In other words, leading with ID represented addressing an issue with the principles of ID before approaching it with technology. The human loop signified maintain one’s independent work while Generative Artificial Intelligence (Generative AI) supplemented it. A misunderstood laggard was an individual who made an educated decision not to adopt an innovation. While each interviewee possessed laggardly characteristics as described by Rogers (2003), each interviewee also expressed a complex set of behaviors that showed informed innovation adoption decisions.

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