Effects of SMEs’ collaboration on service R&D in open innovation

Yongyoon Suh
PhD Candidate, Department of Industrial Engineering, Seoul National University, Republic of Korea

Moon-Soo Kim
Professor, School of Industrial & Management Engineering, Hankuk University of Foreign Studies (HUFS), Kyongki-do, Republic of Korea

Abstract

This study analyses the effects of four types of collaborative activities on the R&D performance of service SMEs in the context of open innovation: in-house R&D (non-collaboration), technology acquisition, R&D collaboration, and networking. For this, the study employs data envelopment analysis, a power tool that uses multiple inputs and outputs to measure the relative efficiency of service SMEs' innovative activity. The results indicate that technology acquisition is the most efficient type of collaborative activity for innovation of service SMEs. More specifically, in-house R&D, technology acquisition, and R&D collaboration are positively related to product/service innovation, patenting activity, and process innovation, respectively. However, networking is not positively related to any three types of innovation outputs. In addition, the SMEs' strategic focus did not match their strategic purposes, suggesting a need for adjusting their innovative activities. The results have important implications for managers and policy-makers interested in facilitating open innovation in service SMEs through various collaborative activities.

Keywords

collaborative activity, collaboration, open Innovation, efficiency, service SMEs, R&D in the service sector, data envelopment analysis

Article Text

Firms typically envision being successful through innovation at the international level. Some of the major factors influencing the success and innovation of firms include R&D and commercialisation based on internal resources (Lichtenthaler, 2008; Chesbrough & Crowther, 2006). Resources developed through internal R&D can act as a formidable barrier preventing potential competitors from entering the same market (Schilling, 2008; Chesbrough, 2003b). However, firms face various problems such as limited amounts of time available for R&D and rapid market changes arising from fluctuating customer needs (Chan et al., 1997). Internal R&D efforts can be inefficient and wasteful in that such efforts require the use of many resources and substantial amounts of time, making it intractably difficult to capture growth opportunities and manage the firm's innovation activity (Kuemmerle, 1997; von Hippel, 1988; Lane & Lubatkin, 1998). Consequently, the firm's organisation and R&D strategies have to be designed such that they can satisfy diverse global needs. Further, its R&D process must increase the likelihood of R&D outputs becoming both technically and commercially successful.

To survive in increasingly competitive markets, firms have started to incorporate external resources from other firms for the growth and success of their business. In particular, the expansion of boundaries between firms has contributed to this phenomenon. Because of the abundance of external ideas in the global market and increasing collaboration among firms, an increasingly large number of multinational firms have been pursuing innovation activity in partnership with other firms (Dodgson et al., 2006). Noteworthy is the use of both internal and external ideas through collaboration for coping with rapid market changes and developing new growth engines. Such initiatives have sparked growing interest in new R&D paradigms for exploring how firms manage their collaborative efforts. In the wake of these changes, the concept of open innovation has been highlighted with collaborative activities such as technology acquisition, R&D collaboration, and joint venture activity.

The concept of open innovation embraces the strategic intent behind the use of both internal and external resources and is defined as the dynamic capability to manage technology both within and outside firms. Collaborative activities for the use of external resources reflect the core role of open innovation, that is, the enhancement of the performance, productivity, and sales of firms (Lichetenthaler, 2008). Open innovation can be achieved by integrating strategic activities such as technology acquisition and transfer, R&D collaboration, joint venture activity, and networking. A number of empirical studies have examined the efficiency of R&D by analysing technology acquisition and commercialisation strategies and evaluating technologically innovative activities (Arora, Fosfuri & Gambardella, 2001; Kline, 2003; Amara & Laundry, 2005; Gassmann & Reepmeyer, 2005). In addition, some studies have reported that goals and determinants of R&D cooperation vary according to partners and the type of R&D (Fritsch & Lukas, 2001; Tether, 2002; Belderbos, Carree & Lokshin, 2004). In this sense, collaborative activities represent one of the most important factors in open innovation, in which the effects of such activities can vary according to the type of activity.

Three key issues remain largely unexplored and unexploited: types of collaborative activities, target industrial sectors, and methods. First, the question of whether innovation performance varies according to the type of collaborative activity has typically remained in the context of open innovation. Previous studies of collaborative activities have typically examined their effects on firm performance by focusing on only one particular type of collaborative activity (Criscuolo & Haskel, 2003; Belderbos et al., 2004; Sampson, 2007; Lhuillery & Pfister, 2009). In addition, rather than types of collaboration, they typically focus on types of collaborative partnerships such as university-firm, university-government, and institute-firm partnerships (Belderbos et al., 2004; Eom & Lee, 2010). Thus, there is a need for a better understanding of how different types of collaborative activities influence firm performance, which should have important implications for R&D management.

Second, in terms of a target industrial sector, most of the previous studies have focused on the manufacturing sector. However, there has been growing interest in innovation in the service sector and R&D in the service sector in the context of the global economy and economic growth. In fact, the value added from the service sector typically accounts for more than 70% of total value added in developed countries (Howells & Barefoot, 2007). With such changes in the economic structure, innovation activity has played an increasingly important role in improving service productivity and quality. Thus, research on collaboration in the service sector has attracted increasing attention from both academia and practice. In particular, open innovation in small and medium-sized enterprises (SMEs) in the service sector has become a topic of special interest (West, Vanhaverbeke & Chesbrough, 2006; Narula, 2004; Edwards, Delbridge & Munday, 2005). SMEs have more difficulty than large firms in exchanging external resources because they have fewer technological resources (Narula, 2004). In addition, SMEs have fewer resources to develop and manage the whole innovation process internally (Edwards et al., 2005). SMEs are more likely to lack sufficient capabilities in manufacturing, distribution, marketing, and extended R&D (Lee et al., 2010). Thus, for SMEs, collaboration is a particularly important factor to improve their own R&D performance.

Finally, in terms of methods, most of the studies conducted a relatively simple quantitative analysis based only on the number of innovation such as how many develop new products or how much improve processes (Klomp & van Leeuwen, 2001; Belderbos et al., 2004; Lhuillery & Pfister, 2009; Eom & Lee, 2010). Although these studies have also examined innovation outputs in terms of labor productivity or sales in either the manufacturing or service sector, they have typically considered such outputs only individually. For example, van de Vrande et al. (2009) examined the differences of SMEs' innovative activities between the manufacturing and service sectors, but the descriptive statistics were only conducted. In this regard, the simultaneous consideration of multiple inputs and outputs can be more relevant for a collective analysis of all the important factors in collaborative activities. For this, data envelopment analysis (DEA) can be used to evaluate relative efficiency based on the simultaneous consideration of multiple inputs and outputs (Cooper, Seiford & Tone, 2000). Because the weight of inputs and outputs can be extracted through DEA, it can be used to compare their effects on firm performance based on the type of collaborative activity.

In this sense, the present paper addresses the effect of collaborative activities on SMEs' efficiency in the service sector by employing DEA within the theoretical framework of collaborative activities for open innovation. The paper provides an in-depth analysis of the effects of different modes of collaborative activities on the performance of service SMEs. In this study, we consider the following three major modes: 1) technology acquisition or licensing as customer-provider relationships, 2) R&D collaboration as a strategic alliance, and 3) networking as an inter-firm alliance. We analyse the effects of each type of collaboration activity by employing various input variables, including R&D investment and labor intensity, and output variables, including the number of service and process innovation outputs and patents. In addition, DEA is employed to assess the R&D performance of service SMEs by using multiple inputs and outputs. By the DEA, efficiency of each collaborative activity can be evaluated based on relative efficiency. Also, the activities that firms engage in today can be found based on the weight of inputs and outputs. To empirically test the effectiveness and strategic focus, we review and use the 2006 Korea Innovation Survey (KIS 2006) - published by the Science and Technology Policy Institute (STEPI) of Korea - for the service sector.

The rest of this paper is organised as follows: Section 2 provides a brief review of open innovation in SMEs by focusing on the characteristics and types of collaborative activities. Section 3 discusses the research methodology and the DEA approach and provides the data, variables, and descriptive statistics. Section 4 presents the results and discusses the managerial and policy implications by focusing on the effectiveness and strategic focus of each collaborative activity and Section 5 concludes.

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