Inicio  /  Algorithms  /  Vol: 16 Par: 3 (2023)  /  Artículo
ARTÍCULO
TITULO

A Cognitive Model for Technology Adoption

Fariborz Sobhanmanesh    
Amin Beheshti    
Nicholas Nouri    
Natalia Monje Chapparo    
Sandya Raj and Richard A. George    

Resumen

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as Industry 4.0. As businesses integrate these technologies into their daily operations, it significantly impacts their work tasks and required skill sets. However, the approach to technological transformation varies depending on location, industry, and organization. However, there are no published methods that can adequately forecast the adoption of technology and its impact on society. It is essential to prepare for the future impact of Industry 4.0, and this requires policymakers and business leaders to be equipped with scientifically validated models and metrics. Data-driven scenario planning and decision-making can lead to better outcomes in every area of the business, from learning and development to technology investment. However, the current literature falls short in identifying effective and globally applicable strategies to predict the adoption rate of emerging technologies. Therefore, this paper proposes a novel parametric mathematical model for predicting the adoption rate of emerging technologies through a unique data-driven pipeline. This approach utilizes global indicators for countries to predict the technology adoption curves for each country and industry. The model is thoroughly validated, and the paper outlines highly promising evaluation results. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning.

 Artículos similares

       
 
Agnieszka Garbacz, Boguslaw Stelcer, Michalina Wielgosik and Magdalena Czlapka-Matyasik    
This cross-sectional study investigated interactions among sugar-related dietary patterns (DPs), personality traits, and cognitive?behavioural and emotional functioning. The study involved working-age women aged 18?54. Data were collected between Winter ... ver más
Revista: Applied Sciences

 
Artur Chudzik and Andrzej W. Przybyszewski    
Neurodegenerative diseases (NDs), including Parkinson?s and Alzheimer?s disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learnin... ver más
Revista: Applied Sciences

 
Ruinan Chen, Jie Hu, Xinkai Zhong, Minchao Zhang and Linglei Zhu    
Existing environment modeling approaches and trajectory planning approaches for intelligent vehicles are difficult to adapt to multiple scenarios, as scenarios are diverse and changeable, which may lead to potential risks. This work proposes a cognitive ... ver más
Revista: Applied Sciences

 
Pratham Grover, Kunal Chaturvedi, Xing Zi, Amit Saxena, Shiv Prakash, Tony Jan and Mukesh Prasad    
Alzheimer?s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of deme... ver más
Revista: Algorithms

 
Muhammad Irfan, Seyed Shahrestani and Mahmoud Elkhodr    
Dementia, including Alzheimer?s Disease (AD), is a complex condition, and early detection remains a formidable challenge due to limited patient records and uncertainty in identifying relevant features. This paper proposes a machine learning approach to a... ver más
Revista: Applied Sciences