סמינר בניהול טכנולוגיה ומידע

הסמינר הפעם יוקדש להצגת מחקרים של דוקטורנטים ופוסט-דוקטורנטים במחלקה.
להלן המחקרים שיוצגו בסמינר :

1.      Open to Everyone? The Long Tail of Crowdfunding: Evidence from Kickstarter
              Ohad Barzilay, Hilah Geva, Anat Goldstein, and Gal Oestreicher-Singer

Crowdfunding platforms remove entrance barriers found in traditional financial markets creating markets that are more democratized. Our work draws from the literature on the “long tail” to investigate how the democratization of crowdfunding markets affects distribution of funds. We utilize a natural experiment in the form of a policy change on Kickstarter.com that resulted in opening the market to more players, effectively raising the level of democratization. We examine how platform-openness affected the distribution of funds and backers across entrepreneurs. Specifically, we ask whether removal of entrance barriers—which increased the quantity and variety of offers—shifted demand from popular to niche offers (a long-tail effect). Counterintuitively, our findings indicate that opening the platform shifted demand towards the head: More funds and backers became concentrated in a smaller number of head-offers. We conclude that a more democratized peer-economy results in a less-even distribution of funds across the marketplace (a superstar effect).

2.      The Information Content of Multiword #Hashtags

              Zvi Ben Ami, Tomer Geva, and Inbal Yahav

In recent years data science had provided us many opportunities to uncover new social phenomena and behaviors in online social networks and to utilize such information for business applications. One such interesting phenomenon is the use of hashtags to emphasize important content.  In this paper, we evaluate the information content of hashtags for sentiment analysis applications. Specifically, we focus on multi-word hashtags, which challenge automated sentiment analysis methods. For this purpose, we develop a new algorithm to split multi-word hashtags into individual terms. We then compare the predictive accuracy of sentiment analysis with and without this finer-grained representation. We find that breaking down hashtags into multiple terms significantly improves the predictive accuracy of sentiment analysis procedures, and more generally, that hashtags are highly informative for sentiment analysis purposes.

3.      From Tailored Calls-to-Action to Subscription, to Consumption of Online News: A Field Experiment
              Sagit Bar-Gill, Yael Inbar, and Shachar Reichman

The transition to subscription-based business models, has led content providers and news websites to use calls-to-actions (CTAs) to drive subscriptions to paid products or services. In this study, we examine the impact of CTAs on subscription decisions and news consumption, specifically testing their potential to elicit consumption patterns referenced in the CTA. We first employ prediction models to identify key consumption patterns associated with subscription to a news product, as well as user groups with high and low subscription likelihood. We then run a field experiment in collaboration with a US-based news website. In the experiment, high and low subscription likelihood users are randomly assigned to receive consumption-based targeted CTAs and control (default) CTAs, where the targeted CTAs are designed based on insights from our prediction models. This research will highlight CTAs’ potential for inducing desired behavior changes on news websites and other e-commerce platforms.