TPRC45

Over the last couple of years, considerable attention has been focused on the Internet of Things (IOT). Through combining a range of technologies with reductions in the cost and size of the components, the IOT has begun to grow – not only is the number of connections rapidly growing, but it can now be found across an ever wider array of sectors. Vodafone alone, for example, now claims to have more than 50 million IOT (Roberts, 2017). While IOT technologies are produced in industries such as aviation/automotive, electronics, medical equipment, software and services, telecommunications and computer hardware sector (Sadowski, Nomaler et al. 2016), they are applied in a large variety of sectors such as smart cities (Baccarne, Mechant et al. 2014; Anthopoulos 2015), smart energy (Gans, Alberini, & Longo, 2013) or smart industries (Da Silveira, Borenstein et al. 2001; Fogliatto, Da Silveira et al. 2012).

The economic literature suggests that patent analysis can be used to examine the knowledge base and the technological diversification of companies (Kogut and Zander 1992; Teece, Pisano et al. 1997; Zack 1999). As the existing knowledge of a firm provides a critical ingredient of competitive advantage and corporate success, the extent to which companies utilize technological diversification as a strategy to enter into new technological areas has only recently begun to be investigated (Kodama 1986; Granstrand 2001; Breschi, Lissoni et al. 2003; Garcia-Vega 2006; Lin, Chen et al. 2006). Technological diversification has been defined as the extent to which firm use their knowledge base to diversify into relevant or irrelevant technological fields (Kodama 1986; Lin, Chen et al. 2006). In this respect technological diversification allows firms to enhance their competitive advantages in the market (Garcia-Vega 2006). In this context, Sadowski, et al (2016) have shown that a higher degree of technological diversification can lead to valuable technological specialization in new emerging technological fields such as the Internet of things (IoT) (Sadowski, Nomaler et al. 2016).

Research has shown that the entry decisions of incumbent companies into new markets are affected by convergence (i.e., the blurring of boundaries between hitherto separate sectors) and increased competition in existing markets (Katz 1996). More recently, it has been demonstrated that firms prepare for a possible entry into these markets by anticipating and monitoring of processes of convergence of different sectors (Curran, Bröring et al. 2010; Curran and Leker 2011). As a response to convergence, companies diversify into new markets based on their existing competencies and resources since they change at a much slower pace than technologies and market conditions in converging sectors. Within the resource-based view theory (Wernerfelt 1984; Barney 2006), diversification into new emerging markets has been conceptualized as a “Blue Ocean” strategy (Kim and Mauborgne 2005; Kim and Mauborgne 2014) aimed a discovering (and benefiting) from pioneering innovations in these markets (van de Vrande, Vanhaverbeke et al. 2011). In exploring new technological opportunities in emerging markets, incumbent companies are able to enter into “blue oceans” of uncontested market space instead of battling competitors in traditional “red oceans”. In entering a new “blue ocean” market incumbent companies are able to unlock new demand as competition is irrelevant in these markets (Kim and Mauborgne 2014). In this tradition, research has rarely addressed the extent to which technological diversification into new markets has improved the knowledge position of incumbent companies. As technological diversification into IoT has been a common strategy of ICT companies over at least the past twenty years, large differences persist with respect to their positioning in these new emerging markets (Sadowski, Nomaler et al. 2016).

We follow Sadowski, Nomaler & Whalley (2016) in terms of defining the IOT. This definition enables us to identify relevant patents, which are then allocated to a specific company. Our study identifies 1322 ICT companies involved in IoT technologies which we classified according to the similarity of their patent profile. We group companies together on the basis of their patenting activity, thereby identifying a series of clusters. Given the volume of IOT patents and the number of companies involved, we then focus our analysis on healthcare and energy. Both sectors are often discussed in terms of being characterised by a series of challenges that the IOT can, at least partially, help to resolve through collecting more data, facilitating its analysis etc.
Not only does our analysis identify the leading actors present in the healthcare and energy areas, as determined by the number of patents and technological diversification, but it also demonstrates that previous experience of ICT patenting does not necessarily result in a substantial presence in these two areas. One way that this can be conceptualised is in terms of “red oceans” and “blue oceans” noted above (Kim & Mauborgne, 2015). We explore this distinction within healthcare and energy by investigating the extent to which the IOT patent portfolios of companies overlap with one another. We find that there is considerable variation in the overlap that exists across our sample.