Market Prediction for High-Technology Innovations

Abstract

To minimise the risk for new product failure and accurately assess the commercial potential of an innovation, companies make predictions. The majority of the available prediction methods are however tailored to either existing products or established companies. For new companies developing high-technology innovations, the literature offers limited guidance on how to navigate the many market uncertainties and perform market predictions in practice.

This thesis aimed to create a framework for performing market predictions for high-technology innovations, both to understand how the prediction process can be structured from start to finish, as well as identify challenges with using a framework for a real prediction situation.

A comprehensive literature review was conducted to identify the key factors influencing the market response of a new innovation, along with an overview of different forecasting methods and their applicability for a company developing a high-technology innovation. These insights resulted in a framework to be used as guidance for the prediction process. The framework was thereafter applied to a case company, to evaluate the functionality of the framework, as well as identify opportunities and challenges with using it.

The results conclude that the main challenge in applying the framework to real-world situations is the lack of suitable data, which necessitates assumptions and introduces uncertainties in the predictions. Nevertheless, the framework supports better market understanding, information gathering, and facilitates a more informed analysis.

Popular Abstract

Many high-technology innovations are developed with high hopes and expectations - only to fail to achieve commercial success. Meanwhile, some of the most successful products in history were initially dismissed by critics. So how can we predict whether a new innovation will succeed or fail?

There are many historical examples of flawed predictions. In 1977, the president of Digital Equipment Corporation famously said, “There is no reason anyone would want a computer in their home.” He could not have been more wrong. When the Segway was launched in 2001, it was hyped as a revolution in urban transportation, and the founder predicted it would replace all cars in cities. Instead, it ended up becoming mostly associated with mall cops and tourists.
These examples highlight a fundamental question: How can we predict which innovations will succeed and which will fail? Large companies with established products and lots of historical sales and market data have many prediction methods to rely on. For new companies or startups, working on high-technology innovations, it is more challenging. They face high uncertainty, limited data and a lack of clear guidance in the prediction process. This project set out to tackle this challenge by developing a structured framework for predicting the market response for new high-technology innovations.

By reviewing existing research and analysing previously launched high-technology innovations, a new framework was created. The framework structures the prediction process from start to finish, and consists of three stages: an information gathering stage, a market framing stage, and a prediction stage. It also includes five critical dimensions: the market, competition, environmental factors, technology and resources, and the product itself. The framework is designed to be iterative, meaning it should be updated when changes occur within any of these dimensions.

To test how well the framework works in practice, it was applied to a real startup developing a high-technology innovation. The results showed that the framework helped improve market understanding and supported a more structured prediction process. However, the study revealed a key challenge: the lack of reliable data for high-technology innovations often creates a need for assumptions, which can introduce uncertainty into predictions. The study also highlights that no single factor can reliably predict market response, and emphasises the importance of combining multiple factors to gain a comprehensive understanding of the market.

While further validation is needed, the developed framework presents a promising step toward making market prediction more structured, accessible, and applicable for companies developing high-technology innovations.

• author: Samuelsson, Hilda
• supervisor: Ola Alexanderson
• organization: Department of Mechanical Engineering Sciences; Production Management
• course: MIOM05 20232
• year: 2025
• type: H2 - Master's Degree (Two Years)
• subject: Business and Economics
• keywords: market prediction, high-technology innovations, new product adoption
• report number: 25/5328
• language: English
• id: 9208966
• date added to LUP: 2025-08-15 14:25:36
• date last changed: 2025-08-15 14:28:41