Accessible products play an essential role in the lives of people with disabilities. This paper aims to identify key user satisfaction with accessible products factors affecting the use of accessible products by people with disabilities that influence user satisfaction. The extended model incorporates the essential elements of the TAM, TPB, and PR models and user satisfaction as an external variable. Data were collected from 339 users of accessible products. Structural equation modeling was used to identify significant variables in this study. SEM considered “behavioral intention” to be the most important among them. This study generated design strategies based on significant factors analyzed in the findings and validated the design cases using the PSSUQ questionnaire, which showed that users had better user satisfaction when using accessible products with the new design strategies.
O’YoungB., GosneyJ. and AhnC., The Concept and Epidemiology of Disability, Physical Medicine and Rehabilitation Clinics of North America30 (2019), 697–707.
JonesM., MorrisJ. and DeruyterF., Mobile Healthcare and People with Disabilities: Current State and Future Needs, IJERPH15 (2018), 515.
5.
RadcliffeE., LippincottB., AndersonR. and JonesM., A Pilot Evaluation of MHealth App Accessibility for Three Top-Rated Weight Management Apps by People with Disabilities, International Journal of Environmental Research and Public Health18 (2021), 3669.
6.
GrayJ.A., ZimmermanJ.L. and RimmerJ.H., Built Environment Instruments for Walkability, Bikeability, and Recreation: Disability and Universal Design Relevant?Disability and Health Journal5 (2012), 87–101.
7.
Mayordomo-MartinezD., Sanchez-AarnoutseJ.-C., Carrillo-de-GeaJ.M., Garcia-BernaJ.A., Luis Fernandez-AlemanJ. and Garcia-MateosG., Design and Development of a Mobile App for Accessible Beach Tourism Information for People with Disabilities, International Journal of Environmental Research and Public Health16 (2019), 2131.
8.
Mayordomo-MartinezD., Carrillo-de-GeaJ.M., Garcia-MateosG., Garcia-BernaJ.A., Luis Fernandez-AlemanJ., Rosero-LopezS., Parada-SarabiaS. and Garcia-HernandezM., Sustainable Accessibility: A Mobile App for Helping People with Disabilities to Search Accessible Shops, International Journal of Environmental Research and Public Health16 (2019), 620.
9.
ZhangM., YuJ., ShenW., ZhangY., XiangY., ZhangX., LinZ. and YanT., A Mobile App Implementing the International Classification of Functioning, Disability and Health Rehabilitation Set, Bmc Medical Informatics and Decision Making20 (2020), 12.
10.
CheraghiS.A., NamboodiriV. and WalkerL., GuideBeacon: Beacon-Based Indoor Wayfinding for the Blind, Visually Impaired, and Disoriented, In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2017.
11.
ChaccourK. and BadrG., Computer Vision Guidance System for Indoor Navigation of Visually Impaired People, In Proceedings of the 2016 IEEE 8th International Conference on Intelligent Systems (IS); 2016.
12.
KoE. and KimE., A Vision-Based Wayfinding System for Visually Impaired People Using Situation Awareness and Activity-Based Instructions, Sensors17 (2017), 1882.
13.
LiuJ., LiuJ., XuL. and JinW., Electronic Travel Aids for the Blind Based on Sensory Substitution, In Proceedings of the 2010 5th International Conference on Computer Science & Education; IEEE: Hefei, China, August 2010; pp. 1328–1331.
14.
LeeS., KangS., HanD.K. and KoH., Dialogue Enabling Speech-to-Text User Assistive Agent System for Hearing-Impaired Person, Medical & Biological Engineering & Computing54 (2016), 915–926.
15.
KimW.-L., HamH.-W., WoonY.S. and JooL.W., A Design and Implementation of Speech Recognition and Synthetic Application for Hearing-Impairment, Journal of The Korea Society of Computer and Information26 (2021), 105–110.
16.
Pan-ngumS., SoonrachT., SeesutasS., NoymaiA. and IsrasenaP., Development of a Low Cost Assistive Listening System for Hearing-Impaired Student Classroom, Scientific World Journal (2013), 787656.
17.
OlsenS.H., SapersteinS.L. and GoldR.S., Content and Feature Preferences for a Physical Activity App for Adults With Physical Disabilities: Focus Group Findings, Jmir Mhealth and Uhealth7 (2019), e15019.
18.
KairyD., MostafaviM.A., Blanchette-DallaireC., BelangerE., CorbeilA., KandiahM., WuT.Q. and MazerB., A Mobile App to Optimize Social Participation for Individuals with Physical Disabilities: Content Validation and Usability Testing, International Journal of Environmental Research and Public Health18 (2021), 1753.
19.
JöreskogK.G., A General Method for Analysis of Covariance Structures, Biometrika57 (1970), 239–251.
20.
TarkaP., An Overview of Structural Equation Modeling: Its Beginnings, Historical Development, Usefulness and Controversies in the Social Sciences, Quality & Quantity52 (2018), 313–354.
21.
ChenH., LiuH., ChuX., ZhangL. and YanB., A Two-Phased SEM-Neural Network Approach for Consumer Preference Analysis, Advanced Engineering Informatics46 (2020), 101156.
22.
SevaR.R., GosiacoK.G.T., SantosM.C.E.D. and PangilinanD.M.L., Product Design Enhancement Using Apparent Usability and Affective Quality, Applied Ergonomics42 (2011), 511–517.
23.
DavisF.D., BagozziR.P. and WarshawP.R., User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science35 (1989), 982–1003.
24.
HoldenR.J. and KarshB.-T., The Technology Acceptance Model: Its Past and Its Future in Health Care, Journal of Biomedical Informatics43 (2010), 159–172.
25.
VenkateshV. and DavisF.D., A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science46 (2000), 186–204.
26.
TroiseC., O’DriscollA., TaniM. and PriscoA., Online Food Delivery Services and Behavioural Intention – a Test of an Integrated TAM and TPB Framework, BFJ123 (2020), 664–683.
27.
TaoD., ShaoF., WangH., YanM. and QuX., Integrating Usability and Social Cognitive Theories with the Technology Acceptance Model to Understand Young Users’ Acceptance of a Health Information Portal, Health Informatics J26 (2020), 1347–1362.
28.
ChenK. and ChanA.H.S., Predictors of Gerontechnology Acceptance by Older Hong Kong Chinese, Technovation34 (2014), 126–135.
29.
AjzenI., From Intentions to Actions: A Theory of Planned Behavior, Action Control (1985), 11–39.
30.
DownsD.S. and HausenblasH.A., Elicitation Studies and the Theory of Planned Behavior: A Systematic Review of Exercise Beliefs, Psychology of Sport and Exercise6 (2005), 1–31.
31.
Ranjbar ShamsiH., Omidi NajafabadiM.and HosseiniS.J.F., Designing a Three-Phase Pattern of Organic Product Consumption Behaviour, Food Quality and Preference79 (2020), 103743.
32.
HaggerM.S., ChatzisarantisN.L.D. and BiddleS.J.H., A Meta-Analytic Review of the Theories of Reasoned Action and Planned Behavior in Physical Activity: Predictive Validity and the Contribution of Additional Variables, Journal of Sport and Exercise Psychology24 (2002), 3–32.
33.
KirkT.N. and HaegeleJ.A., Theory of Planned Behavior in Research Examining Physical Activity Factors Among Individuals With Disabilities: A Review, Adapted Physical Activity Quarterly36 (2019), 164–182.
34.
AB.R., Consumer Behavior as Risk Taking, Proceedings of the 43rd National Conference of the American Marketing Assocation, June 16 (15), 17, Chicago, Illinois, 1960.
35.
PeterJ.P. and RyanM.J., An Investigation of Perceived Risk at the Brand Level, Journal of Marketing Research13 (1976), 184–188.
36.
LeeM.K.O. and TurbanE., A Trust Model for Consumer Internet Shopping, International Journal of Electronic Commerce6 (2001), 75–91.
37.
ChenH.-K., ChouH.-W. and HungS.-C., Interrelationships betweenBehaviour Intention and Its Influential Factors for Consumers ofMotorcycle Express Cargo Delivery Service, Transportmetrica a-Transport Science15 (2019), 526–555.
38.
AlHadidI., Abu-TaiehE., AlkhawaldehR.S., KhwaldehS., Masa’dehR., KaabnehK. and AlrowwadA., Predictors for E-Government Adoption of SANAD App Services Integrating UTAUT, TPB, TAM, Trust, and Perceived Risk, IJERPH19 (2022), 8281.
39.
ParasuramanA., ZeithamlV.A. and BerryL.L., A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing49(4) (1985), 41–50.
40.
WoodruffR.B., Customer value: The next source for competitive advantage, J of the Acad Mark Sci25(2) (1997), 139–153.
41.
OliverR.L., A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions, Journal of Marketing Research17(4) (1980), 460–469.
42.
D’AmicoG., Del BimboA., DiniF., LanducciL. and TorpeiN., Natural Human– Computer Interaction, Multimedia Interaction and Intelligent User Interfaces (2010), 85–106.
43.
ShinD., KeeK.F. and ShinE.Y., Algorithm awareness: Why user awareness is critical for personal privacy in the adoption of algorithmic platforms?International Journal of Information Management65 (2022), 102494.
44.
ShinD., Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm, Journal of Information Science49(1) (2023), 18–31.
45.
HuangZ., LimkeJ. and KongJ., Investigating One-Handed and Two-Handed Inter-Device Interaction, Journal of Visual Languages and Computing42 (2017), 1–12.
46.
ShinD.H., Conceptualizing and measuring quality of experience of the internet of things: Exploring how quality is perceived by users, Information & Management54(8) (2017), 998–1011.
47.
KortumP. and PeresS.C., The Relationship Between System Effectiveness and Subjective Usability Scores Using the System Usability Scale, International Journal of Human-Computer Interaction30 (2014), 575–584.
48.
KarpovA.A. and YusupovR.M., Multimodal Interfaces of Human– Computer Interaction, Her Russ Acad Sci88 (2018), 67–74.
49.
AjzenI., Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior1, Journal of Applied Social Psychology32 (2002), 665–683.
50.
KimY.H., KimD.J. and WachterK., A Study of Mobile User Engagement (MoEN): Engagement Motivations, Perceived Value, Satisfaction, and Continued Engagement Intention, Decision Support Systems56 (2013), 361–370.
51.
XiaX., JiangH. and WangJ., Analysis of User Satisfaction of Shared Bicycles Based on SEM, J Ambient Intell Human Comput13 (2022), 1587–1601.
52.
AmosunS., NdosiA. and BuchananH., Locally Manufactured Wheelchairs in Tanzania – Are Users Satisfied?Afr H Sci16 (2016), 1174.
53.
BragançaS., CastellucciI., GillS., MatthiasP., CarvalhoM. and ArezesP., Insights on the Apparel Needsand Limitations for Athletes with Disabilities: The Design of Wheelchair Rugby Sports-Wear, Applied Ergonomics67 (2018), 9–25.
54.
YeniS., CagiltayK. and KarasuN., Usability Investigation of an Educational Mobile Application for Individuals with Intellectual Disabilities, Univ Access Inf Soc19 (2020), 619–632.
55.
Ju JuY., ShinE.K. and HamY.K., A Study on the Factors Affecting Intention on Continuous Use of Mobile Learning in Cyber University, The Journal of Information Systems23 (2014), 47–71.
56.
KimS. and YoonS., The Influences of Perceived Usefulness and Perceived Ease of Use on Learning Outcomes in Team Project-Based Learning with Naver Band, The Journal of the Korea Contents Association16 (2016), 695–706.
57.
KarahannaGefen, Straub Trust and TAM in Online Shopping: An Integrated Model, MIS Quarterly27 (2003), 51.
58.
DavisF.D., Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly13 (1989), 319.
59.
LiuW. and TangW., Effects of Age-Appropriate Mobile APP Use Behavior on Subjective Well-Being of Young Elderly, Security and Communication Networks2022 (2022), 1–12.
60.
AjzenI., The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes50 (1991), 179–211.
61.
ParkS., ZielinskiS., JeongY. and KimS., Factors Affecting Residents’ Support for Protected Area Designation, Sustainability12 (2020), 2800.
62.
RhodesR.E. and CourneyaK.S., Threshold Assessment of Attitude, Subjective Norm, and Perceived Behavioral Control for Predicting Exercise Intention and Behavior, Psychology of Sport and Exercise6 (2005), 349–361.
63.
YiM.Y., JacksonJ.D., ParkJ.S. and ProbstJ.C., Understanding Information Technology Acceptance by Individual Professionals: Toward an Integrative View, Information & Management43 (2006), 350–363.
64.
LiuL. and MaQ., Perceived System Performance, SIGMIS Database37 (2006), 51–59.
65.
MitznerT.L., RogersW.A., FiskA.D., BootW.R., CharnessN., CzajaS.J. and SharitJ., Predicting Older Adults’ Perceptions about a Computer System Designed for Seniors, Univ Access Inf Soc15 (2016), 271–280.
66.
NamC.S., BahnS. and LeeR., Acceptance of Assistive Technology by Special Education Teachers: A Structural Equation Model Approach, International Journal of Human-Computer Interaction29 (2013), 365–377.
67.
XieI., BabuR., LeeT.H., CastilloM.D., YouS. and HanlonA.M., Enhancing Usability of Digital Libraries: Designing Help Features to Support Blind and Visually Impaired Users, Information Processing & Management57 (2020), 102110.
68.
ChauP.Y.K. and HuP.J., Examining a Model of Information Technology Acceptance by Individual Professionals: An Exploratory Study, Journal of Management Information Systems18 (2002), 191–229.
69.
DuyckP., PynooB., DevolderP., VoetT., AdangL. and VercruysseJ., User Acceptance of a Picture Archiving and Communication System: Applying the Unified Theory of Acceptance and Use of Technology in a Radiological Setting, Methods Inf Med47 (2008), 149–156.
70.
LeeM.-C., Factors Influencing the Adoption of Internet Banking: An Integration of TAM and TPB with Perceived Risk and Perceived Benefit, Electronic Commerce Research and Applications8 (2009), 130–141.
71.
ChengT.C.E., LamD.Y.C. and YeungA.C.L., Adoption of Internet Banking: An Empirical Study in Hong Kong, Decision Support Systems42 (2006), 1558–1572.
72.
WuK., VassilevaJ., NoorianZ. and ZhaoY., How Do You Feel When You See a List of Prices? The Interplay among Price Dispersion, Perceived Risk and Initial Trust in Chinese C2C Market, Journal of Retailing and Consumer Services25 (2015), 36–46.
73.
ChangM.-L. and WuW.-Y., Revisiting Perceived Risk in the Context of Online Shopping: An Alternative Perspective of Decision-Making Styles, Psychol Mark29 (2012), 378–400.
74.
KoH., JungJ., KimJ. and ShimS.W., Cross-Cultural Differences in Perceived Risk of Online Shopping, Journal of Interactive Advertising4 (2004), 20–29.
75.
ShengM. and XiaLu X., An Empirical Study on Influencing Factors of Customer Satisfaction to Individual Internet Banking, In: 2009 International Symposium on Computer Network and Multimedia Technology, IEEE; 2009: 1–4.
76.
FornellC. and LarckerD.F., Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research (1981), 12.
77.
GudigantalaN., SongJ. and JonesD., User Satisfaction with Web-Based DSS: The Role of Cognitive Antecedents, International Journal of Information Management31 (2011), 327–338.
78.
SauroJ., Quantifying the User Experience: Practical Statistics for User Research; Quantifying the User Experience: Practical Statistics for User Research, 2012.
