Whereas a political economy is based on two organizing principles - the economy and politics - a knowledge-based economy is more complex to the extent that technological developments can be expected continuously to upset the equilibrium of the market. The third dynamic of organized novelty production has to be endogenized into a model of technological innovations (Nelson and Winter 1982; Storper 1997). Schumpeter (1939/1964, at p. 62) first defined innovation in relation to the two dynamics of factor substitutions along the production function and shifts of the production function towards the origin. Two dynamics can mutually shape each other and thus form a trajectory; a trajectory can lead to a competitive edge (Leydesdorff and Van den Besselaar 1998). Three dynamics, however, shape an adaptive ecosystem because the additional - third - degree of freedom allows for both globalization and localization (Ivanova and Leydesdorff, 2014a). A regime at the global level and potentially synergetic niches in local optima can then be expected (Dolfsma and Leydesdorff 2009). The system dynamics is adaptive to relations that are added or deleted because the resulting ‘ecosystem’ of communications remains in transition between local and global incentives (Etzkowitz and Leydesdorff 1998).
Under what conditions can a Triple Helix (TH) of university-industry-government (UIG) relations be considered as an ecosystem with synergy? Etzkowitz and Leydesdorff (1995, 2000) considered the synergy in a Triple Helix system as the result of an overlay of exchanges among perspectives on the bi- and trilateral relations. This overlay is constructed in terms of relations but can be expected to add another (hyper-cyclic) dynamic of shared meanings to the events (Leydesdorff 1994, pp. 186f.). Thus, one obtains a double-layered system: at the bottom, a network of exchange relations and a next layer in which these relations are appreciated from different perspectives. Next-order sharing of perspectives - that is, translations among codes of communication - can feedback on or feed-forward to the relations in the network.
As against a double helix, symmetry can be broken in a three- (or more-) dimensional system and the dynamics can therefore be positive or negative (Figure 1; Ivanova and Leydesdorff, 2014a and [b]). The virtuous and vicious circles among three (or more) dynamics contain the options of both globalization and localization at each moment of time. Thus, one is challenged for a balanced response that takes forward and backward loops in consideration.
In terms of the Triple Helix, localizations are needed, for example, in order to retain wealth in the (local) economy from global knowledge; but globalization of the perspectives is also needed in order to make a knowledge-based economy. In empirical cases, one can expect a trade-off between local organization (variation) versus global self-organization (‘beyond control’) in the selection environments of the relevant markets and sciences. In general, the three functional (sub-) dynamics in a Triple Helix can be specified at the system level as interactions among (i) scientific novelty production, (ii) economic wealth generation, and (iii) political control (Leydesdorff and Zawdie 2010). Carayannis and Campbell (2009 and 2010) proposed further extensions of the TH to a Quadruple or Quintuple Helix (cf. Bunders et al. 1999; Leydesdorff 2012).
This complex adaptive system is differentiated horizontally in terms of the three (or more) functional codes of the communication (economic, scientific, political) and vertically in terms of observable relations versus latent functions. The observable exchange relations in the network can be made the subject of network analysis. The relations, however, are differently distributed in terms of the structural components - universities, industries, and governments - and accordingly, what the relations mean can be different from each of these three perspectives.
The specification of how the codes operate as selection environments upon one another requires a systems perspective on the distributions of both relations and non-relations in terms of correlations. Specific relations can also be functionally equivalent. The correlations carry the latent functions that can operate synergetically to a varying extent. The synergy is an interaction effect among the distributions: do the functions fit? Other variants and development patterns are possible in niches (Bruckner et al. 1996; Kemp et al.1998; Schot and Geels 2007). In summary, this TH model of synergy becomes more abstract than a network model because a systems perspective is added that operates in terms of the functions of the relations.
2.1 Mutual information and mutual redundancy in three dimensions
The relative frequency distributions of the relations can be expected to contain uncertainty that can be specified using information theory (Shannon 1948; Theil 1972). Ulanowicz (1986; 1997) proposed to measure synergy as mutual information in bi- and trilateral relations when developing ascendency theory. Ascendency is the organizing power of the emerging next-order level in an ecosystem's trophic network: which combinations of food distributions enable a system to flourish? More recently, Ulanowicz (2009) suggested considering the loop in Figure 1 as potentially autocatalytic. Participation of a third party (or reagent) may enhance the relations between two of them to such an extent that the loop is reinforced by the mediation. In other words, the TH can then slip into another type of communication dynamics.
Can this indicator also be used for the specification of synergy in TH configurations (Ulanowicz, personal communication, October 6, 2001)? The mutual information in three dimensions (which is specified in the Appendix) follows analytically from Shannon's (1948) information theory (e.g., McGill 1954; Abramson 1963, at p. 129; Yeung 2008, at pp. 59f.) but has posed an anomaly within this theory because the results of the measurement can be positive or negative. In Shannon's theory information is defined as uncertainty and is necessarily generated as positive when the system operates (because of Shannon's intentional coupling of the information measure H to the second law of thermodynamics).a Negative information, however, would be a reduction of uncertainty as in a niche. Pockets of relatively less uncertainty are also assumed in Prigogine's (1980) theory of self-organizing systems when order emerges.
Why can a system of three reduce uncertainty? (Burt 1992; Simmel 1902; Tortoriello and Krackhardt 2010). In a system of three (or more) distributions, the correlation between two of them can be spurious upon the third (Sun and Negishi 2010). For example, when two parents answer the questions of a child consistently, uncertainty can be reduced. The answers of one parent inform the child in this case about the expected answers of the other because the two sources of variation are coordinated at a systems level (that is, the marriage as a latent construct). The child will not have direct access to this next-order sharing of expectations. Note that the consequent reduction of uncertainty cannot be attributed to specific agents but is generated in a configuration of relations. In the case of a divorce, for example, positive uncertainty among the answers may begin to prevail over the synergy measurable as negative uncertainty (that is, redundancy) in the configuration.
Similarly, in university-industry-government relations, a strong existing relation between two of the partners can make a difference for the third. The synergy indicator measures both positive and negative contributions to the uncertainty or, in other words, both the necessarily positive interaction information and the potentially negative correlational information. Krippendorff (2009a) showed that this summary measure cannot be considered as Shannon-type information; Shannon-type interaction information (IABC→AB:AC:BC; cf. Krippendorff 1980) can be defined differently. However, Krippendorff (2009b) added that interactions with next-order loops - such as overlays - ‘entail positive or negative redundancies, those without loops do not’ (at p. 676).
The overlay of TH relations provides additional loops of information processing to the interacting agents that may thus reduce uncertainty. Too much reduction of uncertainty, on the one side, may lead to insufficient stimulus to innovate. Too much uncertainty, on the other, may make new relations risky. The TH indicator of synergy indicates local reduction of uncertainty within a system (for example, a regional system of innovations) with a negative sign and opening up to globalization with a positive one. This choice of the signs is technical but makes the indicator consistent with Shannon's information theory (Leydesdorff 2010). Leydesdorff and Ivanova (2014) have shown that the indicator measures ‘mutual redundancy’, that is, the extent to which the same information is coded from two (or more) different perspectives.b The perspectives can also be considered as containing latent codes of communication that interact in the observable relations.
In summary, mutual redundancy in three or more dimensions enables us to indicate the configurations under study in terms of local integration, relations, and synergy versus global differentiation and opening. However, the indicator is not more than a formal instrument. One needs a research design for the appreciation of the results. In other words, interpretation of the results requires a discussion of a TH system in relation to its relevant selection environments (Meyer et al. 2014). As we shall see below in empirical cases, the synergy values can then serve as a heuristics by focusing our attention on intuitively unexpected possibilities.
Because all information measures are based on aggregation (using sigmas; see the Appendix), the synergy can also be decomposed in terms of the dimensions such as geographical regions or sectors such as medium-tech manufacturing and knowledge-intensive services. The indicator provides the option to test the assumptions of systemness such as in national or regional innovation ‘systems’. In recent studies, attempts were made to include institutions without first specifying their national boundaries (Choi, Yang, and Park 2014 forthcoming) and research domains (Khan and Park 2013). One can address these questions empirically: how much synergy is indicated regionally or nationally? Is there surplus between regions, nations, or sectors? Is this different for sectors in regions? The measurement results can be fully decomposed.
For example, when one considers the innovation systems of regions in Northern Italy such as Piedmont (OECD 2009) and Lombardy, the industry in the one region may fit more synergistically to the knowledge base of the other, but the political distribution of authority is regionalized (Beccatini et al. 2003; Cooke and Leydesdorff 2006). In such a configuration, the further development of university-industry-government relations in one of the regions might impede the trans-regional innovation system, but a trans-regional mechanism of political authority across Northern Italy is failing. One can thus indicate limitations on synergy generation and from this perspective provide a policy advice.
In the case of Norway, for example, Strand and Leydesdorff (2013) found that synergy among the three functions could be retrieved in relation to high- and medium-tech offshore activities on the west coast, whereas the traditional university centers (Oslo and Trondheim) have remained more distanced from TH models. In China, Leydesdorff and Zhou (2014) found synergy at the level of the 31 provinces by an order of magnitude more than at the lower level of 339 regional prefectures - with the exception of the four municipalities among them that are centrally administered as provinces (Beijing, Shanghai, Tianjin, and Chongqing). Thus, the knowledge-based economy of China seems not coordinated at the lowest regional level. Expanding the model of the four municipalities to other regions might enhance the knowledge-based pattern in the economy.
In Russia, Leydesdorff, Perevodchikov, and Uvarov, [in press] found that the knowledge-intensive services (KIS) - which are in Russia often related to state apparatuses - were synergetic at the local level but did not enhance globalization. In western countries, KIS is more ‘footloose’ (Vernon, 1979) and thus, a globalizer: when KIS is needed elsewhere, it travels. Similarly, Asian nations such as India and Indonesia show levels of synergy much higher than in China and western nations in a study of UIG co-authorship relations in scholarly publications (Ye et al. 2013). What may be synergetic from one (e.g., industrial) perspective may fail to be so from a perspective of regional government or university development.
2.2 Empirical studies
Using 1,432,401 corporate addresses provided on the CD-ROM version of the Science Citation Index 2000, Leydesdorff (2003) first explored the use of the indicator in terms of the address information in the bylines of these (725,354) records.c The addresses were distinguished as academic, industrial, and governmental using a dedicated routine (Park, Hong, and Leydesdorff 2005). How are UIG co-authorship relations distributed in relation to bilateral relations or single-sector attributions?
In this design, documents are the units of analysis. The corporate addresses to each individual document are scored on three variables: U, I, and G that can be zero or one. If U and I are both positive, this co-authorship counts as a UI relation, etc. For each set, one thus obtains aggregated values for all (seven) possible combinations (UIG, UI, UG, IG, U, I, and G). This design was later extended with international co-authorship relations as a fourth category in a Quadruple Helix model (Kwon et al. 2012; Leydesdorff and Sun 2009). One thus obtains three other possible synergies between university-government-foreign (UGF), UIF, and IGF, and also, the possibility to study the development of the Quadruple Helix of UIGF relations. Both the Korean and Japanese datasets allowed studying these indicators longitudinally (Figures 2 and 3, respectively). A similar design was also used for studies at the internet. The internet also contains documents, and one can count occurrences and co-occurrences of words like ‘university’, ‘industry’, or ‘government’ (Khan and Park 2011; Leydesdorff and Curran 2000; Park et al. 2005; Skoric 2014).
Figure 2 shows the result for the national TH system of publications with Korean addresses in the Science Citation Index (SCI). Several trend breaches are indicated. Under the dictatorship (until 1987), the increases in synergy in TH relations were initially slow. Democratization and civil liberty stimulated TH development in the period of transition. The 1990s witnessed a reversal of this trend; this reversal was reinforced by the increasing globalization (after, for example, the opening of China). Globalization can be expected to make local interactions among and between sectors less important than sector-specific ones because the latter can be more specialized. During the most recent years, the Korean publication system seems to stabilize again into a balance between local and global dynamics.
Using a similar design - with documents in the Science Citation Index as units of analysis - Leydesdorff and Sun (2009) found that the TH synergy in the Japanese set (TUIG) was −67.4 mbits in 1981 but declined to −1.2 mbits in 2004 (Figure 3). The decomposition shows that university-industry (TUI) relations declined during the entire period (1981-2004), but international co-authorship relations (TUF) steadily increased for university-based authors (Wagner, 2008). In other words, international collaboration is a long-term driver for these authors of scholarly articles more than integration with industry at the national level.
The Triple Helix of university, governmental, and non-Japanese authors (TUGF) gained in synergy since the first part of the 1990s, and this is reflected as a trend breach in the Quadruple Helix (TUIGF). In other words, international co-authorship has played a catalytic role in integrating national TH relations in Japan since the mid-1990s, whereas the national system of co-authorship relations tends to erode as a cultural trend (since the early 1980s). Thus, the effects of globalization are integrated in the Japanese case. A similar effect since the mid-1990s was found for Korea, but the composing three-dimensional subsystems of a Quadruple Helix of university-industry-government and foreign relations have been constructed differently in this country (Kwon et al. 2012, at p. 172).
Figure 4 shows synergy values in the systems of co-authorship relations in a number of countries, including the G8, using data from SCI 2011 (Ye et al. 2013). (The results cannot directly be compared with the results of Figures 2 and 3 because of the differences in the data collection). Interestingly, Japan and Korea are positioned in Figure 4 at opposite ends. Japan is third after India and Indonesia in terms of national synergy, whereas in Korea, the system is globalized at the level of advanced western nations.
One of the objectives of this last study was to consider the position of the Chinese Academy of Science (CAS) which is currently in transition from being a government agency towards being a university-type of institution. When CAS is counted as university, the synergy in the Chinese system is much less (−19.4 mbits) than in the case of counting CAS as governmental (−36.0 mbits). In the former case, China is placed among the most globalized countries (in this domain of publishing) such as Korea and Germany, whereas in the latter case, the Chinese system is far more nationally integrated (with a synergy value between Russia and the UK).
In summary, we have witnessed since the mid-1990s an unraveling of the Triple Helix in terms of co-authored publications under the pressure of globalization. Lawton-Smith and Leydesdorff, [in press] note that the Triple Helix model was proposed by Etzkowitz and Leydesdorff (1995) before this change in the system towards globalization became so prevalent. Internationalization was further reinforced by the competitive emphasis on university ranking since the introduction of the Shanghai rankings in 2004 (Shin et al. 2011). Patenting, for example, is no longer an incentive in leading universities (Leydesdorff and Meyer 2010 and 2013; cf. Etzkowitz 2013). Institutional incentives in academia have been increasingly focused on international publications and co-authorship relations during the last decade (Bornmann et al. 2014; Halffman and Leydesdorff 2010; Leydesdorff et al. 2014b).
2.3 Firms as units of analysis
Van der Panne and Dolfsma (2003) used micro-data of Dutch firms which were operationalized in terms of the TH dimensions (Storper 1997): (1) postal codes which indicate the region, etc., in the geographic dimension; (2) technological classifications (NACE codes of the OECD);d and (3) firm sizes indicating the economic dynamics (e.g., small- and medium-sized enterprises versus large corporations). Mutual information (co-variation) between each of the two distributions can be used as a relational measure so that synergy in three dimensions can be estimated. Synergy at the national level can then be disaggregated in terms of geographical levels (regions, provinces) or in the other two dimensions (sectors and size classes).
Leydesdorff, Van der Panne, and Dolfsma (2006) published the results of this analysis in a special issue of Research Policy dedicated to the Triple Helix. This new application of the indicator in the economic domain led to a series of studies at the national level. Leydesdorff and Fritsch's (2006) decomposition of Germany, for example, was published in the same issue. These two studies led to the conclusion that in the Netherlands additional synergy is indicated at the national level (on top of the sum of the regions and provinces), but in Germany, the States (Länder; e.g., Bavaria) of the Federal Republic are the most relevant level of integration. Using the data of 2004, however, the German Länder were still significantly different along the East-west divide, but at the next-lower (Nomenclature of Territorial Units for Statistics (NUTS)-2)e level of regions (Regierungsbezirke), the East-west divide was no longer dominant (Figure 5).
Figure 5 shows the synergy at the NUTS-2 level of regions (Regierungsbezirke) in Germany. In terms of sectors, both studies indicated that medium-tech manufacturing is a crucial factor for generating synergy because of its relative embeddedness (Cohen and Levinthal 1990). High-tech manufacturing and knowledge-intensive services (KIS) can be less embedded and globalized or footloose (Vernon 1979). KIS tends to uncouple from local economies because access to an airport or train station is crucial. This uncoupling from the geographical locus is counter-acted when KIS is high-tech because of the need to maintain a laboratory or other specific installation (e.g., computer centers).
The synergy at the regional level was found in Germany around the major metropolitan centers in the western part of the country such as Munich, Hamburg, and Frankfurt but not in Berlin (cf. Fritsch and Graf 2011; Lengyel, Sebestyén, and Leydesdorff 2013). Thuringia, previously in the German Democratic Republic, however, is also high on synergy. This is a puzzling result which we only understood after further analysis of the Hungarian innovation system in similar terms. In this follow-up study of Hungary, but using a less complete dataset, Lengyel and Leydesdorff (2011) concluded that Hungary no longer provides surplus synergy at the national level to three regional subsystems that are differently integrated.
The most important subsystem in Hungary was found in the metropolitan area of Budapest and its environment. The western parts of the country were no longer integrated nationally but had successfully ‘accessed’ to relevant environments in Austria, Germany, and the remainder of the EU, whereas some eastern provinces had remained synergetic under the regime of the old (state-controlled) system. Although these conclusions were tentative, they could be supported by another reading of existing statistics. This disintegration of the national system followed during the 1990s after the demise of the Soviet Union, when the country first went into transition and then became an accession country to the EU. Given the dynamics of European unification, however, it was too late to construct a national system of innovations when Hungary became a free nation.
In a similar vein, Strand and Leydesdorff (2013) concluded on the basis of the full set of Norwegian firm data that the knowledge base of this national system was no longer primarily integrated in relation to the national universities, but driven by foreign investments in offshore industries in the western parts of the country. Internationalization and globalization in the latter two studies seemed thus core dimensions for understanding how the knowledge-based economy operates. These conclusions, however, made us turn to the Swedish innovation system as a baseline for comparison and validation (Leydesdorff and Strand 2013).
More than any other, the Swedish innovation system has been studied in detail, is documented statistically with great precision, and could thus provide us with a benchmark to test our Triple Helix methodology. Using the full set of data about 1,187,421 firms made available by Statistics Sweden (November 2011), the data was analyzed with a design similar to the previous studies. The Swedish case, however, allows us to specify an expectation.
In accordance with the expectation, three counties dominate the picture of the synergy within this nation (Figure 6) when decomposed in terms of regions: Stockholm (−3.49 mbits), Västre Gotalands län including Gothenberg (−2.91 mbits), and Skåne including Malmö and Lund (−2.31 mbits). Together, these three regions account for (8.71 / 17.95) 48.5% of the summed synergies of regions at the geographical scale of NUTS3. The between-region synergy at the national level is −4.61 mbits or 25.7%, so that the other 18 counties contribute (100 − 48.5 − 25.7) 25.8% to the national synergy.
All relevant distributions (of bi- and trilateral relations) are more skewed in Stockholm than in Gothenburg and Malmö. Most importantly, the synergy function ΔTGTO is far more negative for Stockholm. In summary, the different functions are more concentrated and the distributions operate more synergistically in the Stockholm region than around Gothenburg and Malmö/Lund. The other regions follow these three regions at a considerable distance (Etzkowitz and Klofsten 2005).
The final study which we wish to mention in this context is that of China. In this case, we used data downloaded (December 2012) from the Orbis™ database of Bureau van Dijk (available at https://orbis.bvdinfo.com). This data at the firm level is collected for commercial purposes; the official statistics of China are not available to the public. The results showed that most of the synergy in the Chinese innovation system is generated at the level of the 31 provinces: only 18.0% - that is, less than Sweden - was generated at the next level of the nation between the provinces.
Figure 7 shows an interesting finding of this study. At the next-lower level of the 339 prefectures, the four municipalities which are governed centrally as provinces stand out with together 33.0% of the total synergy at this level. This result came unexpectedly from our analysis but was immediately recognizable to our Chinese colleagues. The success of these four municipalities in constructing a knowledge-based economy regionally can perhaps be used as a role model for other regions.