This study examines how employment anxiety and individual innovativeness shape attitudes toward artificial intelligence amongst social work students in Türkiye. By analysing psychological and structural factors through a mediation model, the research offers practical insights for public servants managing workforce transitions and AI integration in public services. For policy advisors, HR professionals, and digital transformation leads, understanding what drives or hinders AI acceptance among future social workers is increasingly relevant, and this study provides evidence-based guidance on building innovation-ready workforces without assuming that anxiety is simply a barrier to be removed.
Who Wrote This Report?
This report was written by the Department of Social Work at the Patnos Social Work College, Agri Ibrahim Cecen University. It has been published by the British Journal of Social Work. The quantitative data draws from a group of 518 undergraduate social work students.
Remarkable Quote
“Employment anxiety, traditionally viewed as a barrier, may also act as a motivational factor fostering innovative orientations and positive AI perceptions”.
Key Takeaways
Anxiety as a Catalyst, Not Just a Barrier: Conventional workforce thinking frames employment anxiety as something to be managed or reduced. This study challenges that assumption. Among 518 social work students in Türkiye, higher employment anxiety was positively, not negatively, associated with greater self-perceived innovativeness and more favourable attitudes toward AI. For public sector HR and workforce planners, this reframes how anxiety about digital transformation should be understood and responded to.
Innovativeness Is the Bridge: The study's central finding is that perceived individual innovativeness partially mediates the relationship between employment anxiety and AI attitudes. It found that anxious students saw themselves as more innovative, and that sense of innovativeness translated into more positive views of AI. Thus building innovation confidence in public servants may be a more effective lever for AI readiness than simply reducing fear.
Prolonged Direct Effect: Even after accounting for innovativeness, employment anxiety directly shaped attitudes toward AI. This dual pathway, through innovativeness and independently, indicates that technology attitudes in public-facing professions are shaped by both psychological disposition and structural job concerns. Workforce strategies that address only one attribute will fall short.
Innovativeness Can Be Cultivated: Individual innovativeness is not a fixed trait. The study draws on Rogers' diffusion of innovations framework and recent empirical work to suggest that pedagogical and organisational environments that encourage experimentation, idea generation, and creative problem-solving can strengthen innovation orientations, with downstream benefits for AI acceptance.
Multidisciplinary: While the study focuses on social work students, the variables, employment anxiety, innovation self-perception, and AI attitudes, are directly relevant to any public service workforce navigating digital transformation. The mechanisms identified here apply across health, education, welfare administration, and beyond.
What's in this report?
The study addresses a question increasingly urgent for public service leaders: why do some workers embrace AI while others resist it, and what psychological and structural factors drive that difference?
Using a quantitative correlational design, researchers surveyed 518 undergraduate social work students across Türkiye, measuring three validated constructs: employment anxiety (using the Employment Anxiety Scale, α = 0.91), attitudes toward AI (using the General Attitudes Towards Artificial Intelligence Scale, GAAIS, α = 0.82), and perceived individual innovativeness (using the Individual Innovativeness Scale, α = 0.80).
The core analysis tests a mediation model using Hayes' PROCESS macro. Key results include:
- Employment anxiety significantly predicted individual innovativeness (B = 0.39, p < .001), explaining 18.5% of variance.
- Both employment anxiety and individual innovativeness significantly predicted AI attitudes
- The indirect effect via innovativeness was statistically significant (B = 0.11, 95% CI [0.05, 0.18]), confirming partial mediation.
- The direct effect of employment anxiety on AI attitudes remained significant even after innovativeness was included, indicating a dual pathway.
The discussion establishes these findings within social work's evolving relationship with AI, from algorithmic risk assessment to automated case recording, while remaining alert to the profession's ethical commitments around human dignity, professional discretion, and client self-determination.
What's in this report for you?
For public servants working on digital transformation, workforce development, or AI integration, this study offers a reframing that is both evidence-based and practically useful.
For workforce and HR professionals, the finding that employment anxiety can drive rather than dampen innovation readiness challenges the default of anxiety-suppression. Rather than designing programmes solely to reassure workers, it may be equally valuable to design programmes that channel anxiety productively, helping staff understand how their concern for job relevance can become a motivation to upskill and engage.
For development teams, the mediating role of perceived innovativeness points to a concrete intervention target. Programmes that build innovation self-efficacy, through design thinking, technology experimentation, peer learning, and low-stakes prototyping, may strengthen AI readiness more effectively than information campaigns alone.
For policy advisors and senior leaders, the partial mediation model carries a crucial message: employment anxiety maintains a direct effect on AI attitudes, independent of innovativeness. This means that structural concerns, job security, role clarity, and fair workload distribution as AI is introduced, cannot be substituted by psychological or training interventions. Both dimensions require separate attention.
At the micro, medium, and macro levels, the authors outline actionable implications. At the frontline, innovation-confident practitioners are better placed to integrate AI tools into casework collaboratively. At the team level, psychological safety around technology adoption can be designed into supervision and workflow structures. At the systems level, frontline experiences of anxiety and innovation can inform ethical governance frameworks for AI in public services.
The study also presents a useful challenge to risk framing in AI adoption: perceived barriers may contain untapped motivational energy. For public servants designing dynamic management strategies, this is a crucial takeaway.
Who is this report for?
This report is an essential reading for:
- Digital transformation leads seeking to understand the psychological dimensions of AI adoption in public-facing roles
- Workforce planning and HR professionals designing upskilling programmes for services undergoing technological change
- Policy advisors working on AI governance, ethical technology use, or future-of-work strategies in the public sector
- Learning and development practitioners looking for evidence-based approaches to building confidence in innovation sectors
- Social services managers overseeing the introduction of AI tools under welfare, child protection, or community support settings
- Academics and researchers examining technology acceptance in human-service professions
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