Factors Influencing the Usage Behavior of

Digitalized Innovation Environments in Companies:

A Qualitative in-Depth Analysis

Lukas Hellwig

1,2 a

, Jan M. Pawlowski

1,2 b

and Michael Schäfer

Institute of Information Systems, University of Jyväskylä, Jyväskylä, Finland

Institute of Computer Science, Ruhr West University of Applied Sciences, Bottrop, Germany

Keywords: Digitalized Innovation Environment, Knowledge Transfer, Digital Innovation, Open Innovation, Digital

Transformation, Innovation Capacity, Acceptance Model.

Abstract: Digital Innovation Environments (DIE) as an umbrella term for facilities such as FabLabs, Makerspaces and

Innovation Laboratories are already well known in the private and academic sectors. We focus on exploring

the business aspects of DIEs and their role in digital transformation and creation of new opportunities for

companies to increase their knowledge transfer and innovation capabilities. This research is dedicated to

factors influencing the usage behavior of company employees of a DIE. From seven guided interviews, a total

of 27 influencing factors in seven topics were identified through successive in-depth analysis and criterion-

guided interpretation. These factors show the complexity of DIEs and at the same time lay the foundation for

further research. In addition, they are a valuable insight for practice, as they can be used as a basis for

developing new integration and cooperation structures.

1 INTRODUCTION

Digital transformation is an irreversible process that

has now infiltrated all areas of our lives and is

challenging existing structures and processes (Vial,

2019). This affects both private and business

perspectives, so it is not surprising that this

phenomenon is a much-discussed field both in

scientific research (Bharadwaj et al., 2013; Piccinini

et al., 2015) and in practice (Fitzgerald et al., 2014;

Westerman et al., 2011). Since digital transformation

affects all areas of life and has many facets, it is also

the subject of research in a variety of research

disciplines and is viewed from a wide range of

perspectives.

In this paper, we focus on the business perspective

and explore the basis on which digital transformation

and DIEs create new opportunities for companies to

increase their knowledge transfer and innovation

capabilities. The nature of innovation has changed

fundamentally over the past decades. From the former

https://orcid.org/0000-0002-7287-7923

https://orcid.org/0000-0002-7711-1169

https://orcid.org/0000-0003-3521-0975

Schumpeterian model of a single inventor who has an

idea and commercializes it (Schumpeter, 1943),

innovation has become a complex process involving

a variety of different actors (Hippel, 2007; Tidd &

Bessant, 2016). Thus, two different developments can

be observed: On the one hand, innovation processes

are increasingly opening up and integrating external

actors to get new stimuli, which leads to

interdisciplinary innovation teams. This development

is a well-known innovation approach under the term

of Open Innovation (Chesbrough, 2003) and is a

permanent object of the research landscape. Another

striking development is Digital Innovation, which

supports innovations through the use of digital

technologies and methods or leads to digital products

(Iansiti & Lakhani, 2014; Nambisan et al., 2017). Due

to their scalability, these digital products and services

enable enormous growth potential, so that many of

the world's most valuable companies are based on

these digital innovations (e.g. Amazon, Apple,

Microsoft, Alphabet, Alibaba and Facebook (Kantar

Millward Brown, 2020)). Comparatively low

Hellwig, L., Pawlowski, J. and Schäfer, M.

Factors Inﬂuencing the Usage Behavior of Digitalized Innovation Environments in Companies: A Qualitative in-Depth Analysis.

DOI: 10.5220/0010650000003064

In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 3: KMIS, pages 90-101

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

investment costs also enable smaller companies to act

as innovation drivers. Thus, potentially disruptive

innovations are no longer reserved for large and

established companies but can also be realized by

flexible start-ups or small enterprises. The pressure to

innovate has grown steadily due to the new

developments and companies are in a constant

competition for innovation leadership. Faster

iteration cycles are required to keep pace with

progress, otherwise there is a risk of missing

important market trends. The basis of these two

innovation approaches is an efficient transfer of

knowledge between all the players involved.

Based on the mechanisms of open innovation and

digital innovation, innovation environments have

developed in recent years in the private and university

context, which are characterized by their

interdisciplinary users and the use of digital

technologies to implement ideas (Cutcher-

Gershenfeld et al., 2018). There are various names for

comparable facilities such as Makerspaces, FabLab,

Coworkingspace, living Labs, innovation hubs and

innovations laboratory (Capdevila, 2013). In this

paper, we will summarize these facilities under the

umbrella term of Digitalized Innovation

Environments (DIE), as they are all characterized by

the use of digital technologies and methods to support

innovation while providing open access to a wide

range of players (Capdevila, 2018). It is this

combination that leads to faster iteration cycles

through the faster realization of initial prototypes

through the use of digital manufacturing technologies

such as 3D printing and CNC milling (Wolf et al.,

2014). The interdisciplinary exchange between users

on a non-hierarchical ground also supports creativity

in the solution approaches. In the private context,

these environments are used by hobbyists and do-it-

yourself users, the so-called makers, to realize

personal ideas (Dougherty, 2012; Hartmann et al.,

2016). In the academic context, these environments

are often used for hands-on teaching of digital

competencies using concrete examples of

implementation (Konopek et al., 2018).

In the meantime, companies have also become

aware of the potential of such digitalized innovation

environments and are trying to integrate them into

their own innovation processes (Zakoth & Mauroner,

2020). Large companies are taking the path of setting

up their own innovation environments, but these are

usually reserved for the research and development

department, so that they cannot benefit from the

interdisciplinary knowledge transfer and the

innovation potential of their other employees (Lo,

2014). Cooperation between such DIEs and

companies has been underrepresented in the research

landscape to date, although initial studies at the meta-

level have shown promising approaches. The already

hypothetically identified potentials, which result from

a cooperation, could not be retrieved in a structured

way so far (Ruberto, 2015b; Suire, 2016). It has not

yet been possible to unlock the potential known from

the private and academic context and make it fully

usable for companies, although these approaches are

seen as promising drivers of innovation capacity

(Bergner, 2017). This is partly due to the very small

number of companies that have tested

interdepartmental cooperation, so that the empirical

material is very limited.

Against this background, this paper first

empirically examines the basis on which such

cooperation can operate and which factors influence

the integration of DIEs. In our paper, we thus start

from the basic assumption that extended use of DIEs

in companies is a prerequisite for exploiting the

innovation potentials that are already known from

other contexts. This results in the following research

question:

Which factors influence employees´ usage behavior

of Digitized Innovation Environments in companies?

This explorative approach attempts to form an

empirical basis for subsequent research regarding the

integration of such DIEs in companies by identifying

the underlying conditions. The so far only sporadic

use of the potentials known in other contexts suggests

that obstacles and barriers arise here, which must first

be overcome. At this point, the study makes a far-

reaching contribution to the Information Systems (IS)

research landscape by empirically identifying

relevant factors moderating the usage behavior of a

DIE in a company. These results are useful for further

research as a starting point for the development of

suitable cooperation models, as well as for

practitioners for a more targeted integration of DIEs

into their innovation processes. The findings are to be

used as a basis for future theories that have the

potential to take into account the “New Logics of

Theorizing About Digitization of Innovation” by

(Nambisan et al., 2017, p. 227). DIEs, with their

complex interplay of diverse actors and extensive

digital technologies, address all four logics indexed

by Nambisan et al. (2017) and can provide a platform

on which the postulated research questions can be

addressed.

Factors Inﬂuencing the Usage Behavior of Digitalized Innovation Environments in Companies: A Qualitative in-Depth Analysis

2 DIGITALIZED INNOVATION

ENVIRONMENTS

Environments in which targeted innovations are to be

supported and which offer a platform for sharing

knowledge and equipment are already known

instruments, especially in the private and academic

context (Boutillier et al., 2020; Suire, 2016).

However, the first comparable approaches can also be

observed in the commercial sector in the form of

coworking spaces and LivingLabs (Capdevila, 2014).

LivingLabs and their underlying innovation methods

have already been identified as element of user

innovation, which is characterized by its real-life

environment and the user as co-creator (Almirall et

al., 2012). Particularly in the private and academic

contexts, a new way of innovating has developed in

recent years through the integration of a wide variety

of digital components and tools. For example, 3D

printers and CNC milling machines paired with in-

house electronics development enable the short-term

implementation of functional prototypes without

comprehensive craftsmanship (Gershenfeld, 2012).

This allows new user groups to participate in the

innovation process and to be involved by means of

digital communication channels. Based on the new

opportunities made possible by these technologies, a

wide range of different innovation environments have

developed under various names. Fablabs,

Makerspaces, InnovationLabs, Hackerspace or

Cocreation Laboratory are some examples. These

environments have emerged in different contexts and

differ in individual areas with regard to their focus,

orientation and user groups. However, differentiation

by naming is not possible because there is no uniform

understanding of the terms. There is only a Fab

Charter that defines some very generic requirements

of a FabLab (The Fab Charter, 2015). Initial

approaches to differentiate the individual approaches

from one another have produced only insufficient

selectivity and have excluded the digital aspect (cf.

Aryan et al., 2020; Capdevila, 2017). Each innovation

environment is adapted to its specific context and

makes use of a wide variety of elements from the

different streams. In order to respect this richness of

facets, the term "Digitized Innovation Environment"

will be introduced as an umbrella term for these

facilities. These environments are physical spaces

that use a variety of different digital technologies to

support innovation, but are not themselves digital.

They are therefore Digitalized Innovation

Environments rather than digital/digitized innovation

environments such as virtual-reality environments

would be, which convert analog material into a digital

format. We use the following definition of

"Digitalized Innovation Environments":

Digitalized Innovation Environments are physical

spaces that provide both traditional and digital tools

and state of the art technologies to support

collaborative and interdisciplinary innovation and

knowledge transfer.

Some companies have already recognized the

potential of such DIEs and have made various efforts

to integrate them into their innovation processes

(Zakoth & Mauroner, 2020). However, collaboration

has so far mostly been limited to supporting the

research and development departments (Ruberto,

2015a). Some large concerns have gone the way of

making their own DIEs available to their

development teams, but even these have so far mostly

been reserved for a very limited user group and

therefore neglect to exploit the potentials of

interdisciplinary exchange known from the private

and academic context (Lo, 2014). Based on these

findings, potentials and functions that a DIE could

assume within a company has already been identified

(Hellwig et al.), but there is still a lack of knowledge

on how to implement them. As a result, the empirical

data on full-scale collaboration between DIEs and

companies is very limited, which means that only

very generic findings have been obtained so far.

There is a precise concept of use, instruction,

communication, and networking necessary (Bergner,

2017). Before such a concept can be developed, it is

first necessary to define the framework conditions

that influence the use of a DIE offering. Only if the

usability is ensured empirical data on the impact on

the innovation capacity of companies can be

collected. In the next step, this can lead to inductive

theory building, which is a far-reaching contribution

to the IS research landscape as well as to practice.

So far, there are only superficial findings

regarding the factors that influence the involvement

of DIEs in the innovation processes of companies.

This is because cooperation across all departments of

a company with a DIE has so far been a rare approach

to increasing knowledge transfer and innovation

capacity. Thus, the potential cases which allowed an

empirical investigation are very limited.

3 METHOD

Due to the limited prior knowledge in this research

area, an explorative approach was chosen to build a

data base for further investigations. For this purpose,

a qualitative in-depth analysis of an exemplary case

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

was to be conducted and findings were to be formed

by means of inductive theory building (Gregor,

2006). In the context of information systems research,

a case study is an inquiry into single or multiple

instances of observable complex phenomena with the

aim of identifying discrete units of analysis (Turnbull

et al., 2021). In our study, we explore a range of

influencing factors that impact DIE usage. As a

methodological approach, expert interviews (Kaiser,

2014) were conducted with employees from various

departments of a company which, at the time of the

survey, had its own FabLab on its own premises for a

year and which was freely accessible to all

employees. The guidelines for the interviews are

based on established theories and models from

comparable application areas, which were identified

through a literature search (Webster & Watson,

2002). The employees are in a position to report on

their own experiences and, due to their various

departmental affiliations, reflect a wide range of

perspectives and motives for use. In the following

evaluation and data analysis, we structured the

answers, identified influence factors and derived

initial recommendations for practice (Mayring,

2002). The detailed methodological steps are

explained in detail below.

3.1 Case Description

For the case study, we aimed at a company with broad

experiences to capture the broadest possible range of

experiences. For this purpose, we recruited a

company with a long-term cooperation of a company

with a DIE, which was made accessible to all

employees. This is the only way to ensure that the

interviewees can base their statements on experience

and that the greatest possible variance in perspectives

can be taken into account. Since this type of

cooperation is extremely rare, the search for suitable

research cases was limited. It was possible to recruit

a company in Germany for the study, which had been

operating its own FabLab for a year at the time of the

survey. This FabLab meets the definition of a

Digitalized Innovation Environment and houses a

variety of different digital tools and technologies. The

company is part of an international group, but forms

an autonomous unit at the location under

consideration with all the usual organizational units

for medium-sized companies. It is a manufacturing

mechanical engineering company with around 350

employees at the location of the DIE. The employees

are divided equally between the administration,

production and research and development

departments. All employees were free to access and

use the possibilities within the FabLab. At the

beginning, all employees were offered information

events and workshops on how to use individual

technologies. Over time, various concepts were tested

to simplify the integration of the FabLab into daily

business. Finally, one part of the development

department was permanently located in the FabLab

and serves as a contact person for other employees.

This must be taken into account in the upcoming data

evaluation.

In order to identify the widest possible range of

factors influencing usage behavior, employees were

also recruited at different hierarchical levels and from

different departments for an expert interview. A total

of seven stakeholders were identified, all of whom

had gained experience in the DIE but used its

opportunities to varying degrees. Two employees

each from Research & Development (R), Production

(P) and Marketing (M) as well as one employee from

Administration (A) were interviewed. Thus, the

sample represents a heterogeneous cross-section of a

medium-sized manufacturing company.

3.2 Expert Interviews

To ensure a balance between unbiased expression of

opinion and a minimum of structure, the expert

interviews were organized with the help of

predetermined interview guidelines. The employees

were asked to comment on pre-identified dimensions

relating to their usage behavior and were also given

the opportunity to comment on entirely new aspects

(Döring & Bortz, 2016). The interview could be

divided into three parts. In the first part, the

demographic information of the interviewees was

asked to be able to evaluate the subsequent

statements. Then questions regarding usage,

motivation, expectation, and cooperation were asked

to be able to identify first indirect influencing factors

and to gain a broader understanding of the

interviewees' context. he questions were derived from

the dimensions of customer orientation (CO)

(Handlbauer & Renzl, 2009), as this model has

already proven itself in comparable research

approaches and the employees have a customer

relationship with the DIE. In the third phase,

questions were also asked about specific factors

influencing usage behavior. To ensure a basic

structure, the dimensions to be considered were

derived from adjacent theoretical models. The

technology acceptance model (TAM3) (Venkatesh &

Bala, 2008) and the Innovation Diffusion Theory

(IDT) of Rogers (1983) were used as underlying

theories, as these are both established models and

Factors Inﬂuencing the Usage Behavior of Digitalized Innovation Environments in Companies: A Qualitative in-Depth Analysis

Table 1: Dimensions and Guiding Questions within guided Expert Interviews.

No Dimension (Reference) Guiding Question

1 Demographic context

2 Utilization (CO) How have you used the FabLab so far?

3 Motivation (CO) What incentives are there for FabLab use?

4 Barriers (CO) What problems are associated with the use of the FabLab?

5 Expectations (IDT & TAM3) What future possibilities of use do you see in the FabLab?

Competencies

(IDT & TAM3)

To what extent do your own skills and knowledge support you in implementing your own

ideas in the FabLab?

Organization

(IDT & TAM3)

What influence do the organizational structures in the FabLab and at the company level have

on your usage behavior?

Social Aspects

(IDT & TAM3)

To what extent do social aspects influence your decision to use the FabLab?

9 Marketing (IDT & TAM3) How is the public presentation of the FabLab?

10 Feedback (IDT & TAM3) Do you receive feedback on your projects in the FabLab?

cover a wide range of potential influencing factors.

Since DIEs represent a complex structure that

attempts to integrate a variety of technologies into an

existing system, but at the same time can be

interpreted as an innovation itself, both the TAM3

and the IDT were used as a basis. Thus, the

complexity of DIEs should be considered and as

many perspectives as possible should be considered.

The influencing factors listed in the reference models

were generalized in such a way that they allowed

statements to be made regarding FabLab use, while at

the same time leaving room for supplementary

factors. This resulted in the ten dimensions in table 1

that were addressed in the expert interviews.

In addition to the guiding questions, sub-questions

were prepared for each dimension, but these were

optional depending on the progress of the interview.

As guiding questions were formulated to avoid

suggestion, and interviewees were directed only to

broad topics. The sequence of the questions was also

not predetermined. The interviewees were asked to

express themselves as freely as possible and were

encouraged during the interview to explain points that

were not explicitly asked for. An initial pretest with

two volunteers confirmed that the formulated guiding

questions were easy to understand without

prescribing answers to the interviewees. The actual

interviews were conducted in the company itself in an

appropriately relaxed atmosphere, alone with the

interviewer. This ensured that possible critical factors

were also openly communicated. The interviews

lasted between 40 and 65 minutes and were recorded.

3.3 Data Evaluation

Our explorative research approach aims at identifying

factors that influence the use of the company's

internal FabLab by a wide range of employees, a

qualitative data analysis approach was chosen. This is

suitable for explorative research approaches when a

database is to be created first (Döring & Bortz, 2016).

Therefore selective protocols were used. When

identifying influencing factors, the wording is of

secondary importance, which is why selective

protocolling according to Mayring (2002) provides

sufficiently precise results. Parallel to the creation of

the protocols, a category system is to be derived with

the help of the object-related theory formation (cf.

grounded theory (Glaser, 1978)), which permits an

allocation of the statements to differentiated aspects

(Urquhart, 2013). This methodology, which is known

from sociology, enables the formation of categories

already during the data collection or the rehearing of

the recordings by summarizing statements on

superordinate topics. The categorization could be

made inductively, which was further developed in an

iterative process during the recording of the various

interviews. This methodology already falls in part

into the data evaluation and is particularly suitable for

explorative studies (Mayring, 2002). In a further step,

the statements of the interviews were then assigned to

the developed categories. Finally, the statements

within the identified categories were generalized into

precise statements with the help of a qualitative

content analysis (Mayring, 2002). In this way, it was

possible to successively reduce the complex

statements of the interviewees to individual

influencing factors together with their impact on

usage behavior. The evaluation is done in four

analysis steps: paraphrasing, generalization to a

defined abstraction level, first reduction and second

reduction (Mayring, 2002). The results of the data

analysis are presented below.

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

Table 2: Identified Categories of Influence Factors of DIE Usage of Employees.

No Category by objec

-related theory building Interviewees

1 Personal motivation M1, M2, P1, P2, R1, R2, A

2 Relevance for daily business M1, M2, P1, P2, R1, R2, A

3 Integration into the daily business M1, M2, P2, R1, A

4 Previous competencies M1, M2, P1, P2, R1, R2, A

5 Workshops M1, M2, P1, R1, R2, A

6 Trainings according to needs M1, M2, P1, P2, R2, A

7 Software M1, M2, P2, R2, A

8 Guided projects M1, M2, P1, R1

9 Challenges M1, M2, P1, P2, R1

10 Equipmen

M1, M2, P1, R1, R2

11 Usage solicitation M1, M2, P1, P2, R1, A

12 Acceptance of the manage

M1, M2, P1, P2, R1,

13 Communication with other users M1, M2, P1, P2, R1, R2, A

14 Reputation of the FabLab M1, M2, R1, R2, A

15 Presentation of project results M1, M2, P1, P2, R1, R2, A

16 External presentation M1, M2, P1, P2, R1, R2, A

17 Improvement recommendation system M1, P1, P2, A

18 Availability of contact persons M1, M2, P1, P2, R1, R2, A

19 Time flexibility M1, M2, P1, P2, R1, R2, A

20 Requirements of use M2, P1, R1, R2, A

21 DIE Premises M2, R2

22 Personality of contact persons M1, M2, R1, A

23 Restructuring P1, R1

24 Permanent staff in the Fablab M1, P1, P2, R1, R2, A

4 FINDINGS

Through the iterative process of object-related theory

building (Glaser & Strauss, 1979), a total of 24

categories could be identified from the seven

interviews. Some of the categories can be assigned to

the dimensions previously derived through

theoretical considerations, which formed the basis of

the guiding questions of the interview, but some also

go beyond these. Thus, the method of guided

interviews in combination with the inductive category

building is confirmed as appropriate for such an

explorative study. In table 2, the 24 categories will be

presented, and an assignment of the interviewees will

be made.

The identified categories show the multifaceted

nature of the perspectives of the influencing factors.

Some, like "Personality of contact persons," can be

clearly assigned to a previously assumed dimension

(social aspects). Other categories such as "relevance

for daily business", on the other hand, cannot be

clearly assigned and go beyond existing theories in

terms of both content and level of detail. It can

therefore be deduced from the categories identified

that DIEs should not be viewed solely as

technologies, but rather represent significantly more

complex systems with more multi-faceted factors

influencing usage behavior. Single categories such as

"Permanent staff in the FabLab" appear to be a

company-specific category, since at the time of the

interviews the company had some employees from

the development department permanently located in

the FabLab. These company-specific categories,

which cannot provide generalizable factors, were not

considered further for the analysis.

In the next step, the interviewees' propositions

were extracted from the individual interview

protocols and assigned to the categories. Here, the

statements were successively brought to a uniform

level of abstraction with the help of qualitative

content analysis in the steps of paraphrasing,

generalization and reduction in order to identify the

final influencing factors (Mayring, 2002). In doing

so, the interrelationships as well as the impact

(positive or negative factor) had to be considered. In

the following, the 27 identified influencing factors

will be presented and explained. Individual factors

were inverted to present a uniformly positive

influence. Table 3 is already reasonably sorted for a

further generalization loop.

Some of the 27 identified impact factors derive

directly from the previously elaborated categories,

while others only revealed themselves through the

content analysis. In addition to the factors, the in-

depth analysis also made it possible to identify their

effects. For example, the equipment within the DIE

was identified as an influencing factor. However,

without in-depth analysis and description, it would

not be possible to assess how this factor affects usage

Factors Inﬂuencing the Usage Behavior of Digitalized Innovation Environments in Companies: A Qualitative in-Depth Analysis

Table 3: Identified Influence Factors and Description.

No Influence Factor Description

1 Self-Realization Employees can contribute their own ideas and implement them.

2 Fun Employees experience fun in using the DIE.

3 User Competencies Employees have basic skills in handling the equipment.

4 Communication Exchange among employees from different departments and areas of expertise is possible.

5 Promotion Employees are aware of the benefits of using the DIE.

6 Encouragement Employees are regularly encouraged to use the DIE.

7 Image The DIE has a consistent, professional, and positive image among its employees.

8 Outcome Presentation The results and added values are communicated consistently and positively.

9 Workshop There is a changing offer of workshops.

10 Training Individual training on skills acquisition is provided.

11 Projects Guided projects are offered.

12 Competitions Competitions with a business orientation are offered within the DIE.

13 Relevance The work in the DIE is related to the daily business of the employees.

14 Backup Supervisors support employees in using the DIE.

15 Hierarchies There are no hierarchies within the DIE.

16 Terms of use Employees can use the DIE independently of other players

17 Bureaucratic The bureaucratic hurdles for DIE access are low.

18 Access There are uniform regulations on the time of access of the DIE.

19 Structural Inclusion The DIE is part of the company innovation process (e.g. the improvement proposal system).

20 Concept There is a uniform and transparent concept of the DIE.

21 Contact person Personality The contact person within the DIE is helpful, independent, and friendly.

22 Contact Person Availability A contact person is available on a flexible basis.

23 Contact Person Competency

The contact person has all the necessary competencies to support the implementation of

ideas.

24 Equipment The available equipment enables the realization of products with a company connection.

25 Appearance The DIE has a welcoming and visible appearance.

26 Premises The premises are inviting and friendly in a central location.

27 Software The required software is intuitive and user-friendly.

behavior. For example, the equipment requirements

that have a positive effect on usage are described in

more detail in Table 3. All interviewees agreed on the

effects. No factor was evaluated in a contradictory

way. The large number of influencing factors

discussed once again highlights the complexity of the

cooperation system. It also becomes clear that a

digitized innovation environment cannot be

understood purely as a technology, since the

influence factors go far beyond those from familiar

models such as TAM3 (Venkatesh & Bala, 2008) or

IDT (Rogers, 1983). In order to develop a

manageable model despite the large number of

factors, thematically related influence factors were

combined into umbrella terms. Some factors are not

completely clear-cut and contain aspects of several

topics. In this case, the main focus of the factor was

taken as the reference. The factors can be summarized

into the following seven influence categories: user

(influence factor 1-3), presentation (4-8), offer (9-12),

perception (13-15), structure (16-20), contact person

(21-23), and environment (24-27). This results in the

model of factors influencing the use of DIEs by

employees shown in figure 1.

The user himself could be identified as a factor

and thus an influencing factor which can only be

influenced to a very limited extent. Thus, it is difficult

to make changes to the sense of fun, the already given

competencies or the striving for self-realization.

These factors can presumably be favored by other

factors such as an offer that is attractive to the specific

employee, but they cannot be addressed directly by

changing the form of cooperation.

The contact person was cited by all the interviews

as a further exceptional influencing factor. This is

special in the sense that in conventional DIEs in the

private or academic context such a person rarely

exists. In universities, there may well be some kind of

workshop manager, but he or she is assigned other

functions than those mentioned by the interviewees as

being conducive to user behavior. In the private

context, the dissolution of all hierarchies means that

this position does not usually exist at all.

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

Figure 1: Digitalized Innovation Environment Use Model.

From the interviews with the interviewees from

various departments, finally 27 influencing factors

were derived in seven influence topics. These are not

to be understood as stand-alone linear influences, but

also influence each other. These correlations are to be

investigated in further studies but were not the subject

of this first explorative study. Going further, these

factors initially form the basis for enabling the use of

the innovation and knowledge transfer capacities of

DIEs. Utilization alone will probably not necessarily

increase innovative and knowledge transfer capacity

and is likely to depend on other factors. However,

utilization is a fundamental barrier to entry, without

which the potentials known from the private and

academic context cannot be accessed. The indicated

connection between utilization and the actual

function as an innovation driver in figure 1 must be

investigated further in future research.

During the in-depth analysis of the interviews,

several qualitative and interpretative analysis steps

were necessary in order to identify the final

influencing factors (Mayring, 2018). In order to

exclude misinterpretations and to ensure the

robustness of the results, the study was measured

against the established quality criteria according to:

Truth Value, Applicability, Consistency, Neutrality

(Lincoln & Guba, 2007). Truth Value and

Applicability are given by the detailed description of

the research procedure as well as a detailed

description of the underlying context. Consistency

could be achieved by the close link to established

models and theories and their description.

Furthermore, neutrality is ensured by the research

design and the successive criterion-guided

interpretation. Thus, the most important criteria of

credibility of research approaches are met (Schou et

al., 2012). In addition, the results were validated

communicatively in a final iteration with the

interviewees in order to exclude misinterpretations.

No errors or inaccuracies were found during the

communicative validation.

In principle, the identified factors appear to

represent a realistic picture. Comparable factors to the

established acceptance models are found, but further

factors are also identified. Since a digitalized

innovation environment is not just a technology, but

a complex system, the additional factors are

conclusive. The complexity of the influencing factors

also reflects the different perspectives of the

interviewees. For example, it was to be expected that

an employee from the development department

would focus more on technological factors such as

"software” and "equipment", whereas employees

with less technical affinity would focus more on

factors relating to communication and presentation.

The logical validation also confirms the significance

of the identified factors.

5 DISCUSSION

The factors identified as influencing the usage

behavior of employees of a DIE show a very wide

range. It includes aspects of the Innovation Diffusion

Theory (IDT) (Rogers, 1983), Technology

Acceptance Model 3 (TAM3) (Venkatesh & Bala,

2008) and Customer Orientation (CO) (Handlbauer &

Renzl, 2009) identified as potentially relevant in the

preliminary work, but also goes beyond them. This

confirms the assumption that the introduction of a

DIE into a company cannot be understood purely as

innovation diffusion or as a new technology, but

rather represents a multi-layered challenge. The

proximity to the theories and models selected as a

basis confirms on the one hand the selection of the

scientific foundations, but on the other hand also

shows the complexity of the process of integrating a

DIE into a company. Individual aspects such as

"Relevance" (TAM3: "Job Relevance"), "Outcome

Presentation" (TAM3: "Result Demonstrability),

"Backup" (IDT: "Extent of Change Agents'

Promotion Efforts") or "Promotion" (IDT:

"Communication Channels") can already be found

similarly in the underlying theories and models. At

this point, the findings of the study emphasize the

multi-faceted nature of DIEs. They are a complex

system whose use is influenced by factors known

from technologies (TAM3), innovations (IDT) and

customers (CO).

At other points, the in-depth analysis has led to a

specification of the generic factors from the

established models and theories for the specific use

case of DIEs. For example, the factor "Ease of Use"

(TAM3) or "Complexity" (IDT) could be

differentiated in more detail for the context of DIEs

and individual subordinate dimensions could be

identified: e.g. "Terms of use", "Bureaucratic",

"Access" and "Software". This precision provides

Factors Inﬂuencing the Usage Behavior of Digitalized Innovation Environments in Companies: A Qualitative in-Depth Analysis

important insights, especially for further research, but

can also be a first contribution to practice.

Finally, influencing factors could be identified,

which were not considered in previous models and

theories and thus consider the special system of DIEs.

Thus, a special focus was placed on the contact

person and their characteristics. This is interesting

because in privately and academically organized

DIEs there is no contact person in the sense that is

demanded in the corporate context. At this point,

there seems to be a stronger emphasis on results and

outcomes in companies and the try and error

mentality from the maker community is not yet

established (Bergner, 2017) These additionally

identified influencing factors contribute to the

understanding of DIEs and their underlying

structures. In addition, they represent a new

perspective from which to investigate the

phenomenon of DIEs in further research.

In terms of content, the factors range from those

that are easy to influence, such as appropriate

equipment and a welcoming atmosphere, to those that

are much more complex, such as the personality of

the contact person. Here, the various interviewees

also seem to set different priorities depending on their

individual backgrounds. Nevertheless, many factors

were addressed by several department

representatives. In general, there was agreement on

the respective impact of factors among all

interviewees. The different focus could come from

the different usage perspectives of the employees, but

this cannot be validly confirmed on the evidence base.

For example, it seems conclusive that an employee

from the development department is more interested

in the implementation of prototypes and is therefore

focused on the equipment, while an employee from

the marketing department, would rather use the DIE

to acquire competencies and is therefore more

interested in an interesting workshop offer.

It can also be deduced from the identified

influencing factors that employees have a certain

understanding of service from a DIE. A competent

and flexibly available contact person within the DIE

is a central aspect which is considered important by

all department representatives. In a way, this

contradicts the original concept of a DIE, which

benefits from its intrinsically motivated users and

their exchange with each other. In the private and

academic context, such permanently available contact

persons are not envisaged outside of specific event

formats. Rather, the approach relies on the formation

of a community in the DIE that develops its own

momentum and can therefore operate without an

internal structure and hierarchy. Here, the identified

influencing factors are an indication that employees

have a different interpretation of a DIE than private

or academic actors. In the analyzed company, this

may be due to the fact that in the year in which the

DIE was available, different strategies for integrating

it into daily business took place and, in particular,

there was no uniform concept at the beginning.

Basically, the multitude of different influence

topics also illustrates the complexity of the task of

integrating a DIE into an existing company and

supporting cooperation. It becomes clear, for

example, that a room with many digital technologies

without restrictions on use is not sufficient for actual

employee use. The influencing factors go far beyond

those known from other acceptance models and form

a much more complex web. For example,

organizational and social factors also play a decisive

role. In addition to these quite subjectively

perceivable factors, the user himself was also

identified as an influencing topic. Also factors were

mentioned, on which a company can take influence

only indirectly. For example, whether an employee

enjoys using digital technologies depends heavily on

his or her interests. Here, the company can create a

favorable atmosphere through other identified factors

but will not achieve this with every employee. Rather,

the identified influencing factors form a framework

on the basis of which an individual concept can be

developed for a specific company.

6 LIMITATIONS, OUTLOOK AND

CONTRIBUTION

The present study has a couple of limitations, which

are presented below and from which further research

needs are to be derived. Due to the very specific

condition of deriving the factors from employees'

experiences in actually using a DIE, the selection of

possible study participants is severely limited.

Although a broad spectrum of employees from

different departments could be recruited for

interviews, the results cannot be generalized without

restrictions. It must also be critically reflected that

only one company is considered as a case in the study.

This is due to the fact that such an integration of a

DIE into an existing company is a rare constellation

and the recruited company takes a pioneering role. It

is also important to point out the many interpretative

methodological steps, which, although they were

carried out with the greatest care and orientation to

quality criteria, cannot completely rule out bias.

Finally, it remains to be stated that some of the

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

identified influencing factors suggest that there is a

correlation between them, but this could not be

investigated in more detail in the process of this

research.

These correlations should be addressed in further

research to further expand our understanding. Also, a

quantitative evaluation of the influencing factors

would be a logical next step. Thus, further weighting

of the factors could be undertaken. Building on these

findings, it would then be possible to revise existing

models of cooperation and integration. The

phenomenon of DIEs has already proven to support

open innovation and digital innovation (Zakoth &

Mauroner, 2020). With the help of the new insights

gained it will be possible reach the long perspective

goal of unlocking these opportunities for companies

as well and thus address some key blind spots of

innovation and knowledge management research

(Nambisan et al., 2017).

Thus, the findings of this research make a

valuable contribution to the IS landscape on several

levels. On the one hand, it provides an empirical basis

for further research, and on the other hand, it offers

valuable insights for practice. DIEs, with their digital

technologies and communities, constitute a new

phenomenon in the IS discipline and have been little

studied to the current point. The first step is to create

a basis on which empirical research is possible. At

this point, the influencing factors guide a first

important step towards integrating such DIEs into

corporate contexts. The further research approaches

are manifold and can take different perspectives such

as DIE technologies, DIEs as competence incubators

or DIEs as communication platforms (Hellwig et al.).

Complementary to the scientific contribution,

significant added value can also be provided to

practice. The integration of DIEs in innovation

processes has already been identified as a promising

approach (Bergner, 2017), but has so far failed in

implementation. At this point, the factors influencing

user behavior offer initial insights into the design of

efficient integration or cooperation. Thus, the results

of this study form a valid basis for a variety of further

research approaches to better understand the

phenomenon of DIE and, at the same time, to gain an

important contribution to practice.

7 CONCLUSION

Digitized innovation environments as an umbrella

term for various facilities such as FabLabs,

Makerspaces and InnovationLabs are already known

from the private context and have already established

as competence incubators in the academic

environment with their digital technologies.

However, integration or cooperation with companies

has so far taken place only very partially and to a

limited extent. In order to be able to investigate the

potentials of this new phenomenon for companies as

well, the present work intended to identify factors

influencing the usage behavior of employees of a

company of DIEs. By conducting extensive

guideline-based expert interviews with seven

employees from different departments of a company,

it was possible to identify these through successive

analysis and interpretation. The interview guidelines

were based on established theoretical models such as

TAM3 (Venkatesh & Bala, 2008) and IDT (Rogers,

1983). A total of 27 influencing factors were

identified, which can be assigned to the seven topics:

user, presentation, offer, perception, structure,

contact person, and environment. These influencing

factors go beyond known factors from the underlying

models and show the complexity of the phenomenon

DIE. It also illustrates that DIEs should not be

understood as space with digital technology only, but

that many other aspects also influence this construct.

A critical reflection of the identified factors on usage

behavior suggests that a universal concept for

integrating a DIE into a company is a utopian notion

and that there must be specific employee-dependent

approaches.

The findings provide a basis for further

investigation of this still largely unknown

phenomenon. Thus, further investigation of usage

factors and their correlation with each other is a

purposeful next step. In addition to the contribution

to the scientific IS landscape, the findings offer added

value for practical applications, which is a key claim

of IS research (Nambisan et al., 2017). For example,

the factors can support companies in the development

of suitable concepts for the integration of DIEs, which

in turn provide the basis for further empirical

research. Thus, the present work contributes to both

science and practice and has great potential for

connecting research.

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