A critical overreliance on the technical dimension of information security has recently shifted toward more robust, organizationally focused information security methods to countermand $54 billion lost from computer security incidents. Developing a more balanced approach is required since protecting information is not an all or nothing proposition. Inaccurate tradeoffs resulting from misidentified risk severity based on organizational group perceptions related to information risk form information security gaps. This dissertation applies dialogical action research to study the information security gap created by incongruent perceptions of organizational members related to information risk among different stakeholder communities. A new model, the Information Security Improvement model, based on Technological Frames of Reference (TFR), is proposed and tested to improve information security through reduced member incongruity. The model proved useful in realigning incongruent perceptions related to information risk within the studied organization. A process for identifying disparate information characteristics and potential influencing factors is also presented. The research suggested that the model is flexible and extensible, within the organizational context, and may be used to study incongruent individual perceptions (micro) or larger groups such as departments or divisions.

In recent years, Artificial Intelligence (AI) has emerged as a transformative technology, reshaping industries and prompting significant shifts in how businesses operate. The widespread adoption of AI technologies is influenced by various factors, including technological readiness, organizational culture, and external perceptions. Media plays an important role in shaping these perceptions by framing AI in specific ways. Understanding how AI is framed in media and comparing these frames with the perspectives of IT professionals can provide valuable insights into the alignment or discrepancies between public discourse and professional practice. The purpose of this study was to unveil the frames held by Swedish news media, and which of these frames best reflected IT-Professional’s perception of AI. Additionally, we aim to explore the complex interplay of media frames and how such frames potentially shape individual held technological frames of reference surrounding AI adoption. The goal of the study was to contribute to existing research by expanding upon TFR and bridging the existing gap between framing theory, media frames, and AI adoption. We employed a qualitative study with an exploratory, multi-method approach. By drawing inspiration from Framing Theory, we conducted an analysis of Swedish newspaper articles, as well as semi-structured interviews with Swedish IT-Professionals. Our data sample included 946 newspaper articles and 5 interview participants. Through this method, we provided a broader context for the interview findings, which allowed for in-depth insights into how media frames reflect the frames held by IT-Professionals within their professional environments. Based on our findings, we uncovered nine dominant frames depicted in the Swedish news media; Disruptive Technology, Human-Machine Collaboration, Accelerate AI Adoption, Operational Optimization, Ethical Concerns, Regulation & Transparency, Strategize Before AI Adoption, Challenges of AI Adoption, and Dystopian Technology. Additionally, we identified five of these frames to best reflect the perception IT-Professionals had towards AI; Human-Machine Collaboration, Accelerate AI Adoption, Operational Optimization, Regulation & Transparency, and Challenges of AI Adoption.

Today's society is increasingly shaped by new digital technologies and digital work practices. We live in an era that is rapidly changing, and this poses demands on Sweden's industries to meet the requirements and needs of our industrial society. One industry that has struggled to keep up with the pace of digitalization is the construction industry. The construction industry is characterized by high complexity and numerous processes involving various stakeholders, as each building project has its own unique conditions and requirements. In addition to this, the stakeholders have their own conditions and needs related to technology and the use of digital tools. In order to fully leverage the opportunities of digitalization, it is important for actors to be aware of their own, as well as their co-actors', conditions and needs related to technology, especially when implementing new technology and digital work practices, to ensure the implementation is as successful as possible.  This study has been conducted within a project consulting firm with the aim of analyzing differences and similarities in perceptions among actors and groups during the implementation of a changed digital work practice. To achieve this goal, a case study was conducted using data collection techniques such as document analysis, observation, and semi-structured interviews, with the interviews serving as the significant source of empirical data.  Once the empirical data was collected, it was categorized based on how the respondents agreed with each other within their own respondent group and in comparison to another respondent group. Overall, the authors did not find any inhibiting differences in the respondents' answers regarding implementation. However, concerns were raised regarding how the consulting firm's payment models were adapted to the new digital work practice.  After the study, the authors concluded that, in this case, they did not find significant differences in the respondents' answers that could hinder implementation for the consulting firm. The differences that existed were rather due to variations in the respondents' depth of understanding regarding technology. The main challenge highlighted with the new work practice was how the company would charge for the new technology. To fully utilize the opportunities of digitalization and keep up with the ongoing development, one of the suggestions for further research is the payment models of companies.