Futures of shipbuilding in the 22nd century : Explorative industry foresight research of the long-range futures for commercial ship-building, using elements of OpenAI.
Seppälä, Ludmila (2023-06-16)
Futures of shipbuilding in the 22nd century : Explorative industry foresight research of the long-range futures for commercial ship-building, using elements of OpenAI.
Seppälä, Ludmila
(16.06.2023)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2023072691302
https://urn.fi/URN:NBN:fi-fe2023072691302
Tiivistelmä
The shipbuilding industry has historically shaped global trade, logistics, research, and cultural globalization. It was instrumental in exploring and colonizing new continents, thereby significantly shaping our society. Today, it's essential to consider the industry's current transformations and speculate on what shipbuilding might look like in the 22nd century.
This study is dedicated to exploring the possible futures of shipbuilding over a long-range time horizon of 70 -100 years. This thesis applied futures research methods to data collected using OpenAI tools and explored possible transformative pathways within the industry. The research offers potential future scenarios and delineates change pathways from external pressures and internal shifts within the shipbuilding system. Additionally, the study highlights the possible applications and implications of utilizing OpenAI technology in a research context.
The analysis of shipbuilding incorporates the Multi-Level Perspective (MLP) concept, viewing the industry as a system involving ten groups of key actors. This structure guided the data collection process for the input of the research. The primary research process adheres to traditional futures research methods, which include horizon scanning, systems thinking, scenario building, and causal layered analysis (CLA). Furthermore, the methodology was expanded to incorporate AI-assisted techniques. This includes using AI technology for automated data collection and a separate pathway using ChatGPT-4 for computer-generated scenarios and CLA narratives development. The outcomes from both methodologies are compared, and additional literature research about the applicability and implications of using AI in futures studies.
The research has identified critical external drivers of change, originating from fields such as technology, energy, and social development, as well as internal drivers, including biotechnology and diversifying floating structures. The external drivers could influence the future direction of shipbuilding, while the internal factors represent potential changes originating from within the industry. The constructed scenarios are designed to stimulate discussion and provide context for future developmental trajectories of shipbuilding.
This study is dedicated to exploring the possible futures of shipbuilding over a long-range time horizon of 70 -100 years. This thesis applied futures research methods to data collected using OpenAI tools and explored possible transformative pathways within the industry. The research offers potential future scenarios and delineates change pathways from external pressures and internal shifts within the shipbuilding system. Additionally, the study highlights the possible applications and implications of utilizing OpenAI technology in a research context.
The analysis of shipbuilding incorporates the Multi-Level Perspective (MLP) concept, viewing the industry as a system involving ten groups of key actors. This structure guided the data collection process for the input of the research. The primary research process adheres to traditional futures research methods, which include horizon scanning, systems thinking, scenario building, and causal layered analysis (CLA). Furthermore, the methodology was expanded to incorporate AI-assisted techniques. This includes using AI technology for automated data collection and a separate pathway using ChatGPT-4 for computer-generated scenarios and CLA narratives development. The outcomes from both methodologies are compared, and additional literature research about the applicability and implications of using AI in futures studies.
The research has identified critical external drivers of change, originating from fields such as technology, energy, and social development, as well as internal drivers, including biotechnology and diversifying floating structures. The external drivers could influence the future direction of shipbuilding, while the internal factors represent potential changes originating from within the industry. The constructed scenarios are designed to stimulate discussion and provide context for future developmental trajectories of shipbuilding.