Maryam Ghasemaghaei
McMaster University
61 Papers
72 Citations
Maryam Ghasemaghaei is an academic researcher from McMaster University. The author has contributed to research in topics: Computer science & Big data. The author has an hindex of 16, co-authored 50 publications.
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Papers
Are firms ready to use big data analytics to create value? The role of structural and psychological readiness
TL;DR: The findings demonstrate the importance of both structural capability, IT infrastructure capability, tools functionality, employee analytical capability, and bigness of data in enhancing firm value creation through big data analytics usage.
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Big data for social benefits: Innovation as a mediator of the relationship between big data and corporate social performance
Goran Calic,Maryam Ghasemaghaei +1 more
TL;DR: In this paper, the authors explored whether firms use big data to improve social performance and found that these improvements occur through organisational innovation in business practices, workplace organization and external relations.
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Assessment of Complexity in Cloud Computing Adoption: a Case Study of Local Governments in Australia
TL;DR: Assessment of complexity in cloud computing adoption, using the context of the local government sector in Australia, indicates that structural complexity of an organization, structural simplicity of technology, and dynamic complexity of technology are critical complexity aspects to be considered during cloud Computing adoption.
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The Antecedents and Results of Seniors' Use of Activity Tracking Wearable Devices
TL;DR: Seniors’ use of wearable devices is a complex process that involves the interactions of social, psychological, and technological factors that can be categorized as technology related factors such as the complexity and customizability of Wearable devices and individual related Factors such as social influence, self-efficacy.
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eHealth Implementation Issues in Low-Resource Countries: Model, Survey, and Analysis of User Experience
TL;DR: In this paper, the authors developed a structural equation model that reflects the opinions of professional eHealth users who work on low-resource countries (LRCs) health care front lines.