Skip to content

Contrasting Cultural Perspectives in Colombia and the United States

Oct 2026 · Change Management An International Journal
Corporate Management and Leadership

Abstract

This study examines the perspectives of managers from two different cultures, the United States and Colombia, on the impacts of artificial intelligence (AI) on the workplace. It focuses on organizational cultural adjustment and the protection of core values while enabling AI deployment. As organizations continue to integrate AI as a disruptive component of their operations, both the demand for change and the protection of organizational values and culture should be balanced. A conceptual model was developed based on broad organizational change and technological intervention theories. The study relied on a quantitative approach and used surveys as the main method. The research questions were framed from the perspective of middle managers who cope with issues arising from the implementation of new technologies. Their insights are a valuable guide to policy makers and organizational leaders on how to understand the dynamics of cultural change and innovation deployment. Most respondents did not express major concerns regarding AI’s potential to invade privacy. Interestingly, the Colombian group was less concerned with privacy than the US group, and their level of trust in top management was higher. The main conclusion of the study points to the need to formulate implementation and adaptation strategies that increase organizational benefits while mitigating potential cultural drawbacks. When seeking efficiency and effectiveness, organization leadership needs to preserve core cultural values. Although the study used perceptual rather than objective measures, the perceptions of organization members are important to understand the dynamics of culture and change.

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.

Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al. · 109 citations · ⚡19

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.

Jiaqi Xue, Meng Zheng, Yebowen Hu et al. · 109 citations · ⚡8

Related blog posts

MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.