A Cognitive Model for Technology Adoption
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 Industr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/3/155 |
| _version_ | 1827751739340619776 |
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| author | Fariborz Sobhanmanesh Amin Beheshti Nicholas Nouri Natalia Monje Chapparo Sandya Raj Richard A. George |
| author_facet | Fariborz Sobhanmanesh Amin Beheshti Nicholas Nouri Natalia Monje Chapparo Sandya Raj Richard A. George |
| author_sort | Fariborz Sobhanmanesh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:02:23Z |
| format | Article |
| id | doaj.art-3cfff463621041e78dd4ce0e07fccd09 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T07:02:23Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-3cfff463621041e78dd4ce0e07fccd092023-11-17T09:09:20ZengMDPI AGAlgorithms1999-48932023-03-0116315510.3390/a16030155A Cognitive Model for Technology AdoptionFariborz Sobhanmanesh0Amin Beheshti1Nicholas Nouri2Natalia Monje Chapparo3Sandya Raj4Richard A. George5School of Computing, Macquarie University, Sydney, NSW 2109, AustraliaSchool of Computing, Macquarie University, Sydney, NSW 2109, AustraliaFaethm by Pearson, Sydney, NSW 2000, AustraliaFaethm by Pearson, Sydney, NSW 2000, AustraliaFaethm by Pearson, Sydney, NSW 2000, AustraliaSchool of Computing, Macquarie University, Sydney, NSW 2109, AustraliaThe 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.https://www.mdpi.com/1999-4893/16/3/155technology adoptiongenerative AIindustrial revolution 4.0 |
| spellingShingle | Fariborz Sobhanmanesh Amin Beheshti Nicholas Nouri Natalia Monje Chapparo Sandya Raj Richard A. George A Cognitive Model for Technology Adoption Algorithms technology adoption generative AI industrial revolution 4.0 |
| title | A Cognitive Model for Technology Adoption |
| title_full | A Cognitive Model for Technology Adoption |
| title_fullStr | A Cognitive Model for Technology Adoption |
| title_full_unstemmed | A Cognitive Model for Technology Adoption |
| title_short | A Cognitive Model for Technology Adoption |
| title_sort | cognitive model for technology adoption |
| topic | technology adoption generative AI industrial revolution 4.0 |
| url | https://www.mdpi.com/1999-4893/16/3/155 |
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