Networks, weak signals and technological innovations ... - CiteSeerX

ENTREPRENEURSHIP & REGIONAL DEVELOPMENT, ?? (200?), 1–19

Networks, weak signals and technological innovations among SMEs in the land-based transportation equipment sector PIERRE-ANDRE´ JULIEN* and ERIC ANDRIAMBELOSON Institut de recherche sur les PME, Universite´ du Que´bec a` Trois-Rivie`res, Que´bec, G9A 5H7; e-mail: Pierre-Andre´[email protected]

CHARLES RAMANGALAHY De´partement de bibliothećonomie et des sciences de l’information, Universite´ de Montreál, Montreál, Que´bec H3C 3J7

On apprend plus par la conversation des Doctes, que par la lecture de leurs livres Les e´pistres de Seneque Translation by François de Malherbe, Paris, Anthoine de Sommaville, 1639, p. 21

Small and medium-sized enterprises, because of their limited resources, use a variety of sources and are linked to different networks to obtain the information they need to develop their strategy and then to gradually organize their environment. Among other things, networks keep them up-to-date with changes in the economy and allow them to take advantage of opportunities to innovate, thus remaining ahead of their competitors. The networks – personal or business – with which these firms interact the most are usually geographically or sociologically close by, embedded in the environment, and are known as strong tie networks. They generally supply signals in a familiar language, based on habit as well a good reciprocal knowledge, which are easy to understand. In addition to this, however, the most dynamic firms also have contacts with weak tie networks, which are further removed from the usual behaviours of entrepreneurs and provide weak signals that, while difficult to grasp and decode, nevertheless offer new, pre-competitive information that can support major innovations. Very little empirical research has been done so far to test the probability of this theory. This paper reports on the results of a survey involving 147 SMEs, all in the land-based transportation equipment sector. It confirms the importance of weak tie networks as opposed to other types of networks, recognizing their complementary contribution to technological innovation. The organization’s absorptive capacity is also found to be a significant intermediary factor in taking advantage of weak tie networks. Keywords: SMEs; technological innovation; strong tie networks; weak tie networks; strong and weak signals; absorptive capacity.

1.

Introduction

There is no doubt about the importance of innovation, especially technological innovation, for both large corporations and small firms in a new economy based on knowledge derived from training and information (see, for example, Eliasson 1990 or Entrepreneurship and Regional Development ISSN 0898–5626 print/ISSN 1464–5114 online # 200? Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/0898562032000142064

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Foray 2000). In SMEs, innovation is generated by all kinds of accumulated complex information, often driven by clients and changes in raw material supplies, or intuition, the organization’s knowledge and know-how (Gaglio and Taub 1992, Pacitto et al. 2002). Such information is complemented or enriched by networks that may be linked to watch systems, usually informal in nature in SMEs (Julien et al. 1999). Granovetter’s (1973, 1982) sociological theory identifies two main types of networks, namely strong tie networks and weak tie networks. In SMEs, strong tie networks can be subdivided into personal networks that serve basically to criticize, to complete or to support1 managerial decisions if needed and to link with other information sources, and business networks that, as their name suggests, promote business. Weak tie networks are used much less frequently by business people, and their signals are therefore weaker, requiring more effort to be understood, interpreted, complemented and used appropriately.2 Research centres and universities are good examples of weak tie networks that generally provide weak signals for business people (Organization for Economic Co-operation and Development [OECD] 1993). Weak signals are also found within the organizations themselves, for example in the form of tacit information from staff, which requires a special organizational culture to become explicit and rich, as shown by Nonaka (1994). Granovetter (1973, 1982) points out that new ideas and hence change or innovation in organizations derive principally from these weak tie networks, since strong tie networks tend to reproduce the same mental representations and hence support habitus (Bourdieu 1967). Weak tie networks can also act as ‘bridges’ to other social entities, thus multiplying the new ideas that lead to change and innovation (Rothwell 1990, Sundbo 1998). This theory, interesting though it is, has only rarely been tested in economics, and even then, the findings suggest that the impact of weak tie networks on innovation are far from conclusive, despite the fact that growing numbers of researchers make reference to them (Uzzi 1996, Rueff 2001). For example, Hansen (1999), comparing the importance of innovation in 41 subsidiaries of a large American electronics group, showed that Granovetter’s theory was not obvious. To us, this suggests that weak tie networks in the research departments of these subsidiaries need to be complemented by strong tie networks, not only to add information that will allow for the development of research driven by weak signals, but also to make choices among potential innovations, so that resources can be directed appropriately. The research described here addresses this situation. More specifically, our goal is to see whether SMEs in contact with weak signal networks are more innovative than those that generally limit themselves to stronger signal networks. In doing this, we obviously do not wish to denigrate the importance of strong signal networks. The diverse internal and external resources levels of SMEs must also be taken into account when analysing their technological characteristics and hence their ability to convert information into innovation by first identifying it, then assimilating it and finally adapting it to their own needs (OECD 1992).

2.

The theoretical framework

The problem with processing information is that it is a very specific type of entity with characteristics such as intangibility (information only becomes palpable when it is transformed into knowledge and know-how), non-rivalry and non-exclusivity (the fact of being taken up does not remove it from circulation), perishability (it is difficult to

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store and often has value only for a limited time; it often needs redundancy) and variability (information that is good for one person is not necessarily good for another). Its value often depends on the receiver’s confidence in the informer, and on confirmation through other information. However, its final value is obtained from accumulation and complexification; in other words, each additional piece of information complements existing information, leading eventually to a decision or directing the search for new information. In research into information, it is necessary first to identify the constituent elements and measure their effects. It is for this reason that we will begin by defining what we mean by weak tie networks that generally provide weak signals and the organization’s ability to seize them for decisions and technological innovations, and will go on to clarify the relationship between these different aspects.

2.1

Weak signal networks and strong signal networks

The theory of weak tie and strong tie networks was derived from a sociological approach to social group behaviours. It must therefore be adapted for use with SME behaviours and the concept of technological innovation in SMEs. Researchers have used a range of criteria to operationalize the concept of signal strength. For Granovetter (1982), in most social groups or communities, strong tie networks are made up of friends or close acquaintances while weak tie networks are made up of more distant acquaintances or people with whom contacts are less frequent, those with whom there are few interactions over time, a lower emotional intensity, a lower level of confidence and little reciprocity. Other authors have proposed criteria such as low interaction levels, limited affection and short timeframes (Krackhardt 1992); non-reciprocal nomination (Friedkin 1980); or, rarely, frequency of interaction (Woodward 1988). In this latter case, weak tie networks would be composed of sources that are used less frequently than those making up strong tie networks (Granovetter 1973). As mentioned earlier, there are several types of networks in the small business sector, namely personal, business and information networks.3 Personal networks, which are specific to an individual entrepreneur, are generally composed of one or two friends, key staff members and one or two colleagues from school or university, about eight or nine people in northern countries, as some surveys have shown (Birley et al. 1991, Julien 1995, Drakopoulon Dodd and Patra 2002). Business networks are composed of sources with which the firm currently does business, including suppliers, equipment providers, distributors, transporters and so on. Some business network members may eventually join the personal network if contacts are frequent enough. Information networks, unlike personal and business networks, may provide weak or strong signals. They complement the information obtained from the other networks to support the firm’s ongoing development. Information sources can be divided into personal and impersonal, and again into formal and informal sources. As we said earlier, research has shown that small business managers turn most frequently to informal personal sources from their personal networks. These sources include clients, staff members, salespeople and suppliers (Johnson and Kuehn 1987, Brush 1992, Julien 1995). They generally give strong signals because of personal links. Formal sources provide raw information that must be sorted and interpreted. They include specialized publications, brochures and catalogues, business magazines, government publications and other reports.

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Among weak signal sources, some are related more specifically to new technologies. Such sources are located principally in research and educational communities and government organizations. They are composed mainly of research centres and universities, scientific advisors and other related public organizations (Smeltzer et al. 1991). Generally speaking, they are entities with which the entrepreneur has little contact, because of their hermetic language and very different concerns, but they can nevertheless provide a lot of new information (Ansoff 1975, Krackhardt 1992, Hansen 1999). They are particularly important in that they help entrepreneurs think beyond what is known, look beyond what they are used to doing, and spot new opportunities for technological innovation (Hills et al. 1999). 2.2

Technological innovation

There are a number of questions surrounding the concept of technological innovation, concerning first its definition, and second its basis. Innovation is different from invention, although both are part of the same process. The idea of change and novelty derives basically from invention, in the case of both improvements and completely new contributions (Albernathy and Clark 1985, Thom 1990). Invention is often a result of individual capacity, while innovation is necessarily a collective process (Amendola and Gaffard 1988, Foray 2000), mainly disruptive, that improves or changes elements in the value chain, such as products, equipment, processes, distribution methods and so on. The main question concerning technological innovation concerns the nature of technology itself. Some authors, such as Carrier and Garand (1996), state that innovation can only be described as ‘technological’ if it has the effect of transforming the study of technical knowledge. Others regard it simply as the process by which an organization adopts new technologies. The consensus from the literature is that technological innovation can be defined as technical change or the adoption of new processes and technologies (Dewar and Dutton 1986, Pre´fontaine 1994). 2.3

Information absorptive capacity

Not all enterprises innovate. Those that do not often find it difficult to absorb new information from outside their own boundaries, especially the weak signals discussed earlier. Here, the term ‘absorptive capacity’ means the acquisition of new, possibly tacit information, its conversion into new opportunities and its ultimate use. This notion is drawn from some of the work on large enterprise done by Cohen and Levinthal (1990, 1994), and completed by Weick (1993) and Choo (1999); both these authors point out that an organization has a greater capacity to assess the value of new information in a given field, assimilate that information, reduce its uncertainty, give it meaning and apply it, if it already possesses rich knowledge in the same field.4 2.4

Research model and hypotheses

Strong tie networks tend to be composed of the same type of people, and the information they can provide is often redundant or repetitive. They are therefore not a significant channel for new ideas, but serve instead, as we said earlier, to confirm

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the opinions of their members and, in the case of entrepreneurs, to consolidate their business decisions. In contrast, weak tie networks are composed of people who are not used to working together. They facilitate the circulation of new ideas, and hence innovation, precisely because of these personal differences (Fine and Kleinman 1979). Ansoff (1985) said that, in such a case, although the signals may be ambiguous, fragmentary or uncertain, they can nevertheless be anticipatory in that they call existing knowledge into question or add new elements leading to innovation. Based on the above observations, we propose the following two hypotheses. Hypothesis 1: The more innovative the firm is, the more frequent its use of weak tie (and hence weaker signal) networks. Hypothesis 2: Weak signal networks are more likely than strong tie networks to trigger technological innovation. The availability of new information is not sufficient, of itself, for innovation. If its meaning is to be understood, the information has to be decoded, collected and converted into knowledge, know-how and decisions. Hansen (1999) points out that weak tie networks may not be ideal channels for transferring complex knowledge because there are fewer interactions through which the information can be assimilated. Chollet (2002), analysing Burt’s theory (1992) of structural holes, shows that this is not contradictory to the role of weak tie networks linked to the entrepreneur’s needs by strong tie networks. However, partnerships, formal or not, with these types of networks, generally composed of technological sources, need a certain organizational capacity that comes with the presence of gatekeepers and boundary spanners,5 to ‘absorb’ the information, give it meaning and convert it into knowledge or varying levels (intensities) of innovation, as shown in figure 1. Based on these observations, we propose the following hypotheses. Hypothesis 3: The greater the information absorptive capacity of the firm, the more innovative the firm. Hypothesis 4: The greater the firm’s information absorptive capacity, the greater the impact of networks generally providing weak signals. 3.

Research methodology

The problem described above is relatively causal and explanatory in nature, even if the links are exploratory. Yin (1989) and Miles and Huberman (1991) both recommend quantitative methodologies for such problems, based for example on surveys. Information absorptive capacity

Weak signal networks

Figure 1.

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Technological innovative intensity

Research model.


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3.1

The survey questionnaire

The survey was carried out in 1997 by the Land-Based Transportation Equipment Watch Team at the Institut de recherche sur les PME, Universite´ du Que´bec a` Trois-Rivie`res. The questionnaire was divided into four sections, the first covering the general features of the firm, the second its information sources and the frequency of use, the third its current watch process, and the fourth its use of technology, R&D behaviours and specific information needs. In the research described here, we concentrate exclusively on the sections of the questionnaire containing variables that we can use to measure the different aspects of our model (i.e. technological innovation, absorption capacity, information and the different types of sources). Many questions were closed (e.g. types of customers and their importance for turnover, the existence or absence of R&D activities, etc.). Others were presented on an ordinal scale (mainly the frequency of interaction between different information sources or the importance of the type of information needs, etc.), while others were in numerical form (e.g. number of years of experience or number of R&D staff). The questionnaire was mailed to 585 manufacturing firms, most (90%) with fewer than 250 employees. All were members of Que´bec’s land-based transportation equipment round table, as identified by Que´bec’s Department of Industry and Trade. This use of just one market sector helps to reduce variance in the results. The questionnaire was accompanied by a letter from the round table president, a businessman elected by the table members, encouraging the firms to take part in the survey. A total of 146 firms replied, giving a response rate of 25%. Tables 1 and 2 present the sample size and industrial representativity, which is fairly good (with p between