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A Social Influence Perspective on Technology Adoption: The Roles of Social Acceptability and Behavioural Intentions

This study demonstrates that social influence, specifically subjective norms, drives the actual uptake of genetic testing by sequentially enhancing social acceptability and behavioral intentions, highlighting the need for implementation strategies that address social legitimacy alongside service accessibility.

Original authors: Jenny Twyford, Russell Currie, Matti Haverila

Published 2026-10-18

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Jenny Twyford, Russell Currie, Matti Haverila

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Making sense of a new medical technology often feels less like a simple choice and more like a conversation with the world around us. When a doctor suggests a genetic test—a procedure that reads a person's DNA to predict health risks or tailor treatments—the decision rarely happens in a vacuum. It is shaped by what family members think, what friends say, and whether the person feels the test is something a "good" or "responsible" person would do. This is the realm of social influence, where our choices are quietly guided by the expectations of others. For years, scientists have known that these social pressures matter, but they have struggled to explain exactly how a feeling of "what others expect" turns into the actual act of getting tested. Is it simply that people want to please their peers, or is there a deeper layer where society as a whole must first agree that a technology is legitimate and appropriate before individuals feel comfortable using it?

A team of researchers at Thompson Rivers University set out to untangle these threads by looking specifically at genetic testing. They wanted to move beyond the idea that people just decide based on their own logic or a doctor's advice. Instead, they built a model to trace the path from social pressure to real-world action. They focused on three key ideas. First, there are subjective norms, which is a fancy way of saying "what I think my important people expect of me." Second, there is social acceptability, which is the broader feeling that a technology is right, ethical, and approved by society in general. Finally, there is the intention to act, which is the mental step before actually doing something. The researchers asked a simple but profound question: Does the pressure we feel from our circle of friends and family first make us feel that a test is socially acceptable, and does that sense of acceptance then push us to actually get the test done?

To find the answer, the researchers turned to a large group of 833 adults living in British Columbia, Canada. They asked these participants to fill out an online survey about their views on genetic testing. The survey asked people to rate how much they agreed with statements about what their family and friends thought, whether they felt genetic testing was a socially appropriate thing to do, how likely they were to get tested, and whether they had actually done it. After carefully checking the data and removing a few responses that seemed unusual, the team analyzed the answers of 808 people. They used a statistical method designed to find the strongest links between these different ideas, looking for the hidden pathways that connect social pressure to real behavior.

The results painted a clear and detailed picture of how social influence works in this context. The study found that the expectations of family and friends are a powerful force. When people feel that their close circle supports genetic testing, they are much more likely to view the test as something that is socially acceptable and legitimate. This is a crucial link. It suggests that social pressure does not just push people directly to act; it first changes their mind about whether the technology is "okay" to use. Once people feel that genetic testing is socially acceptable, their intention to get tested grows stronger. And finally, this intention is the most reliable predictor of all: if a person intends to get tested, they are very likely to actually go through with it.

The data showed that the path from social pressure to actual testing is not a straight line but a chain of events. The feeling that "my friends think I should" strongly boosts the feeling that "this is a good and normal thing to do." That sense of normalcy then fuels the desire to act, which in turn leads to the action itself. The researchers found that social acceptability acts as a vital bridge. Without that bridge, the pressure from friends might not be enough to convince someone to take the step. In fact, the study showed that the intention to get tested was the single strongest factor leading to actual usage, but that intention itself was heavily dependent on the prior steps of feeling social pressure and feeling that the test was socially legitimate.

This finding challenges the idea that people make these decisions in isolation. It suggests that for a new health technology to succeed, it is not enough to simply make it available or to tell people it is useful. The technology must also feel socially right. If a community or a family does not view a test as appropriate or legitimate, even the most willing individual may hesitate. The study implies that the success of precision medicine depends on building a social environment where these tests are seen as normal, ethical, and endorsed by the people who matter most to us.

The researchers were careful to note that their study looked at a single point in time, so they could not prove that one thing caused the other with absolute certainty, though the patterns were very strong. They also relied on people's own reports of their behavior, which can sometimes be imperfect. However, the consistency of the results across such a large group gives them confidence in the pattern they observed. The study does not claim to have solved the mystery of why people avoid testing, but it does offer a clear map of the social terrain. It shows that to get more people to use genetic testing, health systems and doctors need to do more than just open clinics. They need to foster a sense of social acceptance, ensuring that patients feel their communities support the choice, so that the intention to act can finally become the act itself.

Technical Summary: A Social Influence Perspective on Technology Adoption

Problem Statement
The successful implementation of precision medicine, specifically genetic testing (GT), faces a significant "implementation gap." While clinical availability and utility have expanded, patient uptake remains below expectations. Existing literature suggests that technological capability and clinical effectiveness are insufficient to ensure widespread adoption; rather, the behavioural and social processes influencing individual engagement are critical. Traditional behavioural models (e.g., Theory of Planned Behaviour, Technology Acceptance Model) have largely focused on individual cognitive determinants like attitudes and perceived usefulness. However, these models may not fully explain how social influence translates into actual healthcare utilization, particularly regarding the distinction between perceived expectations from significant others (subjective norms) and the broader societal perception of an intervention's legitimacy and appropriateness (social acceptability). This study addresses the gap in understanding the mechanisms through which social influence shapes the willingness to utilize genetic testing services.

Methodology
The study employed a cross-sectional online survey design to test a behavioural model integrating subjective norms, social acceptability, behavioural intentions, and actual GT usage.

Sample: Data were collected from 833 adults via the Centiment market research platform. After screening and the removal of 25 outliers (identified via Mahalanobis distance), 808 responses were retained for analysis. The sample was drawn from British Columbia, Canada, with a mean age of 47.6 years.
Measures: Constructs were measured using a 5-point Likert scale. The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) rather than Covariance-Based SEM (CB-SEM). This choice was driven by the study's predictive orientation, the complexity of the model, and the need to handle non-normal data distributions common in applied research.
Analytical Rigor: The analysis followed a two-step approach:
1. Measurement Model: Assessed for indicator reliability (loadings > 0.70), internal consistency (Cronbach's alpha and composite reliability), convergent validity (AVE > 0.50), and discriminant validity (using the Heterotrait–Monotrait ratio). Items NORM3 and SOCACC1 were removed to resolve discriminant validity issues between social acceptability and subjective norms.
2. Structural Model: Evaluated for collinearity (VIF < 3.3), predictive power ( $R^2$ ), and predictive relevance (Stone–Geisser's $Q^2$ ). Common method bias was assessed using a full collinearity test, with all VIFs falling below the 3.3 threshold.
Hypothesis Testing: Path coefficients were tested for significance using bootstrapping (5,000 subsamples). Effect sizes ( $f^2$ ) were calculated to determine the magnitude of relationships independent of sample size.

Key Results
The empirical analysis provided strong support for all hypothesized relationships within the proposed structural model:

Direct Effects:
- Subjective Norms $\rightarrow$ Social Acceptability: A strong positive effect ( $\beta = 0.725$ , large effect size), indicating that perceived social pressure significantly shapes perceptions of societal legitimacy.
- Subjective Norms $\rightarrow$ Behavioural Intentions: A significant positive effect ( $\beta = 0.360$ , medium effect size).
- Social Acceptability $\rightarrow$ Behavioural Intentions: A significant positive effect ( $\beta = 0.281$ , small-to-medium effect size).
- Behavioural Intentions $\rightarrow$ Actual Usage: The strongest predictor in the model ( $\beta = 0.649$ , large effect size), confirming the central role of intention in translating perceptions into action.
Indirect (Mediating) Effects: All hypothesized indirect pathways were statistically significant, confirming a sequential mediation process:
- Subjective norms influence actual usage through a chain involving social acceptability and behavioural intentions.
- Social acceptability mediates the relationship between subjective norms and behavioural intentions.
- Behavioural intentions mediate the relationship between social acceptability and actual usage.
Model Performance: The model explained 42.2% of the variance in actual GT usage ( $R^2 = 0.422$ ) and demonstrated low-to-moderate predictive relevance ( $Q^2 = 0.195$ ). Statistical power was estimated at 1.0, confirming robustness.

Key Contributions

Theoretical Extension: The study extends the Theory of Planned Behaviour by introducing social acceptability as a distinct intermediary mechanism. It differentiates between subjective norms (perceived expectations of specific others) and social acceptability (broader societal legitimacy and ethical appropriateness), demonstrating that social influence operates through both interpersonal pressure and broader societal endorsement.
Mechanism Elucidation: By identifying a sequential mediation pathway (Subjective Norms $\rightarrow$ Social Acceptability $\rightarrow$ Behavioural Intentions $\rightarrow$ Actual Usage), the study clarifies how social influence translates into healthcare utilization, moving beyond direct relationship models.
Intention-Behaviour Gap: The research contributes to literature addressing the intention-behaviour gap by showing that favourable intentions are not merely the result of individual cognitive evaluations but are shaped by complex, interconnected social and legitimacy-based processes.
Empirical Evidence on Actual Usage: Unlike many studies focusing solely on intentions, this research examines actual GT usage, providing a more comprehensive view of the adoption lifecycle.

Significance and Claims
The authors claim that successful implementation of precision medicine requires strategies that address social legitimacy alongside service accessibility. The findings suggest that implementation efforts cannot rely solely on expanding access or providing factual information; they must actively foster public trust, ethical acceptance, and community endorsement.

For Health Services Research: The study provides a more comprehensive explanatory framework for patient uptake, suggesting that future models of healthcare technology adoption must incorporate broader social processes and the concept of social acceptability.
For Practice: The authors propose that implementation strategies should leverage trusted social networks (healthcare professionals, community leaders) to normalize GT and address ethical/privacy concerns to enhance social legitimacy. Furthermore, reducing structural barriers is essential to help individuals translate their formed intentions into actual testing.
Scope: The authors maintain a modest scope, noting that while the framework may apply to other emerging technologies (e.g., AI, digital health), the findings are specific to the context of genetic testing and require replication in different jurisdictions and populations to establish generalizability. The study acknowledges limitations regarding its cross-sectional design (limiting causal inference) and reliance on self-reported data.

Technical Summary: A Social Influence Perspective on Technology Adoption

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