Innovation production coupled with rapid application has been a feature of UK and Swedish technology entrepreneurship starting around the time of the industrialists of the Victorian era. The deliberate concatenation of state, university research and concomitant mercantile exploitation, often called the "triple helix", was a feature of the second half of the 20th century, and both the UK and Sweden pushed to emulate the "Stanford/Silicone Valley" effect. More recent data shows that universities are less efficient than other innovation areas in producing and applying innovations. Consequences for the triple helix model are discussed.

The performance of firms involved in projects from 2 UK research councils was investigated; firms in Innovate UK projects receive co-funding while firms in Arts & Humanities Research Council (AHRC) projects do not. Firms in 266 projects 2009–2012 were tracked for Standard Industrial Code (SIC), location and year-on-year financial performance 2012–22. The results show that firms (un- and co-funded) were mainly not local to universities. The growth performance of non-funded firms was steady in the majority of SIC codes, but some SIC codes performed very well, while for co-funded firms, many SICs performed under control but losses were made up for on average by exceptionally high performance in other SIC codes. Overall, non-funded firms achieved average growth of ∼29 % above control while co-funded firms only achieved an average growth of ∼18 % above control. Firms (both co- and un-funded) associated with 21 universities perform consistently well, while other firms (co- and un-funded) associated with 24 other universities perform consistently poorly. This difference in performance was better correlated to degree of business ambidexterity in the tech transfer function, rather than with university reputation.

Forty-five UK universities were approached, drawn from 21 universities whose client technology/knowledge-transfer firms perform consistently well (category 1 universities), and 24 universities whose client technology/knowledge-transfer firms perform consistently poorly (category 2). Contact was established with staff in the Technology Transfer Office (TTO) or similar department, resulting in 72 persons identified as either “leading” TTO staff or “operational” TTO staff. These individuals were subject to semi-structured interviews around a 10-point questionnaire to arrive at consensus opinions. Results indicate that independently of whether the university is regarded as “entrepreneurial” or not, the TTOs of category 1 universities are more ambidextrous than those of category 2 universities.

To investigate successful technology transfer, the potential path of innovations from the university research bench to the knowledge recipient is modelled. Universities exist in highly regulated environments and the initial path of decision-making is a hierarchical model and where decisions flow upward from manager to manager until a small number of candidate innovations for commercialization remain. These are then routed for further processing to the link connecting to the knowledge recipient, the Technology Transfer Office (TTO). In the TTO, a hierarchical decision-making model can be acceptable in terms of outcomes, but ambidextrous co-operative team structures are much superior in cases where staff have good insight and decision-making abilities. This report represents the first Structured Equation Model investigation of the management architecture of a TTO.

This paper investigates the mutual interdependencies between organizational architectures, decision making and performance. Through applying agency-based Monte-Carlo simulations, we reveal how hierarchical, polyarchical and hybrid structures aggregate innovations on the micro level into performance. By considering three different initial project portfolios, we analyze which organizational architectures may be superior regarding selecting good projects, avoiding collective myopia and overcoming organizational inertia. Results reveal that in a risky environment firms with rigid hierarchies can achieve a much higher performance than horizontally-organized firms even if the quality of the decision-making by managers is poor. Results also highlight the dangers involved in erecting a more hybrid organization because such organization might become over-challenged and unable to handle risky innovations adequately. Finally, we discuss how firms could be structured to increase performance and to minimize risks.

Start-up STPs have a central initiative controlling the decision-making. In early maturity, better decision-making is required and decisions are best taken with the input of 10 optimally two on-cluster firms; this ambidextrous situation is superior under all circumstances. Where poor-fit innovations abound and where the STP has been unable to attract large firms, retaining a hierarchical decision process is most helpful, even when the quality of decision-making is poor. This developmental trajectory will lead to market failure as size, and the seriousness of the concomitant potential losses, increases. With time, off-cluster firms move outward, inhabiting a band 4-7 km from the STP; their size remains modest and their innovation output is low. On-cluster firms are resilient to externalities; their innovation output is large and strongly correlated with social/networking expenditure. These new results are reviewed here as a contribution towards a "road map" to help STP decision-making and regional policy.

Science and technology parks (STPs) are curated locations where new technology-based firms (NTBFs) and other SMEs and firms can conglomerate and promote a culture of innovation. Overall, the aim is to construct a sustainable high-value tech entrepreneurship ecosystem, and to this end we present here some recent and novel concepts derived from approaches using a data-driven statistical foundation. This paper considers studies on the organic growth of young start-up science and technology parks by authors who have used big data, econometric analyses, panel data and computer simulations. The results and concepts are derived from industrialized countries, notably Sweden and the UK, and may well be applicable to many regions and emerging economies. The findings are of interest to regional development, technology entrepreneurs considering choosing an STP to inhabit, as well as those in STP central teams, specializing in management and enterprise development, including the sustainable growth of new parks.

Firms with more levels of decision-making hierarchies achieve in average poorer results but have lower overall risks. This is the consequence of poor managerial assessments, where errors of the first and second order reduce the firm’s performance. Despite the negative effect of hierarchies, they reduce the risk of incurring expensive failures. However, the above effect does not hold for scenarios based on setting the minimizing-risk level; under this condition, organizations with hierarchies do worse – both in handling risks and achieving high profits – compared to risk-minimizing organizations without hierarchical assessment. Thus, in a firm with high-ability and knowledgeable risk-reducing employees, imperfect management assessments with errors of the first and second or-der simply increase the total risks. Ambidextrous behaviour with knowledge spillover effects (compared to scenarios without knowledge spillover effects) causes higher average profits as well as higher corporate risks. The reason for this is that an increase in the variety of exploitative and explorative tasks available, means the firm has a wider choice regarding diversifying risks. Interestingly, hierarchies do not cause high trade-offs between risk-reduction and profit-reduction: The relative decrease in returns caused by additional hierarchies is not very pronounced when compared to risk-reduction caused by additional hierarchical levels.

A decade ago Knowledge Valley Theory explained the development of SMEs in terms of provoking innovation by knowledge sharing. In this paper the KVT model will be applied to the wider tech entrepreneurship ecosystem, in particular Science and Technology Parks (STPs). In developing KVT theory further, two new ideas will be incorporated; firstly, Nobel laureate Stiglitz showed that corporate structure determines how decisions are made and thus determines profitability. Secondly is the idea that adopting innovations uncritically can be very expensive, in fact the gain from adopting a useful innovation is much less than the concomitant loss experienced when a poor-fit innovation is mistakenly adopted.

UK Science & Technology Parks (STPs) specialised in pharmaceutical are as were compared with universities scoring highly in pharmaceutical research and with firms returning the corresponding Standard Industry Classification (SIC) codes at Companies House. There was no correlation between the average distance between STPs and highly scoring universities and no evidence that high-ranking universities can attract specialised firms. The ability of STPs to attract specialised firms was investigated and on-campus STPs (within 2 km of the university) were not significantly more successful or less successful than other STPs. To support a specialised STP, an average of 19.15 firms with a similar speciality was found within a 7.89 km radius. In the UK, STPs that are members of the Science Park Association (UKSPA) exist on average 12 km apart but STPs specialised in pharmaceuticals were much further apart, average 32.65 km and this difference is highly significant.

The growth of on-cluster and off-cluster SMEs and municipal science and technology parks (STPs) were compared. The young (2003) Umeå Science Park, accounts for 11% of firms and 29% of employment in Umeå, containing 17 SMEs, with on average 7.42 employees per on-cluster SME compared to 2.14 employees in off-cluster SMEs. The more mature (1998) Skövde Science Park accounts for 30% of firms and 78% of employment in Skövde, with 21 on-cluster SMEs (discounting branches of two large companies) and 49 off-cluster SMEs; the off-cluster firms had 168 employees compared to 598 employees in 21 on-cluster SMEs. In Skövde, STP growth was strong and on- and off-cluster SMEs prospered. In Umeå, STP and on-cluster SMEs grew slowly, while off-cluster SMEs proliferated. These results imply that young STPs grow better when they are interesting enough to be able to attract divisions of larger firms, which in turn improves the STP-level decision-making.

The distribution of off-cluster firms around Science and Technology Parks (STPs) was determined in 4 cases in the UK. Off-cluster firms sharing the same Standard Industry Classification (SIC) codes as on-cluster firms were mashed onto a map of the UK and ArcGIS was used to establish zones around each STP of 1-4, 4-7 and 7-10 km. Around a younger IT-oriented STP, off-cluster IT firms were present in relatively close proximity (1-4km), although they were not in the majority when compared to all those the whole 10 km zone. In an older IT-oriented STP, off-cluster IT firms were present further away (4-7 km) from the STP. In both young and older Biotech-oriented STPs, off-cluster Biotech firms were predominant in the 4-7 km zone. Results may indicate that with time and expansion, and the help of new communications and other technologies, off-cluster firms are able to move away from the STP centre to more attractive locales. Nonetheless off-cluster firms still remain within relatively easy informational and travelling distance of the local STP.

Tech Entrepreneurship is of immense value in regional development and the national economy. Tech Entrepreneurship is partly dependent on economically sheltered environments known as Science and Technology Parks (STPs), who actively seek innovators and also encourage innovation amongst the constituent firms, including by networking and knowledge spill-over between the inhabitants, Universities and sources of capital. The low success rate (~20%) of STPs led us to use the ideas of Stiglitz to investigate how STP architecture can best cope with a changing and challenging innovation environment, through start-up to early maturity and full maturity. Results from using econometric methods (SEM, Monte-Carlo etc) show that it is very beneficial to have a central Cluster Initiative (CI) controlling the decision-making process (“star, hierarchy”) in the early stages of STP development, where potential gains and losses are relatively modest. However in the early maturity stage with commitment to a high-growth trajectory, a high quality of decision–making is required amongst managers and decisions are best taken by the CI with the input of more knowledgeable on-cluster firms. The situation where CI is supported by good-quality knowledge-sharing decisions from on-cluster firms – an ambidextrous situation – is superior when good innovations abound and the STP has acquired some maturity. However, in environments with a surfeit of poor-fit innovations, this becomes a high-risk strategy with high potential losses and indeed in this situation, retaining a hierarchical (CI only) decision process is most helpful, even when the quality of decision–making amongst CI managers is as poor as coin-flipping. To summarise, STP development from small is not linear but Y-shaped: A successful strategy involves the start-up STP attracting enough small innovative firms which – in turn – attract larger firms, whose detailed sector-relevant insight improves CI decision-making. The most valuable ratio includes the CI and only any two of the larger firms; including more decision-makers does not improve the quality of decisions much but does drive the transaction costs up exponentially. If the STP cannot attract larger firms with experienced management, then the STP is best served with the CI continuing alone but, especially under conditions of growth and expansion, the cost of poor decisions will increase until eventually market failure ensues.

Science and technology parks (STPs) foster innovation between firms inhabiting the cluster. Networking channels are considered as integral parts of the knowledge exchange process, and therefore the innovation process. We simulated three organisational topologies for STPs; firstly, in the star model all are connected to the cluster initiative (CI), secondly the strongly connected model, when all are connected to each other, and finally the randomly connected model, where the network follows no centralised topology. Analyses used adjacency matrixes and Monte-Carlo simulation, trading transaction (networking) costs against knowledge benefit. Results show that star topology is the most efficient form from the cost perspective, and this is especially the case for start-up STPs. Later, when the cost of knowledge transformation is lowered, then the strongly connected model becomes the most efficient topology, but this transition to high transaction costs is very risky if direct ties do not quickly result in tangible benefits.

Science and Technology Parks (STPs) are often used as tools to foster regional development. They seek innovations, innovators and encourage innovation amongst the constituent firms, including by networking and knowledge spill over between the inhabitants, Universities and sources of capital. The low success rate of STPs led us to investigate how STP architecture can best cope with a changing and challenging innovation environment, through start-up to early maturity and full maturity, in a preliminary effort to arrive at an evidence-based scheme to help avoid failure. Three different types of architecture were investigated: open (market), star (hierarchy), and closed strong (adhocracy, ambidextrous). Open (market) architecture suffered both from high transaction costs while not protecting against poor decision-making. Results show that it is very beneficial to have a central Cluster Initiative (CI) controlling the decision-making process (star hierarchy) in the early stages of STP development, where potential gains and losses are relatively modest. However in the early maturity stage with commitment to a high-growth trajectory, a high quality of decision–making is required amongst managers and decisions are best taken by the CI with the input of optimally two individual on-cluster firms. The situation where CI is supported by good-quality decisions from on-cluster firms – an ad hoc, ambidextrous situation – is superior when good innovations abound and the STP has acquired some maturity. However, in environments with a surfeit of poor-fit innovations, this becomes a high-risk strategy with high potential losses and indeed in this situation, retaining a hierarchical (CI only) decision process is most helpful, even when the quality of decision–making amongst CI managers is poor. Results indicate that success involves attracting enough small innovative firms which - in turn - attract larger firms, whose detailed sector-relevant insight improves CI decision-making.

This paper investigates the mutual interdependencies between organizational architectures, decision making and performance. Through applying agency-based Monte-Carlo simulations, we reveal how three types of organizational structure (hierarchical, polyarchical and hybrid) aggregate innovations on the micro level into corporate performance. By considering three different initial project portfolios (incremental innovations, innovations with spillover effects and innovations that have to overcome a critical mass), we analyze which organizational architectures may be superior regarding selecting good projects, avoiding collective myopia and overcoming organizational inertia. Results suggest that in a risky environment, firms with rigid hierarchies can achieve a much higher performance than horizontally organized firms even when the quality of the decision-making by managers is poor. Results also highlight the dangers involved in erecting a more hybrid-type organization because such an organization might become over-challenged and unable to handle risky innovations adequately. Finally, we discuss how firms could be structured to increase performance and to minimize risks.