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

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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
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