Anticipating technological convergence: Link prediction using Wikipedia hyperlinks

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Anticipating technological convergence: Link prediction using Wikipedia hyperlinks Juram Kima, Seungho Kimb, Changyong Leea, a b

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School of Management Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea School of Business Administration, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: Technological convergence Predictive analysis Link prediction Wikipedia hyperlinks Technological ecology network

Technological convergence has been the subject of many previous studies, but most have focused on ex post evaluation using patent information. The value of predictive analysis and new data sources has thus seldom been addressed. This study proposes a systematic approach to anticipating technological convergence that can be used to guide organisations towards reacting in a timely manner to challenges posed by increasingly permeable technology boundaries. For this, a technological ecology network is constructed using direct and indirect hyperlinks extracted from the Wikipedia database, and link prediction methods are employed to develop three predictive indicators of technological convergence. A case of 3D printing technology confirms, with statistically significant outcomes, that the proposed approach enables a wide-ranging search for future converging technologies. The systematic process and quantitative outcomes of the proposed approach are expected to be valuable as a complementary tool for strategic decision making regarding emerging technologies in the era of open innovation.

1. Introduction Technological convergence – defined as a breakthrough which combines at least two or more existing technologies into hybrid technologies (Curran et al., 2010) – is currently a dominant trend in many areas, such as information technology, biotechnology, and nanotechnology (Battard, 2012; Borés et al., 2003; Gambardella and Torrisi, 1998; Kim et al., 2015). This phenomenon is considered a major source of new innovations, creating synergies between technologies and further reshaping industry structures and competition rules (Allarakhia and Walsh, 2012; Hacklin, 2007). A variety of issues and suggestions have been proposed to deepen understanding of technological convergence (Choi and Park, 2009; Curran et al., 2010; Curran and Leker, 2011; Fai and Von Tunzelmann, 2001; Geum et al., 2012; Jeong et al., 2015; Karvonen and Kässi, 2013; Kim et al., 2014; No and Park, 2010; Preschitschek et al., 2013), but most have been limited to ex post evaluation using patent information: a major question remains regarding how best to anticipate technological convergence using predictive analysis and new data sources. Using link prediction analysis and Wikipedia databases, this study therefore proposes a novel method of anticipating technological convergence that can be used to guide organisations towards reacting in a timely manner to challenges posed by increasingly permeable technology boundaries.

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Two main approaches have been used to analyse patents for identifying technological convergence: patent citation and co-classification analysis (Caviggioli, 2016). Although these approaches have proved useful for analysing the structures and patterns of technological knowledge flows from voluminous patent data, many researchers and industrial practitioners have identified significant problems and deficiencies in data, methodology, and practicality. First, in terms of data limitations, the reliability of the implicit association between technological fields measured by patents is questionable since it is not guaranteed that patent citation or co-classification information accurately reflect the designated inventors’ knowledge of a given technological field (Criscuolo and Verspagen, 2008). The National Bureau of Economic Research (NBER) documented several sources of noise in patent data and discussed the invalid conclusions that could be made if the collected data was incomplete (Hall et al., 2000, 2001; Jaffe and Trajtenberg, 2002). Thus, new data sources need to be secured which can enhance the reliability of analysis. Second, with respect to methodological limitations, although previous quantitative indicators have proved quite useful (e.g., fusion degree (No and Park, 2010), convergence intensity and coverage (Geum et al., 2012), and forward citation node pairs (Choi and Park, 2009)), they are designed to measure the degree to which technologies have converged rather than the degree to which they will converge. Moreover, validation has usually been

Corresponding author. E-mail addresses: [email protected] (J. Kim), [email protected] (S. Kim), [email protected] (C. Lee).

https://doi.org/10.1016/j.technovation.2018.06.008 Received 2 November 2016; Received in revised form 12 June 2018; Accepted 17 June 2018 0166-4972/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Kim, J., Technovation (2018), https://doi.org/10.1016/j.technovation.2018.06.008

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proposed approach while offering guidelines on their implementation and customisation. Finally, Section 6 concludes with limitations and suggests future research directions.

omitted, and what validation occurred was qualitative and domain specific. Hence, these ex post indicators need to become more futureoriented and establish external validity to effectively assist timely decision making. Finally, from a practical standpoint, there are several existing patent classification schemes, but they are inflexible in determining the scope and level of analysis. Even though most patent systems provide search engines for users to find relevant patents for a technology of interest, difficulties remain in patent retrieval (Huang et al., 2004). To counter these problems, this study proposes a systematic approach to anticipating technological convergence using Wikipedia databases and link prediction methods. The value of Wikipedia as the data source for this study is summarised as follows. First, the Wikipedia database items have global scope and coverage (Halavais and Lackaff, 2008; Medelyan et al., 2009), and so are applicable to a wide range of technologies. Second, Wikipedia provides generally accurate and up-todate information since it is well-organised and updated by thousands of contributors in real time (Milne and Witten, 2013). Its accuracy on scientific and technological information is particularly high (Giles, 2006; Laurent and Vickers, 2009), making it suitable for technology and innovation management research. Finally, it holds every version of an article (Lih, 2004), and is thereby appropriate for assessing the performance of the proposed approach as it allows current information to be compared with prediction results obtained from historical information. Technological changes and the process of innovation are considered to be a search process based on coupling prior technologies (Basalla, 1998; Schumpeter, 1939; Usher, 1954; Rosenberg, 1979). It depends on complex outcomes of interlocking and mutually reinforcing technologies, rather than functioning in isolation (Fleming and Sorenson, 2001; Iansiti, 1995). The premise of this research is that future converging technologies are foreshadowed by current developments, and will be influenced by related technologies. Specifically, this study has defined a computational problem for technological convergence: Given a snapshot of a technology network constructed from the Wikipedia database, is it possible to identify the links that will be added to the network in the future as candidates for future technological convergence? For this, first, a technological ecology network was constructed using direct and indirect hyperlinks extracted from the Wikipedia database after defining a target technology. Second, predictive indicators of which technologies will converge – in terms of technological similarity, technological universality, and technological uniqueness – were developed based on link prediction analysis. This method predicts possible converging technologies by estimating the likelihood of a link between two nodes based on network properties, such as observed links and attributes of nodes. Finally, the accuracy and significance of the proposed indicators were assessed by using quantitative performance metrics. The proposed method was applied to 3D printing technology at the request of the Korea Institute of Science and Technology Information (KISTI). The case study showed that the proposed approach enabled a wide-ranging search for future convergence and gave statistically significant results. It also enabled systematic and continuous monitoring of technological convergence, yielding high potential benefits at relatively low cost. The case study also identified a way to improve the proposed approach, which is expected to be useful for supporting expert decision making, particularly for small and medium-sized high-tech companies that are considering entering new business areas based on existing technological capabilities, but which have little domain knowledge. It is expected that the systematic process and quantitative outcomes offered by the proposed method provide a valuable complementary tool for strategic decision making regarding emerging technologies in the era of open innovation. This paper is organised as follows: Section 2 presents the research background, and Section 3 explains the research framework, which is then illustrated by a case study of 3D printing technology in Section 4. Section 5 discusses the theoretical and practical implications of the

2. Background 2.1. Technological convergence Previously distinct industry boundaries have been rapidly redefined by increasingly merging and overlapping technologies (Athreye and Keeble, 2000). Rosenberg (1963) first coined the term technological convergence as “the process by which two hitherto different industrial sectors come to share a common knowledge and technological base”. Since then, many different terms such as technological fusion (Kodama, 1986; Lee, 2007; No and Park, 2010) and multidisciplinary technology (Rafols and Meyer, 2007) have been used interchangeably to describe similar phenomena at different levels. The definition of the technological convergence in this study follows Kodama's (1995) notion of technological fusion whereby breakthroughs are the result of combining at least two or more existing technologies into a new hybrid technology. Although technological convergence has been more apparent in the past few decades, it remains relatively unexplored both theoretically and empirically. To clarify the conceptual ambiguity and deepen understanding, early research focused on the types and processes of technological convergence. Bröring et al. (2007) proposed supply-side and demand-side convergence on the basis of orientations. Focusing more on the process of convergence, Hacklin et al. (2009) proposed four stages, i.e., knowledge, technological, applicational, and industrial convergence, from a co-evolutionary and sequential perspective. Similarly, Curran et al. (2010) proposed a convergence process model consisting of four phases, i.e., science, technology, market, and industry convergence. Curran and Leker (2011) also argued that industry convergence can occur without going through all the phases, and that industry convergence may evolve as a result of new products/services or new business models. Highlighting possible avenues for methodological adaptation, some recent studies have attempted to identify empirical evidence of technological convergence using patent information. One of the most widely used approaches to capturing technological convergence is based on patent network analysis. Choi and Park (2009) identified patent development paths from a patent citation network by evaluating the weight of citations between patents and interpreted the positions where various development paths meet as converging technologies. No and Park (2010) classified the trajectory patterns of technology fusion using in- and out-degree centralities for a specific patent class. Geum et al. (2012) proposed patent citation and co-classification analysis to measure the intensity and coverage of technological convergence. Kim et al. (2014) conducted centrality-focused patent citation network analysis to identify key technologies driving convergence. Finally, Jeong et al. (2015) investigated the time varying status of technological convergence and growth patterns using patent co-classification analysis. Although these previous studies have proved quite useful for many different purposes, they have certain limitations in terms of data, methodology, and practicality, as discussed in Section 1. These drawbacks provide the underlying motivation and are addressed in this study. 2.2. Link prediction analysis As networks grow and change over time, link prediction analysis has become a major research stream in the field of network theory. This analysis seeks to model the evolving mechanism of a dynamic network or to infer the missing links from an observed, often incomplete network, using features intrinsic to the network (Liben-Nowell and 2

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possible to track every version of an article and discuss issues. An expert-led investigation by a Nature research team found Wikipedia's accuracy and coverage, at least on scientific and technological information, to be reliable, even though anyone can edit any article (Giles, 2006). Wikipedia has also been cited by many news publications on subjects such as animal extinction, and even used in United States litigation (Lih, 2004). Other advantages in using Wikipedia data for anticipating converging technologies are summarised in Section 1. Wikipedia is structured as an interconnected network of articles, where each article explains a single concept and contains hyperlinks to other articles within or outside the Wikipedia database. These hyperlinks are used to guide the reader to more detailed descriptions of the concepts in the article. Wikipedia's hyperlink structure has been validated as representing conceptual similarity and semantic relatedness (Witten and Milne, 2008). For example, the Wikipedia article on 3D printing contains hyperlinks to articles on the international space station (ISS), stem cells, and jewellery manufacturing, where 3D printing technology can be applied. Therefore, this study employs direct and indirect hyperlinks extracted from the Wikipedia database to anticipate which technologies will converge.

Kleinberg, 2007). Given a snapshot of a network, link prediction analysis estimates the likelihood of the addition or deletion of a link between two nodes based on network properties, such as attributes of nodes and observed links. Approaches to link prediction analysis can be classified into three main categories (Lü and Zhou, 2011). Firstly, similarity-based methods assign a score based on the similarity between two nodes, postulating that links connecting more similar nodes have a higher likelihood of existence. Secondly, maximum likelihood methods assume some organising principles of the network structure (e.g., hierarchical structure), with detailed rules and specific parameters obtained by maximising the likelihood of the observed structure. Finally, probabilistic methods abstract the underlying structure from the observed network, and then predict missing links using the trained model. Similarity-based methods can be applied to large and complex networks, whereas maximum likelihood and probabilistic methods are time-consuming and complex, and fail to deal with large scale networks (Clauset et al., 2008). Furthermore, many empirical studies have shown that similarity-based methods outperform maximum likelihood and probabilistic methods, particularly where there is little theoretical understanding of network structures (Lü and Zhou, 2011). Therefore, similarity-based methods are more appropriate for anticipating converging technologies, and form the underlying methodology in the current study. Link prediction analysis has been employed in many research fields, such as transportation (Zhang et al., 2007), bioinformatics and biology (Lei and Ruan, 2013), quantitative marketing (Schafer et al., 2001), and security (Clauset et al., 2008). In particular, this method plays an important role in bioinformatics and biology because it can significantly reduce the high experimental costs involved in the analysis of biological interactions, such as protein-protein and disease-gene interactions (Lei and Ruan, 2013), where approximately 99% of the molecular interactions in human cells are unknown (Stumpf et al., 2008). However, despite its potential utility, this method has not been fully explored for technology and innovation management research. Kim et al. (2017) developed a distance-based link prediction analysis to identify potential areas for concentric diversification using patent-product databases. Link prediction offers a number of advantages for technological convergence research. Previous expert-centric approaches become extremely time-consuming and labour-intensive in practice, as the number of extant technologies and the complexity of technological knowledge increases. Time, cost, and effort associated with expertcentric approaches can be reduced significantly by good quality and well-organised information. If link prediction is integrated into objective and reliable databases, it can provide insight into future technological convergence, supporting timely decision making in emerging technologies. Therefore, link prediction analysis is applied to the Wikipedia database so as to anticipate technological convergence. The proposed approach can be extended to other data types, e.g., patents or newspapers; and methods, e.g., maximum likelihood or probabilistic. The current outcomes are expected to serve as a starting point for developing more general models.

3.2. Methodology Fig. 1 shows the overall process of the proposed approach. Given the complexities involved, the proposed approach is designed to be executed in four discrete steps: data collection and pre-processing; constructing a technological ecology network; anticipating converging technologies; and interpretation and validation. 3.2.1. Data collection and pre-processing Once a technology area of interest is chosen, relevant Wikipedia articles on the area of interest and other articles connected via hyperlinks are collected. When synonyms exist for a technology of interest (e.g., 3D printing), its synonyms and equivalent terms (e.g., additive manufacturing) are automatically redirected to the appropriate article in the Wikipedia system (Hu et al., 2009). Articles collected at this stage need to be pre-processed since they include superfluous information such as images and talk pages. Specifically, Wikipedia articles are collected from two sources through data parsing techniques. First, the basic information such as title, description, hyperlinks, and article ID is collected from each article. Here, the hyperlinks include the outbound hyperlinks that go to other articles and the inbound hyperlinks that come from other articles. Second, the page information of each article provides additional information, including the number of page watchers, the number of page views in the last 30 days, date of article creation, date of latest edit, details of edits, and the number of other language versions. These items are stored in a structured database. 3.2.2. Construction of a technological ecology network This step constructs a technological ecology network to identify the relationships between technologies using the direct and indirect hyperlinks extracted from the Wikipedia database. A node within the network represents an article that contains information about a technology, while an edge denotes a hyperlink between articles. Three issues need to be considered to define the scope of analysis and to manage a large scale network. First, the use of different network sizes and depths have different implications for the scope of the analysis. One of the primary dimensions used to distinguish innovation type is the continuum between radical versus incremental innovation (Schilling, 2005). In this context, considering only technologies that are connected by direct hyperlinks with the technology of interest is simple but less effective in identifying new and different areas from existing practices. Incorporating technologies that are connected by indirect hyperlinks with the technology of interest is complex but helpful in detecting the candidates for future converging technologies that could

3. Data and methodology 3.1. Data The Wikipedia database was launched in 2001 by James Wales, and is now the world's largest internet-based, user contributed encyclopaedia (Tapscott and Williams, 2008). This database was built on the concept of collective intelligence to create knowledge that can be shared and spread, encouraging people to easily change and improve the quality of that knowledge. However, concerns have been raised about its accuracy, since there is no gate keeping function to ensure quality material. Significantly, to enable this completely open system to function, Wikipedia makes it 3

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Fig. 1. Overall process of the proposed approach.

be the sources of radical innovation. Therefore, the zero level technology set includes the articles that have technological information about a given field of interest; the first level technology set includes articles that have direct inbound and outbound hyperlinks with the articles in the zero level technology set; the second level technology set contains articles that have direct inbound and outbound hyperlinks with the articles in the first level technology set, but are unconnected with the articles in the zero level technology set. This connectivity model continues to subsequent levels of technology sets. The second issue to consider is how the forecast origin and time period should be defined. Wikipedia provides the change history of articles from page creation to the latest version. Data prior to the forecast origin is used to anticipate future converging technologies while the remaining data is used to evaluate model performance. Last, but not least, it is more efficient in terms of time and space complexity to use an edge list structure than a matrix, since the constructed matrix would be sparse due to the fact that it would have many unconnected nodes. Thus, an edge list with identifiers is employed to visualise and compute the network information. The resultant technological ecology network represents the direct and indirect relationships between technologies.

TSxy =

•

Γ (x ) ∩ Γ (y ) Γ (x ) ∪ Γ (y )

where Γ (x ) is the set of neighbours of x and |Q| is the cardinality of the set Q. Technological universality (TU): The basic assumption of this indicator is that the probability of a new link being connected to node x is proportional to the degree of x. Therefore, the probability that this new link will connect x and y is proportional to the degrees of x and y (Holme et al., 2002; Xie et al., 2008). Motivated by this mechanism, TU is defined as

TUxy = | Γ (x ) | × | Γ (y ) |

•

3.2.3. Anticipation of converging technologies With the technological ecology network constructed, a procedure is proposed based on the link prediction algorithm, where each pair of nodes, x and y, is assigned a score sxy, which is defined as the proximity between x and y. This procedure tests whether the scores for all nonobserved links in the technological ecology network prior to the forecast origin discern the links that will be added to the network in the future as candidates for converging technologies. Specifically, three quantitative indicators are employed to capture the proximity between x and y, as shown below:

Although this indicator has the least computational complexity, the assumption underlying this indicator has been used to quantify the significance of links in various dynamic networks with considerable success (Lü and Zhou, 2011). Technological distinctiveness (TD): The motivation for this indicator is that rare features are more relevant. TD thus refines simple counting of common neighbours by weighting the less connected neighbours more heavily (Adamic and Adar, 2003). For example, articles that share “technology” are probably less semantically related than the articles that share “laser cutting technology”. TD uses direct neighbours, but also considers neighbours of neighbours, and is defined as

TDxy =

∑ Z ∈ Γ (x ) ∩ Γ (y )

• Technological similarity (TS): This indicator assumes that a pair of

1 log Γ (z )

Fig. 2 illustrates a simple example of a technological ecology net2 work for seven technologies. In this example, TSxy = 5 with | Γ (x ) | = 4, | Γ (y ) | = 3, | Γ (x )∩Γ (y ) | = 2 and | Γ (x )∪Γ (y ) | = 5; 1 1 1 1 TUxy = 12; and TDxy =4.75, with log Γ (t ) = log5 and log Γ (t ) = log2 .

nodes is more likely to form a link in the future if they have a large set of common neighbours. Hence, TS is defined as the proportion of common neighbours relative to the total number of neighbours for a pair of nodes, x and y (Kossinets, 2006; Newman, 2001), as shown below:

1

4

3

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4.2. Anticipating converging technologies 4.2.1. Data collection and pre-processing Considering topical scope, accuracy, and reliability, the English version of the Wikipedia database was employed in this study. The Wikipedia database provides every version of an article according to the date and time of each revision, with a detailed revision history that contains a list of the article's previous changes, the usernames of those who made it, and their edit summary. In this context, the last versions of the article on 3D printing technology of each year since its creation was collected. For this, such search terms as 3D printing, 3D printer, additive manufacturing, additive fabrication, and layer manufacturing were used, which were redirected to the appropriate article (i.e., 3D printing) in the Wikipedia system. Similarly, different versions of other articles connected via direct and indirect hyperlinks to the corresponding version of the article on 3D printing technology were also collected. Because the number of articles increased exponentially with each level of analysis, a Java-based crawling system was developed using the MediaWiki API offered by Wikipedia. The first, second, and third level technology set included hundreds, tens of thousands, and millions of articles, respectively. Finally, Microsoft Office Access was utilised to construct the Wikipedia database based on data parsing techniques. The resulting database included information on title, description, hyperlinks, article ID, date of page creation, number of views, etc. The article ID, title, and hyperlink information were employed to construct the technological ecology network, while the remaining information was used to examine the characteristics of converging technologies identified through the proposed approach. The constructed database is not reported here in its entirety owing to lack of space, but a part of the 2010 database is shown in Table 1.

Fig. 2. Example of a technological ecology network.

4. Empirical analysis and results 4.1. Overview 3D printing technology was chosen as a case study for three reasons. Firstly, although 3D printing technology has tremendous potential for industrial applications, little effort has been made to assess its current status and prospects for future development (Berman, 2012). Secondly, since this technology is considered one of the fastest-growing generalpurpose technologies, industrial practitioners require a systematic approach to the anticipation of technological convergence so as to assist timely decision making in regard to 3D printing and potential related technologies (Lipson and Kurman, 2013). Finally, although 3D printing technology was first developed in the 1980s, it had been rarely utilised in practice (Gebler et al., 2014). However, with recent significant price reductions, sales of 3D printers have increased dramatically, and there has been a significant increase in the application of 3D printing technologies in various industries, including aerospace, architecture, automotive, and medicine (Guo and Leu, 2013). Thus, 3D printing provides a suitable test case for assessing the performance of the proposed method by comparing current practice with prediction results.

4.2.2. Construction of technological ecology network The 2010 database was selected to anticipate converging technologies, as the use of 3D printing technology at this time was not widespread and there were few industrial applications, whereas the 2015 database was used to assess the performance of the proposed approach. The number of nodes and edges increases exponentially as the level of analysis increases, and when setting the analysis level, there is a tradeoff between complex analysis using the whole Wikipedia database and the simplest analysis using just the first level technology set. This study employed searches up to the second level technology set, although the proposed method and the software system developed are not limited to

Table 1 Part of the Wikipedia database. ID

Title

1305947

3D printing

27059

Stainless steel

… 1894504

… Brain implant

1780366

Space manufacturing

Hyperlinks

Stainless steel Rapid prototyping … Instant manufacturing Metallurgy Chromium … Martensitic stainless steel … Cerebral cortex Biological neural network … Human enhancement Planet Solar System … Megascale engineering

Page statistics Date of page creation

Number of page watchers

…

Date of last edit

Number of other languages

11:04, 21 December 2004

444

…

00:42, 30 October 2016

54

12:55, 15 May 2001

247

…

17:46, 8 October 2016

69

… 11:04, 16 May 2005

… 87

… …

… 12:43, 8 October 2016

… 8

16:01, 22 April 2005

46

…

08:37, 28 September 2016

2

5

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shown below:

Table 2 Part of the edge list. Title for source node

Title for target node

3D printing 3D printing 3D printing 3D printing … Stainless steel Stainless steel Stainless steel Stainless steel

Additive manufacturing Rapid prototyping Moulding Instant manufacturing … Surface finishing Nanowire battery Spray forming Brain implant

Normalised score =

s − Min Max − Min

where s is the current score, and Max and Min represent the maximum and the minimum score, respectively, for each indicator. The possible pairs of technologies were assigned link prediction scores and classified into two categories, candidates for converging technologies and independent technologies, according to pre-determined cut-off values. Here, how cut-off values are defined is subject to the context of analysis and the technology area. For instance, if a company carries out exploratory research to discover potential converging technologies, using a large number of potential converging technologies with a low cut-off value may create more meaningful results. If a company is interested in minor innovation, restricting the scope of analysis to a small number of potential converging technologies with a high cut-off value will give a practical solution (Lee et al., 2015). Considering the scope of analysis an organisation might choose (i.e., narrow scope, medium scope, and broad scope), three different cut-off values were used for each indicator (i.e., TS, TU, and TD), identifying a total of 500, 1000, and 2000 candidates for converging technologies. Moreover, borrowing the concept of ensemble methods in data mining and machine learning research, two more indicators, i.e., majority rule and disjunction rule, were defined by aggregating the results of the three indicators. In short, ensemble methods are algorithms that construct a set of classifiers (i.e., TS, TU, and TD) and then make a final decision by synthesising the prediction results of individual classifiers (Opitz and Maclin, 1999). Specifically, the disjunction rule classifies a technology as a candidate if at least one indicator identifies the technology as a candidate, whereas the majority rule classifies a technology as a candidate if the majority of indicators identify the technology as a candidate. Table 3 shows a part of the results of link prediction analysis.

this level, and can allow for more complex analyses. Specifically, as of 2010, the zero level technology set included one node (i.e., 3D printing technology), the first level technology set included 113 nodes and 257 edges, and the second level technology set included 20,700 nodes and 245,832 edges. Here, it should be noted that the proposed method and the software system can incorporate multiple nodes in the zero level technology set, although only one node was utilised in this study. The edge list was then constructed to represent the connections between the zero and the first level technologies, and between the first and second level technologies, as shown in Table 2. This list was used to visualise and compute the network information. Finally, the technological ecology network for 3D printing technology was developed by using Gephi, an open platform for network analysis and visualisation. The resulting network is not reported here in its entirety owing to lack of space, but a part of the network is shown in Fig. 3, where the zero level technology is represented by a white node, the first level technologies by grey nodes, and the second level technologies by black nodes. Such technologies as computer graphics, modelling, and digital materialisation are directly connected with 3D printing technology, whereas such technologies as jewellery manufacturing and bone fracture, which are regarded as potential application areas for 3D printing technology, are not yet connected with it.

4.2.4. Validation Even though many examples of future converging technologies were found from the results (i.e., which are classified as candidates for converging technologies), newly developed methods should be carefully deployed, since there is no absolute confirmation regarding the validity of the results. Here, the performance of the proposed approach is ultimately related to the ability to identify the links that will be added to the network. For this reason, the significance and effectiveness of the proposed indicators were assessed by using a t-test and several performance metrics.

4.2.3. Anticipation of converging technologies Three quantitative indicators, i.e., TS, TU, and TD, as defined in Section 3.2.3, were calculated using the ‘Network X’ package implemented in Python to anticipate technologies likely to be connected with 3D printing technology. The values of these indicators cannot be directly compared since the minimum and maximum values are different. For this reason, the link prediction scores were normalised, as

Fig. 3. Part of the technological ecology network for 3D printing. 6

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Table 3 Part of the results of link prediction analysis. Technology

TS_Norm

TS_Class

TU_Norm

TU_Class

TD_Norm

TD_Class

Disjunction rule

Majority rule

Condition in 2015

Injection moulding technology Laser cutting technology Casting technology Molecular assembler technology Space manufacturing technology Product design technology Holography technology Carbon nanotube technology … Microcontroller Sucrose Woodblock printing technology Solar cell technology Opto-isolator technology Superconductivity technology 3D television technology

0.859 1 0.607 0.476 0.333 0.599 0.529 0.247 … 0.204 0.204 0.224 0.189 0.245 0.190 0.233

CT CT CT CT CT CT CT IT … IT IT IT IT IT IT IT

0.093 0.024 0.096 0.385 0.076 0.027 0.070 0.079 … 0.044 0.044 0.032 0.044 0.022 0.053 0.028

CT IT CT CT CT IT CT CT … IT IT IT IT IT IT IT

0.767 0.612 0.490 0.028 0.324 0.298 0.456 0.185 … 0.132 0.202 0.086 0.118 0.118 0.119 0.121

CT CT CT IT CT CT CT CT … CT CT IT IT IT IT IT

CT CT CT CT CT CT CT CT … CT CT IT IT IT IT IT

CT CT CT CT CT CT CT CT … IT IT IT IT IT IT IT

CT CT CT CT CT CT CT CT … CT IT CT IT IT IT IT

* Cut-off values for TS, TU, and TD are 0.274, 0.064, and 0.126, respectively. Table 4 Examples of candidates for converging technologies. Technology

Relevant article

Interplanetary spaceflight Self-replicating spacecraft Space construction Holography Brain implant Moulding Concept car Laser cutting Surgical instrument Dental restoration Carbon nanotube … Motorcycle design Microfabrication Miniature figure Game controller Tobacco pipe Skeletal animation Jewellery manufacturing Organic chemistry Plastic recycling Optical fiber

https://www.nasa.gov/mission_pages/station/research/news/3Dratchet_wrench http://canprint3d.com/possible-future-self-replicating-spaceship/ http://www.space.com/topics/3d-printing http://www.zebraimaging.com/products/3D-Holographic-Prints http://www.wired.co.uk/news/archive/2014-03/26/3d-printed-skull http://www.stratasys.com/solutions/additive-manufacturing/injection-molding https://3dprintingindustry.com/news/bmw-conceives-of-4d-printing-hyper-futuristic-concept-car-67921/ http://www.instructables.com/id/Laser-Cut-3D-Printer/ https://www.ncbi.nlm.nih.gov/pubmed/24721602 http://3dprintingdentalmarket.com/3d-printing-dental-news/ https://3dprint.com/3701/3dxtech-carbon-nanotube-3d-printer-filament/ … http://www.motorcyclenews.com/news/new-bikes/2016/may/worlds-first-3d-printed-motorcycle-revealed/ http://www.3ders.org/articles/20150301-nanoscribe-brings-maximum-precision-to-3d-printing-of-microfabrication.html http://www.imagine3dminiatures.com/ http://game-accessibility.com/2016/02/17/4841/ https://3dprintingindustry.com/news/vauen-3d-printed-pipe-38360/https://3dprintingindustry.com/news/vauen/ https://3dprint.com/147616/skeleton-puppet-kubo-two-strings/ http://www.protoforming.com/products/jewellery-rapid-prototyping/ http://www.zmescience.com/science/chemistry/3d-printer-small-molecules-05634654/ https://www.3dprinteros.com/3d-printing-recycling-plastic-waste-and-saving-the-world/ https://3dprintingindustry.com/news/first-3d-printed-fiber-optics-created-by-university-of-sydney-researchers-with-desktop-3d-printer-55047/

Firstly, the overall effectiveness of the proposed indicators was examined to discern the links that will be added to the network in the future. Specifically, the t-test was conducted to statistically compare the mean values of the link prediction scores in two different sets. Of technologies that belonged to the second level technology set, the first set consisted of 221 technologies that were newly connected with 3D printing technology in 2015, and the second set consisted of 20,479 technologies that were not connected with 3D printing technology in 2015. The two-tailed Student's t-test for unequal sample size and unequal variance was performed to test the null hypothesis (X1 = X2), where X1 and X2 denote the mean values for link prediction scores for the two sets. As Table 4 summarises, all three indicators (i.e., TS, TU, and TD) have the ability to discern candidates for converging technologies, showing significant differences in link prediction scores between the two sets. Secondly, the detailed performance and reliability of the proposed indicators were examined by using quantitative metrics. Anticipating technological convergence corresponds to a binary classification problem, and the results may vary depending on the pre-determined cut-off values. In this context, three different cut-off values were used for each indicator (i.e., narrow scope, medium scope, and broad scope), as shown in Table 5. As stated earlier, using these cut-off values, the

Table 5 Summary of t-test results.

t-statistic Degree of freedom Mean of scores in group 1 Variance of scores in group 1 Mean of scores in group 2 Variance of scores in group 2 p-value

TS_Norm

TU_Norm

TD_Norm

7.3935 224 0.1360 0.0115 0.0832 0.0009 0.0000

5.8048 226 0.0179 0.0008 0.0070 0.0003 0.0000

6.6880 224 0.0544 0.0064 0.0187 0.0002 0.0000

proposed approach identified 500, 1000, and 2000 candidates for converging technologies corresponding to the first, second, and third set of cut-off values. Although accuracy is a basic metric, it cannot be guaranteed in this study since it yields misleading results when the data set is unbalanced. For this reason, the diagnostic odds ratio (DOR) (Glas et al., 2003) and Youden's J statistic (Youden, 1950) were employed to assess the effectiveness of the proposed indicators after the relevant confusion matrices were constructed. Specifically, DOR is defined as sensitivity × specificity Diagnostic odds ratio = (1 − sensitivity) × (1 − specificity) , 7

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5. Discussion

where sensitivity is the number of true positive results (i.e., technologies newly connected with 3D printing technology in 2015 among candidates for converging technologies in 2010) divided by the number of positive results (i.e., technologies newly connected with 3D printing technology in 2015) that should have been returned; specificity is the number of true negative results (i.e., technologies not connected with 3D printing technology in 2015 among independent technologies in 2010) divided by the total number of all negative results (i.e., technologies not connected with 3D printing technology in 2015). This indicator is independent of prevalence, and ranges from zero to infinity. A DOR of exactly one means the test is equally likely to predict a positive outcome whatever the true condition, and thus gives no information, whereas higher DOR indicates better performance. Youden's J statistic also captures the effectiveness of a binary classifier, and is defined as

5.1. Implications for theory, practice, and policy The proposed approach provides an extensive understanding of future converging technologies, and therefore has novel implications to theory, practice, and policy. First, the application of link prediction methods in exploring and anticipating future converging technologies contributes to the foundation of future research by extending previous ex post evaluation to predictive analysis. Although this study focused on the anticipation of technological convergence, the proposed approach could be useful for many different purposes such as technology foresight and technology planning. Second, the use of Wikipedia data to forecast new links between different concepts is brand new. Wikipedia analysis, vis-à-vis patent analysis, provides results with different levels of technological characterisation. The proposed approach can support strategic decision making in emerging technologies since the results generated via Wikipedia analysis comprise terms that are business related and readily understandable. Third, the proposed method and software system developed in this study enable the quick analysis of a wide range of technologies, and support decision making within acceptable limits of time and cost. Moreover, the software system allows even those unfamiliar with Wikipedia databases and computational models to assess converging technology predictions. It is expected that the proposed approach and software system could serve as a monitoring tool for identifying future converging technologies, as the data and throughput are reusable, and new data can be added and analysed easily. Finally, recent global ICT trends have been characterised by an orientation to technology interdependence, convergence, and fusion (Shin and Park, 2010). Since the critical technologies identification program in the US, most European countries and Japan have conducted similar national technology foresight exercises (Keenan, 2003), focusing more on ICT driven converging technologies. In this respect, the potential benefits of the proposed approach may be helpful in portraying an overall picture of what the future of technology holds.

J = sensitivity + specificity −1, and ranges from −1–1, with J = 0 meaning the test gives the same proportion of positive results for groups regardless of true conditions, and J = 1 meaning there are no false positives or false negatives, so the test is perfect. Table 5 shows that the proposed indicators are effective and reliable in identifying the links that will be added to the network in the future. In particular, TD is found to be the most effective and reliable indicator among the three individual indicators. Moreover, it is also noteworthy that the aggregation of the results of the three indicators (i.e., majority rule) shows better performance than individual indicators. Taking the results of qualitative (i.e., expert judgement) and quantitative analysis (i.e., Student's t, DOR, and J statistic) together, the proposed approach is proved to be superior in terms of both accuracy and significance, supporting the contention that it provides more appropriate indicators of anticipating technological convergence. 4.2.5. Examples of candidates for converging technologies The technologies with high link prediction scores, i.e., which are classified as candidates for converging technologies, include many interesting and novel technologies across many industries with which 3D printing technology has converged. For example, space manufacturing technology is identified as a candidate for converging technologies by all five indicators. This technology was recently endorsed by the European Space Agency (ESA) as a potential means of facilitating lunar settlement with reduced logistics (Cesaretti et al., 2014). ESA has devised a weightbearing catenary dome design with a cellular structured wall using 3D printed lunar soil to help shield against micrometeoroids and space radiation. Medical applications for 3D printing technology, such as organ transplantation, dentistry, magnetic resonance imaging, and brain implants are also identified as candidates for converging technologies. The applications of 3D printing technology in the medical industry have been expanding rapidly and are expected to revolutionise health care. 3D printing technology can now create exact replicas of organs and human body parts, such as titanium pelvises and lower jaw bones (Dawood et al., 2015; Schubert et al., 2014). The technology uses a patient's MRI or CT scan images as the template (Rengier et al., 2010). Researchers have developed a 3D structure that closely resembles layered brain tissue using 3D printing (Lozano et al., 2015). Although it has not been developed to grow replacement brain parts at this early stage, if all goes as planned, it is expected to provide transferable tissue that could eventually be implanted into a human brain. Moreover, in the automotive industry, concept car technology and intelligent design technology are identified as candidates for converging technologies. 3D printing technology provides faster product innovations (e.g., design verification and prototyping) and has transformed the supply chain ecosystem (Bogue, 2013). Automotive manufacturers have developed supercars and concept cars with 3D printed parts and 3D printed spare parts for production and after-sale, and miniature demonstration models (Ratto and Ree, 2012). Many other examples can also be found in Table 6.

5.2. Implementation and customisation of the proposed approach A number of considerations should be made before applying a novel method. First, it is important to understand that the objective of the proposed approach is not to produce a definitive set of converging technologies, but rather to screen technologies that have a relatively high possibility of convergence. The use of computational methods should be limited to automating experts’ routine work and offering information that cannot be easily produced by humans. Moreover, the communication between experts from different domains still remains crucial after this process to discover and crystallise potential technology opportunities. Second, the candidates for converging technologies generated via the proposed approach may vary according to the presence of nodes in the zero level technology set. Most computerised approaches do not eliminate the need for human input. In particular, these approaches require experts to set the objective and scope of analysis (e.g., to determine the zero level technology set to collect Wikipedia articles of concern) and interpret the results of the analysis (e.g., to determine the cut-off value to identify candidates for converging technologies). The role of experts should be emphasised, although most parts of the proposed approach can be automated. Third, this study constructed classification models to identify converging technologies by using similarity-based link prediction methods. The integration of machine learning models (e.g., support vector machines and random forests) and link prediction methods could be useful for improving the performance of the proposed approach. Moreover, the proposed approach could be elaborated to provide the likely timings of a predicted technological convergence by adding to the analysis other factors such as dates of page creation and edits. Fourth, the proposed approach could be extended further by adopting text mining and entity 8

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Table 6 Summary of performance metrics for different cut-off values. TS-based classification

TU-based classification

TD-based classification

Disjunction rule-based classification

Majority rule-based classification

(a) Number of candidates for converging technologies = 500 Cut-off value 0.293 0.104 Accuracy 0.968 0.967 Sensitivity 0.204 0.095 Specificity 0.977 0.976 DOR 10.691 4.335 Youden's J 0.180 0.071

0.174 0.971 0.271 0.978 16.961 0.250

– 0.944 0.308 0.951 8.561 0.258

– 0.974 0.240 0.982 17.519 0.222

(b) Number of candidates for converging technologies = 1000 Cut-off value 0.274 0.064 Accuracy 0.946 0.945 Sensitivity 0.235 0.186 Specificity 0.953 0.953 DOR 6.300 4.598 Youden's J 0.189 0.138

0.126 0.949 0.357 0.955 11.806 0.312

– 0.896 0.416 0.901 6.484 0.317

– 0.957 0.290 0.964 10.851 0.253

(c) Number of candidates for converging technologies = 2000 Cut-off value 0.218 0.038 Accuracy 0.901 0.898 Sensitivity 0.443 0.312 Specificity 0.906 0.905 DOR 7.691 4.308 Youden's J 0.350 0.217

0.098 0.904 0.548 0.908 11.941 0.456

– 0.817 0.611 0.819 7.113 0.430

– 0.908 0.538 0.912 12.164 0.451

should be complemented by future research. Firstly, the proposed approach cannot provide information about the likely timing of a predicted technological convergence. This should be further investigated by integrating time dimensions into link prediction analysis. For this purpose, stochastic models based on the dates of page creation and edits could provide a probabilistic prediction for a convergence occurring over a given period. Secondly, the proposed approach is dependent on the level of accumulation of available information, which is affected by several non-technological factors. Hence, other data types, such as patents, need to be investigated as well to improve the accuracy and reliability. Moreover, the proposed approach could be extended further by adopting entity recognition techniques or category information so as to incorporate technological concepts that do not yet have a dedicated page into analysis and rule out non-technological hyperlinks more efficiently. Thirdly, the proposed method cannot distinguish between new applications of an existing technology and new technologies resulting from two converging different ones. This should be further investigated by employing the page information of each article such as date of article creation, date of latest edit, the number of page views, and the number of other language versions. Fourthly, many issues remain as to how to improve the performance of the proposed approach. More advanced analysis methods, such as maximum likelihood and probabilistic approaches, could also be helpful. Finally, this study considered a single case study, 3D printing technology. Further testing on technologies across different areas is essential to confirm the validity of the proposed approach. Nevertheless, the systematic processes and quantitative outcomes of the proposed approach offer a substantial contribution to both current research and future practice.

recognition techniques so as to incorporate concepts that do not yet have a dedicated page into analysis and rule out non-technological hyperlinks efficiently. Finally, the systematic processes for updating the database and the model need to be defined, although they may differ across organisational contexts. 6. Conclusions This study proposed a systematic approach to anticipating technological convergence to guide timely organisational reactions to challenges posed by increasingly permeable technology boundaries. A central tenet of the proposed approach is that technological linkages captured by Wikipedia hyperlinks can provide clues to future prospects for technological convergence. To this end, a technological ecology network was constructed using direct and indirect hyperlinks extracted from the Wikipedia database, and link prediction methods were employed to identify which technologies will converge. The specific case of 3D printing technology confirmed that the proposed method enables a wide-ranging search for future converging technologies, with statistically significant outcomes. The contributions of this research are two-fold. From an academic perspective, the proposed method contributes to technological convergence research by extending previous ex post evaluations that used patent information, to predictive analysis using Wikipedia hyperlinks. The consideration of indirect hyperlinks enabled a more comprehensive search for future converging technologies. The proposed method provides information regarding future prospects for technological convergence, enables the quick analysis of a wide range of technologies, and supports decision making within acceptable limits of time and cost. Although this study focused on the anticipation of technological convergence, the approach could be useful for many different purposes such as the recommendation of collaborative partners and the identification of emerging technologies. From a practical standpoint, a software system was developed as part of this study to automate the proposed method, allowing even those unfamiliar with Wikipedia databases and computational models to assess converging technology predictions. It is expected that the proposed approach and software system could be useful as a complementary tool for supporting expert decision making, particularly for small and medium sized high-tech companies that are considering entering new technology areas, but which have little domain knowledge. Despite the confirmed validation, this study has limitations that

Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIP) (No. 2014R1A1A1005931 and No. 2017R1C1B2011434) References Adamic, L.A., Adar, E., 2003. Friends and neighbors on the web. Soc. Netw. 25 (3), 211–230. Allarakhia, M., Walsh, S., 2012. Analyzing and organizing nanotechnology development: application of the institutional analysis development framework to nanotechnology consortia. Technovation 32 (3), 216–226. Athreye, S., Keeble, D., 2000. Technological convergence, globalisation and ownership in

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Technovation xxx (xxxx) xxx–xxx

J. Kim et al.

technological capabilities: link analysis of products and technologies. Technol. Forecast. Soc. 118, 246–257. Kim, N., Lee, H., Kim, W., Lee, H., Suh, J.H., 2015. Dynamic patterns of industry convergence: evidence from a large amount of unstructured data. Res. Policy 44 (9), 1734–1748. Kodama, F., 1986. Japanese innovation in mechatronics technology. Sci. Public Policy 13 (1), 44–51. Kodama, F., 1995. Emerging Patterns of Innovation: sources of Japan's Technological Edge. Harvard Business School Press. Kossinets, G., 2006. Effects of missing data in social networks. Soc. Netw. 28 (3), 247–268. Laurent, M.R., Vickers, T.J., 2009. Seeking health information online: does Wikipedia matter? J. Am. Med. Inform. Assn. 16 (4), 471–479. Lee, C., Kang, B., Shin, J., 2015. Novelty-focused patent mapping for technology opportunity analysis. Technol. Forecast. Soc. 106, 53–64. Lee, K.R., 2007. Patterns and processes of contemporary technological fusion: the case of intelligent robots. Asian J. Technol. Innov. 15 (2), 45–65. Lei, C., Ruan, J., 2013. A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics 29 (3), 355–364. Liben-Nowell, D., Kleinberg, J., 2007. The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Tec. 58 (7), 1019–1031. Lih, A., 2004. Wikipedia as participatory journalism: reliable sources? metrics for evaluating collaborative media as a news resource. Nature. Lipson, H., Kurman, M., 2013. Fabricated: the New World of 3D Printing. John Wiley & Sons. Lozano, R., Stevens, L., Thompson, B.C., Gilmore, K.J., Gorkin, R., Stewart, E.M., Panhuis, M., Romero-Ortega, M., Wallace, G.G., 2015. 3D printing of layered brain-like structures using peptide modified gellan gum substrates. Biomaterials 67, 264–273. Lü, L., Zhou, T., 2011. Link prediction in complex networks: a survey. Phys. A. 390 (6), 1150–1170. Medelyan, O., Milne, D., Legg, C., Witten, I.H., 2009. Mining meaning from Wikipedia. Int. J. Hum.-Comput. St. 67 (9), 716–754. Milne, D., Witten, I.H., 2013. An open-source toolkit for mining Wikipedia. Artif. Intell. 194, 222–239. Newman, M.E., 2001. Clustering and preferential attachment in growing networks. Phys. Rev. E 64 (2), 025102. No, H.J., Park, Y., 2010. Trajectory patterns of technological fusion: trend analysis and taxonomical grouping in nanobiotechnology. Technol. Forecast. Soc. 77 (1), 63–75. Opitz, D., Maclin, R., 1999. Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198. Preschitschek, N., Niemann, H., Leker, J., Moehrle, M.G., 2013. Anticipating industry convergence: semantic analyses vs IPC co-classification analyses of patents. Foresight 15 (6), 446–464. Rafols, I., Meyer, M., 2007. How cross-disciplinary is bionanotechnology? Explorations in the specialty of molecular motors. Scientometrics 70 (3), 633–650. Ratto, M., Ree, R., 2012. Materializing information: 3D printing and social change. First Monday 17 (7). Rengier, F., Mehndiratta, A., von Tengg-Kobligk, H., Zechmann, C.M., Unterhinninghofen, R., Kauczor, H.U., Giesel, F.L., 2010. 3D printing based on imaging data: review of medical applications. Int. J. Comput. Assist. Radiol. 5 (4), 335–341. Rosenberg, N., 1963. Technological change in the machine tool industry, 1840–1910. J. Econ. Hist. 23 (4), 414–443. Rosenberg, N., 1979. Technological interdependence in the American economy. Technol. Cult. 20 (1), 25–50. Schafer, J.B., Konstan, J.A., Riedl, J., 2001. E-commerce recommendation applications. Data Min. Knowl. Discov. 5 (1/2), 115–153. Schilling, M.A., 2005. Strategic Management of Technological Innovation. Tata McGrawHill Education. Schubert, C., Van Langeveld, M.C., Donoso, L.A., 2014. Innovations in 3D printing: a 3D overview from optics to organs. Br. J. Ophthalmol. 98 (2), 159–161. Schumpeter, J., 1939. Business Cycles. McGraw-Hill, New York, NY. Shin, J., Park, Y., 2010. Evolutionary optimization of a technological knowledge network. Technovation 30 (11), 612–626. Stumpf, M.P., Thorne, T., de Silva, E., Stewart, R., An, H.J., Lappe, M., Wiuf, C., 2008. Estimating the size of the human interactome. Proc. Natl. Acad. Sci. USA 105 (19), 6959–6964. Tapscott, D., Williams, A.D., 2008. Wikinomics: How Mass Collaboration Changes Everything. Penguin. Usher, A., 1954. A history of mechanical invention. Dover, Cambridge, MA. Witten, I., Milne, D., 2008. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: Proceedings of the AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 25–30. Xie, Y.B., Zhou, T., Wang, B.H., 2008. Scale-free networks without growth. Phys. A 387 (7), 1683–1688. Youden, W.J., 1950. Index for rating diagnostic tests. Cancer 3 (1), 32–35. Zhang, G.-Q., Wang, D., Li, G.-J., 2007. Enhancing the transmission efficiency by edge deletion in scale-free networks. Phys. Rev. E 76 (1), 017101.

the UK computer industry. Technovation 20 (5), 227–245. Basalla, G., 1998. The Evolution of Technology. Cambridge University Press, Cambridge. Battard, N., 2012. Convergence and multidisciplinarity in nanotechnology: laboratories as technological hubs. Technovation 32 (3), 234–244. Berman, B., 2012. 3-D printing: the new industrial revolution. Bus. Horiz. 55 (2), 155–162. Bogue, R., 2013. 3D printing: the dawn of a new era in manufacturing? Assem. Autom. 33 (4), 307–311. Borés, C., Saurina, C., Torres, R., 2003. Technological convergence: a strategic perspective. Technovation 23 (1), 1–13. Bröring, S., Martin Cloutier, L., Leker, J., 2007. The front end of innovation in an era of industry convergence: evidence from nutraceuticals and functional foods. R&D. Manag. 36 (5), 487–498. Caviggioli, F., 2016. Technology fusion: identification and analysis of the drivers of technology convergence using patent data. Technovation 55–56, 22–32. Cesaretti, G., Dini, E., De Kestelier, X., Colla, V., Pambaguian, L., 2014. Building components for an outpost on the Lunar soil by means of a novel 3D printing technology. Acta Astronaut. 93, 430–450. Choi, C., Park, Y., 2009. Monitoring the organic structure of technology based on the patent development paths. Technol. Forecast. Soc. 76 (6), 754–768. Clauset, A., Moore, C., Newman, M.E., 2008. Hierarchical structure and the prediction of missing links in networks. Nature 453 (7191), 98–101. Criscuolo, P., Verspagen, B., 2008. Does it matter where patent citations come from? Inventor vs. examiner citations in European patents. Res. Policy 37 (10), 1892–1908. Curran, C.S., Leker, J., 2011. Patent indicators for monitoring convergence–examples from NFF and ICT. Technol. Forecast. Soc. 78 (2), 256–273. Curran, C.S., Bröring, S., Leker, J., 2010. Anticipating converging industries using publicly available data. Technol. Forecast. Soc. 77 (3), 385–395. Dawood, A., Marti, B.M., Sauret-Jackson, V., Darwood, A., 2015. 3D printing in dentistry. Br. Dent. J. 219 (11), 521–529. Fai, F., Von Tunzelmann, N., 2001. Industry-specific competencies and converging technological systems: evidence from patents. Struct. Change Econ. Dyn. 12 (2), 141–170. Fleming, L., Sorenson, O., 2001. Technology as a complex adaptive system: evidence from patent data. Res. Policy 30 (7), 1019–1039 (2001). Gambardella, A., Torrisi, S., 1998. Does technological convergence imply convergence in markets? Evidence from the electronics industry. Res. Policy 27 (5), 445–463. Gebler, M., Uiterkamp, A.J.S., Visser, C., 2014. A global sustainability perspective on 3D printing technologies. Energ. Policy 74, 158–167. Geum, Y., Kim, C., Lee, S., Kim, M.S., 2012. Technological convergence of IT and BT: evidence from patent analysis. Etri. J. 34 (3), 439–449. Giles, J., 2006. Internet encyclopaedias go head to head. Nature 438, 900–901. Glas, A., Lijmer, J.G., Prins, M.H., Bonsel, G.J., Bossuyt, P.M., 2003. The diagnostic odds ratio: a single indicator of test performance. J. Clin. Epidemiol. 56 (11), 1129–1135. Guo, N., Leu, M.C., 2013. Additive manufacturing: technology, applications and research needs. Front. Mech. Eng. 8 (3), 215–243. Hacklin, F., 2007. Management of Convergence in Innovation: Strategies and Capabilities for Value Creation Beyond Blurring Industry Boundaries. Springer Science & Business Media. Hacklin, F., Marxt, C., Fahrni, F., 2009. Coevolutionary cycles of convergence: an extrapolation from the ICT industry. Technol. Forecast. Soc. 76 (6), 723–736. Halavais, A., Lackaff, D., 2008. An analysis of topical coverage of Wikipedia. J. Comput.Mediat. Comm. 13 (2), 429–440. Hall, B.H., Jaffe, A.B., Trajtenberg, M., 2000. Market Value and Patent Citations: A First Look. National Bureau of Economic Research. No. w7741. Hall, B.H., Jaffe, A.B., Trajtenberg, M., 2001. The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools. National Bureau of Economic Research. No. w8498. Holme, P., Kim, B.J., Yoon, C.N., Han, S.K., 2002. Attack vulnerability of complex networks. Phys. Rev. E 65 (5), 056109. Hu, X., Zhang, X., Lu, C., Park, E.K., Zhou, X., 2009. Exploiting Wikipedia as external knowledge for document clustering. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 389–396. Huang, Z., Chen, H., Chen, Z.K., Roco, M.C., 2004. International nanotechnology development in 2003: country, institution, and technology field analysis based on USPTO patent database. J. Nanopart. Res. 6 (4), 325–354. Iansiti, M., 1995. Technology integration: managing technological evolution in a complex environment. Res. Policy 24 (4), 521–542 (1995). Jaffe, A.B., Trajtenberg, M., 2002. Patents, Citations, and Innovations: a Window on the Knowledge Economy. MIT Press. Jeong, S., Kim, J.C., Choi, J.Y., 2015. Technology convergence: what developmental stage are we in? Scientometrics 104 (3), 841–871. Karvonen, M., Kässi, T., 2013. Patent citations as a tool for analyzing the early stages of convergence. Technol. Forecast. Soc. 80 (6), 1094–1107. Keenan, M., 2003. Identifying emerging generic technologies at the national level: the UK experience. J. Forecast. 22 (2/3), 129–160. Kim, E., Cho, Y., Kim, W., 2014. Dynamic patterns of technological convergence in printed electronics technologies: patent citation network. Scientometrics 98 (2), 975–998. Kim, H., Hong, S., Kwon, O., Lee, C., 2017. Concentric diversification based on

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