Technological Scanning for Foresight
The case of Metaverse applications for Healthcare
Bibliographic Data
| ID | 9842315 |
|---|---|
| Authors | Francesca Zoccarato (0000-0002-2825-4112, University of Milan, corresponding author), Antonio Ghezzi (0000-0002-2084-4521, University of Milan), Emanuele Lettieri (0000-0003-1235-989X, University of Milan), Giovanni Toletti (0000-0003-2579-9683, University of Milan) |
| Year | 2024 |
| Volume | 164 |
| Pages | 103476 |
| Publication date | 2024-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Futures (JOURNAL) |
| Journal identifiers | ISSN: 0016-3287 • E-ISSN: 1873-6378 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.futures.2024.103476 |
| OpenAlex | W4402611267 |
| Language | EN |
| Citations received | 2 |
| References cited | 56 |
The process of foresight, which allows companies and organizations to build scenarios and inform the creation and sustainment of their competitive advantage, relies on the integration of several steps. Scanning is a crucial step of foresight, as it informs and influences the results of the whole process and, thus, the strategic decision-making of the company. Sources and methods of scanning for foresight analysis can be diverse and lead to different results, although few studies investigate such differences: more specifically, the informative power of academic and non-academic articles and reports has not been assessed yet. This study aims to shed novel light on how the different analysis methods of full reading of records and text mining analysis isolate and gather forces of change differently, based on the source analyzed. The study’s empirical context is the metaverse and its application in healthcare. We find that each source and method by itself is unable to fully gather the whole set of forces of change; however, each source presents some topics that are specific to the target readers of the source, and each methodology presents some advantages as well as some limitations. From the comparison of the results, theoretical and managerial implications are drawn. • The informative power of academic and non-academic articles and reports is compared. • Extensive reading and text mining analyses are applied. • Results differ based on the combination of sources and methods. • There is the need of expertise on both text mining and Foresight
Business · Economic growth · Economics · Futures studies · Health care · Human–computer interaction · Knowledge management · Metaverse · Virtual reality · Artificial Intelligence · Big Data and Business Intelligence · Computer Science · Digital Transformation in Industry · Ethics and Social Impacts of AI
How are foresight methods selected?
Foresight in science and technology
Probabilistic topic models
Trends and Innovations of Simulation for Twenty First Century Medical Education
My doctor is an avatar! The effect of anthropomorphism and emotional receptivity on individuals' intention to use digital-based healthcare services
Leveraging blockchain technology for green innovation in ecosystem-based business models
Digital business models and ridesharing for value co-creation in healthcare
A review of the key challenges of non-fungible tokens
Expert biases in technology foresight. Why they are a problem and how to mitigate them
Exploring the impact of technological disruptions in the automotive retail
Scenario development using web mining for outlining technology futures
Business model theory-based prediction of digital technology use
The future of ICT for health and ageing
Developments and challenges of foresight evaluation
The landscape evolution model
The evolution of Foresight
How to visualise futures studies concepts
The Big Picture – trends, drivers, wild cards, discontinuities and weak signals
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |