Key driving factors for product service innovations in UK university spinoffs_final draft (a)

Abstract: University spin-offs are a vital firm class since they are an economically important sub-group of high-tech start-up firms They have engendered a high volume of academic studies However, the focus on the economic returns and contributions from university spin-offs has been criticised Moreover, what the firms deliver by way of innovation in the form of new products and services has largely been missing from the literature This study demonstrates the driving factors for the success of products and services development by spin-off firms, such as understanding needs of customers, networks, clear market analysis, application of technology, and vision, mission and value of the company The findings resonate with various studies on the key elements of the products and services performance predictors The study contributes to filling a gap in the literature by providing an understanding beyond the success factors in setting up university spin-offs This can help inform academic entrepreneurs, Technology Transfer Offices (TTOs), university senior managers, or policy makers on the design or change of policy within the university in order to support university spin-offs Keywords: Academic spin-offs; innovation; service innovation university spin-offs performance; product University spin-offs have engendered a high volume of academic studies They are a vital firm class since they are an economically important sub-group of high-tech start-up firms Many have sought to clarify the diversity of spin-off activities; for example by sector, by their employment and wealth generation possibilities, and by institutional and public policies designed to escalate this activity (see Druilhe and Garnsey, 2004; O’Shea et al., 2004; Shane, 2005; Siegel et al., 2003 in the UK for example) However, the focus on the economic returns from university spin-offs has been criticised with questions having been raised about their longer-term impact (Harrison and Leitch, 2010; Colombo et al., 2010; Siegel and Wright, 2015) Moreover, what the firms deliver by way of innovation in the form of new products and services has largely been missing from the literature (but see Druilhe and Garnsey, 2004, and to some extent Shane 2005; Stephan, 2014) Innovation is often regarded as the fundamental part of many firms’ operations, being key to driving their growth and development Additionally, firms that have maintained their leading position in the market have established an ability to develop products effectively and successfully In other words, there is an evident connection between innovation and economic advancement The effective management of the product/service development process can bring success to the business (Cooper and Kleinschmidt, 1995) In addition, product and service development have made key contributions to the surge of diversity in the market Both forms can become prospective sources of competitiveness for many companies (Shepherd and Ahmed, 2000) There has also been a rising number of studies carried out to ascertain driving factors of innovation (Rhee et al, 2010) However, there is a lack of research on the university and the wider context in which university spin-offs have developed product/service innovations, and the driving factors for their development In this paper, contributions to markets made by UK university spin-offs through transferring and transforming newly invented knowledge and technology into products and services are examined (Shane, 2005) The importance of having an understanding of the factors that contribute to success of the development of products and services is considered Therefore, this study aims to address two questions: 1) What are driving factors in the development of products and services in the UK university spin-off context? 2) What is the relationship between the driving factors and the success of product and service innovations? The answers are derived from a mixed methods study comprising in-depth interviews with 20 university spin-off founders and a survey of 204 UK university spin-off companies The structure of the paper is as follows It begins with a discussion of the economic contribution of UK universities through spin-off activity This is followed by the identification of driving factors in successfully developing products and services in university spin-offs Next, findings from in-depth interviews and a survey are reported and discussed The paper concludes with the insights and implications for entrepreneurial universities University spin-offs and product and service innovations The economic contribution of UK universities: through university spin-offs Universities have played a crucial and creative role in translating knowledge for economic and social development (Etzkowitz, 2016) A number of studies have agreed that the development of high-technology clusters and the growing number of knowledge exchange activities are generally a consequence of contribution from universities (Chapman et al 2011) In the UK government or public funding for universities has become increasingly focused on whether they will have a direct ‘impact’ on the economy In order to respond to the government policy towards commercialising science and technology developed within universities, the creation of university spin-offs has steadily increased (Etzkowitz, 2000; Iacobucci and Micozzi, 2014) Numerous universities have founded technology transfer offices (TTOs) to facilitate the diffusion of entrepreneurial culture and the commercialisation of research (Algieri et al 2013) As noted by Wright et al (2002), university research and technology commercialisation activities speeded up in the late 1990s when university technology transfer offices were established by many UK universities A survey conducted by Higher Education Business-Community Interaction (HE-BCI) in 2004 showed that in the UK, between 1999 and 2002 there was a rapid growth in the number of spin-off firms While the number of spin-offs has slowed down since, more are surviving By 2014/15, the number of three year-old or older spin-off companies had risen to approximately 1,013 (See Figure 1) Figure 1: University spin-offs formed in the UK, 2003-04 to 2014-15 Source: Higher Education – Business and Community Interaction Survey 2014-15 While the direct creation of jobs and wealth by university spin-offs is not enormous (Harrison and Leitch 2010), they are potentially an effective means of transferring novel technological knowledge to the market in the form of new products and services (Sternberg, 2014) The majority of the studies of university spin-offs not reflect this importance They appear to focus more on the macro-economic and infrastructural perspectives that support the formation of university spin-off firms rather than on the firm-level innovations (Druilhe and Garnsey, 2004) Only a handful of studies of academic entrepreneurship have looked at the innovation offerings contributed to the market The study of the products and services development process of university spin-offs from MIT in the US by Shane (2005) seems to be the only one currently looking at this topic Driving factors of successful product/service innovations in firms The importance of innovation to the success of firm is frequently referred to as a very significant factor in firms’ higher performance (Ngo and O’Cass, 2013) The management of the product/service development process can bring success to the business (Cooper and Kleinschmidt, 1995) In addition, product and service development has also made key contributions to the surge of diversity in the market Both forms can become a prospective source of competitiveness for many companies (Shepherd and Ahmed, 2000) Therefore, the development and launch of new products is not only critical to the growth and success of firms, but also creates new markets, which in turn provides economic growth and employment (Ahlstrom, 2010) The development process has associated huge uncertainties together with a high chance of failure; this has made product/service innovation one of the more perilous activities for the business (Cooper, 2003) Discerning elements supporting the success of new product development continues being an important managerial interest (McNally, 2011) A review of empirical research by Ernst (2000) underlined the success driving factors of products and services development for firms in general, such as the existence of either formal or informal development processes within the company, the formation of a devoted project team, the awareness and understanding of senior management etc Later, Bessant and Tidd (2011) found that generally, funding, resources and identified target markets are considered critical success factors in products and services development However, within the university spin-off context, there is little evidence on those factors which drive success in product and service development Only a few studies have touched upon the factors contributing to the success of products and services of science or technology-based firms For example, Wright et al (2007) noted the importance of human capital by showing a significant relationship between both the general and specific human capital of the technological entrepreneurs and the innovative products and services offered to the market by their ventures In addition, Knockaert et al (2011) found that, in science-based entrepreneurial firms (SBEFs), factors leading to the delivery of first product to market are the combination of staff with commercial experience together with prior technical expertise or background Therefore, this study aims to address two questions: 1) What are driving factors in the development of products and services in the UK university spin-off context? 2) What is the relationship between the driving factors and the success of product and service innovations? Methodology and data The data for this research are obtained from the use of a mixed methodology Given the exploratory and descriptive nature of the research questions, a mixed method research approach is appropriate A combination of in-depth interviews and survey was employed The drivers of product/service innovations within university spin-offs are required to be explored; a qualitative method was firstly chosen to explore and ascertain these drivers Then, a questionnaire survey was employed to empirically test the drivers obtained from the qualitative stage The population and sampling The population in the study is university spin-off companies in the UK In this study, the definition given by Higher Education Funding Council (HEFCE) is followed, but the scope is more focused on spin-off firms that have been established by academic or university staff (where the university owns the intellectual property (IP) or academic entrepreneurs own the IP it is then easier to identify the population) In addition, firms in the service sector, in the definition set in the Higher Education Business-Interaction (HEBCI) surveys for Higher Education Funding Council (HEFCE) is broad and expansive by embracing new legal entities and enterprises created by the Higher Education Institute or its staff to allow the commercialisation of knowledge from academic research The universities may or may not have a stake in these firms In addition, the term “spin-offs” includes start-up firms established by university staff and students beyond the exploitation of IP which firms are set up without any appropriating of IP, are included as well as are technology-based spin-off firms The sampling frame of this study was drawn from public websites of universities in the UK The list of 133 universities was obtained from Universities UK (http://www.universitiesuk.ac.uk), which is the central organisation supporting all universities in the UK It has a comprehensive list of UK universities The list was cross-checked with information provided by Higher Education Funding Council (HEFCE), and Scottish Funding Council (SFC) The database of university spin-offs was constructed by searching through the business and innovation centres of universities, such as Oxford University Innovation as well as departmental websites Since some universities not provide a list of spin-off firms on their public website, the relevant people in the university were contacted to ensure that there was no omission of any university spin-off firm Then, it was merged and reconciled with the company list shown on website: www.spinoutsuk.co.uk, which provides a list of all spin-off companies from universities in the UK In order to ensure that all those included are university spin-offs from academic or university staff, the names of company directors were checked against the university’s website to see if they were affiliated with the university From 1356 spin-out companies in the database, 844 companies are actively in operation In addition, 87 companies have been merged or acquired (M&A) These companies are simply excluded because after the M&A process they have become part of either a big conglomerate or another firm Hence, knowledge and resources tend to be integrated and transferred between two firms (Gomes et al., 2013) Within the active companies in the database, there is no information available on 144 companies This process also helped in the collection of founders’ contacts, i.e name, e-mail and telephone number, for the purpose of the empirical research Data collection method A qualitative method, i.e in-depth interviews with founders of university spin-offs, was employed to explore important factors in successfully developing products and services Indepth interviews were conducted with academic founders of 20 university spin-offs The sampling at this stage was purposively selected from the database of UK university spin-offs developed for the purpose of this study, aiming to represent the various sectors, firms’ size and different regions within which university spin-offs operate The respondents were selected based on the following criteria: - being a founding member of a university spin-off firm - owning an equity in the firm - used to/ currently hold an academic position when establishing the company - having product/service offerings in the market Convenience also plays a secondary role in selection process, i.e., how easy it is to get access and get an agreement from the founders to set up a 30 to 45 minute interview The firms’ locations are spread throughout the UK There are firms in software, in consultancy, in biotech, in engineering, in pharmaceutical, and the geography consultancy sector The majority of the firms are categorised as micro with only 1-10 employees; only one in the sampling is a medium-size firm (with more than 50 staff) Most of the founders interviewed were men, though female founders were also interviewed Additionally, 14 founders in the sample still maintain their academic position while running the firm’s operations The findings from the in-depth interviews allowed the development of observed variables in the survey questionnaire Subsequently, the collection of quantitative data used a structured on-line and postal questionnaire The founders were targeted for the survey since they usually have a broad knowledge on the firm’s history (Carter et al., 1994) The sampling (n=844) at this stage was from the database of UK university spin-offs developed for the purpose of this study The survey questionnaires were pre-tested as thoroughly as possible through discussion with founders and product development managers of university spin-offs prior to distribution The survey questionnaire and its observed variables are derived and developed from the in-depth interviews, which are identified and summarised in Table The survey was conducted from October 2013 to March 2014 Both online survey and paperbased questionnaires were sent to 844 university spin-offs altogether; out of 844 firms, 322 firms were sent paper-based questionnaires by post For the online survey, e-mails were bounced back and another 20 firms stated that they had no interest in doing the survey In total, 212 questionnaires were filled out both via online platform and via post However, some parts of 18 questionnaires were not fully completed So, those 18 firms were contacted once again by phone in order to get information of the missing parts Only firms could be reached and the missing information obtained Therefore, the total number of completed questionnaires of this study was 204 and the response rate was 24% Findings The findings are divided into two parts in order to address the research question Those on success driving factors in developing product/services, derived from the in-depth interviews with 20 founders of university spin-offs in the UK are presented, followed by the empirical evidence from the survey Driving factors in developing products and services in the university spin-off context The data from in-depth interviews and survey have highlighted the driving factors that contribute to the success in developing products and services within university spin-offs as presented in Table Some of these are factors which apply to new innovative firms generally; others relate more directly to the university environment Those, which relate most to the academic context, are numbers (Application of technology to the needs of the market), (Networks) and (Funding and investment) as these cover the nature and context of the academic commercialisation process, in particular the importance of building links external to the university in order to develop a viable business model These data show the patterns within a sample of university spin-offs and point to areas where technology transfer support systems need to be effective, which are further complicated by the diversity of product and service categories offered by university spin-offs The relationship between the driving factors and the success of product and service innovations This section examines the relationship between the driving factors identified from previous sections and the success of products/service innovations The number of products and services is used as a proxy for the success of product/service innovations in order to find out which driving factors can predict and have effect on the success of product/service innovations This gives further insights and some considerations on what driving factors can attribute to the higher number of product/service offering A multinomial logistic regression was employed to examine the relationship The equation for multinomial logistic regression is described as: Pr(yi=j) = exp(xiβj) Σ J j exp(xiβj) where pr(yi=j) is the likelihood of being a member to group j, x i is a vector of explanatory variables and Bj are the coefficients, which are calculated using a maximum likelihood estimation In this case, the dependent variables are categorical of number of products and services offered, while the independent variables are important factors in developing products and services, which have been identified from the in-depth interviews These together with the sectors, wherein the firms operate, and with the size (based on number of employees) of the firms, are chosen as controlled variables The output shows the R2; the value on Cox and Snell measure is 0.5 and the value of Nagelkerke’s measure (adjusted R2) is also 0.5 See Table They are similar values and indicate decent-sized effect Table 1: Summary of important factors contributing to the success of products and services from in-depth interviews Table 2: The pseudo R-Square Pseudo R-Square Cox and Snell 0.5 Nagelkerke 0.5 McFadden 0.2 In the likelihood ratio tests, “understanding needs of customers”, “application of technology”, “vision and mission of the company”, “funding and investment”, “capable staff” and “networks” are predictors that significantly allow us to predict the outcome category, though the effect is not presented See Table Table 3: Likelihood ratio tests Likelihood Ratio Tests Model Fitting Criteria Effect Intercept factor- identify right target customers factor-understand needs of customers factor-clear market analysis factor-application of technology factor-pricing model factor-vision and mission of the company factor-funding and investment factor-capable staff factor-networks size-1-30 employees size-31-49 employees size-50-99 employees size-150+ employees sector-biotech sector-engineering sector-medical sector-pharmaceutical sector-software sector-telecommunications sector-web/internet -2 Log Likelihood of Reduced Model 378.2 381.7 443.0 354.3 411.1 377.8 420.0 407.2 409.7 433.6 378.2 378.2 378.2 378.2 378.2 378.2 378.2 378.2 378.2 378.2 378.2 Likelihood Ratio Tests Chi-Square 0.0 3.4 64.8 32.9 41.8 29.0 31.5 55.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 df Sig 5 5 5 5 0 0 0 0 0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model The reduced model is formed by omitting an effect from the final model The null hypothesis is that all parameters of that effect are a This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom b Unexpected singularities in the Hessian matrix are encountered This indicates that either some predictor variables should be excluded or some categories should be merged In Table 4, the summary of parameter estimates shows the result of the predictors’ effect by category These parameters summarise the results of the compared pairs of outcome 10 categories and there are categories of products and services The category 0-1 products and services is used as a reference category (baseline category) This means category 2-5 products and services, for example, is comparing against 0-1 product and service Table 4: Summary of parameter estimates* products/servicea 2-5 6-10 1115 1620 20+ B Std Error Wald df Sig Exp(B ) 95% Confidence Interval for Exp(B) Lower Upper Bound Bound [understand needs of customers=0] -1.1 0.6 3.2 0.1 0.3 0.1 1.1 [application of technology=0] -0.9 0.5 3.5 0.1 0.4 0.2 [sectortelecommunications =0] -2.1 6.7 0.1 0.8 0.1 2.57E-07 63242.7 [understand needs of customers=0] -1.3 0.8 2.4 0.1 0.3 0.1 1.4 [networks=0] -1.4 0.7 4.2 0.2 0.1 0.9 [sectortelecommunications =0] -2.9 6.8 0.2 0.7 0.1 9.08E-08 30340.1 [networks=0] -2.9 1.2 0.1 0.6 [understand needs of customers=0] -2.3 2.3 1 0.3 0.1 [application of technology=0] -1.4 1.2 1.3 0.2 0.3 2.5 [sector-engineering =0] -1.8 2.3 0.7 0.4 0.2 13.3 [sector-pharmaceutical=0] -3.2 2.2 2.1 0.1 0 3.1 [understand needs of customers=0] -2.3 2.2 1.1 0.3 0.1 7.2 [application of technology=0] -1.9 1.8 1.1 0.3 0.1 4.9 [networks=0] -1.3 1.7 0.6 0.4 0.3 7.6 [understand needs of customers=0] -2.7 1.2 5.1 0.1 0.7 [vision and mission of the company=0] -1.2 0.9 1.8 0.2 0.3 0.1 1.7 The category 0-1 product/service is used as a reference category (baseline category) *This table only summarises the predictors that show a strong effect towards each product category Explaining the predictor’s effect - Category 2-5 products and services – the predictor “understand customers’ needs” significantly predicted whether producing 2-5 products and services or producing 0-1 products and services, b= -1.1, Wald X2(1) = 3.2, p = 0.1 and Exp.(b) = 0.3 Since = 11 understanding customers’ needs is not important and 1= understanding customer needs is important, this is the effect of no importance of understanding customer needs compared to the importance of understanding customers’ needs The odds ratio (Exp.b) shows that when this predictor changes from no importance to importance, the odds of having 2-5 products and services is increased to 3.33 times more than not giving an importance to understand customers’ needs In the same way, the predictor “application of technology” significantly predicted whether producing 2-5 products and services or producing 0-1 products and services, b= -0.9, Wald X2(1) = 3.5, p = 0.1 and Exp.(b) = 0.4 The chance of having 2-5 products and services is increased to 2.5 (1/0.4=2.5) times more than not giving an importance to application of technology Further, the “sector-telecommunications” also predicted whether producing 2-5 products and services, b= -2.1, Wald X2(1) =0.1, p = 0.8 and Exp.(b) = 0.1 Though, the effect is not as significant; the odds of having 2-5 products and services is increased to 10 times - Category 6-10 products and services – the predictor “understanding customers’ needs” again significantly predicts whether producing 6-10 products and services or producing 0-1 products and services, b= -1.3, Wald X2(1) = 2.4, p = 0.1 and Exp.(b) = 0.3 The odds ratio (Exp.b) shows that, when this predictor changes from no importance to importance, the odds of having 6-10 products and services is increased to 33 times more than not giving an importance to understand customers’ needs The predictor “network” predicted whether producing 6-10 products and services or producing 0-1 products and services, b= -1.4, Wald X2(1) = 4.2, p = 0.0 and Exp.(b) = 0.2 When this predictor changes from no importance to importance, the odds of having 6-10 products and services is increased to times more than not giving an importance to network In addition, the “sector-telecommunications” also predicted whether producing 6-10 products and services or producing 0-1 products and services, b= -2.9, Wald X2(1) = 0.2, p = 0.7 and Exp.(b) = 0.1 The odds of having 6-10 products and services are increased to 10 times more if in the sector telecommunications - Category 11-15 products and services – The predictor “network” predicted whether producing 11-15 products and services or producing 0-1 products and services, b= -2.9, Wald X2(1) = 6.0, p = 0.0 and Exp.(b) = 0.1 When this predictor changes from no importance to importance, the odds of having 11-15 products and services is increased to 10 times more than not giving an importance to network The predictor “understanding customers’ needs” also predicted whether producing 11-15 products and services or producing 0-1 products and services b= -2.3, Wald X2(1) = 1.0, p = 0.3 and Exp.(b) = 0.1 The odds of having 11-15 products and services is increased to 10 times more than not giving an importance to understanding customers’ needs In the same way, the predictor “application of technology” predicted whether producing 11-15 products and services, b= -1.4, Wald X2(1) = 1.3, p = 0.2 and Exp.(b) = 0.3 The odds of having 11-15 products and services are increased to 3.33 times more than not giving an importance to application of technology In addition, the pharmaceutical and the engineering sector predicted producing 11-15 products and services or producing 0-1 products and services The value of b= -3.2, Wald X2(1) = 2.1, p = 0.1 and Exp.(b) = 0.0 for pharmaceutical sector, and b= -1.8, Wald X2(1) = 0.7, p = 0.4 and Exp.(b) = 0.2 for engineering sector respectively The 12 odds of having 11-15 products and services is increased to 25 (1/0.0=25) and (1/0.2=5) times more if in the pharmaceuticals and engineering sector respectively - Category 16-20 products and services – The predictor “understanding customers’ needs” predicted whether producing 16-20 products and services or producing 0-1 products and services, b= -2.3, Wald X2(1) = 1.1, p = 0.3 and Exp.(b) = 0.1 As when this predictor changes from no importance to importance, the odds of having 16-20 products and services is increased to 10 (1/0.1=10) times more than not giving an importance to the understanding of customers’ needs The predictor also “application of technology” predicted whether producing 16-20 products and services, b= -1.9, Wald X2(1) = 1.1, p = 0.3 and Exp.(b) = 0.1 This means, the odds of having 16-20 products and services is increased to 10 (1/0.1=10) times more than not giving an importance to application of technology In addition, the predictor “network” predicted whether producing 16-20 products and services or producing 0-1 products and services, b= -1.3, Wald X2(1) = 0.6, p = 0.4 and Exp.(b) = 0.3 When this predictor changes from no importance to importance, the odds of having 16-20 products and services is increased to 3.3 (1/0.3=3.3) times more than not giving an importance to network - Category more than 20 products and services - The predictor “understanding customers’ needs” predicted whether producing more than 20 products and services or producing 0-1 products and services, b= -2.7, Wald X2(1) = 5.1, p = 0.0 and Exp.(b) = 0.1 As when this predictor changes from no importance to importance, the chance of having 20+ products and services is increased to 10 (1/0.1=10) times more than not giving an importance to understand customers needs The predictor “vision and mission of the company” also predicted whether producing more than 20 products and services or producing 0-1 products and services, b= -1.2, Wald X2(1) = 1.8, p = 0.2 and Exp.(b) = 0.3 As when this predictor changes from no importance to importance, the chance of having 20+ products and services is increased to 3.33 times more than not giving an importance to vision Discussion The evidence suggests that the factor “understanding needs of customers” is a predictor of the higher number of products and services (>0-1 product and service) with generally a strong effect As noted by Skarzynski and Gibson (2008), product/service innovation should be generated by empathy with customers as well as an intention to mitigate customers’ problems Hence, an understanding of customers’ needs and experience is necessary in the process of products and services development In addition, the study by Cooper and Kleinschmidt (2011) highlighted the products and services development process that begins with clear definitions is 3.3 times more likely to be successful, with 85.4% success rate of end product Clear definitions can be spelled out as: - the target market- exactly who the intended users are; - customer needs, wants, and preference; - the product concept – what the product would be and do; - the product’s specifications and requirements Additionally, the factor “networks” can predict a higher number of products and services (>0-1 product and service) with reasonably strong effect This finding agrees with previous studies that the degree of product/service innovations and a firm’s competitiveness depends 13 on the success with which firms can form collaborations with external partners and get access to external technological knowledge and skills (Faems et al., 2005) As also noted by Pullen et al (2012), the diversity of networks has positively linked and increased the performance of internal innovation capabilities It is noted further by Haeussler et al (2012) that many high technology new firms (most of the university spin-offs can be categorised as new and high-tech firms) have formed strategic collaborations to gain an access to knowledge, skills, resources and expertise in order to develop new products and services The factor “clear market analysis” can also predict the higher number of products and services (>0-1products and services) Clear market analysis and market potential are also stipulated as one of the key predictors among others (e.g product advantage, meeting customer needs, predevelopment task proficiencies, and dedicated resources) that have significant impact on new product/service performance (Henard and Szymanski, 2001) Market assessment of the potential of the market and customers’ needs should be done at the beginning of product and service development projects (Cooper and Kleinschmidt, 2007) As highlighted by Cooper and Edgett (2008), approximately twice as many companies with high productivity carry out an initial market assessment for their products and services development projects in comparison with firms with low productivity In addition, the factor “application of technology” can be a predictor for a higher number of products and services (>0-1 product and service) with reasonably strong effect Having to find the application or market relevancy is important to successful product/service development The main factors distinguishing winners from losers, according to Bessant and Tidd (2011), are product advantage or product superiority in the eyes of the customers, real differential advantage, high performance-to-cost ratio, and delivery of unique benefits to users This element also links with the points above on understanding both customers’ demands and the market It is interesting that “vision, mission and value of the company” only appears as a predictor of a category of more than 20 products and services The findings resonate with the study of Reid and De Brentani (2010), which shows that having the right vision of the market for new product enables companies to attain competitive advantage For instance, clarity in vision has a positive relationship with the success in technical and radical innovations, while the stability of vision has a positive association with the success in incremental innovation projects This point corresponds with the study by Lynn and Reilly (2002) on the significance of a well-defined and well-constructed vision playing a role in the success of products and services development It is very important to have a clear and stable vision and clear guidelines for implementation In addition, an awareness of the mission is regarded as a strong predictor of R&D projects’ achievement Kahn et al (2012) also placed an emphasis on the importance of the company’s strategy and mission as one of best practice in new products /services development Some factors not predict a higher number of products and services These include “capable staff”, “identifying the right target customers”, “funding and investment” and “pricing/revenue model” These factors, however, have agreed with previous studies that highlight the significance of these factors towards the success of products and services development in general For instance, the point on “capable staff” has also been highlighted by Chen and Huang (2009) with respect to the crucial roles of human resource in the innovation process To design and develop products and services, human skills (both 14 management and technological) have to be readily available (Bessant and Tidd, 2011) When new products and services are developed, the motivation and capabilities of staff are needed in order to generate creative ideas and novel approaches, as well as to bring new opportunities into play (Scarbrough, 2003) Hence, companies need to increase their human capital for the development of new products and services (Skarzynski and Gibson, 2008) In addition, the factor “identifying the right target customers” has been regarded as a cornerstone of product/service development This factor somehow links closely with the factor “understanding customers’ needs” As suggested by Desouza et al., (2008), firms can arrange and categorise customers by segmenting them based on characteristics and attributes; this will enable firms to ascertain target markets and understand their specific needs If companies aptly manage these customers’ features, the performance of products and services are deemed to be improved accordingly Hence, the pivotal initial step in products and services development processes is to define a target market (Crawford, 1980) Neglecting to describe the exact characteristics of the product that fits with its target market before the start of the development project has caused the failure of new products and services (Cooper and Kleinschmidt, 2007) The importance of funding and investment towards product/service innovations has also been outlined by Heirman and Clarysse (2007) Bigger funding and investment at the initial stage are likely to accelerate the launch of products and services to the market Additionally, pricing and revenue model is rated as an important factor in developing products and services within the university spin-off context As highlighted by Teece (2010), new product and service development, especially high-tech offerings, should be combined with developing a business model that outlines strategies on market and value capturing It can also be noted that the factors, such as “identifying right target customers”, “funding and investment”, and “pricing/revenue model” seem to be important for the first launch of products and services to the market, especially “funding and investment” This can possibly explain why they not predict a higher number of products and services Unlike the factors “understanding the needs of customers” and “networks”, they continually play an important role not only throughout the development process, but also beyond the first launch of the products and services For example, “networks” would enable firms to expand or increase their products and services because firms can gain access to knowledge, expertise or market, which they may not necessarily have at the initial launch of their first product/service However, some of the factors presented in the findings as the cause to allow university spinoffs to develop many products and services, e.g “networks” or “understanding the needs of customers”, may well be an ex post of the success in product/service innovations and the growth of the company Zheng et al (2010) offered the view that when spin-off or start-up firms become more mature and have successful product/service innovations, they tend to develop routines that allow them to extract higher value from networks as well as to manage more effectively through either internal innovation or external collaboration In addition, Joshi and Sharma (2004) have noted that knowledge about customers’ demands is an evolutionary learning process of organisations and can occur at all phases or stages of product/service development Resources have played an important role and capturing this process When firms have grown or have become successful, more resources and a routine can be set up to better attain, analyse and utilise the knowledge about customers’ needs Hence, the factor “understanding the needs of customers” can also be as a result of the success of the product/service innovations 15 Conclusions The findings of this research have contributed to filling a gap in the academic entrepreneurship literature by shedding light on driving factors in successfully developing products and services within the university spin-off context They provide an understanding beyond the success factors in setting up university spin-offs, since this can help inform academic entrepreneurs, Technology Transfer Offices (TTOs), university senior managers, or policy makers on design or change of policy within the university to support the university spin-off In addition, the study shows that certain activities or driving factors involved in product/service innovations are rather foreign to typical academic cultures and environments These include clear market analysis or setting vision, mission and value goals of company Where entrepreneurship is regarded as a strategic objective of the university then a policy for supporting these entrepreneurial skills should be established (Hofer and Potter, 2010) To conclude, university spin-offs can stimulate economic growth by introducing and adding the innovative element of their technology to the other components of the innovation network This can add dynamism to the regional innovation systems (Pérez Pérez and Sanchez, 2003) However, to date there is little in the literature on driving factors in successfully developing product/service within a university spin-off context This study is the first to empirically identify and examine the different key factors that contribute to the success of product/service innovations within the 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