Research Project

From Institutional Pressure to Responsible Innovation: The Role of AI Capability in Chinese Big Health Industry SMEs

Artificial intelligence is increasingly presented as a strategic priority, but acquiring AI tools does not automatically create organizational value.

Ning Yibing’s research examines the organizational conditions that allow firms to translate external pressures to use AI into meaningful internal capability and, ultimately, into more responsible forms of innovation. Her research asks a complementary question:

The study distinguishes three major sources of institutional pressure:

1. coercive market pressure from customers, suppliers and business partners;
2. coercive regulatory pressure arising from government requirements, inspections, compliance and regulation;
3. mimetic pressure, whereby firms observe and benchmark competitors and peers when deciding how to use AI.

It then examines whether these pressures contribute to the development of data- and technology-based AI capabilityand whether stronger AI capability supports responsible innovation through anticipation, reflexivity, inclusion and responsiveness.

From a Management Challenge to Doctoral Research

Companies increasingly encounter expectations to adopt AI. Customers may demand faster, smarter or more data-driven services. Regulators may require stronger traceability, compliance, documentation or risk control. Competitors may create pressure to follow emerging industry practices.

But responding to these pressures by simply purchasing new technology does not necessarily mean that an organization has developed the capability to use AI effectively—or responsibly.

Ning’s research therefore moves the discussion from: “Should firms adopt AI?”

toward a more demanding question: “What organizational capability must firms develop so that AI can support better and more responsible innovation?”

This distinction is especially important for SMEs, where resources and formal governance systems may be more limited than in large organizations.

Empirical Research

269 organizational respondents across China’s Big Health ecosystem

Data were collected in April 2026. Following screening, the final quantitative sample consisted of 269 valid organizational respondents from across China. Respondents included senior and middle managers, supervisors, executives, technical staff and company owners working in areas such as operations, production, supply chain management, technology, R&D and innovation.

The study examines

1. Coercive market pressure
2. Coercive regulatory pressure
3. Mimetic pressure
4. Data- and technology-based AI capability
5. Responsible innovation

The project also includes a qualitative follow-up phase based on semi-structured interviews with managers and professionals. This phase explores how different forms of AI-related institutional pressure influence responsible innovation in organizational practice.

Emerging Findings

Pressure explains why firms act—capability helps explain whether they can act responsibly

The results suggest that external pressure alone does not tell the whole story.

Coercive market pressure, regulatory pressure and mimetic pressure are all positively associated with the development of data- and technology-based AI capability.

Among these pressures, market pressure has the strongest relationship with AI capability, suggesting that customers, suppliers and other business partners may play a particularly important role in encouraging firms to develop AI-related resources.

The study also finds that institutional pressures are directly associated with responsible innovation.

Most importantly, however, data- and technology-based AI capability is the strongest predictor of responsible innovation in the model. Firms with stronger AI-related data and technological infrastructure are better positioned to anticipate consequences, reflect critically on innovation choices, include relevant stakeholders and respond to emerging expectations.

Research Interests

Artificial Intelligence Capability · Responsible Innovation · Institutional Theory · AI Adoption · SMEs · Digital Transformation · Innovation Management · Big Health Industry · Organizational Capability · Responsible AI

Practitioner Perspective

From pressure to adopt to capability to act

A distinctive contribution of Ning Yibing’s research is that it avoids treating AI adoption as an end in itself.

Organizations can face strong pressure to appear technologically advanced without necessarily possessing the internal resources required to use AI effectively or responsibly.

Her research therefore distinguishes between external pressure to adopt and internal capability to act.

This distinction has practical importance. Responsible AI transformation depends not only on technology investment, but also on whether firms can develop the data, infrastructure, governance and organizational capacity needed to anticipate consequences, reflect on choices, involve stakeholders and respond to change.

This reflects CEIBA’s approach to doctoral research: starting from an emerging management challenge, developing a rigorous explanatory model, testing it empirically, and then returning to practice to understand the mechanisms behind the results.

Her DBA Journey