Projects within "Networks & Organizations"

The establishment, use, and replacement of resources in enterprises are significantly affected by network structures. Although this topic is highly relevant to business administration, there is still a need for clarification. How do network structures affect and are affected by individual and collective consumer behaviors, ranking algorithms, and firms’ innovation activites? How do they affect the use of resources in strategic enterprises and in the economy overall?

Many managerial decisions aim to influence collective consumer behavior, e.g., to stimulate the adoption of a new service or to promote sustainable consumption. Collective consumer behavior results from the aggregation of many interconnected individual-level choices, whose dynamics determines the success or failure of new products and their creators. There exists a rich literature on both the drivers of individual-level choices and the collective dynamics of adoption processes, but only few studies integrating both perspectives. In this research direction, we perform systematic efforts to connect empirical individual-level choices with the collective success of new products and their creators in social systems. We aim to (1) understand how individual-level creative strategies predict and drive the long-term collective success of product creators; (2) understand the role played by different groups of early adopters in the collective success of a new product; (3) understand how to integrate individual-level behavioral heterogeneity and social network structure to predict the collective success of a diffusion process as well as improve seeding policies. For each of the three goals, we aim to not only advance theoretical understanding, but also derive managerial implications for seeding policies and influencer marketing. These ambitious goals are achieved via a rare combination of traditional methods in consumer behavior (such as discrete-choice models and experiments) with social network and machine learning methods.

In this research direction, we aim to determine how changes in digital platforms simultaneously affect multiple platform-level variables of interest, including the platform’s turnover, efficiency, and diversity as well as consumers’ satisfaction and level of sustainable consumption. Currently, our research focuses on the design of ranking algorithms and on the communication of price information. Ranking algorithms are essential components for the success of modern digital platforms in e-commerce and social media. To understand rankings’ performance in different scenarios, we develop theoretical agent-based models which reveal several nonlinear dependencies typical of complex system dynamics, usually undetected by observational studies. Based on our theoretical analyses, we provide practitioners with roadmaps of the most relevant factors which could help them determine the ranking algorithms that best suits their needs, as well as practical tools to calibrate the proposed models with large-scale behavioral data to anticipate their platforms’ performance under different algorithm choices. Our research on the communication of price information reveals how different price signals may influence consumer search and purchase behavior, and how price transparency affects product sales after promotional sales.

The economic complexity field aims to better understand how the network position of economic actors — such as companies and nations — may drive and predict the actors’ economic performance. In this research direction, we aim to derive new network-based indicators to infer the competitiveness and sustainability of firms’ and nations’ innovation activities. At the firm level, we combine network-based and machine-learning approaches to forecast firms’ research and financial performance from their innovation activities. At the nation level, ongoing projects aim to forecast national development and sustainability-related variables from new network-based indicators.

The Blockchain ecosystems is a new type of ecosystem based on Blockchain technologies: By design, it can create secure and trustworthy peer-to-peer interactions on this type of ecosystems mainly due to their decentralised control and governance mechanisms. In one study, we try to understand the role of social interactions in the creation of price bubbles. Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. By using Bitcoin data, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters.