Despite the rapid growth in development of technology, such as software or hardware devices, its implementation among stakeholders working with individuals with autism spectrum disorder (ASD) is slow. This suggests that there might be a number of challenges that hinder technology adoption or its continued usage. The current project aimed to investigate stakeholders’ perceptions on potential barriers in adopting technology. We involved 17 stakeholders, including parents of children with ASD, service providers, and administrators of ASD organizations, in interviews. Thematic analysis yielded three themes: (a) making the right choice, (b) apprehension and concern about uptake, and (c) external obstacles to implementation. This project was the first study to involve key stakeholders to determine components that take place in utilization and adoption of technologies in the field of ASD.
AmendahD.GrosseS.PeacockG.MandellD. (2011). The economic costs of autism: A review. In AmaralD.DawsonG.GeschwindD. (Eds.), Autism spectrum disorders. New York, NY: Oxford University Press.
2.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Washington, D.C: American Psychiatric Association.
3.
BaioJ.WigginsL.ChristensenD.MaennerM.DanielsJ.WarrenZ.…DowlingN. (2018). Prevalence of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 Sites, United States, 2014. MMWR Surveillance Summaries, 67, 1–23.
4.
BoeijeH. (2002). A purposeful approach to the constant comparative method in the analysis of qualitative interviews. Quality & Quantity, 36, 391–409.
5.
BölteS.GolanO.GoodwinM. S.ZwaigenbaumL. (2010). What can innovative technologies do for autism spectrum disorders?Thousand Oaks, CA: Sage.
6.
CobbS.BeardonL.EastgateR.GloverT.KerrS.NealeH.…MitchellP. (2002). Applied virtual environments to support learning of social interaction skills in users with Asperger’s Syndrome. Digital Creativity, 13, 11–22.
7.
CreswellJ. W.MillerD. L. (2000). Determining validity in qualitative inquiry. Theory into Practice, 39, 124–130.
8.
CroenL. A.NajjarD. V.RayG. T.LotspeichL.BernalP. (2006). A comparison of health care utilization and costs of children with and without autism spectrum disorders in a large group-model health plan. Pediatrics, 118, e1203–e1211.
9.
DavisF. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Cambridge: Massachusetts Institute of Technology.
10.
De Boer-OttS.GriswoldD. E.MylesB. S.ByrdS. E.GanzJ. B.CookK. T.…AdamsL. G. (2004). Autism spectrum disorders: Interventions and treatments for children and youth. Thousand Oaks, CA: Corwin Press.
11.
DobbinsM.DeCorbyK.TwiddyT. (2004). A knowledge transfer strategy for public health decision makers. Worldviews on Evidence-Based Nursing, 1, 120–128.
12.
DunnG.LaingM. (2017). Policy-makers perspectives on credibility, relevance and legitimacy (CRELE). Environmental Science and Policy, 76, 146–152.
13.
GambleM. J.DowlerD. L.OrsleneL. E. (2006). Assistive technology: Choosing the right tool for the right job. Journal of Vocational Rehabilitation, 24, 73–80.
14.
GolanO.Baron-CohenS. (2006). Systemizing empathy: Teaching adults with Asperger syndrome or high-functioning autism to recognize complex emotions using interactive multimedia. Development and Psychopathology, 18, 591–617.
15.
GolanO.AshwinE.GranaderY.McClintockS.DayK.LeggettV.Baron-CohenS. (2010). Enhancing emotion recognition in children with autism spectrum conditions: An intervention using animated vehicles with real emotional faces. Journal of Autism and Developmental Disorders, 40, 269–279.
16.
GrynszpanO.WeissP. L.Perez-DiazF.GalE. (2014). Innovative technology-based interventions for autism spectrum disorders: A meta-analysis. Autism, 18, 346–361.
17.
HammellK. W. (2002). Informing client-centred practice through qualitative inquiry: Evaluating the quality of qualitative research. British Journal of Occupational Therapy, 65, 175–184.
18.
JacobsonJ. W.MulickJ. A. (2000). System and cost research issues in treatments for people with autistic disorders. Journal of Autism and Developmental Disorders, 30, 585–593.
19.
KaminskiJ. (2011). Diffusion of innovation theory. Canadian Journal of Nursing Informatics, 6, 1–6.
20.
McNaughtonD.LightJ. (2013). The iPad and mobile technology revolution: Benefits and challenges for individuals who require augmentative and alternative communication. Augmentative and Alternative Communication, 29, 107–116.
21.
MoldinS. O.RubensteinJ. L. (2006). Understanding autism: From basic neuroscience to treatment. Boca Raton, FL: Taylor & Francis.
22.
MooreD.ChengY.McGrathP.PowellN. J. (2005). Collaborative virtual environment technology for people with autism. Focus on Autism and Other Developmental Disabilities, 20, 231–243.
23.
MooreD.McGrathP.ThorpeJ. (2000). Computer-aided learning for people with autism—A framework for research and development. Innovations in Education and Teaching International, 37, 218–228.
24.
National Autism Center. (2009). National standards report. Randolph, MA: Author.
25.
ParsonsS.CobbS. (2011). State-of-the-art of virtual reality technologies for children on the autism spectrum. European Journal of Special Needs Education, 26, 355–366.
26.
ParsonsS.MitchellP. (2002). The potential of virtual reality in social skills training for people with autistic spectrum disorders. Journal of Intellectual Disability Research, 46, 430–443.
27.
PenningtonR. C. (2010). Computer-assisted instruction for teaching academic skills to students with autism spectrum disorders: A review of literature. Focus on Autism and Other Developmental Disabilities, 25, 239–248.
28.
PloogB. O.ScharfA.NelsonD.BrooksP. J. (2013). Use of computer-assisted technologies (CAT) to enhance social, communicative, and language development in children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 43, 301–322.
29.
Riemer-ReissM. L.WackerR. R. (2000). Factors associated with assistive technology discontinuance among individuals with disabilities. Journal of Rehabilitation, 66, 44–50.
30.
RogersE. M. (1995). Diffusion of innovation theory. New York, NY: Free Press.
31.
RogersE. M. (2003). The diffusion of innovation (5th ed.). New York, NY: Free Press.
32.
RosenstockI. M.StrecherV. J.BeckerM. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15, 175–183.
33.
ScottS.AlbrechtL.O’LearyK.BallG.HartlingL.HofmeyerA.…DrydenD. (2012). Systematic review of knowledge translation strategies in the allied health professions. Implementation Science, 7, 70–70.
34.
ScottS.OsmondM.O’LearyK.GrahamI.GrimshawJ.KlassenT.,…Pediatric Emergency Research Canada (PERC) MDI/Spacer Study Group. (2009). Barriers and supports to implementation of MDI/spacer use in nine Canadian pediatric emergency departments: A qualitative study. Implementation Science, 4, 65.
35.
StrausS. E.TetroeJ.GrahamI. (2009). Defining knowledge translation. Canadian Medical Association Journal, 181, 165–168.
36.
ThorneS. (2016). Interpretive description: Qualitative research for applied practice. Routledge.
37.
VenkateshV.DavisF. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186–204.
38.
VenkateshV.MorrisM. G.DavisG. B.DavisF. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27, 425–478.
