Abstract
Fundamental changes have prepared the grounds for a rapid movement towards becoming data and insight-driven. Businesses are continually seeking approaches to create more value from data. The main purpose of this article is to propose a model by which experts as Human Intelligence, can participate to share their expectations to orient the data processing towards the generation of insights needed to target industries and consequently, the realization of indirect data monetization. A set of recommendation systems as Artificial Intelligence, facilitate the submission and validation of expectations, access to data, and selling insights. The model also encompasses a direct data monetization strategy, wherein participants access or request their requirements in an Online Insight Marketplace. We have used the design science methodology to develop and validate our proposed model. The model is validated by comparison with competitive models from the literature, and also by bringing evidence from real-world applications which relate to the components of our model.
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Appendix
Appendix
Glossary of Acronyms
AI: Artificial Intelligence
BPMN: Business Process Model Notation.
CIST: Collective Intelligence Social Tagging.
DOR: Data Offering Repository.
DSR: Design Science Research.
DVC: Data Vendor Community.
FR: Feedback Repository.
HI: Human Intelligence.
HIT: Human Intelligence Tasks.
IaaS: Insight-as-a-Service.
IH: Insight Hunter.
IP: Insight Provider.
IR: Insight Repository.
IT: Information Technology.
MEEM: Monetizing Expert Expectation Model.
MEET: Monetizing Expert Expectation Token.
PC: Professional Community.
SaaS: Software-as-a-Service.
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Hanafizadeh, P., Barkhordari Firouzabadi, M. & Vu, K.M. Insight monetization intermediary platform using recommender systems. Electron Markets 31, 269–293 (2021). https://doi.org/10.1007/s12525-020-00449-w
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DOI: https://doi.org/10.1007/s12525-020-00449-w