Jason Miklian

Jason Miklian, Kristian Hoelscher, John E. Katsos

How well can AI-derived synthetic research data replicate the responses of human participants? An emerging literature has begun to engage with this question, which carries deep implications for organizational research practice. This article presents a comparison between a human-respondent survey of 420 Silicon Valley coders and developers and synthetic survey data designed to simulate real survey takers generated by five leading Generative AI Large Language Models: ChatGPT Thinking 5 Pro, Claude Sonnet 4.5 Pro plus Claude CoWork 1.123, Gemini Advanced 2.5 Pro, Incredible 1.0, and DeepSeek 3.2. Our findings reveal that while AI agents produced technically plausible results that lean more towards replicability and harmonization than assumed, none were able to capture the counterintuitive insights that made the human survey valuable. Moreover, deviations grouped together for all models, leaving the real data as the outlier. Our key finding is that while leading LLMs are increasingly being used to scale, replicate and replace human survey responses in research, these advances only show an increased capacity to parrot conventional wisdom in harmony with each other rather than revealing novel findings. If synthetic respondents are used in future research, we need more replicable validation protocols and reporting standards for when and where synthetic survey data can be used responsibly, a gap that this paper fills. Our results suggest that synthetic survey responses cannot meaningfully model real human social beliefs within organizations, particularly in contexts lacking previously documented evidence. We conclude that synthetic survey-based research should be cast not as a substitute for rigorous survey methods, but as an increasingly reliable pre- or post-fieldwork instrument for identifying societal assumptions, conventional wisdoms, and other expectations about research populations.

Jason Miklian

With the international business landscape becoming more crisis-ridden as risks proliferate, how do the professionals who implement corporate social initiatives in high-risk environments perceive their work, and what can this reveal about the forces shaping business engagement with society in crisis contexts? We present findings from a synthetic survey of 400 corporate professionals working on social impact in fragile and conflict-affected settings to understand conventional wisdoms and best practices on corporate strategy and activity in high-risk settings. Drawing on political corporate social responsibility (CSR), synthetic survey, and international business literatures, we test seven hypotheses about how regulatory environments, political polarization, sector characteristics, and organizational structures shape corporate social engagement in high-risk contexts. The synthetic results suggest that European professionals report significantly higher strategic integration of social impact across all measured dimensions, while US professionals overwhelmingly report that political polarization hinders social initiatives, yet this perception does not predict unreported social activities, complicating the emerging "quiet CSR" narrative. Extractive industry professionals deliver both the highest operational preparedness and the highest complicity awareness, a pattern we conceptualize as presence-dependent reflexivity. These patterns deliver a baseline to detect the theorized dynamics and offer preliminary theoretical propositions for future real-world empirical testing.

Jason Miklian, Kristian Hoelscher

Digital technologies are transforming democratic life in conflicting ways. This article bridges two perspectives to unpack these tensions. First, we present an original survey of software developers in Silicon Valley, interrogating how coder worldviews, ethics, and workplace cultures shape the democratic potential and social impact of the technologies they build. Results indicate that while most developers recognize the power of their products to influence civil liberties and political discourse, they often face ethical dilemmas and top-down pressures that can lead to design choices undermining democratic ideals. Second, we critically investigate these findings in the context of an emerging new digital divide, not of internet access but of information quality. We interrogate the survey findings in the context of the Slop Economy, in which billions of users unable to pay for high-quality content experience an internet dominated by low-quality, AI-generated ad-driven content. We find a reinforcing cycle between tech creator beliefs and the digital ecosystems they spawn. We discuss implications for democratic governance, arguing for more ethically informed design and policy interventions to help bridge the digital divide to ensure that technological innovation supports rather than subverts democratic values in the next chapter of the digital age.