L
Lior Zalmanson
Researcher at University of Haifa
Publications - 27
Citations - 845
Lior Zalmanson is an academic researcher from University of Haifa. The author has contributed to research in topics: Social media & Willingness to pay. The author has an hindex of 8, co-authored 21 publications receiving 653 citations. Previous affiliations of Lior Zalmanson include Tel Aviv University & New York University.
Papers
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Journal ArticleDOI
Content or Community? A Digital Business Strategy for Content Providers in the Social Age
TL;DR: Firms whose digital business models remain viable in a world of "freemium" will be those that take a strategic rather than techno-centric view of social media, that integrate social media into the consumption and purchase experience rather than use it merely as a substitute for offline soft marketing.
Journal ArticleDOI
Content or community? a digital business strategy for content providers in the social age
TL;DR: In this article, the authors show that willingness to pay for premium services is strongly associated with the level of community participation of the user and the volume of content consumption on Last.fm, a site offering both music consumption and online community features.
Proceedings Article
Hands on the Wheel: Navigating Algorithmic Management and Uber Drivers’ Autonomy
Mareike Möhlmann,Lior Zalmanson +1 more
Journal ArticleDOI
Minimal and Adaptive Coordination: How Hackathons’ Projects Accelerate Innovation without Killing it
TL;DR: The innovation journey of new product development processes often spans weeks or months, but recently hackathons have turned the journey into an ad hoc sprint of only a couple of days using new tools and techniques.
Journal ArticleDOI
The Persuasive Power of Algorithmic and Crowdsourced Advice
TL;DR: Examining the effects of algorithmic and social advice on decision-making in the context of an online retirement saving system finds that both types of advice have a positive effect on users’ saving performance, and that users follow advice presented as coming from an algorithmic source more closely than Advice presented as crowdsourced.