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Keywords:

  • nanotechnology;
  • social network;
  • semantic network;
  • triple helix;
  • research network

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

This study investigates the interorganizational communication structures of the academic, private, and public sectors of nanotechnology in cyberspace. Few studies have examined systematically the ways these three major sectors contribute to the social construction of nanotechnology. The current study examines hyperlink and semantic networks of nanotechnology websites. Results show that academic websites revealed specific aspects of nanotechnology such as the uses and scales (units) of nanotechnology. However, the business sector exhibited a salient clustering of words regarding the developers and possible applications of nanotechnology. In hyperlink networks, the industry website nanobot.blogspot.com demonstrated high centrality. There were transitive and cyclic linkages among the 3 sectors. Findings offer important implications for the social construction of nanotechnology.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

Previous studies investigating the relationship between nanotechnology and society include the scientometric analysis of nanotechnology (Porter, Shapira, & Youtie, 2009) and hyperlink and content analysis of nanotechnology industry websites (Ackland, Gibson, Lusoli, & Ward, 2010). In addition, the European Union initiated the Sixth Framework Program to support research investigating the societal dimensions of nanotechnology. Further, in 2009, the National Science Foundation (an independent U.S. government agency) allocated 2.7% of the annual budget to the study of societal and educational concerns regarding nanotechnology (Porter et al., 2009). However, there is still a notable lack of research examining the relationship between nanotechnology and society.

This study is an analysis of the communication patterns and the interorganizational affinity among the three nanotechnology sectors. This study employs the triple helix approach, which argues that the empirical analysis of communication patterns and organizational linkages between the government, industry, and academia is vital for understanding knowledge-economy systems (Leydesdorff & Etzkowitz, 1996; Etzkowitz & Leydesdorff, 2000). This study evaluates whether the three sectors are closely involved in the development and diffusion of nanotechnology through online communication. The government sometimes initiates research and provides grants and support for academia while various industries commercialize research outcomes and new technological developments.

Goktepe (2003) suggested that the “Magnet Program,” one of Israel's policy initiatives, facilitated communication among and organizational changes between government, industries, and academia. The program also helped form an “innovation network” among those sectors and worked to advance relevant technologies (Goktepe, 2003). Furthermore, the program boosted people's positive perceptions of new technologies.

The three sectors are related to public opinion formation regarding new technologies. Mass media rely on the government for fundamental information for their news coverage (Herman & Chomsky, 1988). In addition, academic research conducted by universities or industries relating to new technology has occasionally featured newspaper and magazine headlines, along with internet news sources. Thus, these three sectors exert their influence on public opinion in diverse ways.

We employed hyperlink and semantic network analyses to examine the relationships and interactions among the three sectors. Hyperlink network analysis investigates social roles/connections online, prestige, reputation, centrality, and credibility of actors in cyberspace (Ackland et al., 2010; Thelwall, 2004; Park & Jankowski, 2008; Park & Thelwall, 2003; Park, Barnett, & Nam, 2002). Hyperlink network analysis evaluated the relationships among the three sectors and identified the major actors and clusters within them. Semantic network analysis examines word frequency of websites and determines the most frequently occurring words and shared words among those websites (Doerfel & Barnett, 1999; Zywica & Danowski, 2008). The analysis also retrieves semantic clusters of words by considering the co-occurrence of and distances between words. In semantic network analysis, each word is a node, and the shared words of different texts indicate the similarity of semantic structure (Doerfel & Barnett, 1999; Zywica & Danowski, 2008).

The study examines the interorganizational communication by using hyperlink network analysis and their shared perception of nanotechnology by using semantic network analysis. In this way, the study contributes to the literature by analyzing how nanotechnology is portrayed and addressed by major actors in cyberspace.

Theoretical Framework

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

Theoretical Approaches to Innovation and Major Actors

Leydesdorff and Meyer (2003) compared two existing systematic approaches for analyzing the actors involved in technological innovation. First, theorists taking the “Mode 2” position state that discipline-based knowledge production, or “Mode 1,” is disappearing and that integration now reigns throughout society, science, and technology (Shinn, 2003). The “Mode 2” position also argues that the role of science, technology, and society has become less differentiated (Gibbons et al., 1994; Nowotny, Scott, & Gibbons, 2001). However, Leydesdorff and Meyer (2003) criticized the “Mode 2” position:

[D]ifferentiation and integration do not exclude each other, but rather depend on each other as different dimensions of the communication. The communication enables us to construct and sometimes stabilize an innovative integration, but the underlying structures compete both in terms of their definitions of social realities and in terms of the representations that can be constructed at the localizable interfaces (pp. 195–196).

The evolutionary economics model of innovation in the national, not international, context (Freeman, 1988; Lundvall, 1988, 1992; Nelson, 1993) is the second position that Leydesdorff and Meyer (2003) criticized. This model stipulates that “national systems of innovation” should be analyzed structurally. However, they argue that it overlooks transnational corporations, international nongovernmental organizations, and regional communities such as the European Union. According to Leydesdorff and Meyer (2003, p. 196), this failure is critical because “these [nation-based] systems are continuously being restructured under the pressure of the global differentiation of expectations.”

The third position raised by Leydesdorff and Etzkowitz (Leydesdorff, 2003; Leydesdorff & Etzkowitz, 1996; Etzkowitz & Leydesdorff, 2000) is the triple helix model. This model posits that three sectors (helices), university-government-industry (UGI), communicate with one another and can occasionally, and partially, take on each other's role. According to the concept of the “entrepreneurial university” (Leydesdorff & Meyer, 2003, p. 196), universities may adapt to changing environments by coordinating functional and institutional roles (Etzkowitz & Leydesdorff, 2000) in order to take on or share industry or government roles. This flexibility can occasionally lead to the reorganization of “institutional arrangements” (Leydesdorff & Meyer, 2003, p. 196).

Among these three models, we follow the triple helix model because the three helices interact continuously and this interaction may lead to institutional innovation or degeneration. For example, Park and Leydesdorff (2010) analyzed South Korean national science and technology (S&T) research policies of the 2000s. The new policies evaluated domestic scientists' academic performance based on their international publication numbers rather than on the level of cooperation among academic, private, and public domains resulting in a decreased interinstitutional collaboration among UGI (Park & Leydesdorff, 2010; Shapiro, So, & Park, 2010). This case shows that globalization is not necessarily helpful in promoting the cooperation among UGI.

Convergence Theory. Convergence theory envisions the flow of information through a communication network (Barnett, 2008; Centola & Macy, 2007; Kincaid, 2002; Nam & Barnett, 2010; Rogers, 1995; Rogers & Kincaid, 1981), and is useful for examining interorganizational networks. In an interorganizational setting, information exchange can exert considerable influence on the network members by affecting each organization's collective attitude, belief system, and occasionally changing the relationships among participating organizations.

If the agent is located in a closed system, the recurrent exchange of information is likely to lead to value set convergence. This value sharing may promote the interchangeability of the role and function of participating organizations. For instance, the South Korean Sungkyunkwan University, whose foundation is owned by Korean conglomerate Samsung, established information technology-related departments to facilitate the collaboration between the electronics industry and the university. Theoretically, all participating organizations (in this paper, the UGI organizations) in a social system would converge over time into similar value sets, beliefs, and institutional arrangements if communication were to continue indefinitely.

Barnett (2008) posited that two additional propositions could be added to convergence theory. First, the stronger the link between individual nodes or organizations, the greater their reciprocal influence will become. They will converge faster on a common set of beliefs. Second, the greater the proportion of messages initiated by an individual node or organization, the more similar the final equilibrium will be to that of the initial state of beliefs (Barnett, 2008). Thus, the equilibrium values/beliefs, or culture, is likely to become most similar to that of the organization (e.g., a university or a government agency) accounting for the greatest proportion of the system's messages. Differences between members can be reduced through the iterative process of information exchange.

In addition, convergence can be delayed or reversed by the introduction of new information and/or the formation of boundaries that restrict information flows. Relatively bounded and isolated groups are more likely to converge to their local system than to the larger global system, even though the net convergence of the entire system would continue.

In sum, convergence theory can explicate the communication structures and their changes. If there are multifaceted communication flows among the three sectors we can infer that their beliefs and values converge over time.

Hyperlink and Semantic Networks as a System Mirror. We now discuss how the communication flows of the three helices can be examined. A macroapproach is required because the relationship among the UGI sectors is inherently interorganizational. Furthermore, the value sets revealed by written messages online should be examined. For these two purposes, hyperlink (relationship) network analysis and semantic (content) network analysis are appropriate. Hyperlink network analysis reveals social connections and relationships in cyberspace (Adamic & Adar, 2003; Park & Thelwall, 2003), while semantic network analysis, “a structure of analysis based on shared meaning (Doerfel & Barnett, 1999, p. 589),” investigates shared perceptions and sentiments among organizations.

Semantic network analysis examines the relationship among words in terms of their co-occurrence, frequency, and distance and reveals the semantic organization of the text (Lee, Kim, & Rosen, 2009; Chung & Park, 2010). Semantic network analysis is a semiautomated text analysis for determining the most frequently used words and the differences among texts in terms of word frequency and clusters (Lee, Kim, & Rosen, 2009; Doerfel & Barnett, 1999).

Research Questions. Based on the triple helix model and convergence theory we examine evidence of symbolic convergence used among organizations as well as the intersector (and interwebsite) communication flow as a dynamic mechanism promoting such a trend. This communication flow can be examined by determining whether there is a relational pattern such as a transitive or cyclic pattern in the hyperlink network.

RQ1: Semantic Networks. To what degree have the three helices (UGI) converged in their portrayal of nanotechnology?

RQ 1-1. What are the most frequently used words on websites of the three helices?

RQ 1-2. What are the words that are shared (cognitive convergence) or unique on websites of the three helices?

RQ 1-3. How are words clustered in terms of their distance and co-occurrence?

RQ2: Hyperlink Networks. How much do the three helices communicate with one another through hyperlinks?

RQ 2-1. What are the link patterns of their websites and sectors?

RQ 2-2. Are there transitive or cyclic relationships in interlink and co-inlink networks of the websites of the three helices?

Method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

Data collection

We used the snowballing method to gather websites related to nanotechnology. The snowball sampling technique is particularly useful in situations where the population is hard to define or a priori unclear (Garton et al., 1997). For example, Park and Thelwall (2008) used the technique to visualize political parties in the blogosphere and detected clusters representing each party's network.

Using the Yahoo.com database, we collected websites related to nanotechnology that were hyperlinked to Wikipedia's nanotechnology entry (http://en.wikipedia.org/wiki/Nanotechnology) on July 9, 2009. Eight hundred seventy-one nanotech-related webpages were compiled using Yahoo's advanced search option. We used Yahoo.com because it was the only search engine that supported colink queries then (Park, 2010). Wikipedia was selected as the focal node for mapping the online discourse landscape because it is currently the world's fastest and largest web-based collaborative encyclopedia in terms of the number of contributors. In addition, we compared the search results with those obtained using Google.com to verify the coverage of our data. First, we analyzed all 346 websites linked to the Wikipedia article based on their number of linkages. Second, we selected 20 websites that 1) included “nano” in their domain name; 2) featured the highest frequency of inbound or outbound hyperlinks (inlinks and outlinks) and 3) represented one of the three sectors. This two-step process encompasses both a totality of the network, selected integral websites from each sector and their interactions online.

Data Analysis

We conducted webometric analysis focusing on the semantic structure and hyperlink network of the 871 webpages. Webometrics is broadly defined as “the study of web-based content with primarily quantitative methods for social science research goals and using techniques that are not specific to one field of study” (Thelwall, 2009, p.6), and following Park (2010), we performed three types of webometric analyses. First, webpage authors were classified into the following categories based on the triple helix model: public/nonprofit organization websites (government), academic organization/university websites (university), and private firm/industry websites (industry). We sorted the websites using public information in the “about” section, and we did not use any software for automatizing the classification process. Second, we extracted the most prominent words and their linkages from the summary information of returned webpages. This semantic network analysis was accomplished using FullText.exe …….(www.leydesdorff.net/software/fulltext), which is a free software package. This package identifies the most frequently occurring words in the webpage text and produces a co-occurrence matrix. Third, we conducted a hyperlink network analysis to investigate the structure of the web-based nanotechnology discourse. More specifically, hyperlinks embedded in webpages returned by Yahoo.com were parsed using LexiURL Searcher, a webometric analysis tool developed for social scientists (Thelwall, 2009; http://lexiurl.wlv.ac.uk). Network indices used include centralization, degree centrality, and density. Degree centrality is the sum of the values of the ties. The normalized degree centrality is the degree (binary data, e.g., 1: connection exists; 0: no connection exists) divided by the maximum possible degree signified in a percentage (Freeman, 1979). Density is the sum of all values divided by the number of possible ties.

Finally, we drew a network diagram using NodeXL embedded in Excel 2007(NodeXL can be freely downloaded from http://nodexl.codeplex.com).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

RQ1. Industry webpages accounted for 76.7% of the 871 webpages from 346 websites, followed by academic and public organization webpages. This is consistent with the findings of Ackland et al. (2010) suggesting that most of the nanotechnology-related websites tend to be “.com.”Table 1 summarizes the 871 nanotechnology-related webpages in terms of the institutional identity of the author. One blog, nanobot.blogspot.com, included the highest number of returned documents (374 pages) among those sampled, followed by foolscleverbag.info (22 webpages), divedi.blogspot.com (13 webpages), nano.foe.org.au (8 webpages), nanoart.blogspot.com (4 webpages), and nanomedicinecenter.com (4 webpages).

Table 1.  Composition of nanotechnology-related webpages
AuthorFrequencyPercentage
Public organizations (nonprofit organizations, online communities, and government agencies)536.1%
Business organizations (firm websites and individual blogs related to business)66876.7%
Academic organizations (academics, schools, and universities)9811.3%
Author not available526.0%
Total871100.0%

The word frequency analysis (Figure 1) shows the distribution of words that appeared at least 10 times. The most frequently used words were device, material, molecular, nanotechnology, research, and science. There were 52 words that were used at least 10 times in U, I, or G sectors. Among these 52, eight words were shared by all three sectors: control, developing, device, material, molecular, nanometer, nanotechnology, and science. These eight words represent the research subject (control, material, device, nanotechnology, and science) and the scale (nanometer and molecular). The words shared by two of the three sectors were drug/food (industry and the university); size/technology (the university and the government); and atomic/environment (industry and the government).

image

Figure 1. Frequently occurring keywords on nanotechnology-related webpages

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The words that are not shared with other groups in their frequency list represent the indigenous identity of each group. The unique words on university websites were nanomachine and structure and those for the government sector were debate, matter, product, and smaller. Here matter and product represent the research subject; debate stands for the government sector's duty; and smaller stands for the definition of nanotechnology. There were considerably more unique words on industry websites (20): chemistry, development, energy, future, nanobot, nanomarket, nanomaterial, nanoscientist, nuclear, government, health, innovation, machine, molecule, nanoart, nanoparticle, revolution, scientist, theory, and treatment. Here nanoscientist, scientist, theory, treatment, revolution, nanoparticle, development, and innovation indicate nanotechnology and developer (development), and chemistry, energy, future, nanobot, nanomarket, nanomaterial, nuclear, government, health, innovation, machine, molecule, nanoart, nanoparticle, and treatment signify the applications of nanotechnology in the future. The log-transformed distribution of frequently occurring words (Figure 2) provides a picture of the differences among the sectors.

image

Figure 2. Frequently occurring keywords on nanotechnology-related webpages (log-transformed)

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Table 3 indicates semantic network characteristics including degree centralities, density, and network centralization. The government sector has the highest normalized mean of degree centralities (mean = 8.720), while the university sector shows the greatest absolute values among the three sectors (mean = 612.038). Also, the density of university is the highest among the three sectors. Network centralization indices signify that the industry sector has the highest outdegree network centralization (20.07%), while the government sector has the lowest among the three sectors (19.33%). The top 15 centrality list shows that nanotechnology, control, material, device, nanometer, and developing are shared by the three sectors. Unique words of each sector are structure (university), scientist and human (industry), and technology, matter, smaller, and environmental (government).

Table 3.  Semantic network characteristics: Centralities, density, network centralization
 University Industry Government
Out DegreIn DegreeNrm OutDNrm InDegOut DegreIn DegreeNrm OutDNrm InDegOut DegreIn DegreeNrm OutDNrm InDeg
  1. Note. Only the 15 most central words are presented here.

Nanotech2561.0002561.00019.93019.930Nanotechnology3240.0002375.00022.82316.730Nanotech874.000874.00026.90926.909
Technology2289.0002289.00017.81317.813Scale1970.0001970.00013.87713.877Nanotechnology754.000407.00023.21412.531
Nanotechnology2107.0002107.00016.39716.397Material1516.0001471.00010.67910.362Technology666.000839.00020.50525.831
Scale1000.0001005.000 7.782 7.821Molecular1478.0001433.00010.41110.094Atomic261.000261.000 8.036 8.036
Molecular 846.000 846.000 6.584 6.584Device1305.0001296.000 9.193 9.129Material249.000249.000 7.666 7.666
Control 800.000 800.000 6.226 6.226Control1119.0001119.000 7.883 7.883Molecular244.000244.000 7.512 7.512
Size 755.000 756.000 5.875 5.883Atomic1093.0001093.000 7.699 7.699Matter244.000244.000 7.512 7.512
Material 749.000 749.000 5.829 5.829Science 837.0001069.000 5.896 7.530Device243.000243.000 7.482 7.482
Device 739.000 739.000 5.751 5.751Nanometer 810.000 810.000 5.706 5.706Control214.000246.000 6.589 7.574
Structure 613.000 613.000 4.770 4.770Developing 776.000 942.000 5.466 6.636Size197.000228.000 6.065 7.020
Nanometer 540.000 540.000 4.202 4.202Nanoscale 743.000 743.000 5.234 5.234Nanometer192.000192.000 5.911 5.911
Study 523.000 523.000 4.070 4.070Research 513.000 683.000 3.614 4.811Developing172.000198.000 5.296 6.096
Developing 494.000 498.000 3.844 3.875Study 496.000 625.000 3.494 4.403Science155.000155.000 4.772 4.772
Nanoscale 311.000 316.000 2.420 2.459Scientist 318.000 331.000 2.240 2.332Smaller137.000159.000 4.218 4.895
Research 255.000 257.000 1.984 2.000Human 313.000 318.000 2.205 2.240Environmental 75.000 96.000 2.309 2.956
Mean 612.038 612.038 4.763 4.763Mean 457.395 457.395 3.222 3.222Mean283.235283.235 8.720 8.720
Std Dev 681.567 682.089 5.304 5.308Std Dev 641.701 564.930 4.520 3.979Std Dev233.819222.446 7.199 6.849
N of Obs 26.000 26.00026.00026.000N of Obs 43.000 43.00043.00043.000N of Obs 17.000 17.00017.00017.000
DensityAvg Value 24.4815Std Dev53.7373DensityAvg Value 10.8904Std Dev31.0633DensityAvg Value 17.700Std Dev26.700
Network Centralization (Outdegree) 15.77%   N Cent (Outdegree) 20.07%   N Cent (Outdegree) 19.33%   
Network Centralization (Indegree) 15.77%   N Cent (Indegree) 13.83%   N Cent (Indegree) 19.33%   

Figure 3 presents a graphical representation of semantic networks retrieved using the results of word co-occurrence and distance..The sematic network of the industry sector encompassed both development and application of nanotechnology to the real world. The network was composed of five salient clusters. The largest one included words representing nanotechnology research, including nanotechnology, nanoscale, nanometer, developing, atomic, molecular, control, device, scale, study, and material. The second largest cluster included words indicative of the application of nanotechnology to the real world, such as nanoparticle, human, energy, revolution, machine, molecule, nanomarket, development, nanoart, treatment, health, future, drug, and cancer. The other three clusters were composed of fewer words than the two major clusters and did not represent any specific aspect of nanotechnology.

image

Figure 3. A semantic network diagram (industry) Legend. circle: academic organization/university; square: industry/firm; up-triangle: public/government organization; diamond: not available. The current study used Ward's method of retrieving clusters by their central point and optimizing the minimum variance within clusters (Ward, 1963).

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The diameter of each node indicated that nanotechnology, material, molecular, device, scale, science, scientist, and research were the most central nodes (Table 3 and Figure 3).

Figure 4 shows the semantic clusters of university websites. The only major cluster included the words nanotechnology, nanoscale, nanometer, control, molecular, scale, study, device, and material, all of which overlapped with the largest cluster of the industry sector. This suggests that major agendas on university websites are closely related to those on industry websites. The shared words represented the research subject and the scale. The largest cluster also included the words developing, atomic, size, nanotech, technology, cancer, and structure. The other three clusters include only a few words. Centralities indicated by the diameter of each node demonstrate that nanotechnology, device, molecular, material, and research are relatively higher than other nodes (See Table 3 for details).

image

Figure 4. A semantic network diagram (university) Algorithm: Ward (1963)

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Figure 5 shows the semantic network of the government sector. Each of the six small clusters was composed of a few words such as atomic and nanometer; product, debate, and environmental; and smaller and size. These words did not show specific commonalities with one another.

image

Figure 5. A semantic network diagram (government) Algorithm: Ward (1963).

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RQ 2. We investigated the overall linkages of 346 websites to “nanotechnology” articles in Wikipedia. The matrix 346*1 was transformed to a 346*346 adjacency matrix. The most central nodes in the network were mostly industry websites, including Node 50 (divedi.blogspot.com), 69 (findmeacure.com), 98 (Kempton.wordpress.com), 204 (www.bookyards.com), 227 (www.fooscleverbag.info), and 236 (www.ibm.com). Node 114 (meta.wikipedia.org) was the only exception. To sum up, the linkages to the Wikipedia article are unevenly distributed (Sum = 871, Mean = 2.52, SD = 20.09).

Table 2 shows the composition of 20 websites selected based on the criteria presented in the method section. The websites can be classified into: academic organization/university (5 websites), industry/firm (6 websites), public/government organization (5 websites), and not available (4 websites). We conducted a hyperlink network analysis using these websites.

Table 2.  Sample websites and institutional types
Institutional typeWebsite
Academic organization/universitynanotechlaw.blogspot.com
 nanomedcanada.org
 nanotech.upenn.edu
 safenano.org
 nanomedicinecenter.com
Industry/firmnanobot.blogspot.com
 nanoart.blogspot.com
 nano118.com
 nanotechnologywiki.com
 nanoparticletoxicology.com
 nanotubefabrication.com
Public/government organizationnano.foe.org.au
 crnano.typepad.com
 nanotechnological.org
 nanoceo.net
 nanodic.com
Not availablenanopowders-nanotechnology.blogspot.com
 nanobroadcast.com
 nanotechmedicines.com
 nanotechmicro.com

Figure 6 shows the interlink network of the 20 websites along with their incoming and outgoing links to each other. According to the relationship between the websites measured by aggregated hyperlink relations, the visibility, impact, and reputation of nanobot.blogspot.com (belonging to the industry/firm category) was strong. Further, crnano.typepad.com (the public/government) was another important information hub among academic and public websites. The map clearly partitioned relatively peripheral and isolated websites (the upper left corner) from central ones (the right-hand side) in the online network landscape of nanotechnology.

image

Figure 6. The interlink network of 20 websites Legend. circle: academic organization/university; square: industry/firm; up-triangle: public/government organization; diamond: not available.

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One noteworthy finding is that nanobot.blogspot.com, crnano.typepad.com, and nanotechlaw.blogspot.com showed the following transitivity: A[RIGHTWARDS ARROW]B, B[RIGHTWARDS ARROW]C, and A[RIGHTWARDS ARROW]C. Here A[RIGHTWARDS ARROW]C was weaker than A[RIGHTWARDS ARROW]B and B[RIGHTWARDS ARROW]C. In addition, the relationships between A and B, and, between B and C were bidirectional (A[LEFT RIGHT ARROW]B and B[LEFT RIGHT ARROW]C), whereas the relationship between A and C was unidirectional. This result indicates a series of relationships: industry [RIGHTWARDS ARROW] public/government [RIGHTWARDS ARROW] academic/university. Although the previous section suggests a strong similarity between industry and university in terms of semantic networks, the results here exhibit a bridging role of public/government websites.

In particular, 11 websites that were connected to one another did not show an “assortative linking” pattern (Lusher & Ackland, 2011; Ackland, 2007) of hyperlinks based on their institutional identity. Instead, the interlink diagram indicates that academic websites (e.g., nanotech.upenn.edu) were scattered around their counterparts in the industry and public domains. Being a recipient of hyperlinks in the nanotechnology field can represent a form of power that can be referred to as hyperlink capital (Ackland et al., 2010). Therefore, the interlink structure suggests that business and government organizations are likely to play a major role in the formation and deployment of nanotechnology.

The structure of the co-inlink network in Figure 7 displays the relationship between websites from an external websites perspective and co-inlinks denote shared sources of influence. The density of the co-inlink network was much higher (0.9132) than that of the interlink network (0.2682, Table 4). The co-inlink network for 11 websites formed a single group. This may be because all 11 websites, regardless of their institutional type, offered basic information for people to keep abreast of news and trends in the nanotechnology field. Interlink mainly indicates the relationship between website A and website B and the colink represents shared interests or citations (Park & Jankowski, 2008; Park & Kluver, 2009; Park & Thelwall, 2003), the results indicate that the public/government sector is more of information (citation) sources (colinks) than of relational counterparts (interlinks).

image

Figure 7. The co-inlink network of 20 websites

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Table 4.  Hyperlink network characteristics: Centralities, density, network centralization
 Inlinks Colinks
Out DegreeIn DegreeNrm OutDNrm InDegOut DegreeIn DegreeNrm OutDNrm InDeg
  1. Note. See table 2 for each website's complete address.

nanobot.bl35.60010.50018.737 5.526crnano.ty56.60056.60029.78929.789
nanoce18.700 0.000 9.842 0.000safena44.90044.90023.63223.632
nanobroad16.900 1.000 8.895 0.526nanobot.bl40.00040.00021.05321.053
crnano.ty16.40024.900 8.63213.105nanotechlaw.37.60037.60019.78919.789
nanotechlaw. 7.000 8.300 3.684 4.368nanomedicin36.30036.30019.10519.105
nano.foe 3.70016.500 1.947 8.684nanoart.bl32.30032.30017.00017.000
safena 3.60015.900 1.895 8.368nano.foe27.90027.90014.68414.684
nanomedicin 0.00010.000 0.000 5.263nanotech.24.00024.00012.63212.632
nanotechnol 0.000 0.000 0.000 0.000nanobroad22.20022.20011.68411.684
nanoart.bl 0.00012.800 0.000 6.737nanoce16.60016.600 8.737 8.737
nano11 0.000 0.000 0.000 0.000nanodi 8.600 8.600 4.526 4.526
nanopowders-nanotech 0.000 0.000 0.000 0.000nanopowders-nanotech 0.000 0.000 0.000 0.000
nanodi 0.000 0.000 0.000 0.000nanotechnol 0.000 0.000 0.000 0.000
nanomedca 0.000 0.000 0.000 0.000nanomedca 0.000 0.000 0.000 0.000
nanoparticlet 0.000 0.000 0.000 0.000nanoparticlet 0.000 0.000 0.000 0.000
nanotech. 0.000 2.000 0.000 1.053nano11 0.000 0.000 0.000 0.000
nanotechmed 0.000 0.000 0.000 0.000nanotechmed 0.000 0.000 0.000 0.000
nanotechm 0.000 0.000 0.000 0.000nanotechm 0.000 0.000 0.000 0.000
nanotubefabr 0.000 0.000 0.000 0.000nanotubefabr 0.000 0.000 0.000 0.000
nanotechnol 0.000 0.000 0.000 0.000nanotechnol 0.000 0.000 0.000 0.000
Mean 5.095 5.095 2.682 2.682Mean17.35017.350 9.132 9.132
Std Dev 9.295 7.328 4.892 3.857Std Dev18.39918.399 9.684 9.684
N of Obs20.00020.00020.00020.000N of Obs20.00020.00020.00020.000
DensityAvg Value 0.2682Std Dev 1.2499DensityAvg Value 0.9132Std Dev 2.0644
Network Centralization (Outdegree)16.90%   N Cent (Outdegree)21.75%   
Network Centralization (Indegree)10.97%   N Cent (Indegree)21.75%   

Further, there were many bidirectional cyclic relationships (A [LEFT RIGHT ARROW]B, B[LEFT RIGHT ARROW] C, and C[LEFT RIGHT ARROW] A) among the three sectors. The most salient one was among crnano.typepad.com (public/government), nanomedicinecenter.org (public/academic), and nanoart.blogspot.com (industry/firm).

Table 4 indicates that most central nodes in inlinks and colinks ranking overlap with each other, but the centralities of nanoceo.net and nanobroadcast.com are the greatest in inlinks and their rankings are lower in colinks. Also, nanotech.upeen.edu shows much higher ranking in colinks than inlinks.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

In web-mediated environments, semantic networks signify that shared cognitive traits and hyperlinks can be an important proxy for interorganizational linkages. In addition, from a convergence theory perspective, linkages and shared keywords in cyberspace can increase the level of multilateral interactions during early periods of technology development, particularly through partnerships and alliances (Rafols, 2007; Rafols & Meyer, 2007; Marres & Rogers, 2000). Nonetheless, few studies have examined the relational and semantic patterns of internet-based communication networks of innovative technology websites.

Few studies have taken the triple helix approach to examine online networks among academic, public, and business websites. These networks denote interorganizational interactions and shared semantic traits between social actors in the science and technology fields (Leydesdorff & Zawdie, 2010). This study contributes to the literature by examining the ways in which two or more social actors can become connected to and share information with each other to achieve the mutual adaptation and/or collective identity discussed in Rogers and Kincaid (1981) and Barnett (2008).

As documented by Shapira, Youtie, and Porter (2010), there is a growing interest among social scientists in nanotechnology. Although previous studies have typically used scientometric data (e.g., data generated by Web of Science, Scorpus, and/or Google Scholar), the data from this study include both hypertext linkages and text-mediated symbolic ties between websites. Accordingly, this study provides a more thorough understanding of the social dynamics of nanotechnology than previous studies. The following section provides a brief review of the results and their theoretical and policy implications.

Implications for Understanding the Triple Helix Structure of Nanotechnology. First, industry websites accounted for most of the nano-related websites (76.7%) and formed more salient clusters with highly central nodes within them than the other two sectors' semantic networks (Figure 3). However, the university sector showed the highest mean absolute value of degree centrality (Table 3).

The words shared by the three helices represented what they studied, ie. the research subject, and the scale. The unique words on industry websites stood for nanotechnology, developers, and possible applications of the technology. The unique words on government websites represented the debate over nanotechnology and the research subject.

According to the semantic network diagram, the clusters of university and industry websites were very similar, particularly their major-word clusters. The major agendas on industry and university websites included research subjects such as device and material; scale and nanoscale; and nanometer. Convergence theorists argue that shared symbols can represent the degree to which two sectors are or can become homogeneous. Thus, these results are consistent with the recent “entrepreneurial university” trend (Leydesdorff & Meyer, 2003), where the university sector is becoming more similar to the industry sector, accepting it's cultures, norms, and even organizational systems. This trend also supports the triple helix scholars' contention that functional and institutional roles are being replaced by mutual adaptation (Etzkowitz & Leydesdorff, 2000; Etzkowitz, 2008).

Second, hyperlinks can indicate social connections, credibility, and reputation (Park, Barnett, & Nam, 2002; Marres & Rogers, 2000; Terveen & Hill, 1998). The results of the interlink analysis indicate that industry (nanobot.blogspot.com) and public/government (crnano.typepad.com) websites were central in the network. In addition, there was a transitivity of relationships among industry inline image public/government inline image academic/university websites. Further, the colink structure can represent “structural embeddedness” (Gulati & Gargiulo, 1999, p. 1446) by signifying shared relationships among various sectors. According to Gulati and Gargiulo (1999), social embeddedness encompasses relational, structural, and positional dimensions. Structural embeddedness represents shared relationships with the third party (1999, p. 1446). In this research, co-inlinks stand for structural embeddedness, and their pattern shows reciprocal interactions among the three helices and their components themselves. The co-link results also indicate that the three sectors were densely linked. The bidirectional and multifaceted cyclic relationships provide support for the view that information sharing indicated by colinks among three helices occurs more frequently than the information flow represented by the interlink structure. This communication pattern provides support for the triple helix model and convergence theory because shared words among the industry, university, and government sectors may be an outcome of their communication with one another through online (hyperlinks) or offline interactions (e.g., Face to face communication).

According to Barnett (2008), intersite linkages can promote their reciprocal influence on each other and their belief system will ultimately converge. The semantic network results are clearly consistent with his argument. In addition, he posits that if one sector (in this study, the industry sector) accounts for a substantial share of communication, the ultimate equilibrium of beliefs should be closer to that sector. Differences between the three helices have been decreasing, and iterative information exchange should ultimately result in the reorganization of “institutional arrangements” (Leydesdorff & Meyer, 2003, p. 196).

Conclusion and Suggestions for Future Research

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

This study used the triple helix model to understand the structure and influence of interorganizational communication of the three helices. The results obtained using the model suggest that the university, government, and industry sectors are becoming increasingly interdependent and flexible through communication and that such a process has promoted nanotechnology development. The results of the hyperlink analysis indicate that the three helices follow a frequent, transitive, and cyclic communication pattern. In addition, the industry and university sectors are likely to use the same words, indicating a convergence of the two sectors.

Future research should apply the convergence theory and the triple helix model in this study to the telecommunications field (Nam & Barnett, 2010) or to other “grand” disciplines (Yang, Park, & Heo, 2010). Using hyperlink network analysis as well as semantic network analysis, this study demonstrates the ways in which people use relational (hyperlink) and topical (semantic) features of the Web and how such use influences the underlying system. A comparison between applied science (e.g., management or communication) and “fundamental” science (e.g., philosophy) can be made by examining their relevant websites and determining their content similarities and hyperlink exchange.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

An earlier version of this article was presented at International Communication Association annual convention, Boston, MA, USA, in May 2011. The author thanks Dr. Han Woo Park, Ting Wang, and editors and anonymous reviewers for kind comments and insights.

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  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author
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About the Author

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical Framework
  5. Method
  6. Results
  7. Discussion
  8. Conclusion and Suggestions for Future Research
  9. Acknowledgement
  10. References
  11. About the Author

Jang Hyun Kim is Assistant Professor in the Department of Communicology at The University of Hawaii at Manoa. His research interests include social/semantic networks and social media, culture and digital contents, and organizational systems.

Address: 2560 Campus Rd. George Hall 330. Honolulu, HI 96822, USA. E-mail: jangkim@hawaii.edu