Technology and Information Management11

 

Researchers in the Technology and Information Management specialization are concerned with the way in which technology changes the business world, the organizations and the society around us.  These issues include the design and use of information technologies, the management of information and data resources, economic influences of new technologies, electronic commerce, crowdfunding and uses of data in medicine.

The research covers a range of approaches, including the use of data analytics and machine learning, the construction of models of consumer and user behavior, and their examination in the laboratory and the field, as well as the development and evaluation of economic models.

 

 

 

Faculty

 

 

Dr. Shachar Reichman

Head of Technology and Information 
Management Department

Prof. Moshe Zviran

Dean

Prof. Gal Oestreicher-Singer

 

 

 

Dr. Tomer Geva

 

Dr. Sagit Bar-Gill

Dr. Lior Zalmanson

Dr. Inbal Yahav Shenberger

Dr. Hilah Levin Geva

Dr. Yael Inbar


​Publications

 

 

Bar-Gill S., Inbar Y. and Reichman S. (2020), “The Impact of Social vs. Non-Social Referring Channels on Online News Consumption”, Management Science, Forthcoming. (Published Online: 8 Jun 2020).

 

Bar-Gill S. and Reichman S. (2020), “Stuck Online: When Online Engagement Gets in the Way of Offline Sales”, MIS Quarterly, Forthcoming.

 

Geva, T., and Saar-Tsechansky, M. (2020), “Who Is a Better Decision Maker? Data-Driven Expert Ranking Under Unobserved Quality.” Production and Operations Management (Forthcoming).

 

Geva, T., and Yahav, I. (2020), "Data-Driven Link Screening for Increasing Network Predictability." IEEE Transactions on Knowledge and Data Engineering (Forthcoming).

 

Bar-Gill S. (2019), “Game of Platforms: Strategic Expansion into Rival (Online) Territory” Journal of the Association of Information Systems, 20(10), pp. 1475-1502.

 

Geva, H., Barzilay, O., and Oestreicher-Singer, G. (2019), “The Potato Salad Effect: The Impact of Competition Intensity on Outcomes in Crowdfunding Platforms” MIS Quarterly, 43(4).

 

Geva, H., Oestreicher-Singer, G., and Saar-Tsechansky, M. (2019), “Using Retweets to Shape our Online Persona: A Topic Modelling Approach”, MIS Quarterly, 43(2), pp. 501-524.

 

Geva, T., Saar-Tsechansky, M. and Lustiger, H. (2019), “More for less: adaptive labeling payments in online labor markets” Data Mining and Knowledge Discovery, pp.1-49.

 

Cascavilla, G., Conti, M., Schwartz, D. G., and Yahav, I. (2018), “The insider on the outside: a novel system for the detection of information leakers in social networks.” European Journal of Information Systems, 27(4), 470-485

 

Shmueli, G., and Yahav, I. (2018), “The Forest or the Trees? Tackling Simpson's Paradox with Classification Trees.” Production and Operations Management, 27(4), 696-716.

 

Yahav, I., Shehory, O., and Schwartz, D. (2018), “Comments Mining With TF-IDF: The Inherent Bias and Its Removal.” IEEE Transactions on Knowledge and Data Engineering, 31(3), 437-450. (A)

 

Geva, T., Oestreicher-Singer, G., Efron, N., Shimshoni, Y. (2017), “Using Forum and Search Data for Sales Prediction of High-Involvement Projects.” MIS Quarterly 41, no. 1:  65-82.

 

Brynjolfsson, E., Geva, T., and Reichman, S. (2016), “Crowd-Squared: Amplifying the Predictive Power of Search Trend Data”, MIS Quarterly, Vol. 40 No. 4, pp. 941-961.

 

Bertsimas, D., Brynjolfsson, E., Reichman, S., and Silberholz, J. M. (2015), “Tenure Analytics: Models for Predicting Research Impact,” Operations Research, 63(6):1246-1261.

 

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