Do Large Language Models Reduce Research Novelty? Evidence from Information Systems Journals

Large language models such as ChatGPT have increased scholarly output, but whether this productivity boost produces genuine intellectual advancement remains untested. I address this gap by measuring the semantic novelty of 13,847 articles published between 2020 and 2025 in 44 Information Systems journals. Using SPECTER2 embeddings, I operationalize novelty as the cosine distance between each paper and its nearest prior neighbors. A difference-in-differences design with the November 2022 release of ChatGPT as the treatment break reveals a heterogeneous pattern: authors affiliated with institutions in non-English-dominant countries show a 0.18 standard deviation decline in relative novelty compared to authors in English-dominant countries (beta = -0.176, p < 0.001), equivalent to a 7-percentile-point drop in the novelty distribution. This finding is robust across alternative novelty specifications, treatment break dates, and sub-samples, and survives a placebo test at a pre-treatment break. I interpret these results through the lens of construal level theory, proposing that LLMs function as proximity tools that shift researchers from abstract, exploratory thinking toward concrete, convention-following execution. The paper contributes to the growing debate on whether LLM-driven productivity gains come at the cost of intellectual diversity.

Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions

This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment.