## 4 Research Question Results
### First Research Question
#### 4.1.2 Thematic Evolution  
An evolutionary analysis based on author keywords within the three periods was performed. These are Period 1 (2015-2017), Period 2 (2018-2019), and Period 3 (2020-2022). In the proposed study, co-word analysis was performed on author keywords in three time periods and returned several word clusters. These clusters of keywords are named as themes. Each theme has two parameters: \"density\" and \"centrality\". The placing of a theme in one of the four quadrants depends on these parameters. They indicate the similarity of items within and between themes. The similarity of an item can be calculated based on keywords' co-occurrence frequency. Density implies [PERSON]'s density [32], which measures the strength of a keyword's interaction with other keywords within a theme. Conversely, centrality implies [PERSON]'s centrality [32], which measures strength of interactions of a keyword in a theme to keywords in other themes. Therefore this value indicates the importance of the theme in the development of the target research domain, whereas density implies the themes' development stage [33]. An evolutionary map consists of four quadrants (i.e., Figure 9):  
* Upper-right quadrant: motor themes--higher values of development and relevance define motor themes. These themes are well-developed and relevant to the domain. * Lower-right quadrant: basic themes--higher values of relevance and lower values of development define basic themes. These themes are significant for the domain; however, they are not well-developed. * Lower-left quadrant: emerging or declining themes--lower values for relevance and development define emerging or declining themes. These themes are not directly connected to the domain, and full development has not been achieved. * Upper-left quadrant: very specialized/niche themes--lower-relevance values and higher development values define niche themes. These themes are highly developed, but their relevance to the domain is marginal. **Period 1 (2015-2017):** In Figure 9, the circle with keyword \"Digital Twin\" and \"manufacturing\" is a transversal theme that is related to different research fields of the manufacturing domain. The circle size indicates that these keywords (\"Digital Twin\" and \"manufacturing\") are emphasised by the authors in Period 1 more than \"big data\" and \"cloud computing\". **Period 2 (2018-2019):** In Period 2 (Figure 10), the keywords \"Digital twin\", \"machine learning,\" and \"Internet of things\" are emphasised as emerging themes. Additionally, \"manufacturing\", \"data models\", and \"deep learning\" are considered as emerging themes. The trajectory through these two time periods (Period 1 and Period 2) indicates that DT is evolving from only applications in the manufacturing domain to a robust architecture. A new motor theme appeared in period 2: \"smart manufacturing\", \"cyber physical systems\", and \"virtual reality\". This theme is highly relevant and has been developed for the manufacturing domain. Another motor theme appeared in Period 2, which is \"artificial intelligence\", with high density and centrality. Figure 9: Thematic evolution period 1, 2015–2017. A figure with four quadrants representing specific relevance and development degree. Two themes in period 1, 2015–2017. **Period 3 (2020-2022):** In period 3 (Figure 11), deep learning remained an emerging theme, similarly to in Period 2. Conversely, \"artificial intelligence,\" \"big data,\" and \"smart manufacturing\" degraded from the motor theme (Periods 1 and 2) to the basic theme (Period 3). Additionally, ML, DT, and the data model evolved from emerging themes (Period 2) to basic themes (Period 3). Several new themes that appeared as basic themes were \"solid modelling\", \"real-time systems\", \"reinforcement learning\", \"simulation\", \"machining\", and \"process mining\". It can be seen from the analysis of the three time periods that DT evolves from a basic shadow of a physical system to a cutting-edge digital counterpart.