AI adoption in SMEs tied to employee fear and flexibility gains

Results show that employees are more likely to embrace job-crafting and demonstrate readiness for change when leaders visibly endorse and embody AI-driven innovation. However, ambiguous or poorly supported AI messaging from leadership often triggers fear, anxiety, and perceptions of job displacement.

A new study has found that small and medium-sized enterprises (SMEs) adopting symbolic leadership practices to signal artificial intelligence (AI) integration are seeing a boost in employee adaptability and job-crafting behaviors - but also a rise in perceived threats and job insecurity. The findings, published this week in the journal Systems, underscore the dual-edged nature of symbolic AI leadership and the vital role of organizational support in managing its outcomes.

The research, led by scholars at the Harbin Institute of Technology, surveyed 376 employees across AI-integrated healthcare and e-commerce SMEs in China. Using a Python-based structural equation modeling approach, the study offers one of the first empirical evaluations of how symbolic representation of AI by leaders influences employee behavior, perception, and readiness for technological change in resource-constrained organizations.

Results show that employees are more likely to embrace job-crafting and demonstrate readiness for change when leaders visibly endorse and embody AI-driven innovation. However, ambiguous or poorly supported AI messaging from leadership often triggers fear, anxiety, and perceptions of job displacement.

Leadership signaling was found to be a decisive factor. Drawing from signaling theory, the study argues that visible endorsement of AI - from championing AI-driven initiatives to integrating AI into workflow discussions - serves as a strong behavioral cue to employees. These symbolic actions significantly increased employee change readiness and enhanced job-crafting tendencies. Job crafting refers to employees proactively redesigning their roles to align with new technologies and workflows.

But the study also warns of a downside. When employees interpreted symbolic AI leadership without sufficient organizational support, perceived threat increased significantly, leading to disengagement and resistance to change. Perceived threats included fears of redundancy, diminished role importance, and anxiety over AI’s implications for job security, particularly in SMEs, where reskilling pathways and safety nets are limited.

Organizational support proved to be a powerful moderator. When companies backed symbolic AI leadership with training, communication, and tangible resources, the negative effects were significantly reduced. The study found that organizational support amplified the positive effects of AI symbolization on change readiness and diminished the negative effects on perceived threats.

The research also established indirect effects. Leaders’ symbolic AI actions improved job crafting indirectly by enhancing change readiness and, conversely, reduced job crafting through increased perceived threats. Organizational support moderated both of these pathways, strengthening the positive indirect effects while weakening the negative ones.

This dual nature of symbolic leadership presents both a challenge and an opportunity for SMEs. Unlike large corporations, Chinese SMEs often lack the institutional structures and HR systems to buffer rapid technological shifts. That makes leadership messaging especially influential and potentially volatile in shaping employee attitudes toward AI adoption.

The findings are especially timely as Chinese SMEs accelerate their AI adoption in healthcare diagnostics, retail personalization, and inventory management. While AI implementation is expected to increase productivity and innovation, the study urges leaders to understand the psychological toll symbolic cues can have on employees in environments with limited formal support.

In practice, the researchers recommend that SME leaders integrate symbolic AI behavior with a parallel investment in organizational support. This includes conducting transparent internal communications about AI’s intended role, offering AI literacy and upskilling workshops, and creating feedback mechanisms to address employee concerns. Leaders are also advised to engage in honest messaging, avoiding overly optimistic portrayals of AI benefits that may later be perceived as disingenuous.

Moreover, the study calls for policy-level incentives to encourage SMEs to invest in employee support systems. As AI adoption intensifies in the private sector, such investments could mitigate potential workforce disruptions and improve innovation outcomes.

The study used a rigorous methodology, drawing on validated scales for measuring symbolic leadership, change readiness, perceived threat, organizational support, and job crafting. Python's semopy and statsmodels libraries were used to perform the structural equation modeling, with fit indices and variance inflation factors confirming the model's robustness. Discriminant validity was verified through Heterotrait–Monotrait Ratio and Fornell–Larcker criteria, and no multicollinearity or method bias was detected.

Healthcare and e-commerce sectors were specifically targeted due to their early and heavy integration of AI technologies in China. Among the 376 respondents, 53.2% were from healthcare SMEs and 46.8% from e-commerce firms. Participants included software developers, data analysts, and researchers all working in AI-rich environments.

The study's lead authors, Dr. Chunjia Hu, Dr. Qaiser Mohi Ud Din, and Aqsa Tahir, emphasized the critical role of leadership in shaping AI transitions not just technologically, but culturally and psychologically. They argue that symbolic AI leadership, when deployed alongside robust support structures, can unlock significant employee potential in dynamic and competitive sectors.

Still, the study acknowledged several limitations. Its focus on Chinese SMEs may limit generalizability to other cultural or economic contexts. It also assessed short-term employee responses, leaving long-term impacts of symbolic leadership on workforce development and innovation adoption for future investigation.

The authors recommend future research explore longitudinal effects, the role of employees’ prior AI experience, and cross-cultural comparisons to better understand how symbolic AI leadership plays out in different organizational ecosystems.

The paper, titled "Artificial Intelligence Symbolic Leadership in Small and Medium-Sized Enterprises: Enhancing Employee Flexibility and Technology Adoption," is available open-access in the April 2025 issue of Systems.

DOI: https://doi.org/10.3390/systems13040216