Surgeon Adoption of 3D-Printed Implants: Behavioral Factors and TAM2 Analysis

Introduction to the Research

The medical device industry constantly seeks competitive advantage through innovative product launches, but the adoption of these new technologies by their primary users—surgeons—is not always guaranteed and is influenced by various factors. Understanding these factors is crucial for the continued growth of organizations that invest significant resources in research, development, and marketing. While traditional diffusion models describe sales and marketing timing, they often fail to provide insights into surgeons’ decision-making processes, highlighting a need for more defined strategies to successfully target surgeons and understand their adoption behaviors.

This study specifically addresses this gap by examining the behavioral factors that influence orthopedic surgeons’ adoption of innovative technologies, with a particular focus on 3D-Printed (3DP) implants. The 3DP implant market is a rapidly expanding sector, projected to grow from $973 million in 2018 to $3.69 billion by 2026, driven by groundbreaking applications and benefits. These benefits include reduced medical costs, enhanced surgical planning, the creation of customized, patient-specific implants, and standardized tools that improve procedural workflow, patient fit, and clinical outcomes like fusion rates.

Theoretical Framework and Constructs

To investigate surgeons’ behavioral intentions (BIs) and their likelihood of adopting technology, this research employs the Technology Acceptance Model 2 (TAM2), an extension of the widely validated Technology Acceptance Model (TAM). TAM2 is considered a robust framework for studying the cognitive and social factors influencing surgeon technology adoption. The foundational TAM constructs—Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)—are central to understanding users’ attitudes and behavioral intentions.

TAM2 expands upon these core constructs by incorporating social influence factors and cognitive instrumental processes as determinants of PU and intention to use (IU). The study specifically investigates the following constructs and their hypothesized relationships:

• Perceived Usefulness (PU) and Perceived Ease of Use (PEOU): Expected to positively influence surgeons’ Intention to Use (IU) and subsequently, adoption.
• Subjective Norms (SN): Reflecting surgeons’ perceptions of what important individuals (e.g., mentors, peers, key opinion leaders at conferences) believe about adopting a technology.
• Image (I): The degree to which using an innovation is perceived to enhance one’s status or reputation within their social system.
• Job Relevance (JR): A surgeon’s perception of how applicable a technology is to their professional tasks and surgical skillset.
• Output Quality (OQ): The extent to which a system performs its tasks effectively, producing reproducible, measurable results that improve patient outcomes.
• Result Demonstrability (RD): The tangibility of the results from using a technology and a surgeon’s ability to understand and communicate these benefits to others.
• Technology Readiness Index (TRI): Measures individual motivations (optimism, innovativeness, discomfort, insecurity) that influence the adoption of new technologies.
• Voluntariness: The perceived extent to which the adoption decision is non-mandatory.

Methodology

The study utilized primary data collected through a Qualtrics survey administered to approximately 2,500 orthopedic surgeons and neurosurgeons globally, operating weekly. The recruitment involved various methods, including email lists, corporate office visits, LinkedIn connections, surgeon conferences, and assistance from sales representatives. A total of 100 completed surveys were obtained, yielding an effective response rate of 4%. The survey instrument incorporated construct-scale items adapted from established studies, and questions were reworded for clinical relevance and to enhance surgeon engagement.

Data analysis was performed using IBM SPSS v. 27 and SmartPLS3 v 3.3.3. The analysis included assessing multicollinearity, performing correlation analysis, and utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the hypotheses and the full model. Descriptive statistics revealed demographic information of the participants, including specialty (69 Ortho, 31 Neuro), clinical position, tenure, age, gender, and geographic location. The study also conducted a multigroup analysis to compare factors influencing surgeons who had adopted 3DP implants (74 participants) versus those who had not (26 participants).

Key Findings and Implications

The empirically supported model explained 50% of the variance in surgeons’ Intention to Use (IU) new technologies. The study found that subjective norms, image, and the Technology Readiness Index (TRI) significantly affected the core TAM variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (IT/IU). Specifically, PU and PEOU were found to significantly influence IU, supporting the idea that surgeons will intend to use technology if they perceive it as useful and easy to use. The path coefficients indicated strong and significant relationships, particularly between TRI and PEOU, and PEOU and IU. The positive relationship of subjective norms with IU and image with PU also demonstrated significance, albeit borderline in some cases, highlighting the social influences on surgeons’ adoption decisions.

Interestingly, factors such as job relevance, output quality, and result demonstrability did not show a statistical relationship in the final PLS-SEM model, although initial SPSS analysis showed correlations with PU. A multigroup analysis revealed that non-adopters showed higher R-square values, suggesting the model captured behaviors discouraging their adoption, with output quality specifically explaining 41.5% of non-adopters’ behavior.

The findings of this study provide several significant contributions:

• Theoretical Contribution: It extends the application of TAM2 to a non-IT, clinical technology adoption context, specifically for innovative surgical technologies like 3DP implants, demonstrating TAM’s utility in measuring surgeons’ behaviors.
• Practical Contribution: The research offers valuable insights for medical device companies. It identifies that social influences (subjective norms and image) and cognitive readiness (technology readiness) are key intrinsic behaviors driving surgeons’ technology adoption. Companies can leverage these insights to target their marketing efforts more precisely by implementing behavioral marketing and social influence campaigns that focus on key opinion leaders and influencers within surgeons’ networks. Additionally, organizations can utilize the TRI to assess a surgeon’s technology readiness, which is a strong predictor of PEOU.

This study underscores that orthopedic surgeons, being inherently inclined to use newer technologies to improve patient outcomes, are significantly influenced by peer perceptions and their own readiness to embrace innovation, making TAM2 a valuable tool for predicting their clinical adoption behaviors.

APA Reference:

Reynolds, S., Donthu, N., & Campagna, C. (2024). Factors Influencing Orthopedic Surgeons’ Clinical Adoption of Innovative Technologies. Ortho Surg Ortho Care Int J, 3(2), 000556. DOI: 10.31031/OOIJ.2024.03.000556.