Forecasting of Potential Impacts of Disruptive Technology in Promising Technological Areas: Elaborating the SIRS epidemic model in RFID technology

Yu Cheng a, Lucheng Huang a, Ronnie Ramlogan b,, Xin Li a

a School of Economic and Management, Beijing University of Technology, Beijing, Chinab Manchester Institute of Innovation Research, University of Manchester, Manchester, United Kingdom

ABSTRACTDisruptive technology introduces new competitive platforms, possesses the ability of initiating new markets, and changes firms’ technological competition status. To address the potential impacts of disruptive technology, disruptions can be measured through two dimensions, namely industrial disruption and technological disruption. Early identification of candidate application areas will allow for timely adjustment of technology innovation strategy and minimization of risks at firm level. However, disruptive technology follows a non-linear development process and this requires that forecasting of potential application areas should be discussed in a different paradigm. In this paper we extract technology diffusion information from patent data, and introduce a SIRS epidemic model analogically by measuring transition velocity of all the entities in the technology diffusion system respectively. We implement the model deterministically to forecast the potential of industrial and technological disruption in the short run, and stochastically to forecast the disruptive technology’s major outbreak in candidate application areas in the long run. Radio-frequency identification technology is selected as case study. We conclude by discussing the major outbreak probabilities and potential disruptions of RFID in three different application areas. The results will provide practical suggestion to firms and other stakeholders to facilitate their strategy making when faced with disruptive technologies.

Keywords: Disruptive technology; SIRS epidemic model; Application areas; Technology forecasting; Technological disruption; Industrial disruption1 Introduction

Some emerging technologies have the potential to disrupt the status quo by altering patterns of resource use, working relationships and consequently rearranging value pools. Such technologies have been characterized by Christensen (1997) as disruptive technologies. In his later work, Christensen introduced the concept of disruptive innovation (Christensen & Johnson, 2002, 2006), to take account of not only disruption from new technologies that surpass the performance of the existing technology dominant in a market. Disruptive innovation may also result but from changes in the business model or underlying processes that enable superior or novel value to be delivered to consumers (Christensen, 2006). In this paper we focus specifically on the idea of disruptive technology. Two classes of disruptive technologies are generally observed in the literature. One displaces an incumbent technology along with attribute dimensions transition or changes the competitive situation in existing industries or both. The second class of disruptive technologies creates a new market or capability where none had previously existed

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(CFFDT, 2010). While disruptive technology is a much debated concept (Christensen and Overdorf, 2000, Christensen and Raynor, 2003) from a practical perspective clarifies that the emergence of disruptive technologies poses a dilemma for firms as they have the capacity for altering the competitive landscape and this creates strategic risks for industry’s incumbents. Unless it has invented the disruptive technology, it is only by periodically scanning the technological horizons can firms become aware of potentially disruptive technologies and make decisions on whether or how to incorporate such technological advances into new product designs to preemptively upstage their rivals in the marketplace (Teece, 2007). A wide variety of methodological approaches have been developed to identify the potential application pathways for technology in general (Robinson et al., 2013; Ma & Porter, 2015; Jin et al., 2015; Zhang et al., 2014; etc.). The extant literature is rich in empirical studies of potential applications for incremental or sustaining technologies (Christensen, 1997) in many areas (Yoon et al., 2014; Yoon & Kim, 2011; Zhang et al., 2014), However because many disruptions emerge when ‘seemingly unrelated resources, people, events, and technologies converge, current methodologies for technology forecasting are generally incapable of predicting extreme scenarios, especially those in which the most beneficial or catastrophic events occur’ (CFFDT, 2010, p.57).Products based on disruptive technologies can provide dramatic improvements to current product market paradigms, or produce the physical and service products that initiate new industries. Such regime changes may define new product platforms, which are different from what the market would have experienced with 'only' incremental changes (Kostoff et al., 2004). Moreover, despite considerable promise, disruptive technology may involve a high risk of commercial failure if potential users are reluctant to change their behaviors to gain the advantages such technologies have to offer (Veryzer, 1998). In the theoretical and practical senses therefore, seizing opportunities from disruptive technology relies much more on proactive initiatives than that in incremental technology (Wan, 2015). Third, disruptive technologies will induce huge impacts to both technological developments and industrial changes. Thus, in order to provide a method to forecast the possibilities of different scenarios along these two dimensions, we differentiate between the concepts of technological disruption and industrial disruption to provide an overall outlook into the future (Naumov, 2013). In this paper, we undertake an empirical study for the identification of potential application areas for disruptive technologies using Derwent patent data. Patent data are used as proxies for technology or knowledge itself and patent analysis plays an important role in identifying technology development trends and opportunities (Tsuji, 2012). Although not the only form of intellectual property protection1, patents may help firms gain competitive advantage by providing a temporary technological lead as well as by protecting brand names and forming industry standards (Reitzig 2004).Detailed information in patent documents reflects technology strategy, intended niche markets and preference for cooperation with other firms and organizations. Therefore, we argue that analysis based on patent data is capable of forecasting the application areas of certain technology. For our analysis we draw from the literature on epidemic models. These have been used widely in technology diffusion research (i.e., Geroski, 2000; Gupta & Jain, 2012; Karshenas & Stoneman, 1993; Rao & Kishore, 2010; Pulkki-Brännström & Stoneman, 2013) but they have not considered the impact of firms’ decision making with respect to adopting or not adopting disruptive technology.

1Other forms of IP protection include trademark, copyright and trade secret

We select radio-frequency identification (RFID) technology as our case study. This is an emerging technology posing disruptive challenges to a wide range of existing markets (Sengupta and Sethi, 2007; Taghaboni-Dutta and Velthouse, 2006). The technology is based on the concept of an RFID “tag” – a microchip connected to an antenna. The tag’s proximity reading and information storage capabilities and can substitute for traditional automatic identification and data capture technologies such as barcode systems in tracking items. Applications include, but are not limited to access control systems, resource and asset tracking methods, time and attendance management, package handling and routing and location- based computing.While the specific impacts of RFID on the value chain differs from industry to industry, its attributes leads to various benefits including improved data accuracy, information visibility, process innovation, service quality and ultimately provide customers with easier, less costly, and more efficient products. There has been a heated discussion on the most promising applications of RFID which includes supply chain & inventory technology (Lefebvre et al., 2006), telemedical technology (Zailani et al., 2015) and wearable technology (Thierer, 2015). The aim of this case study is to make assessments on whether RFID will induce disruptive changes and, if so, whether the disruption will be expressed in the context of industrial change or in technology development. The rest of this paper is organized as follows. Section 2 proposes brief reviews of related studies concerning potential application areas identification. Section 3 introduces the whole research process, which is mainly based on analogy between SIRS model and disruptive technology diffusion model and in which both deterministic and stochastic models are illustrated. Section 4 organizes an empirical study in RFID, analyzing the possibility of "major outbreak" in each potential application area and defining the potential disruption in each area respectively. Finally, we provide a conclusion about our research and briefly discuss future work.

2 Literature review

In this section, we first review the studies related to extended definition of disruptive technology,

and then introduce the forecasting methods for disruptive technology’s application areas in prior

arts.

2.1 Extended definition of disruptive technology

Christensen (1997) first introduced the term "disruptive technology", to refer to a new technology

having lower cost and performance measured by traditional criteria, but having higher ancillary

performance. Christensen (1997) suggested that disruptive technologies may enter and expand

emerging market niches, improving over time and ultimately attacking established products in

their traditional markets.

A review of the literature shows that, to describe unexpected and unusual technological changes,

different terms including "radical technology" and "discontinuous technology" are also used and

sometimes are confused with each other. Actually, these concepts are often independently

discussed with overlapping connotations. In order to define "disruptive technology" in a clearer

and precise way and make Christensen’s conceptual apparatus more useful, several researchers

argue that there should be improvement to it. Table 1 shows the conceptual evolution of disruptive

technology over the years.

Table 1.

Comparison of original and extended scope of disruptive technologies

Disruptive technology’s characteristics extended from Christensen’s

Original scope from Christensen Extended scope Representative literature

1 Low-end entrance Low-end/High-end encroachmentsUtterback (1994); Acee (2001); Utterback

& Acee (2005)

2 Dismissed by incumbentsAdopted by incumbents as

frequently as by new entrantsSood & Tellis (2011)

3 Entirely replace incumbents Incumbents preserved Naumov (2013)

Disruptive technology’s characteristics consistent with Christensen’s

4 Driving force: Follow discontinuous technological trajectories which can change the firms’ profit model

5 Market encroachment: Create niche/fringe markets initially based on higher ancillary performance

6 Influence to competitive pattern: Incumbents take more risks, while new entrants get new opportunities

7 Influence to industries: Explore unexpected application areas and change the existing value network

First, not all the disruptive technologies follow the path of "attack from below" (Utterback, 1994;

Acee, 2001; Utterback & Acee, 2005), and customers actually make purchasing decisions based

on complex criteria rather than just price (Daneels, 2004; Dombrowski & Gholz, 2009). Daneels

(2004) argued that not all the disruptive technologies enter the market from the low-end. For

example, digital cameras are more expensive than traditional film cameras to purchase but less

expensive to use and digital video discs always have had higher image quality than videocassettes.

Second, not all the incumbents experience failure in launching a disruptive technology, rather, as

Christensen (1997) pointed out a disruptive technology creates a dilemma for incumbents, as they

are less inclined to adopt a disruptive technology than new entrants. However, based on some

prior researches, claiming that incumbents may be better positioned to take advantage of new

technologies because of superior financial and managerial resources, R&D capability and

complementary assets, Sood & Tellis (2011) argued that, potentially disruptive technologies are

introduced as frequently by incumbents as by new entrants. Third, one of the main claims of

Christensen (1997) is that disruptive technology is often associated with replacing the incumbent

firm’s market leadership. However, an entrant strategy of initially competing followed by later

cooperating would suggest that, in some cases of disruptive technology, old technology will be

totally replaced, while incumbents’ market leadership might still be preserved. Therefore, in

different circumstances Naumov (2013) argues that disruptive technology will lead to various

types of disruption, namely industrial disruption and technological disruption, which will be

illustrated in detail later. Based on the above arguments, the original scope of disruptive

technologies (proposed by Christensen) can be extended in several aspects. As described in Table

1, Christensen identifies disruptive technologies by seven main characteristics. We propose an

extended concept of disruptive technology adapted from Christensen’s theory. Thus a disruptive

technology is an emerging technology following a different technological trajectory from existing

technology. It has higher ancillary performance which can create niche/fringe markets initially and

finally be dominant in unexpected application areas. Disruptive technology will often bring great

challenges to incumbents, because it changes firms’ profit models and existing value networks.

2.2 Technology forecasting methods

Forecasting disruptive technologies is fraught with many challenges. While technology forecasting

(TF) in general has been acknowledged as an effective tool in order to anticipate and understand

the potential direction, rate, and effects of technological change (Porter & Roper, 1991), the

methods do not readily lend themselves to anticipating disruptive technologies. Over the decades,

technology forecasting has been used in several areas of technology management including

detection of technological trends, discovering technological opportunities, identifying emerging

trends and evaluating technology development. Moreover, there has been a variety of technology

forecasting methods developed to meet the demands of decision makers. Table 2 provides an

overview of the different methods in these prior arts.

Table 2.

Main stream technology forecasting methods

Representative

literatureMethod

Representative

literatureMethod

Delphi approach

Cho et al. (1991) Semi-Markov model

Bibliometric

technique

Yoon & Park (2005)Keyword-based

morphology analysis

Rowe & Wright (2001) Expert opinion Lee et al. (2009)Keyword-based patent

map

Yousuf (2007) Expert opinion Shen et al. (2010) Patent co-citation method

Shen et al. (2010) Fuzzy Delphi method Choi & Jun (2014) Bayesian patent clustering

Curve fitting

techniqueYoon & Lee (2008)

S-curve fitting with patent

dataMa & Porter (2015)

Patent topical statistic and

cluster analysis

Ryu & Byeon (2011)Technology growth curve

fittingGuo et al. (2016)

Subject–action–object-

based morphology

analysis

Kim et al. (2012)Decision tree and statistical

feature analysisJeong et al. (2016) Hazard function

Gao et al. (2013) Technology life cycle analysis Technology Walsh (2004) Technology roadmapping

roadmapping based on patent data

Data envelopment

technique

Lamb et al. (2012) Data envelopment analysis Jeong & Yoon (2015)

Lim (2015) Data envelopment analysis Lee et al. (2015)

Environmental

scanning

Fahey & King (1977) Comparison study Li et al. (2015)

Drew (2006) Scenarios analysis Cho et al. (2016)

However we argue that the extent to which these methods are able to forecast disruptive

technologies is limited. Qualitative studies for example were put forward to meet the demands of

technology forecasting analysis in the earlier years. These included the Delphi method, scenario

planning, interview analysis, etc (Rowe & Wright, 2001; Daim et al., 2006; Drew, 2006; Shen et

al., 2010; Cho et al., 2016, etc). These methods suffer many limitations but most significantly, the

data sources for these researches are subjective and expensive to collect. For the sake of the data

reliability, many researchers conducted technology forecasting by combining qualitative studies

with objective data. For example, Daim et al. (2006) presented the forecasts for three emerging

technology areas by integrating the use of bibliometrics and patent analysis into scenario planning,

growth curves and analogies. Shen et al. (2010) proposed a technology selection process

integrating fuzzy Delphi method, analytic hierarchy process, and patent co-citation approach.

Hsieh (2013) created a model that combined the Delphi method, fuzzy measurement, and a

technology portfolio planning (TPP) model to analyze patents in large quantities before they were

commercialized. However, due to the fact that these qualitative methods cost so much and are

unable to provide ex-ante implication to decision makers, they found limited usage over the years.

Other researchers promote technology roadmapping as an ideal approach for technology

forecasting (Walsh, 2004; Jin et al., 2015; Lee et al., 2015; Li, 2015; Cho et al., 2016; Phaal et al.,

2004, etc). This method employs various data sources and is used to explore and communicate the

evolution of markets, products and technologies, together with the linkages and discontinuities

between various perspectives (Phaal et al., 2004). Although the technology roadmapping method

better addresses the reliability of data source than qualitative methods, there are still limitations to

it. There are two kinds of technology development paths, namely continuous and discontinuous

development. While the scope of technology forecasting is limited somewhat to ‘posteriori

trends’, discontinuous technology evolves along totally different trajectories, thus technology road

mapping will only provide implications to firms who are focusing on continuous or sustaining

technology, and do not provide answers for firms who are faced with discontinuous or disruptive

technology (Kim, et al., 2016). Most of the analyses are limited to ‘technology’ viewpoints, but

this is not sufficient for technology forecasting especially for disruptive technology, since the

ultimate purpose of technology forecasting is to guide the R&D activities and finally facilitate the

firms’ profitability after the market encroachment. To be specific, although the disruption stems

from the emergence of a disruptive technology (Kostoff, et al., 2004; Walsh, 2004), actual

‘disruption’ occurs when a disruptive technology is introduced to certain application area and meet

the new demand of customers.

In recent years, bibliometric methods, especially those which are based on patents/academic

papers are widely used to conduct quantitative technology forecasting. In bibliometric analysis, it

is assumed that the number of patents or papers is related to the validity and quality of R&D

activities (Narin et al., 1994). The researches of relationships between patents or papers are well

organized in order to discover the underlying pattern of technology development. Specifically,

various methods are proposed including Bayesian models for patent clustering (Choi et al., 2014),

bathtub-curve and the hazard rate analysis (Jeong et al., 2016), keyword-based patent maps (Lee et

al., 2009; Jin et al., 2015), subject-action-object semantic analysis (Yoon & Kim, 2011; Guo et al.,

2016), topical analysis (Ma & Porter, 2014), etc. The advantages of bibliometics mainly lie in the

reliability of data sources. Even though there are several limitations about patents being used as a

proxy of innovative activities2 (Audretsch, 2004), patents are a direct output category of industrial

R&D and other inventive activity, and they are ideal for mirroring the cumulative process of

technological development. When applying for a patent, the assignee has to prove the novelty,

non-obviousness and usefulness of his invention. Moreover, a patent provides information not

only about their new inventions but also about their potential or target application areas and their

technological domains, which can be proxies for firms’ market strategies. However, the

relationship between patent information and market strategies has not been well addressed in

existing literature.

Moreover, some researchers approach technology forecasting in various other ways, including the

importance-performance matrix (Hung & Lee, 2016), data envelopment analysis (Lamb et al.,

2012), TRIZ evolution theory (Sun et al., 2008), and heuristic methodology (Vojak & Chambers,

2004).

2.3 Disruptive technologies: Methods for forecasting new application areas

At the firm level, potential application areas will not be considered until after the R&D process is

completed. And, since technology development is done by technologists who may have little or no

market knowledge, the application areas they choose may sometimes not have sufficient revenue

potential to satisfy the demands of the firm’s financial supervisors (Leifer, 2000). In this context,

application area forecasting is vital for firms who are willing to organize the R&D process of

disruptive technologies well and successfully build the bridge between the lab and the market (Jin,

2000). Therefore, some research has been done, including consistent and overall scanning of

application areas, considering the technological distance between disruptive technology and

existing knowledge base (Datta & Jessup, 2013), measuring the cognitive distance between the

acquired knowledge and the problem to be solved (Enkel & Gassmann, 2010), and increasing the

external sources’ diversity to solve internal problems (Jeppesen & Lakhani, 2010).

2 First, a patent can demonstrate the outcome of research and development, but can hardly guarantee a significant and positive economic value. Second, partly due to concerns of secrecy and non-patentability, many inventions which result in innovations are not patented. Therefore, not all the patents indicate firms' market orientation, and not all the market trend can be embodied in patent data. Other forms of IP protection include trademark, copyrightand trade secret

In the case of incremental technological development, performance improvement is unlikely to

cause alterations to the existing technological and market paradigms. Thus, the application areas

associated with incremental technologies are relatively fixed. However, for disruptive technologies

the situation is going to be quite different. As a typical case of disruptive technology, Kodak’s

failure aroused vigorous discussion among researchers. In the first place, Kodak was the pioneer

of digital photography and secured many patent applications from it. However, Kodak followed its

historical trajectory by investing in traditional products and services and by building on analogue

photography instead of investing in the new digital technology. It also did not pay attention to the

number of new entrants into the market investing and patenting in digital technology (Momeni &

Rost, 2016). Kodak’s failure fully demonstrated the importance of technology forecasting, which

can serve as a bridge between technology R&D and market encroachment. Systematic approaches

for identifying potential application areas for disruptive technologies are scarce. Danneels (2004)

first questioned the importance of disruption theory’s predictive use, which can potentially

facilitate firms’ ex-ante decision making. Technology roadmapping (TRM) methods help

managers and policy makers to identify and track global trends in disruptive technology research

and its following influence to the markets. For example, Walsh (2004) used International

Industrial Microsystems and Top-Down Nanosystems Roadmap (IIMTDNR), to facilitate the

commercialization of disruptive technology. In addition, some researchers implement TRM jointly

with quantitative analysis. Jin et al. (2015) proposed a framework for solar light-emitting

diode technology mainly applying two quality function deployment matrices to draw up the

technology roadmap in order to identify both profitable markets and promising product concepts.

Li et al. (2015) used bibliometric method and technology roadmapping to strategize the future

development from technology to application and marketing in the case of dye sensitized solar cell

technology. Lee et al. (2009) argued that there exists valuable strategic information in patent

documents, and used roadmapping techniques for business opportunity analysis and market

planning for products location in the area of RFID technology. Momeni and Rost (2016)

developed a network analysis based on patent-development paths, k-core analysis and topic

modeling of past and current trends of technological development to identify technologies that

have the potential to become disruptive technologies.

Sood & Tellis (2011) describe disruptive technology from three domains, namely firm

(competitive survival), demand (market acceptance) and technology (performance evolution).

Considering that it is very hard to evaluate the level of market acceptance through patent data, we

mainly focus on the disruption from the perspective of firm and technology. Firm disruption

occurs when the market share of a firm whose products use disruptive technology exceeds the

market share of the incumbents, and finally the new entrants become the dominant entities in the

application areas. Technology disruption occurs when the disruptive technology surpasses the

performance of the incumbent technology on the ancillary dimension of performance in the first

place, and finally surpasses the incumbent technology on the primary dimension of performance.

Different archetypes of disruption will bring about diverse influence to firms, so the confirmation

of potential disruption archetypes should be well studied. Therefore, in general, the "disruptive

technology" we discussed here is not limited to low-end market entrance, and may induce various

disruptions under different circumstances.

So far as we can determine from the literature, no one has studied the application areas forecasting

of disruptive technology based on the diffusion theory using a stochastic procedure. We fill this

gap by introducing epidemic model analogically using patent data and forecast the probability of

disruptive technology’s ‘major outbreak’ in candidate application areas. The advantages of this

method are threefold. First, from the perspective of data source, it is suggested that patent

applications and citations are appropriate objective measures of performance if the construct of

interest is technological disruptiveness (Katila, 2007). Second, technology diffusion is driven by

information diffusion which is similar to the process of disease diffusion (Geroski, 2000; Chang,

2009). The epidemic model we apply here is capable of building the analogous relationship

between the infectious process and technology diffusion process, and analyzing the disruptive

technology’s diffusion in a non-linear way. Therefore, the high degree of uncertainty of the

disruptive technology's development can be well addressed. Third, this model enables the

forecasting of potential application fields from the perspective of both technological and industrial

impacts the disruptive technology is likely to produce. Therefore, we propose a systematic model

which is different from the above studies, and addresses the issue of application filed forecasting

for disruptive technology in a more appropriate way.

3 Research Strategy and Methodology

In order to solve the challenging problems emerging in the research of disruptive technology application areas forecasting, two questions are discussed here. First, after entering into a certain application area, will the disruptive technology replace the existing technology and become dominant in the short run? Second, given the condition that the disruptive technology may be totally neglected by the market when the initial diffusion rate is extremely low, will it generate disruptions in the long run? Third, considering there are two kinds of disruption, namely industrial disruption and technological disruption, then what kind of disruption will the technology induce?Therefore, we introduce SIRS (Susceptible, Infectious, Recovered and Susceptible) epidemic model and answer the above question with corresponding deterministic and stochastic models (Lahodny et al., 2015). Deterministic analysis is applied to first, calculate the disruptive technology’s overall diffusion rates and then evaluate the probability of its outbreak in certain application area in the short run, and second, assess the probability of occurrence of industrial and technological disruption respectively. The stochastic model is combined with branching process to evaluate the probability of disruptive technology’s ‘major outbreak’ in candidate application areas in the long run.Fig. 1 shows the research framework mentioned above. It consists of four steps: understanding and linkage between disease epidemic model and disruptive technology diffusion process; description of information flow in technology diffusion process by imitating the SIRS model; evaluation of

the disruptive probability in the short (long) run based on deterministic (stochastic) model; forecasting of the probability of industrial and technological disruption. Finally, we can come to the conclusion about the most promising application areas and the likely archetype of disruption.

Fig. 1.

Application areas forecasting process for disruptive technology

3.1 Stage 1: Understand & Link

The feasibility of applying epidemic model in disruptive technology diffusion forecasting lies in three aspects. First, both disease and disruptive technology emerge in a similar context. In the early stage, a small number of infectious individuals (or pathogens) emerge in new regions by chance. Similarly, in the first place, the future of disruptive technology is vague, so that it is vital to form niche markets in order to select the most promising application areas. Second, technology is possessed, diffused, exchanged in the form of information flow, which can be infectious (Geroski, 2000). In similar fashion, disease can be spread from two channels, one is directed diffusion by infectious individuals and the other one is indirect diffusion by pathogens existing in the environment. Analogously, in the bibliometrics and social network analysis of technology forecasting, researchers have defined two diffusion channels, namely undirected diffusion (co-existence) and directed diffusion (citation) (Huang et al., 2012; Ding, 2011). We introduce the above concepts and define the undirected diffusion by patents’ co-existences in two technological areas, and the directed diffusion by patents’ backward citations. Third, the diffusion of disease and disruptive technology possess a similar growth trend. Both of them experience exponential growth

in the early stage of development. Then with an explosive tendency of growth, both of them can invade successfully the target areas, pose risks and generate destructive influence to residents or incumbents respectively. Therefore, we argue that disruptive technology diffusion can be defined in an evolutionary perspective. Even though there is not a very precise analogy between patented inventions and forms of life, it is feasible to describe disruptive technology’s patent activities using an epidemic model.As depicted in the first phase of Fig. 1, the SIRS model proposed by Bani-Yaghoub et al. (2012) is introduced. The analogies between key elements, including susceptible agents, infectious agents and recovered agents, of SIRS model and technology diffusion is summarized in Fig. 2.

Fig. 2.

Analogy between SIRS model and disruptive technology diffusion model

3.2 Stage 2: NAUN model description and parameter settings

Shown in the second phase of Fig. 1, we adapt the SIRS epidemic model (Lahodnyv et al., 2015) and build a NAUN model to describe disruptive technology diffusion process. ‘N’ denotes non-adopter firms who have not adopted disruptive technology yet, but they have potential intentions to adopt disruptive technology for higher business profit. ‘A’ denotes actual adopter firms who have already adopted disruptive technology, and have their achievements embedded in patents. We assume that firms will stop investing in disruptive technology if they fail in earlier adoption and experience disappointing performance, so ‘U’ stands for un-adopter firms who used to be adopters but already have decided to stop investing in disruptive technology.Fig. 3 illustrates the relationships among the three above entities. Since knowledge flow occurs in different channels, two kinds of disruptive technology diffusion process, namely directed and undirected diffusion are discussed here. The former one is derived from co-existent patents which lie in both patent sets of two technology areas, while the latter one is applied based on patent citation records among different technology areas. ‘P’ denotes patents applied by disruptive technology adopters, and their forward-citing patents. This patents set serve as a typical source of undirected diffusion. From the biological point of view, a given patent is considered to be born when the patent is granted by the patent office. A patent is considered to reproduce whenever it is cited by another patent, during which process the contribution of previous patent is passed on. Old patents never die. When they stop being cited, or stop being licensed from the perspective of economic value (Bessen & Meurer, 2008), they simply

become dormant indefinitely (Bedau, 2013). All the entities and their interrelationships mentioned above are depicted in Fig. 3.

Fig. 3.

Relationships among entities in disruptive technology diffusion process

Let N ( t ) , A (t ) and U (t ) denote the number of non-adopters, actual adopters and un-adopters at time t, respectively in a certain technological area. Let P(t) denote the number of disruptive technology patents applied by firms in this area at time t. Suppose that Λ>0 represents the emerging firms in this application area, μ>0 denotes the failure rate of firms after adopting disruptive technology, and d>0 denotes the failure rate of firms due to other possible reasons, such as mergers & and acquisitions, or temporary dormancy derived from R&D process slowdown. For the sake of quantitative measurement, we assume that if there is a three-year gap without further patent applications in the same area, firms are thought to lose interest in developing this area. Directed and undirected technology diffusion are modeled with diffusion parameters β1>0 and β2>0, respectively. The former one is measured with patents co-existences in the two technology areas, while the latter one is calculated by patent citation data. After adopting disruptive technology, there is a chance that firms are unable to achieve their expected return, so that they will stop their R&D process on disruptive technology, which will lead them to be un-adopters at the rate of γ>0. Un-adopters will not adopt disruptive technology again temporarily, which is analogous to ‘immunity’ as used in infectious system. But the ‘immunity’ will disappear and the un-adopters will get into the business of disruptive technology again in the rate of ν>0 along with the evolution of disruptive technology or strategy alteration of the firms.Assuming that there are several candidate technological areas under discussion, not all the disruptive technology patents are covered in the above process. Every public patent conveys information of disruptive technology to the firms, so it is vital to measure the growth trend of all the published patents. First, technology adoption can be reflected in the number of disruptive technology patents applied by firms in the technological area, and the rate is represented by α >0. Second, forward-citing patents of the patents mentioned above can also demonstrate the diffusion of disruptive technology, which is represented by r>0. Historical studies proposed the concept of patent saturation within a technology area based on patents’ logistic growth trends (Chiu & Ying, 2012; Chang & Fan, 2015), so carrying capacity of patent growth is used here, demonstrated by K. Meanwhile, patents will become dormant in the rate of ξ>0 (Litan & Singer, 2014; Bowman, 2001).The corresponding system of differential equation is

dNdt

=Λ−dN−β1 NA−β2 NP+νU (1)

dAdt

=β1 NA+β2 NP−(d+μ+γ )A (2)

dUdt

=γ A−(d+ν)U (3)

dPdt

=α A+rP(1− PK )−ξ 　 (4)

With non-negative initial conditions N (0 ) , A (0 ) ,U (0 ) , P(0). Table 3 summarizes the model variables and their descriptions.

Table 3.

Variables and parameters with units for the model (1)-(4)

Description

N Number of non-adopting firms

A Number of adopting firms

U Number of un-adopting firms

P Number of disruptive technology patents in this area

Λ Rate of new firms’ emergence

μ Rate of failure firms due to disruptive technology adoption

d Rate of failure firms due to possible reasons beyond disruptive technology adoption

β1 Rate of directed technology diffusion

β2 Rate of undirected technology diffusion

γ Average rate of firms who did not apply for disruptive technology patents in successive 5 years

ν Average rate of firms who apply for disruptive technology patents again in no more than 3 years

α Rate of disruptive technology patents applications

r Rate of patents’ forward citation

ξ Rate of patents dormancy

K Carrying capacity of patent applications

In biological epidemic models, the basic reproduction number is a well-known threshold which can be used to determine whether an outbreak will occur or not. The basic reproduction number, denoted by R0, is defined as the expected number of secondary infections produced by a typical infectious individual during an infectious period in a completely susceptible population (Diekmann, 1990). Similar to the basic reproduction number, we propose an index of basic diffusion number ( D0) of disruptive technology to determine the dynamics of the model (1)-(4) regarding technology prosperity or extinction. Obtained from the next-generation matrix approach (Tien & Eam, 2010):

D0=12 [ D01+D03+√(D01−D03)

2+4 D 02] (5)

Where

D01=Λ β1

d (d+μ+γ)，D02=

Λ β2 αd ξ(d+μ+γ )

，D03=rξ (6)

The terms D01 and D02 represent the average number of disruptive technology adoption activities caused by disruptive technology adopters through directed diffusion and undirected diffusion respectively. The term D03 represents forward-citation number of a single disruptive technology patent.In general, if D0>1, the disruptive technology will be dominant in the considered technological area in the short run, otherwise, it will be eliminated. Similarly, if D01>1 or D02>1, the disruption will be mainly induced by directed or undirected diffusion.

3.3 Stage 3: Forecasting of disruptive technology’s promising technological areas

The basic diffusion number for the deterministic model can be used to determine, on average, whether the appearance of a small number of early adopters or their patents will result in a major outbreak or extinction. However, even if D0>1, it is possible that disruptive technology is eliminated from a certain technological area prior to its prevalence. In order to determine the probability of a major outbreak of disruptive technology for certain technological area in the long term, it is necessary to apply a stochastic model. Therefore, we introduce a stochastic analysis using CTMC model. A multi-type branching process is used to calculate probability of disruptive technology’s prevalence in each candidate technological area. Finally, the technological area with higher probability is determined and deemed as more promising for disruptive technology.To delineate the CTMC model, let N (t ) , A (t ) and U (t ) denote discrete-valued random variables for the number of non-adopters, adopters and un-adopters at time t. Let P(t) denotes a discrete-valued random variable for the number of disruptive technology-related patents at time t.A same set of notations is used for the discrete random variables and parameters as for the deterministic model. The state transitions and rates for the CTMC model are given in Table 4.

Table 4.

State transition and rates for the CTMC model

Description Diffusion,△ X⃗ (t ) Transition rate, p

Recruitment (1,0,0,0 )T Λ

Failure of N (−1,0,0,0 )T dN

Directed diffusion (−1,1,0,0 )T β1 NA

Undirected diffusion (−1,1,0,0 )T β2 NP

Failure of A (0 ,−1,0,0 )T (d+μ) A

Recovery (0 ,−1,1,0 )T γA

Regain of expectation (1,0 ,−1,0 )T νU

Failure of U (0,0 ,−1,0 )T dU

Patent application (0,0,0,1 )T αA

Forward citation of patents (0,0,0,1 )T rP (1− PK

)

Dormancy of P (0,0,0 ,−1 )T ξP

Degeneration of the whole industry (0,0,0 ,−1 )T δP

A multi-type branching process can be used to approximate the dynamics of the nonlinear CTMC process. We adopt a biological concept of 'offspring' to denote firms or patents in this diffusion system. The expectation matrix of the offspring probability generating functions is given by

M=[ ∂ f 1

∂ x1

∂ f 2

∂ x1

∂ f 1

∂ x2

∂ f 2

∂ x2]|⃗

x=I⃗

=[ 2 β1Λd

+α

β1Λd

+d+μ+γ+α

β2Λd

β2Λd

+ξ+δ+r

α

β1Λd

+d+μ+γ+α

β2 S+2r

β2Λd

+ξ+δ+r ] (7)

The entries m11 and m22 represent the expected number of adopters and related patents, respectively, produced by one earlier adopter. Similarly, the entries m12 and m22 represent the expected number of adopters and related patents, respectively, produced by one disruptive technology patent.The elimination probability of disruptive technology from a certain technological area can be derived from the multi-type branching process. That is

P0=[ d+μ+γ

β2Λd ]

i0[ ξ+δα ]

p0[ β2Λd

+ξ+δ

d+μ+γ+α ]i0− p0

(8)

3.4 Stage 4: Forecasting of disruption’s archetype

As the concept of disruption is widely used in numerous areas including, academia, policy circles and industry, the term disruptive technology has been used in a multitude of ways and has become separated from its initial theoretical basis (Danneels, 2004). Therefore, different cases should not be considered in the same paradigm, and the types of disruption should not be restricted to the narrow concept proposed by Bower and Christensen (1995). To address this issue, disruption is

characterized in two levels, namely industrial level and technological level. Within these two dimensions, there are three main outcomes, namely disruption, co-existence and no disruption. The matrix formed in stage 4 (shown in Fig. 1) can provide us the basis of forecasting disruption archetypes (Naumov, 2013).Based on the interdisciplinary patents’ co-existence and interdisciplinary patent citation data sets, potential disruption can be classified quantitatively in corresponding archetype and considered in a wider spectrum. More specifically, patent data contain both information of new technology’s influence to industries and technology development. Representing the directed diffusion of disruptive technology, the interdisciplinary cooperative patent set provides the opportunity for us to identify disruptive technology’s potential application in candidate areas. D01 is measured to evaluate the extent to which firms adopt disruptive technology to grab its potential economic value in target area, which indicates involvements of new entrants from the disruptive technology area. D02 can be a proxy of technology invasion into the new area through interdisciplinary patent citation, from which channel, disruptive technology will induce more impacts to existing technology development trend, so incumbents will be encouraged to undertake disruptive technology’s R&D processes. Therefore, D01 and D02 are used separately as quantitative measurements for industrial and technological disruption. If the parameters are larger than 1, then there will be industrial/technological disruption. If the parameters equals to 1, then the firms/technologies will co-existent. In the other conditions, there will not be any disruption in both industrial and technological dimensions.4 RFID’s potential impacts on promising technological areas

RFID is a technical system that offers the possibility of reading data through radio waves without the need for contact. This allows the automatic identification and location of objects and makes the collection of data easier (Hutter & Schmidt, 2013). RFID technology involves the use of radio frequency to identify and track the items that have been implanted with a coded electronic chip. Unlike bar codes, these chips can be read from several meters away and beyond the line of the sight; therefore, the items do not need to be positioned precisely relative to the reader. This provides a significant enhancement on the accuracy of the information currently obtained through bar code scanning. Moreover, with RFID systems, it is possible to obtain real-time information about every item and its physical location without conducting time consuming audits (Sari, 2010).There seems to be a consensus in academia that RFID technology will induce disruption to a wide range of areas. We thus evaluate the disruptive potential of RFID according to the criteria proposed by Christensen (1997) and the extended concept of disruptive technology we propose above. First, disruptive technology follows a different technological trajectory from existing technology. RFID is identified as an enabling technology since it can facilitate organizations implementing data collection and then create value from it. In particular, it will fulfill those targets in an easier (Lui et al., 2016; Kamoun et al., 2015), more efficient, and less costly approach (Kamoun, 2008). Second, disruptive technology emerges together with new value network and calls for new business model. Academic research suggested that RFID has the potential of enabling new value networks that will profoundly change our lives (Oinonen et al., 2012) and introducing new ways of conducting business (Krotov & Junglas, 2008), and further, it appears to form disruptive innovation as it calls for new business models (Hossain & Quaddus, 2015) and

redesign of existing processes. Third, firms are exposed in high risks when undertaking disruptive technology’s R&D processes. Because firms undertaking RFID need to bear the cost of changing business processes and business models, it is very hard especially for incumbents, to launch such projects. Thus, the case of Radio-frequency identification (RFID) technology serves as the disruptive technology analyzed here. It is notable that some researches highlight the potentially disruptive impact of RFID in the following three engineering areas. First, Lefebvre et al. (2006) verified that RFID is a disruptive technology as it supports a new business model, entails major redesign of existing processes and fosters a higher level of electronic integration between supply chain members. Second, the use of RFID technology in the healthcare industry is rather new and it’s generally considered as the "next disruptive technology in healthcare". Due to its capability for remote tracking and data collection, RFID possesses the disruptive potential in telemedicine area (Zailani et al., 2015). Third, as a vital part of Internet of Things (IoT), RFID facilitates the realization of most wearable devices. Specifically, the application of RFID in wearable devices will induce disruption in the near future (Thierer, 2015). Besides, considering the accessibility and rationality of patent data, we follow two rules. First, we try to avoid those application areas which are likely to overlap with each other in the patent datasets, so the potential application areas we chose are quite different from each other in practical economic activities. Second, in order to show that disruptive technology could have different impacts on technology development and industry evolution, we select areas which show different features. This will generate different kinds of results which facilitate our later research, and also, the time span or total number of patents will not influence the result, rather, the records of patent citation and patent co-existence really matter in this context. So, it assures the external validity of the results. Therefore, in the following empirical study, we use the NAUN model and apply the theory of CTMC process to estimate the probability of RFID's major outbreak both determinately and stochastically in three different technological areas, namely supply chain & inventory technology, telemedicine technology, and wearable technology.

4.1 Data collection and preliminary data statistics

The Derwent Innovation Index database (DII)3 serves as the source for patent documents collection. The data in DII can identify the firms or institutions that deposit patents and is the most comprehensive database covering the data of the main leading patent-issuing authorities including United States Patent and Trademark Office, Japan Patent Office, European Patent Office, World Intellectual Property Organization and Sino Intellectual Patent Office. Structured data can be retrieved to delineate companies’ innovation strategies and patent landscapes. We consider that increasing the precision ratio and recall ratio is not the main purpose of our study, so we follow the most commonly seen retrieval strategy, which combines keywords and IPC codes together to fulfill our goal (Gao et al., 2013).Specifically, this empirical study uses four patent data sets in corresponding technology areas, namely, RFID, supply chain & inventory technology, telemedicine technology and wearable technology. For the first data set, general terms of "RFID" and "radio frequency identification" are utilized for patent retrieval. For the second data set, IPCs of "G06F", "G06Q" and "G06K" are

3 Patent retrieval website: http://www.webofknowledge.com.

selected to define the technological area. Terms such as "supply chain", "inventory" and "warehouse" are key retrieval terms. For the third patent data set, key retrieval terms are "remote health", "remote medical", "tele-medical", "telemedical", "remote clinical", "tele-clinical" and "teleclinical". At the same time, IPC of "A61" is introduced along with other terms, such as "remote monitor", "teleconference", "telenursing", "telerehabilitation", etc. For the fourth data set, a set of 10 IPCs, such as "E04F", "E21B", and "F16L", are selected to demonstrate the corresponding technological area. Also, terms such as "wearable*", "iWatch", and "google glass*" are key search terms which can narrow the retrieving result to a reasonable scope. Table 5 shows the statistical results of patent retrieval.

Table 5.

Description of patent retrieval results

RFID

technology

Supply chain & inventory

technology

Telemedicine

technology

Wearable

technology

Time span 1995-2015 1992-2015 1974-2015 1974-2015

Total no. patents 53311 20358 9980 25032

Total no. patentees 30914 20284 8859 16381

Then, we need preliminary data statistics for indicators in Table 3 based mainly on data of interdisciplinary patent co-existence, interdisciplinary patent citation, inner-disciplinary patent citation, and co-existence of year-patentees. Table 6 lists the model parameters related to RFID diffused separately in supply chain & inventory technology, telemedicine technology and wearable technology areas. The statistical frequency of patents is measured yearly. All the data here are calculated by Java programming mainly through screening and counting processes.

Table 6.

Model parameters related to RFID diffusion in three areas

ParameterValue

Supply chain & inventory technology Telemedicine technology Wearable technology

Λ 0.001 0.002 0.018

μ 2.38×10-4 2.06×10-4 4.7×10-4

D 0.00491 0.00616 0.0018

β1 0.137 0.022 0.015

β2 0.323 0.18 0.009

γ 9.1×10-5 4.5×10-5 0.00109

ν 0.03 0.014 7.8×10-4

α 0.39 0.46 0.38

R 3.24 4.56 0.32

ξ 0.8 0.8 0.8

K 45000 30000 50000

4.2 Results and discussion

In order to generate and exhibit the dynamics of the diffusion process, we introduce the program of Ordinary Differential Equations (ODE) model and then adapt stochastic epidemic model proposed by Allen (2008). Finally we run our programs in a MATLAB environment. Before we can make an assessment of a "major outbreak", it is worth considering that there are three main reasons influencing disruptive technology’s diffusion rate. First, if there is a clear routine for disruptive technology transferring from the laboratory to the market (Ferrary, 2003), then the disruptive technology can be widely used immediately in firms’ product design and is likely to be highly profitable in the near future, so that it will be appraised in a relatively short time. Second, adopting disruptive technology calls for different levels of paradigm shifting in external value network and internal operation structure (Oinonen et al., 2012). So for incumbents, if there is lower shifting cost, there will be higher diffusion rate. Third, from the perspective of investors, they may search for markets that have threats, needs, or demands that could be addressed by emerging technologies (Lehoux et al., 2014). So if there exists a higher hazard rate of current core technology or value network, then this area will be more susceptible to disruptive technology, hence the diffusion to this area will be faster.4.2.1 RFID in supply chain & inventory technology

Based on the values in Table 6, the diffusion numbers of RFID in supply chain & inventory technology area are D01≈ 5.33 , D02≈ 6.12 , D03≈ 4.05 and D0 ≈ 7.24. For the CTMC model, there are two outcomes: either RFID is eliminated from the supply chain & inventory technology area or the technology will be dominant in this area in the long term. The ODE solution is plotted in Fig. 4, while one sample path of the CTMC illustrating RFID’s prevalence is plotted in Fig. 5.

Fig. 4.

ODE solution for RFID diffusion in supply chain &

inventory area

Fig. 5.

One sample path of CTMC for RFID diffusion in supply

chain & inventory area

The value of D0 calculated from deterministic model is greater than 1, which indicates that there is a greater possibility for RFID technology diffusing in supply chain & inventory technology area with an obvious prevalence. It is remarkable that both D01 and D02 are greater than 1, which demonstrates that RFID will induce significant impact to both technological transformation and industrial evolution in supply chain & inventory technology area. Meanwhile, the probability of

being eliminated from the supply chain & inventory technology area is P0=0.1745 (calculated from the branching process approximation), illustrating a major outbreak in the long run.Therefore, paths of the ODE solution and the CTMC illustrate a lasting existence for RFID in supply chain & inventory technology area and a prominent market share in the future. Meanwhile, both the value of diffusion numbers and eliminated rate (P0) show a successful disruption both in the short and long term.4.2.2 RFID in telemedicine technology

Based on the values in Table 6, the diffusion numbers of RFID in telemedicine technology area are D01≈ 1.02 , D02≈ 4.79 , D03 ≈5.70 and D0 ≈ 6.56. Similarly, for the CTMC model, there are two outcomes: either RFID is eliminated from the telemedicine technology area or will be dominant in this area. The ODE solution is plotted in Fig. 6, while one sample path of the CTMC illustrating RFID’s prevalence is plotted in Fig. 7.

Fig. 6.

ODE solution for RFID diffusion in telemedicine area

Fig. 7.

One sample path of CTMC for RFID diffusion in

telemedicine area

The value of D0 calculated from deterministic model is greater than 1, which indicates that there is a greater possibility for RFID technology diffusing in telemedicine technology area with an obvious prevalence in the short run. Similar to the supply chain and inventory area, D01 is slightly greater than 1 and D02 is much greater than 1, demonstrating that RFID will have a slight impact on industrial evolution in telemedicine area, but will greatly influence the trajectory of technology development and have the potential of replacing existing technology to a large extent. Meanwhile, the probability for RFID of being eliminated from the wearable devices area is P0=0.2207 (calculated from the branching process approximation), showing a major outbreak in the long run.Therefore, paths of the ODE solution and the CTMC illustrate a lasting existence for RFID in telemedicine technology area and a prominent market share in the future. At the same time, the value of diffusion numbers and eliminated rate show a successful disruption both in the short and long run. Incumbents should be prepared for disruptive technology’s impacts to existing technology trajectory and make their R&D strategy wisely.4.2.3 RFID in wearable technology

Based on the values in Table 6, the diffusion numbers of RFID in wearable devices area are

D01≈ 4.46 , D 02≈ 0.21 ,D03≈ 0.40∧D 0≈ 4.52. Similarly, for the CTMC model, there are two outcomes: either disruptive technology is eliminated from the wearable devices area or will be dominant in this area. The ODE solutions for relative individuals are plotted in Fig. 8, while one sample path of the CTMC illustrating RFID’s prevalence is plotted in Fig. 9.

Fig. 8.

ODE solution for RFID diffusion in wearable devices area

Fig. 9.

One sample path of CTMC for RFID diffusion in

wearable devices area

The value of D0 calculated from deterministic model is greater than 1, which indicates that there is a greater possibility for RFID technology diffusing in wearable devices area with an obvious prevalence. It is remarkable that the value of D01 is much greater than1, while D02 is less than 1, which demonstrate that RFID will be largely used in wearable devices area but will not substitute existing dominant technology. Meanwhile, to determine the outcome of CTMC model, the multi-type branching process is used. Consequently, the probability of RFID being eliminated from the wearable devices area is P0=0.7878, showing that there will not be major outbreak in this area in the long run.Therefore, paths of the ODE solution and the CTMC illustrate a lasting existence for RFID in wearable devices area. However, its market occupancy is not promising. Meanwhile, both the value of diffusion numbers and eliminated rate demonstrate that RFID will be adopted by firms in wearable devices area, and most probably, it will generate a niche market, but will not largely replace the market of existing dominant technology.4.2.4 Assessment of potential disruption archetype

In general, both graphs of deterministic and stochastic model show a similar trend of technology diffusion of RFID technology in the three potential areas respectively. However, there is a difference between basic diffusion rate and major outbreak rate. The former is widely used for short-term forecasting, while the latter is used for long-term forecasting over decades. Therefore, the two ways of evaluation can be used separately. First, basic diffusion rate in deterministic model is eligible for disruptive technology’s diffusion trend assessment in the short run, and potential impact of RFID to industrial evolution/technology development in candidate areas. Second, the major outbreak rate in stochastic model can be a proxy to make an overall forecasting of major outbreak of RFID in candidate areas in the long run. In order to forecast the impact of RFID in the three areas, we first list the basic diffusion rate (for deterministic model) and major

outbreak rate (for stochastic model) for RFID technology diffusion in the three areas, as shown in Table 7.

Table 7.

Comparison of basic diffusion rates and prevalence rates in three areas

Deterministic model Stochastic model

Value of basic diffusion rate Value of major outbreak rate

Supply chain & inventory

technologyD0 ≈ 7.24

D01≈ 5.331−P0=0.8255

D02≈ 6.12

Telemedicine technology D0 ≈ 6.56D01≈ 1.02

1−P0=0.7793D02≈ 4.79

Wearable technology D0 ≈ 4.52D01≈ 4.46

1−P0=0.2122D02≈ 0.21

In the long run, RFID will substitute existing dominant technologies in supply chain & inventory, telemedicine, and wearable technology areas with probabilities of 0.8255, 0.7793 and 0.2122 respectively. Based on the value of basic diffusion rates, we can then forecast RFID’s impact on industrial evolution and technology development respectively in the three application areas, which can be defined by industrial disruption and technological disruption. Based on this two dimensional assessing framework, the full mapping of the above three cases’ disruption is shown in Fig. 10. RFID shows different potential impacts to the three candidate application areas. First, RFID will bring forward both technological and industrial disruption to supply chain & inventory technology area. Second, old technology in telemedicine technology area will be disruptively replaced by RFID, however it will have little influence on the industrial evolution and competitive situation. Third, RFID will not substitute existing dominant technology in wearable technology area, whereas, niche market will be built in this area and changes for industrial competitive situation will be greatly influenced.

Fig. 10.

Potential disruptions allocated in a two-dimensional map

Profound implications from the results can be provided for RFID technology’s firms and other stakeholders. First, the possibility of technological disruption decides whether the new technology will displace old technology completely or not. As we know, entrants seek new attributes the

disruptive technology provides, while the incumbents provide higher quality with old technology. The potential of disruptive technology’s impact on technological transformation creates a dilemma for incumbents. Therefore, forecasting the possibility of technological disruption will provide a decision basis for incumbents. In our case study, RFID will introduce technological disruption to both supply chain & inventor area and telemedicine area, thus incumbents in both areas will need to undertake R&D activities in advance. In the wearable technology area, RFID does not show its disruptiveness to existing dominant technology, hence incumbents can still benefit from existing technology for a relatively long time. Second, a higher possibility of industrial disruption demonstrates higher penetration of disruptive technology into candidate areas. It is likely that disruptive technologies need a large amount of prior-period investment and their adoption pose serious risk. In the case of firms, new entrants should make an assessment about whether they can get incumbents involved in the development of disruptive technology, and then decide on how to spread risks during the market encroachment. In our case study, RFID will bring about industrial disruption to both inventory technology and wearable technology areas, in which cases, incumbents should look out for potential changes to existing competitive paradigm and further win market share through technology R&D or strategic alliances. However, RFID will not show industrial disruption in telemedicine technology area, thus incumbents will be faced with less pressure from RFID’s market encroachment.Third, the forecasting of promising disruptions will influence investor’s expectations. Due to high risk of disruptive technology, venture capital is the most commonly seen channel for financing. When venture capitalists select potentially disruptive technologies for the purpose of investment, they may attach importance to markets which have threats, needs, or demands that could be addressed by emerging technologies. They may also concentrate on markets which are in desperate need of innovation and renewal, and could be disrupted through the introduction of a new technology (CFFDT, 2010). Apparently, if disruptive technology is deemed to introduce industrial disruption, it will stimulate the expectations of investors. Therefore, in our case, investors should pay more attention on the financing of RFID in the area of supply chain and telemedicine which shows higher economic potential. Undoubtedly, forecasting potential disruption through only patent analysis is not enough. We therefore expect more research in the future both at the product-level and market-level.

5 Conclusion

This paper should be seen as a preliminary exploration of how to forecast the impacts of disruptive technology on candidate application areas both in the short and long run. In order to involve the non-linear feature of disruptive technology development into our research, we introduce both deterministic and stochastic processes of SIRS epidemic model. Various data sets, including interdisciplinary co-existent patents, interdisciplinary citation patents, and inner-disciplinary citation patents, are used to calculate the basic diffusion rate in the short run and major outbreak rate in the long run. Our research provides a feasible framework for firms to better forecast future trends of disruptive technology, and provides a practical tool for other stakeholders to evaluate the potential of disruptive technology.In the perspective of theoretical work, this study illustrates the importance of forecasting

disruptive technology’s impact on candidate areas in a non-linear way and initiates a new way of analogically adopting the SIRS epidemic model to analyze disruptive technology diffusion which offers an alternative approach compared to prior arts in the same area. First, in prior research on diffusion of disruptive technology, the feature of exponential growth has not been considered. To improve on that, we propose this framework which is adapted from the SIRS epidemic model, and through which we can describe the process of disruptive technology diffusion in a more accurate way. Second, prior arts did analyze the diffusion of disruptive technology separately in the short run and long run. Due to the high risk of disruptive technology both in technical innovation and economic value, we consider it very important to evaluate this process from both the short and long run. We deem it as a significant improvement for theoretical study in disruptive technology’s forecasting analysis which will hopefully open up new avenues for researchers. Third, there is no prior art trying to involve patent analysis into potential disruption forecasting. To fill this gap, we initiate this research by categorizing different disruptions in a two dimensional framework. Based on this classification, further researches of disruptive technology diffusion can be done according to its potential disruption to existing industry and technology, in order to improve the efficiency and effectiveness of forecasting analysis.From a practical perspective, this study can be instructive to firms and other stakeholders. First, technology managers can adjust their marketing strategies based on the analysis of industrial and technological impacts. For example, in the context of both high probability of industrial and technological disruption, disruptive technology will induce great changes to firms' competitive landscapes as well as their current technology development trends. Incumbents should therefore either consider R&D investment in disruptive technology activities, or engaging in trans-disciplinary mergers and acquisitions (M&As) involving new entrants. Therefore, early adopters would be in a strategic position to develop market share and core technologies. In the context of high probability of technological disruption and low probability of industrial disruption, disruptive technology will induce more impacts in terms of technology development. Incumbents should focus on acquisition of disruptive technology to sustain their profit levels. However, if the disruptive technology shows a greater influence on firms' potential market share, incumbents should explore new frontiers by M&As which reduce R&D risks. Second, technologies with disruptive potential such as RFID are characterized by powerful initiators or authorities, so government's policy makers are expected to facilitate disruptive technology development on the basis of forecasting analysis as well. For example, if the technology shows more technological disruption potential, policy makers should focus more on loose intellectual property right policy, which encourages technology diffusion and accelerates technology upgrading. Despite the work that has been done, there still exists limitation that should be studied in the future. First, due to the limitations of patent data as a proxy of innovative activities, the accuracy of the results may be affected in several ways. For one thing, patent thickets or low-quality patents may produce noise in the process of technological trend forecasting. For another, the probability of being disrupted in any application field is affected by the tendency to patent inventions. For example, if firms protect intellectual property through secrecy rather than patenting then there is a strong likelihood that the disruptive probability will be underestimated. Second, despite attempts to improve the accuracy of forecasting methods, the development of technologies, including disruptive technologies is subject to unanticipated factors from the social and economic environment. High level of uncertainty therefore is characteristic of the process. Therefore, as a

concluding remark it is important to bear in mind that in most circumstances, disruptive technology emerges by chance and diffuses in varies technological areas explosively. Even if patents are agreed as the most useful proxy for understanding technology development trends, there is still need for complementary methods of analysis.

Acknowledgments

This paper is mainly supported by the National Social Science Foundation of China (11&ZD140) and the Ministry of Education Social Science Youth Foundation of China (Grant 14YJC630071). During the author’s visit in the University of Manchester, her work got financial support from Chinese Scholarship Council and academic support from Manchester Institute of Innovation Research at the Alliance Manchester Business School.

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