What knowledge management approach do entrepreneurial universities need?

Information Systems 85 (2019) 21–29

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Information Systems journal homepage: www.elsevier.com/locate/is

What knowledge management approach do entrepreneurial universities need? ∗

Nuria Calvo a , David Rodeiro-Pazos b , , María Jesús Rodríguez-Gulías a , Sara Fernández-López b a

Department of Business, Universidade da Coruña, Campus Elviña, 15701, A Coruña, Galicia, Spain Department of Finance and Accounting, Universidade de Santiago de Compostela, Avda. do Burgo, s/n., 15782 Santiago de Compostela, Galicia, Spain b

highlights • Entrepreneurial universities connect IT solutions and KM processes. • Data grouping infrastructure increases some entrepreneurial outcomes of universities. • Process-oriented KM results in some entrepreneurial outcomes.

article

info

Article history: Received 21 January 2019 Received in revised form 11 June 2019 Accepted 11 June 2019 Available online 17 June 2019 Recommended by D. Shasha Keywords: Knowledge management Entrepreneurial university IT solutions Panel data Dynamic simulation Resource-based view

a b s t r a c t This paper explores how universities’ entrepreneurial outcomes (patents, university spin-offs, research projects and R&D contracts) relate to the availability and use of information and telecommunications (IT) solutions for knowledge management (KM), looking at the period 2011–2014. We hypothesized that entrepreneurial universities may benefit from a good connection between knowledge infrastructure (IT solutions) and KM processes. We tested this hypothesis by estimating generalized least squares models and negative binomial regression models for a sample of 63 Spanish universities over the period 2011–2014. The results show that using a data grouping infrastructure improves several measures of entrepreneurial outcomes of universities. Unexpectedly, institutional tools for collaborative working and data warehouses significantly decrease the number of patents. From these results, we suggest that process-oriented approaches for KM may worsen the entrepreneurial outcomes of universities. The contribution of this analysis is twofold. First, it gives a better empirical understanding of the way in which IT solutions for KM affect universities’ entrepreneurial outcomes. Second, this analysis could guide a new design for IT solutions in order to improve these outcomes. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Since the Triple Helix model highlighted the importance of partnerships among universities, industry and government in building a knowledge society in a balanced configuration [1], universities have changed from being producers of scientific papers to becoming providers of knowledge transfers. In this context, academics and policy makers have coined the term ‘entrepreneurial university’ [2–5] to describe a university that is effective in performing its ‘third mission’ of contributing to regional economic growth [3]. To define the university activities involved in this ‘third mission’, the E3M [6] has grouped them ∗ Corresponding author. E-mail addresses: [email protected] (N. Calvo), [email protected] (D. Rodeiro-Pazos), [email protected] (M.J. Rodríguez-Gulías), [email protected] (S. Fernández-López). https://doi.org/10.1016/j.is.2019.06.002 0306-4379/© 2019 Elsevier Ltd. All rights reserved.

into three dimensions: technology transfer and innovation, continuing education, and social engagement. To date, the literature on entrepreneurial universities has mostly focused on the first dimension of the third mission [7,8], by analysing universities’ capacity to transfer knowledge through contracts with firms, university spin-offs (USOs), patenting, and licencing activities in order to generate, use, apply and exploit their knowledge base [9]. As this third mission has gained acceptance, universities have invested a great deal of publicly funded resources to help the transfer of research from academics to society. In particular, university managers have used information technology (IT) solutions to support knowledge management (KM) systems, mainly to facilitate knowledge sharing [10]. However, the lack of a common understanding of KM at universities means that there are few studies that provide empirical insights into the use of IT solutions in entrepreneurial activities at university level.

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This paper seeks to contribute towards filling this gap. Although the literature on KM has generally found that IT solutions have a positive effect on KM for firm performance, there is a lack of studies addressing this issue at university level, which is surprising since universities are mainly knowledge-driven organizations. In addition, the few studies focused on universities mostly offer theoretical insights [11] coming from case studies [12–15]. Moreover, most of them analyse only a few universities [16,17] or only explore the effect of KM practices on scientific production [10]. Stimulating the entrepreneurial mission of universities has become a priority of political agendas in recent years. Governments have funded the development of IT solutions for KM, expecting these solutions to improve the entrepreneurial outcomes of universities. However, there is little empirical evidence of this cause and effect relationship. This study therefore explores the influence of knowledge infrastructure (i.e. IT solutions for knowledge sharing) and KM processes on entrepreneurial outcomes in a sample of 63 Spanish universities over the period 2011–2014. This paper makes certain contributions. First, while a large number of studies of KM and performance focus on firms, the literature to date has paid little attention to how KM furthers the competitive advantage of universities. Second, unlike other studies, we use as our dependent variables a rich set of indicators that is broadly employed to define an entrepreneurial university. Third, in contrast to previous studies, we use panel data methodology and a large sample of universities in the empirical part of the paper, which allows us to offer robust results. Finally, we base our analysis on Spain because this country can be considered a pioneering case study. In 2009, the Spanish government launched an initiative called the ‘2015 University Strategy’, which funded universities on the basis of their scientific performance. However, the economic crisis shifted the strategy towards universities’ selffunding via R&D transfers, so this case study becomes a potential example for other countries in the same situation. This paper is organized as follows. In Section 2 we review the previous evidence on the contribution of KM to a university’s performance, and discuss the approach of the previous papers. In Section 3 we describe the methodology. In Section 4 we present the empirical results of the analysis. In Section 5 we discuss the main results of the paper. Finally, in Section 6 we conclude the analysis and point to the limitations and future research work. 2. Theoretical framework 2.1. University entrepreneurship and KM In the past three decades, universities have gained attention from academics and policy makers as key actors for economic growth, and this has led to a burgeoning set of works in this field. Whereas a significant number of the studies have tried to deal with the concept of an entrepreneurial university [2,3,5,18– 20], another stream of the literature has explored why some universities are more successful than others in carrying out entrepreneurship activities (i.e. university entrepreneurship) (see [21]). Most of these studies follow an institutional approach and use the resource-based view (RBV) [22–25] to understand the determinants of entrepreneurial activities in universities. In this approach, knowledge is seen as the most significant resource of entrepreneurial universities, which hope to transform individual and group-level knowledge into successful products, services or decisions through the dynamic interaction between tacit and explicit knowledge [26]. In particular, the literature on university entrepreneurship has assigned a key role in promoting university entrepreneurship to both a university’s interface infrastructure (e.g. Technology Transfer Offices (TTOs), incubators, science parks, entrepreneurship

centres, etc.) and its researchers’ performance. Thus, the members of interface infrastructures (i.e. the TTO staff) usually accumulate tacit knowledge about the ways of promoting entrepreneurship outcomes (e.g. launching USOs, licencing, patent applications, research contracts, etc.), and the TTO members exchange this knowledge through interactions with other TTO members and with researchers, as well as with industry and society. Consequently, the entrepreneurial outcomes of a university rely to a great extent on the effectiveness of this knowledge exchange. In this respect, IT solutions for KM (knowledge infrastructure) can help to leverage university entrepreneurship in different ways. First, IT solutions for KM support new practices and applications that increase the efficiency of the knowledge flows (knowledge management processes) among individuals in order to enhance the organization’s performance [27,28], and this is also the case at universities [10]. Second, IT tools employed by universities to facilitate the diffusion of their knowledge and technology act as alternative channels to offer research knowledge that can be exploited by outsiders (i.e. industry, government and society). Finally, to a certain extent, IT solutions allow part of the knowledge embedded in individuals to be made explicit (through documenting and sharing) [29,30], contributing to the creation of new knowledge and its application [31–34]. This aspect is particularly relevant at universities, where the knowledge necessary to generate innovations and the knowledge necessary to generate entrepreneurial outcomes are often embedded in, respectively, researchers and TTO members. Thus, an individual’s learning depends on the way in which explicit knowledge is structured, used and transmitted (i.e. KM), affecting an employee’s contributions to the organization’s success [35,36]. 2.2. KM at universities: literature review In spite of the aforementioned arguments, there is a lack of empirical studies that connect the effect of IT solutions for KM to the entrepreneurial outcomes of universities. Moreover, a review of the KM literature focused on universities shows that the studies are divided into two groups depending on the level of analysis used: the individual level (studies focused mostly on researchers) and the university level. The second group of studies, focused at the university level, can, in turn, be divided into case studies and quantitative studies (Table 2). The former, mostly carried out in Asian countries, show the importance of IT in enabling communication and collaboration between researchers and external partners [47], and in creating a better environment for KM exchange [12]. Nevertheless, KM resources must be managed properly if effective governance is to be achieved by the university [15]. There are just a handful of quantitative studies that have explored the effect of KM in university activities, using a mix of quantitative techniques. Thus, the study of Geng et al. [16] shows that Chinese universities are more process-oriented, and that their KM priorities focus on addressing university-wide information technology (IT) goals. By comparison, American universities are more performance-oriented, and their KM priorities are based on aligning the needs of end-users for knowledge storage by addressing priorities like institutional research, libraries and information centres. In both countries, universities have carried out similar missions and achieved similar goals in KM, even though they use different KM tools that respond to different realities. The results of Mohayidin et al. [17], who analysed a sample of ten Malaysian universities, indicate that info-structure support, infrastructure capacity, info-culture, and knowledge acquisition, generation, storage and dissemination are important factors in shaping KM initiatives. According to these authors, the sociotechnical components supported by a university improve the implementation and application of KM throughout the university.

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Table 1 Summary of results for KM literature focused on researchers (individual level). Authors (Year)

Country

Sample

Main results

D’Este and Patel [37]

UK

4337 university researchers in the UK (1995–2003)

University researchers interact with industry through the creation of new physical facilities, consultancy and contract research, joint research, training, meetings and conferences. Policies to incentivize knowledge transfer are likely to have a limited impact on encouraging university–industry interactions, unless they take better account of the individual characteristics of the researchers engaged in such interactions.

Rego et al. [38]

Portugal

152 researchers at Portuguese universities

Individual interaction among people is the most relevant factor for knowledge flow, although technology works as an important facilitator.

Allameh et al. [39]

Iran

430 Isfahan University staff

Organizational culture is related to knowledge capture rather than to knowledge storage or knowledge application.

Aming’a [40]

Kenya

172 academic and non-academic staff of Kisii University

KM practices positively affect organizational performance in terms of efficient and effective productivity, innovation and the generation of new knowledge, quality of services delivered, employee morale and decision making and problem solving.

Fullwood et al. [41]

UK

230 academics from 11 universities in UK

The role of organizational structure and IT in knowledge sharing with colleagues and external partners for opportunities creation is not clear for academics.

Mohammad and Arul Jose [42]

Oman

64 academic and non-academic staff

There is a significant relationship between gatherings of academic and non-academic staff and KM. However, internal procedures and general administration and human resource management applied to KM have no relevance for the knowledge orientation of a university.

Omogeafe and Evoreime [43]

Nigeria

389 respondents from Nigerian universities

KM practices are significantly related to innovation, competitive advantage and growth.

Tan and Noor [44]

Malaysia

421 respondents from five Malaysian research universities

Trust and interactive communication between academics positively influence knowledge systems (KS). In contrast, knowledge self-efficacy, the perceived degree of reciprocal benefits for researchers and a perception of top management support are not relevant factors for KS. Organizational incentives are positively related to KM systems. Organizational culture encourages academics to share their knowledge. The KM system infrastructure has no significant relationship with KS members. KM system quality is a significant facilitator of KS practices.

Jamil and Lodhi [45]

Pakistan

450 employees of Pakistani universities

The dimensions of KM infrastructure (culture and human resources) and processes (acquisition, storage and application) positively predict university performance.

Masa’deh et al. [46]

Jordan

207 university lecturers

There is a positive relationship between KM processes (knowledge identification, creation, collection, organization, storage and knowledge application) and the job and knowledge performance of universities.

In a more recent study, Fernández-López et al. [10], using a sample of 70 Spanish universities, concluded that KM based on IT influences a university’s scientific performance. This paper fits into the second group of studies. As Table 2 reports, only a few studies have explored the effect of KM on universities’ performance at the university level using a quantitative approach. Moreover, the previous studies (Tables 1 and 2) have usually measured a university’s performance through its scientific production, instead of using a wider range of entrepreneurial outcomes. Finally, even when the analysis is at individual level (Table 1), it adopts the perspective of a university manager; that is, the analysis emphasizes the role of managers in supporting the positive relationship between KM and the university’s performance [13,40,41,43–45]. This approach pays little attention to the importance of the researchers’ involvement in KM [12,14,42] to support their interactions with industry [37,48,49], but instead focuses on managers’ need to gather information for the effective governance of a university, becoming more processoriented than performance-oriented, or more ‘technocratic’ than ‘economic’ [50]. In this respect, Wilson [51] has already warned us that academics and professionals tend to confuse ‘knowledge’ and ‘information’, and that having good ‘information management’ applications and work routines does not mean having good ‘knowledge management’. This warning is even more pertinent for KM at entrepreneurial universities, which require a fluid exchange of knowledge between the university’s internal actors (i.e. TTO staff

and researchers) and external actors [17,47,52]. In this context, adopting a process-oriented approach for KM might worsen the entrepreneurial outcomes of the university [16]. To sum up, this paper explores how IT solutions for knowledge-sharing processes (knowledge infrastructure) and decision-making processes (knowledge management processes) at universities may influence their entrepreneurial outcomes (an assessment of knowledge management). In doing this, we draw to a certain extent on the conceptual framework of the Enterprise Knowledge Management Model (EKMM), commonly known as the ‘knowledge tower’, which was proposed by Oztemel and Arslankaya [53] as a way to study the hierarchical structure of knowledge in organizations (Fig. 1). This perspective is related to the literature review of KM performed by Galvis-Lista and Sanchez-Torres [54], which points out that the first generation of KM literature states that knowledge is something that can be captured and stored in repositories of knowledge-based technology, while the second generation of KM literature considers knowledge as a complex phenomenon with socio-cultural, political and technological aspects. 3. Methodology 3.1. The data and sample We constructed an original dataset from two sources of information the UNIVERSITIC project (http://tic.crue.org/universitic/),

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Table 2 Summary of results of KM literature at the university level (case studies are in shaded boxes). Authors (Year)

Country

Sample

Main results

Numprasertchai and Igel [47]

Thailand

Multiple case study (three R&D units)

Collaboration with industrial partners is the most relevant resource to enable universities to acquire missing in-house knowledge in order to create new knowledge and achieve research goals. Communication, collaboration and storage technologies are relevant factors for communication and collaboration with external partners, but are not sufficient for ensuring the success of research collaboration.

Bechina et al. [12]

Thailand

Case study, Bangkok University

The use of appropriate ICT in order to create a more favourable environment for KM exchange helps to improve the results of universities as knowledge-based learning organizations.

Blackman and Kennedy [13]

Australia

Case study

Knowledge creation and utilization are relevant factors for the effective governance of the university in the study.

Tian et al. [14]

Japan

Case study, Japanese national institute

The main KM obstacles perceived are the lack of technological support for KM, the lack of involvement in creation activities and cultural and organizational dysfunction.

Yapa [15]

Sri Lanka

Case study

The lack of management of IT resources for KM can explain academics’ poor research results.

Geng et al. [16]

USA and China

28 Chinese universities, 11 American universities

In Chinese universities the four dimensions of KM (priorities, needs, tools and support) are significant and positively related, whereas in American universities the relation between KM priorities and administrative support was the only dimension that was not significant.

Mohayidin et al. [17]

Malaysia

685 respondents from ten public and private universities (individual, faculty and university level)

Info-structure support, infrastructure capacity, info-culture, and knowledge acquisition, generation, storage and dissemination are important factors in shaping KM initiatives. The infrastructure of knowledge management was the most relevant factor in shaping KM initiatives in Malaysian universities.

Fernández-López et al. [10]

Spain

70 universities

The use of applications for data grouping is positively related to university performance. The use of institutional tools for collaborative work is negatively related to the scientific production of universities.

Fig. 1. Conceptual framework. Source: The knowledge tower [53].

and the IUNE Observatory (http://www.iune.es/). The UNIVERSITIC project was used to obtain data about universities’ IT solutions for KM (independent variables). This project was launched by the CRUE (the Conference of Spanish University Rectors) in 2004, and is supervised by an IT committee composed of IT

directors and IT vice rectors (CIOs) from all Spanish universities. The UNIVERSITIC project measures the state of IT at each Spanish university through an annual survey that uses a catalogue of IT indicators [55]. To the best of our knowledge, it is the only potential source of information about how universities use IT

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solutions for KM. The initial year for our study was 2011, because this is the first year in which all the data considered in our analysis were collected. After filtering for respondent universities giving data referring to KM, a sample of 63 Spanish universities was formed. We completed this dataset with the most common indicators of the performance of entrepreneurial universities, such as patents, USOs and contracts [56,57], obtained from the IUNE Observatory. In turn, the IUNE Observatory takes this information from other sources. Thus, the data about patents were collected from the Spanish Patent and Trademark Office, and particularly from INVENES. The USO information was gathered from the universities’ TTOs and the number of projects in an EU Framework Programme from the Centre for Industrial Technological Development (CDTI). We completed the database with data from the National Statistics Institute and the Ministry of Education of Spain. Given that data referring to the universities’ activities in 2015 were not available from the IUNE Observatory, we selected 2011 through to 2014 as the period of analysis. To sum up, the final dataset was an unbalanced panel consisting of 63 Spanish universities observed between 2011 and 2014. 3.2. Dependent and independent variables As mentioned, in the empirical literature on university entrepreneurship the most commonly accepted indicators of universities’ entrepreneurship [58] are patents, USOs and contracts. Following this approach, the outcomes of entrepreneurial universities were measured using four variables: the number of patents granted by the Spanish Patent and Trademark Office (PAT), the number of USOs created (USOS), the number of research projects obtained within an EU Framework Programme (FPP), and the natural logarithm of the value of executed R&D and consultancy contracts (in thousands of euros) (LNCONT). Regarding the independent variables, we included the natural logarithm of the budget for centralized IT services (excluding personnel expenses), in euros, (LNBUDGETTI) in the analysis, to measure the availability of IT resources at universities. We considered the percentage of the university’s researchers who use institutional tools for collaborative work (PTRSHCOLABORA) as a proxy of the level of IT use among researchers. Additionally, four variables referring to the availability of IT solutions were included in the analysis. All of them were dummies, and, respectively, they took the value 1 if the university had documental workflow (BWFLOW), filing applications (BARCHIVODOC), institutional content repositories (BREPOSITORIO), or a data warehouse (BDATAWH). We also included a set of control variables widely used in the empirical literature on university entrepreneurship [59–61]. These control variables were the natural logarithm of the number of teachers and researchers (LNTRS), the number of first quartile publications (1QPUB), the natural logarithm of the university’s total budget in euros (LNBUDGET) and the natural logarithm of the age of the TTO (LNTTOAGE). 3.3. Model specification We used panel data to test the effect of IT solutions for KM on the outcomes of entrepreneurial universities. Unlike crosssectional analysis, panel data allows the researcher to control for the individual heterogeneity that may characterize the behaviour of a particular university. This heterogeneity is modelled as an individual effect (αi ) in order to control it and avoid biased results. Accordingly, the basic specification of our model is as follows: UEOit =β1 LNBUDGETTIit + β2 PTRSHCOLABORAit + β3 BWFLOWit

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+ β4 BARCHIVODOCit + β5 BREPOSITORIOit + β6 BDATAWHit + β7 LNTRSit + β8 1QPUBit + β9 LNBUDGETit + β10 LNTTOAGEit + αi + λt + εit where UEOit refers to each of the four dependent variables capturing the university’s entrepreneurship outcomes — namely patents granted, USOs, number of projects within an EU Framework Programme, and R&D contracts and consultancies. As mentioned, αi is the individual unobserved heterogeneity, λt is a set of dummy variables for years that incorporate time-specific effects common to all universities, and εit is the random disturbance. The first three dependent variables used in the analysis (PAT, USOS and FPP) have a discrete nature, with only non-negative integer small values and a preponderance of zeros, so they are count data. In order to estimate the three outcomes, we applied negative binomial regression models under the assumption of random effects. Finally, the continuous nature of the fourth dependent variable (LNCONT) allowed us to apply generalized least squares models (GLS) to the analysis. Since time-invariant dummy variables are present, we applied random effects, where the estimator assumes that the individual effects (αi ) are independent (uncorrelated) from the explanatory variables (xit ). 4. Results 4.1. Descriptive analysis Table 3 displays the main descriptive statistics of the dependent, independent and control variables considered in the empirical analysis. Concerning the dependent variables proposed as measures of the outcomes of entrepreneurial universities, the annual average number of patents granted is greater than 11 for each university. The annual mean number of USOs created by academics is around two. On average, there are around six projects within an EU Framework Programme for each university and each year. In terms of R&D contracts and consultancies, the mean annual amount reached is around 6 million euros (Table 3). In order to perform a deeper analysis of the dependent variables, Fig. 2 shows the annual evolution of their mean values over the period 2011–2014. The mean number of patents granted by the Spanish Patent and Trademark Office increases slightly over the period of study, while the mean number of USOs created and the mean number of projects within an EU Framework Programme increases until 2013 and then decreases in the next year (2014). The mean contract values of R&D and consultancy contracts show a decreasing trend. Regarding the independent variables, the annual mean budget for centralized IT services is about 2.5 million euros. The annual average percentage of researchers who use institutional tools for collaborative work is about 81%. On average, 37% of universities have a documental workflow, 61% have a filing application, 75% have a repository, and 68% have a data warehouse (Table 3). Concerning the control variables, by university, 1.909 is the average number of teachers and researchers (TRS), the annual mean number of publications in the first quartile is around 373, the average total university budget is about 179 million euros and the mean age of the TTO is 20 years (Table 3). Finally, Table 4 shows the correlation matrix for the continuous variables used in the analysis. In view of the correlation matrix data, we noted some possible multicollinearity problems. The variance inflation factor (VIF) was then checked for each independent variable and was less than 6, which is considered to be an acceptable level [62].

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Table 3 Descriptive statistics of dependent, independent and control variables. Variable

Obs

Mean

Std. Dev.

Min

Max

DEPENDENT

PAT USOS FPP CONTa b

260 230 255 246

11.06 2.26 6.88 5,914.80

10.84 3.54 7.91 7,549.49

0 0 0 1.00

53.00 22.00 56.00 57,878.00

INDEPENDENT

BUDGETTIa b PTRSHCOLABORA BWFLOW BARCHIVODOC BREPOSITORIO BDATAWH

189 186 217 220 222 224

2,420.45 0.81 0.37 0.61 0.75 0.68

2,094.70 0.35 0.48 0.49 0.44 0.47

0 0 0 0 0 0

14,100.00 2.00 1.00 1.00 1.00 1.00

CONTROL

TRSb 1QPUB BUDGETa b TTOAGEb

225 236 195 184

1,909.77 373.01 178,792.10 20.24

1,328.59 433.44 123,391.80 4.44

123.00 0 6,321.80 9.00

6,206.00 2,368.00 595,577.00 28.00

Notes: a Variables are in thousands of euros. b Variables are in absolute values (not in logs). Table 4 Correlation matrix. PAT USOS FPP LNCONT LNBUDGETTI PTRSHCOLABORA LNTRS 1QPUB LNBUDGET LNTTOAGE

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1)

(2)

(3)

(4)

1 0.556* 0.5107* 0.6345* 0.2807* −0.4065* 0.5260* 0.4700* 0.5980* 0.4723*

(5)

1 0.5404* 0.3978* 0.0687 −0.2848* 0.3825* 0.2853* 0.4360* 0.2243*

1 0.5895* 0.2357* −0.2116* 0.5180* 0.5787* 0.5457* 0.3309*

1 0.4196* −0.1736* 0.7097* 0.7014* 0.8167* 0.4888*

(6)

(7)

(8)

(9)

1 0.7823* 0.9439* 0.4943*

1 0.8542* 0.6099*

1 0.5928*

1

−0.0218

1

0.5826* 0.4634* 0.5846* 0.2830*

−0.1740* −0.1570* −0.1604* −0.2739*

Notes: The table shows the Pearson correlation coefficients for the continuous variables considered in the empirical analysis.

Fig. 2. Evolution of the entrepreneurial outcomes of Spanish universities (2011–2014).

4.2. Multivariate analysis The results of the negative binomial models for patents granted (PAT), USOs created (USOS) and projects within an EU Framework Programme (FPP), as well as the results of the random effects GLS models for the value of R&D and consultancy contracts signed (CONT), are presented in Table 5. In all cases, the models include the group of independent variables (LNBUDGETTI, PTRSHCOLABORA, BWFLOW, BARCHIVODOC, BREPOSITORIO and BDATAWH), the group of control variables (LNTRS, 1QPUB, LNBUDGET and LNTTOAGE) and the year’s dummy variables (λt ). The results show that KM based on IT influences the entrepreneurial outcomes of universities. Thus, we found a positive effect in the case of IT solutions for the infrastructure of

(10)

*

p < 0.05;

**

p < 0.01;

***

1 p < 0.001.

data storage. In particular, the availability of filing applications (BARCHIVODOC) positively influence the funds obtained through R&D contracts and consultancies and the availability of an institutional content repository (BREPOSITORIO) positively influence the number of USOs launched. Nevertheless, we also found negative effects of IT solutions for KM on the entrepreneurial outcomes of universities. Thus, the percentage of researchers using institutional tools for collaborative work (PTRSHCOLABORA) has a strongly significant negative effect when the entrepreneurial outcomes are measured in terms of the patents granted by the Spanish Patent and Trademark Office. A tentative explanation of this result is that when researchers intend to apply for a patent, they do not use institutional tools for collaborative work because the nature of these tools does not guarantee that the novelty step requirement for a patent is met. In contrast, the users of these collaborative tools in the university context could be more oriented to outcomes other than patents (such as teaching and training collaborations). Another explanation for this result is that researchers do not use these institutional tools of collaborative work properly, wasting their time and damaging their patenting activity. Similarly, the availability of a data warehouse (BDATAWH) negatively affects the university’s patenting activity. An explanation for this is that the data stored in these tools are not useful for the patenting activity of researchers, and the time required to look for these data and to administer their storage reduces the time available to researchers for developing new inventions. There is also a negative relationship between the budget for centralized IT services and the funds obtained from R&D contracts and consultancies. In this respect, it is noteworthy that the budgets of Spanish universities experienced substantial cuts after the economic downturn started in 2007. In this context of

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Table 5 Negative binomial and random effects GLS panel regressions. LNBUDGETTI PTRSHCOLABORA BWFLOW BARCHIVODOC BREPOSITORIO BDATAWH

LNTRS 1QPUB LNBUDGET LNTTOAGE CONSTANT Years dummy University-year obs. Unique universities Log likelihood Wald χ 2

PAT

USOS

FPP

LNCONT

−0.021

−0.056

(0.075) −0.513*** (0.197) 0.030 (0.159) −0.202 (0.180) 0.127 (0.144) −0.710*** (0.161)

0.028 (0.139) −0.378 (0.400) 0.003 (0.267) 0.036 (0.297) 0.453* (0.238) −0.535* (0.292)

(0.061) −0.119 (0.236) 0.085 (0.165) 0.242 (0.192) −0.018 (0.167) −0.164 (0.205)

−0.085** (0.034) 0.114 (0.204) −0.175* (0.094) 0.330** (0.166) 0.084 (0.145) −0.521 (0.320)

(0.436) 0.751* −0.282** (0.126) 0.609 (0.377) 0.350 (0.515) −12.825*** (4.396)

(0.758) 0.837 −0.216 (0.200) 0.468 (0.653) −0.055 (0.821) 1.692 (485.613)

(0.503) 0.110 0.499*** (0.159) 0.410 (0.439) −0.227 (0.524) −7.144 (5.206)

(0.576) 0.594 −0.092 (0.086) 1.160** (0.475) −0.884* (0.469) −13.470** (5.497)

YES 117 37 −347.77 105.93****

YES 108 35 −194.47 34.24***

YES 117 38 −315.90 85.36****

YES 112 36 – 303.84****

Notes: This table presents the results for the negative binomial models (Models 1 to 3) and the random effects GLS model (Model 4). Robust standard errors are in parentheses. * p < 0.10; ** p < 0.05; *** p < 0.01; **** p < 0.001.

limited total budgets, an increase in the budget for centralized IT services actually means a decrease in the budget for other university services. Therefore, the negative effect might reflect the fact that other university departments, such as the department for interface infrastructure, have fewer financial resources to develop their activities, damaging the university’s entrepreneurial outcomes. It is also noteworthy that, whereas the positive effects of IT solutions for KM on the entrepreneurial outcomes of universities are weakly significant, the negative effects are strongly significant. Taking the aforementioned results together, we conclude that IT solutions for KM in Spanish universities erode some of their entrepreneurial outcomes, especially when these outcomes are measured through the number of patents granted and the funds obtained from R&D contracts and consultancies. These results are, to some extent, consistent with those of Numprasertchai and Igel [47], Geng et al. [16], Masa’deh et al. [46], and Yapa [15]. As mentioned, these findings could be due to the fact that these IT solutions are more process-oriented than performanceoriented. Additionally, the KM systems could have been designed mostly to answer the needs of university managers, leaving in the background the needs of researchers and other internal (i.e. TTO staff) and external (i.e. industry, government and society) actors who are also involved in the entrepreneurial processes of universities. According to the aforementioned conceptual framework of the ‘knowledge tower’ (Fig. 1), these results contribute towards showing how the connection between knowledge infrastructure processes (IT solutions) and knowledge management processes can be useful in explaining the assessment of KM at universities (entrepreneurial outcomes).

Fig. 3. Causal analysis of results.

5. Discussion Despite governments investing a considerable amount of public funds every year to support university entrepreneurship through the development of IT solutions for KM, the empirical analysis does not show a clear return on this investment in entrepreneurial outcomes. By investigating the dynamics of the entrepreneurial learning process [8], we used a system dynamics approach [63] to explain the results through a causal diagram. The process-oriented approach of KM at universities makes the TTO staff continually demand data from researchers about their research lines, the economic justification for projects, their external agreements, etc. Time spent answering these demands is time that researchers cannot spend innovating, which hampers innovation transfer in the long term. The poor position of the university in the innovation rankings increases the pressure on its managers to use the KM system to diagnose the causes of the problem, and so they demand new data. This reinforces a perverse loop (Fig. 3) that maintains the problem of the lack of innovation transfer at the university. This diagram demonstrates the problems in the role played by the current IT solutions for KM in the entrepreneurial activities of Spanish universities. As mentioned, a potential explanation lies in the fact that most of these knowledge infrastructure and KM processes are designed for administrative purposes and use a process-oriented approach, instead of enabling knowledge flow between the different stages of the knowledge tower [53] and an approach for the KM system based on the researchers’ needs for innovation. 6. Conclusions Over the last three decades, universities have drawn attention from academics and policy makers as a key ingredient for a region’s prospects for growth. Indeed, not surprisingly, the literature devoted to entrepreneurial universities and university entrepreneurship is flourishing. In particular, the literature on entrepreneurial universities has focused on exploring why some universities are more successful that others in achieving entrepreneurial outcomes. Broadly speaking, most studies have agreed that support from the university’s interface infrastructures (i.e. TTOs), as well as its researchers’ performance, exerts a positive effect on entrepreneurial outcomes. Both researchers and the personnel of TTOs use IT solutions to accumulate tacit knowledge, and the exchange of this tacit knowledge helps in creating new

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knowledge and commercializing it. The way in which explicit knowledge is structured, used and transmitted in a university’s KM system may affect its entrepreneurial activities. In particular, we focus on the role played by the IT solutions developed for the university’s KM strategy. This study concludes that the IT solutions for KM available within universities (knowledge infrastructure) affect the way in which the actors involved in entrepreneurial activities store and exchange the knowledge required for generating entrepreneurship (knowledge management processes). Moreover, adopting a process-oriented approach for KM may result in a worsening of the entrepreneurial outcomes of universities (assessment of knowledge). These results are aligned with those of Numprasertchai and Igel [47], Geng et al. [16], Masa’deh et al. [46], and Yapa [15]. From our point of view, universities and governments should reflect on the strategic impact of IT solutions at universities, reorienting these solutions to serve as tools for the knowledge sharing and innovation transfer of researchers and TTOs instead of being mere administrative channels. In this respect, the previous literature has shown that KM is not just an IT issue. It is necessary to take into account the priorities and needs for KM, its organizational procedures, its culture and, above all, the endusers’ involvement [44,64–68]. We do not yet know whether the use of IT solutions will increase the transfer capacity of universities, but it is clear by now that the process-based approach of these applications leads away from this goal. Future research could shed light on the effects of the use of IT solutions for knowledge sharing at entrepreneurial universities if the university managers and government officials agree to design these solutions according to the needs of researchers and firms in order to build knowledge, innovation and consensus spaces [69]. Finally, this paper also has some limitations that should be addressed in future research. Some variables reflect the existence of KM systems based on IT tools rather than the way in which IT solutions are used in KM. Future research on this topic might benefit from measuring the specific uses of the IT solutions for KM and the channels through which KM systems based on IT influence universities’ performance. Moreover, the use of longitudinal datasets could provide new insights about the effect of the IT usage (over time) on a university’s performance. Finally, future work could benefit from extending the analysis to other knowledge-driven institutions. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] H. Etzkowitz, L. Leydesdorff, The dynamics of innovation: From national systems and mode 2 to a triple helix of university–industry–government relations, Res. Policy 29 (2) (2000) 109–123. [2] H. Etzkowitz, Entrepreneurial scientists and entrepreneurial universities in American academic science, Minerva 21 (2–3) (1983) 198–233. [3] B. Clark, Creating Entrepreneurial Universities: Organizational Pathways of Transformation, McGraw-Hill, Oxford: New York, 1998. [4] M. Guerrero, D. Urbano, The development of an entrepreneurial university, J. Technol. Transf. 37 (1) (2012) 43–74. [5] H. Etzkowitz, Innovation lodestar: The entrepreneurial university in a stellar knowledge firmament, Technol. Forecast. Soc. Change 123 (2017) 122–129. [6] E3M 2010. Needs and constraints analysis of the three dimensions of third mission activities, available at: http://e3mproject.eu/Three-dimthird-mission-act.pdf (accessed November 2018). [7] R. Lambert, Lambert Review of Business–University Collaboration: Final Report, HMSO Licensing Division, London, 2003.

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