Mercury Talk: Data-Driven Modelling of Technology Acceptance: A Machine Learning Perspective
| Dates: | 11 December 2019 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Computer Science |
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| Who is it for: | University staff, Adults, Current University students |
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| Speaker: | Asim Alwabel |
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Join us for the next Computer Science Mercury Talk with speaker Asim Alwabel in Kilburn L.T 1.5
Forecasting and understanding technology adoption have dominated research of Information Systems (IS) for more than two decades. Although structural equation modelling is suitable for explanatory modelling, it was regarded by majority of IS research as a predictive technique. On the other hand, despite development and utility of predictive analytics, its use in estimating technology usability remains scarce. This research demonstrates a unique data driven approach utilising linear and non-linear machine learning (ML) techniques, predictive analytics-based modelling, to advance technology adoption modelling, assess its predictability, evaluate relevance of its current determinants, and introduce new ones. Inspired by current literature, the resulting model comprised 37 features and was tested on 32 technologies with heterogeneous subjects. Content validity of the model was attained applying Twitter API, text-mining technique and interviews. Before modelling, the discriminant validity was accomplished applying multitrait-multimethod analysis. Performance of the proposed model was estimated employing multiple linear regression, k-nearest neighbour, decision tree, multilayer perceptron, and support vector machine with mean absolute percentage error as loss metric. The model was benchmarked against the current literature to highlight differences and similarities. As a result, ML-based modelling revealed the distance between theory and practice.
Speaker
Asim Alwabel
Role: PhD student
Organisation: University of Manchester
Travel and Contact Information
Find event
L.T 1.5
Kilburn Building
Manchester
