Industry Institutions, Social Capital and Firm ...

Industry Institutions, Social Capital and Firm Participation in Industrial Development Philip R. Tomlinson School of Management, University of Bath (UK) Address for Correspondence: School of Management University of Bath Bath, BA2 7AY Tel: +44 (0) 1225 383798 Fax: +44 (0) 1225 386473 Email: [email protected]

Revised January 2011 Abstract Industry institutions and trade associations represent the ‘collective interests’ of an industry’s constituent firms and play a prominent role in industrial development. Yet the strength and efficacy of industry institutions to promote these ‘collective interests’, depends upon the active support and participation of member firms. This is essentially a collective action problem. By aligning Olson’s (1971) logics of collective action framework with Nahapiet and Ghoshal’s (1998) dimensions of social capital, this paper uses survey data from 381 firms from across 5 UK manufacturing sectors to explore the factors that affect the propensity for firms to participate in industry institution led initiatives. The results suggest the propensity of firms participating in collective activities rises where ‘shared interests’ emerge, although the over-riding factor is the extent to which firms perceive their own ability to influence and shape the direction of such activities (the logic of influence).

Keywords: Industry Institutions, Industrial Development, Logic of Collective Action, Social Capital and Firm Participation. JEL Codes: D71, L14, L16

1.0 Introduction Industry institutions have long been regarded as playing a significant role in industrial development (see, for instance, Malerba and Orsenigo, 1996); both as service providers and as the industry’s ‘collective voice’ in representations with the state and other interlocutors. Both roles are important in aiding firm performance, promoting wider industry interests in the socio-political domain and in determining the industry’s long run trajectory (see Le Gales and Voelzkow, 2001; De Propris and Wei, 2007). Indeed, while often being categorised under the general sphere of ‘business associations’, the industry specific nature of their role distinguishes industry institutions (and trade associations) from more generic business associations such as geographically bounded Chambers of Commerce, which cover a range of (local) business sectors (for a typology of business associations, see Bennett 1998a, p.248-249).

However, in terms of being a ‘collective voice’, the strength of industry institutions and their ability to clearly define and their subsequent efficacy to deliver collective objectives largely depends upon the active participation of their member firms (Aldrich and Fiol, 1994; Donor and Schneider, 2000). This is a collective action problem often beset with issues of ‘free-riding’ and difficulties in aligning diverse member interests (Olson, 1971, Streeck, 1989). In the UK, this problem can be particularly acute since membership of business associations is largely voluntary, while diverse business groupings and interests have sometimes led to a degree of a fragmentation in membership with in some cases, numerous (often overlapping

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and/or competing) associations emerging (Bennett, 1998b; Streeck and Schmitter; 1985; Lanzalaco, 1992).

Previous studies of industry institutions (and business associations) have tended to focus upon their role and type, their activities and the nature of their membership bases (see, for instance, Bennett, 1998a, b and c). The aim in this paper, however, is to focus upon the collective action problem that industry institutions face in encouraging member firms to actively participate in joint actions; namely industry institution led initiatives relating to industrial development (such as organising and participating

in

industry

meetings,

campaigns

and

promoting

industry

issues/development). Participation though is costly for firms in terms of time and additional resources and some will choose to take either a passive role (i.e. ‘freeride’) or an active role. The extent of firm participation will largely depend upon their own perceived marginal benefit and influence in undertaking such activities and, crucially the degree of social capital existing between firms, which can encourage collective action. In exploring the factors affecting the propensity of firms to participate, this study therefore aligns Mancur Olson’s (1971) logics of collective action framework with Nahapiet and Ghoshal’s (1998) structural, relational and cognitive dimensions of social capital. This facilitates a novel exploration of industry institutions’ (governance) structures, social capital and the extent to which firms share collective goals within an industry, and how these impact upon collective action. The study itself is largely empirical, drawing upon survey data from 381 firms from across 5 UK manufacturing sectors (aerospace, ceramics, information technology and software, textiles and healthcare) and which sought information upon 2

the extent of firm participation in industry institution led initiatives, the nature of industry institution structures and collective (shared) values within each sector.

The remainder of this paper is set out as follows. Section (2) provides a brief contextual overview of the role and functions of industry institutions. Section (3) then considers the factors affecting the propensity of firms to participate in industry institution led initiatives, drawing upon the logics framework and Nahapiet and Ghoshal’s (1998) dimensions of social capital. From this discussion, two testable hypotheses are conceived which consider the competing logics of collective action and institutional structures and also the degree to which a ‘shared vision’ influences joint action. In Section (4) details of the study’s research methodology, survey database, model specification and estimation procedures are provided. Section (5) reports and discusses the empirical results for both a pooled (across industry) model and a supplementary model, which considers specific sectors. Finally, Section (6) concludes.

2.0 The Role of Industry Institutions: An overview The main functions of industry institutions (and trade associations) relate to representation and lobbying on the industry’s behalf and the provision of member services, although additionally (particularly in local associations) community support, collective marketing and ‘club’ and social/peer support are also important (see Olson, 1971, Bennett, 1996, 1998a). For Olson (1971), firms primarily form and join associations to provide access to collective (industry) services and the representative function is a ‘by-product’: a cross-subsidy exists to fund 3

representation and lobbying activities so as to overcome a collective action problem whereby firms opt out and free-ride (see also Bennett, 1998c).

In recent decades, the service function has become more extensive, assuming greater significance for many firms; the general withdrawal of state industrial support has encouraged industry institutions to fill the vacuum and provide collective services or ‘semi’ public goods (see Helmsing, 2001; Best, 1990; Meyer-Stamer, 1997). Service provision varies across industry institutions, but can include specialist advice, assistance and information on a range of business and industry wide issues (such as accountancy and tax advice/information on environment, labour and industry specific regulation), along with the organisation of trade fairs and industry wide marketing promotions as well as export services. They may also help to run public-private partnerships and co-ordinate networks of firms on particular projects, while some are also actively involved in education and vocational training for industry employees and/or providing firms with access to specific Research and Development centres (Nelson and Nelson, 2002) [1].

Theoretically, firms may undertake (some of) these activities in-house or access them through professional service providers; indeed, the latter can potentially undermine industry institutions by offering cut-price service provision since they do not subsidise the representative function (see Stigler, 1974). However, there are a number of factors that generally favour industry institutions. First, collective service provision allows industry institutions to benefit from scale economies and staff expertise, while the general accessibility of their assets to member firms, reduces the 4

transaction costs firms entail when using market providers (Bennett, 1998b). Secondly, industry institutions are information exchanges which, under bounded rationality and partial information sets, can reduce market uncertainties and enhance firm performance (Simon, 1979). Kirby (1988), for instance, demonstrates that trade associations act as efficient conduits for information exchange, particularly when these are based upon quid pro quo arrangements by which only participating firms in the information sharing process receive the aggregated information collated by the trade association [2].

It is, however, the representation function that differentiates industry institutions from being mere private service providers giving the industry credibility and legitimacy in the public domain. Industry institutions provide the social context and opportunity for securing what Aldrich and Fiol (1994) define as i) cognitive legitimacy - the degree of acceptance among relevant (industry) stakeholders, particularly in relation to industry norms (and firm behaviour), quality standards and ‘best practice’ [3] and ii) socio-political legitimacy - the degree of conformity to the wider public’s expected levels of industry competence(s) and the degree of awareness of the industry’s wider relevance in the socio-political domain (see also Hannan and Freeman, 1986, 1989). Indeed, in the political sphere, industry institutions can provide members with a recognised legitimacy in leading industry initiatives,

lobbying

for

state

support/contracts

and/or

‘favourable’

legislation/regulation (Nelson, 1994). Given their industry specific knowledge and expertise, they tend to have a significant degree of influence with policy-makers at both national and supra-national levels (Bennett, 1998c). Successful lobbying in such 5

cases can both raise and stabilise the organisation’s membership, which in turn strengthens the industry’s collective strength and legitimacy (see Aldrich and Staber, 1983, 1989).

3. Firm Participation in Industry institution led initiatives 3.1 Collective interests and the Logics Framework The efficacy of industry institutions to play a key role in industrial development depends to a large extent upon the active participation of member firms and their provision of additional resources in industry institution led initiatives (see Bennett, 1998c, Aldrich and Fiol, 1994). These resources may include time commitments, additional finance and possibly employees in support of industry wide and institution led activities. However, while a close and prominent relationship with their industry institutions can infer prestige and status upon firms among their peers (and possibly enhance networking opportunities), such costs can be prohibitive and limit firm participation. More generally, firm participation is tempered by collective action problems which typically beset organisations of collective interests. These relate to ‘free-riding’ and trying to accommodate diverse membership interests (and territories). In the first instance, there is an incentive for firms to ‘free-ride’ upon the collective actions of others which, in the extreme, can ensure that collective action does not occur even where there are large groups of common interests (Olson, 1971) [4]. Secondly, diverse member interests and demands can make it difficult for industry institutions to formulate and pursue consistent long term collective objectives. This is particularly the case where large groups of firms are involved, since the costs of organising collective action are greater and the incentive for 6

collective action diminishes (see also Streeck, 1989). Such collective action problems weaken the industry’s legitimacy in the public domain and may even hamper industrial development.

As representatives of collective interests, an industry institution’s organisation and objectives are largely determined by the strength of competing forces: the ‘logic of membership’ and the ‘logic of influence’ (Olson, 1971; Schmitter and Lanzalaco, 1989). The ‘logic of membership’ relates to an institution’s responsiveness to the varying interests and demands of its members. In this regard, ‘inclusive’ structures provide a sense of collective identity, but may lack the organisational stability and ability to formulate and pursue long-term objectives. The ‘logic of influence’ relates to the role that associations play in negotiating on their members’ behalf with public agencies and other state interlocutors, which often involves compromise and a possible revision of collective objectives by those negotiating on the industry institution’s behalf. This risks the danger of becoming co-opted within the state apparatus and losing the support of their wider membership. Thus there is an inherent tension between the two logics (Streeck, 1989; Schmitter and Lanzalaco, 1989).

3.2 The role of social capital Within this sphere, a critical facet in the propensity of firms to participate in institution led initiatives is the nature and degree of social capital existing between firms. The concept of social capital has become widely used in the social sciences with a range of interpretations, although it is generally regarded as a collective resource that arises from (and is shaped by) social relations between actors within a 7

network (Adler and Kwon, 2000, Payne et.al, 2010). In the context of the current study, it is particularly instructive to consider Nahapiet and Ghoshal’s (1998) structural, relational and cognitive dimensions of social capital. While sometimes considered as separate facets of social capital, they are in fact highly interrelated (ibid, 243).

The structural dimension relates to the loci of actors within a network, the network structure and the degree to which it encourages social interaction between actors. In the context of the current study, one might consider how the industry institution’s organisational and governance structure facilitates firm participation in collective action (see Section 3.3). The relational dimension explores the nature of linkages and the degree to which actors are embedded in such networks (as defined by the structure). This facet embodies business liaisons, particularly behaviour, trust and attitudes that exist between firms (and their representatives) within the network or via the industry institution. Finally, the cognitive dimension refers to the collective goals or ‘shared vision’ that emerge (via the relational dimension) between actors within the network or through the institution (ibid, 1998; see also Tsai and Ghoshal, 1998). By aligning Nahapiet and Ghoshal’s (1998) dimensions of social capital within the logics framework, a number of useful insights emerge in relation to firm participation in industry institution led initiatives and these are considered in further detail below.

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3.3 The paradox of inclusive institutions With specific regard to the structural dimension and in particular, the institutions’ organisational and governance structures, Sacchetti and Sugden (2003, 2009) have argued that relatively ‘open’ and ‘inclusive’ industry forums (such as through trade associations) are likely to encourage more firms to exercise their Hirschmanian (1970) ‘voice’ and participate in the determination of their industry’s development. Stiglitz (2002) expresses similar sentiments in favouring ‘inclusive’ (industry) governance structures so as to enhance wider participation in development. Inclusive structures of course, will also reflect the underlying relational dimension of social capital; particularly the reciprocity of participating firms (and industry institutions) in their interactions.

Within the specific context of industry associations, Donor and Schneider (2000) emphasise the role of internal mediation between member firms for industry development. This is achieved where institutional structures are transparent and accountable and crucially facilitate intensive deliberation (among a wide set of members); this can promote homogeneous preferences among a diverse interest group leading to consensus over collective actions (in larger groups this may require extensive and frequent deliberation through various sub-committees). They suggest that such processes strengthen the institution and its remit. Similarly, Annen (2003) has also demonstrated that inclusive social networks, where there is a high capacity for communication and on-going dialogue, are particularly conducive for increasing participation in collaborative activities.

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This might imply institutional structures more closely aligned to the ‘logic of membership’ will increase firm participation. However, Annen (2003) also warns that the sustainability of collaborative activity is threatened when structures, particularly those affecting communication paths and levels of dialogue, are not raised concomitantly with higher degrees of ‘inclusivity’ within the network. In such cases, communication lines may become ‘fuzzy’, while violations of norms may go undetected and unpunished, reducing the degree of trust and social capital (see also Coleman, 1988) [5]. There may thus be an optimum level of ‘inclusivity’, with ultimately restrictions being enforced so as to facilitate cohesion between members and the effective implementation of an institution’s goals (see also, Adler and Kwon, 2000). Indeed, where inclusive and democratic processes do not lead to stable governance structures and clear collective objectives being formed the industry’s legitimacy in the socio-political domain may be compromised, which in turn will reduce the institution’s effectiveness and influence as a collective body (Streeck, 1989; see also Aldrich and Fiol, 1994). If the ‘logic of influence’ is therefore too heavily diluted, firms may be deterred from participating in industry institution led initiatives, since the perceived costs of participation may outweigh the potential benefits (Stigler, 1974).

Indeed, more generally, institutional governance structures geared towards the ‘logic of influence’ are particularly attractive for large(r) firms (Bennett, 1998a). Not only do their larger resources allow them wider scope to undertake institution led initiatives, but they are attracted by the opportunity such forums provide to engage with public agencies and interlocutors, where their ‘voice’ is likely to have greater 10

influence than smaller firms (Stigler, 1974). This may indicate that large(r) firms have a greater collective responsibility for the industry than smaller firms; in such cases, the latter might be content to reap the spill-overs of a strong institution - led by larger firms – in pursuing industry wide objectives. However, where industry institutions are overly reliant upon the resources of larger firms they may become, in the terms of Pfeffer and Salancik (1978, p. 258) ‘resource dependent’ upon the latter and subject to undue influence and pressure. Larger firms may thus seek the spheres of influence within their industry institutions to shape the industry development path and extract special value for themselves (Bennett, 1998a; Useem, 1984). If small firms’ interests are not represented or severely compromised, then they may refrain from participating in institution led initiatives (Stigler, 1974) [6].

In terms of encouraging active firm participation in industrial development, the structural dimension of social capital (and the related governance structure) within industry institutions thus highlights a paradox. In seeking to be ‘open’, ‘representative’ and ‘inclusive’ of all member interests (the logic of membership), industry institutions may encourage wider participation; however this participation maybe tempered by a dilution of the ‘logic of influence’. This leads to the following hypothesis:

H1: An industry institution’s degree of ‘inclusivity’ positively/negatively affects firm participation in institution led initiatives (collective action) in relation to industry development issues depending upon the relative importance of the logic of membership vis-à-vis the logic of influence. 11

This hypothesis might be further interpreted as follows; where firms are predominantly attracted by open forums and the logic of membership, then a positive association between the degree of inclusivity within the industry institution and firm participation (in collective activities) might be expected. On the other hand, a negative association will result if firms are primarily attracted by the logic of influence and the institution’s effectiveness. Furthermore, it might be expected that the relative importance and significance of these competing logics may well differ across industry environments and cultures (see Schmitter and Lanzalaco, 1989; see also Section 5).

3.4 Shared vision and collective action The propensity for firm participation in institution led initiatives is also likely to be shaped by relational and cognitive dimensions of social capital, both of which maybe entwined to promote collective goal formation (see Nahapiet and Ghoshal, 1998; Tsai and Ghosal, 1998). Recall, the relational aspect refers to the nature of inter-firm ties and the degree of ‘social embeddedness’ (Granovetter, 1985; see also Jessop, 2001). According to Granovetter (1985, 2005), social embeddedness’ is generally stronger in environments where there are high and frequent levels of social interaction between actors. Time is also an element, with relations carefully nurtured and maintained by participating actors over the long term being more likely to lead to stronger, enduring ties and higher levels of social capital (Anderson and Jack, 2002). Such environments facilitate reciprocity and enhance reputation for reliability among actors, which can generate trust and perceptions of trust, defined by Granovetter 12

(2005, p.33) as ‘confidence in others to do the ‘right thing’ (despite incentives to the contrary)’. The establishment of trust between actors is in turn an important facet in the cognitive dimension of social capital, which relates to the promotion of shared/common values and/or social norms. Shared values and norms are an embodiment of the degree of trust existing between actors and the extent to which they bring and keep actors together (Tsai and Ghosal, 1998; Barber, 1983).

Within industry networks, shared values and norms enhance cognitive legitimacy between stakeholders and acceptable behaviour which, re-enforced with collective sanctions, can promote a harmony of interests and a reduction in opportunistic behaviour (see Ouchi, 1980; Granovetter, 1992; also Jessop, 1998). Yet shared values and norms are not stationary but rather evolve over time, shaped by the (changing) collective decisions of member firms (see Hodgson, 2006). It is here where industry institutions play a critical role as the central forum for industry decision-making (see Le Gales and Voelzkow, 2001), facilitating co-ordination and extensive coalition building and interaction between firms. Amin and Thrift (1994) consider this process as constituting the ‘institutional thickness’ of an industry, with the degree of congruence emerging (between firms) being an indication of the industry’s collective strength. Nelson and Sampat (2001) have also likened (industry) institutions to evolving ‘social technologies’, which through firm interaction and participation, facilitate the social construction and evolution of (industry) shared values and beliefs, thus setting the pathways for industry progression and possibly providing a low (transaction) cost means of co-ordinating joint (industry) action.

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Indeed, in terms of promoting collective action, Donor and Schneider (2000) have noted how in the developing world, business and trade associations influence and shape the behaviour of firms, encouraging them to participate in (joint) activities that ‘they would not otherwise do’ (ibid. p.270). They suggest an institution’s strength lies in its capacity to induce member firms to commit resources and pursue collective goals in the industry’s development. Again, such joint commitments are more likely to occur where participating firms share beliefs and values (see also Taylor and Singleton, 1993); this being particularly important where firms and industry institutions seek to identify and pursue a common industrial strategy (Amin and Thrift, 1994) [7]. In exploring this cognitive dimension of social capital, Tsai and Ghosal (1998) define the construct ‘shared vision’; a cognitive process reflecting a set of collective values (or goals) within an organisation. They argue this process encourages individuals and groups to undertake (extra-curricular) actions and responsibilities - and also possibly combining and exchanging their resources and ideas - which can benefit the whole organisation (p.465). In applying the construct at an industry level, Molina-Morales and Martinez-Fernandez (2006) regard ‘shared vision as capturing the collective goals and aspirations of (industry) networks’ (p.509). In line with the above reasoning, the following hypothesis is therefore formulated:

Hypothesis 2 [H2]: The more firms ‘share a vision’ or believe in collective goals for their industry, the more likely they are to participate in institution led initiatives (collective action) in relation to industry development issues.

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4.0 Research Methodology 4.1 Database The study seeks to explore the factors affecting the propensity for firms to participate in their respective industry institution led initiatives over industrial development, focusing upon the two hypotheses relating to social capital outlined in the previous section. Data for the study was therefore collated from a postal survey of 2,537 firms across five important UK industrial sectors: aerospace, ceramics, information technology and software, textiles and healthcare (i.e. the manufacture of medical equipment and instruments). These sectors have a number of widely known and established industry institutions, which encourage dialogue and close relations with and between constituent firms. Indeed, the sample was drawn from the membership directories of the respective main industry trade associations – The Ceramics Industry Forum, Society of British Aerospace Companies (SBAC), Intellect and The Entertainment and Leisure Software Publishers Association (ELSPA), British Apparel and Textile Confederation (BATC) and the Association of British Healthcare Industries (ABHI).

The questionnaire was part of a wider study into these five sectors which also considered inter-firm co-operation and innovation, along with firm relationships with their industry institutions and industrial development issues. The survey also included questions on the firms’ characteristics and their business background. This paper is primarily based upon information obtained from the sections relating to firm relations with institutions and industry development. The questions covered a range of issues relating to institutions’ support for firms, the effectiveness and 15

representativeness of institutions and the attitude of firms towards towards collective goals in the industry (see Table 1). The questions themselves related to the previous three years of business trading (i.e. 2005/06-2007/08). In order to quantify the answers, a structured set of 5 point Likert scales were employed.

INSERT TABLE (1) HERE

The membership directories provided contact and background information on the majority of firms operating at the 4 digit Standard Industrial classification level in each of the five sectors. The postal questionnaire was sent out in early September 2008 and was addressed to the Managing Director of each firm listed on the respective membership directories and for which contact information was available (or could be attained from the company web-site). Where larger firms had subsidiaries, care was taken to exclude these and to approach only their parent company. In order to facilitate a higher response rate, a £1 donation was promised to a recognised charity for each completed and returned questionnaire received. A reminder was sent out three weeks after the initial mail-out and the final reminder was mailed two weeks later. In total, there were 455 responses providing a response rate of 17.9%. Tests for non-response bias were conducted by comparing the size of firms and the means of the variables under consideration of the early and late respondents (Armstrong and Overton, 1977) [8]; t-tests revealed no significant differences. Unfortunately a number of questionnaires contained incomplete responses in the sections relating to industry institutions and industry development issues. Consequently, the analysis was restricted to the 381 valid responses which 16

provided full and complete answers to the relevant questions. With 381 usable responses, the sampling error was 5.01% at the 95% confidence interval and is within the acceptable limits for survey research (see Oerlemans et.al, 2006) [9].

Data on the proportion of responses by firm size and age stratification is provided in Tables A1 and A2 in the Appendix. With regards to firm size, Table A1 compares the sample with data from the UK National Office of Statistics (2008) on the proportion of UK VAT registered units by employment sized bands (1-49, 50-249, greater than 250) across all five sectors. Apart from ceramics (where the sample appears to mirror the size distribution of the population of firms in the sector), the sample appears to be skewed towards larger firms (vis-à-vis the industry population). This apparent skew may be qualified somewhat in that the UK Office for National Statistics (2008) data measures the proportion of VAT registered units (based upon 2003 SIC codes) and does not specifically account for the ownership of such units. Since firms may own multiple units, the National Statistics data may overstate the number of smaller firms in the actual population. Hence, the differences in the size distribution of firms in the composition of the sample and the notional measure of the population may not be as considerable as appears. However, by design, the sampling frame has an inherent bias in surveying current members of industry associations, and membership may disproportionately cover larger firms. Insofar as the study does not explicitly claim to represent an isomorphic measure of the notional population of firms but rather aims to explore the propensity of member firms to actively participate in institution led initiatives the validity of the ensuing analysis should not be unduly compromised. This point though should be borne in mind when making 17

industry-wide generalisations from the study. Finally, while there are some sectoral differences with regard to younger firms in the sample, 76.4% of all firms are greater than 10 years old and have significant industry experience (Table A2).

4.2 Variables 4.2.1 Dependent Variable: Firm Participation Firms were asked whether they had been involved with their industry institutions in issues relating to the development of their industry. They were asked to indicate the extent of their participation on a scale ranging from 0 for ‘no involvement’, 1 for ‘occasional involvement’ and 2 for ‘high involvement’. The dependent variable takes these values and is thus categorical in nature.

Details of the firm’s responses to this question are presented in Table (2), which indicates the level of involvement by all firms across the five sectors. This data provides some interesting cross-industry comparison, and it is worth briefly deliberating further here. In particular, the data highlights that it is in the healthcare sector that the highest proportion of firms participate with their respective industry institutions over industrial development issues. This is perhaps not surprising given the use of medical equipment and instruments is closely regulated and the Association of British Healthcare Industries (ABHI) has been able to acquire wide legitimacy in representing the industry with state interlocutors and in maintaining industry standards and reputation. Related to this has been the significant increase in UK public health expenditure particularly following the Wanless Report in 2004. This may have encouraged firms to take a more active role in their industry 18

institutions, as they sought to influence the distribution of increased health budgets [10].

At a lower level, both the software and aerospace industry have similar levels of participation with industry institutions. These are both seen as strategically important sectors in terms of UK exports and recent UK industrial strategy, and the industry institutions themselves have been at the forefront in promoting their respective industry cases at the state level. For instance, ELPSA have recently co-ordinated a highly prominent campaign for tax credits in the games industry so as to bring the sector in line with competitor countries; this is likely to have raised awareness and participation among constituent firms. Similarly, in aerospace, SBAC play a continual role in UK Defence management and procurement and in managing industry supply chains (Graves et.al. 2001); in such a hierarchical production structure, larger firms may particularly seek influence over such issues through their industry association [11]. Finally, both the traditional industries of ceramics and textiles appear to have lower levels of firm participation with their respective industry institutions. This may reflect of a general ‘air of despondency’ within these industries which have been particularly adversely affected by global competition, and firms may thus be refraining from further participation in collective action (see Sacchetti and Tomlinson, 2009).

INSERT TABLE (2) HERE

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4.2.2 Independent Variables

The main variables of interest relate to the two hypotheses outlined in Section 3. These were constructed as follows:

i). Institutional Inclusiveness (H1): This is a construct variable which explores the extent to which industry institutions are perceived as being ‘inclusive’ to all firms within the industry. In this respect, the items utilised (see Table (1)) asked about accountability, accessibility and responsiveness and openness and dialogue within industry institutions. These are items mentioned by Jessop (1998) in his discussion of heterarchy, Sacchetti and Sugden (2003, 2009) in relation to heterarchical governance structures and also Donor and Schneider (2000) in their discussion of open forums within business associations. As discussed in Section (3), the expected sign of the estimated co-efficient will depend upon the relative importance of the logic of membership vis-à-vis the logic of influence.

ii). Shared Vision (H2): This is a construct variable that relates to Nahapiet and Ghoshal’s (1998) cognitive dimension of social capital within the industry. Specifically, it captures the degree to which firms perceive as there being a set of ‘collective goals’ or a ‘shared vision’ existing within the industry. To operationalise the variable, items were based upon previous research by Tsai and Ghosal (1998) and also Molina-Morales and Martinez-Fernandez (2006), who employed the variable in their study of Spanish industrial districts. A positive correlation with firm involvement in institution led initiatives is expected. 20

4.2.3 Control Variables In addition to the two main variables of interest, a number of control variables are also included. These controls are referred to in the discussion in Sections (2) and (3) and further details are provided below:

i). Firm Resources: Larger resourced firms are more likely to become involved with industry institutions in relation to industrial development, because they are attracted by the logic of influence and, by definition, they have the resources to undertake an active role (Stigler, (1974), Pfeffer and Salancik (1978), Aldrich and Fiol, (1994) and Bennett (1998a). The variables Firm Age, Firm Size and Sales Revenue growth are thus used to capture the extent of a firm’s resources. Age is the number of years since formation and captures the possibility that older firms are more likely to have established long term relations with industry institutions (see Bennett, 1998c). Firm Size is measured in terms of the number of employees (segmented using a categorical scale), while Sales Revenue growth is a binary variable capturing growth over the previous three years. It is expected that all three variables will be positively correlated with firm involvement in institution led initiatives over industrial development.

ii). Institutional Support: Olson’s (1971) by-product theory suggests that the service function is likely to subsidise related collective and representative activities of industry associations. An extension of this is where firms regularly utilise the services function, they are more likely to become closely involved in wider 21

institution led activities and undertake collective actions; this is more likely where the service function has grown in importance (Helmsing, 2001). Such close associations augment the ‘institutional thickness’ of the industry (Amin and Thrift, 1994; see also Johannisson et.al, 2002, Parrilli, 2009). This construct variable captures the impact of previous support provided by institutions using the items listed in Table (1). Support can take the form of advice (either formal/informal), training or the dissemination of industry research. The items included here are guided by the literature on the service function of business associations, in particular, Bennett (1998 a, b, c) and also Helmsing (2001). A positive correlation is with firm involvement in institution led initiatives over industrial development is expected.

iii). Institutional Effectiveness: The logic of influence is entwined with the institution’s degree of legitimacy in the wider public domain, particularly its perceived effectiveness in promoting industry development. In this regard, the more effective the institution is perceived to be, the more likely that firms will actively support and become involved in institution led activities (Aldrich and Fiol, 1994, Aldrich and Staber, 1983, 1989). This construct variable aims to capture firms’ perception of their industry institutions’ ability to successfully represent, promote and develop the industry and lobby effectively for the industry’s interests. The items used in Table (1) are guided by the literature on the effectiveness of institutions and the representation function, notably that of Streeck (1989) and Bennett (1998 a, b and c). A positive correlation is firm involvement in institution led initiatives over industrial development is expected.

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iv). Industry Dummies: Industry dummies are included to capture differences across sectors. The base industry is Healthcare, and in each case the dummies take the value 1 if belonging to either of Aerospace, Ceramics, Information Technology and Software and Textiles and zero if otherwise.

A summary of the items used for the independent and control constructs are provided in Table (1). For each item, the responses were scored using a 5 point Likert scale where 1 = Strongly Disagree and 5 = Strongly Agree. To measure each variable, the mean of all items for each construct (per firm) was calculated, while Cronbach’s alpha was run to validate the aggregation of the items. These are reported in Table (2).

4.2.3 Descriptive Statistics Details of the descriptive statistics, Cronbach’s alpha for multiple-item variables and the Pearson’s correlations for all combinations of variables are provided in Table (3). The values of Cronbach’s alpha appear within the normal bounds of tolerance (i.e. greater than 0.70) for the multiple-item variables, apart from ‘Shared Vision’, where the alpha score is slightly below at 0.64. However (as with the other scales utilised), this is a relatively recent summated scale and in such circumstances it seems reasonable to concur that the alpha value here is also within the limits of tolerance (see Hair et. al., 2007). In short, the summated scales employed in the constructs appear reliable and internally consistent.

INSERT TABLE (3) HERE 23

Turning to the Pearson’s correlations, there are significant correlations between the independent variables, in particular those between the construct variables where the correlations are highly significant (at the 1% level). To some extent, this may be expected given that these variables largely capture the firms’ perception of their institutions’ different activities, effectiveness and inclusiveness and positive scores on one aspect are likely to be positively related to scores on others. Of course, a concern arises that sensitivity between the independent variables can give rise to problems of multi-collinearity in estimation. However, the size of the correlation coefficients and subsequent tests for multi-collinearity (using variance inflation factors) in the ensuing multivariate analysis suggested this was not a problem. Finally, following Hair et al (2007), for each construct, factor scores were calculated in SPSS – this provides a standardized mean of 0 and a standard deviation of 1 for each construct and these were used in the ensuing regression analysis.

4.2.4 Method of Estimation Following the above discussion, the model is formulated as:

Firm Involvement = β0 + β1 Firm Age + β2 Firm Size + β3 Sales Revenue Growth + β4 Institutional Support + β5Institutional Effectiveness + β6 Institutional Inclusiveness + β7 Shared Vision (β8… β11 Industry Dummies) + εi

24

(1)

Equation (1) is estimated by multinomial logistic regression. This is an appropriate technique where the dependent variable is categorical and where there are more than 2 outcomes to consider (in this case there are 3). Other techniques might have been employed such as discriminant analysis, but logistic regression is preferred since it requires fewer assumptions in relation to the distribution of the independent variables, is also generally more statistically robust in practice and generates results that are more intuitive (Borooah, 2002). A related estimator to the multinomial logit is the ordered logit model, which tends to be employed where there is a clear (preferred) ranking in outcomes and which involves imposing the restriction of parallel slopes across all of the categories. In the case of the dependent variable in Equation (1) however, it is not readily apparent whether the categories are ordered or not since ‘firm participation’ is a choice variable and ‘higher’ participation might not necessarily be considered more desirable from the firm’s perspective. The use of an ordered logit in such circumstances may well lead to biased estimates and so the multinomial technique is considered the more reliable estimator (Borooah, 2002).

The model was estimated in three stages. The first model included only the control variables (Table 4). In the second model, the two social capital constructs (Institutional Inclusiveness and Shared Vision) were added (Table 5). Finally, a third model included multiplicative sectoral dummies for both of these constructs to try and capture industry specific effects (Table 6).

25

5.0 Results and Discussion Tables (4) to (6) present the results of the multivariate analyses across all industries. In this regard, multinomial logit uses comparison categories for the dependent variable and thus estimates two coefficients for each independent variable. This facilitates easy comparison across the categories, and the empirical results themselves are thus specified and discussed in relative terms. In columns (1) and (2), the reference category is set to ‘non-involvement’; the estimated coefficients (βi1) for each independent variable (Xi) thus describe how the independent variable affects the probability of a firm being ‘highly involved’ with industry institutions (in relation to industry development issues) relative to ‘no involvement’ (column 1). The estimated coefficients (βi2) in column (2) describe how the independent variable affects the probability of a firm being ‘occasionally involved’ relative to ‘no involvement’. In column (3), the reference category is altered to ‘occasional involvement’, and the estimated coefficients describe how the independent variable affects the probability of a firm being ‘highly involved’ relative to ‘occasional involvement’.

INSERT TABLES (4) to (6) HERE

The results in Table (4) relate to the control variables only. Here, column (1) shows that both firm size and sales growth are positive and significantly related to firm ‘high involvement’ (relative to ‘no involvement’) in institution led industry development issues. These results are unsurprising since firms with larger resources are more likely to become involved as they are particularly attracted by the ‘logic of influence’. Both the other two control variables, Institutional Support and 26

Institutional Effectiveness, are all also positive and highly significant. In the case of Institutional Support, it appears that firms utilising the service function are also more likely to participate in institution led initiatives. This is an extension of Olson’s (1971) by-product theory, whereby the service function engineers closer relationships between industry institutions and member firms, encouraging the latter to engage in wider (institution led) industry activities. The significance of Institutional Effectiveness again reflects the relevance of the logic of influence. Firms are more likely to participate (and invest their own time and resources) if they believe their industry institutions are likely to be successful in promoting industry interests. To encourage participation, institutions may therefore need to demonstrate efficient implementation of collective goals, while acquiring a visible presence and strong negotiating skills with relevant parties (see Streeck, 1989). The significant industry dummy variables (which are relative to the Healthcare sector) are consistent with the earlier observations outlined in Section (4.2.1), namely there is significant systematic variation across sectors in the propensity for participation in institution led initiatives. Finally, there are fewer significant coefficients in the comparisons between ‘occasional’ to ‘no’ involvement (Column 2), and between ‘high’ and ‘occasional’ involvement (Column 3), although where these occur the interpretations are similar to the results for Column (1).

Turning to the specific hypotheses, Table (5) shows the results when the two social capital constructs are included. The inclusion of these variables appears to improve the explanatory power of model as indicated by the higher Nagelkerke R2. The results themselves are also particularly revealing. First in column (1), the coefficient 27

on Institutional Inclusiveness (H1) is negative, although insignificant, while in column (2), the variable is again negative but this time marginally significant. While these results suggest that inclusive governance structures do not encourage wider firm participation, the evidence is weak in relation to the reverse hypothesis. There is insufficient evidence to suggest that a high level of firm involvement (relative to ‘no involvement’) is primarily driven by governance structures favouring the logic of influence vis-à-vis the logic of membership (column (1)), although in column (2), there is marginal support for the logic of influence when comparing ‘occasional’ to ‘no’ involvement. However, if these results are also considered alongside the positive and significant impact of the control variables, particularly Firm Resources and Institutional Effectiveness - both of which relate to the logic of influence (see above) - it might be reasonable to view the logic of influence as being the dominating factor affecting firm participation. This conclusion certainly concurs with recent (related) work by De Propris and Wei (2007) who explored firm participation in local decision making processes in the UK’s Birmingham Jewellery quarter. They found the few firms that actually engaged with local institutions in such processes were those that felt they could exert sufficient influence over the policy agenda.

The second hypothesis (H2) relating to the cognitive dimension of social capital, is clearly supported. Shared Vision (H2) is highly significant and positive in Column (1), and positive and marginally significant in Column (3). Where there is consensus between firms, they are more likely to participate in collective industry activities. Consensus and shared values themselves evolve through close relational ties between firms and also industry institutions. To encourage wider participation in collective 28

actions, industry institutions should perhaps consider an enhanced role as a conduit for nurturing and supporting inter-firm ties and as a coalition-building mechanism within the industry (see also Amin and Thrift, 1994). More generally, the promotion of shared values between firms may be worth exploring further, particularly since this cognitive dimension of social capital may aid industrial development. MolinaMorales and Martinez-Fernandez (2006), for instance, have found that shared vision between firms significantly enhanced innovative activity across 5 industrial districts in the Valencian region of Spain.

Following the techniques outlined in Aiken and West (1991), Table (6) provides the results of the estimations which include multiplicative sectoral dummies for the two social capital variables. The results reported are relative to the healthcare sector (which again acts as the base) and again there is an improvement in the Nagelkerke R2. However, the interpretation of the results should be treated with caution as the inclusion of multiplicative dummies can raise the possibility of multi-collinearity and reduce levels of individual coefficient significance (see also Greene, 2008). In addition, when interaction terms are included, the estimated coefficients (βi1) on the main variables (in this case, the social capital constructs) should be considered in conjunction with the corresponding coefficients on the interaction terms (XiZi) i.e. the impact of the main variable is conditional upon the value of other independent variable(s) [12] (see Aiken and West, 1991). In terms of the results in Table (6), the two social capital variables now become insignificant across all sectors, although the interaction terms reveal varying degrees of significance/non-significance (columns

29

(1) and (2)). This suggests moderating effects with industry specific differences in relation to role of social capital may be important.

The results in Table (6) are generally in line with those in Table (5). Institutional Inclusiveness is again negative and significant in textiles (columns 1 and 2), aerospace and ceramics (Column 2). This implies the logic of influence is the preriding factor in affecting the propensity for firm participation in institution led initiatives in these sectors. Shared Vision appears significant in both software and textiles (column 1). In software, as noted in Section (4.2.1), ELPSA have recently played a prominent role in building a coalition of software producers in relation to tax issues in the sector. The story in textiles is particularly interesting. The sector has the one of the lowest levels of firm participation in institution led initiatives (Table 2) and firms appear less likely to participate in structures which dilute the logic of influence. Yet, where a shared vision emerges, participation is more likely. In exploring this further, it is worth noting the sector is highly fragmented in terms of products (ranging from wool, fibre, yarn and fabric to household textiles and garments) and firms are also members of various regional and sub-sector institutions in addition to the BATC. In addition, global competition and industry issues affect these sub-sectors differently. Such fragmentation might reduce the industry’s effectiveness in the policy arena (Streeck, 1989). The results here may suggest textile firms participate in institution led initiatives, when their own sub-sector is particularly affected by policy issues and the firms themselves feel they can actively shape the collective goals (and shared vision) that emerges. Finally, Shared Vision appears insignificant in relation to the other two interactive sector dummies 30

(aerospace and ceramics); this may suggest a collinearity issue given the strong result for this variable across all sectors in Table (5).

6.0 Conclusion This paper has sought to consider the factors that affect the propensity for firms to participate in industry institution led initiatives relating to industrial development. This is an important issue, since firm participation can affect the strength and efficacy of the industry institution in delivering collective goals and more widely, the industry’s legitimacy in the socio-political domain and its subsequent development path. The study’s main focus was to explore Nahapiet and Ghoshal’s (1998) dimensions of social capital within the logics of collective action framework. This allowed an examination of the extent to which institutional governance structures and inter-firm relations encourage greater participation. The study finds that where firms are able to generate shared values and goals, then collective action is more likely to occur. Firms, though, also seek the logic of influence in deciding the extent to which they are prepared to participate in institution led initiatives. This may well be the case for larger firms; indeed, it is noticeable that larger firms tend to be disproportionately (well) represented on boards of directors of trade associations (Bennett, 1998a; Aldrich and Fiol, 1994). Conversely, more ‘inclusive’ and ‘open’ governance structures do not appear attractive for participating firms. This result may be particularly germane to the sectors studied, although more generally it may reflect that such structures often lack stable leadership and clear objectives (Streeck, 1989). The general conclusion for industry institutions appears to be that to encourage greater firm participation, the institution must demonstrate effective leadership, while 31

also building coalitions around salient industry issues which allow firms to identify with (and indeed formulate) clear collective (shared) goals.

Achieving such a balance is no easy task for industry institutions. Indeed, more generally the results are somewhat paradoxical, since ‘inclusive’ environments are sometimes regarded as providing the conditions for the promotion of shared values (Donor and Schneider, 2000). Yet, if firms are attracted by the logic of influence, more open environments are unlikely to be effective. The interpretations here may also have implications for the often positive role associated with social capital. While it is beyond the scope of this paper to explore this issue in depth, it is worth noting Anderson and Jack’s (2002; p.196) point that ‘‘social embeddedness’ can have negative effects because of group expectations….. which may seek to exclude and include; to consolidate or to share power’. As noted in Section (3.2), there may be an optimum level of ‘inclusivity’ that participating firms are willing to accept; nonconformers may be excluded. Moreover, firms only appear willing to participate if the emerging ‘shared goals’ largely converge with their own perspectives.

In arriving at these conclusions, it is important to note the limitations of the current study and offer some tentative suggestions for future research. The first is the assumption regarding the direction of causation in relation to the social capital variables affecting the propensity of firms to become actively involved in industry wide initiatives. In a cross-sectoral study of this type, there may be a degree of endogeneity with greater participation enhancing social capital. For future research, it would be therefore be useful to supplement the current approach, perhaps through 32

more qualitative techniques to unpick the precise nature of relationships between firms and industry institutions. Secondly, the results presented here relate to the membership bodies of industry associations in 5 established UK manufacturing sectors. It would therefore be unwise to generalise the conclusions too widely and further work might seek to explore the nature of such relationships across other sectors, such as the service sector and also across other countries for comparison. As an adjunct, further research might also seek to survey non-member firms to possibly uncover reasons for their general non-involvement and their perspective on the collective action problem. This would be particularly useful in perhaps gauging the extent of the ‘free-rider’ problem over such issues.

Finally, the time window of the current study was relatively short (3 years). In part, this was deliberate so as to focus respondents upon their recent relations with their industry institutions. As such the empirics reflect a point in time. It is highly probable that the dynamics of the relationships covered will change over time, particularly as new firms enter/exit industries and new industry issues become in vogue (which may impact upon firms (and sub-sectors) differently). Moreover, managers may change in firms and they will have different perceptions on the extent to which their firms become aligned with their trade associations. Such changes will affect the propensity of firms to participate in industry institution led collective actions. These dynamics might be better captured through periodic follow up surveys and/or longitudinal data that takes account of a longer time window.

33

Endnotes [1]. There are numerous examples in the literature on the role industry institutions play in facilitating research and development. Nelson and Nelson (2002), for instance, draw upon the experience of the post-War German industrial research laboratories (which were largely industry funded) to emphasise the importance of such institutions in aiding the innovation process, while Balthasar et.al (2000) view industry specific research institutions as the interface between science and industry: a catalyst for nurturing inter-firm innovation networks and facilitator of the exchange of experiences and knowledge transfer (see also Abramson et.al, 1997). More recently and particularly relevant to the current study, Sacchetti and Tomlinson (2009) describe the role of the UK ceramic industry institutions in establishing ‘The Hothouse’ ceramic shape and design centre, which serves the whole industry and provides users with the latest 3D printing and prototype technology and CAD and CAM tools. The Hothouse has become a widely used centre of excellence, enabling users to bring new designs to the market more quickly, and allowing firms to take advantage of the centre’s facilities (for a set fee) and expertise, without incurring the high sunk costs associated with investing in specific technologies. [2]. The quid pro quo arrangements enhance the aggregated information, by encouraging collective action (participation) in the process, thus in turn negating the free-rider problem (see Kirby, 1988). [3]. Another example from the UK ceramics industry is useful here. Warren et.al (2000) document the important role played by one of the industry’s leading research institutes, CERAM Research, in disseminating new best practices and innovations within the sector in the second half of the twentieth century. CERAM’s role was seen as being a ‘technical gate-keeper’ and thus provided confidence (legitimacy) for firms in adapting new standards and product and process innovations. [4]. Stigler (1974), perhaps more accurately, refers to this as the ‘cheap rider’ problem since non-participating firms (i.e. the ‘free-riders’) actually incur a cost as the collective action is less likely to occur, thus reducing the expected universal benefits. Where collective action does occur, the outcome is also likely to be lower, since the overall contribution of the collective interests is less due to ‘cheap riders’. [5]. In social networks, collective sanctions/incentives are necessary to ensure compliance. Examples include social sanctions where participants are ostracised by others if they engage in opportunism or breach accepted norms and/or reputation effects where reliability and commitment among participants is rewarded (see Jessop, 1998). [6]. In such cases, sub-sectoral or territorial groups may break away and emerge as independent institutional actors leading to fragmentation within the industry and possibly a general weakening of national representative institutions (Streeck, 1989). See Bayirbağ (2009) for a recent and detailed account of this occurring in business associations in South East Turkey. 34

[7]. Both Axelrod (1997) and Jessop (1998) have also emphasised the importance of social norms in supporting co-operative behaviour and joint actions among firms. [8]. Late responses were defined as those received after three weeks from the initial mail-shot. [9]. The number of useable responses per sector were as follows (sector response rate in parenthesis): Aerospace 103 (25.2%), Ceramics 86 (19.16%), Health 46 (14.3%), Software 64 (15.3%) and Textiles 82 (15.5%). [10]. I am grateful to an anonymous referee for making this point. At the Academy of Management Conference, Montreal, August 6-11th 2010, Scott Latham of UMASS, made a similar observation in relation to US medical equipment providers becoming more closely involved with their trade associations as a result of increased health budgets. [11]. In the case of ELPSA’s campaigns, see http://www.elspa.com/. For SBAC and details of the role of its Defence Industries Council, see http://www.sbac.co.uk/pages/53214663.asp [12]. Specifically, if Ŷ = β0 + β1X1 + β2Z1 + β3X1Z1 where β1 measures the main effect of X1 on Y, and β2 is the interaction effects, then the marginal effect of X1 on Y is given by: δY/δX1 = β1 + β3 Z1. i.e. the impact of X1 on Y, is conditional on the value of Z1 (see Aiken and West 1991; also Brambor et.al 2005).

35

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Table (1): The variables used Variables

Method used to construct the variables

Involvement with Institutions in Industry Development

0-2, where 0 = no involvement, 1 = occasional involvement and 2 = high involvement

Firm Age

Firm Age in Years

Firm Size

Number of employees (Scale 1-7; where 1 = less than 10, 2 = 10-49, 3 = 50-99, 4 = 100-250, 5 = 250499, 6 = 500-999, 7 = greater 1000)

Sales Revenue Growth

1/0, where sales revenue growth over past three years equals 1; 0 otherwise a). You have received support for firm activities (such as R&D/marketing)

Institutional Support

from industry institutions b). You and your employees have received specific training by industry institutions c). Your firm has received benefits from research activities carried out by industry institutions d). In seeking support for your business, you prefer to liaise with institutions as opposed to other firms e). The services provided by your industry institutions are important for our firm’s business

Institutional Effectiveness

a). The institutions representing our industry are a strong lobbying group b). The institutions representing our industry aid and promote the industry’s development c). You consider the role played by your industry institutions as being strategically important

Institutional Inclusiveness a). Overall, institutions in our industry represent the interests of all firms within the industry b). In general, institutions in our industry encourage an open dialogue and exchange of views about industry issues c). In general, institutions in our industry are responsive to the needs of firms like ours in the industry d). In general, institutions in our industry are accessible for all firms in the industry e). The institutions in our industry are accountable to their members.

Shared Vision a). You and the people in your firm share the same ambitions and vision as other firms in your industry b). You consider that your firm’s future is related to that of other firms in your industry c). There is some kind of shared strategy or plan for firms in your industry d). People in your firm are encouraged and motivated to pursue the shared goals and strategy of your industry

Note: All constructs use a 5 point Likert scale, where 1 = Strongly Disagree and 5 = Strongly Agree

41

Table (2): Proportion of Firms Participating with Industry Institutions over Industrial Development All Firms 16.7% 28.1%

Involved Occasional Involvement Not 55.2% Involved

Aerospace

Ceramics

Software

Textiles

Healthcare

19.1% 37.4%

9.3% 23.7%

16.6% 37.5%

11.9% 17.9%

38.8% 24.5%

43.5%

66%

45.8%

70.2%

36.7%

Table (3): Descriptive statistics, Cronbach’s alpha and Bivariate correlations N=398

Mean

S.D

α

1

Firm Age

39.9

Firm Size

2

3

4

5

6

44.5

N/A

1

2.60

1.71

N/A

0.418***

Sales Revenue Growth

0.59

0.49

N/A

-0.113** 0.199***

Institutional Support

2.82

0.79

0.73

0.095*

0.076

Institutional Effectiveness Institutional Inclusiveness Shared Vision

3.27

0.80

0.70

-0.066

0.051

3.08

0.72

0.84

0.061

0.104**

0.048

0.465*** 0.643***

3.0

0.69

0.64

-0.068

0.012

0.024

0.191*** 0.418*** 0.345***

7

1 1 0.036

1

0.114** 0.458***

α = Cronbach’s alpha for all multiple-item variables *** Pearson’s Correlation is significant at the 0.01 level (2-tailed). ** Pearson’s Correlation is significant at the 0.05 level (2-tailed). * Pearson’s Correlation is significant at the 0.10 level (2-tailed).

42

1 1 1

Table (4): Multinomial Logit: Probability of firm involvement institution led initiatives (Control Variables Only)

Independent Variables

Intercept Firm Age Firm Size Sales Growth Institutional Support Institutional Effectiveness Aerospace Ceramics Software Textiles Nagelkerke R2 - 2 Log-Likelihood χ2 (22 d.f) N = 381

(1) ‘High’ versus ‘No’ involvement -1.282 (6.497)** 0.003 (0.668) 0.154 (2.870)* 0.631 (3.383)* 0.609 (10.502)*** 0.443 (5.416)** -0.966 (4.314)** -1.586 (8.531)*** -0.476 (0.842) -1.383 (6.936)***

(2) ‘Occasional’ versus ‘No’ involvement -1.166 (5.972)** 0.002(0.316) 0.093(1.262) 0.453(3.060)** 0.275 (3.839)** 0.101 (0.490) 0.415(0.830) -0.011(0.001) 0.386(0.613) -0.608(1.470)

0.204 731.023 78.237

Notes: a. *** p