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MIS Quarterly Executive

 

Forthcoming Papers

 

BBVA’s Data Monetization Journey
Data monetization occurs when companies convert data and analytics into financial returns. BBVA, a global financial group, established a data science center of excellence in 2014 to lead its data monetization activities. By 2017, BBVA had developed a rich data monetization portfolio by making pre-existing data monetization activities more effective, by pursuing new data monetization approaches and by investing in a larger number of data monetization projects. Based on BBVA’s efforts, we provide recommendations for others as they embark on their data monetization journeys.

Elena Alfaro
BBVA Group (Spain)
Marco Bressan
Satellogic (Spain)
Ida Asadi Someh
University of Queensland (Australia)
Fabien Girardin
Near Future Laboratory (Spain)
Juan Murillo
BBVA Group (Spain)

 

Three Differentiation Strategies for Competing in the Sharing Economy
Executives of organizations entering the sharing economy face the challenge of selecting the right differentiation strategy. This article describes three sharing economy differentiation strategies—technology, partnership and user experience. We provide nine questions to guide the selection of a differentiation strategy when entering the sharing economy, and recommend three actions for implementing the strategy.

Alexander Frey
University of Augsburg (Germany)
Manuel Trenz
University of Augsburg (Germany)
Daniel Veit
University of Augsburg (Germany)

 

Designing Ethical Algorithms
Algorithms drive critical decisions such as which patient is seen or who is offered insurance. Such algorithmic decisions, like all decisions, are biased and make mistakes. Yet, who is responsible for managing those mistakes? This article focuses on the responsibility of developers and users of algorithms to ensure algorithms support good decisions – including managing mistakes. First, while mistakes may be unintentional, ignoring or even fostering mistakes is unethical. Second, by creating inscrutable algorithms, which are difficult to understand or govern in use, developers may voluntarily take on accountability for the role of the algorithm in the decision.

Kirsten Martin
George Washington University School of Business (U.S.)

 

A Ten-Step Decision Path to Determine When to Use Blockchain Technologies
Many organizations are looking at blockchain technologies. However, the drawbacks of blockchain databases (e.g., scalability, capacity, latency, privacy) mean that the technology is not always appropriate. This article presents a ten-step decision path that can help determine whether the application of blockchain is justified and, if so, which kind of blockchain technology to use. We describe how this decision path was used to develop a blockchain prototype for the Danish maritime shipping industry.

Asger B. Pedersen
Netcompany Group (Denmark)
Marten Risius
University of Queensland (Australia)
Roman Beck
IT University at Copenhagen (Denmark)