Fostering adoption, acceptance and assimilation in ... - KnowMiner

Fostering adoption, acceptance, and assimilation in
knowledge management system design
Maximilian Hecht Ronald Maier Isabella Seeber Gabriela Waldhart
ABSTRACT
Designing information and communication technologies (ICT) for
knowledge work is a primary challenge in research and practice of
knowledge management. Knowledge workers supposedly
organize and manage their workplaces, at least partly themselves,
which needs to be considered when designing ICT for supporting
their daily knowledge-intense activities. It is considered useful for
designers of knowledge management systems (KMS) to look into
the results of behavioral science in information systems
concerning the adoption, acceptance and assimilation of ICT.
Thus, this paper proposes a model that contributes to bridging the
gap between design science and behavioral science in the domain
of knowledge management. In this regard, widely recognized
behavioral models that aim at explaining organizational and
human behavior in conjunction with ICT are analyzed in order to
extract important factors influencing the successful application of
KMS with respect to the adoption by an organization or
organizational unit, acceptance by individual knowledge workers,
and assimilation into knowledge processes and practices. By
combining, categorizing, and structuring these factors, we
developed a comprehensive model to be taken into account in
software design and evaluation processes from various
perspectives. Moreover, we discuss a case example in which this
model is applied to the design of a KMS.
Categories and Subject Descriptors
D.2.1, H.1.1, H1.2, K.6.1
General Terms
Design, Human Factors, Theory
Keywords
Knowledge management systems, behavioral science, design
science, system design, decision-support model, adoption,
acceptance, assimilation
1. INTRODUCTION
Taking knowledge management systems (KMS) from
experimental pilot environments into organizations and from
availability in the organizations to the use by employees and from
University of Innsbruck
School of Management
Information Systems
[First name].[Surname]@uibk.ac.at
there to the full integration and alignment with business and
knowledge processes is a challenge that KMS providers and
customers are struggling with. Hence, competitive pressure
among software providers is high. Also, many organizations
gathered experiences with unsuccessful knowledge management
(KM) projects [1] and became cautious towards adopting new
systems. However, if this barrier is overcome and organization
leaders decide to adopt a system the user’s willingness to actually
use another new KMS might be little, unless the use is specially
fostered [2]. And not only individual users are reluctant when
accepting new tools, but also benefits typically only come
apparent when a critical mass of users accepts a system and
collaborates with its help which requires re-designing work
practices and processes. However, business process management
rather accepts the coexistence of independent systems than putting
additional effort into difficult integration and assimilation [3].
Reasons for these developments are manifold and out of the scope
of this paper. While multiple models have been established for the
different steps in such an adoption, acceptance, and assimilation
process, on the basis of positivistic empirical research, there is,
however, no combined model covering the complete process from
adoption to assimilation that could be used to support and inform
KMS design activities.
Goal of this paper is to combine, categorize, and structure factors,
researched in behavioral models that have been suggested as
having an influence on adoption, acceptance or assimilation to
develop a comprehensive model supporting KMS design and
evaluation processes. The assumed causal relationships in this
comprehensive model, though individually supported, are
certainly difficult to test in their entirety from a behavioral science
perspective, but deemed useful as a process model helping
understand and guiding the process of turning design-based KMS
solutions into organizational successes. Based on a comprehensive
literature review, we created the following understanding
regarding the three main concepts focused in this paper. Adoption
describes an organization’s decision to make use of an
information technology (IT) product [4], e.g., a company or an
organizational unit that is considering or willing to make use of a
KMS. Acceptance focuses on the level of individuals, e.g., an
employee, and relates to the decision of a user to use IT [5]. In
addition to an organization adopting a system, and individuals
accepting the software, it also needs to be diffused into
organizational work processes and corresponding daily activities,
which is denominated as assimilation [3].
Along the KMS design and implementation process, the factors of
our model can be addressed at different stages and in different
tasks. As a second value of the paper, we therefore demonstrate
the application of the introduced comprehensive model in the
requirements elicitation, packaging and offering activities of a

large-scale European project in the domain of technologyenhanced
learning.
The paper starts out with an introduction into the models and
theories (section 2) underlying our model before presenting all
factors influencing adoption, acceptance and assimilation
extracted from the models and theories and forming a new
comprehensive model (section 3). Section 4 outlines the
application of this model in a KMS design process as well as
related approaches found in literature, followed by possible
limitations (section 5) of the model and a conclusion including an
outlook on future research (section 6).
2. EXPLAINING ORGANIZATIONAL AND
HUMAN BEHAVIOR TOWARDS IT
Numerous models and theories aim at explaining organizational
and human behavior in respect to using IT systems. Many of them
are originally rooted in behavioral science, sociology and
psychology [6]. However, many of them have been transferred
into an Information Systems (IS) context and have been applied in
IS studies. In order to get a comprehensive understanding and
overview, we structure the factors into adoption, acceptance and
assimilation and examine models and theories in these three
domains.
2.1 Adoption
In the context of the organizational decision to adopt an IT system
or an IT innovation such as a KMS, the research literature
presents various models and theories. In the following, the models
and theories that are used in this paper are shortly described. For
further information about a single model or theory, please refer to
the given literature.
The Technology-Organization-Environment Framework (TOE)
[7] explains the process by which a firm adopts and implements
technological innovations, influenced by the technological
context, the organizational context, and the environmental
context. TOE has been widely applied in IS research, e.g., to
explain the adoption of open systems [8] or to investigate the
adoption of electronic businesses by European firms [9].
The Diffusion of Innovations (DOI) theory, based on sociological
research [4], has been used since the 1960s to study several
innovations regarding adoption and acceptance by both,
individuals and organizations [7]. There have been various
applications of DOI to evaluate adoption on the organizational
level, e.g., in the context of IS process innovations [10] or in the
context of financial EDI [11].
The IS Success Model (ISSM) [12] does not explicitly focus on
organizational adoption, but rather presents a framework for
conceptualizing and operationalizing IS success. However,
organizations preparing for a decision about adopting a
technology will consider whether and to what extent the
introduction of the technology will result in a positive impact on
the organization and thus, some of the factors found influential for
IS success within a company are expected to directly relate to the
adoption of an IS.
Finally, the Fit-Viability Model (FVM) [13] is based on the theory
of task-technology-fit [14] enhanced with organizational viability.
In a first application, it was used to examine the adoption of
mobile technology in businesses [13].
2.2 Acceptance
Various models and theories exist that seek to explain a user’s
decision to make use of an ICT system. Those that are deemed
relevant for acceptance are listed in the following.
The Theory of Reasoned Action (TRA) [15], was drawn from
social psychology and is one of the most fundamental and
influential theories of human behavior in IS research. TRA
theorizes that the behavior of an individual can be predicted based
on the person’s behavioral intention. In other words, if a person
intends to behave in a certain way then he or she is likely to do so,
based on one’s attitude and the influence of other people’s
opinions (subjective norm). TRA was applied to explain the
acceptance of technology by users and found that their results
were consistent with TRA applications in other research fields
[16].
The Theory of Planned Behavior (TPB) [17] extends TRA with
the construct of perceived behavioral control as an additional
determinant of behavioral intention that takes one's perception of
the difficulty of performing a behavior into account. It has been
successfully applied to explain individual acceptance and usage of
various technologies [8,18].
Furthermore, the Technology Acceptance Model (TAM) is an
adaptation of TRA tailored to the field of IS to predict information
technology acceptance and usage on the job [16] and has been
widely applied to various technologies and user groups [6]. An
extended version of TAM is TAM 2 which includes subjective
norm as an additional factor to predict intention in the case of
mandatory settings [19].
As already introduced above, the Diffusion of Innovations (DOI)
theory has been used to study several innovations regarding
adoption and acceptance by both, individuals and organizations
[20]. Consequently, it can also be used in the context of
acceptance as conceptualized here. For this purpose, DOI was
transferred into an IS context and refined to study individual
technology acceptance [19].
The Motivational Model (MM) has a different focus than the ones
mentioned above [21]. It was influenced by research in
psychology and represents an application of motivational theory
to understand new technology acceptance and use [21,22].
Consequently, it focuses on motivational constructs, i.e., intrinsic
and extrinsic motivation, in order to explain and predict user
acceptance.
The Model of Personal Computer Utilization (MPCU) [23] is
based on the theory of human behavior. It was originally
introduced to predict the way of usage rather than the decision to
use it or not. However, it comprises factors that are deemed
suitable to predict individual acceptance as well [6].
A model that is helpful for both, predicting individual and group
behavior is the Social Cognitive Theory (SCT) [24] which
identifies human behavior as an interaction of personal factors,
behavior, and the environment. An application in the field of KM
is given within a study on using a laissez-faire approach for
improving knowledge work methods that traditionally have
employed a top-down approach [25].
The Unified Theory of Acceptance and Use of Technology
(UTAUT) [6] groups the most important constructs of all
acceptance models named above describing behavioral intention
and usage behavior with these groups, influenced by so-called
moderators, e.g., the user’s age and gender [6]. However, all
factors taken into account in this model are mentioned in at least

one of the models before. Therefore, there were no additional
factors collected directly from the UTAUT model.
2.3 Assimilation
The following models aim at explaining the assimilation of a new
technology by collectives of users into their work processes and
practices.
Institution Theory (INST) states that organizations’ and
individuals’ choices and behavior are influenced by social
structures embedded in the environment. In this regard, three
general differentiations comprising regulative, normative, and
cognitive elements can be made. Regulative elements refer to the
avoidance of violations against laws and regulations by
individuals to avoid organizational sanctions against themselves.
Normative elements are understood as embedding social
understanding into decision-making rules or procedures on an
organizational level. Cognitive elements on an organizational
level describe social structures that constitute the nature of reality
and development of meaning. Individual behavior in such cases is
informed by cognitive guidance that allow them understanding
how they should behave [3,26].
Structuration Theory (ST) is a frequently used theory in IS
research [27] and is the basis for Adaptive Structuration Theory
(AST). These theories are appealing to IS research, since they
focus on social aspects of IS, in particular they analyze structure
and the processes by which structures are used and modified over
time [28]. The theories comprise a number of concepts that help to
understand how users interpret IS and how they actually use them
compared to the intended usage by designers and developers,
called appropriation. This is an inherently social process relating
to the collective behavior of the group of users and stakeholders
affected by a new system. It explains how organizational
structures and routines are impacted by the use of IS on the one
hand and how IS are interpreted from the perspective of the
organizational structures and routines on the other hand [29].
An interpretation of the DOI theory emphasizes the role of
knowledge and organizational learning as potential barriers to
successful rollout of innovations and provides a
reconceptualization of the diffusion theory for complex
organizational technologies [30]. These complex technologies
create a knowledge barrier to the users that inhibits diffusion.
Therefore, technology suppliers should actively encourage
diffusion by developing procedures that lower knowledge barriers
in order to improve the assimilation [31]. Hence, DOI is also
useful for explaining assimilation of IT into processes and
practices, e.g., it was applied to examine the assimilation of
collaborative information technologies [32].
The Knowledge-based Theory of the firm (KBT) considers
knowledge as a resource that has strategic significance for
organizations. Potential competitive advantage and sustainability
can be achieved due to the fact that knowledge comprising
policies, routines, or employees is difficult to imitate and socially
complex in the sense that it is embedded in the social fabric of
collectives of people which cannot easily be transferred [33, 34].
Besides the models and theories above, we looked into
characteristics of (business) processes in order to help decision
makers judge whether changes considering work processes and
practices have improved organizational performance. Business
process management (BPM) [35] has also been extended to cover
processes of knowledge work [36]. As assimilation refers to the
actual use of the adopted technology within processes, it seems
useful to consider the following factors that correspond to
qualitative and quantitative process goals: process quality
(qualitative and quantitative process goals), process costs
(quantitative process goals), and process time (temporal process
goals) and customer process satisfaction (process-external view of
quality) [37,38].
3. METHOD
The models presented above give valuable insights into adoption,
acceptance or assimilation from different points of view with
different focuses and partly overlapping concepts. However, in
order to consider and improve these topics within the design of a
KMS, a more comprehensive and consolidated model is needed
that gives an overview of factors influencing the adoption,
acceptance and assimilation of KMS, thus reducing complexity
and presenting the models and theories in a form that is more
easily understandable and applicable for decision makers
responsible for design, development and implementation of KMS.
Additionally, it would be helpful to distinguish between factors
that can be influenced by KMS design and factors that cannot.
Therefore, we combined the models and theories named above
explaining behavior of individuals and organizations using IT
with the aim to use the resulting model as guidance for KMS
design purposes. For each of the three topics, i.e. adoption,
acceptance and assimilation, we collected highly cited models
which we analyzed sequentially, extracting influencing factors
given in the model or theory. In doing so, we got three sets of
factors that influence adoption, acceptance or assimilation
respectively and originate in one or more of the models and
theories discussed in Section 2. In order to improve
understandability and reusability of the 81 collected factors, we
further categorized them thematically into 17 factor groups. Thus,
we provide a structured basis for subsequent work with our
comprehensive model. The factors are listed in tables in the
following three subsections. A definition of the single factors can
be found in the literature given for the respective model or theory.
The factors need to be operationalized according to the specific
context of a situation of an organization willing to adopt a KMS,
e.g., characteristics of the business sector to be addressed by the
KMS. An example for the interpretation of factors within a special
context is given as part of Section 4.
In a last step, we analyzed the factor groups in respect to whether
they can be influenced within the design or not, i.e. if the factors
within one group can be influenced by design decisions. In the
following, these steps are presented in more detail in the
respective subsections.
3.1 Adoption Factors
We collected 37 factors influencing the organizations’ decision on
adopting a KMS which are presented together with their
originating model or theory in table 1.
Furthermore, we categorized the factors influencing the adoption
into eight groups depending on the thematic origin of the factors.
The Environment group combines factors external to the adopting
organization; the Innovation Characteristics groups collects
factors that are defined only by the KMS product itself and the
type of innovation it represents; Fit combines factors related to the
suitability of the KMS solution to the tasks it is intended to
support and the infrastructure it is intended to be applied in
influenced by the solution itself as well as the organization;
Expected Results combines factors related to the perceived quality
and impact of the KMS’s adoption; the factors within the group
Organizational Characteristics correspond to general properties
of the adopting organization, while the groups Communication

Characteristics, Technological Infrastructure and Resources
combine factors that are related to particular organizational
properties in the context of its communication processes and
practices, the technology already in place with which the KMS
needs to be connected and monetary and human resources related
to the adoption of the KMS. The groups are graphically presented
within the final model in figure 1.
Table 1: List of Factors for Organizational Adoption
Factor name Model/Theory
Adopter type DOI
Availability TOE
Characteristics TOE
Commercial Advantage DOI
Communication Process TOE
Community Norms DOI
Compatibility DOI
Complexity DOI
Cultural Values DOI
Formal and Informal Linking Structures TOE
Funding DOI
Government Regulations TOE
Industry Characteristics and Market Structure TOE
Informal Communication DOI
Information Quality ISSM
Management Hierarchy DOI
Observability DOI
Opinion Leaders and Change Agents DOI
Organizational Impact ISSM
Organizational Viability FVM
Price DOI
Problem Solver DOI
Relative Advantage DOI
Risk DOI
Size TOE
Service Quality ISSM
Slack Resources TOE
Standard DOI
System Quality ISSM
Task-Technology Fit FVM
Technological Edge DOI
Technological Experience DOI
Technological Infrastructure DOI
Technology Support Infrastructure TOE
Trialability DOI
User Need Recognition DOI
User Resistance DOI
The factors within the groups Environment, Organizational
Characteristics, Technological Infrastructure and Resources
cannot be directly influenced by KMS design, as they are
organization specific path-dependent idiosyncrasies or externally
determined. In contrast, the factors categorized in Innovation
Characteristics, Communication Characteristics and Expected
Results are affected by decisions related to the KMS design, as
many of them correspond to qualities and features of the KMS
product. Fit is a mapping between KMS and the selected tasks and
environment it should support and thus supposedly can also be
influenced by KMS design.
3.2 Acceptance Factors
We identified 22 factors explaining the acceptance of KMS by
users as listed in table 2. The factors influencing the acceptance
have been categorized into four groups. Two groups combine all
expectancy-related factors: Effort Expectancy collects factors
related to users’ perceptions of what they need to contribute to
work with the KMS (input) and Performance Expectancy collects
factors related to users’ perceptions of what the impact of working
with the KMS will be (output). The factors in the third group are
moderating factors from the social environment of a user (Social
Influences) and the factors in the last group are personal factors
impacting the acceptance decision (Attitude towards Technology-
Use).
Consequently, factors within the groups Attitude towards Using
Technology and Social Influence are depending on culture,
organizational peculiarities and personal preferences and thus
cannot be directly influenced by design. The factor groups
Performance Expectancy and Effort Expectancy are also
underlying subjective influences, however, these factor can be
influenced by KMS design, e.g., by ensuring high quality
standards and providing useful product features.
Table 2: List of Factors for User Acceptance
Factor name Model/Theory
Affect Towards Use SCT; MPCU
Anxiety SCT
Attitude Toward Behavior TRA; TPB
Compatibility DOI
Complexity MPCU
Ease of Use DOI; TAM; TAM2
Extrinsic Motivation MM
Facilitating Conditions MPCU
Image DOI
Intrinsic Motivation MM
Job-Fit MPCU
Long-term Consequences MPCU
Outcome Expectations - Performance SCT
Outcome Expectations - Personal SCT
Perceived Behavioral Control TPB
Usefulness TAM; TAM2
Relative Advantage DOI
Results Demonstrability DOI
Self-efficacy SCT
Social Factors MPCU
Subjective Norm TRA; TAM2; TPB
Visibility DOI
3.3 Assimilation Factors
Lastly, table 3 presents 22 factors that influence the assimilation
of a KMS. The collected factors were categorized into four
groups: Institutional Characteristics, combining factors related to
certain characteristics of the organization adopting the KMS,
Social System Characteristics combining factors describing social
structures, as well as communication and collaboration
peculiarities of the organization, Process Characteristics,
combining factors describing processes in terms of a set of
standard process goals; and lastly Management Characteristics,
which include factors related to management quality, support and
selected practices deemed important for the success of KMS
assimilation into the organization.

In consideration of the fact that assimilation as a third step refers
to the diffusion of a solution into organizational processes and
work practices, nearly all of the factors correspond to
organization-specific peculiarities. However, when looking at the
factors that are highly affected by a new KMS, we expect Process
Characteristics and Social System Characteristics to change
depending on the design of the introduced KMS. Consequently,
the factors within these two groups can be indirectly influenced by
design decisions. In contrast, the introduction of a KMS will
probably have less impact on Management Characteristics or
Institutional Characteristics.
Table 3: List of Factors for Process Assimilation
Factor name Model/Theory
Communication Channels Use DOI
Decision-making Patterns DOI
Extent of Coordination INST
Functional Integration DOI
Knowledge Barrier DOI
Knowledge Embeddedness DOI
Management Championship INST
Methodology Influence INST
Organizational Size KBT
IT Function Size DOI; INST
Process Cost BPM
Process Quality BPM
Process Time BPM
Process Satisfaction BPM
Promotion of Collaboration DOI
Quality of Senior Leadership KBT
Sophistication of IT Infrastructures KBT
Strategic Investment Rationale INST
Structures of Significance ST; INST (cognitive)
Structures of Domination ST; INST (regulatory)
Structures of Legitimation ST; INST (normative)
Top Management Championship ST
3.4 Resulting Model
Figure 1 presents the comprehensive model that combines factors
impacting on the process of adoption, acceptance and assimilation
of KMS. Moreover, the factors have been categorized so that
guidance can be offered on two levels of granularity, namely the
level of groups of factors and the level of individual factors.
Furthermore, the model visually separates the groups that can be
influenced within the design phase from groups that cannot be
influenced as argued above.
This differentiation seems useful because designers of KMS can
distinguish between factors that can be influenced and thus are
important when eliciting functional and non-functional
requirements while others can be considered as environmental
limitations which need to be taken into account particularly when
packaging a KMS and offering it to various target groups. The set
of groups that cannot be influenced by design partially consists of
environmental characteristics which cannot be directly
manipulated, neither by providers of KMS nor by organizations
using them. Other factors that cannot be influenced correspond to
characteristics of an organization and individuals willing to adopt
a new technology that are comparably stable over time. However,
some factors in this set of groups can be changed when using a
KMS. Examples are Social Systems Characteristics which are
influenced by IT and also influence how IT is used. According to
AST, designers should particularly be aware of these
characteristics and should explicitly state the intended use of the
KMS which, however, needs to be distinguished from the KMS’s
actual use in a particular organization.
4. RELATED WORK & APPLICATION
Thus far, we have presented a comprehensive model of factors
relevant for KMS design impacting the adoption, acceptance, and
assimilation of IT. The model intends to provide guidance for
analyzing and reflecting decisions related to KMS design,
development and implementation in organizations based on the
collected factor groups and their factors for each phase in the
context of an ongoing software project. This manner of informing
software design, comprising for example phases such as
requirements elicitation, configuration, packaging, and offering, is
not yet widely observable in the domain of KM. However, some
comparable work in the general domain of IS research can be
named.
In ongoing research, an evaluation model for Question Answering
Systems has been developed that focuses on user-centered
evaluation rather than on system-centered evaluation. The
proposed model comprises constructs taken from behavioral
science models, such as TAM, UTAUT, TRA, and TPB, to
increase user acceptance when designing Question Answering
Systems [39,40]. Another study focuses on Employee
Relationship Management systems and investigates the impact of
usefulness on systems quality perceptions. Specific user needs
should be considered in the analysis phase of system design and
therefore need to be assessed properly. The model draws upon
TAM among others to explain perceived usefulness [41]. It is
rather common to use research results amalgamated into a single
model for predicting behavior to inform system design. For
example, a study used TAM to measure perceived usefulness
when analyzing the connection between website design and
impulse buying in the domain of e-commerce. Subsequently,
research results were used to give concrete recommendations for
e-commerce website designers [42].
In the application of our model we followed a similar approach. In
context of a large-scale research and development project, we
applied the proposed model for informing and extending
requirements elicitation (especially for non-functional
requirements), as well as supporting decisions with respect to
packaging and offering of the KMS implemented in this project.
The project aims at designing an ICT solution supporting and
enhancing employees in their daily activities related to Human
Resource Management, Business Process Management, KM and
Innovation Management. In our efforts to increase adoption,
acceptance, and assimilation of this KMS, 77 recommendations
were developed for the design of the platform and addressed to
platform architects, designers and developers of related business
plans. These recommendations were developed by evaluating so
far defined requirements, technological trends, and the ICT
landscapes of pilot organizations in respect to our model.
The remainder of this section will exemplify the use of the
presented model to describe a collection of additional
requirements and recommendations in two application areas.
Here, application areas describe project-specific work groups that
work on requirements elicitation, packaging and offering solutions
for the KMS.

Innovation Characteristics
· Availability
· Characteristics
· Complexity
· Price
· Relative Advantage
· Standard
· Technological Edge
· Trialability
Fit
· Compatibility
· Task-Technology Fit
· User Need Recognition
Expected Results
· Commercial Advantage
· Information Quality
· Organizational Impact
· Problem Solver
· Risk
· Service Quality
· System Quality
· Observability
· Communication Process
· Community Norms
· Informal Communication
Can be
influenced by
design
Cannot be
influenced by
design
Communication Characteristics
Environment
· Government Regulation
· Industry Characteristics and Market
Structure
Technological Infrastructure
· Technological Infrastructure
· Technology Support Infrastructure
Resources
· Funding
· Slack Resources
Organizational Characteristics
· Adopter Type
· Cultural Values
· Formal and Informal Linking Structures
· Management Hierarchy
· Organizational Viability
· Size
· Technological Experience
· User Resistance
· Opinion Leaders and Change Agents
Effort Expectancy
· Complexity
· Ease of Use
· Facilitating Conditions
Performance Expectancy
· Job-Fit
· Long-term Consequences
· Outcome Expectations – Performance
· Outcome Expectations – Personal
· Usefulness
· Relative Advantage
· Results Demonstrability
· Compatibility
Social System Characteristics
· Communication Channels Use
· Extent of Coordination
· Knowledge Barrier
· Structures of Significance
· Structures of Domination
· Structures of Legitimation
Process Characteristics
· Process Cost
· Process Quality
· Process Time
· Process Satisfaction
Adoption Acceptance
Assimilation
XOR
Social Influences
· Image
· Social Factors
· Subjective Norm
· Visibility
· Extrinsic Motivation
Attitude towards Technology-Use
· Affect Towards Use
· Anxiety
· Attitude Toward Behavior
· Perceived Behavioral Control
· Intrinsic Motivation
· Self-efficacy
Management Characteristics
· Decision-making Patterns
· Management Championship
· Methodology Influence
· Promotion of Collaboration
· Quality of Senior Leadership
· Top Management Championship
Institutional Characteristics
· Functional Integration
· Knowledge Embeddedness
· Organizational Size
· IT Function Size
· Sophistication of IT Infrastructure
· Strategic Investment Rationale
Figure 1: Model of Factors influencing the Adoption, Acceptance and Assimilation in KMS Design
Requirements Elicitation. Within this application area, system
designers developed user requirements for the KMS, comprising
functional and non-functional requirements. To support adoption
by organizations, a recommendation was based on the factors
Task-Technology Fit and User-Need Recognition in the factor
group Fit. Task-Technology fit is understood as the degree to
which the requirements of the tasks that should be supported by
the new technology and the nature of the new technology match
[13]. User-Need Recognition describes the degree to which an
innovation matches the user needs in the tasks [10]. In this
context, the architecture team was advised that the final
architecture should allow for high configurability of the user
interface (UI) and provided services. Personalized services, which
in turn draw upon existing system functionality, can be composed
and further customized in order to adapt to a user’s context and
needs. Users hold various needs, which can be met by providing
configurable software to increase successful acceptance of KMS.
The factor Complexity in the Effort Expectancy group describes
the perception of the user to which degree it is difficult to
understand and use the new technology [23] while Ease of Use
explains the degree to which a new technology is perceived as
being difficult to use [6]. Hence, additional functionalities to
provide a number of exemplary configuration for different user
needs in order to improve acceptance among users were
recommended. To increase assimilation of the KMS into
organizational processes, a detailed planning of the intended use
during the design phase is essential. In this regard, a
recommendation based on Process Satisfaction, which measures
how well the customer judges the quality of a process, product or
service [37], was made which recommends monitoring
functionalities to increase process transparency and hence
improve process satisfaction. Consequently, monitoring
functionalities were defined, e.g., allowing users to rate suggested
items.
Packaging & Offering. This phase describes an application area
that is not directly connected to the development of the system
itself, but rather relates to activities relevant to make the product
ready for the market. Such activities comprise amongst others the
definition of various distribution channels, product/software
package, marketing intentions etc. In this regard, the factor
Trialability in the group Innovation Characteristics describes the
degree to which using an innovation can be experimented with,
prior to its adoption [11]. Hence, it was defined that the final
product of the project includes demonstrators and/or an online
portal to allow showing characteristics and advantages of the

KMS. In order to increase acceptance on the individual level, the
factor Facilitating Conditions in the group Effort Expectancy,
describing the degree of provided organizational and technical
infrastructure which supports system use [23], was used to
formulate a recommendation for this application area. User
support, expressed by trainings, tutorials, step-by-step guides,
videos, and wikis were planned to be available and communicated
in a proper way.
5. LIMITATIONS
The current research combines factors taken from several models
and theories explaining human behavior using IT on an individual
as well as an organizational level into one comprehensive model
covering adoption, acceptance and assimilation of KMS.
Consequently, validity is challenging in design and a complete
validation is problematic and probably not feasible due to the
large number of variables that would need to be controlled over a
period of time due to the fact that adoption, acceptance and
assimilation are regarded as a process taking place in a dynamic
socio-technical environment. However, all single models and
theories used to extract factors, were individually validated
As the model is intended to be used to inform design activities,
empirical validation of the model is not our primary concern, but
we rather long for demonstrating its usefulness and practicability
as an explicit guide that helps investigating and adjusting design
activities in respect to the adoption, acceptance and assimilation
by critically examining the single factors for each phase in the
context of the project. Such an application was demonstrated in
the case example above, i.e. a large scale project in which this
approach was welcomed and resulted in a number of
recommendations informing project decisions. The proposed
categorization of factors and the distinction whether they can be
influenced by KMS design or not is therefore argued for on the
basis of a first application within a large-scale R&D project in the
field of KMS with multiple instantiations for several distinct
organizations.
6. CONCLUSION AND OUTLOOK
To conclude, the proposed model represents a comprehensive
basis for a systematic consideration of factors influencing
adoption, acceptance and assimilation within the field of KM. Our
contribution comprises a structured review of the literature, the
consolidation of factors, selected as relevant for designing KMS
for the three topics adoption, acceptance, and assimilation as well
as an example application of the model in a large-scale KMS
project. Moreover, we differentiated between factors that can be
influenced by design and those that cannot be influenced by
design. As described and demonstrated, there are various
applications in KMS design, e.g., evaluating (non-)functional
requirements, eliciting additional requirements, or influencing
packaging and offering of KMS in order to enhance the adoption
by organizations, the acceptance by users and the assimilation of a
KMS solution into organizational processes and work practices.
Consequently, the model supports and informs KMS design with
the aim to improve the take-up of the resulting KMS solution.
There are a number of future research directions and opportunities
implied by the results of this paper. One is that the categories
within the model should be reviewed in the context of further
applications. Moreover, additional applications and project
experiences with the model may also allow a more detailed
evaluation of the relevance of the factors specifically for KMS
design. Besides that, it would be interesting to examine additional
factors proposed in KM literature that are particularly important to
improve the adoption, acceptance and assimilation of KMS.
However, they are typically not validated upon strong empirical
basis in contrast to most of the factors extracted for our model.
Furthermore, we see other possible areas of application that are
worth to be examined, e.g., the use of the comprehensive model
from the perspective of organizations that aim to adopt and
introduce a KMS within their organization. Factors that cannot be
influenced by KMS design are suggested to be considered from an
organizational perspective, e.g., the users’ attitude towards
technology can be influenced by guidance, motivation or trainings
when rolling-out the KMS.
7. ACKNOWLEDGMENTS
This work was co-funded by the European Commission under the
Information and Communication Technologies theme of the 7th
Framework Programme, Integrating Project ARISTOTELE
(contract no. FP7-257886).
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