1
Impact of technology incubators on the
underlying processes of innovation in the
South African ICT sector
Erika Kraemer-Mbula
Institute for Economic Research on Innovation (IERI), Tshwane University of Technology, South Africa
Abstract
The contribution of innovation to social and economic progress of developing countries is widely recognised. Governments and other actors are actively seeking to increase the firms‟ ability to deliver successful innovation in multiple sectors, particularly in those related to emerging technologies – such as ICTs. Technology incubation programmes have arisen as a major instrument to promote the advance of indigenous ICT industries in developing countries. This paper analyses the impact that three technology incubators in South Africa have on the development of technological capabilities of ICT firms. The methodology allows accounting for the accumulation of technological capabilities in processes and products, as well as internal and external mechanisms of technological learning. The results of the analysis indicate that the impact of technology incubators in directly promoting learning and technological capabilities in firms is rather limited, although they can play an important role as networking mediators.
Keywords: technological capabilities, technological learning, innovation, technology incubators, South Africa, ICTs.
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1.
Introduction
The existing literature widely recognises the importance of innovation for the social and economic progress of developing countries. At the level of the firm, innovation has been associated to the existence of certain types of capabilities, more specifically, „technological capabilities‟. Technological capabilities have been defined as the knowledge and skills that a firm needs to acquire, use, adapt, improve, and create technology (Lall, 1992, Bell and Pavitt, 1993). These capabilities are not acquired easily or automatically, but are the result of extensive and deliberate efforts to learn and improve both the process of production and the final products. In order to build technological capabilities, firms need to engage into deliberate and intensive efforts to learn from many sources, internal and external to the firm. Therefore, the rate of innovation of a company depends not only on firm-specific forces but also on other factors external to the firm, such as support programmes – for instance, technology incubators.
South Africa embraced the promotion of ICT innovation with its „ICT Sector Development Framework‟, launched in 2000. This framework envisaged the establishment of incubators to encourage and support innovative and entrepreneurial behaviour (SAITIS, 2000). This paper examines three of the incubators that emerged out of this initiative. The existence of technology incubators is often assumed to have a positive effect on the innovative performance of firms leading to higher technology-based competitiveness of the sector. However, their impact has not been empirically assessed so far. This paper addresses the following question: “what is the impact of the observed technology incubators on the underlying processes and activities that lead to innovation in ICT firms?” This paper aims to answer this question through a detailed comparison between firms that participate in these programmes and other set of „independent‟ firms, in terms of learning, accumulation of technological capabilities and innovation, as well as other goals of the incubators such as facilitating networking in firms and improving their business strategy.
In the literature, empirical testing of specific sectoral innovation policies has been much neglected, partly owing to lack of detailed data on the use of various policy instruments in firms and the limited understanding of how innovation takes place within firms. The exercise done in this paper would benefit largely from an examination of technology incubators over time; however, such an analysis is beyond the scope of this study.
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2.
The conceptual framework
The identification of the underlying processes leading to innovation, adopted in this paper, is based on various contributions from the existing literature. Technological learning is here understood as the origin of innovation. Learning processes in the firm, shaped by internal and external stimuli, translate into the accumulation of capabilities to generate changes in processes and products in the firm. Parallel to Winter‟s (2003) view of organisational capabilities, technological capabilities are here considered as high-level routines that an organisation has available for producing significant outputs of a particular type, in this case, innovations. Although having the capabilities to generate innovation does not mean that the firm actually undertakes innovation, it has been recognised (Prahalad and Hamel, 1990) that the capability is a necessary condition for it to happen. Therefore, technological capability has the potential to transform knowledge into new products and processes; that is, innovation. Figure 1: Theoretical relationship between TL, TC and innovation
It is important to note that despite the representation in figure 1 the relationships between learning, accumulation of capabilities and innovation are not assumed to be linear. Far from that, this conceptualisation regards these processes as highly dynamic, cyclical and cross-feeding. Technological capability is understood as a stock of technological knowledge that remains in the firm, diminishing the risk and uncertainty of future actions, generating innovation and also feeding future learning processes. Similarly, innovations generated as a result of these capabilities would influence the further development of technological capabilities. This circular view of the processes of learning, capability building and innovating makes it harder to establish causality.
Innovation Technological Learning
External Internal
Technological Capabilities
Products Process Products Process
4 The literature of technological capabilities in developing countries has enlarged the current understanding of these processes at the firm level, based on new evidence. This literature has placed the firm at the centre of analysis, making it a useful tool for empirical research. In recent years there have been multiple studies using the technological capabilities approach – some recent contributions include Pietrobelli (1998); Dutrenit (2000), Lall and Pietrobelli (2002), Wignaraja (2002), Cassiolato et al. (2003), Figueiredo (2003), Mani and Romijn (2004) and Oyeyinka (2004, 2006). These studies have shed new light on how developing country firms undertake learning and accumulate capabilities, suggesting alternative paths for firms to overcome technological constraints. However, the distinction between technological learning and technological capabilities has not been often made explicit, and it tends to be implicitly assumed that one leads to the other. This paper attempts to distinguish measures for each of these processes.
2.1.Measuring technological learning (TL)
Developing a single measure of TL is inherently difficult, because of the complexity of the codified and tacit knowledge that a variety of agents within a firm acquire from multiple sources. This paper makes use of relevant contributions from case studies on developing countries, such as Hobday (1990), Kim (1997), Lall and Pietrobelli (2002), Viotti (2002), Figueiredo (2003) and Marcelle (2004) who have identified specific learning mechanisms.
Several authors have observed that companies do not operate in isolation, but in constant interaction with their environment (Muchie et al., 2003). In order to acquire more complex technological capabilities, firms generally combine internal and external channels of learning. Few firms count with all the inputs required for successful and continuous TL and they often make use of external sources to fulfil their knowledge requirements. For instance, suppliers, buyers, universities, consultants, government agencies and competitors have long been considered as sources of vital knowledge for the firm, and recent empirical studies continue to confirm their relevance for latecomer firms (Almeida et al., 2003; Marcelle, 2004; Arvanitis et al., 2006).
Hobday (1990) developed a typology of learning mechanisms including learning by searching, learning by setting up electronic capital goods, learning by training and hiring,
5 learning by designing and adaptation of products designs, and learning by installing information feedback systems. These learning mechanisms were identified in a study of telecommunication companies in Brazil, and most mechanisms are also appropriate for our sample of ICT firms in South Africa. These categories implicitly involve the transition through phases, from elementary to intermediate and advanced forms of learning – although categories were not explicitly established, nor it was differentiated between internal and external mechanisms.
Learning by imitation and by copying foreign technologies has been found to explain much of the fast technological advance in the New Industrialised Economies (NIEs) of Asia (Hobday, 1995; Lall, 1996; Kim, 1997, 2001). Imitation ranges from producing mere copies or duplications to highly innovative new products inspired by the pioneering brand, or purchasing technology licences and royalties. Duplication and creative imitation have different associated learning mechanisms, although their boundaries are difficult to distinguish. Whilst learning from copying or duplicating typically involves low levels of learning since the firm is not required to generate new knowledge, other forms of creative imitation may involve purposeful search for information, systematic experimentation and interactions with actors outside the company.
In relation to this last point, Lall highlighted the deliberateness of learning, placing it at the centre of firms‟ business and technological strategy. He considered TL as a conscious and purposive process (Lall, 1992, 2000). Viotti (2002) distinguished between passive and active learning processes – depending on the company‟s intention to either simply acquire capabilities for production through absorbing foreign technologies or to develop more sophisticated capabilities for the improvement of those technologies.
Bell and Albu (1999) elaborated a taxonomy including some of these points, acknowledging that technological knowledge may be acquired from either internal or external sources that could also involve either relatively passive routine activities or more deliberate and active efforts. In their taxonomy, internal sources include passive learning based on the observation of routine production activities or through repairing and maintenance, as well as more active learning based on formally organised learning practices within the firm. External sources could also be either relatively passive as a by-product of various kinds of interaction with the outside world – such as selling by-products to
6 customers – or from more deliberate and active search efforts. According to their view, passive learning processes are those in which firms play a very limited role in either acquiring new knowledge from outside the company or generating such new knowledge themselves; whereas active learning processes would be those in which firms themselves are responsible for a relatively large proportion of the new technological knowledge that is either acquired by, or generated within, the firm1.
Drawing on these contributions, this paper classifies and measures TL at six different levels of effort (expanding on the binary distinction between passive and active) and in two dimensions (internal and external) – see table 1 below. „Effort‟ is clearly difficult to measure objectively, and some learning processes may involve a mixture of active and passive stages – for example, serendipitous acquisition of external knowledge may trigger active internal learning. However, this framework has proved to be helpful for classifying ICT companies and comparing their levels of capabilities.
7 Table 1: Classification of technological learning (TL) in the ICT sector
Internal TL External TL
Passive
Level 1 -Learning by doing from normal operations: based on informal staff interactions - Routine correction of defects and problems
- Learning by doing from normal operations: based on routine interaction with customers and suppliers
Level 2 - „Learning by doing‟ within the firm for specific upgrading purposes: existence of a variety of media for transmission of technical information (formal and informal approaches); interaction and exchange of information between managers and other workers
(vertical/horizontal) on innovation aspects
- „Learning by doing‟ with external actors for specific purposes:
external training (local and
overseas) funded by the supplier to accommodate specific demands; purchasing equipment packages linked with technical assistance from the supplier
Level 3 - Interaction and exchange of information between technical personnel and other specialists - Continuous in-house
technical and management training
- Copying and duplication - Direct application of a foreign license
Active
Level 4 -Promotion of internal networks among workers: mentorship and staff
motivation in other ways (team building exercises)
- Imitation
- Regular use of external
consultants to improve technical and production methods
- Regular participation in conferences and related events Level 5 - Development of an innovative
organisational structure - Systematic experimentation
- External technical and
management training at company‟s initiative (local and overseas) Level 6 - Development of staff
interactive tools within the company.
-Financial incentives to staff for innovation
- Deliberate promotion and high intensity of external linkages (with other companies and organisations) for various aspects of production and innovation (i.e. collaborative projects with universities for innovation)
8 Using this classification, this paper has developed three indices of TL: internal, external and a combination of internal and external. These indices will be utilised for assessment of the impact of technology incubators, based on the comparison between „incubated‟ and „independent‟ firms.
2.2.Measuring technological capabilities (TC)
The measures adopted for technological capabilities are based on the framework developed by Lall (1992) and later adaptations by Bell and Pavitt (1995). These frameworks have decomposed different types of technological activities within firms that define their technological capabilities. Drawing on the approach proposed by these influential contributions, further empirical studies have enriched the analytical framework to examine TC in developing countries. These studies are less numerous in Sub-Saharan Africa than in other developing regions2, although the existing ones have provided valuable insights on the circumstances under which TC are developed in Africa in various types of firms, sectors and countries (Some include Wignaraja; 2002; Oyelaran-Oyeyinka; 2006; Sawers et al, 2008).
Based on the observations of some these studies Table 2 identifies the various technical functions performed by enterprises, categorising them into levels.
2
Recent studies on TC are more abundant for Latin America (e.g. Pietrobelli, 1998; Dutrenit et al., 2002; Ariffin and Figueiredo, 2004; Padilla-Perez, 2006; Dutrenit and O‟Vera Cruz, 2007, etc), and Asia (Hobday, 1995; Ernst et al., 1998; Gammeltoft, 2003; Berger and Revilla-Diez, 2006; Rasiah, 2007, etc).
9 Table 2: Classification of technological capabilities (TC) in the ICT sector
TC in Process TC in Product
Basic
Level 1 Sub-assembly and assembly of components and final goods
Replication of fixed specifications and designs Level 2 Minor adaptations of
process to local conditions, informal routine quality control
Minor adaptations to product driven by market needs
Intermediate
Level 3 Adoption of organised quality control systems, testing, adoption of international standards, international certificates (ISO 9000)
Significant adaptations to products, including external means
Level 4 Automation of processes, focus on core activities and outsourcing process activities Reproducing existing technologies, reverse engineering/ development of prototypes Advanced
Level 5 Writing original software to allow new
processes/embedded software
Redesigning product to meet local/regional/international consumer needs, creative introduction of existing technologies/creative
recombination of technologies to provide new products and services
Level 6 Process-oriented R&D R&D on new product generation
Source: Adapted from Lall (1992), Bell and Pavitt (1993), Ariffin and Figueiredo (2004), Padilla-Perez (2006) and own fieldwork.
Similarly to TL, this classification allows developing three measure of TC: TC in processes, TC in products and a combination of the two. These indices will also be utilised below for assessment of the impact of technology incubators, based on the comparison between „incubated‟ and „independent‟ firms.
2.3.Measuring innovation
Many definitions have been devised for innovation. However, since its inception, innovation has been generally related to new ideas, new technologies, new products and
10 services. This paper adopts what could be considered as a broad view of innovation, as proposed by Nelson: „the process(es) by which firms master and get into practice product designs and manufacturing processes that are new to them, whether or not they are new to the universe, or even to the nation‟ (Nelson, 1992: 349).
This definition is adopted for one main reason. This paper is not focused on the novelty of the innovations per se but on learning and the accumulation of capabilities that underlie those innovative activities. As Nelson (2004) also recognises, the efforts of becoming the leader in the introduction of a new product or process and those associated with catching up, are not necessarily much different.
Some features of innovation captured in this study are:
- „Degree of novelty‟: this variable captures whether the new products/services are new to their firm, their local market, the national market or global markets. This variable takes values from 1 to 4 in relation to the reach of innovations, according to these options respectively.
- „Innovation outputs‟: This variable measures the percentage of turnover accounted for by the new products/services.
3. Methodology
This paper draws on the empirical evidence of 81 ICT firms in South Africa. In this sample, 31 firms participated in one of the three incubation programmes for the ICT industry located in two provinces: Gauteng and the Western Cape. Fieldwork for this research was designed to minimise the potential biases often noted in the literature3. However, despite the preventive measures some inherent biases to the nature of the research are worth mentioning:
(a) There is an element of self-selection into incubation programs. Firms that apply for subsidies or support programmes that promote innovation are more likely to be involved in innovation and technology development than firms that do not. The mere process of application
3 Such as: sample bias (Heckman, 1979), response bias (Locander et al., 1976) and interviewer bias (Dohrenwend et al., 1968; Fowler, 1991)
11 for support involves a certain level of organisational structure and attitude towards searching for external sources of knowledge and resources. Companies participating in support programmes are thus be likely to present higher levels of technological learning, capabilities and innovation. This bias has been noted in the evaluation of incubators elsewhere (Colombo and Delmastro, 2002).
(b) Technology incubators in developing countries often operate in context where they fight over scarce public resources. Programme managers face strong pressures to achieve high „success rates‟. This might provide an incentive for incubation programmes to select participant firms with innovative and commercially attractive project proposals (Wallsten, 2000). Therefore, companies that participate in incubation programmes might be likely to also have better overall performance than firms that do not.
(c) However, incubation does not always imply a positive bias on the firm innovative performance. Some of the benefits of incubation might emerge only after the incubation period, at a later stage of the company‟s business life, which would cause the „success‟ of the incubating programme to be understated by measuring only performance during the incubation period. Technology incubators have a very short history in South Africa. Emerging from different regional and national initiatives the three incubators that are part of this study were established in 2001 and 2002. Three incubators have been chosen for this study – the Innovation Hub (in Gauteng), Softstart-BTI (Gauteng), and the Bandwidth Barn (BWB) (Cape Town). They are the only incubators (to date) in South Africa supporting specifically the ICT industry. The three incubators selected for this research offer a number of services which include: affordable work space and shared facilities (e.g. conference room), access to ICT infrastructure, counselling, mentorship and networking activities for their clients and technology support services. Despite their name, the provision of technological support was not among their frequently provided services, but appeared to be tailored to individual requests when they could be matched with the resources available at the incubator.
The analysis of the impact of the incubators is based on counterfactual of what would have happened if no intervention had taken place, by using a control sample. It is important to recognise that this type of analysis does not take place without bias and problems with endogeneity, since the differences between the control sample and the incubated sample can be partly explained by other factors – as mentioned above. This paper examines the differences between a sample of incubated firms and a control sample of non-incubated firms (named here
12 „independent‟ firms) in relation to a number of „impact‟ variables (as oppose to focusing on their goals). The differences between these two sub-samples are examined by statistical tests of differences of means of outcome variables (ANOVA).
4. Impact of technology incubators: empirical relationships
The uncertainty about which are the most appropriate type of programmes to support innovation and whether any action should be considered at all, are relevant subjects for study from both theoretical and empirical points of view. Over the past decade, governments in developing countries have increasingly turned their attention from merely offering financial assistance or support to SMEs, to providing direct services to business in strategic sectors, such as incubators. ICT incubators are specifically designed to offer solutions to firms‟ challenges, extending their networks, facilitating the exchange of ideas, information and knowledge and assisting management and technological development. Evaluations of direct support programs vary in their methodologies (Colombo and Delmastro, 2002; Lalkaka, 2003), but studies conventionally suggest a positive (although partial) impact on firms‟ growth and economic performance. Empirical analyses of their effects on learning, TC and innovation practices are rare.
4.1.Description of incubated and independent firms
As mentioned above, the establishment of incubators has accelerated in South Africa in recent years as instruments of facilitating entry of small firms into emerging sectors, such as ICTs. The three incubation programmes described in section 3 support small start-ups in the ICT industry (except the Innovation Hub which also hosts other companies in high-tech sectors). Therefore, companies that participate in incubation programmes were on average younger and smaller (in relation to turnover and number of employees) than the sub-sample of „independent‟ firms – table 3 below summarises their key features. Incubated companies also tend to have a higher percentage of qualified personnel in their employees base (71% compared to 62% for independent firms), but only in relation to the percentage of staff with university degrees, since the average percentage of staff with Diplomas appears to be higher in the independent sub-sample. Companies in incubating programmes also appeared to have a higher propensity to
13 export, with an average percentage of exports in total sales almost double than that of independent firms.
Table 3: Key figures of incubated companies
Incubator Independent
Total average
Age of the firm (years) 4.6 9.1 7.4
Turnover 05/06 (Rand million) 6.4 252 158
Number of employees 10 102 67
Highly qualified personnel (%) 0.71 0.62 0.66
Staff with University degree (%) 0.41 0.26 0.33
Staff with Diploma (%) 0.29 0.36 0.34
Intensity of exports (% of sales 2006) 23.9 14.4 18.1
4.2. Impact of incubators on TL, TC and innovation
The three incubators intended to assist new firms in taking their innovative ideas to the point of commercialisation. Discussions with managers of the incubators indicated that they understood this process as bounded by the entrepreneur‟s constraints in mind-set, money, management and knowledge of the market. This journey implicitly involves a process of learning and accumulation of TC to finally deliver tradable innovations that translates into a competitive and self-sustainable firm.
The analysis in this section attempts to depict the impact of the incubators through the comparison of various activities and the accumulation of TC between incubated firms, and a control sample of what we call here „independent‟ firms – that is, companies that do not participate in incubators. ANOVA tests have been performed to detect significant differences between the samples of incubated and independent firms.
Table 4 present the results of the ANOVA test. Although the levels of TL, TC and innovation are consistently higher in incubated firms compared to independent firms, the average levels of these indicators in the two sub-samples are only significantly different in four cases: internal TL, overall TC, TC in products and degree of novelty of innovations.
14 Table 4: Differences in TL, TC and innovation between independent and incubated firms (2006)
Pooled sample
Incubated Independent F-value p-value
TL 8.68 7.44 3.48 0.06 TL Internal 4.35 3.58 4.33* 0.04 TL External 4.32 3.86 1.49 0.22 TC 8.81 7.18 7.24** 0.00 TC Products 5.13 3.72 11.38** 0.00 TC Process 3.68 3.46 0.61 0.43 Degree of novelty 3.06 2.08 11.31** 0.00 Innovation output 0.55 0.42 2.00 0.16
* Significant at the 5% level, ** Significant at the 1% level
These results can be interpreted as an indication of the positive impact that these incubators have in the TL, TC and innovation of the participant firms. However, as was mentioned in section 3, there are a number of biases that need to be taken into consideration when interpreting higher innovation levels in incubated firms. For instance, incubators were inclined to give entry priority to firms that showed propensity to deliver innovative products and services – see Box below. Their entry requirements are likely to affect the differences between the levels of TC in products and the degree of novelty of the innovations. However, these requirements do not necessarily explain the existing significant differences in internal TL between the two subsamples. This is a first indication of the potential positive impacts from incubation programmes, perhaps explained by their provision of managerial advice.
15 4.3.Impact of incubators on improving networking
Numerous studies testify to the importance of firms extensively 'networking' in order to improve innovation potential. The ability of South African incubators to build and develop the TC of their participants through networking has been stressed by Chan and Pretorius (2007) and Sawers et al. (2008). Business incubators are more likely to succeed if they are supported by a broadly based partnership of public and private sector sponsors. Particularly important is their capacity to leverage private sector inputs, facilitating support from other external agents (e.g. expertise, access to finance, corporate venturing).
Common to all the incubators in this study was that they offered physical shared office space, which automatically connected all incubated firms with each other. Additionally, they also held:
- Partnerships with strong local companies and foreign firms to potentially assist marketing deals and promote mentorship.
- Formal networking events: organising workshops, inviting external experts for occasional seminars, regular meetings of incubated firms, etc.
- Partnerships with local authorities, education institutions, financial institutions, professional associations and other national and international incubating programmes to promote the interests of ICT companies.
Box: Admission criteria to incubation programmes
The profile of the firms participating in technology incubators is likely to be shaped by the entry criteria to these programmes. Although there were differences in the requirements of each of the incubators, they shared a clear preference towards young firms with ambitious projects and innovative ideas that presented high chances of success. For instance, the Innovation Hub in Tshwane (Gauteng) described some of their entry criteria as follows: Preference will be given to companies that are:
- Potential world leaders in innovative technology and brand recognition.
- Inclined to develop synergies with other companies in The Innovation Hub.
- Have a long-term commitment to building technology competence in Gauteng through investment in people and indigenous intellectual property.
- Provide a significant number of high value-added innovation and technology jobs locally
These features gave incubated firms an advantage in TC and innovation over independent firms.
16 Companies in the sample were asked to rate the interaction in ideas related to innovation and technological advance with a number of external agents. They rated the intensity of their interactions in relation to innovation from 1 to 5 (1=no interaction, 3=moderate intensity, 5=very intense). The results from the ANOVA test of means for incubated and non-incubated firms are summarised in table 6 below. They show significant differences between the strength of linkages of independent firms and incubated firms with three external actors, namely suppliers, competitors and higher education institutions (universities and „technikons‟4). These results suggest the effectiveness of the incubators in widening the networking opportunities of their tenants, not only through the facilitation of linkages between incubates (mentorship programmes5 and networking events) but also through their referrals to business professionals outside the incubators (such as suppliers). Personal interviews also revealed that tenants often outsourced certain non-core activities (such as website development, or specific software writing) to other companies in the incubator, reinforcing their exchange of technical information, collaboration in innovation projects and development of supply chains.
Table 5: Profile of external linkages of firms: results from the ANOVA test
4
Now called Universities of Technology.
5 One of the most acclaimed services offered by the three incubators covered in this study was the mentorship programme. Mentoring had a different character in each incubator. However, they all shared some common features: (a) Mentoring was an educational process where the mentor provided advice for professional development, growth and support to less experienced firms within the incubator; (b) Mentors provided
information, encouragement and advice to younger firms as they planned and developed the growth strategy for their business; and (c) A mentor could be an experienced manager of a company inside or outside the
incubator, as well as a retired person with long experience in the field. Mentorship sessions had targeted learning as the ultimate goal. By arranging regular sessions, young companies were motivated to actively develop interactive tools within the company, which was automatically reflected in more dynamic learning within the firm. In independent firms, the benefits of mentorship had to be either contracted through consultants or by hiring highly experienced personnel, which was in any case much more expensive than the mentorship offered in the incubators. In independent firms mentorship was mainly out of the reach of the average firm in terms of costs.
Incubated Indep.
ANOVA F- value
p-value (Values from 1= not relevant to 5= very relevant)
Clients 4.2 4.1 0.26 0.61
Internet 4.1 4 0.06 0.80
Joint or cooperative ventures 3.3 3.1 0.18 0.67
Suppliers 3.6 3 4.17* 0.04 Professional publications 3 2.9 0.12 0.73 Trade fairs/conferences/meetings 2.8 2.8 0.01 0.90 Competitors 2.6 1.9 6.23** 0.01 Sector associations 2.1 1.8 1.51 0.22 Universities/Technikons 2 1.6 3.13* 0.05
17 * Significant at the 5% level, ** Significant at the 1% level
However, these results also indicate that incubators fail to strengthen the linkages between firms and other critical actors in the innovation system, such as financial institutions, sector associations, and government agencies, as well as assisting firms in developing joint or cooperative ventures – these are also considered essential goals of incubation programmes.
4.4.Impact of incubators on the business performance: business strategy and export activities
The success of an incubation programme depends ultimately on the performance of its tenants. Therefore, incubators dedicate substantial efforts to ensuring that their tenants have a coherent and well-functioning business strategy. As mentioned above, incubators generally provided their clients with a range of business services and assistance that included advisory services in management, human resources, new markets and products, and feasibility assessment of overall business strategy.
This study also collected evidence on various aspects of the business strategy. Business strategy is captured in this study using by the scores that interviewed companies assigned to five factors: markets and products, technology, production inputs, management and human resources. Decisions on markets and products are concerned with the intensity that firms devote to the commercialisation of new products and services. Decisions on technology are concerned with the search for and acquisition of technologies that can be used to develop innovative products and services. Decisions on production inputs are concerned with upgrading and reducing product costs. Decisions on management and human resources are related to the ways in which the company promotes the development of human capital and innovative behaviour at various levels
Related firm (parent or subsidiary) 1.8 2.3 2.5 0.12 Government regulations/standard agencies 1.8 1.7 0.2 0.66
Consultants 1.7 2.1 1.73 0.19
Software houses 1.6 1.9 1.36 0.25
Government industrial development & technology transfer agencies
1.4 1.2 1 0.32
Financial institutions 1.4 1.7 1.85 0.18
Chambers of commerce 1.3 1.2 0.28 0.60
18 of the corporation. The relevance of each of these dimensions of the business strategy was rated from 1 to 5.
Figure 2 below shows that on average, incubated firms have more proactive strategies in all areas of the business strategy than independent firms. This advantage becomes more apparent in relation to their technology strategy.
Figure 2: Differences in business strategies: independent and incubated firms (2006)
Note: values of indicators range from 1 to 5
A one-way ANOVA test of means – see table 6 below – reveals that this difference in technology strategy is in fact significant statistically. However, participation on incubating programmes does not seem to have any impact on other key areas of the business strategy such as markets and products, management or human resources. This result contrasts with the central services provided by the incubator targeted to assistance to firms on decision making, management and commercialisation. Additionally, the significant differences in the technology strategy between samples can be partly explained by the specific entry requirements for certain incubators, as explained above. 0 0.5 1 1.5 2 2.5 3 3.5 4 Markets and Products
Technology Inputs Management Human resources Incubated Independent
19 Table 6: Profile of the business strategies of incubated and independent firms
Incubated Independent
ANOVA F- value
p-value
Markets and Products 3.78 3.61 0.58 0.44
Technology 3.59 3.12 2.99* 0.08
Inputs 2.77 2.37 2.57 0.11
Management 3.39 2.99 2.53 0.13
Human resources 3.59 3.34 0.94 0.33
* Significant at the 5% level, ** Significant at the 1% level Note: values of indicators range from 1 to 5
Finally, the impact of incubators was also assessed in relation to the export activities of firms. Patterns of export activities were compared between incubated and independent firms on the basis of their percentage of exported sales in 2006, the sophistication of export markets both in 2006, and the predicted sophistication of export markets in 2010. Table 7 shows that although on average incubated firms present higher values for these indicators, the differences between the two sets of companies are not significantly different. From these results, it can be inferred that participation on incubators has no significant impact on the exporting activities of firms.
Table 7: Profile of export activities of independent and incubated firms Incubated Independent ANOVA F- value p-value Exports 2006 23.95 14.48 2.02 0.15 SEM 2006 2.33 2.20 2.22 0.63 SEM 2010 2.32 2.19 2.32 0.57
Note: „Exports 2006‟ measure the percentage of sales abroad in 2006; the sophistication of export markets (SEM), measures the difficulty of technological-entry level to different export markets. It takes value 1 for exports to regional low-technological entry markets, 2 for exports to medium-technological entry markets and 3= exports to high-medium-technological entry markets.
5.
Conclusions
This paper has examined the impact of three technology incubators in South Africa on the underlying processes of innovation in ICT firms. These underlying processes refer to the processes by which firm obtain knowledge and accumulate the capabilities necessary to engage in successful innovations. In order to assess the impact of incubators on these processes, measure of technological learning, technological capabilities and innovation have been adopted and constructed. The evaluation of technology incubators was conducted by comparing the performance of incubated firms with a control sample of non-incubated firms
20 Technology incubators are an important tool of innovation policy, and they are becoming increasingly prevalent in South Africa. Three technology incubators were selected for this study, due to their shared commitment to nurturing ICT firms. The evidence, however, shows that their impact is not as strong as it would be expected. The comparison between incubated and non-incubated firms shows that non-incubated firms perform better in terms of internal TL, TC in products and degree of novelty of innovations. However, this finding has been interpreted cautiously and in conjunction with the overall impact of incubators, since priority to these programmes is given to companies that have higher (actual or potential) innovative performance. The examination of other areas of impact showed that incubators have a weak incidence on four of the five the dimensions of the business strategy identified in this research, with the exception of the technology strategy. Incubators were particularly active in providing management support and commercial advisory services to firms. However, they were not found to induce proactive approaches to markets and products, management or human resources, despite the fact that they were established with that goal in mind. On a more positive note, incubators seem to succeed as networking institutions, and incubated firms seem to have a higher propensity to exchange ideas on innovation with suppliers, competitors and high education institutions.
The analysis of incubators from multiple dimensions, has allowed distilling the real impact that incubators have in the accumulation of TC of firms, providing more agile internal TL, and coordinating the external links of firms. Beyond those impacts the role of government support on the accumulation of TC in South African ICT firms appears to be quite limited.
6.
References
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