The concept of digitizing everything is already a reality. With artificial intelligence, IoT, machine learning and other advanced technologies capturing and analysing a wealth of data.  One of the major challenges we face is to change the way we think, train and work with data in order to create value through advanced technologies.

Track 7.5 in the  R&D Management conference 2019- ‘Emerging landscapes. New skills, new technologies and new organizational challenges in the 4.0 age’ – aims to critically analyze the opportunities, dark side and challenges created by a move to 4.0.

Track 7.5 chairs are: Cristina SIMONE, Associate Prof., Sapienza University of Rome; Giuliano MAIELLI, Queen Mary University of London; Marcelo Enrique CONTI, Sapienza University of Rome; Laura RIOLLI, California State University.

Julien Cloarec, Graduate Engineer in Computer Science and Ph.D. Candidate and Teaching Assistant in Management Science at Université Toulouse will present his research. We asked him about developments in this fascinating area of research. 

What do you think are the most disruptive influences impacting developments in your track?

While consumers’ attention is fragmented by a multi-screen environment, marketers are still looking for the best way to capture attention through personalization.

While personalization is considered to be a privacy benefit, consumers are reluctant to provide personal information and marketers’ exploitation of consumer personal information to provide personalization can trigger consumers’ privacy concerns.

Scholars are thus looking for approaches to better understand this issue at a societal level. The evolution of Big Data, over the last decade, has brought attention to this important core research area which is of interest to economists and choice theorists.

Can you describe some recent findings in this area that are of interest to you personally?

The scientific consensus on privacy is clear: the stakeholders (e.g., firms and consumers) have often conflicting objectives. Privacy concerns are constantly evolving over time, and privacy regulation should be individualized to specific markets.

There is a need to implement an effective attention management policies to undermine the personalization-privacy trade-offs’ pressure, especially for consumers who surrender to technology.

The literature on the personalization-privacy trade-offs provide some policy recommendations. However, their major limitation is that they are reactive. To fully encompass the issues regarding both the personalization-privacy trade-offs and attention management, education is necessary.

However, a balance must be reached. If consumers become too reluctant to enter online exchanges, they can be harmed. Indeed, recent works that studied privacy in a context of limited attention showed that there exists a prisoner’s dilemma situation: if all consumers opt out of personalization-privacy trade-offs, price competition is weakened, which is costly for consumers.

If someone was new to this topic what would you suggest they read to get a quick overview of the issues?

Anderson, S. P., & de Palma, A. (2012). Competition for Attention in the Information (Overload) Age. The RAND Journal of Economics, 43(1), 1–25.

Andrews, M., Luo, X., Fang, Z., & Ghose, A. (2016). Mobile Ad Effectiveness: Hyper-Contextual Targeting with Crowdedness. Marketing Science, 35(2), 218–233.

Casadesus-Masanell, R., & Hervas-Drane, A. (2015). Competing with Privacy. Management Science, 61(1), 229–246.

Falkinger, J. (2007). Attention Economies. Journal of Economic Theory, 133(1), 266–294.

Falkinger, J. (2008). Limited Attention as a Scarce Resource in Information-Rich Economies. The Economic Journal, 118(532), 1596–1620.

Gal-Or, E., Gal-Or, R., & Penmetsa, N. (in press). The Role of User Privacy Concerns in Shaping Competition Among Platforms. Information Systems Research.

Sutanto, J., Palme, E., Tan, C.-H., & Phang, C. W. (2013). Addressing the Personalization-Privacy Paradox: An Empirical Assessment from a Field Experiment on Smartphone Users. MIS Quarterly, 37(4), 1141–1164.

For more information about Track 7.5 click here.