The Uneven Impact of Generative AI on Entrepreneurial Performance

Published: Dec. 21, 2023

Scalable and low-cost AI assistance has the potential to improve firm decision-making economic performance. However, running a business involves myriad of open-ended problems, making it difficult know whether recent advances can help owners make better decisions in real-world markets. In field experiment with Kenyan entrepreneurs, we assessed impact advice on small revenues profits by randomizing access GPT-4-powered assistant via WhatsApp. While are unable reject null hypothesis that there is no average treatment effect, find effect for entrepreneurs who were high performing at baseline be 0.27 standard deviations greater than low performers. Sub-sample analyses show performers benefited just over 15% from assistant, whereas did about 8% worse. This increase performance inequality does not stem differences questions posed or received AI, but how selected implemented they received. More broadly, our findings demonstrate generative already capable impacting—though uneven unexpected ways—real, open-ended, unstructured decisions.

Language: Английский

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Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Large language models (LLMs) such as OpenAI's GPT series have shown remarkable capabilities in generating fluent and coherent text various domains. We compare the ideation of ChatGPT-4, a chatbot based on state-of-the-art LLM, with those students at an elite university. ChatGPT-4 can generate ideas much faster cheaper than students, are average higher quality (as measured by purchase-intent surveys) exhibit variance quality. More important, vast majority best pooled sample generated ChatGPT not students. Providing few examples highly-rated further increases its performance. discuss implications these findings for management innovation.

Language: Английский

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Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

International Journal of Production Research, Journal Year: 2024, Volume and Issue: 62(17), P. 6120 - 6145

Published: Jan. 31, 2024

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) particular supply chain operations management (SCOM). Through lens resource-based view based on key capabilities such as learning, perception, prediction, interaction, adaptation, reasoning, we explore how GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, design, risk management. With its outcomes, this study provides a comprehensive understanding GAI's functionality applications context, offering practical framework for both practitioners researchers. The proposed systematically identifies where be applied SCOM, focussing enhancement, process optimisation, investment prioritisation, skills development. Managers use it guidance evaluate their operational processes identify deliver improved efficiency, accuracy, resilience, overall effectiveness. underscores that GAI, with multifaceted applications, open revolutionary substantial implications future practices, innovations, research.

Language: Английский

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AI literacy and its implications for prompt engineering strategies

Computers and Education Artificial Intelligence, Journal Year: 2024, Volume and Issue: 6, P. 100225 - 100225

Published: April 18, 2024

Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) increasingly being used when humans interact with systems based on artificial (AI), posing both new opportunities and challenges. When interacting LLM-based AI system in a goal-directed manner, prompt engineering has evolved as skill formulating precise well-structured instructions to elicit desired responses or information from the LLM, optimizing effectiveness interaction. However, research perspectives non-experts using through how literacy affects prompting behavior is lacking. This aspect particularly important considering implications LLMs context higher education. In present study, we address issue, introduce skill-based approach engineering, explicitly consider role non-experts' (students) their skills. We also provide qualitative insights into students' intuitive behaviors towards systems. The results show that higher-quality skills predict quality LLM output, suggesting indeed required for use generative tools. addition, certain aspects can play targeted adaptation within We, therefore, argue integration educational content current curricula enable hybrid intelligent society which students effectively tools such ChatGPT.

Language: Английский

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Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models

The Journal of Legal Analysis, Journal Year: 2024, Volume and Issue: 16(1), P. 64 - 93

Published: Jan. 1, 2024

Abstract Do large language models (LLMs) know the law? LLMs are increasingly being used to augment legal practice, education, and research, yet their revolutionary potential is threatened by presence of “hallucinations”—textual output that not consistent with facts. We present first systematic evidence these hallucinations in public-facing LLMs, documenting trends across jurisdictions, courts, time periods, cases. Using OpenAI’s ChatGPT 4 other public models, we show hallucinate at least 58% time, struggle predict own hallucinations, often uncritically accept users’ incorrect assumptions. conclude cautioning against rapid unsupervised integration popular into tasks, develop a typology guide future research this area.

Language: Английский

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A Design Space for Intelligent and Interactive Writing Assistants

Published: May 11, 2024

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various communities. We seek to address this challenge by proposing a design space as structured way examine and explore multidimensional intelligent interactive assistants. Through large community collaboration, we five aspects assistants: task, user, technology, interaction, ecosystem. Within each aspect, define dimensions (i.e., fundamental components an aspect) codes potential options dimension) systematically reviewing 115 papers. Our aims offer researchers designers practical tool navigate, comprehend, compare possibilities assistants, aid in envisioning new

Language: Английский

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Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review process

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2023, Volume and Issue: 62(5), P. 835 - 843

Published: Nov. 29, 2023

In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns.

Language: Английский

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AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business

AI and Ethics, Journal Year: 2024, Volume and Issue: 4(3), P. 791 - 804

Published: Feb. 23, 2024

Abstract This paper examines the ethical obligations companies have when implementing generative Artificial Intelligence (AI). We point to potential cyber security risks are exposed rushing adopt AI solutions or buying into “AI hype”. While benefits of for business been widely touted, inherent associated less well publicised. There growing concerns that race integrate is not being accompanied by adequate safety measures. The rush buy hype and fall behind competition potentially exposing broad possibly catastrophic cyber-attacks breaches. In this paper, we outline significant threats models pose, including ‘backdoors’ in could compromise user data risk ‘poisoned’ producing false results. light these concerns, discuss moral considering principles beneficence, non-maleficence, autonomy, justice, explicability. identify two examples concern, overreliance over-trust AI, both which can negatively influence decisions, leaving vulnerable threats. concludes recommending a set checklists implementation environment minimise based on discussed responsibilities concern.

Language: Английский

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Artificial intelligence as a tool for creativity

Journal of Creativity, Journal Year: 2024, Volume and Issue: 34(2), P. 100079 - 100079

Published: Feb. 5, 2024

The release of ChatGPT has sparked quite a bit interest about creativity in the context artificial intelligence (AI), with theorizing and empirical research asking questions nature (both human artificially-produced) valuing work produced by humans means. In this article, we discuss one specific scenario identified community – co-creation, or use AI as tool that could augment creativity. We present emerging relevant to how can be used on continuum four levels creativity, from mini-c/creativity learning little-c/everyday Pro-C/professional Big-C/eminent discussion, is defined broadly, not include only large language models (e.g., ChatGPT) which might approach general AI, but also other computer programs perform tasks typically understood requiring intelligence. conclude considering future directions for across c's.

Language: Английский

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How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries

Published: May 11, 2024

Generative AI is expected to have transformative effects in multiple knowledge industries. To better understand how workers expect generative may affect their industries the future, we conducted participatory research workshops for seven different industries, with a total of 54 participants across three US cities. We describe participants' expectations AI's impact, including dominant narrative that cut groups' discourse: largely envision as tool perform menial work, under human review. Participants do not generally anticipate disruptive changes currently projected common media and academic narratives. however amplify four social forces shaping industries: deskilling, dehumanization, disconnection, disinformation. these forces, then provide additional detail regarding attitudes specific conclude discussion implications challenges HCI community.

Language: Английский

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