Core Projects - Work, Organizations, and Digital Well-Being

Digital phenotyping is a promising and emerging field that uses the power of technology, particularly personal digital devices, to understand and analyze human behavior and health. By collecting and analyzing data from devices like smartphones and wearables, researchers can gain insights into various aspects of a person’s life and health, including their mobility patterns, physical activity, social interactions, and mental state.

This approach utilizes both active and passive data collection methods. Active data involves direct participation from individuals, such as responding to surveys or inputting information into apps. Passive data collection on the other hand, relies on sensors and data logs from devices to track activities without direct user input. This can include GPS data to understand mobility patterns, accelerometer data for physical activity analysis, or phone usage patterns for insights into social behavior and mental health.

The key advantage of digital phenotyping is its ability to provide a more continuous and objective view of an individual's behavior and health in their natural environment. For instance, by analyzing patterns in mobility or phone usage, researchers can potentially identify early signs of mental health issues or monitor the progression of physical diseases.

In the broader context, digital phenotyping has implications beyond healthcare. It can be used to understand behaviors related to workplace well-being and productivity, lifestyle choices. This tool can finally disentangle the holy grail of the “happy and productive worker” trying to explore the various shades of this phenomenon.

Overall, digital phenotyping represents an interesting convergence of technology, healthcare, and behavioral science, offering new possibilities for understanding and improving human health and behavior.

Digital technologies and remote or hybrid work arrangements have reshaped entrepreneurial activity, increasing cognitive demands, relational challenges, and exposure to work-related stress. Entrepreneurs often operate under conditions of uncertainty, continuous connectivity, and limited organizational buffers, making the identification of scalable mechanisms that support stress regulation and adaptive functioning particularly relevant.

This project examines the effects of compassion-oriented framing on work-related stress and on the activation of personal and relational resources in digital entrepreneurial work. Drawing on the Job Demands-Resources framework, the study conceptualizes compassion as a socio-emotional mechanism that may support emotional regulation, foster prosocial orientations, and improve functioning under demanding work conditions.

The research adopts an experimental design based on brief scenario-based framings that simulate digitally mediated work interactions. Participants are exposed to alternative framings - compassion-oriented, conflict-oriented, or neutral - and complete a single-session protocol assessing stress, emotional resilience, work engagement, and prosocial behavior, measured through both validated self-report instruments and incentivized behavioral tasks. An entrepreneurship-specific module allows the examination of heterogeneity across entrepreneurial profiles. 

The study is hypothesized to show that compassion-oriented framing is associated with

lower perceived work-related stress, higher emotional resilience, and stronger prosocial

orientations compared with neutral or conflict-oriented framings. These effects are expected to be modest in magnitude but meaningful, given the low-intensity nature of the intervention and the cumulative relevance of small changes in stress regulation and relational behavior for entrepreneurial functioning.

From a practical perspective, the findings may inform entrepreneurial support initiatives,

incubators, and policy programs by highlighting the potential of brief, low-cost socio-emotional interventions to improve stress management and cooperation in digital work

contexts. More broadly, the project contributes experimental evidence to debates on sustainable entrepreneurial work by clarifying how socio-emotional framing can influence stress and resource dynamics in digitally mediated environments.

In the context of the New Product Development (NPD) process, many product innovations fail not because of a lack of functionality, but due to excessive complexity. The continuous addition of features, often equated with greater value and innovation, can result in products that are difficult to use, costly to develop, and misaligned with users’ actual needs. This phenomenon, known as over-featuring (OVF), is widely recognized as one of the top ten risks contributing to NPD failures.

This research project addresses this challenge by systematically investigating how and why products become overly complex, examining decision-making processes from both the consumer and the firm perspectives. On the demand side, it explores how users evaluate feature-rich products prior to use and how these expectations evolve following actual experience. On the supply side, it analyzes how managers and development teams make feature selection decisions under conditions of uncertainty, time pressure, and resource constraints.

The objective of the project is to identify the behavioral, cognitive, and strategic mechanisms that drive systematic misalignment between product configurations that maximize user value and those ultimately developed by firms.

The experimental analysis is conducted on digital products, which offer a highly controllable and scalable environment for manipulating features and observing behavior. However, the broader aim of the project is to generate insights that can be extended to physical products, where feature proliferation, production constraints, and lifecycle costs further amplify the consequences of excessive complexity.

Through an innovative experimental approach, this research contributes to a deeper understanding of the relationship between product complexity, usability, and value, with practical implications for the design of more efficient, sustainable, and user-centered innovations across both digital and physical domains.

This project aims to study how uncertainty in the completion time of a task influences labor supply choices, with a particular focus on how variation in supply at both the extensive and intensive margins is determined by mismatches in expected and accumulated effort. The literature on labor supply models generally ignores uncertainty about working hours or sets it as a disturbance factor to be removed. However, in many modern labor markets where flexible arrangements are becoming more common, such as in platform work, workers face considerable uncertainty regarding the duration of tasks and unpaid waiting times, which can substantially alter labor supply behavior.

This project aims to fill this gap by studying how workers respond to uncertainty and sunk costs in task duration when deciding whether to continue working or switch to an alternative task with a known duration. We explicitly focus on: (i) how expected effort and the degree of uncertainty in this expectation affect the decision to persist or switch; (ii) how the accumulated time already invested influences switching behavior beyond standard discounting mechanisms; and (iii) how these effects are mediated by reward levels and behavioral traits, such as loss aversion and patience. To answer these questions, the project will conduct a randomized online experiment in which participants perform real-effort tasks where task completion time is uncertain and drawn from a known distribution, while, at exogenously assigned points, participants are offered the option to switch to an alternative task with a known duration. This design allows for clean identification of the causal effects of time uncertainty and sunk time on both the extensive margin (whether to switch) and the intensive margin (when to switch).

The project is expected to contribute to the labor economics literature by providing novel evidence on labor supply responses to time uncertainty. By improving our understanding of how workers adapt their labor supply decisions in environments characterized by flexibility, uncertainty, and incomplete information, the findings will inform theoretical models that incorporate behavioral frictions into labor supply and have direct relevance for the design and regulation of digital labor markets.