Technology and AI in Emerging Markets
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Item Adaptive Hotel Rate Prediction Using External Data: A Competitor-Driven Approach(2025-01-07) Kim, Jungwoo; Kwon, Soonjae; Park, Sung-HyukTraditional dynamic pricing in the hotel industry has predominantly relied on detailed occupancy data, which often disadvantages smaller independent hotels due to their limited access to such comprehensive datasets. This study presents CAMP (Competitor-based Accommodation Market Pricing), an innovative model designed to overcome these limitations by forgoing occupancy data. Instead, CAMP leverages competitor pricing and a range of external factors, including regional search trends, weather conditions, and economic indicators. CAMP accurately identifies competitors through hierarchical clustering and advanced regression techniques and optimizes pricing strategies. By providing a robust and flexible revenue management tool, CAMP enables independent hotels to adjust their pricing strategies dynamically, maximize revenue, and improve social welfare by balancing profitability and consumer satisfaction. This research fills a critical gap in revenue management literature and introduces a novel, data-driven approach to dynamic pricing that anticipates market demands without relying on occupancy data.Item Introduction to the Minitrack on Technology and AI in Emerging Markets(2025-01-07) Lee, Gene Moo; Oh, Wonseok; Han, Sang Pil; Park, SunghoItem Applying Byzantine Fault Tolerance Concept to Overcome Remote Work Challenges: Strategies for Effective Monitoring(2025-01-07) Lei, Zixi; Wen, Wen; Whinston, AndrewThe transition to remote work, accelerated by the COVID-19 pandemic, has reshaped the professional landscape, offering both opportunities and challenges for companies. One significant issue is managers’ reliance on reported information from employees without direct observation or supervision, which might complicate monitoring decisions and impact project productivity. By integrating concepts from Byzantine Fault Tolerance (BFT) into remote work dynamics, we develop a model to examine the optimal level of monitoring intensity for managers under different levels of observability. Our work bridges a critical gap between computer science and organizational studies by extending the well-established concepts of BFT, traditionally used in distributed computing and blockchain technology, to the context of human teamwork and organizational behavior. This study offers new insights into monitoring and managing dispersed teams.Item AI and Freelancers: Has the Inflection Point Arrived?(2025-01-07) Qiao, Dandan; Rui, Huaxia; Xiong, QianArtificial intelligence (AI) has seen tremendous improvements in capabilities recently, yet how AI impacts different labor markets remains unclear. Leveraging the launch of ChatGPT, this study aims to elucidate how AI influences freelancers across different online labor markets (OLMs). Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT’s launch: 1) displacement effects in translation & localization OLM, reducing freelancers’ work volume and earnings; 2) productivity effects in web development OLM, increasing freelancers’ work volume and earnings. Theoretically, we developed a Cournot competition model to explain and identify that an inflection point exists for each occupation. Before this point, human workers benefit from AI enhancements; beyond this point, human workers would be replaced. Additional analyses across various occupations consistently demonstrate the two scenarios caused by AI. Further investigation into the progression from ChatGPT 3.5 to 4.0 reveals three evolving patterns of AI’s effects, thereby reinforcing our inflection point conjecture.