The Innovation Dilemma: Creativity vs. Digital Nativity

Imagine a team brainstorming the next groundbreaking idea. On one side, there's Alex, brimming with creativity, constantly coming up with novel concepts and unconventional solutions. On the other side is Taylor, a digital native who grew up with technology and effortlessly navigates complex IT systems. Both are valuable, but who will drive the innovation your organization needs?

This was the central question behind our latest study, recently published in Information Technology & People, where we explored how creativity and digital nativity influence two types of innovation: general innovative performance (broad, idea-driven innovation) and personal innovativeness using IT (PIIT) (tech-specific innovation).

This blog post summarizes a research project that was a joint effort with three exceptional coauthors: Matej Černe , Aldijana Bunjak , and Jestine Philip Matej Černe, a professor at the University of Ljubljana, brought profound expertise in creativity, innovation, and organizational behavior. Aldijana Bunjak, based at the University of St. Gallen at the time of research, contributed cutting-edge insights into digital innovation and workplace dynamics. Jestine Philip, from the University of New Haven, offered a wealth of knowledge in IT-enabled innovation and technological adaptability.

Together, we explored how creativity and digital nativity shape different types of innovation in today's workplace.

Methodology

Phase 1: Broad Innovation Assessment

- Surveyed nearly 500 professionals via the Prolific platform

- Participants completed idea generation tasks to assess general innovative performance

- Evaluated responses based on originality and usefulness

- Measured self-reported levels of personal innovativeness using IT (PIIT)

- Assessed traits like creativity and digital nativity using established scales

Phase 2: Focused Application

- Engaged over 300 participants through Amazon Mechanical Turk

- Introduced a specific task: proposing creative ideas for a new digital app

- Evaluated both general and IT-specific innovation

- Included observer ratings to complement self-reports

- Ensured balanced perspective through multiple evaluation methods

Our analysis incorporated:

- Key control variables (age, gender, digital literacy, creative job roles)

- Regression models and relative importance analysis

- Assessment of both direct relationships and comparative impact of creativity vs. digital nativity

What We Discovered

Our findings revealed a fascinating split. Creativity emerged as the stronger driver of general innovation. Whether it’s finding new ways to streamline processes or developing out-of-the-box product ideas, creative thinkers excelled across diverse scenarios. Interestingly, their performance wasn’t significantly influenced by PIIT.

In contrast, digital nativity took the lead in IT-related innovation. Digital natives—those who seamlessly integrate technology into their daily lives—excel at leveraging digital tools to create and implement tech-driven solutions. However, their reliance on multitasking and preference for instant gratification occasionally hindered their ability to produce innovative ideas outside the tech domain.

Lessons for Leaders

This duality offers actionable insights for organizations:

• When to Bet on Creativity: For projects requiring broad, unconventional thinking, prioritize employees who demonstrate creative strengths. Their ability to generate unique ideas transcends technical know-how.
• Leveraging Digital Natives: For IT-heavy tasks or technology-driven innovation, digital natives are invaluable. Their fluency in digital tools makes them agile problem-solvers in tech-centric scenarios.

Beyond the Stereotypes

One surprising revelation was the disconnect between age and innovation. While younger workers often embody digital nativity, creativity spans all generations. Organizations must look beyond age and stereotypes, not using age as a proxy or even a substitute for measuring digital nativity.

The GAI Shift: New Challenges and Opportunities

Our data collection took place before the explosion of generative artificial intelligence (GAI) tools. Now, the proliferation of tools like ChatGPT, Claude, and others will likely reshape innovation dynamics. On one hand, GAI democratizes access to creative ideation and technological expertise, potentially blurring the lines between creativity and digital nativity. On the other hand, it raises new research questions: How does the availability of GAI tools affect individual contributions to innovation? Will these tools amplify inherent creativity or reduce the importance of traditional creative traits?

Potential Impact of Generative AI on Data Collection in the future

The findings of this research were entirely derived from data collected prior to the widespread adoption of generative AI (GAI) tools. The use of GAI in online settings introduces a huge limitation for follow-up studies. Participants may use these tools to "cheat" or artificially enhance their responses, especially in tasks requiring creativity, idea generation, or IT-related problem-solving. This shift requires researchers to implement safeguards, such as designing tasks that are less easily completed by AI or even collecting data in an offline setting.

What It Means for You

Innovation isn’t a one-size-fits-all game. By understanding the unique roles of creativity and digital nativity, organizations can make smarter decisions—whether it's assembling teams, assigning tasks, or recruiting talent. So, the next time you're building your innovation dream team, ask yourself: Do you need a visionary like Alex or a tech-savvy trailblazer like Taylor?

Final Disclaimer and GAI use

The entire research paper, including its methodology, data analysis, and findings, was conducted and written without the assistance of generative AI tools which were only used very little in response to reviewers in the final stage of the process.

This blog post summarizing the study was created with the help of GAI. I believe that the use of to effectively communicate the findings to a broader audience is not problematic at all.

This further illustrates how challenging it is to distinguish GAI-assisted content from human-created material. I used the “human-generated” research paper to turn it into a blog post with AI's help. This blog post also serves as an example of how AI can assist in sharing complex ideas. While the findings remain untouched by AI, this post demonstrates the fine line between human-authored and AI-assisted content. It raises an important question: how do we evaluate authenticity and originality in broader settings?