Does Social Software Support Service Innovation?

Abstract

Recent Internet technologies and web‐based applications, such as social software, are being increasingly applied in firms. Social software can be employed for knowledge management and for external communication enabling access to internal and external knowledge. Knowledge, in turn, constitutes one of the main inputs to service innovation. Hence, social software has the potential to support service innovation. Using data from German IT and knowledge‐intensive service firms, this is the first paper that empirically analyses whether the use of social software applications triggers innovation. It refers to a knowledge production function in which social software use constitutes the knowledge sourcing activity. The results reveal a positive relationship between social software and service innovation. Since this result is robust when controlling for former innovative activities and the previous propensity to adopt new technologies and to change processes, the analysis suggests that the causality runs from social software to innovation.

Key Words:

• Social Software
• Web 2.0
• Service Innovation
• Knowledge Management

JEL classifications:

• O31
• O33
• M10

Notes

1. A blog is a web page where entries are ordered chronologically, beginning with the latest entry and the possibility to comment on the entries. A wiki is a web page where every user can add or change content. An online community is a virtual community of users in the Internet.

2. Viral marketing refers to marketing techniques that use existing social networks to produce increases in product, brand or campaign awareness. The spread of communications is based on word‐of‐mouth and enhanced by the network effects of the internet (Göhring et al., 2006).

3. Service products are harder to store, to transport and to export compared to manufacturing products (intangibility). There is intense interactivity between supplier and client and, in most service processes, both have to be present for the transaction (interactivity). The production and consumption of services occur at the same time and place (coterminality).

4. See, for instance, Barras (1990), Evangelista (2000), Evangelista et al. (1998), Freel (2006), or Licht and Moch (1999).

5. A great deal of the literature on service innovation considers knowledge‐intensive business services (KIBS). Although this paper uses data from ICT and knowledge‐intensive services, it will not focus on the KIBS‐literature and the innovation in KIBS. For a review, see, for instance, Koch and Strotmann (2006) or Leiponen (2005).

6. See, for instance, Arvanitis (2008), Arvanitis and von Arx (2004), Hipp et al. (2000), Koch and Strotmann (2006), Leiponen (2005, 2006), Love et al. (2009), Schibany et al. (2007) and Tether (2005).

7. O'Reilly (2005) coined and clarified the term Web 2.0 in his seminal article. According to him, the basic characteristics of applications typical for Web 2.0 are: the web as a platform, harnessing collective intelligence, data‐driven applications, end of the software release circle (‘perpetual beta’), lightweight programming models, software above the level of a single device and rich user experiences.

8. See, for instance, Alby (2007) and Hippner (2006) for a definition of these applications.

9. See, for instance, Raabe (2007), Döbler (2008) and the articles in Back et al. (2008) or in Hildebrandt and Hofmann (2006) for details, case studies and examples of the social software adoption in several business areas.

10. For further details on the nine industries, their industrial classification and their distribution within the sample, see the data description and Table A1 in the Appendix.

11. Former waves of the data have previously been used to analyse, for instance, the productivity effects of organisational change (Bertschek and Kaiser, 2004), the relationship between managerial ownership and firm performance (Mueller and Spitz‐Oener, 2006) and the impact of the age structure of the workforce on technology adoption (Meyer, 2009).

12. Love and Roper (1999) use an extended model of innovation activity and identify three main routes by means of which to obtain main ingredients for innovation: R&D, technology transfer (intra‐firm phenomenon) and networking (involves inter‐firm relationships), which in turn are the main inputs in their innovation production function. Acs et al. (2002) implement a Cobb–Douglas function with two inputs as their knowledge production function. These two inputs are industry R&D and university research.

13. For more details on the Probit model, see Wooldridge (2002). All calculations and estimations of this paper were done with STATA 10.0.

14. Summary statistics of the variables can be found in Table A2 in the Appendix.

15. In the third quarter of 2005 and first quarter of 2007, the firms were asked ‘Have you offered a new or significantly improved service during the last twelve months?’ (product innovation) and ‘Have you adopted new or significantly improved technologies (e.g. new data processing systems, Internet) in your company during the last twelve months?’ (process innovation).

16. Only the average marginal effects (sample averages of the changes in the quantities of interest evaluated for each observations) are discussed in the following. A table containing the coefficient estimates is available upon request.

17. The same regression has also been run, including only the dummy variable for former product innovation, including only the dummy variable for former process innovations and including only a variable representing at least one of the two. The results did not change qualitatively and are available upon request.

18. A table containing the coefficient estimates is available upon request.

19. A table of results is available on request.