Currently submitted to: JMIR Formative Research
Date Submitted: Jul 6, 2026
Open Peer Review Period: Jul 7, 2026 - Sep 1, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Predictors of the intention to adopt a hypothetical artificial intelligence-enabled professional networking platform among orthopedics in a high-income Middle Eastern country: A cross-sectional study
ABSTRACT
Background:
Musculoskeletal disorders increased by 152% in the Middle East and North Africa region with Kuwait leading in prevalence between 1990 and 2019. Yet, artificial intelligence (AI)-enabled professional networking solutions to speedify inter-professional consultations toward reducing burnout are barely used in Kuwait. Concerns over poor adoption intention considering over-reliance on informal channels for professional communication in Kuwait may be hindering market entry into MENA countries.
Objective:
This study aimed to identify predictors of adoption intention of a hypothetical AI-enabled professional support solution among orthopedics in Kuwait.
Methods:
A cross-sectional offline survey was conducted among orthopedics in Kuwait to assess their agreement with statements on trust, social identify (SI), perceived usefulness (PU), and perceived ease of use (PEOU) regarding a hypothetical AI-enabled professional networking solution. Path coefficients were calculated to determine the relationships between the factors in multivariable regression and structural equation modelling. Mediation, moderation, importance performance, and dominance analyses were conducted to characterize the relationships between factors.
Results:
A total of 326 orthopedics mainly surgeons and physiotherapists with varying years of experience working in hospitals participated in the survey. Perceiving profession’s success as personal success and pride in the professional group, aspects of social identity, were the strongest positive and negative individual predictors of adoption intention (β = 0.55 to 0.58, p < 0.001 and β = -0.34 to -0.35, p < 0.001, respectively in both models. Trust was the strongest direct positive predictor (β = 0.6, p < 0.001), the mediator of the relationship between PU and SI, the factor requiring the most attention, and the factor with the highest dominance weight (average contribution = 0.28, 53.8% of R2) followed by SI (0.148, 28.1% of R2).
Conclusions:
Trust significantly predicted adoption intention of AI-enabled professional networking solution either directly or by influencing PU and SI. SI, the second most significant predictor of adoption intention, is complex. Developers of and researchers on the AI-enabled professional networking solutions should align with the social identities and trust-building essentials of Kuwaiti orthopedics to improve adoption intention and prediction models.
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