Scientific and technical human capital

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Bozeman and colleagues (2001, p. 718) more abstract formulation of ‘scientific and technical human capital’ pairs an ‘expanded notion of human capital’ with a ‘productive social capital network´. Or alternatively, ‘the sum of researchers’ professional network ties and their technical skills and resources’ (Bozeman & Corley 2004, p. 599). They argue that educational qualifications should not be understood as either an indicator of homogeneous human capital or as an end point in human capital acquisition. Rather both training and experience are heterogeneous, and individual scientific careers are somewhat unique trajectories of ongoing human capital accumulation.

Scientists’ technical human capital is defined by three dimensions:

1. Cognitive skills – those cognitive abilities (maths reasoning, memory, ability to synthesize) that are largely independent of context or more likely interact but are not determined by context. Not only ‘scientific’ abilities (2001, p. 726)
2. Substantive scientific and technical knowledge – formation and education, understanding or experimental and research findings (2001, p. 727)
3. Context skills – knowledge accumulated by doing and creating and including tacit knowledge, craft skills, and knowledge specific to the design and implementation of specific research or experimentation plans (2001, p. 727) not directly applicable but provide heuristics and analogies for other contexts.

The extent to which an individual scientist has particular ‘loadings’ of these factors will shape their career path. Evolution in capacities of these dimensions over time also shapes the possibilities in terms of career trajectories. These dimensions overlap and are in part co-constitutive of each other, but relative weightings determine to some degree the kinds of work roles within teams or other collectives that a scientist is most suited for (cf. habitus, Bourdieu, 1987).

However, human capital represents only half the resources available to scientists, the remainder being available through a researchers’ accumulated social capital. Social capital is embodied in the sum of professional and personal interactions and relationships in which an individual is embedded and which increase the resources available to them.

Social capital is defined along two dimensions:

• The institutional setting of the network partner (firm, NGO, Govt institute, etc.)
• Role of the partner (entrepreneur, colleague, funding agency, etc.)

These dimensions combine into the social capital network. In terms of the formal analysis of a social capital network, insights from Social Network Analysis (SNA; Burt, 1992; Granovetter, 1973) also show that the configuration of extended network roles in terms of centrality, density and brokerage can also affect the resources that are available to an individual. Positioning within networks thus has career implications as well.

The most important parts of the social capital network are the ‘knowledge value collectives’ in which a researcher is involved. Knowledge value collectives (KVCs) are a ‘set of individuals connected by their uses of a body of scientific and technical knowledge’ and are smaller and less durable than scientific disciplines (Bozeman & Rogers, 2002, p. 777). The basis for choices of research questions or collaborators in the S&T human capital model may thus be somewhat different to that in a model of disciplinary peer communities.

Empirical Application

Following from the work of Stephan and Levin (1997) to some extent, empirical studies utilizing the S&T human capital approach have made a considerable contribution to understanding the role of research collaboration in research careers. For example, collaboration with industry has been shown to have beneficial effects on scientific productivity (Lee & Bozeman, 2005; Lin & Bozeman, 2006). Different collaboration strategies among researchers have been linked to different sets of motivational factors (Bozeman & Corley, 2004; Bozeman & Gaughan, 2011). While earlier studies (Bozeman & Corley, 2004; Lee & Bozeman, 2005) suggested men have greater numbers of collaborators than women, a more recent study found women and men to have similar levels of research collaboration – prompting suggestions that policies promoting gender equity in U.S. university careers may be paying dividends (Bozeman & Gaughan, 2011, p. 1399).

Dietz and Bozeman (2005) look at the impact of time spent/jobs (n) in the private sector on scientific productivity measured exclusively as publications and patents. An index of career ‘homogeny’ is constructed to measure the extent to which an individual career conforms to a very standard or normal vision of an academic career (PhD, post doc, assistant Prof, tenured Prof). They found that among research centre based academics there is significantly more time being spent in the private sector (often through dual appointments) than life course productivity literature had shown in the past. This may be because the centres in the study were mission centres funded by government and hence, perhaps more likely to be strongly engaged with industry issues and problems. Career diversity was found to create some productivity boosts around both sides of a ‘job transition’ – so-called because of dual and hybrid appointments (industry PhDs). The overall change in universities toward a more ‘business’ model was also seen as a potential explanator for some of the emergence of these different career structures (see Lam, 2005). This problematic makes it seemingly important to better distinguish between career steps or job transformations (such as taking on a dual industry appointment or chairing a spin-off) and changes (moving from one University research centre to another).

In the STHC model, the interaction between these elements and career stages is not explained. Rather the tenure track process is said to be under stress (see Ziman, 2000) from the proliferation of post-docs trapped in sequences of temporary positions. A much greater proportion of PhD graduates are doing post-docs reflecting institutional changes that are affecting careers (from 27% in 1973 to 63% in 1995). Problems include oversupply, ‘steady state funding’, cheap labour, discrimination against women, minorities and other relatively weak labour market actors, the most talented go straight to tenure-track leaving a pool of lesser lights to try an establish their credentials, whilst this may be a much bigger problem in some fields than others. For example, lack of research funding in some parts of SSH mean there are too few post doc opportunities.

In summary, whilst scientific and technical human capital is what scientists bring to their jobs or collaborations, these contexts are also sites for the continuous augmenting of capitals. The scale of S&T human capital is enhanced by increasing the volume of collaborations. The scope of S&T human capital is enhanced through the diversity of collaborations, with different types of organizations or researchers from different disciplines, for example. The degree of social capital diversity will determine whether an individual engages in a relatively generalist or specialist career.

In this model, collaboration and networking simultaneously contributes to the advancement of individual careers and capabilities and the enhancing of systemic capacities. Jobs and collaborations also provide a context for further learning, knowledge transfer and skills development. They also facilitate the core network building and professional connections that will shape a professional career.

From a theoretical perspective, human and social capital are regarded as indivisible, and their ‘interplay’ as ‘so fundamental, intimate, and dynamic that neither concept is fully meaningful by itself’ (Bozeman et al., 2001, p. 723). Scientific careers can thus be understood in this model as a function of the acquisition and interplay of complements of S&T capitals and how this impacts on the evolution of research capacities and performances over time.

An advantage of the STHC approach is that its concepts are applicable across a fuller range of institutional settings. The framework enables the exploration of hybrid careers in EILMs (Lam, 2005) and careers that move back and forth between industry and universities or other public sector research organizations. Empirical analyses can be conducted on a large scale, including through the use of surveys and CV coding, to draw out patterns that can help identify different types of ‘standard’ careers and the impact of independent variables on career trajectories.

Contributions to measurement concepts

Research collaboration and networking

This concerns Social capital, and Collaboration.

Inter-sectoral mobility

Intersectoral Mobility is related to the work-experience (Work Experience) of individuals.

Scientists’ technical human capital

Scientists’ technical human capital is defined by three dimensions Cognitive skills, substantive scientific and technical knowledge and context skills.