IGL Trials Database

This study investigates the integration of artificial intelligence (AI) in the screening processes of early-stage innovations, traditionally conducted by human evaluators, across various professional and competitive settings. Through a randomized controlled trial involving around 400 participants from the MIT Solve expert internal screener team and from community leveraged startups screeners, this research explores whether AI-assisted human evaluators or AI-only evaluations enhance the efficiency and quality of decision-making compared to traditional human-only evaluations.

This study is focused on the relationship between borrowing constraints, access to cutting-edge technology and information about cutting-edge technology on the performance of U.S. online businesses. With the help of two large U.S. technology companies we will be able to randomize access to loans and free cloud computing credits (as well as information about the potential use of technology) to otherwise identical (generally small, but fast growing) firms, to see if they will have a causal impact on firm development.

Can artificial intelligence (AI) assist human employees in increasing employee creativity? Drawing on research on AI-human collaboration, job design, and employee creativity, we examine AI assistance in the form of a sequential division of labor within organizations: in a task, AI handles the initial portion which is well-codified and repetitive, and employees focus on the subsequent portion involving higher-level problem-solving. First, we provide causal evidence from a field experiment conducted at a telemarketing company.

Can a set of low-cost behavioural nudges encourage more small businesses to adopt productivity-raising digital technologies? This randomised controlled trial sought to test whether businesses could be nudged into using a cloud-based system to improve the efficiency of invoice processing. All participants in the trial were offered access to the system free of charge for a 12-month period, with a treatment group receiving weekly email reminders to make use of the system.

The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company.

Across developing economies, cash is the conduit for retail transactions. Policymakers, multinational product manufacturers and marketers of electronic payment systems are interested in understanding how to stimulate the growth of electronic payments in emerging markets. In this paper, we investigate what hinders the adoption of e-payment technology by traditional retailers, in particular, whether barriers to adoption are technological, informational or financial in nature.

I investigate how firms should design human/AI collaboration to ensure human workers remain engaged in their activities. I developed a formal model that explores the tension between AI quality and human effort. As AI quality increases, humans have fewer incentives to exert effort and remain attentive, allowing the AI to substitute, rather than augment their performance. Thus, high-performingalgorithms may do worse than lower-performing ones in maximizing combined output.

Using a new survey of firms in New Zealand, we document how exogenous variation in the macroeconomic uncertainty perceived by firms affects their economic decisions. We use randomized information treatments that provide different types of information about the first and/or second moments of future economic growth to generate exogenous changes in the perceived macroeconomic uncertainty of some firms. The effects on their decisions relative to their initial plans as well as relative to an untreated control group are measured in a follow-up survey six months later.

We examine the influence of physical proximity on between-startup knowledge spillovers at one of the largest technology co-working hubs in the United States. Relying on the random assignment of office space to the hub's 251 startups, we find that proximity positively influences knowledge spillovers as proxied by the likelihood of adopting an upstream web technology already used by a peer startup.

This paper studies the impact of business modernization on the sales performance of traditional retailers. We define modernization as adopting tangible structures and business practices of organized retail chains (for example, exterior signage with store name and logo, or a database to record product-level information). To address our research question, we implement a randomized field experiment in Mexico City with 1148 traditional retail firms.

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize that domain expertise of users can complement ML by mitigating this bias. Our observational and experimental analyses in the patent examination context support this conjecture.

This trial proposes to evaluate a model for scaling up affordable access to effective STEM education through national online education platforms.

The study is an impact evaluation of a training program that induced SMEs to adopt broadband connections, establish presence on online retail and potentially export their goods or services.