About

Jackson G. Lu is the General Motors Associate Professor of Work and Organization Studies at MIT Sloan School of Management. His research focuses on topics related to generative AI, including its application in entrepreneurship, cultural tendencies in generative AI, and its impact on creativity. He has contributed to understanding AI aversion or appreciation through an integrative framework and meta-analysis, and has explored issues such as breaking ceilings with debate training and stereotypes related to creativity and the Bamboo Ceiling. His work also addresses the problematic categorization of 'Asian' in research and practice, reflecting his interest in diversity and organizational behavior. Additionally, Jackson Lu serves as a senior editor for Organization Science and an associate editor for the Journal of Personality and Social Psychology, indicating his active engagement in scholarly publishing and thought leadership in his field.

Research topics

• Psychology
• Computer Science
• Social psychology
• Epistemology
• Advertising
• Internet privacy
• Computer Security
• Medicine
• Political Science
• Ecology
• Internal medicine
• Economics
• Applied psychology
• Environmental resource management
• Virology
• Environmental science

Selected publications

• Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment–Entrapment Framework

  2026-04-01

  articleOpen access1st authorCorresponding

  Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment–Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI’s role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel “workslop” and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs’ metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment–Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.

  PublisherDOI

• Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework

  arXiv (Cornell University) · 2026-04-02

  articleOpen access1st authorCorresponding

  Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment-Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI's role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel "workslop" and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs' metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment-Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.

  PublisherOA PDF

• Think Political Leader — Think He or She? A Multi-Country Test of a Gender Fair Language Intervention for Stereotype Bias

  PsyArXiv (OSF Preprints) · 2026-05-10

  preprintOpen access1st authorCorresponding

  Women are greatly underrepresented in positions of political leadership around the world. In seeking to explain this underrepresentation, some researchers have pointed to people’s tendencies to stereotype leaders as more similar to men than women as these tendencies can support the belief that women are unsuited to leadership. This project aimed to test whether a subtle linguistic intervention was able to ameliorate this gender bias in political leadership stereotypes across different languages and national contexts (country N = 42, sample N = 22,995). Specifically, this project examined whether gender fair language (e.g., the use of paired pronouns ‘he or she’) can reduce the tendency for people to stereotype political leaders as more similar to men than women. To increase the rigor with which these stereotypes were measured, this project complemented the dominant ‘cheap talk’ measure of personal stereotype content with a novel incentivised community measure of this content. As expected, participants stereotyped political leaders as more similar to men than women; this pattern was stronger on the incentivised measure of community stereotypes. Unexpectedly, there was no evidence that paired pronouns reduced this bias (there was weak exploratory evidence that paired nouns increased participants’ tendencies to stereotype political leaders as more similar to women). This project suggests that the ability of gender fair language interventions to ameliorate gender bias in the political leadership domain may be limited.

  Publisher

• Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment–Entrapment Framework

  PsyArXiv (OSF Preprints) · 2026-04-01

  preprintOpen access

  Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment–Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI’s role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel “workslop” and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs’ metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment–Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.

  Publisher

• A Megastudy of Behavioral Interventions to Increase Voter Registration Ahead of the 2024 U.S. Presidential Election

  PsyArXiv (OSF Preprints) · 2026-05-10

  preprintOpen access1st authorCorresponding

  In the United States, in nearly all cases, one must register in order to vote—yet, a substantial portion of the eligible electorate remains unregistered. Despite this, relatively little is known about how to increase the likelihood that a voter registers. Here, we tested the impact of 10 expert-crowdsourced, theoretically-based psychological interventions on a sample of eligible, yet unregistered, U.S. voters ahead of the 2024 presidential election (N = 12,896). Eight of the interventions increased intentions to vote, and five led individuals to click on the voter registration website. Escalating Commitment, which sequentially employed several social pressure strategies, was the strongest intervention across these outcomes. However, none of the interventions had a significant effect on actual voter registration or voter turnout. The results highlight a substantial disconnect between voters’ intentions and their ultimate behaviors. We discuss potential structural and psychological barriers that undermine the translation of intent into action.

  Publisher

• Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework

  arXiv (Cornell University) · 2026-04-02

  preprintOpen access1st authorCorresponding

  Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment-Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI's role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel "workslop" and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs' metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment-Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.

  PublisherDOI

• Investigating the analytical robustness of the social and behavioural sciences

  MetArXiv (OSF Preprints) · 2026-03-30

  preprintOpen access

  The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1–3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, where for one claim per study, at least five re-analysts independently re-analysed the original data. The statistical appropriateness of the re-analyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent re-analyses yielded the same result (within a tolerance region of +/- 0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator rose to 57%. Regarding the conclusions drawn, 74% of analyses were reported to arrive at the same conclusion as in the original investigation; 24% to no effects/inconclusive result, and 2% to the opposite effect as in the original investigation. This exploratory study suggests that the common single-path analyses in social and behavioural research should not simply be assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

  Publisher

• Artificial Intelligence Quotient (AIQ)

  2025-10-31 · 2 citations

  articleOpen access

  We introduce the concept of Artificial Intelligence Quotient (AIQ)—defined as a person’s ability to use AI to perform a wide variety of tasks—and provide evidence for its existence using five studies (archival, lab, and online) across different AIs and samples. Study 1 (an 18-year global dataset of human+AI chess tournaments) and Study 2 (a three-wave longitudinal study of human+AI renju games) show that individuals have stable human+AI performance over time (controlling for human’s own capability and AI’s capability), suggesting the existence of a stable human+AI capability. Study 3 shows that a general AIQ factor can be statistically extracted from individuals’ performance on a variety of tasks completed with ChatGPT, a more general AI tool. Besides replicating Study 3’s findings in larger samples, Study 4 and Study 5 (preregistered) show that the extracted AIQ factor has both concurrent validity and prospective validity. Regarding concurrent validity, the extracted AIQ factor can predict human+AI performance on a new task using the same AI (ChatGPT) on the same day. Regarding prospective validity, the extracted AIQ factor can predict human+AI performance on other new tasks using different AIs (renju AI or Gemini) in the future. Across studies, we ascertain the unique explanatory power of AIQ by controlling for individual’s IQ, social intelligence (SQ), AI literacy (knowledge about AI), and/or computer literacy. We also explored potential correlates of AIQ (e.g., personality traits, previous AI use, and demographics). Together, our findings suggest that AIQ exists and is measurable. By establishing this new type of intelligence (AIQ), we shed light on individual differences in the ability to use AI, which is increasingly important for individuals, organizations, and society.

  PublisherDOI

• Mapping and Increasing Error Correction Behaviour in a Culturally Diverse Sample

  2025-01-14

  preprintOpen access

  Intuition often guides our thinking effectively, but it can also lead to consequential reasoning errors, underpinning poor decisions and biased judgments. Little is known about how people globally self-correct such intuitive reasoning errors and what enhances their correction. Defying prevailing models of reasoning, recent research suggests that people spontaneously correct only a few errors during deliberation; however, enhancing error monitoring and motivating further effort should increase error correction. Here, we study whether these mechanisms apply to reasoning across individualistic and collectivistic cultures (expected N = 33,000 participants from 67 regions). Participants will solve problems that elicit incorrect intuitions twice: first intuitively and then reflectively, allowing them to correct initial errors, in a 2 (feedback: absent vs present) × 2 (answer justification: absent vs present) between-participants design. The study will shed more light on the nature, generalisability, and promotion of corrective behaviour, crucial for understanding and improving reasoning worldwide.

  PublisherDOI

• The Social Class Gap in Negotiation

  Academy of Management Proceedings · 2025-07-01

  article

  A class ceiling persists in organizations: People from a lower social class are often paid less than those from a higher social class, despite having comparable credentials. In this research, we argue that one factor contributing to this class ceiling is class differences in the propensity to negotiate. Specifically, we posit that lower-class individuals are less inclined to initiate negotiations than their higher-class counterparts, a tendency associated with adverse economic outcomes. We provide empirical support (total N = 11,344) for this hypothesis through field data from a nationally representative sample of employees (Study 1) and an MBA student sample (Study 2), a field study of an online labor market (Study 3), and a survey about workplace negotiation (Study 4). We find that this class gap in negotiation propensity is explained by differences in sense of power and concerns about social backlash. A final experiment among HR professionals in charge of recruiting and hiring shows that the concerns of lower-class individuals reflect realistic perceptions of differential treatment (Study 5): Negotiation incurs a greater social backlash for lower-class than higher-class individuals. We discuss implications for future research connecting social class and negotiation and for organizational efforts to address class disparities.

  PublisherDOI

Frequent coauthors

• Adam D. Galinsky

  18 shared

• Krystian Barzykowski

  Jagiellonian University

  18 shared

• Asil Ali Özdoğru

  Üsküdar University

  18 shared

• Robert M. Ross

  Macquarie University

  17 shared

• Martin Obschonka

  14 shared

• Carlota Batres

  13 shared

• Michał Misiak

  13 shared

• Mariola Paruzel‐Czachura

  13 shared

Awards & honors

• 2025 Cummings Early to Mid-Career Scholarly Achievement Awar…
• 2025 Best Paper in Management Education and Development Awar…
• 2025 Best Conference Micro Paper Award from the Internationa…
• Wegner Theoretical Innovation Prize (2024) from the Society…
• Early Career Achievement Award from the Human Resources (HR)…