The purpose of this study is to investigate what new challenges CRM systems, Customer Relationship Management, have for the protection of personal data and security in retail. Today's IT development offers companies effective solutions for acquiring new customers and for retaining current customers. IT solutions such as data collection, data storage and analysis of data in real time are just a few examples of what a successfully implemented IT system can offer. CRM systems are the overall IT system solution that companies use, or show interest in using, to be competitive and ultimately achieve higher profits. The method chosen for this study is the research philosophy of pragmatism because it enables the use of different research methods which give a more accurate result. The study is mainly based on surveys and interviews and therefore both qualitative and quantitative methods are used. This study has examined four important variables, ie Privacy, Confidentiality, Integrity and Data Protection, to interpret and explain how these significantly affect the protection of personal data in the retail trade. Another result that emerged from the study is that the overall goal of CRM systems is to develop a strong and long-term customer relationship that minimizes costs and improves efficiency to meet and meet customer needs. Finally, the results of the study show that there is a great deal of awareness about the protection of personal data among consumers and in doing so it is concluded that the lack of protection of personal data among companies will lead to consumers choosing to leave companies. / Syftet med denna studie är att undersöka vilka nya utmaningar CRM-system, Customer Relationship Management, har för skyddet av personliga data och säkerhet inom detaljhandeln. Dagens IT-utveckling erbjuder företagen effektiva lösningar för att skaffa nya kunder samt för att behålla aktuella kunder. IT-lösningar såsom datainsamling, datalagring och analys av data i realtid är bara några exempel på vad ett lyckat implementerat IT-system kan erbjuda. CRM-system är den övergripande IT-systemlösningen som företagen använder, eller visar intresse för att använda, för att vara konkurrenskraftiga och slutligen uppnå högre vinster. Metoden som valts för denna studie är pragmatismens forskningsfilosofi eftersom den möjliggör användningen av olika forskningsmetoder vilka ger ett mer rättvisande resultat. Studien bygger huvudsakligen på undersökningar och intervjuer och därför används både kvalitativa och kvantitativa metoder. Denna studie har undersökt fyra viktiga variabler, det vill säga Privacy, Confidentiality, Integrity och Data Protection för att tyda och förklara hur dessa på ett väsentligt sätt påverkar skyddet av personliga data inom detaljhandeln. Ett till resultat som framkommit av studien är att det övergripande målet med CRM-system är att utveckla en stark och långsiktig kundrelation som minimerar kostnaderna och förbättrar effektiviteten för att möta och tillgodose kundens behov. Slutligen visar resultaten från studien att det finns stor medvetenhet kring skyddet av personliga data hos konsumenterna och därvid dras slutsatsen att det bristande skydd av personliga data hos företagen kommer att leda till att konsumenterna väljer att lämna företagen

This dissertation explores applicant reactions to Internet-based selection procedures in order to advance theory and practice related to the use modern employee selection tools. Previous authors have explored this topic area (e.g., Harris et al., 2003). However, this dissertation goes beyond previous research by proposing and testing a model that incorporates the measurement of multiple constructs that are highly relevant to organizations when utilizing Internet-based selection procedures. Such constructs include privacy concerns, explanations, control, fairness perceptions, litigation intentions, organizational intentions, and test-taking motivation. Current organizational justice theory, previous findings from studies on applicant reactions to selection procedures, and research on Internet privacy concerns provided the foundation on which this research is based. This dissertation also pulls from theory in the legal, information sciences, and psychology literatures. A model of applicant reactions that included privacy concerns and multiple outcomes relevant to organizations was proposed. Hypotheses examining this model were tested via a high-fidelity laboratory study with student participants. One-third of the participants in this study were seeking jobs at the time of participation. Findings indicated that privacy concerns are an important predictor of both proximal (i.e., fairness perceptions) and distal (i.e., organizational intentions, test-taking motivation) applicant reaction outcomes. Results also demonstrated support for a mediating role of fairness perceptions in the relationships between privacy concerns and organizational intentions as well as between privacy concerns and test-taking motivation. Providing applicants with control and explanations were found to have no moderating effect on the relationship between privacy concerns and fairness perceptions. However, post-hoc analyses indicated that excuse explanations moderated the effect of privacy concerns on test-taking motivation. Theoretical implications of this dissertation include support for a one-factor model of organizational justice as well as a call for more integration of research from outside of industrial-organizational psychology. Additionally, areas for future research, including opportunities for improvement of study design involving timing of measures, are presented. Finally, implications for practice are discussed in regard to the possible impact of privacy concerns to large numbers of applicants participating in Internet-based selection processes, including a discussion on the importance of applicant privacy concerns to organizations and the use of multiple, inexpensive methods that may aid organizations in increasing fairness perceptions among applicants.

För varje dag som går möter stora industrier en ökad mängd intrång i sina IT-system. De flesta befintliga verktyg som använder sig utav maskininlärning är starkt beroende av stora mängder data, vilket innebär risker under dataöverföringen. Därför har syftet med denna studie varit att undersöka om en decentraliserad integritetsbevarande strategi kan vara ett bra alternativ för att minska effektiviteten av dessa attacker. Mer specifikt skulle användningen av Random Forests, en av de mest populära algoritmerna för maskininlärning, kunna utökas med decentraliseringstekniken Federated Learning tisammans med Differential Privacy, för att skapa en ideal metod för att upptäcka nätverksintrång? Med hjälp av befintliga kodbibliotek för maskininlärnings och verklighetsbaserad data har detta projekt konstruerat olika modeller för att simulera hur väl olika decentraliserade och integritetsbevarande modeller kan jämföras med traditionella alternativ. De skapade modellerna innehåller antingen Federated Learning, Differential Privacy eller en kombination av båda. Huvuduppgiften för dessa modeller är att förbättra integriteten och samtidigt minimera minskningen av precision. Resultaten indikerar att båda teknikerna kommer med en liten minskning i noggrannhet jämfört med traditionella alternativ. Huruvida precisionsförlusten är acceptabel eller beror på det specifika användningsområdet. Det utvecklade kombinerade alternativet lyckades dock inte nå acceptabel precision vilket hindrar oss från att dra några slutsatser. / With each passing day, large industries face an increasing amount of intrusions into their IT environments. Most existing machine learning countermeasures heavily rely on large amounts of data which introduces risk during the data-transmission. Therefore, the objective of this study has been to investigate whether a decentralized privacy-preserving approach could be a sensible alternative to decrease the effectiveness of these attacks. More specifically could the use of Random Forests, one of the most popular machine learning algorithms, be extended using the decentralization technique Federated Learning in cooperation with Differential Privacy, in order to create an ideal approach for network intrusion detection? With the assistance of existing machine learning code-libraries and real-life data, this thesis has constructed various experimental models to simulates how well different decentralized and privacy-preserving approaches compare to traditional ones. The models created incorporate either Federated Learning, Differential Privacy or a combination of both. The main task of these models is to enhance privacy while minimizing the decrease in accuracy. The results indicate that both techniques comes with a small decrease in accuracy compared to traditional alternatives. whether the accuracy loss is acceptable or not may depend on the specific scenario. The developed combined approach however, failed to reach acceptable accuracy which prevents us from drawing any conclusions.

As society progresses towards a more algorithmic era, the influence of artificial intelligence (AI) is driving a revolution in the digital landscape. At its core, AI applications aim to engage customers by providing carefully tailored and data-driven personalization and customization of products, services, and marketing mix elements. However, the adoption of AI, while promising enhanced personalization, poses challenges due to the increased collection, analysis, and control of consumer data by technology owners. Consequently, concerns over data privacy have emerged as a primary consideration for individuals. This paper delves deeper into the implications of the personalization- privacy paradox, aiming to provide a comprehensive analysis of the challenges and opportunities it presents. The purpose of this thesis is to understand users’ privacy concerns and willingness to disclose their personal information to AI technologies by addressing the limitations of previous research and utilizing qualitative methods to gain a more in-depth understanding of consumer views. To understand users’ privacy concerns and willingness to disclose personal information to AI technologies, a qualitative approach was followed. Combining a deductive and inductive approach to fulfill the purpose of the study, empirical data was collected through 20 semi- structured interviews. The participants were chosen using a purposive sampling technique. Users’ privacy concerns and willingness to disclose personal information to AI technologies differ significantly. It depends not only on the individual, but also on the type of AI technology, the company providing the AI technology, the possibility of obtaining additional benefits, and whether the company is transparent about its data collection and can provide proof of security.

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Digital marketing is constantly adapting and evolving in line with technological advances. One of these advances is the digitalization, which has given rise to Programmatic Advertising (PA). In order to practice PA, the companies need to collect data about consumers' preferences and personal interests. On the other hand, consumers have a need to protect their privacy. The needs of these two parties cross each other which creates the tension, called the Personalization-privacy paradox. In this study, we intend to investigate consumers' attitudes towards PA and whether personalization gives rise to improved browsing experiences or evenviolates their privacy. A quantitative study was conducted, where the independent variable was called Personalization and the dependent variables - Attractiveness, Annoyance, Invasiveness and Trade-off. The results of bivariate regression analysis showed that all of the dependent variables of the study were statistically significant. The results also illustrated that the majority of the respondents experienced PA ads as beneficial, but also invasive. These results are in line with the Utility maximization theory, as PA ads were considered both beneficial and risky. Therefore, consumers would have incentives to disclose their personal information as long as the percieved benefits would outweigh perceived risks, generated by PA. We concluded that there are no clear, predetermined answers to what attitudes consumers have towards PA, but this can vary from case to case, which is in line with both the Privacy calculus theory and the Utility maximization theory. This involves a risk-benefit analysis, performed by consumers, where perceived benefits exceeding perceived risks would generate positive attitudes and vice versa

When joining a new service, in order to access its features, users are often required to accept the terms of service and privacy policy. However, the readership of these documents is mostly non-existent, leaving an information asymmetry, the imbalance of knowledge between two parties. Due to this, users are sacrificing their online data privacy without being aware of the consequences. The purpose of this work is to investigate the readership of terms of service and privacy policies among users of social media services. We implemented a prototype called ‘ShareIt’, which resembles a photo-sharing platform to gain insight about readership, behavior and effectiveness of our adjusted presentations of terms of service and privacy policies in regard to readership and comprehension. We conducted a survey experiment using the prototype with 31 participants and concluded, that 80,6% of our participants did not spend more than ten seconds in our terms of serviceand privacy policy. The observed behavior suggests, that social media users are used to sharing information on the internet which in addition to their trust towards online services leads to the aforementioned low readership. We presented adjustments to the presentation of terms of service and privacy policies which showed a slight tendency of higher engagement in comparison to the current way of accessing these documents. This result however, due to the lack of readership examined for our participants, has to remain debatable and needs further investigation.

In Essay 1, we seek to expand the insights on an individual's decision to share group content. Social networking sites (SNS) have become a ubiquitous means of socializing in the digital age. Using a survey, we collected data from 520 respondents with corporate work experience to test our research model. Our analysis highlights the complex interplay between individual and group factors that shape users' risk-benefit analysis of sharing group content on social networking sites. Furthermore, the results of this study have important implications for social networking site design and policy, particularly with regard to providing granular control over the privacy settings of group content and clear and concise information about the potential risks and benefits of sharing group content. Essay 2 aims to extend the knowledge of information security policy (ISP) compliance. Using a comprehensive approach, we extended the perspective of control mechanisms in the context of ISPs. It is evident that maintaining information security is an important concern for organizations of all sizes and industries. Organizations can establish policies and procedures to regulate and ensure compliance with information security policies, and various control mechanisms can be employed to ensure compliance. Among these control mechanisms, enforcement, punishment, evaluation, and recognition have been identified as important factors that influence information security policy compliance. In Essay 3, we delve deep into the current digital era and the reality of individuals becoming particularly vulnerable to privacy breaches. In the third essay, we offer a thorough examination of existing literature to gain insight into the disparities between users' stated privacy concerns and their actual information-sharing behavior. Our analysis reveals that, in addition to technological and environmental factors, cultural and personal differences significantly contribute to the paradoxical behavior observed among individuals. Utilizing the S-O-R (stimulus-organism-response) framework, we emphasize the necessity of examining the intricate interplay between technological aspects, individual attributes, and environmental factors in order better to understand the complexities of individuals' privacy decision-making processes. By addressing these factors and their interactions, we can develop more effective strategies to improve individuals' privacy awareness, decision-making, and overall online experiences. This will ultimately create more secure and privacy-respecting digital communities for users with various characteristics.

[pt] A adoção de modelos de inteligência artificial está alterando a relação entre organizações e consumidores e o volume de produtos digitais dependentes de dados pessoais cresce a cada dia. As políticas de privacidade são o principal instrumento de informação do cidadão sobre como suas informações serão tratadas por empresas com as quais se relaciona. Porém, atualmente, as interfaces destes instrumentos não comunicam de forma objetiva suas informações. O presente estudo demonstra que a aplicação de diretrizes de design nas políticas de privacidade promove uma experiência mais satisfatória e uma aquisição de informação mais rápida de seu conteúdo pelos usuários. A metodologia do estudo abarcou uma revisão bibliográfica, pesquisa documental, adaptação da escala Internet Users’ Information Privacy Concerns, teste de usabilidade e análise de conteúdo. Literaturas de direito e design foram relacionadas para identificar requisitos legais que poderiam ser melhor atendidos por meio do design, o nível de preocupação com a privacidade dos participantes foi verificado e um teste comparativo de usabilidade foi conduzido. Uma réplica da política do Facebook foi comparada à nova proposta, que contava com elementos que representavam diretrizes de design. Os dados mostraram redução no tempo para localização de informações e na taxa de erro entre os usuários que acessaram a nova proposta, assim como maior frequência de declarações positivas a respeito desta versão. A pesquisa contribui para a ampliação do conhecimento sobre a influência do design de interface na construção destes instrumentos ao esclarecer que a consideração de boas práticas deste campo facilita a aquisição de informação. / [en] The use of artificial intelligence models is changing the relationship between organizations and consumers, and the volume of digital products dependent on personal data is growing every day. Privacy policies are the primary tool for informing citizens about how their information will be handled by companies with which they interact. However, currently, the interfaces of these instruments do not objectively communicate their information. The present study demonstrates that the application of design guidelines in privacy policies promotes a more satisfactory experience and faster user acquisition of information from its content. The study methodology encompassed a bibliographical review, documentary research, adjustment of the Internet Users Information Privacy Concerns scale, usability testing, and content analysis. Literature from law and design fields could be interconnected to identify legal requirements that could be addressed more effectively through design, the level of privacy concern of the study s participants was verified, and a comparative usability test was conducted. A replica of Facebook s policy was compared to a new interface, which included elements that represented design guidelines. The data showed a reduction in the time to find information, the error rate among users who accessed the new proposal, and a higher frequency of positive statements regarding this version. This research enhances the understanding of how interface design affects the creation of such instruments by showing that following best practices in this area facilitates information acquisition.

This study examined stepparents' privacy boundary management when engaging in communicative interactions with stepchildren. I utilized Petronio's communication privacy management theory to investigate stepparents' motivations of disclosing or concealing from stepchildren as well as how stepparents' gender influences such motivations. Moreover, present research also explored types of privacy dilemma within stepfamily households from stepparent perspectives. Fifteen stepfathers and 15 stepmothers received in-depth interviews about their self-disclosing and concealment experiences with stepchildren. I identified confidant dilemma and accidental dilemma in stepfamily households from stepparents' perspectives, as well as stepparents' gender differences in self-disclosing and concealing motivations. Findings also suggest that stepparents reveal and conceal from stepchildren out of same motivations: establishing good relationships, viewing stepchildren as own children, helping stepchildren with problems resulting from the divorce and viewing stepchildren as "others." The result also indicates that stepparents experienced dialectical tensions between closedness and openness during the decision of revealing or concealing from stepchildren.