As a result of tough competition in the marketplace and the shift form a resource-based economy to a knowledge-based economy, companies are looking more and more at gaining competitive advantage through managing and maximizing their most valuable asset ¡V knowledge. In line with the increasing need to manage knowledge systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are growing in popularity. Given the fact that it is not difficult to find applications relevant to KMS in organizations, the topic of KMS has not been well explored by researchers and scholars. Besides, even among the limited literature on KMS, there is a scarcity of studies on the empirical perspective of KMS, especially in the area of adoption, which is an important issue of managerial capacity. This research tries to address this gap by studying the adoption of KMS in Taiwanese organizations. Specifically, we want to find the significant factors of the KMS adoption. This study is based on innovative perspective combining with some important factors to mold an integrated model. Three dimensions are involved in, including: (1) Innovative characteristics of KMS: includes relative advantage, complexity, compatibility and cost of KMS (2) Organizational factors: includes IT infrastructure inside organization, employees¡¦ IS knowledge, management support, slack resources and business size (3) External factors: only includes competitive pressure. We try to find out whether there is something critical to adopt KMS. An empirical survey methodology is applied to test the research model and hypotheses proposed in this study. Eight out of nine hypotheses are validated in our research model with Discriminant Analysis. The research result reveals that management support of a firm has the strongest discriminability; on the other hand, competitive pressure also affects the adoption of KMS of a firm strongly. In conclusion all the variables have discriminant power expect relative advantage.

In a supply chain, a warehouse is a crucial component for linking all chain parties. Automatic identification and data capture (auto-ID) technology, e.g. RFID and barcodes are among the essential technologies in the 21st century knowledge-based economy. Selecting an auto-ID technology is a long term investment and it contributes to improving operational efficiency, achieving cost savings and creating opportunities for higher revenues. The interest in auto-ID research for warehouse management is rather stagnant and relatively small in comparison to other research domains such as transport, logistics and supply chain. However, although there are some previous studies that explored factors for the auto-ID selection decision in a warehouse environment, those factors (e.g., operational factors) have been examined separately and researchers have paid no attention to all key factors that may potentially affect this decision. In fact, yet there is no comprehensive framework in the literature that comprehensively investigates the critical factors influencing the auto-ID selection decision and how the factors should be combined to produce a successful auto-ID selection process in warehouse management. Therefore, the main aim of this research is to investigate empirically the auto-ID technology-selection process and to determine the key factors that influence decision makers when selecting auto-ID technology in the warehouse environment. This research is preceded by a comprehensive and systematic review of the relevant literature to identify the set of factors that may affect the technology selection decision. The Technology-Organisation-Environment (TOE) framework has been used as lens to categorise the identified factors (Tornatzky & Fleischer, 1990). Data were collected by conducting first a modified (mixed-method) two-round Delphi study with a worldwide panel of experts (107) including academics, industry practitioners and consultants in auto-ID technologies. The results of the Delphi study were then verified via follow-up interviews, both face-to-face and telephone, carried out with 19 experts across the world. This research in nature is positivist, exploratory/descriptive, deductive/inductive and quantitative/qualitative. The quantitative data were analysed using the statistical package for social sciences, SPSS V.18, while the qualitative data of the Delphi study and the interviews were analysed manually using quantitative content analysis approach and thematic content analysis approach respectively. The findings of this research are reported on the motivations/reasons of warehouses in seeking to use auto-ID technologies, the challenges in making an auto-ID decision, the recommendations to address the challenges, the key steps that should be followed in making auto-ID selection decision, the key factors and their relative importance that influence auto-ID selection decision in a warehouse. The results of the Delphi study show that the six major factors affecting the auto-ID selection decision in warehouse management are: organisational, operational, structural, resources, external environmental and technological factors (in decreasing order of importance). In addition, 54 key sub-factors have been identified from the list of each of the major factors and ranked in decreasing order of the importance mean scores. However, the importance of these factors depends on the objectives and strategic motivations of warehouse; size of warehouse; type of business; nature of business environment; sectors; market types; products and countries. Based on the Delphi study and the interviews findings, a comprehensive multi-stage framework for auto-ID technology selection process has been developed. This research indicates that the selection process is complex and needs support and closer collaboration from all participants involved in the process such as the IT team, top management, warehouse manager, functional managers, experts, stockholders and vendors. Moreover, warehouse managers should have this process for collaboration before adopting the technology in order to reduce the high risks involved and achieve successful implementation. This research makes several contributions for both academic and practitioners with auto-ID selection in a warehouse environment. Academically, it provides a holistic multi-stage framework that explains the critical issues within the decision making process of auto-ID technology in warehouse management. Moreover, it contributes to the body of auto-ID and warehouse management literature by synthesising the literature on key dimensions of auto-ID (RFID/barcode) selection decision in the warehouse field. This research also provides a theoretical basis upon which future research on auto-ID selection and implementation can be built. Practically, the findings provide valuable insights for warehouse managers and executives associated with auto-ID selection and advance their understanding of the issues involved in the technology selection process that need to be considered.

The aim of this paper is to demonstrate whether stakeholders activating a project shape team building, the structure and magnitude of resource investment levels, and to what extent these levels impact ERP project effectiveness. The process view of an ERP project includes project initiation, system justification and funding, implementation, and early system use. Results from a nationwide empirical survey conducted in Austria (N = 88) show that activating actors influence team formation and resource investments, which impact project effectiveness levels. Resource-intensive justification and funding phases tend to precede resource-intensive implementations in heavy-weight projects, which seem to be less effective than light-weight projects. Resource and change conflicts are associated with lower project performance and are more common in resource-intensive ERP projects, where early system use appears to be relatively less stable. (author's abstract)

Det svenska sjukvårdssystemet är konstant under hög press och situationen benämns ofta i media som en sjukvårdskris. Radiologin är en av de medicinska discipliner som drabbats av en kontinuerligt ökande arbetsbelastning och personalbrist. Detta sätter sjukvården i en situation att konstant tvingas väga effektivitet mot kvalitet. Trots höga förväntningar på att innovationer som Artificiell Intelligens (AI) ska kunna bistå behoven, används AI idag i en mycket begränsad utsträckning. Denna studie syftar till att utreda påverkande faktorer för införandet av AI inom radiologin. För att besvara arbetets forskningsfråga har HA Adoption-Decision Model, en modifierad version av det väletablerade Technology-Organization-Environment Framework (TOE), tillämpats. Ramverket innefattar tre kontexter; teknologisk, organisatorisk och extern kontext. Varje kontexts delaspekter, så kallade faktorer, följer under respektive kontext. Dessa tio faktorer utvärderades för att besvara studiens forskningsfråga om vilka faktorer som har störst påverkan på införande av AI inom radiologi. Genom att förena tidigare forskning med resultatet från sex intervjuer visade sig affärsvärde , strategisk lämplighet , ledningsstöd och reglering av datahantering ha störst påverkan. Avslutningsvis presenteras ett förslag om att introducera en elfte faktor, IT-mognad, till ramverket.

The rapid changes in the global economy have imposed high pressure on small and medium-sized enterprises (SMEs) and their operations. Many SMEs have realized how important it is to integrate all units in the business by introducing Enterprise Resource Planning systems (ERP systems).    The adoption decision phase in ERP systems is an early phase and is often confused with implementation, which is a major cause of failure. This confusion can especially hit hard on small and medium-sized companies that may lack sufficient resources. The adoption decision phase can be particularly crucial in such case. The purpose of this study is to investigate which driving forces that influence the adoption decision phase of ERP systems within SMEs. The Technology-Organization-Environment (TOE) theoretical framework was used to create a theoretical foundation for finding drivers from previous research.   Semi-structured interviews were then conducted with six ERP consultants. Respondents' responses were analyzed thematically, and 12 themes were identified as driving forces. These could then be compared with previous research. The driving forces identified were management and decision makers, benefits and efficiency, functionality, regulations and security, competition and the future, industry type and complexity, education and competence, finance and resources, supplier and solutions, integration with existing systems, maintenance and support, and challenges and uncertainty. The driving forces that were identified but could not be linked to previous research were functionality, and challenges and uncertainty.   Keywords: Adoption decision, driving forces, Enterprise Resource Planning systems (ERP system), small and medium-sized enterprises (SME), Technology-Organization-Environment (TOE).

Object of study: The innovation “Digital health care center” from a multi-level stakeholder’s perspective. Problem: A new technology era has opened up for new kind of innovations. Digital health care centers are a service that recently has been introduced on the Swedish market, which needs further investigation. To be able to better understand, explain and predict future behavior of an innovation the innovation could be theoretical conceptualized and classified. In the specific area of health care, new innovation should preferable be investigated in from a multilevel perspective, including different stakeholders opinions. One if the stakeholders are the customers. If new innovative products and services want to be successful, it required consumers to adopt the product or service, but relatively few studies have focused on the adoption of technology services among customers. Purpose: The purpose of this thesis is to gain a better understanding of the innovation “digital health care center” in Sweden. Research question: How can the innovation “digital health care center” be described through a stakeholder perspective? Method: The empirical data were collected through qualitative semi-structured interviews and a structured quantitative questionnaire. Conclusions: The innovation digital health care center can from a multi-level perspective be described as an innovation that contributes and have an impact on the market and the healthcare industry in many ways. The innovation could be described as a complement to traditional health care. The innovation has influences from different theoretical classes of innovation which means that the innovation cannot be categorized in a specific class. The innovation can be considered successful because it facilitates for the patient.. According to the stakeholder group potential patients, a majority of the respondents thinks that increased availability and time-efficiency would be facilitating factors and reasons for using the service. The innovation is described by the various stakeholders as contributing to a better society. The care becomes more productive, cost effective, more available, and in the broader perspective, the innovation contributes to increased digitalization of the healthcare sector as a whole. There are many new possible fields of application which in the healthcare industry which could develop the innovation further. Strengths and opportunities with the innovation can be considering outweighing weaknesses with the innovation and potential threats of the innovation.

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