Manfred Bornemann* Roswitha...

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Intellectual Capital in education – a value chain perspective

Manfred Bornemann*

Intangible Assets Consulting GmbH Dr. Anton Schlossar Weg 16 A-8010 Graz, Austria

Roswitha Wiedenhofer

FH JOANNEUM University of Applied Sciences Alte Poststrasse 149, A-8020 Graz, Austria * Corresponding author

Structured Abstract

Purpose – Educational institutions are highly regulated and regularly in the focus of political as well as professional reflection for improvement. This paper aims to apply the concept of Intellectual Capital (Edvinsson, 1997, Guthrie 2000) to assess intangible resources as crucial for the quality of educational processes as well as to identify patterns of interdependence between drivers of Intellectual Capital and generic processes of educational institutions (Bornemann, 2007) as a prototype study in Austria. Starting with elementary schools, secondary as well as tertiary levels of education are analyzed and related to each other in order to identify the need to differentiate drivers of Intellectual Capital for each type of school or to apply standardized drivers independent from the operational focus (Alwert et al, 2010).

Design/methodology/approach – Educational institutions are typically regulated by governmental procedures and hence do not follow entrepreneurial management models. This paper suggests the assumption of a value chain of schools with the pupil or learner as the customer as well as the object of intervention. With this analogy, experiences from the application of Intellectual Capital assessment in value chains of the automotive industry are transferred and applied to education. The methodology of “Wissensbilanz – made in Germany” (BMWI, 2004) is applied in action research oriented prototypes along this

value chain (Bornemann et al 2011). Results are consolidated according to procedures for stock noted companies (Alwert 2009).

Originality/value – Austria officially implemented the legal obligation for Intellectual Capital reporting for universities in 2007. The implementation of these procedures was discussed controversially in some articles, and certainly did not come to an end. Up to date, no test on the usefulness of Intellectual Capital assessment in other educational institutions such as elementary schools, secondary schools or professional schools was reported. Based on data from 12 case studies collected over a time frame of 2011 and 2012, this paper will report preliminary insight and call for further research along the value chain of education.

© Institute of Knowledge Asset Management - ISBN 978-88-96687-01-7 Proceedings of IFKAD - ISSN 2280-787X

Zagreb, Croatia, 12-14 June 2013 www.knowledgeasset.org/IFKAD

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Practical implications – Applying methodologies for Intellectual Capital reporting in educational institutions and integrating the management of these resources along the value chain of education seems to be very beneficial not only for ministries of education as the major common stakeholder but for de-central decision makers as well. Prioritizing scarce resources and systematically observing intangible assets in public as well as privately management educational intuitions contributes to economic improvement and better accomplishment of strategic objectives.

Keywords –intellectual capital, education, value chain, Wissensbilanz made in Germany

Paper type – Academic Research Paper

We would like to express our gratitude to the authors of the case studies, all of them participants of the master class for knowledge management at the Applied University of Eisenstadt. We would like to express our gratitude to the participants of two projects in the business of continuing education, who need to remain unidentified for reasons of confidentiality. We would like to express our appreciation for the feedback from S. Eschenbach (fh Burgenland), R. Reinhardt (fh Riedlingen) and K. Alwert (AKWB):

1 Introduction and Observations

According to high level international sources (OECD, 1998; OECD, 2000; OECD,

2012; EC, 2012), education systems are crucial for the productivity and future prosperity

of societies. Large budgets are allocated by national governments as well as private

institutions and participants in education processes. All of them rightfully expect a

reasonable return from their investments, but regularly, respected sources report troubles,

albeit on different levels. The Human Development Report (UNDP/DGVN, 2013) is a

source that relates the level of education to human development and documents relevant

challenges for significant parts of the world. This supports the ambitions to optimally

manage education in order to improve well being.



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Figure 1: Population per nation as function of education and human development index, i.e. a composite index measuring average achievement in three basic dimensions - a long

and healthy life, knowledge and a decent standard of living (UNDP/DGVN, 2013).

Independent from the relative level, national policies try to align their education

systems to the (local) labour market and particularly to demands by leading industries and

service providers. Governments (from developed as well as emerging economies)

regularly publish strategy papers and plans how they intend to accomplish these targets,

but only very few provide insights how they would like to proceed. Even so, elaborated

national educational strategy and implementation plans most often cover only a special

segment within the educational system (e.g. tertiary education) and beyond that strongly

focus on structural and resource related issues within the chosen segment.

1.1 Value chains as an approach to improve results

In complex industrial production systems with partially global sourcing strategies,

concepts such as supply chains were implemented decades ago and improved ever since.

The general benefits of supply chain management approaches support (among others)

improvement of operations, increase of profits and an enhanced quality of outcome. Later

on, these concepts were also applied to service industries and in a few cases to

educational contexts. First suggestions of educational supply chains in tertiary education

were provided by O´Brien and Dean (1996). Lau (2007) introduced a supply chain

management approach to suggest innovative management ideas in higher education with



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the case of University of Hong Kong. Habib (2010) elaborated on Lau towards an

integrated model.

Beyond the complex logistically centered challenges of manufacturing supply chains,

additional services before and after production lead to an added value for the end

customer. Focusing on interdependencies of value activities across organisations and

optimizing these links to create competitive advantage lead to the concepts of value

chains as introduced by Porter (1998). Some of these ideas were applied to education, e.g.

by Pathak and Pathak (2010), who map and reconfigure the activities of Porter’s value

chain model within a single organisation to a higher educational institution aiming to

optimise the value added for the customers. Williams (2012) takes a broader perspective

by using industrial value chains to derive skill maps for workforce development for

selected industries.

1.2 Education as a major driver for productivity

The application of value chain ideas to the requirements of a knowledge society

(Drucker 1993) were - surprising enough - not yet fully transferred to education systems.

In this article, we would like to address an education system as a multi-layered construct

of various institutions that provide education, starting with kindergarten, as the first

formal contact of a young person with institutional instruction, then preschool and

primary school. Obligatory education in the EU regions then continues with secondary

education and vocational training. Increasingly higher relative shares of a cohort continue

their education with one or two advancements in higher education and complete first

college, then university degrees. In the European Union, the Bologna Process (EU, 1999)

brought a new framework at the beginning of the New Millennium and introduced Anglo-

American concepts of bachelor and master into tertiary education. After completing these

education process, the former student regularly enters the labour market and sometimes

receives a professional education, either immediately or as an upgrade later on, e.g. as

continuing education or “lifelong learning” concept. Figure 2 illustrates this education

process.

Figure 2: schematic structure of the value chain in education



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We suggest understanding these development levels of a student in the process of

education as a value chain with the pupil and student as the “object” to be developed in

several different but consecutive institutions over several years.

As mentioned above, the existing literature applies value chain and supply chain

approaches to education mainly on various “needs for supplies” of education providers or

concepts for a single educational level. These approaches are explicitly not the focus of

our research.

Instead, we would like to reconstruct the development and education of people along

a time perspective. As educational careers are slightly different, depending on national

context and regulation, we would like to discuss our approach by the case of the Austrian

education system, which is compatible to most EU countries. As it would exceed the

capacity of this article, we will not cover the details of the Austrian education system

(BMUKK 2013).

Educational institutions in Austria, as well as in most constituencies, are highly

regulated and regularly in the focus of political as well as professional reflection for

improvement. In addition to the already mentioned Bologna process, the Austrian

Government implemented a pioneering regulation to report the Intellectual Capital of

universities on a regular basis as part of the performance monitoring processes (Republic

of Austria: § 13 Abs. 6 Universitätsgesetz 2002 ). The law is of particular relevance, as it

expressed the assumed connection of Intellectual Capital as a relevant driver of national

performance. In a similar direction argues Hattie (2008) with his synthesis of over 800

meta-analyses relating to achievement in the education process.

1.3 Intellectual Capital Perspectives in educational value chains

This paper aims to apply the concept of Intellectual Capital (Edvinsson, 1997,

Amidon 1997, Guthrie 2000) as a relevant driver for performance, not only to tertiary

education (universities) but to all the educational processes starting from kindergarten and

primary level to lifelong learning. We inquire the need to differentiate drivers of

Intellectual Capital for each type of organization in contrast to the opportunity to apply

standardized drivers. In any case, they are different from available templates in industry

(Alwert et al, 2010).

Intellectual Capital is discussed intensely in the literature (representative since 2000:

Journal of Intellectual Capital, Reinhardt, R. et al (2001); Choo, C. and Bontis, N. (2002);

Mouritsen, J. Et. al. (2002)), with early adopters in academia, industry (Sveiby 1997,

Steward, 1997, Dumay, 2009 and Dumay, 2012) and public administrations (The Danish

Trade and Industry Council, 1997). Based on the experiences in Scandinavia and later in

Austria (Bornemann et al, 2003), the German Federal Ministry for Economy and

Technology initiated a multi-year program to measure and manage Intellectual Capital

(BMWI 2004). Among the results were a well received “Guideline for measuring

Intellectual Capital - made in Germany” (BMWI 2004) and a toolbox to support large

scale implementation of Intellectual Capital Reporting in small and medium sized

enterprises. The concept was successfully rolled out all over Germany (see AKWB, 2012



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for more details). With the EU-funded project InCaS (InCaS 2008) the concept became

available all over Europe.

Initially intended to improve the performance of SMEs, the “Wissensbilanz” was

applied to other systems as well, such as large stock noted enterprises (ENBW 2004-

2012), regional innovation regions and systems (NOEST, 2004; Wiedenhofer, 2009 and

2012) and clusters (Bornemann 2013, Cadic 2012). The topic “Intellectual Capital

Reporting” is increasingly covered by universities and applied universities as part of their

regular management training (e.g. Wirtschafts Universität Wien, Donau Universität

Krems, Campus02, FH Burgenland, FH Riedlingen, Technical University Dresden,

Fraunhofer Academy) and thus becomes visible for several students as a field of

application and research.

2 Challenges

2.1 Research questions

As part of these educational activities, several case studies emerged as prototypes as

well as regular application of the concept of Intellectual Capital Reporting and

Management and provide the qualitative source to investigate the research questions for

this paper.

· We would like to explore the current status quo of taxonomy for Intellectual

Capital in educational institutions for four levels of education (we do not

have data for the preschool level): Do we have an established language for

Intellectual Capital in Education?

· With this semantic analysis, we would like to identify the degree of

connectedness of IC drivers and exchange between these institutions: Can we

construct a value chain perspective?

· Finally, we would like to investigate the current management of this assumed

value chain and explore whether or not the concept of “Wissensbilanz made

in Germany” as the management methodology for intangibles could

contribute to improve the overall performance. How could we measure and

manage Intellectual Capital in education in order to achieve better results

from the value chain and improve general welfare?

2.2 Data

Based upon current Austrian legislation and in line with international practice, we

identified five levels of an educational value chain (see: Figure 2). For each level but

preschool or Kindergarten, one or more case studies to measure Intellectual Capital could

be accessed for secondary analysis. Three sources of data were utilized, as the authors had

not direct access to all institutions:

· Case studies conducted by the authors as part of their research and consulting

activities



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· Diploma and master thesis papers of students focussing on Intellectual

Capital with at least one semester (4 ECTS) of formal training on the

methodology of Intellectual Capital Reporting and additionally more than

350 hours of their own research, and

· Seminar papers provided by master students with at least one semester (4

ECTS) of formal training on the methodology of Intellectual Capital

Reporting.

Table 1 covers the distribution of the papers and their relative focus. Twelve case

studies involved 55 teachers and administrative staff from the institutions under analysis

and thus draw on first hand experience. All participants invested a series of 4 full day

workshops with a total of 80 hours of their time into the reports and thus provide very

privileged sources of data. Hence, validity as well as reliability of the retrieved statements

are supposed to be very high and exceeding the quality of other sources of data, such as

(extended) interviews or surveys. All case studies were double checked by the authors.

Table 1: distribution of case study sources

master seminar master thesis consulting

primary 2 2

secondary 3 3

tertiary 1

quartery 2

Restrictions and limitations As this paper reports the findings of an ongoing research project, only one case study

for the tertiary level could be considered. To avoid eclectic results, at least one additional

case should be covered in a next stage. However, the results of this case in the tertiary

level are - according to the author’s best knowledge - in line with other results from

higher education institutions, but their data could not be used because of methodological

limitations and confidentiality reasons.

2.3 Method

Wissensbilanz - made in Germany (2004, 2007 and 2013) as well as InCaS (2007) are

available as open source guidelines in English. The method builds on 8 steps to come up

with a full report as well as management priorities on Intellectual Capital. The process is

a team effort with typically not less than 4 and not more than 12 participants. For this

paper, the following activities are most relevant:



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· Definition of the system boundaries: As the focus of providing Intellectual Capital

Statements is on internal management (and - so far – not (!) an integrated

management approach covering several institutions), the objective and limit of the

case studies was typically a distinct school or institution.

· Definition of IC drivers: Even though some templates for drivers of Intellectual

Capital are available in the guideline as well as in the toolbox, the participants were

encouraged to identify the drivers of Intellectual Capital within their system and

depending on the individual strategic objectives. The

· Qualitative assessment of Intellectual Capital is based on three dimensions. Two of

them cover the present (status quo of quantity and quality of a driver), one focuses on

the management and future development (systematic management). All three

dimensions are measured according to the strategic fit on a scale from 0 to 100.

· Indicators to support and supplement the qualitative assessment are part of the

method but were not included in this study. This is because of so far not available

standard definitions for indicators (including a standardized mathematical definition

and standardized procedures to collect the data) with a binding definition for all

institutions covered in this study. But even a standard definition would not guarantee

availability or access to the data.

· Business dynamics or cross impact analysis: All case studies include an analysis of

cross-interdependence to assess the impact of an improvement (change project) for

one driver on all the others. This analysis allows to model causality patterns (see

Sterman, 2000; Vester, 1999; Senge, 1990) and thus supports organizational learning

as well as prioritization for change projects.

· This paper does not focus on individual results on the status quo of Intellectual

Capital or management actions focussing on the elimination of identified bottlenecks

for further organizational development. This paper does not cover internal or external

communication issues.

Based on this methodological setting and the data obtained from 12 case studies, we

would like to explore the research questions as mentioned before.

3 Analysis and Findings

3.1 Do we have an established language for Intellectual Capital in Education?

Based on 12 case studies, we conducted a semantic analysis for the drivers of

Intellectual Capital. Most of the authors adhered to the “Guideline for Intellectual Capital

Reporting” and identified 3-5 drivers for each of the Intellectual Capital categories of

Human Capital, Structural Capital and Relational Capital.



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Table 2: distribution of drivers of IC for each case

System A B C D E F G H I J K L

Number of HC drivers 4 5 3 4 4 5 9 3 4 3 5 3

Number of SC drivers 5 6 5 3 4 4 6 4 3 3 4 5

Number of RC drivers 3 4 4 3 4 5 7 3 3 3 5 3

Number of IC drivers 12 15 12 10 12 14 22 10 10 9 14 11



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Table 2 covers the number of drivers for each system as well as the aggregate number

in the bottom line. We can see an almost normal distribution with the minimum of 9

drivers - the minimum of the recommended 3 drivers for each category. The highest

number is 22 drivers. Because of the comparative large gap of 7, this constitutes a clear

exception. In



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Table 2 we see that Human Capital was covered with almost double the amount of

drivers. This can be explained by the differentiation of two groups of employees in this

particular institution (teachers and educators). A similar explanation applies for Relational

Capital, as the author of this case investigated various stakeholders. However, the cases

are acceptably similar with regard to the level of differentiation in Intellectual Capital.

In order to test for an established set of drivers for Intellectual Capital, we identified

the individual drivers for Human Capital and for Relational Capital (Structural Capital

provides similar results, but they are not covered because of space restrictions).

Fachkompetenz 6

Fachliche Qualifikation (inkl. Weiterbildung) 1

Fachliche Kompetenz 3

Methodisches und didaktisches Fachwissen der Lehrer 1

Didaktische und Methodenkompetenz 1

Qualifikation der Lehrkräfte 1

MitarbeiterInnenqualifikation 1

Erfahrung der Lehrkräfte 1

Fachkompetenz und method. Kompetenz 1

Fortbildung 1

allgemeine Mitarbeiterkompetenzen entwickeln 1

Fachqualifikationen für die Akademie aufbauen 1

FACHKOMPETENZ 19

Mitarbeitermotivation und Zufriedenheit 1

Motivation der LehrerInnen 1

Mitarbeitermotivation 3

Motivation 1

Motivation der Lehrkräfte 1

Mitarbeiterzufriedenheit 2

Identifikation 1

Engagement der Lehrer 1

Engagement der Erzieher 1

MITARBEITERMOTIVATION 12

soziale Kompetenz 5

Sozialkompetenz 4

Selbst- und Sozialkompetenz 1

Soft Skills 1

Sozialkompetenz der Lehrkräfte 1

SOZIALKOMPETENZ 12

Führungskompetenz 5

Führungskompetenz weiterentwickeln 1

Führungsstärke der Direktion und der Lehrer 1

Führungsstärke der Erzieher 1

FÜHRUNGSKOMPETENZ 8



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Persönliche Einstellung 1

Authentisches Christsein 1

Veränderungsfähigkeit unterstützen 1

SONSTIGE 3

and Table 5 in the appendix contain all drivers in German language - the very subtle

differences are difficult to translate and would not make a difference for this analysis.

From

Fachkompetenz 6









Fortbildung 1



FACHKOMPETENZ 19




Motivation 1



Identifikation 1




soziale Kompetenz 5

Sozialkompetenz 4


Soft Skills 1


SOZIALKOMPETENZ 12








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SONSTIGE 3

in the appendix we see that the authors used 33 different names for a cluster of only 5

major drivers of Human Capital.

Table 5 in the appendix offers 30 different drivers for Relational Capital, but needs 7

subsets, one a residual, as some drivers could not be assigned to a “major driver”. Even

more scattered are the drivers for Structural Capital which needs up to 12 different

subsets.

We have to attest a highly differentiated set of drivers for each level of the value chain

with a relatively uncontested set for Human Capital (5 drivers) and diverse Structural (12)

and Relational (7) Capital. This supports the hypothesis that Structural Capital constitutes

a major competitive advantage of an organization as employees are free to move and - at

least in small organizations - as employees regularly control the relations to external

groups.

From the perspective of a value chain analysis, Relational Capital is particularly

relevant. Hence, we would like to investigate the drivers of Relational Capital with some

qualitative details:

3.1.1 Customer Capital (Beziehungen zu Eltern, Schülern und Alumni (Kunden)):

While all SMEs agree upon “customer relations” (Alwert et al 2011) this is not the

case in education. Of 12 case studies only 6 explicitly used the term “customer relations”,

while several other stakeholders were identified as crucial to accomplish their strategic

objectives: pupils, alumni, parents and custodians were considered as relevant. However,

no case identified industry as the likely future employer of successful graduates as

relevant to be included in the list of drivers for Relational Capital. And no case

considered “(future) taxpayers” - as distinct from parents - as a relevant stakeholder of the

public funded education sector. This maybe reveals something about the strategic

positioning of the case-study teams that provide the reports.

3.1.2 Relations to cooperation partners (Beziehungen zu Kooperationspartnern)

10 out of 12 case studies refer to some external parties as privileged partners. This

term comes very close to what could be understood as a connector between institutions

from the same and maybe even different levels of the value chain in education. (In the

SME context we see this driver comparatively frequent. “Relations to cooperation

partners” refers to organizations that have some impact on each other for various reasons

such as sharing (production-) capacity, access to technology or markets or even joint

activities such as research and development that are too complex for only one to handle.)

When we drill down to the definitions behind the headlines, we learn that most case

studies provide only ambiguous hints, whom they consider as distinct from “other

suppliers” and for what reasons. Educating institutions sometimes “collaborate” to



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compensate for infrastructure restrictions (sharing / substituting laboratory space or sports

facilities) or to handle local events such as organizing conferences, cultural events or

distinct projects outside their core activities. One case explicitly states to nurture / foster

relations to a neighbour institution in order to benefit from lessons learned in each others

domain or to collaborate in regard to administrative issues. Another case illustrates a very

particular type of collaboration, as their core activity is the training of future pedagogues

and teachers, who - naturally - need to train their skills in other schools. We could not

identify relations to “suppliers” as understood in industrial contexts. However, this driver

and its various definitions constitute a perfect example of what we understand with the

analogy of value chain in education.

3.1.3 Relations to suppliers and service providers (Beziehungen zu Lieferanten

und Dienstleistern) Only four of 12 case studies cover this dimension that connects an educational

institution to its environment and keeps its physical structures as well as maintenance or

access to services “alive”. This is another interesting deviation from ICS reports form

“typical SMEs” (Alwert et al 2010), as most of them nurture relations to their key

suppliers. Maybe we could explain this difference with the relative autonomy of most

educational institutions that do not really depend on external service providers in a similar

way as do (industrial) enterprises.

3.1.4 Relations to Authorities (Beziehungen zu Aufsichtsbehörden)

This category is covered by only three out of 12 cases. However, it might be wrong to

imply a relaxed atmosphere between schools and their governing authorities. Rather, this

indicates a relevant part of the systems borders that sometimes get blurred in daily

routines. However, the teachers - in contrast to administrative staff - are rarely in direct

contact with the authorities but still might be very aware of the external influence.

3.1.5 Relations to the public (Beziehungen zur Öffentlichkeit)

This category is covered by 8 of 12 case studies and shows only very little semantic

deviation. “Public relations” and “relations to the public” are extremely close. Both terms

indicate the awareness of the participants of the relative impact of their activities on the

public prestige. But given the relative small size of the majority of local educational

institutions (schools have typically less than 1000 pupils and less than 50 teachers), they

are depending on public budgets but in turn have little to no influence on the federal

budgeting process, for example via an electorate. After inquiring deeper, we identified

considerable competition among various schools to attract pupils and students and thus

secure the teacher’s jobs. This, of course, applies primarily to urban areas, where

geographical distances between competing institutions are smaller.



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3.1.6 Relations to Investors (Beziehungen zu Kapitalgeber und Investoren)

This is an interesting driver for two reasons: it is a default driver in both, the

“guideline” and the “toolbox”, whose primary focus is on SMEs and enterprises, who

have (weak) ties to the capital market. This is regularly not the case for schools, and only

one group suggested it. The authors of this case did not provide a convincing rationale for

including this driver in their analysis. Hence, it servers as “controlling variable” for the

students ambition to come up with education-specific drivers for Intellectual Capital as

well as to accept the other 11 cases as “qualitatively reasonable”. But we probably can

learn another lesson from this (minor) deviation: users tend to use what is already

available. They readily accept whatever “standard” is proposed and subordinate (maybe

not only in educational systems) to more or less meaningful default settings. When

investigating a sector such as education with an alien concept that originally was

developed in a for-profit-environment of industry and service providing organizations, we

must be very careful to premature adoption of “established truth” or standards.

After reviewing 12 case studies of Intellectual Capital Statements in educational

systems, we can conclude that there is a widespread understanding for Intellectual Capital

drivers in general. We can document a huge variety of terminology with slightly differing

focus and interpret this as a signal for the need of a distinct terminology in educational

systems that support contextual understanding, meaning and governance.

However, we could demonstrate a semantic consolidation of various drivers of

Intellectual Capital to more general clusters. We would not suggest that this aggregation

is supporting a (simple) benchmarking of results, as the understanding and interpretation

of the participants are diverse.

With this analysis, we have to conclude this chapter with the observation that

currently, we do not have an established set of drivers for Intellectual Capital. But we

have candidates that might qualify for a future recommendation.

3.2 Can we construct a value chain perspective?

Postulating a connection between consecutive schools might appear quite obvious.

However, so far we could identify only few international examples of education providers

that collaborate along a progression path with the explicit understanding to advance the

career of their students. Among them are Anglo-American based feeder schools (WSJ,

2007) for the Ivy League universities that actively communicate their mission to serve as

“feeder schools”. Interestingly, all of them are private schools with a different

organizational structure than are established in Austrian schools. So, we would like to

investigate the phenomenon of “non-integration” in the following paragraphs.

The 12 case studies contain data of cross-impacts for each driver of Intellectual

Capital as well as on the core processes and results. The processes are quite diverse.

However, a common pattern can be identified as a “minimum common denominator”. All

education institutions have a process for knowledge transfer (education in a wide

interpretation), a process for administrative agendas (such as managing the organization)

and sometimes a (not very distinct) process for facility management in a wider sense.



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More interesting from a value chain perspective are the outputs of the processes, the

results of educational organizations. Applying the industrial concept of value chains as

implemented in the aviation and automotive industry (Porter 1985) to educational systems

as described in Figure 2, and combining them with concepts of system dynamics

(Sterman, 2000, Vester 1999) and Intellectual Capital Management (Bornemann et. al

2007), we could define a cross-impact matrix that takes advantage of the data we can

obtain from our case studies.

Wissensbilanz - made in Germany applies these ideas and uses an interdependency

matrix that relates drivers of Intellectual Capital to core processes and strategic objectives

(=business results). Figure 3 illustrates the structure.

Figure 3: example of the IC matrix of one education provider from the second level The values in the matrix have the following connotation:

· 0 - a change in the driver of the first column has “no effect”

· a change in the driver of the first column has “weak effect”

· 2 - a change in the driver of the first column has “proportional effect”

· 3 - a change in the driver of the first column has “exponential effect”

on the other drivers in the matrix.

Generally, these matrixes are heterogeneous. This reflects the differences in

organizational structure, organizational maturity, culture and general orientation of the

institutions. In this article, we will not discuss internal management issues - they are

discussed in Bornemann 2013 - but focus on the external connections that might support

our interpretation of comprehensive education as a value chain.

This matrixes provide deep insight into the context of an organization and allow to

observe, how the members of an organization understand the cross dependencies of

strategy-relevant drivers on each other and, ultimately, on the strategic objectives.

We can aggregate the data for each level and reconstruct a value chain for the

education system in order to better understand the interdependencies between intangible

assets of the whole sector as well as the relations between different levels of education.

Additionally, we could learn about governing interventions into the system - and probably

derive some learning’s that are interesting for designing policy instruments. The matrix of

on educational institution (see Figure 3) is represented in one of the grey fields.

GP-1

GP-2

GP-3

GE-1

GE-2

HK-1

HK-2

HK-3

HK-4

SK-1

SK-2

SK-3

SK-4

BK-1

BK-2

BK-3

BK-4

Active sum

GP-1 Unterrichtsablauf x 3 0 3 0 0 0 0 0 1 0 1 2 3 3 1 1 18GP-2 Wissenstransfer 1 x 0 1 0 0 0 2 0 0 0 0 1 -2 0 -1 1 9GP-3 Individualisierung 0 2 x 2 1 0 0 2 0 0 0 0 2 1 0 1 0 11GE-1 Image/Kundenzufriedenheit 0 0 0 x 3 0 0 1 0 0 0 1 0 3 1 2 1 12GE-2 Anzahl der Einschreibungen 0 0 -1 0 x 0 0 -1 0 0 2 0 1 -1 0 1 1 8HK-1 Fachkompetenz 3 2 0 2 2 x 0 0 0 2 0 1 3 0 0 0 1 16HK-2 Soziale Kompetenz 3 1 0 2 1 0 x 0 0 3 0 2 0 3 1 0 0 16HK-3 Motivation der LehrerInnen 2 3 0 1 1 0 0 x 0 3 0 1 1 0 0 0 1 13HK-4 Didaktische und Methodenkompetenz 3 3 0 3 1 0 0 1 x 3 0 1 0 3 0 0 0 18SK-1 Kooperation und Wissenstransfer 3 3 1 2 2 3 1 3 3 x 0 3 2 1 0 0 0 27SK-2 Informationstechnik 2 2 1 2 1 0 0 2 1 3 x 1 2 1 0 0 1 19SK-3 Unternehmenskultur 2 2 1 3 3 0 2 3 0 3 0 x 1 3 1 2 1 27SK-4 Wissensobjekte 3 2 0 0 0 2 0 0 0 0 0 0 x 0 0 0 0 7BK-1 Kundenbeziehungen 1 1 1 3 0 0 2 2 0 0 0 3 0 x 2 2 1 18

BK-2 Beziehung zu Volksschulen im 20. Bezirk 0 0 0 1 3 0 0 2 0 1 0 1 0 2 x 1 0 11

BK-3 Beziehungen zur Öffentlichkeit 0 0 0 1 2 0 0 1 0 0 0 2 0 2 2 x 1 11BK-4 Beziehungen zu Kooperationspartnern 1 2 2 1 2 0 0 1 0 1 0 1 0 2 1 1 x 15

passiv sum 24 26 7 27 22 5 5 21 4 20 2 18 15 27 11 12 10 256



586

Figure 4: Integrated system perspective of the value chain

From Figure 4 we see that according to our understanding of an educational system

that accompanies students from early childhood to a job in a knowledge based

organization, the output of the first level (primary schools) becomes the input for the

second level (secondary schools). Graduates from secondary level become input for the

learning processes of the tertiary level and so on.

As we are constructing this concept, we see that the data based on case studies with

focus on one organization rarely cover impacts on other systems. However, some drivers

of Relational Capital and some drivers describing the strategic objectives do support this

assumption (highlighted in Figure 4):

· We identified one example of “Improving relations to a (specific) school in the

same district” as being part of the narrative of Relational Capital.

· We found one example where “improving relations to the next higher educational

institutions” constitutes an explicit strategic objective.

· And, as suggested in the hypothesis, we found six cases with “improving the

qualifications of students” as being an explicit strategic objective.

As an intermediate summary, we can confirm the availability of “connectors” between

individual institutions along a value chain. However, this is not valid for all covered case

studies. We can not document an established and comprehensive value chain as e.g. in the

automotive industries.

Figure 4 suggests how a formal complete description of the value chain could be

established. It illustrates how an improvement of Intellectual Capital as a major resource -

apart from financial means - influences the business processes of each institution. Right

now, we can not report homogeneous patterns, but highly individual perceptions of

interdependencies.

Pre-School Primary Secondary Tertiary QuartaryHC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR HC SC RC BP BR

Pre-School HCSCRC

BPBR

Primary HC

SCRC

BPBR

Secondary HCSCRC

BPBR

Tertiary HCSCRC

BPBR

Quartary HCSCRC

BPBR



587

3.3 Can we assume typical patterns in “similar organizations”?

Even though we have only a very small sample of case studies available, we tested for

similarities and differences in the cross-impact matrices of various institutions. A simple

comparison of Figure 3 and Figure 5 demonstrates the huge variety.

Figure 5: example of the IC matrix of one education provider from the second level

The first matrix (Figure 3) contains an almost equal distribution, the later (Figure 5) a

rather extreme frequency of “exponential effects”. This can be explained by very different

maturity levels or structural change in strategy and the organization. The second case

represents such a situation: it is a pilot organization for a completely new set up education

model. As a consequence of the restructuring, changes, even small ones, have still a

remarkable impact on the whole system. However, with the distance of desktop research

analysis, the case certainly represents an extreme one and would be realistic only for a

very short period of time, until change efforts will be stabilized.

Table 3: dynamics of various educational institutions within the same level

level 2 seminar 1 seminar 2 seminar 3 master 1 Master 2

matrixmax 768 675 867 588 507

system 256 235 353 424 451

dynamic 0,33 0,35 0,41 0,72 0,89

We tested for the dynamics of organizations with a simple measure that relates the

maximum “energy” of a system the actual value of the matrix.

The maximum is calculated by the number of drivers times the number of drivers

minus one (there is no impact on the factor itself) times 3 as the maximum score.

What we see from Table 3 is a strong spread of dynamic in various systems. This

indicates some challenges in “simply comparing” the systems without taking into account

for the fundamental assumptions and the context in the individual organizations. Thus, we

can not assume typical patterns of organizations, not even within one level (here, the

secondary level) of the education value chain. However, we could use these 12 cases as

prototypes to identify some further questions and formulate initial learnings.

GP-1 GP-2 GP-3 GE-1 GE-2 HK-1 HK-2 HK-3 SK-1 SK-2 SK-3 BK-1 BK-2 BK-3 active sumGP-1 Fort- und Weiterbildungskonzept x 3 3 3 3 3 3 2 3 3 3 2 3 2 36GP-2 Unterrichtsmethodik 1 x 3 3 3 2 1 3 3 3 3 3 3 1 32GP-3 alternative Formd der Leistungsrückmeldung 2 3 x 3 3 2 1 1 2 2 1 3 3 1 27GE-1 Kundenbindung 3 3 3 x 3 3 3 3 3 2 2 3 3 1 35GE-2 Image 3 3 3 3 x 3 3 3 3 3 3 3 3 3 39HK-1 MitarbeiterInnenqualifikation 3 3 3 3 3 x 3 3 3 3 3 3 3 3 39HK-2 Selbst- und Sozialkompetenz 3 3 3 3 3 3 x 3 3 3 3 3 3 36HK-3 Authentisches Chrsitsein 3 2 3 3 3 3 3 x 3 3 3 3 3 3 38SK-1 Produktinnovation 3 3 3 3 3 3 1 x 2 2 3 3 3 32SK-2 Kooperations- und Kommunikationsformen innerhalb der Schule3 3 3 3 3 3 3 2 2 x 2 3 3 3 36SK-3 Wissenstransfer und Wissenssicherung 3 3 2 2 2 2 2 2 2 3 x 2 2 2 29BK-1 Beziehungen zu Eltern und Erziehungsberechtigten 2 2 3 3 3 2 2 2 2 2 1 x 2 2 28BK-2 Beziehungen zur Öffentlichkeit 2 1 1 2 3 1 1 1 2 0 0 2 x 2 18BK-3 Beziehungen zu Kooperationspartnern 2 2 2 3 3 2 2 1 2 1 2 2 2 x 26

33 34 35 37 38 32 28 26 30 30 28 35 36 29 451



588

4 Discussion

Applying methodologies for Intellectual Capital Management in educational

institutions and integrating the management of these resources along the value chain of

education seems to be very beneficial not only for ministries of education as the major

common stakeholder but for de-central decision makers as well. Prioritizing scarce

resources and systematically observing intangible assets in public as well as privately

management educational intuitions contributes to economic improvement and better

accomplishment of strategic objectives.

If we can construct a value chain similar to industrial value chains in education, then

processes to integrate the organizations and to streamline the education processes should

be possible. This assumes, however, a shared understanding of the final objective, for

example “serving a customer need”. According to our findings (

Fachkompetenz 6









Fortbildung 1



FACHKOMPETENZ 19




Motivation 1



Identifikation 1




soziale Kompetenz 5

Sozialkompetenz 4


Soft Skills 1


SOZIALKOMPETENZ 12



589









SONSTIGE 3

and Table 5), it can not be assumed that all players in the value chain have a shared

understanding, who their actual customer is and what the customers primary demands

might look like.

The value chain perspective comes not “naturally” from the so far highly autonomous

educational institutions. We need to define the linking pins between the systems which

are already visible, but not yet established. Their impact on the education system as a

whole would be considerable.

We added the perspective of Intellectual Capital, not only as a resource and input, but

- simultaneously - the intangible output of education systems as well. Given the complex

and knowledge based processes of education and the nature of knowledge transfer,

training and feedback, there might be plenty of room for further research to better

understand the management process of educational institutions.

Another challenge relates to the governance process itself. Of course, the education

systems in general and the education system in Austria in particular, are subject of heavy

regulation. But these regulations regularly are influenced by various stakeholders such as

(competing) teacher unions, political parties and government administration. As a

consequence, the governance structures are quite complex and not prone to managerial

optimization methods which are established in the industry.

Finally, when reflecting on the process of Intellectual Capital Reporting and the

people involved - our cases covered more than 55 people for several days - we have to

acknowledge their desire for transparency in order to align their individual behavior and

daily decision making to a comprehensive education process, ideally tailored for each

pupil or student. This seems to be extraordinarily difficult, given the complexity of the

system, the subtle differences and the context dependency.

5 Outlook

There seems to emerge a growing demand of more entrepreneurial approaches

especially in tertiary educational governance as a consequence of enhanced (international)

competition and limited public resources and funding. Thus the implementation of a

strategic management processes in education provides as future opportunity. Intellectual

Capital Management provides an effective methodology to support such governance

mechanisms in a participatory manner. Intellectual Capital concepts are established in



590

industry and SMEs in various configurations. The initial learning curve is successfully

completed; a transfer to education is not only possible but entirely feasible as

demonstrated with this paper.

However, there are several challenges to be overcome:

· Definition of individual sets of drivers of Intellectual Capital which are relevant

for each individual institution while simultaneously allowing for consolidation

into a larger framework.

· Definition of connecting drivers of Intellectual Capital that establish not only

relations but support procedural exchange in order to optimize parts and later the

whole value chain.

· Definition of an appropriate scale with inter-subjective and auditable measures

that support comparability and benchmarking. This, however, is clearly of

secondary priority.

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Appendix Table 4: list of drivers of Human Capital Fachkompetenz 6









Fortbildung 1



FACHKOMPETENZ 19




Motivation 1



Identifikation 1




soziale Kompetenz 5

Sozialkompetenz 4


Soft Skills 1


SOZIALKOMPETENZ 12









SONSTIGE 3



594

Liste unterschiedlicher Begriffe BK Table 5: list of drivers of Relational Capital Beziehung zu den Schülerinnen 1

Beziehung zu Absolventen 1

Kenntnis der intellektuellen Fähigkeiten und Begabungen der Schüler 1

Kenntnisse über Neigungen und Interessen der Schülerinnen 1

Beziehungen zu Schülern und Eltern 1

Beziehungen zu Eltern und Erziehungsberechtigten 1

Unterstützung und Engagement durch Eltern 1

Beziehung zu Kunden 1

Kundenbeziehungen 6

Kundenbeziehungskapital durch Vernetzung entwickeln und laufend pflegen 1

Beziehungen zu Eltern, Schülern und Alumni (Kunden) 15

Beziehung zu externen Dienstleistern 1

Lieferanten 1

Beziehungen zu Dozenten 1

Externe Kooperation mit Institutionen und externer Wissenserwerb 1

Beziehungen zu Lieferanten und Dienstleistern 4

Beziehungen zu Kooperationspartnern 6

Kooperationspartner 1

Wissensaustausch 1

Beziehung zu Volksschulen im 20. Bezirk 1

Kooperationen pflegen 1

Beziehungen zu Kooperationspartnern 10

Beziehung zu Institutionen 1

Beziehungen zu Behörden 1

Kenntnisse über außerschulische Institutionen 1

Beziehungen zu Aufsichtsbehörden 3

Öffentlichkeitsarbeit 4

Beziehungen zur Öffentlichkeit 4

Beziehungen zur Öffentlichkeit 8

Kapitalgeber und Investoren 1

Beziehungen zu Kapitalgebern und Investoren 1

Stakeholder-Kontakte 1

Partizipative Zusammenarbeit aller Beteiligten 2

Kooperation mit schulinternen und externen Personen 1

Aufgeschlossenes und dynamisches Lehrerteam 1

Effiziente Kommunikationsprozesse 1

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