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All eyes on me: Pre...
All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning
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- Mikalef, Patrick (författare)
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Norway; Department of Technology Management, SINTEF Digital, Trondheim, Norway
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- Sharma, Kshitij (författare)
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Norway; University of Science and Technology, Trondheim, Norway
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- Chatterjee, Sheshadri (författare)
- Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur, India
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- Chaudhuri, Ranjan (författare)
- Indian Institute of Management Ranchi, Jharkhand, India
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- Parida, Vinit (författare)
- Luleå tekniska universitet,Industriell ekonomi,School of Management, University of Vaasa, Vaasa, Finland
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- Gupta, Shivam (författare)
- Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, Reims, France.
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Decision Support Systems. - : Elsevier. - 0167-9236 .- 1873-5797. ; 175
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Understanding what information is important for consumers when making a purchase-related decision has been a key question for researchers and practitioners ever since the advent of empirical research in commerce. Nevertheless, our knowledge of what information is important has been formed primarily through post-purchase conscious capturing approaches, such as surveys and questionnaires. To overcome these limitations, we ground this research on an exploratory study that captures eye-tracking data during a decision-making task of product selection. Grounded on the dynamic attention theory, we utilize different information types and formats present on a popular social commerce platform, to identify elements which are important when deciding about online product purchase decision. Specifically, we employ a series of prediction algorithms and use an ensemble learning setup to predict the aspects that contribute to product selection by consumers. Our analysis highlights the most important informational cues to accurately predict product selection among alternatives. In addition, the results showcase how such elements shift in importance during the temporal sequence of comparing different product alternatives. Our results provide insight into how we can understand the journey of decision-making for social commerce customers when navigating through information to select a product. In addition, it opens the discussion about the shifts that eye-tracking in combination with machine learning can create for researchers and marketers.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Information Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Ensemble learning
- Eye-tracking
- Machine learning
- Prediction
- Social commerce
- Entreprenörskap och innovation
- Entrepreneurship and Innovation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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