The speed of technological change makes it difficult to see the future clearly. Technology Outlook provides orientation in the technological landscape of tomorrow. It is a travel guide for the future, explaining trends in technology, ranking their importance for Switzerland as a centre of knowledge and industry, and comparing developments in Switzerland with those abroad. Technology Outlook identifies opportunities and challenges, and is thus an important basis for the strategic work of experts from the worlds of industry, location promotion and administration.
The Swiss Academy of Engineering Sciences SATW is involved in early identification of technologies (in short: foresight) on behalf of the Swiss Confederation. Technology Outlook is a result of these foresight activities and presents future-oriented technologies that will be of relevance to Switzerland in the coming years. As a unique expert organisation with high credibility, SATW conveys independent, objective and holistic information on technology – as a basis for establishing informed opinions. It is politically independent and non-commercial.
The SATW Scientific Advisory Board is responsible for selecting the technologies. Firstly, the technology readiness level (TRL) of all technologies from Technology Outlook 2021 was reassessed , (see NASA, Technology Readiness Level). Secondly, a provisional list of new, potentially disruptive technologies was drawn up in collaboration with the heads of the SATW topical platforms. The TRL and significance of these technologies for Switzerland were assessed by experts. Technologies of major relevance to Switzerland with a TRL between 4 and 7 were included in the final technology list on which the 2023 edition of Technology Outlook is based. For each technology, interviews were conducted with experts. In most cases, there were two or more such interviews. These were based on a standardised questionnaire. The SATW foresight team then incorporated the answers into articles, which were checked for accuracy by the respective experts.
The selected technologies’ importance for Switzerland, as visualised in the four-quadrant diagram, was estimated on the basis of eight parameters, for which values were obtained as part of the standardised questionnaire. Four were used to determine economic importance and four to determine the level of research competence in Switzerland. The eight parameters were as follows:
The experts’ answers were transferred to a point system.
Sales revenue in 2021 (R), based on expert estimates, sector and company reports, statistics databases and self-conducted research:
| Value (in CHF mn) | < 10 | 10–99 | 100–499 | 500–999 | ≥ 1000 |
|---|---|---|---|---|---|
| Points | 1 | 2 | 3 | 4 | 5 |
Market potential in the coming five years (M), as estimated by experts:
| Value | Low | Medium | High |
|---|---|---|---|
| Points | 0.4 | 1 | 1.6 |
Legal and regulatory framework conditions in Switzerland (FL), as estimated by experts:
| Value | Unvafourable | Neutral | Optimal |
|---|---|---|---|
| Points | 0.8 | 1 | 1.2 |
Acceptance in Swiss society (FS), as estimated by experts:
| Value | Inhibiting | Neutral | Encouraging |
|---|---|---|---|
| Points | 0.9 | 1 | 1.1 |
Number of relevant academic research groups in Switzerland (RA), based on information provided by experts and self-conducted research:
| Value | <20 | 20–39 | ≥40 |
|---|---|---|---|
| Points | 1 | 3 | 5 |
Competence of academic research groups (CA), based on the average h-index of research groups in Switzerland that are active in the given field. The h-index is a metric pertaining to the worldwide perception of a scientist in professional circles, derived from citations of publications by the respective specialist.
| Value | <20 | 20–34 | ≥35 |
|---|---|---|---|
| Points | 0.8 | 1 | 1.2 |
Number of firms in Switzerland with topic-related R&D (RI), based on information provided by experts, sector and company reports, and internet research:
| Value | <30 | 30-69 | ≥70 |
|---|---|---|---|
| Points | 1 | 3 | 5 |
Competence of these firms in the international context (CI), as estimated by experts:
| Value | Low | Medium | High |
|---|---|---|---|
| Points | 0.8 | 1 | 1.2 |
The values determined with this point system were converted into a position on the horizontal axis (economic significance) using the following formula:
R ∙ (M+FL+FS)
Different parameters were given different weightings in the formula. Sales revenue, which is based on reliable figures, was defined as the main parameter, while the other three values modulated the revenue. The influence of market potential on sales revenue development was considered to be greater than that of the legal and regulatory framework conditions or of acceptance in society. This weighting formed the basis of the point system and thus determined how the resulting values were obtained.
Each technology’s position on the vertical axis (research competence in Switzerland) was computed using the following formula:
RA ∙ CA + RI ∙ CI
The given numbers of academic and industrial research groups were defined as the two main parameters, which were then modulated by the respective groups’ competence, thus affecting how the values were transferred to the point system.
These calculations yielded values between 2.1 and 19.5 for the horizontal axis and between 1.6 and 12 for the vertical axis. To simplify visualisation, these values were converted using a linear transformation, yielding final positions between 0 and 10 on both axes.
In total, 19 of the technologies that had already been addressed in Technology Outlook 2019 are also represented in the 2023 edition’s four-quadrant diagram. In order to determine how they have developed, including any positional changes, the values from the two four-quadrant diagrams were compared directly: To detect changes in economic importance (horizontal axis), the difference between the x-values from 2023 and 2019 was calculated; for changes in the research competence in Switzerland, the difference between the y-values from 2023 and 2019 was calculated (vertical axis). Changes are expressed in absolute terms. The figures from the 2019 edition were converted to take into account the slightly adjusted methodology used for the 2023 edition. This applies to market potential, which is now divided into three instead of four levels, as well as the number of academic and industrial research groups, for which the experts’ responses are now assigned to one of three levels, rather than five.
SATW uses the semantic search engine provided by Swiss firm LinkAlong. This searches Twitter (rebranded as X in 2023) and the websites referred to in the posts. With the aid of this search engine, all posts on the official Twitter accounts of universities in Switzerland, Germany, France, the UK, Italy, Austria and the USA were collated and analysed with regard to the individual technologies described in Technology Outlook.
For each technology described in Technology Outlook, a list of search terms that clearly identify the technology in question was compiled. These lists incorporate the national languages of the monitored countries, as well as the different names and spellings used for each technology. This makes it possible to determine how many universities in a country tweet about a particular topic.
For the purpose of analysing technological trends, the technologies were assigned to the four research categories ‘digital world’, ‘energy and environment’, ‘manufacturing processes and materials’, and ‘life sciences’. In order to describe changes in these research categories, the number of universities commenting on at least one technology in the respective category was determined. As the number of universities in the monitored countries varies greatly, the relative number of commenting universities was determined as a percentage for each research category in each country, with 100 percent representing the total number of universities in the respective country. Two periods were examined for all four research categories – 2018–2019 and 2021–2022 – and the values for each two-year period were averaged. The trend in each country was determined from the difference between these two periods in percentage points.
Similarly, for analysis at the level of the individual technologies, the number of universities with at least one tweet on the respective technology was counted. Each of these values was converted into a percentage of the total number of universities in the respective country. The averages for 2018–2019 and 2021–2022 were calculated. The difference between the two averages shows the trend for the respective technology in each country.
For analysis of trends at national level, the percentage of Swiss universities commenting on selected technologies on Twitter was assessed. Here, the two-year average for 2021–2022 was compared with the average for 2018–2019; the figures for 2020 were influenced by the coronavirus pandemic and were excluded. In the chart, the change between the two periods is shown in absolute terms.
Technology Outlook is now also addressing societal aspects of technologies, on the basis of a Delphi survey. The Delphi method is a technique for conducting systematic multi-stage surveys. Developed by the RAND Corporation in the 1950s, it has been used ever since to address issues about which the existing knowledge is uncertain or incomplete. The aim is to obtain subjective intuitive knowledge that is nevertheless based on personal expertise. Delphi studies are characterised by multiple rounds of questioning. In the Delphi survey for Technology Outlook, there were two rounds.
Identification of topics
In interviews, experts suggested topics of particular importance to society. Based on assessment of this information and under the condition that one topic be selected from each research category, the foresight team, in cooperation with David Marti from the think tank Pour Demain, identified possible topics: AI in healthcare, energy, materials and point-of-care testing. Preparations for the subsequent session included drawing up the questionnaire and procedure.
Development of hypotheses
Taking the topic-identification workshop as a starting point, a set of questions was compiled (see below). This served as the basis of a workshop, with the SATW Scientific Advisory Board and other experts from science and politics, for the purpose of formulating hypotheses.
This set of questions was slightly adapted for each of the four topics, in order to reflect the different perceptions and problems associated with them.
During the workshop, the 20 participants formed four groups, each of which worked on the questions for one topic. Grouping was done in such a way that experts on the respective topic worked together with experts from other areas of expertise. The plenary discussed the results from the individual groups and derived hypotheses.
Delphi survey and analysis
Based on the workshop’s results, the foresight team developed the questionnaire for the first round of the Delphi survey, in which there were 94 respondents. The questionnaire was in four parts and comprised a total of 24 questions with numerous sub-questions.
Most of the questions on AI in healthcare were about the use and protection of sensitive personal data, and the study participants’ trust in various players within the healthcare system. The block of questions about energy concentrated on tensions between different goals that turn out to be in conflict with each other, and on responsibilities with regard to achieving a sustainable and secure energy supply. The questions on materials primarily focused on measures to improve the level of knowledge among the population and on the recycling of new types of materials. The block of questions on point-of-care testing was dominated by this technology’s influence on the individual and the healthcare system.
The SATW foresight team analysed the responses, then drew up the second round’s questionnaire. This comprised a total of ten questions on the four topics. Its purpose was to clarify ambiguities from the first round and to ask follow-up questions in greater depth where needed. The second questionnaire was completed by 72 participants from the first round of the survey.
Most questions were analysed according to response frequencies, without any statistical procedures beyond the calculation of means and percentages. As patterns were noticed in the responses about preferences regarding possible conflicting goals in the context of energy supply, the corresponding answers were grouped by means of cluster analysis. The grouping method was based on minimising the sum of squared errors.
