Process Model for Digital Transformation of University Knowledge

Transfer

Claudia Doering

, Finn Reiche

and Holger Timinger

Institute of Data and Process Science, University of Applied Sciences, Landshut, Germany

Keywords: Digitalization, Digital Transformation, Knowledge Management, Transfer, Higher Education Institution,

Universities, Modeling for Digitalization.

Abstract: The digital transformation is still a volatile concept with different understandings between researchers and

practitioners. Nevertheless, digital technologies and concepts are finding their way into all areas of life, be it

private or professional life. Even universities are not spared from digital transformation. They need to

incorporate innovations not only within their curricula, but also in their inner structures and administration to

ensure up to date research and transfer. Therefore, a structured model for the digital transformation of transfer

in universities was created. The main purpose of this paper is to provide practical support and break down

barriers in the digital transformation of knowledge transfer in and out of universities.

1 INTRODUCTION

Economy, society and research are currently

experiencing a global surge in digitization, which is

partly due to the pandemic, or at least reinforced by

it. Processes, procedures and production steps are

being digitized, just as meetings, schooling and

studying.

At the same time, the importance of knowledge

transfer is continuously increasing due to various

stakeholder groups. Companies, society, universities

and other research institutions, as well as politicians,

are recognizing this importance and are promoting

transfer and calling for it to be intensified. For

example, in Germany, a Higher Education Innovation

Act is planned, which is expected to be passed this

year or next year and which emphasizes a central role

of transfer (Bavarian Ministry of Science and Art,

2021). Knowledge and Technology Transfer, is one

of the central tasks of universities, like research and

teaching (Bavarian Ministry of Science and Art,

2021).

Digitization in the context of universities affects

all areas of higher education institutions. Research is

already in the transformation process through an

increasing establishment of research information

https://orcid.org/0000-0002-3727-8773

https://orcid.org/0000-0003-2066-7323

https://orcid.org/0000-0001-7992-0392

systems, and teaching is also in the process of this

transformation. In addition to pandemic online

teaching, MOOCs (massive open online courses)

have already gained attention for themselves in recent

years. These are online courses, mostly free of charge

or accessible for small fees, which students from all

over the world can use to educate themselves in a

wide variety of topics.

However, the knowledge and technology transfer

of universities has not yet been sufficiently digitized

(Doering and Timinger, 2020).

Transfer often takes place “via heads”, which

means that people spread knowledge and technology,

for example as part of a student's thesis in a company

(Liyanage et al., 2009). Such knowledge is called tacit

knowledge, which is non-codified knowledge that is

acquired through informal behavior and procedures

(Howells, 1996). In this case, knowledge from

academia is brought into the company, but on the

other hand, practical application cases are also

brought from the company via the student into the

university (Roessler, 2015). Other types of transfer

include cooperation projects between companies and

universities, patents, or presentations and workshops.

For successful tacit knowledge transfer, universities

Doering, C., Reiche, F. and Timinger, H.

Process Model for Digital Transformation of University Knowledge Transfer.

DOI: 10.5220/0010672700003064

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 3: KMIS, pages 153-160

ISBN: 978-989-758-533-3; ISSN: 2184-3228

153

need to address the human, social and organizational

culture factors (Joia and Lemos, 2010).

In order to intensify the transfer between

companies and universities, target group-specific

information is needed. Target groups are in this case,

collaborating partners from society and industry, but

also employees of universities, in knowledge transfer

departments. Furthermore, studies have shown that

the geographical proximity between companies and

universities influences the intensity of transfer

(Arundel and Geuna, 2001; Laursen et al., 2011). If

project consortia are made up of various specialists

from different sectors of the economy, it can be

assumed that the physical distance between these

specialists and the universities is large. In most cases,

the distances are not limited to individual regions or

countries; in many cases, the parties are even

distributed globally. The proximity needed for

satisfactory knowledge transfer is missing. In order to

establish this, a way often chosen is the digitization

of the transfer process in all its facets. In this context

the term digitization means the transformation of a

process from analogous to digital data, whereas

digitalization is to use digital technologies to

transform business processes and business models

and create new revenue and value opportunities. It is

the process of using digital technologies and

information to transform business operations. The

often used term digital transformation refers to a

strategic transformation that requires both,

organizational change, and the implementation of

digital technologies.

By means of this digitalization, continuous

dissemination of research results – and thus, transfer

of knowledge and technology – is possible. The

advantage of digital transfer is that dissemination can

take place globally and continuously.

Universities must use digitalization in order to be

able to exploit the digital transformation for their own

benefit. Many universities are active in the area of

teaching and research in the subject area of

digitization, although some internal procedures and

processes are not digitized. Digitalization is being

taught by universities but not applied in their own

structures and processes (Doering and Timinger,

2020).

In order to give universities an impulse for

digitalization, a generic process model is necessary

which describes the various steps and stages of the

digitalization of a transfer process. Digital platforms

for knowledge transfer can be considered as an

enabler for innovation and problem solving within

transfer projects (Hossain and Lassen, 2017).

Therefore, this paper proposes an approach for a

process model for digital transformation of university

transfer processes in order to qualitatively and

quantitatively increase knowledge transfer with the

economy and society.

RQ1. How can the process of the digitalization of

university transfer processes be displayed in a

structured process model?

RQ2. What are possible challenges in the digital

transformation in universities and in which ways can

digitalization of university transfer processes be

initiated?

This article is divided in the following sections: at

first, the relevant research design is summarized. The

framework for digital transformation is then outlined

in the following section to answer RQ1. The next

sections cover RQ2 and outlines several challenges in

the digital transformation within universities.

An overview of the evaluation of the results with

a theoretical case study and an outlook completes this

contribution.

2 RESEARCH DESIGN

A comprehensive research method is needed to

ensure a high degree of quality of research. Therefore,

Design Science Research according to H

EVNER and

CHATTERJEE is used within the scope of this research

(Hevner and Chatterjee, 2010). They describe two

approaches, the Design Science and the Behavioural

Science. Whereas the latter aims at the construction

and validation of hypothesis, Design Science

Research focuses on the creation and evaluation of

IT-artefacts, which are build and evaluated in

alternating and iterative phases. In this context the

presented model is the artefact of research. H

EVNER

and CHATTERJEE present seven guidelines for the

rigorous research.

As this research method aims at solving an

essential business problems, a systematic literature

review according to the guidelines of V

OM BROCKE

was conducted to prove the relevance and fulfil the

request for rigorousness and support the research as a

search process (Vom Brocke et al., 2009). To

disseminate the information and the related model as

well as to fulfil the seventh guideline of H

EVNER and

CHATTERJEE, it will be published in this conference

and in an accompanying doctoral thesis.

To evaluate the process model for digital

transformation, expert interviews were conducted.

All expert interviews were executed as semi-

structured interviews according to the principles of

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154

Figure 1: Overview of Roles of Experts.

EUSER AND

AGEL

(Meuser and Nagel, 2009).

According to these guidelines an expert is a person

with special knowledge which is accepted by society

as relevant expert knowledge. In order to be able to

select the experts on the basis of specific criteria, the

guidelines of M

EUSER

and N

AGEL

were applied

(Meuser and Nagel, 2009). According to these

guidelines, an expert is a person with specialized

knowledge, which can often have been acquired

through the specific position in the company or in the

university. In total 13 experts, all situated in

Germany, were surveyed with a length of at least 30

minutes up to 90 minutes. They were carried out in

2021 using the online platform Zoom. The first

questions dealt with the experience and the

background of the experts (Figure 1). More than half

of the experts, namely seven, are employed in transfer

administrations of higher education institutions. Two

are researchers in universities, and two experts are

researchers in a company. Two of the experts are Vice

Presidents of Research and Transfer of a university.

The types of higher educational institutions, in which

the experts are employed, ranges from universities,

universities of applied sciences and technical

universities. Furthermore quality criteria according to

AYER

were considered (Mayer, 2013). The

objectivity of the interviews is ensured by the

independence of the experts. The data acquisition was

conducted under equal conditions, with concrete

specifications for the provision, evaluation and

interpretation of the interviews. Reliability

guarantees that the same results appear at the end of

the research, if it was conducted under the same

conditions. This quality criterion was ensured through

the conduction of pre-interviews. Validity ensures

that a suitable research design was chosen according

to the specific research questions. As the results of the

interviews can be generalized for knowledge transfer

situations for all kinds of transfer possibilities, the

validity of the interviews is ensured.

3 PROCESS MODEL FOR

DIGITAL TRANSFORMATION

IN UNIVERSITIES

The model consists of four phases, three of them

consisting of several views each. The phases are

shown in ascending order of the maturity level to the

right; the different views symbolize that there are

several potential processes or possibilities in these

phases (Figure 3).

The first phase is to elicit the current status in

relation to the existing analogous and digital

structures and data. This is exemplified by the Design

Thinking process, but the other views also indicate

other possible variations of the elicitation. The

following phase is the Enabling Phase, in which the

data is already available digitally, but is not linked to

each other. Furthermore, the processes are not

modeled or captured. Here, too, different views are

possible. The initiation of process changes or general

changes in the course of digitization can take place in

different ways, also represented by different views

(cf. RQ2). The Development & Implementation

Phase is the first phase in which steps backward, i.e.,

iterations, are possible. First, the structures and data

are available digitally, which means that processes

have already been differentiated, process owners have

been selected and named, and digital process

management is already in place. This means that the

Development and Implementation Phase is therefore

firstly characterized by a basic digitization level.

Within departments process management systems

can be used, but there are no service-oriented or

cloud-based approaches for cross-department tasks

within transfer activities.

The next step, but still in this third phase, is

process automation, which involves, for example, the

rollout of automated workflow. When moving on to a

process automation approach cross-department

digitization can occur, when process are implemented

in e.g. a research information system.

The last phase is the Sustaining & Systematic

Change Phase, which contains new business models.

In this case, this means that new areas and ideas can

be addressed and dealt with. A digital transfer process

has therefore been achieved and a new business

model or a new strategy can be implemented. Phase

two, three and four are underpinned by a foundation,

it is the Research Information System, starting partly

integrated in the Enabling Phase.

The roof is filled with different gears implying

different initiation ways of the digital transformation.

Depending on the current circumstances, the gears

Process Model for Digital Transformation of University Knowledge Transfer

155

rotate around the Organizational Culture gear and

digitization processes are initiated via different

participants – symbolized by the different gears.

The gears act as a unified and closed system.

Some of these areas have an influence on areas

outside the university. At the same time, these areas

also have an influence on the university. In

collaborations with the economy, the areas influence

each other, and the digital transformation can take

place from the university to the economy or vice

versa. Organizational culture is presented as the

largest gear in the middle and connects all others. In

this aspect, culture refers exclusively to the culture

within the organization and not to country-specific

cultures. It is a key position that can be both, an

obstacle, and an enabler of digital transformation.

However, the organizational culture gear is always at

the center, and the others are moving around, which

determines the translation and thus, the power

transmission of the changes. If the organizational

culture is very entrepreneurial and focused on quickly

changing processes, it acts as an accelerator of

change. If the internal culture is focused on

maintaining the status quo, it can act as an inhibitor.

The whole process model embodies the idea on

continuous learning and adapting. Therefore, not only

the steps backward are included, but also the whole

digital transformation process maintains the idea of a

PDCA-cycle (Figure

2). PDCA stands for Plan-Do-

Check-Act and illustrates that the process of digital

transformation will never finalize. Not only technical

solution and processes will always develop over time

and need to be evaluated over again, but also new

ideas or even business models can change the whole

previous process steps.

Figure 2: Continuous improvement process of the digital

transformation process.

Figure 3: Process Model for Digital Transformation in Universities (cf. RQ1).

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4 CHALLENGES IN DIGITAL

TRANSFORMATION IN

UNIVERSITIES

The implementation of a process model for digital

transformation in universities faces some limitations

and challenges, which were assessed through the

expert interviews (RQ2). Having presented our

definition of digital transformation and a brief

description of the process model, the experts were

asked to name challenges, which they emphasized

with digital transformation. In the following, the most

commonly named challenges are presented (Figure

4).

4.1 Lack of Long-term Strategy

All the interviewees agree that digital transformation

is a strategic issue for universities. As stated by the

experts, often only short-term digital initiatives, like

the implementation of new software, are conducted

without establishing or communicating a long-term

strategy.

The strategic impetus for this change should come

from the university management and its governance,

which should not only advocate digitization, but also

be an example for it. This is seen as the only way to

promote an openness to change the way of working,

according to all experts.

4.2 Lack of Resources

The lack of resources as a limitation for digital trans-

transformation relates, according to the experts,

mainly to lack of resources in the IT departments of

universities. These departments are often

understaffed and are not able to support a digital

transformation besides their daily business.

4.3 Investment Costs

Investment costs for passing through the process of

digital transformation were not seen as a major

challenge. Process modelling tools or research

information systems are normally totally free of

charge or quite cheap. For example the Camunda

Modeler, where users can model processes in BPMN

2.0 or create decision-tables in DMN, is free of charge

(Camunda, 2019).

4.4 Lack of or Insufficient Know-how

The lack of sufficient know-how within the university

administration was assessed as the other main

challenge of digitalization besides the lack of a long-

term strategy. Employees in the university

administration are often not involved or enabled in

processual, tool or strategic changes, which can result

in a lack of understanding. For example, modelling

tools can be quite hard to understand in first place,

without any explanation. Three experts mentioned

that digitalization needs tools and processes, which

are usable without being an IT-expert.

Therefore, the employees might see problems in

the transformation, because they were not explained

target-group oriented to them.

Figure 4: Challenges in Digital Transformation in Universities (cf. RQ2).

01234567

No Challenges

Availability of suitable solutions

Uncertainty of data security or legal issues

Lack of acceptance of employees

Lack of or insufficient know-how

Investment costs too high

Lack of human/time resources

Lack of long-term strategy

Process Model for Digital Transformation of University Knowledge Transfer

157

4.5 Lack of Acceptance

The lack of acceptance of this change relates also to

the lack of or insufficient know-how of the employees

in university administrations. According to the

experts, the fear of change or the fear of losing the

personal impact are major challenges, which need to

be addressed by a university management.

4.6 Uncertainty of Data Security/Legal

Issues

The experts mentioned, that there are often

reservations with the implementation of new

tools/processes concerning data security or legal

issues concerning cyber security. But all these

reservations can be disproved, as the changes are

conducted internally within a university and can be

solved with legal support.

4.7 Availability of Solutions

The availability of suitable solutions for digitalization

was not considered as a major obstacle by the experts.

One expert even stated that this point is only used as

an “excuse for not changing anything”.

Another expert suggested the usage of internal

expertise by the researchers, as they often have a very

deep understanding of the processes and possibilities

of digitalization. Nevertheless, a lack of networking

possibilities for all university departments, in current

solutions, was assessed as a possible limitation in

digitalization.

5 EVALUATION

The digital transformation of the field of transfer and

the underlying processes at universities primarily

addresses internal university processes and structures,

as well as those of some experts from other fields. In

order to adequately capture this situation, expert

interviews were conducted, on the one hand with

experts within universities and on the other hand with

experts from external, for example from a company,

which conducts transfer projects (e.g. research

projects and thesis from students) on a regular basis

with universities (Figure 1).

5.1 Evaluation Design

The evaluation includes a case study in which the

digitalization of a single process is explained as an

example. The process is the "application of funded

research and development projects". In order to

illustrate this, the various phases of the Process Model

will be stepped through in the context of the

digitization of this university transfer process.

5.2 Case Study

The target of this case study is to demonstrate the

capability of the process model for its application in

the digital transformation of a transfer process in a

university. To this end, the process of the application

of funded research and development projects is

considered exemplarily and theoretically.

This process is initiated through the different

gears (Figure 3). For example, it can be initiate as a

result of the fact that public funding agencies require

a purely digital submission of the application, which

requires a digital application instead of an analog

application, for example in paper form. This gear

(external funding agencies) thus drives the

“Organizational Culture” gear and thus, “Initiation

and Purpose”.

The Elicitation Phase concerns a survey of the

current status in all departments involved and affected

by this process. This includes the identification of all

relevant stakeholders within the university. The

design thinking methodology can be used to capture

all data necessary for this process and form a joint

vision of this transformation process.

In the Enabling Phase all processes (digital and

analogous) are brought together from their isolated

form (from for example research information

systems).

When moving from Enabling to the Development

& Implementation Phase, this process is modelled, for

example with the BPMN2.0 modelling language.

This is a formal modelling language maintained by

the Object Management Group (Object Management

Group, 2010). This modelling language has its

benefits in its syntactic clarity and its prevalence. The

modelling involves all concerned departments, so that

they are integrated as a whole.

In the Development & Implementation Phase, the

interfaces between the departments are implemented

by the research information system. It is also possible

to automate parts of the process, in this case for

example the pre-filling or partially automated filling

in of applications. Keywords can also be marked in

tender texts or parts can be transferred to the

corresponding application.

The last phase describes a final, digital and

automated process. The capacities of employees that

are freed up by automation and digitization can be

KMIS 2021 - 13th International Conference on Knowledge Management and Information Systems

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used for other activities, such as in a deeper support

in the initiation of other transfer projects. This means

that more work can be done with the same utilization

of resources and capacities. This new process can

serve as a starting point for the redesign of other and

similar processes such as the application of non-

public research and development projects.

6 CONCLUSION AND

OUTLOOK

Digitalization and the provision of digital processes

and platforms have been seen as an enabler for

knowledge transfer, as they offer innovative

possibilities for collaboration and further

development of organizations (Hossain and Lassen,

2017).

In this article, the two research questions RQ1 and

RQ2 have been answered. The first question dealt

with the design of a structured process model to

display the reality of the digitalization of university

transfer processes adequately. The target of research

was to design an adaptive process model, which

displays possible ways and methods of a digital

transformation within a university. When using the

process model, the users may customize the model

and only use the processes or methods, which are

relevant to them. With its iterative approach the

model supports the concept of a learning organization

and allows for setback to continuously improve itself.

Also, the various possibilities to initiate a

digitalization of university transfer processes are

displayed through different views and gears in the

process model (cf. RQ2).

To evaluate if the process models is suitable for

the digital transformation in universities, in-depth

expert interviews were conducted. It was found that

multiple challenges and limitations exits in the digital

transformation in universities (cf. RQ2). The experts

assigned the “Lack of long-term strategy” and “Lack

of or insufficient know-how” as the most challenging

aspects.

The use of automatized or even a whole digital

transformation has multiple benefits for universities,

e.g. a faster and enhanced availability of internal

services or an improved external image of universities

(Doering and Timinger, 2020).

The individual components of the process model

will be further evaluated in an international

perspective, as the current solution was only

evaluated within the scope of German universities.

Furthermore, aspects from other types of higher

educational institutions need to be taken into account

to evaluate whether the model is also suitable for all

types of universities, for example for private

universities, vocational academies or teacher training

colleges.

ACKNOWLEDGEMENTS

The transfer project "Transfer and Innovation East-

Bavaria" is funded by the "Innovative University of

Applied Sciences" East-Bavaria 2018 – 2022

(03IHS078D).

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