Patent indicators for monitoring convergence – examples from NFF and ICT

Technological Forecasting & Social Change 78 (2011) 256–273

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Technological Forecasting & Social Change

Patent indicators for monitoring convergence – examples from NFF and ICT Clive-Steven Curran ⁎, Jens Leker Institute of Business Administration at the Department of Chemistry and Pharmacy, University of Münster, Leonardo-Campus 1, 48149 Münster, Germany

a r t i c l e

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Article history: Received 10 December 2009 Received in revised form 21 June 2010 Accepted 26 June 2010 Keywords: Converging industries Patent analysis IPC co-classification Technology forecasting Nutraceuticals and Functional Foods Information technologies Consumer electronics Telecommunications

a b s t r a c t The blurring of boundaries between hitherto distinct scientific disciplines, technologies or markets is a common and powerful phenomenon. Concentrating on monitoring convergence through patent indicators, we discuss convergence with examples from the area of Nutraceuticals and Functional Foods (NFF)/Cosmeceuticals as well as information technologies, consumer electronics, and telecommunications (ICT). We analyze 7455 scientific and patent references on phytosterols with the aid of SciFinder Scholar and 3836 documents employing STN AnaVist. Furthermore, we test an IPC co-classification based approach on 859,469 ICT-related and 341,846 NFF-related patents. Our results show clear indications for convergence and a proof of principle for our monitoring concept. Academics may better evaluate environmental parameters, like cases of convergence, influencing companies' actions. Implications for practitioners are based on a more reliable assessment prior to the forming of strategic alliances or mergers and acquisitions. This could help avoid costly adventures such as the mergers and acquisitions seen in ICT. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Ever since their first appearance, industries have undergone evolution, decisive or radical changes or dematerialized completely over time. These changes were caused by very different drivers like technological developments, changes in regulations or new customer preferences. But within the last few decades, a new and powerful phenomenon has been identified – the convergence of industries. In industries such as information technologies, consumer electronics, and telecommunications (ICT),1 formerly distinct sector boundaries have already largely faded [1,2]. While the merging of different technologies and customer demands has undoubtedly led to many cross-industry products like Apple's widely discussed iPhones or RIM's several BlackBerry models (two examples for the group of products termed ‘smartphones’2), scholars are still discussing the degree of complete industry convergence [3]. In managerial practice several strategic decisions, especially between the 1980s and the early 2000s, have been associated with actual or expected occurrences of this phenomenon [4]. Strategic actions, divestitures, and even corporate mergers like that of AOL and Time Warner in 2000 have been set off by top-managers, who felt the necessity to cope with increasingly permeable industry boundaries. More recently, this phenomenon can also be found in other industries like the chemical, the food, or the pharmaceutical industry. The world of fading industry boundaries and more and more cross-scientific research [5–7] enables the chemical sector to utilize the often tremendous technological developments in its neighboring disciplines (e.g. physics or ⁎ Corresponding author. Tel.: +49 251 8331820; fax: +49 251 8331818. E-mail address: [email protected] (C.-S. Curran). 1 Many different definitions exist for the abbreviation ‘ICT’. Within this study, it is used to encompass the three broad sectors of computers and information technology (I), consumer electronics and media appliances (C), and telecommunication devices and technology (T). All of these have something in common —they generate, process, and store (now) digital information. 2 Smartphones enable users to place phone calls, read their e-mails, make pictures, listen to music or the radio, and make their way to unknown places with the aid of their built-in navigation system. They are a combination of a selection of previously disjoint devices such as mobile phones, media players, cameras, or personal digital assistants (PDA). 0040-1625/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2010.06.021

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biotechnology) and at its outposts (e.g. electronics or agriculture). And the food and the pharmaceutical sectors are competing with each other on the basis of foods enriched with quasi-medical functions in the area of Nutraceuticals and Functional Foods (NFF). The resulting new industry segments present incumbents with a selection of opportunities and threats. On one hand a plethora of opportunities for new fields of business and economic growth emerges. On the other hand the new setup is often quite challenging as firms have to employ knowledge and technologies not within their traditional framework of expertise. Naturally, in most cases of convergence sourcing the essential knowledge and experience from beyond their own factory gate is necessary and key to successful innovation management. Furthermore, they are facing many new competitors, who may have been strong incumbents in their own segments prior to the formation of the new segment [8]. On that account, anticipating convergence would enable firms to form strategic alliances or acquire new technologies already at an early stage and to prepare for the challenges and pitfalls of new segments in advance. A timely reaction to challenges posed by these external events may be decisive to find the best possible partner [9]. Thus the question remains: how to foresee or at least detect fading industry boundaries at the earliest possible moment? And how to distinguish mere utilization of a common technology platform or a merging market from cases of real industry fusion? Being the first mover will be yielding substantial rewards in many cases. But if the anticipated fading of boundaries does not occur, or occurs under completely different parameters, waiting to be second may prove to be the more sustainable survival strategy for managers [10]. Hence, the challenge is not only to spot weak signals, but at the same time to develop a reliable and robust early warning system [11]. We introduced the idea of a comprehensive approach towards anticipating and monitoring industry convergence, based on publicly available data in earlier papers [12,13]. To the best of our knowledge, ideas of multiple indicators based on scientific and patent literature have so far not been applied in the area of convergence anticipation. Patents are generally regarded as precursors of technological developments. Long before the general public is realizing new developments, scientists and companies are trying to secure their inventions through filing of patent applications. While a patent is mainly intended for specialists and hence describes the technological aspects of the invention in great detail, especially its relation to other patents and publications can make it valuable also for non-specialists trying to identify general trends. While most scientific publications are as well not more insightful for the layman, they are written on the basis of a different motivation. Most articles are written by scientists at a time when they are either not able to already grasp the commercial applicability of their findings, or again when the most important parts have already been protected by patents. Albeit the differences in both types of “publications”, e.g. in regard to legal parameters and the existence of highly trained and full time patent examiners and attorneys, they have much in common. Like patents, papers are structured to a certain degree and follow scientific discipline (and journal) specific regulations. And with a peer review system in place and with specialists in the same area as readers, most weaknesses and errors will not go unnoticed. Hence, assessing industry convergence on the basis of such data sources should yield robust results. Building on the outline of our anticipation approach [12,14,15], we have thus far discussed different indicators and their results regarding the example of one class of NFF ingredients (phytosterols) [13,16,17]. Whilst different stages of the convergence process can be assessed employing a selection of indicators from various sources, patent documents have been identified to be the most important source for a reliable anticipation of industry convergence. Consequently, this paper will focus on the application of different patent indicators and compare potential sources. The present study will also introduce a way of monitoring convergence in patents independent of search-terms in the broader NFF sector. As the intersection of the pharmaceutical and the food industry is not yet a fully established example of convergence (despite several studies in this area, see especially Bröring's works [6,18–20]), this approach will be tested against the results from the ICT sector. Thus, the remainder of this paper is structured as follows. In the next section we will define industry convergence and delineate it from industry fusion. Additionally, patents will be briefly introduced as a means of anticipating convergence. Following Section 3 with our research setting in ICT-related industries and the chemical and pharmaceutical industry, Section 4 presents the results from three different databases, comparing our indications for convergence in NFF and those on the ICT-example of smartphones. Finally, a discussion of the developed model and future research strands will conclude our paper in Section 5. 2. Industry convergence 2.1. Defining convergence Although ‘convergence’ has been attested “broad implications for economic welfare” [21], it has at the same time been coined “an often used but rarely defined” [22] buzzword [23,24]. The use of the term ‘convergence’ in the English language dates back to the 17th century. It is of Latin origin “convergere” (inclining together) and its dictionary definition encompasses basic definitions like “(of lines) tend to meet at a point” or “(of a number of things) gradually change so as to become similar or develop something in common” [25]. Furthermore, individual disciplines ranging from biology, meteorology, and mathematics to the political and social sciences have applied it with individualized meanings. More generally, it describes the concept of at least two discernable items moving towards union or uniformity or the merging of distinct technologies, devices, or industries into a unified whole. In a managerial context the first use of the term convergence is attributed to Nathan Rosenberg [26], who in his article on changes in the machine tool industry in the second half of the 19th century coins the expression of “technological convergence”

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in contrast to “sequences of parallel and unrelated activities”. He employs the phrase to describe processes that are commonly used by different (unrelated) industry sectors and different stages of tool production. In this early work a foundation for the commonplace understanding of industry convergence equaling technological convergence is laid. While in many cases like the telecommunications, information technologies, and electronics industries technological convergence and industry convergence will be strongly related, they are still not interchangeable [10]. Within this paper we will use the term for the description of a blurring of boundaries between two or more industries, thus called industry convergence [18,27]. While the terms industry convergence and industry fusion [7] have been used interchangeably in most cases, there is a considerable difference: Convergence describes a process, where objects move or stretch farther from their prior and discrete spots, to a new and common place. Fusion describes a process, where objects begin to merge with each other in the very same place of at least one of the objects. Hence, the difference lies in the movements of the involved objects, e.g., industries would be labeled ‘converging’, if they (or parts thereof) began to merge with each other in a new field. This field must not have been part of any of the hitherto distinct industries. In contrast to this, industries would be labeled ‘fusing’, if the new replacing segment or sub-segment would substitute (parts) of the prior segments. Accordingly, Nutraceuticals and Functional Foods can be interpreted as an example of convergence, as they are replacing neither the demand for nutrition nor the demand for full-scale medication. On the other hand, smartphones are a fusion product of cameras, cellular phones, and portable computers (etc.). All of the attached demands had been fulfilled prior to the fusion process. Afterwards a new sub-segment of the established industries was created in the spot of the hitherto disjoint devices. Fig. 1 depicts the difference with an illustrative example. Consequently, building on the convergence/fusion delineation, this study defines convergence in a managerial context as: Convergence [fusion] is a blurring of boundaries between at least two hitherto disjoint areas of science, technology, markets, or industries. Through this convergence [fusion], a new (sub-)segment is being created in a new spot [the same spot] as a merger of (parts) of the old segments. It is marked by an increase of interchangeability and connectedness between the respective areas, as can be seen in collaboration, licensing, patenting, or publishing behavior. This delineation is not reflected in other researchers' works. As discussed, most appear to not discern between both terms at all, but rather use either ‘convergence’ or ‘fusion’ [7,8,11,28,29]. As the present study aims at developing a monitoring concept for a blurring of boundaries, both cases are of high interest – convergence as well as fusion. For increased legibility, fusion will be implicitly included where convergence is mentioned – if not stated otherwise. 2.2. Sequential process Convergence has been discussed mainly in connection with industries that are to a high degree science and technology driven, even though it has been shaping industries throughout history [30]. In both fields of the present study, information technology as well as chemistry and their neighboring industries, pivotal developments within the last few decades have led to shortened lifecycles of often radical innovations and to up to two-digit R&D spending. This has often been triggered by developments in the

Fig. 1. The process of convergence (a/b) graphically delineated from the process of fusion (a/c).

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Fig. 2. Sequential process of industry convergence. Dashed lines indicate facultative dependencies, whereas solid lines represent obligatory connections between individual sequences.

respective scientific areas, and thus we argue that in most cases a convergence of technologies or even whole industries will succeed a growing overlap of scientific disciplines. All in all we discern between four loci of convergence, with a short description for the pre-convergence status given in parentheses: • • • •

Science (different scientific disciplines or areas) Technology (central technologies of different application areas) Market (markets for different sets of demands) Industry (set of companies with (partly) different technology bases, different application fields and different target groups in different markets)

All of them are possible answers to the question ‘On what level is convergence happening?’ and are partly related or dependent on each other. The nature of their hierarchical interdependencies is the starting point for the sequential convergence process, which is being depicted in Fig. 2. This is a refinement of hitherto discussed time series approaches [12,13,31–34]. In order to build a concept for measuring convergence at each locus, a clear distinction between each of the steps is mandatory. In our idealized sequential process depicted, industry convergence is evolving when scientific disciplines and technologies and/or markets have converged. Starting with scientific disciplines that begin to use more and more research results of one another [35], a scientific convergence will start with cross-disciplinary citations and eventually develop further into closer research collaborations. After the distance between basic science areas has been decreasing for some time, applied science and technology development should follow [36,37], leading to technology convergence. This can trigger market convergence, with new product–market combinations. Finally, firms begin to merge with each other, completing the convergence process with industry fusion. Whilst in most cases convergence will thus build up over each of the steps, on some occasions industry convergence may evolve as a direct result of just one of both developments – either new business models or new products/services. For instance, convergence of the banking and the insurance sectors (sometimes labeled ‘bankassurance’) has not been driven by technological developments, but rather by new business models. We have thus developed further our idealized time series of events introduced in earlier papers [12,13], to also include such phenomena. Also, convergence does not necessarily imply the development of a new industry. In some cases industry areas or scientific disciplines may create a new area of collaboration, without changes in the anteceding areas and even nothing more than loosely coupled to them. This may mean a movement from both3 areas, but as well only one of the fields may be evolving their expertise further in such a direction. Being aware that this is a considerably simplified and idealized process, we still deem this sequential model useful to discuss different means for anticipating and (later in the time series) realizing occurrences of convergence. 3. Cases of industry convergence 3.1. Convergence in ICT-related industries As discussed earlier, most scholars have thus far concentrated on media-, information technology-, or electronics-related industries for studying effects of industry convergence. Partly, this may be due to the strong technological focus, the search for 3 When speaking of ‘two areas’ or ‘both’, this is only the smallest possible number. Convergence may as well happen between three or more areas, of course. But for increased legibility ‘two’ or ‘both’ always represents ‘at least two’ in this context.

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signs of industry reactions, and the necessity of comprehensible and illustrative examples, marking a considerable portion of the literature. But also the extent of influences on peoples' everyday life is probably unrivalled by other occurrences of convergence. Firstly, a decisive customer surplus is often a result of new combinations of products or services. Secondly, the omnipresence of (digitalized) information in the form of text, pictures, sounds, or a combination of these is altering the global economy. Strikingly, convergence involving the computer and the communication sector has been discussed at least since the early 1970s. As early as 1971 Oettinger spoke of “computers and communications [having] long since become inseparable” [38]. Retrospectively, this was a time at which a merger between both sectors was far from being finalized (if it really had begun at all). Indeed, while some classify it as a clear trend observed by many and as a well-suited example [39], others question the connection between an undisputed technological convergence in ICT and blurring market boundaries [40]. Notwithstanding this discussion, many areas appear to have in the meantime merged into common markets. Voice and data streams are increasingly managed by the same providers and delivered through one single line to the end customer. And smartphones are an illustrative example of ICT's convergence tendencies. In 1965, at the time of Intel-cofounder Moore's groundbreaking paper on the expected evolution of integrated circuits, most processes were still based on analog technologies. In his article he envisions a future with “such wonders as home computers […] and personal portable communications equipment” [41], and discusses different approaches to miniaturize and reduce the costs of electronics equipment. In the remainder of the paper he derives time-dependent cost curves for the number of components per integrated circuit, and predicts the number of components (at minimum costs) to be exponentially rising for at least a decade.4 Two decisive technological drivers of convergence in ICT are listed here. Firstly, digitization – enabling a free transfer of information (no matter whether pictures, sounds, or text) through various transport mechanisms. Secondly, miniaturization – securing the applicability of integrated circuits in a wide array of increasingly small equipment. Digitization most likely plays the central role [43]. It could even be classified a prerequisite for convergence in ICT, instead of just one of many triggers and drivers. Without the switching from analog to digital means of information transportation, the different branches would have had no common ‘language’ to use. To that extent, grounding products and services on bits and bytes may be regarded a ‘generic technology’ [44], as it can be employed similarly across different industry boundaries. Indeed, digital technology probably is a generic technology. And much more, as the notion ‘language’ would suggest. Consequently, it is not only a technology to send information from A to B in certain settings. As a result, these products or services have now been replaced into a competitive environment, addressing similar demands and markets. Furthermore, digitization actually changed underlying paradigms of, for instance, telecommunication and computers. When the Internet began to receive increased attention during the 1990s, people used analog modems and their telephone lines to connect to it. Nowadays, more and more consumers and companies use their digital broadband access to administer their telephoning activities. Also, VoIP handsets have been introduced by mobile device vendors and the underlying technology is being switched to packet-switched data transmission [31,39,45]. Miniaturization, on the other hand, is part of a larger set of technological developments making the process of convergence more likely and easier, but this convergence was beginning to speed up already at a time when ‘mobile phones’ would still fill the trunk of a car. Miniaturization definitely plays a central role when it comes to the performance of microprocessors as well as the possibility to assemble devices with multiple functionalities. The calculation power of any microprocessor-containing device, from dish washers and cars to telephones and personal computers, is still exponentially rising, and accordingly are the possibilities for computer-aided applications [43]. After a ground was thus laid for a blurring of boundaries by technological change, another important driver was the deregulation of the telecommunications markets [39,46]. In the USA, e.g., the “Telecommunications Act of 1996” aimed at letting anyone compete in any market with any communications business [47]. This act was the first amendment to the “Communications Act of 1934” and opens with the pledge “To promote competition and reduce regulation in order to secure lower prices and higher quality services for American telecommunications consumers and encourage the rapid deployment of new telecommunications technologies” [48]. Another good example is Europe, where the “Commission Directive 90/388/EEC on competition in the markets for telecommunications services” was introduced in 1990 to progressively introduce competition to the telecommunications sector [49], followed by further legislation in the years afterwards [10]. Twelve years later a series of directives (including 2002/77/ EC) was trying to introduce a common regulatory framework for “all electronic communications services and/or networks which are concerned with the conveyance of signals by wire, radio, optical or other electromagnetic means (i.e. fixed, wireless, cable television, satellite networks)” [50]. Next to technological (especially digitization) and regulatory aspects [43], a third trend has propelled developments in this industry – the trend for one-stop shopping or multi-purpose devices [43,51]. In this context, Kaluza et al. [43] highlight increased pressure from substitutive products during the process of convergence. This can be well observed in the domain of Internet service providers (ISPs), cable, and satellite television providers, and telephone providers. Each of them has faced competition from other providers in its area since deregulation. But today, all of them are directly competing with each other. In many markets telecommunication companies offer broadband, telephone, and television access – just like cable providers or ISPs. Thus, convergence

4 With a few alterations this prediction has been named “Moore's Law” after him. Moore's Law describes the historical trend in computing that the number of transistors on one single computer chip will double about every two years. This forecast has proven to be correct for more than forty years and is expected to be still valid for the next years. In 1965 an average chip would hold approximately 50 transistors. This number has now reached 2 billion with Intel's newest Itanium processor range “Tukwila”. Closely related to the number of transistors are also related parameters like the size of chip components, the number of calculations per time, memory capacities, and the number of pixels on an average digital camera chip (cf. [41,42]).

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leads to intensified competition and results in decreasing prices for these services. This prospect is actually one of the reasons for the deregulation activities of the past two decades [48]. 3.2. Convergence in the chemical and pharmaceutical industry The chemical and pharmaceutical industry is full of examples of blurring boundaries. It is in the middle of a paradigm shift. Firstly, most firms have closed down their huge central laboratories and redeployed their researchers to smaller units managed by the individual business unit. This setup is aiming at less basic research and more customer-oriented development, with customers altering their demands and buying behavior substantially. Secondly, this traditionally technology-driven industry has realized that most impulses for innovations have in the recent past come from its neighboring disciplines. While their substantial R&D budgets for the assessment of inventions used to be spent on experimental trial and error, the so-called ‘innovation scissors’ (the difference between the increasing development time and decreasing commercial exploitation time) became a serious problem since the 1960s [52]. Most companies tried to battle the fact that they were developing fewer really new molecules that often only showed incremental changes in properties, compared to existing materials, by increasing the speed of their trial and error approach. These ultra high throughput screening methods5 are still industry standard today, but they are supported by methods developed in neighboring fields, especially informatics (leading to computational chemistry and molecular modeling) and biotechnology. Working with informatics allows fewer physical experiments and thus increased turnover and methods developed by researchers in biotechnology make a directed approach possible. Through understanding of physiological mechanisms within a plant or the human body, researchers can start to look for a specific key to a lock much more target-oriented. Another important driver for a reorganization of industry structure is the alteration of the raw materials base. For about half a century almost all chemicals for the different kinds of applications were synthesized from oil. But while the demand for oil has surged for decades, the world's reserves are definitely limited. Additionally, environmental awareness has increased in most countries and led to an increasing interest in products derived from renewable raw materials. This has led to the blurring of boundaries between the incumbent materials manufacturers and new competitors with access to renewable raw materials. Hence, chemical companies like Dow or Bayer are today competing with agricultural companies like Cargill or Archer Daniels Midland. In contrast to this competition between similarly sized firms, the pharmaceutical industry has in the past mainly taken over considerably smaller biotechnological firms to fill its product pipeline or to broaden its technological knowledge base [8]. This has to be seen in the light of a setting, where on one hand one active ingredient alone may account for over US$ 1 billion p.a. in sales. However, on the other hand, companies have to deal with extremely high failure rates of lead compounds tested in the discovery phase: the estimated success rates are about 0.01–0.02% [53]. Hence, many firms experiencing backlashes with predicted blockbusters (i.e. pharmaceuticals that account for more than US$ 1 billion in sales p.a.) have turned to biotechnological firms to fill their drying product pipelines. While this often results in acquisition of the biotech firm (e.g. Roche buying Genentech, Merck KGaA acquiring Serono, or AstraZeneca's US$ 15 billion takeover of MedImmune), building alliances with others outside the firm's boundaries may often be preferable in regard to costs, flexibility, and broadness of cooperation possibilities. In the pharmaceutical industry's relation with biotech firms, often not convergence but mere utilization of technologies or even whole companies plays the decisive role. But at the borders of this segment two clear examples of industry convergence have earned some considerable attention – the emerging sectors of NFF and of Cosmeceuticals [15]. The NFF sector has been evolving since the early 1990s at the intersection of pharmaceutical and food industry sectors [6,18,19], starting in Japan, which is still a very strong market for NFF [54]. The NFF sector is marked by different influences triggering convergence. Firstly, food technology competencies are converging with LDL-cholesterol lowering technologies. Secondly, regulations regarding pharmaceutical products are increasingly transferred also to food products with enhanced characteristics. And finally, customers are seeking to purchase foods with added health-benefits in an attempt of one-stop shopping [18]. Thus firms do not only need knowledge and technological skills formerly unimportant to them, but also find themselves competing with one another in one unified market of nutrition and disease prevention [55]. A similar setting can be found in the sector of Cosmeceuticals, a term coined by Klingman in the 1970s [56]. It is reported to be the fastest growing segment of the “natural personal care industry” with cosmetic–pharmaceutical hybrids aiming at enhancing both “beauty” and health [57]. While health benefits appear to be more disputed than for most products in the NFF sector,6 some experts are calling for a harmonized regulation through bodies like the FDA (Food and Drug Administration, USA) [57]. The broader the health claims marketed with the Cosmeceuticals, the more likely they will have to undergo scrutiny by drug administration bodies, leading to a convergence of regulations and standards. For our illustrative analysis we chose phytosterols. With many applications in the areas of NFF and Cosmeceuticals, this term's use is pretty much restricted to the natural sciences and thus proves to be easily identifiable in publications. Phytosterols (or plant sterols) are a subclass of the chemical steroid group. Their structure is very similar to the well known sterol “cholesterol”. The class of phytosterols can be subdivided into plant sterols and stanols and consists of about 250 different molecules, with β-sitosterol, campesterol and stigmasterol being the most common ones. As the name indicates, they are found in plants with the highest concentrations in corn, sunflower or safflower oil [58]. Because of the high structure analogy to cholesterol it competes with the absorption of dietary cholesterol in the intestines and thus leads to lower blood concentrations of LDL-cholesterol (low density 5 UHTS (ultra high throughput screening) is based on fully automated testing of tens of thousands of substances from a company's substance library on single targets, especially in the development of new pharmaceutical drugs or chemicals to be used in crop protection. 6 See e.g. Crompton [56] who also cites Dr. Nicholas Perricone, founder of NV Perricone MD Cosmeceuticals, opposing accusations of Cosmeceuticals' low credibility: “Cosmeceuticals are science-based rather than marketing-driven, giving the customer results and value for their investment.”

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lipoprotein). Bearing in mind that high concentrations of LDL-cholesterol are a risk factor for cardiovascular diseases (CVD) [18] such as near term coronary heart disease (CHD), several studies have undertaken research on possible uses in primary and secondary prevention of CHD [58,59]. Phytosterols could also help patients suffering from chronically high concentrations of LDL-cholesterol due to one of the most frequent monogenic hereditary disorders, the familial hypercholesterolemia [59] or help in cancer prevention [60]. Besides their possible applications in pharmaceuticals and foods, phytosterols are also commonly topically applied in cosmetics, e.g. as hair care agents, moisturizers, skin barrier strengtheners, or in treating cellulite as well as in deodorizing preparations [61–64]. 4. Monitoring convergence indicators 4.1. Patent analysis as a means to monitor convergence If managers as well as policy-makers and other stakeholders are to ground their strategic decisions on an assessment of a possible convergence status in their field, this assessment will have to be timely and reliable. Naturally, alterations in market or industry structure would be a stronger proof for the presence of fading boundaries than developments in scientific or technological publications (i.e. mainly patents). But waiting for them would hardly qualify for taking ‘proactive’ actions. In most cases, analyses at this point would only satisfy an academic interest in a comprehensive ex post discussion of the whole convergence process. And according to prior research science convergence may happen long before technology convergence (or even industry convergence) will follow. Scientific publications – to a large extent the result of academia-driven research – have been found to appear up to 30 years in advance of industrial application [65]. Similar results could be shown for the example of phytosterols in NFF and consequently, patent literature is expected to be the single most important possibility to anticipate converging industries [13]. They also have a decisive practical advantage over other forms of (at least non-scientific) publication – the patent literature is already structured to a high degree. Due to requirements of patent offices, they entail clear identification of, e.g., applicants, references, and fields technology. Furthermore, extensive databases have gathered comprehensive collections of patents, added information, e.g., on subject area and keywords, and made them full-text searchable. Thus, identifying patterns at “the touch of a button” becomes possible, appealing to practitioners and academics alike [66]. Against this background, Pennings and Puranam [1] argue that based on a validity assumption for classification schemes like the IPC (International Patent Classification) or the SIC (Standard Industrial Classification) codes, convergence can be found in patent data through growing overlap among SIC codes or IPCs and through an increase in patent citations between different classes. Patent analysis has been employed in the context of technology-driven convergence of electronics, computers, and telecommunication [2,18,40] as patents are often regarded as outcome indicators for organizations' R&D activities [66–68]. It is also the basis for the analyses presented in the remainder of this section.7 According to our argument in Section 1, we focused on deriving different patent indicators from different sources. In a first step, we employed SciFinder Scholar. This product entails not only patent data but also scientific articles and can thus serve well for a first analysis of a new research field. As these analyses have been published in a previous article [12], only a brief summary of the most important results is provided in Section 4.2. Section 4.3 covers the results of the STN AnaVist aided analysis of our phytosterols example. These results include opportunities for visual inspection as well as IPC co-classification and IPC/industry sector classification. Finally, the results of IPC co-classification analyses conducted with STN Express on the basis of the INPADOCDB database (International Patent Documentation DataBase) are presented in Section 4.4. These analyses are search-term independent and also possible with large amounts of data. Thus we could compare the two broad convergence areas of NFF and ICT (341,846 and 859,469 documents, respectively). 4.2. SciFinder ScholarTM We used SciFinder Scholar to analyze the 29 million patent and journal article references from more than 10,000 scientific journals and 57 patent authorities. Entering phytosterols, plant sterols and their derivatives as search terms and afterwards removing duplicate entries resulted in 7455 references ranging from 1897 to 2008. These consisted of 6212 scientific references and 1243 references to patents, reflecting an overall patent share of 16.7%. If patent analysis is to make sense for the assessment of the state of technology convergence, there has to be a considerable commercial interest in the covered topics. While the number is difficult to judge per se, a growing patent share should be grounded on an increase of commercial interest. The results depicted in Fig. 3 show such an increase. Until interest in phytosterols grew at the beginning of the 1990s due to their possible application in the Cosmeceuticals and NFF sectors, the patent share was on average well under 10% (the strong peaks result from the overall considerably lower amount of publications on phytosterols until the early 1990s with on average 30–70 publications per year). Within the last two decades the share of patents has risen exponentially to about a third today. With the more apparent commercial interest, the total number of publications has reached about 370 in 2006 and 2007. To further asses the degree of commercial interest, a patents' applicants8 list was compiled. Analysis of the filing organization for these patents yielded 654 organizations, including countries, research institutions and companies. Excluding all non7

For further discussion of the underlying concept and the strengths and weaknesses of patent analysis, see [13,14,69]. In contrast to the ‘inventor’, being an individual person or a group of people, the applicant is in most cases a company, university, or other form of organization. As a commercial interest can be expected from firms filing a patent application, but not necessarily from state-owned entities, these were excluded from further analysis. Additionally, we concentrated on the more active players in the field to reduce ambient noise. 8

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Fig. 3. Development of share of patents (as percentage of all publications) on phytosterols between 1897 and 2007. For easier assessment we included a fourth order polynomial trend line (black line) (NSF = 7455).

companies from the 50 organizations with the highest numbers of patents and including their subsidiaries led to 451 remaining patents, filed by 28 firms. With these patents we conducted our further analysis. Firstly, we assigned the firms to four categories as follows: • Personal Care, nine firms who are selling or conducting R&D on phytosterols applied in Cosmeceuticals. • Food & Agriculture, nine companies who are either active in agricultural processing or selling food and food ingredients under their brand names. • Pharmaceuticals, the smallest group, consisting of four firms specialized in developing and marketing drugs in a highly regulated industry sector. • Chemicals, six companies producing phytosterols and selling it in the B2B (business to business) market. Secondly, the 451 patents were organized according to these four industry sectors. Thirdly, they were analyzed in regard to subject areas. Subject areas are assigned content based by CAS (the division of the American Chemical Society responsible for SciFinder®). We structured subject areas into four disjunctive groups, with the first three representing the core businesses of the industry sectors Personal Care, Food & Agriculture, Pharmaceuticals, and the last one including all the remaining subject areas. These analyses resulted in the following central findings. Personal Care, Food & Agriculture, and Pharmaceuticals each filed patents mainly in the subjects areas closely related to their core businesses (87%, 71%, and 43%, respectively). Only the Chemicals group filed two thirds of their patents in the areas of Personal Care and Food & Agriculture (33% each). The Pharmaceuticals group filed 19% of the patents in Personal Care and 16% in Food & Agriculture. Each of the latter two exhibited only little emphasis on Pharmaceuticals (9% and 7%). Additionally, examination of the WAY (weighted average year), representing the distribution of patents over time regarding subject area and industry sector, substantiated these first indications of convergence in separate areas of Cosmeceuticals and NFF. More detailed results on the basis of this sample have been discussed in earlier papers [12,15]. 4.3. STN® AnaVistTM In a second step, STN International provided us with a test sample of 3836 references compiled through STN Express (aggregated in the database CA, provided by CAS). Although the number of references is smaller (due to differences in the databases used) the sample does not substantially deviate from that used in Section 4.2. It consists of 785 patent and 3051 nonpatent documents. Identification of the same 28 firms led to 391 documents. In contrast to the SciFinder software, STN AnaVist is a frequency-based analysis and visualization software that is designed for interactive and statistical examinations [70,71]. Next to a wide array of analysis possibilities, one of its strongest assets is the research landscape functionality. On the basis of documents' contents it automatically organizes the selected documents in a three dimensional topographical map. As Fig. 4 demonstrates, research hotspots can be easily measured by identifying research clusters (symbolizing proximity) and their individual peak height (as a proxy for the number of documents). This clustering can then be further refined by selecting subsets, adjusting the maximum appearance of clustering concepts (terms may be included in a maximum of 10% to 70% of all documents) and grouping or excluding certain terms. Dots are representing individual documents and are concentrating on concepts like food, plant physiology, pharmaceutical applications, procedures to extract phytosterols and cosmetic applications. In a next step we focused on the 785 patents and assessed their classification by IPC. IPCs are dividing technological inventions into more than 70,000 categories, organized in a hierarchical way (for a detailed example see Appendix A). The patents of our sample used 1033 different IPCs, with single patents being assigned one main IPC and in most cases a few

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Fig. 4. Research landscape (max. occurrence in 40% of documents) on phytosterols (NAV = 3836).

secondary (sub)classes. While for many patents those will be very close to the main class, phytosterols present quite a different setting. In analogy to the SciFinder analyses we grouped all IPC classes into five larger groups. With the fifth one containing all those IPCs not to be assigned to any of the four industries. Fig. 5 shows the development over time of the five groups. While the first patents had been filed in 1925, it was not until the late 1980s that interest in phytosterols triggered more substantial numbers of patents and only within the last ten years have numbers multiplied. Whereas patents in the Chemicals' IPCs have fallen back from about 25 per year at the beginning of the decade to around 15 in the last years, those of the other three areas appear to be still rising. Personal Care and Food & Agriculture are at about the same level with 38 and 41 patents in 2007, respectively. As this might be only an indication for parallel (but independent) growth in interest, a closer look at co-classification of the 785 patents demonstrates how strongly interwoven the field of phytosterols is (Table 1). The IPC co-classification shows about even distribution across all four industry-related groups. As AnaVist does not allow deciding whether an IPC is main or secondary, the sum of all five groups adds to 1169 – 384 more than the actual number of documents. Nevertheless, there is an obviously strong connection, especially between Pharmaceuticals and Food & Agriculture and Personal Care, respectively. 25.6% of all patents on Personal Care are also classified as Pharmaceuticals-related patents. For Food & Agriculture this link is even stronger – 39.5%. As already seen in Fig. 4, the number of patents with a (additionally) strong chemical focus is smaller (17.1% of Personal Care and 28.5% of Food & Agriculture patents) but still decisive. Then we identified the 28 firms of the four industry segments in all filing organizations for the 785 patents. This led to a sample of 391 references (451 in Section 4.2). To test whether the co-classification of patents resulted from all of the industry segments we compared the patenting behavior regarding IPCs of each of the segments (Table 2). Strikingly, 92% of patents

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Fig. 5. Development over time of IPC groups (np = 785).

from Personal Care firms are at least also in Personal Care IPCs, whereas the share is considerably lower for the other three segments (48%, 52%, and 35%). This is basically in line with the findings from employing SciFinder, which, based on CAS' subject areas, found 87% for Personal Care, 43% for Pharmaceuticals, and 18% for Chemicals [16]. Numbers were tentatively lower as the assignment to subject areas was carried out exclusively to one area per document. Only the share for Food & Agriculture firms is extremely low (also compared to 71% in Section 4.2). One reason might be the high number of references not containing information on IPCs in this segment. On the basis of the assumption that most of the 66 patents would be containing Food & Agriculture IPCs, this share would probably rise to over 75%. As already seen in the co-classification analysis, patents by Personal Care and Food & Agriculture firms have much stronger connections to Pharmaceuticals and Chemicals than to the respective other's IPCs. All the above mentioned results would only be suitable to answer the question whether there is an overlap in segments, but not whether a process of convergence is actually developing over the course of time. Thus we took the 391 patents as the basis for a series of research landscape analyses during the last ten years. Due to small numbers of patents prior to 1998, outcomes were not expected to be reliable up to then. Additionally, since these research landscapes can only be compiled with a minimum of input data, we added two years, resulting in the five research landscapes depicted in Fig. 6 a–e (max. occurrence in 50% of documents). Starting with a large number of hotspots (Fig. 6a) these landscapes show the emerging of two research hotspots within ten years (Fig. 6e) – a bimodal NFF hotspot in the lower left corner and the Cosmeceuticals-

Table 1 Co-classification of patents on phytosterols according to main and secondary IPCs (np = 785). IPC group

Personal Care

Food & Agriculture

Pharmaceuticals

Chemicals

Rest

Personal Care Food & Agriculture Pharmaceuticals Chemicals Rest

234 15 60 40 12

291 115 83 17

283 87 29

279 40

82

Table 2 Patents by firms according to IPCs (nf = 391).

No. of No. of No. of No. of No. of No. of No. of SUM

patents patents patents patents patents patents patents

per segment in PC IPCs in F&A IPCs in Pharmaceuticals IPCs in Chemicals IPCs in remaining IPCs not containing IPCs

Personal Care firms

Food & Agriculture firms

Pharmaceuticals firms

Chemicals firms

88 81 4 19 6 0 3 113

188 22 90 30 35 7 66 250

52 12 13 27 24 4 5 108

63 20 26 23 22 6 11 108

SUM 391 135 133 99 87 17 85 556

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Fig. 6. Research landscapes 1999–2008 on phytosterols patents of sample firms (nf = 391).

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related peak in the upper right corner. Furthermore, by having a closer look at individual documents, we could show that the patents of the four industry segments increasingly began to concentrate on common research topics. Whereas in the beginning each industry segment mainly concentrated on its own topics, Fig. 6e demonstrates how they have moved towards the two common concepts of Cosmeceuticals and NFF. 4.4. Comparing NFF to ICT in STN Express® All of the results reported in Sections 4.2 and 4.3 were derived from search-term dependent data collection. This should not pose a problem in the present case, as the term ‘phytosterols’ and its derivatives are – as chemical names – with limited application and employed independently of possible convergence trends. The term has been used already at a time where industry convergence was far from being even discussed. This may be different in other settings, where applicable search terms are either too broad to be employed or describe the convergence product itself. For instance, when analyzing developments in ICT ‘phone’ would probably be too narrow (it rules out other competing means of communication), ‘communication’ would most likely be too broad and ‘smartphone’ is only in use since these convergence products are actually being produced. Thus we conducted a last set of analyses on the basis of IPCs. We used STN Express to compare results from the limited area of one NFF ingredient (phytosterols) to NFF as a sector in general. Furthermore, we gathered data on co-classification of patents on different fields of the ICT industry sectors. This comparison with a widely accepted case of convergence should enable an approximate positioning of the case in the convergence continuum. Through STN Express, a software package designed to search scientific and technical databases online, we accessed INPADOCDB. INPADOCDB is being maintained by the European Patent Office (EPO) and contains bibliographic data of patent documents of 90 patent-issuing organizations, including the EPO and the World Intellectual Property Organization (WIPO). In total, more than 55 million records are available dating back to the year 1790 [72]. On the basis of WIPO's catchword index those IPCs were selected reflecting the broader areas of Food and Pharmaceuticals for the NFF assessment (cf. Appendix B). The same was carried out for ICT on the basis of ‘Telecommunication’, ‘Computer’, ‘Camera’, and ‘Navigation’ (cf. Appendix C). These IPCs are representing the broader ICT development regarding computer and telecommunication industries, as well as the narrower example of smartphones, with its further integration of consumer electronics. Then all documents containing those IPCs were searched for patents and patent applications filed with the United States Patent and Trademark Office (USPTO), and their overlap calculated. Results are shown in Table 3 for ICT and Table 4 for NFF. In ICT there is a decisive co-classification of patents in Telecommunication and Computer. 15.9% of Telecommunication patents are also classified as Computer patents. Interestingly, this number decreases to 2.0% when taking the Computer perspective. A very similar trend is valid for Music and Computer (12.1% and 1.0%, respectively) as well as Navigation and Computer (17.0% and 0.9%, respectively). Whereas many shares are below 1% and thus probably negligible, Telecommunication and Music still exhibit a noteworthy interconnectedness (2.9% and 4.5%, respectively). The results of the analysis of US patents on Food and Pharmaceuticals can also be taken as an indication for convergence. 10.9% of patents on Food topics are also related to Pharmaceuticals. And 2.5% of all Pharmaceuticals patents are carrying at least one Food-related IPC. As introduced in the second section, the definition of convergence explicitly stresses its process character. Accordingly, the results shown in Tables 3 and 4 underline a degree of connectedness of NFF and ICT. But they fall short of showing a growing corresponding trend. Thus we analyzed both fields regarding their developments in the last few decades. The most interesting overlap of IPCs in Table 3 certainly was between Telecommunication/Computer patents. Not surprisingly, their graphs (Fig. 7) differ greatly in the share of co-classified patents, but run rather parallel when considering local minima or maxima. While both exhibit an increase of co-classification from the 1950s to the early 2000s (about 2%/1% to about 27%/3.5%, respectively), the shares have again decreased to 17%/1.5% in 2008. A similar development can be seen in Fig. 8 on NFF patents. Again, the share of co-classified patents is much larger in the Food area than in Pharmaceuticals. Beginning with shares under 1% in the 1950 s, they reach 26% for Food and 3.5% for Pharmaceuticals in 2003 before decreasing to 18.5% and 2% in 2008, respectively. To assess, whether this co-classification is a general phenomenon we analyzed the co-classification of the 341,846 patents from the areas of Food and Pharmaceuticals in respect to their co-classification in all eight sections (Fig. 9). Not surprisingly there is a substantial co-classification with non-Food/Pharmaceutical patents of section A (e.g. personal care) and also section

Table 3 Share of co-classified patents according to IPCs, compiled in classes related to ICT (NICT = 859,469). IPC group

Telecom-munication (nT = 81,606)

Computer (nC = 641,352)

Music (nM = 51,628)

Camera (nCa = 83,211)

Navigation (nN = 32,883)

Telecommunication Computer Music Camera Navigation

– 15.9% 2.9% 0.2% 0.3%

2.0% – 1.0% 0.5% 0.9%

4.5% 12.1% – 0.3% 0.4%

0.2% 4.1% 0.2% – 0.9%

0.8% 17.0% 0.6% 2.4% –

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Table 4 Share of co-classified patents according to IPCs, compiled in classes related to NFF (NNFF = 341,846). IPC group

Food (nF = 65,328)

Pharmaceuticals (nP = 283,648)

Food Pharmaceuticals

– 10.9%

2.5% –

C (chemistry), here especially for the Pharmaceuticals patents. Furthermore, a still substantial amount of Food patents is also classified as section B – performing operations and section F – engineering (mainly due to production technologies). Due to the need for advanced methods of analyses and testing of medical substances, nearly every tenth Pharmaceuticals patent is co-classified as section G (physics).

5. Discussion and conclusion In this paper we aimed at deepening our concept for monitoring convergence, by testing different patent indicators and different databases. Furthermore, we wanted to broaden the underlying search possibilities through comparing our NFF and Cosmeceuticals example of phytosterols to the ICT case of smartphones, thus shedding more light on the applicability of our concept. Employing the three software products SciFinder Scholar, STN Express, and STN AnaVist we analyzed four samples with 7455 (SciFinder), 3836 (AnaVist), 859,469 (ICT, STN Express), and 341,846 (NFF, STN Express) patent (for SciFinder: and scientific) references. Based on our results presented in Section 4, we will discuss their significance as indicators for convergence. The share of patents in all publications on phytosterols has significantly risen within the last two decades, demonstrating the growing commercial interest and technological applicability of phytosterols. Although patents only constitute 17% of all publications, their share has reached 35% in 2007. These first results supported our argument of convergence through patenting developments over time regarding classification by subject area and industry sector. The topics identified through the first AnaVist research landscape (Fig. 4) are in line with the main clusters identified in our SciFinder analyses. Besides the rather basic research-oriented field of plant physiology they evolve around the four main industrial segments. This proves our assessment of the main technological and industrial segments represented by the subject areas. On this basis we could furthermore show a parallel development for the two main IPCs Personal Care and Food & Agriculture (Fig. 5). While the results of the IPC co-classification (Table 1) demonstrate a clear blurring of boundaries in the converging areas of NFF and Cosmeceuticals they also show only a very weak direct link between Personal Care and Food & Agriculture. Accordingly, we argue that one unified whole of a common NFF and Cosmeceuticals industry is not to be expected. The results of the classification by IPCs and industry segment (Table 2) support this finding at least for Personal Care. A deviation in results for the Food & Agriculture sector is most likely because of the large number of patents not containing information on IPCs.

Fig. 7. Share of co-classified US patents on ICT according to IPCs (NICT = 859,469).

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Fig. 8. Share of co-classified US patents on NFF according to IPCs (NNFF = 341,846).

These analyses provided indications of convergence, but the strongest argument for a development towards the two converged industry segments of NFF and Cosmeceuticals could be achieved by inspection of the research landscapes depicted in Fig. 6 a–e. In the final landscape two clearly distinct areas can be seen, one on topics around NFF and one on such dealing with Cosmeceuticals. Due to the clear trend from a very scattered picture of individual documents to these two merged areas, we argue that indeed the pharmaceutical industry is involved in a convergence process with the cosmetics industry on one hand and with the food industry on the other hand to form the substitutive inter-industry segments of Cosmeceuticals and NFF, respectively. Finally, we added a brief analysis of an overlap in the broader areas of ICT and NFF, to compare them to the results from the field of phytosterols. Strikingly, both fields demonstrate a clear connection within the respective different branches of a possible convergence process. In ICT, Telecommunication patents show a strong tendency to be also related to Music IPCs and especially Computer IPCs. Whereas this overlap is even larger from the Music patents' perspective, this is quite different for the Telecommunication/Computer link. Apparently, companies filing the respective patent applications in the Computer domain have a much broader focus than those in Telecommunication. This may also explain the roughly 8-fold difference in the amount of patents. Whereas the Camera and Navigation domains seem to be rather independent of technological developments in Telecommunication (and vice versa), they are much closer to Computer developments. All in all, this would indicate an asymmetry in convergence effects. From these figures one should expect much more significant changes in e.g. the telecommunications sector than in the personal computers sector. Albeit it has to be born in mind that the IPCs are not necessarily evenly distributed representatives of industrial sectors, but per se only of technological and applicational fields. This is also to be seen in connection with differences between Tables 1 and 2, where IPC co-classification yields results basically similar in tendency but different in strength to those derived from industry sector/IPC classification.

Fig. 9. Co-classification of Food and Pharmaceuticals US patents with remaining sections (NNFF = 341,846).

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A comparable distribution can be found in NFF, where the share of Food-related patents concerned with medical effects is four times larger than from the Pharmaceuticals-related patents' viewpoint. Again, the pharmaceutical industry (encompassing more than four times as many patent documents) should experience weaker effects of the convergence process than the food sector. This is also in line with earlier findings, indicating a much stronger interest and activity level of food companies on NFF when compared to pharmaceutical companies [13]. Interestingly, both cases exhibit similar patterns in co-classification. In each case there is one very dominant technological area (related to an industry sector), when it comes to the number of patents. This indispensably leads to a lower overlap from their respective perspective. As IPCs are not anymore classified as main and secondary, it cannot be assessed whether the co-classification group is led by either of the involved technological areas. These findings are at least to some extent in line with Fai and von Tunzelmann [67], who used the University of Reading database9 for their study of patterns of possible technological convergence at a broadly defined industry sector level. They also found through their analysis of patenting behavior that patterns of technological convergence could be identified when looking at changes within one technological field across all industry sectors. Additionally, they report a high degree of path-dependency with industrial sectors remaining to engage most heavily in patent activities within their core technological field. When analyzing both fields' patenting behavior over time, the clear increase in co-classification is obvious. Even the less affected Pharmaceuticals and Computer clusters exhibit this increase. In both areas, convergence appears to have gained momentum in the early to late 1980s. This is in line with our expectations, as industrial activities became more visible from the late 1980s on as well. The decreasing overlap in the last five years could be attributed to a decreasing interest in the respective technological developments. Bearing in mind that patenting activities normally lead market developments by up to a decade, this does not necessarily have to transpire into market activities in the very near future. A different explanation could also be grounded on a shift in company activities. In both areas a large portion of patentable inventions may already have been made, leading to a lower level of activity in the respective fields. Finally, Fig. 9 represents a form of negative test. An evenly distributed co-classification of NFF patents across all eight sections would have thwarted our line of argument. On the contrary, the distribution is varying considerably, based on the closeness of technological fields. In connection to Section 2.1's discussion of a difference between convergence and fusion, it could be expected that this difference in co-classification shares between Pharmaceuticals/Food or Telecommunication/Computer might indicate a fusion pattern with the larger area remaining largely in place. However, we do not think that this would correctly reflect the underlying structure of ICT developments. At least when looking at the subfield of smartphones, companies in the computer industry are as much confronted with a new environment as are telecommunication companies. Hence, according to the definition provided earlier, ICT appears to be an example of industry convergence. Contrastingly, the evolution of NFF bears more resemblance to industry fusion. It can be expected that NFF will (at least partially) compete with pharmaceutical drugs in lowering the risk or effects of human diseases. In consequence, they are potential substitutes for products sold by pharmaceutical companies. Notwithstanding these encouraging results, they only serve as a further case in point for the applicability of our monitoring concept. We refined our idea of a comprehensive approach towards anticipating industry convergence on the basis of publication data analysis. But as we aim at developing a reliable concept for monitoring such phenomena of blurring industry boundaries that can act as an early warning system, we have to take into consideration that many signals will be rather weak at the beginning [73]. The early stages of a convergence process are the most interesting ones, if a firm wants to prepare for substantial industrial reorganization. In this paper we could only concentrate on one part of the convergence process. Further research will have to show whether these results can be reproduced at other stages of the convergence process as well as in analyzing deeper different examples of converging industries. In most cases managers will not be concentrating on an area as restricted and clearly laid out as phytosterols. Thus, we have tried to address the challenge of keyword selection or definition of areas that might be converging or fusing by employing IPCs to delineate different fields of activity. In a next step these results will have to be assessed as a development over time. Where a differentiation of possible areas of convergence is more difficult, the question will have to be addressed, how to draw from the vast amount of scientific and patent literature the right selection of documents. Too many and too diverse documents might make it impossible to identify any patterns, while selecting too few or too restricted in scope might lead to results that cannot be generalized. Accordingly, the applicability of software like STN AnaVist is restricted to analyses with a limited amount of documents (up to 20,000 references can be visualized in one approach). Once sets of documents, like those on all IPCs of a broader industrial sector, approach multiples of this threshold, AnaVist-based analyses will become too costly and too complex. Their potential lies more in a scrutiny of fields already identified as interesting and narrowed down to sample sizes suited for AnaVist. Summing up, despite the long history of examples for converging industries, the drivers and effects are increasingly being addressed by academics and practitioners alike only within the more recent past. In the light of many failed convergence predictions on the one hand and their high strategic importance on the other hand, a clear demand for means to anticipate convergence has been identified [30]. Especially with recent developments in research-intensive industries like the chemical industry and in light of an exponentially growing amount of data, a closer look on effective tools for harvesting the world's knowledge wealth appears to be necessary. We contributed to the existing body of literature, discussing a monitoring concept 9 This patent database was compiled by Prof. John Cantwell, based on all patents registered at the USPTO between 1890 and 1995 and grouping 399 technological classes into five broad technological fields while compiling four broad industrial groupings from 875 companies or affiliates [67].

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for the anticipation of converging industries, filling the gap between the largest part of the still rather scarce convergence literature (mainly concerned with strategic reactions to it) and the extensive literature on forecasting emerging technologies. Furthermore, we applied it to an example from NFF and Cosmeceuticals, demonstrating the reliability of this first step towards such an early warning system and relating it to results from ICT. Such a system can only help firms to build a clear margin over their current and future competitors if it proves to be reliable and easy to use. For that reason, we enriched our approach by adding an indicator on the basis of large sets of documents with common IPCs. Future studies will certainly have to further refine our proposed system to that end. A reliable system to monitor the degree of convergence in a given industrial environment could benefit not only industrial managers in search for new markets or new business models. Also, academics could delineate strategic actions in real from those in only perceived convergence settings. And they would be able to engage earlier in cross-disciplinary collaboration as well as collaboration with companies in more distinct industry sectors. This is particularly important in the light of recent findings that – although the degree of interdisciplinarity increases overall [74,75]– most fields are mainly drawing from neighboring fields [76]. Finally, policy makers could build (de-)regulation activities as well as funding schemes on it – in a constant battle for a nation's ideal atmosphere for economic growth. Acknowledgments The authors are indebted to Dr. Walter Buckel and his colleagues at FIZ Karlsruhe/STN International for their support with and access to STN AnaVist and the respective databases. We would also like to thank Gudrun Maria John, for her help in collecting patent data. Finally, our research approach was substantially improved due to the constructive discussions and recommendations during our presentation of earlier versions of this paper at the PICMET '09 and the IAMOT 2010 conference. Appendix A. Hierarchical construction of IPCs E.g. Forbes Medi-Tech's patent application AU002005211536A1 on “Conjugates of phytosterol or phytostanol with ascorbic acid and use thereof in treating or preventing cardiovascular disease” is classified as: IPC main class*: C07J 51/00

IPC secondary class*: A61K 31/59 A61P 3/06 A61P 9/10 C07J 17/00

Its main class for example can be hierarchically divided up into: C C07 C07J C07J 51 C07J 51/00

Chemistry; Metallurgy Organic Chemistry[…] Steroids Normal steroids with unmodified cyclopenta[a]hydrophenanthrene skeleton […] (/00 means there is only this level of hierarchy)

* Main and secondary classes were only used up to the 7th version of the IPC (IPC-7). Since 2006 (IPC-8) there is no differentiation on this level anymore.

Appendix B. Grouping of IPCs in phytosterols- and NFF-related patents in this paper

For easier analysis we grouped the 1033 IPCs into five main groups according to this scheme: Subject

IPC main groups*

Meaning of IPC

Personal Care

A61K 7 A61K 8 A61Q A21D

Cosmetics or similar toilet preparations** Cosmetics or similar toilet preparations** Use of cosmetics or similar toilet preparations Treatment, e.g. preservation, of flour or dough for baking, e.g. by addition of materials; baking; bakery products; preservation thereof Foods or foodstuffs; their treatment, not covered by other classes Preparations for medical, dental, or toilet purposes […] Therapeutic activity of chemical compounds or medicinal preparations Preservation of bodies of humans or animals or plants or parts thereof […]; biocides, e.g. as disinfectants, as pesticides or as herbicides […] Organic chemistry Organic macromolecular compounds […] Dyes; paints; polishes; natural resins; adhesives […] Animal or vegetable oils, fats, fatty substances or waxes […] Biochemistry; beer; spirits; wine; vinegar; microbiology; enzymology; mutation or genetic engineering …

Food & Agriculture

Pharmaceuticals Chemicals

A23 A61K (except 7&8) A61P A01N C07 C08 C09 C11 C12

Rest

All remaining IPCs

* Given is the highest hierarchical level possible. ** IPCs with the same content appear (e.g. A61K 7 and 8) as the classification scheme is regularly updated and then all patents are reclassified (if necessary). Thus A61K 7 is the IPC of an older version than A61K 8.

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Appendix C. Assignment of IPCs in smartphone-related patents in this paper IPCs were assigned to five subjects on the basis of WIPO's IPC catchword index [77]. Subject

IPC main groups*

Meaning of IPC

Telecommunication Computer Music Camera Navigation

H04M G06 G10 G03B G01C

Telephonic communication […] Computing; calculating; counting […] Musical instruments; acoustics Apparatus or arrangements for taking photographs or for projecting or viewing them […] Measuring distances, levels or bearings; surveying; navigation; gyroscopic instruments; photogrammetry or videogrammetry […]

* Given is the highest hierarchical level possible.

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His research interests particularly include industry convergence, forecasting, patent analysis and the life sciences industry. Jens Leker is a Professor of Business Administration at the Department of Chemistry and Pharmacy at the University of Münster. He obtained his PhD in Business Management from the University of Kiel. His institute’s interdisciplinary approach combines business management research with natural sciences in order to get a deeper understanding of R&D processes.