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.

Social Network Site (SNS) users’ disclosed personal information is beneficial for marketers, as targeted advertising can be provided accordingly. This study identifies the three privacy concepts of concerns, attitudes and intentions, along with culture (i.e. Hofstede’s masculinity) to be key drivers of users’ self-disclosure on SNSs. Hence, this study evaluates these antecedents and their potential effect on self-disclosure, considering Facebook as the SNS of choice. To account for potential cross-cultural differences, data has been gathered via two online questionnaires, resulting in one German and one Norwegian sample of respondents. Multiple regression analyses were carried to evaluate the antecedents of self-disclosure and one-way ANOVA to examine potential differences amongst Germans and Norwegians. Results indicate that privacy intention is the strongest predictor of self-disclosure on SNSs; whereas privacy concerns and attitudes only reveal an indirect effect on self-disclosure. In addition, significant differences have been found for all of the constructs, indicating that there exist fundamental differences between Germans and Norwegians with regards to their cognitive processing behind personal information disclosure on SNSs.

The Internet of Things (IoT) has been acknowledged as one of the most innovative forms of technology since the computer, because of the influence it can have on multiple sectors of physical and virtual environments. The growth of IoT is expected to continue, by 2020 the number of connected devices is estimated to reach 50 billion. Recent developments in IoT provide an unprecedented opportunity for personalised services and other benefits. To exploit these potential benefits as best as possible, individuals are willing to provide their personal information despite potential privacy breaches. Therefore, this paper examines factors that influence the willingness to disclose personal information in the use of IoT in South Africa (SA) with the use of the privacy calculus as the theoretical underpinnings of this research. The privacy calculus accentuates that a risk-benefit trade off occurs when an individual decides to disclose their personal information, however, it is assumed that there are more factors than perceived risks and perceived benefits that influence information disclosure. After analysing previous literature, this study identified the following factors; information sensitivity, privacy concerns, social influence, perceived benefits, (perceived) privacy risks and privacy knowledge as possible key tenants in relation to willingness to disclose personal information. This research took on an objective ontological view, with the underlying epistemological stance being positivistic. The research incorporated a deductive approach, employing the use of a conceptual model which was constructed from a combination of studies orientated around privacy, the privacy calculus and the privacy paradox. Data for this research was collected using the quantitative research approach, through the use of an anonymous online questionnaire, where the targeted population was narrowed down to the general public residing within SA that make use of IoT devices and/or services. Data was collected using Qualtrics and analysed using SmartPLS 3. SmartPLS 3 was used to test for correlations between the factors which influence information disclosure in the use of IoT by utilising the complete bootstrapping method. A key finding was that the privacy paradox is apparent within SA, where individuals pursue enjoyment and predominantly use IoT for leisure purposes, while individuals are more likely to adopt self-withdrawal tendencies when faced with heightened privacy concerns or potential risks.

The essay examines whether there is a difference in concern and actual behavior of peopledepending on whether they use tracking protection or not when browsing the web. The thesisis based on previous research on the privacy paradox, information privacy concern andprotection motivation theory. The approach is quantitative with an accompanyingquestionnaire. The answers were collected via a self-developed test environment which at thesame time collected information about any measures the respondents had taken to protectthemselves against tracking. The analysis was made through a correlation analysis betweenthe survey questions to find the survey questions convergent validity. Furthermore, adescriptive analysis was made of the responses from the respondents in each group and aMann-Whitney U test was conducted to see if there were any statistically significantdifferences between the groups. The thesis found that the correlation between the questionswere overall strong, which shows a well-designed research model. The thesis has also foundsigns that privacy paradox is present in certain parameters such as information privacyconcern and percieved vulnerability, while it is not in other parameters such as percievedseverity and response efficacy. The conclusion drawn is that the respondents are aware andconcerned that they could be tracked when they browse the web, but not as many believe thatthe severity is high if one were to be tracked. / Uppsatsen undersöker om det finns skillnad i oro och uppskattning av att hantera denna orohos personer beroende på om de använder spårningsskydd eller ej när de surfar på nätet.Uppsatsen utgår ifrån tidigare forskning kring privacy paradox, information privacy concernoch protection motivation theory. Tillvägagångssättet är kvantitativt med tillhörande enkät.Svaren samlades in via en egenutvecklad testmiljö som samtidigt samlade in information omeventuella åtgärder respondenterna tagit för att skydda sig mot spårning. Analysen gjordesgenom en korrelationsanalys mellan enkätfrågorna för att undersöka frågornas konvergentavaliditet. Samtidigt gjordes en deskriptiv analys av svaren från respondenterna i varje gruppsamt ett Mann-Whitney U test för att se om det uppstod signifikanta skillnader i hurgrupperna svarade på enkäten. Uppsatsen har funnit att korrelationen mellan frågorna överlagär stark, vilket visar på en bra utformad forskningsmodell. Uppsatsen har även funnit att detfinns tecken på att privacy paradox är närvarande vid vissa parametrar, såsom informationprivacy concern och percieved vulnerability medans den inte är det hos andra parametrar sompercieved severity och response efficacy. Slutsatsen som dras är att respondenterna kännermedvetenhet och oro för att spåras när de surfar på nätet men inte lika många anser attallvarlighetsgraden är hög om man skulle bli spårad.

I den aktuella avhandlingen kommer Alphabet Inc och Facebook Inc som är två av de fem stora techbolagen som ingår i det digitala ekosystemet att analyseras. Alphabet Inc och Facebook Inc har valts ut på grund av deras affärsmodell bygger på att använda privatdata som handelsvara. Dessa företag kommer att analyseras utifrån den privacy paradox som uppstår när användare har kännedom kring deras privata integritet samtidigt som de godkänner användarvillkoren utan att läsa dessa som motstrider deras integritet. Denna studie har använt en enkätundersökning som underlag för att stödja litteratur och författarnas hypotes för att kunna svara på den givna forskningsfrågan. Resultatet visar att dessa företag använder sig av svårförståeliga, långa och komplicerade användarvillkor vilket resulterar till att användaren inte läser igenom dem. Samtidigt finns det en medvetenhet kring den privata integriteten vilket skapar en paradox då användaren ej läser avtalen. De slutsatser som kommer med arbetet är faktorerna som leder till privacy paradoxen samt dess effekter. Samtidigt som implikationer gällande konsekvenser på samhället. / In the current thesis, Alphabet Inc and Facebook Inc, which are two of the five major tech companies included in the digital ecosystem, will be analyzed. Alphabet Inc and Facebook Inc have been selected because their business model is based on using private data as a commodity. These companies will be analyzed based on the privacy paradox that arises when users have knowledge of their privacy while still agreeing to the terms of service without reading those that conflict with their privacy.  This study has used a survey as a basis to support literature and the authors hypothesis to be able to answer the given research question. The results show that these companies use language that is difficult to understand, long and complicated terms of service, which results in the user not reading through them. At the same time, there is an awareness of the privacy paradox, which creates a paradox when the user does not read the user agreements. The conclusions that come with the work are the factors that lead to the privacy paradox and its effects. At the same time as implications regarding consequences on society.

What lies beneath the surface of modern day interfaces? And what are the consequences of sharing our digital self with companies in exchange for free services? We report on a study of smartphone users in Sweden where behaviours and reactions are assessed from a critical viewpoint using the suggested method “Clear the palace”. Users express concerns regarding management of the digital self as well as showing awareness of the limitations within personalization as a result of data mining. Strategies to limit mobile overuse are discussed. The analysis is based on; Shklovski’s notions privacy paradox and creepiness and Pierce’s framework of undesigning technology. Clear the Palace as a methodology is assessed aswell as societal implications of data mining are problematized. / Vad pågår under ytan av moderna gränssnitt? Och vad är konsekvenserna av att vi delar våra digitala jag med företag i utbyte mot gratis tjänster? Vi redogör för en studie av mobilanvändare i Sverige där beteenden och reaktioner studeras ur ett kritiskt perspektiv med metoden “Röj palatset”. Användare uttrycker oro gällande hanteringen av det digitala jaget och uppvisar medvetenhet angående begränsningarna av personalisering som en följd av data mining. Strategier för att begränsa överanvändning av smartmobiler diskuteras. Analysen baseras på; Privacy Paradox, Leakiness/Creepiness enligt Shklovski och Pierce ramverk Undesigning Technology. Röj palatset som metod utvärderas och implikationer av data mining för samhället problematiseras.

Smartwatches can collect health data, location data and other sensitive information about users, and privacy concerns arise. This thesis aimed to investigate how older people (50-80 years old) in Sweden behave when it comes to privacy and health data. The data were analyzed according to the privacy paradox, which describes the discrepancy between how people behave and how they intend to behave in relation to risk and trust. The research approach was qualitative, and twelve semi-structured interviews were conducted. The interviews were coded and thematized following the chosen theory. Among the twelve participants in the study, a majority did not see, understand, or behave consciously towards the risks of sharing health data. Instead, trust was related to both the disclosure behavior and the intentional behavior among several of the participants in this study. This study indicates that for some of the participants, there are also other factors that determine their behavior, and the privacy paradox alone is not complete. Four of the findings when it comes to participants' behavior towards their health data and privacy were: trust-based decisions, lack of knowledge, low value of personal data, and value benefits more than privacy. Among several of the participants in this study, when trust towards an actor increase, the participant’s risk awareness decreases. It can be discussed whether the participants in the study value the opportunities more than the risks, and this impacts their behavior. Most of the participants think that sharing location data infringes more on their privacy than sharing health data, and self-education might be a reason the behavior and the level of privacy differ among the participants.

Syfte: Studien syftar till att förklara hur olika användare upplever de tre nivåerna av AI-genererat innehåll inom marknadsföring i företagsekonomi. Studien syftar även till att se om deras handlande går i linje med deras attityder eller om det finns utrymme för en paradox. Teoretisk bakgrund: I bakgrund och teori redogörs för begrepp som är relevanta för studien så som de olika nivåerna av AI, attityders beståndsdelar och flera modeller kopplat till handlande. Även begreppen integritetskalkylen och integritetsparadoxen behandlas samt påverkansfaktorerna upplevda fördelar, upplevt förtroende och upplevd risk.  Metod: En post-positivistisk och deduktiv ansats användes för studien i form av en kvantitativ surveyundersökning. Data samlades in genom en online-enkät som förmedlades genom sociala medier och genererade 113 svar. Resultatet analyserades med hjälp av univariata tabeller samt en regressionsanalys. Undersökningskvaliteten säkrades med hjälp av enkätfrågor förankrade i teori samt Cronbach’s Alpha och korstabeller. Resultat & Slutsatser: Resultatet av studien visar på att de tre faktorerna fördelar, förtroende och risk påverkar användares attityder kring alla nivåer av AI-baserad marknadsföring. Användarna hyste mest negativa attityder mot kännande AI och mest positiva mot mekanisk AI. Attityderna visade sig ha en påverkan på handlandet vilket inte bekräftar teorin om en paradox. Resultatet är inte applicerbart på populationen i stort och därför behövs vidare studier innan långtgående rekommendationer kan ges till managers. / Purpose: The study aims to explain how users experience the three levels of AI-based marketing within business administration. The study also aims to see if users' actions are in line with their attitudes or if a paradox is applicable.  Theoretical background: In the background and theory, concepts that are relevant to the study are described, such as the different levels of AI, components of attitudes and several models linked to behavior. The concepts of the privacy calculus and the privacy paradox are also addressed, as well as the influencing factors perceived benefits, perceived trust and perceived risk. Method: A post-positivist and deductive approach was used for the study in the form of a quantitative survey. Data was collected through an online questionnaire that was distributed through social media and generated 113 responses. The results were analyzed using univariate tables and a regression analysis. The research quality was ensured with the help of questionnaire questions rooted in theory as well as Cronbach's Alpha and crosstabs. Results & Conclusions: The results of the study show that the three factors benefits, trust and risk affect users' attitudes of all levels of AI-based marketing. Users had the most negative attitudes towards feeling AI and most positive towards mechanical AI. The attitudes showed to have an influence on the behavior, which does not confirm the theory of a paradox. The result is not applicable to the population as a whole and therefore further studies are needed before far-reaching recommendations can be given to managers.

Background: Digital loyalty programs collect extensive personal data, but literature has so far neglected the aspect of privacy concerns within the programs. The privacy paradox denotes the contradictory behavior amongst consumers stating privacy risk beliefs and actual behavior. Existing literature is calling for a dual perspective of the privacy paradox and digital loyalty programs to find the underlying reasons for the contradictory behavior. Purpose: The purpose of this study was to explore (1) if and when privacy concerns existed in digital loyalty programs and (2) why consumers overruled their privacy concerns in digital loyalty programs. Method: A qualitative method with 18 semi-structured interviews were conducted through a non-probability purposive sampling of consumers within digital loyalty programs. The findings were then analyzed through a thematic analysis to finally construct a model based upon the given research purpose.  Conclusion: The findings suggest that consumers experience privacy concerns in digital loyalty programs from external exposure to privacy breaches and when consumers felt their mental construct of terms and conditions were violated. Four themes were found to influence why consumers overrule their privacy concerns and share personal data with digital loyalty programs, relating to cognitive biases, value of rewards received, and digital trust for the program provider. The findings were synthesized into a model illustrating the consumer assessment of personal data sharing in digital loyalty programs and the interconnection between the influences.

The deployment of human-augmented machine learning (ML) systems has become a recommended organizational best practice. ML systems use algorithms that rely on training data labeled by human annotators. However, human involvement in reviewing and labeling consumers' voice data to train speech recognition systems for Amazon Alexa, Microsoft Cortana, and the like has raised privacy concerns among consumers and privacy advocates. We use the enhanced APCO model as the theoretical lens to investigate how the disclosure of human involvement during the supervised machine learning process affects consumers' privacy decision making. In a scenario-based experiment with 499 participants, we present various company privacy policies to participants to examine their trust and privacy considerations, then ask them to share reasons why they would or would not opt in to share their voice data to train a companies' voice recognition software. We find that the perception of human involvement in the ML training process significantly influences participants' privacy-related concerns, which thereby mediate their decisions to share their voice data. Furthermore, we manipulate four factors of a privacy policy to operationalize various cognitive biases actively present in the minds of consumers and find that default trust and salience biases significantly affect participants' privacy decision making. Our results provide a deeper contextualized understanding of privacy-related concerns that may arise in human-augmented ML system configurations and highlight the managerial importance of considering the role of human involvement in supervised machine learning settings. Importantly, we introduce perceived human involvement as a new construct to the information privacy discourse. Although ubiquitous data collection and increased privacy breaches have elevated the reported concerns of consumers, consumers' behaviors do not always match their stated privacy concerns. Researchers refer to this as the privacy paradox, and decades of information privacy research have identified a myriad of explanations why this paradox occurs. Yet the underlying crux of the explanations presumes privacy concern to be the appropriate proxy to measure privacy attitude and compare with actual privacy behavior. Often, privacy concerns are situational and can be elicited through the setup of boundary conditions and the framing of different privacy scenarios. Drawing on the cognitive model of empowerment and interest, we propose a multidimensional privacy interest construct that captures consumers' situational and dispositional attitudes toward privacy, which can serve as a more robust measure in conditions leading to the privacy paradox. We define privacy interest as a consumer's general feeling toward reengaging particular behaviors that increase their information privacy. This construct comprises four dimensions—impact, awareness, meaningfulness, and competence—and is conceptualized as a consumer's assessment of contextual factors affecting their privacy perceptions and their global predisposition to respond to those factors. Importantly, interest was originally included in the privacy calculus but is largely absent in privacy studies and theoretical conceptualizations. Following MacKenzie et al. (2011), we developed and empirically validated a privacy interest scale. This study contributes to privacy research and practice by reconceptualizing a construct in the original privacy calculus theory and offering a renewed theoretical lens through which to view consumers' privacy attitudes and behaviors. / Doctor of Philosophy / The deployment of human-augmented machine learning (ML) systems has become a recommended organizational best practice. ML systems use algorithms that rely on training data labeled by human annotators. However, human involvement in reviewing and labeling consumers' voice data to train speech recognition systems for Amazon Alexa, Microsoft Cortana, and the like has raised privacy concerns among consumers and privacy advocates. We investigate how the disclosure of human involvement during the supervised machine learning process affects consumers' privacy decision making and find that the perception of human involvement in the ML training process significantly influences participants' privacy-related concerns. This thereby influences their decisions to share their voice data. Our results highlight the importance of understanding consumers' willingness to contribute their data to generate complete and diverse data sets to help companies reduce algorithmic biases and systematic unfairness in the decisions and outputs rendered by ML systems. Although ubiquitous data collection and increased privacy breaches have elevated the reported concerns of consumers, consumers' behaviors do not always match their stated privacy concerns. This is referred to as the privacy paradox, and decades of information privacy research have identified a myriad of explanations why this paradox occurs. Yet the underlying crux of the explanations presumes privacy concern to be the appropriate proxy to measure privacy attitude and compare with actual privacy behavior. We propose privacy interest as an alternative to privacy concern and assert that it can serve as a more robust measure in conditions leading to the privacy paradox. We define privacy interest as a consumer's general feeling toward reengaging particular behaviors that increase their information privacy. We found that privacy interest was more effective than privacy concern in predicting consumers' mobilization behaviors, such as publicly complaining about privacy issues to companies and third-party organizations, requesting to remove their information from company databases, and reducing their self-disclosure behaviors. By contrast, privacy concern was more effective than privacy interest in predicting consumers' behaviors to misrepresent their identity. By developing and empirically validating the privacy interest scale, we offer interest in privacy as a renewed theoretical lens through which to view consumers' privacy attitudes and behaviors.