The personalization of telehealth services to accommodate patient preferences and interaction abilities could significantly improve patient adherence to telehealth treatment plans. Long-term adherence can be as low as 25% among chronic patients for reasons related to ease-of-use and personal preferences, which can be attributed to factors associated with the patient, physicians, and healthcare systems. Poor adherence in the long term can cause increased morbidity, poorer quality of life, a higher risk of mortality, and greater health care utilization. Poor adherence is partly driven by generic telehealth services that are not adapted to individual patients' lived experiences. Recent research calls for the personalization of telehealth services in a manner that addresses long-term adherence. This thesis views the telehealth service context from a multilevel service systems perspective. This perspective enables the articulation of the contextual differences between standardized and personalized services. This thesis proposes a service design method (SerViU: pronounced Serv You) to support a continuous Use - Assess - Personalize process; this design method focuses on the patient personal service encounter level within a telehealth service. SerViU is anchored in the service-dominant logic concept of value-in-use, and it assesses the patient's individualized experiences with the telehealth service and accordingly recommends a suitable personalization. SerViU guides decision-making about telehealth personalization by integrating an existing information communication technology (ICT) service personalization framework that identifies three types of ICT personalization: architectural, relational, and technological. A design science research methodology (DSRM) was used to guide the research activities underlying the development and validation of SerViU. Within this methodology, the SerViU Personalize Tool was selected to demonstrate SerViU's ability to personalize telehealth services by accounting for patient-related, service context-related, and technology-related factors. A multiple case study with embedded units of analysis was conducted at a Canadian hospital to simulate personalization decision-making using the SerViU Personalize Tool. The same participants were then asked to fill out a questionnaire to evaluate the tool's usefulness for decision-making, its relevance to the telehealth context, and whether it contained sufficient information to make personalization decisions. Results show that SerViU was relevant to telehealth contexts, useful for making personalization decisions, and provided sufficient information to make relevant decisions. The collected data were analyzed using cross- and within-case analysis by comparing decisions in different telemonitoring service modes. The comparisons included personalization options, feature selection, scores, rationales, and resource-related information. The results of this research provide a means to operationalize telehealth personalization as proposed in telehealth research. This study provides a method which can guide the transformation of generic telehealth services into personalized services. This research contributes to service design by differentiating between standard and personal service encounter levels, which is paramount for supporting the personalization of ICT-enabled services. This research contributes to the telehealth practice by presenting an ongoing telehealth personalization process that involves patients in decision-making throughout their treatment processes as a means to improving long-term adherence.

Personalized advertising is possible by tracking consumers' online activities and customizing ads for individual consumers. These ads are often more relevant, interesting, and appreciated by the consumer. However, there is a sense of worry about how personal information is gathered, stored, and used. While companies share the information with each other, consumers worry that the collection and usage of their personal information may damage their personal privacy. The personalization-privacy paradox describes this relationship between appreciated personalized advertising and breaches of privacy. The purpose of this study is to explore what elements of personalized marketing consumers find to be a breach of their personal privacy. By analyzing earlier research and literature, a theoretical framework was developed. The study uses a qualitative method utilizing semi-structured interviews to collect empirical data, which is then analyzed using the theoretical framework. The findings show that there are four primary elements in personalized advertising that consumers find to be a breach of privacy. These are recurring ads, feelings of being watched, personal information being shared and covert data collection. / Individanpassad marknadsföring är möjligt genom att spåra konsumenters onlineaktivitet och anpassa reklam för individuella konsumenter. Denna reklam är ofta mer relevant, intressant och uppskattad av konsumenten. Det finns dock en känsla av oro om hur personlig information samlas in, lagras och används. Medan företag delar informationen med varandra oroar sig konsumenter för att insamlingen och användandet av deras personliga information kan kränka deras personliga integritet. Personalization-privacy paradox beskriver detta samband mellan uppskattad individanpassad marknadsföring och kränkningar av den personliga integriteten. Syftet med denna studie är att undersöka vilka element av individanpassad marknadsföring konsumenter upplever som integritetskränkande. Genom att analysera tidigare forskning och litteratur skapades ett teoretiskt ramverk. Studien använde sig av semistrukturerade intervjuer för att samla in empiriska data, som sedan analyserades med hjälp av det teoretiska ramverket. Resultaten visar att det finns fyra huvudsakliga element i individanpassad marknadsföring som konsumenter anser vara integritetskränkande. Dessa är återkommande reklam, känslan av att vara övervakad, delande av personlig information och hemlig datainsamling.

Background: Artificial intelligence (AI) personalization approaches can increasingly be seen used in society today from businesses using it to analyze the behavior of their consumers to consumers using it to find for example jobs that match their persona. AI personalization delivers unique messages to an individual based on their previous data to improve the consumer experience. Previous research has not focused from the perspective of the consumer in combination with their trust towards AI personalization. Since AI personalization could be beneficial for society at large, stakeholders, policymakers, companies and the consumers, the understanding of consumers' perspectives are now more important than ever.  Purpose: The purpose of this study is to explore consumers' trust towards AI personalization.  Methodology: Since this bachelor thesis aims to fill a gap in the literature on providing insight on consumers' trust towards AI personalization, a qualitative research approach was used where seven semi-structured interviews were performed. Thus, gaining a deep insight of the subject in question which allowed for flexibility in the data collection. An interview guide was prepared and used to help guide the interviews to stay on topic. The sample method was made with purposive sampling to ensure previous experience and therefore the possibility to answer the questions asked.   Findings: This bachelor thesis identified three main findings in regards to consumers’ trust towards AI personalization to be influence of trust, mistrust and skepticism.  Conclusion: Concluding the thesis, three aspects of consumers' trust towards AI personalization was found having somewhat of a connection with each other implying that consumers base their trust, mistrust or skepticism differently.

The advent of the digital era has profoundly transformed marketing and variousorganizational functions, largely driven by the accessibility to vast repositories ofdigitized data and the integration of artificial intelligence (AI). The purpose of this studyis to examine the impact of artificial intelligence-driven personalization on customerexperience within the context of TikTok, a leading social media platform known for itsinnovative use of AI technology. TikTok leverages AI to analyze user behaviors andpreferences, delivering tailored content that enhances user engagement and satisfaction.The deductive approach is the selected approach to fulfill this study is objective.Particularly, the research hypothesis (H1) posits that AI-driven personalization positivelyinfluences customer experience on TikTok. To test this hypothesis, we conducted aquantitative study involving structured questionnaires distributed to a diverse sample ofTikTok users via social media platforms, resulting in 365 usable answers. The findingsconfirm a significant positive relationship between AI-driven personalization andcustomer experience, underscoring the critical role of AI in shaping user interactions andexperiences on social media platforms - TikTok. This study contributes to the literatureon digital marketing and customer experience management by highlighting theeffectiveness of AI-driven personalization in fostering engagement and satisfaction.

The advent of information services introduces many advantages, for example, in trade, production and services. While making important descisons today, people increasingly rely on the information gleaned from such services. Presumably, as such, information from these services has become a target of manipulation. During the past decade, we have already observed many forms of information manipulation that misrepresents or alters reality. Some popular manipulation -- we have ever witnessed on the Internet -- include using black hat SEO techniques to drive up the ranking of a disreputable business, creating disinformative campaigns to conceal political dissidence, and employing less-than-honest product assessments to paint a rosy picture for inferior wares. Today, emerging web services and technologies greatly facilitated and enhanced people's lives. However, these innovations also enrich the arsenal of manipulators. The sheer amount of online information available today can threaten to overwhelm any user. To help ensure that users do not drown in the flood of information, modern web services are increasing relying upon personalization to improve the quality of their customers' experience. At the same time, personalization also represents new ammunition for all manipulators seeking to steer user eyeballs, regardless of their intents. In this thesis, I demonstrate a new unforeseen manipulation that exploits the mechanisms and algorithms underlying personalization. To undermine the effect of such manipulation, this thesis also introduces two effective, efficient mitigation strategies that can be applied to a number of personalization services. In addition to aforementioned personalization, increasingly prevalent browser extensions augment the ability to distort online information. In this thesis, I unveil an overlooked but widespread manipulation phenomenon in which miscreants abuse the privilege of browser extensions to tamper with the online advertisement presented to users. Considering that online advertising business is one of the primary approaches used to monetize free online services and applications available to users, and reckless ad manipulation may significantly roil advertising ecosystem, this thesis scrutinizes the potential effect of ad manipulation, and develops a technical approach to detect those browser extensions that falsify the ads presented to end users. Although the thesis merely discusses several manipulation examples in the context of the Internet, the findings and technologies presented in this thesis introduce broad impacts. First, my research findings raise Internet users' awareness about pervasive information manipulation. Second, the proposed technologies help users alleviate the pernicious effects of existing information manipulation. Finally, accompanying the findings and technologies is publicly available open-source software and tools that will help an increasing number of users battle against the growing threat of information manipulation.

Background: With the adoption of smartphones, a new mean of communication emerged for businesses, calling for deep knowledge on how to leverage this profitable direct-link to consumers. However, previous literature has mainly studied the subject from a quantitative standpoint with a theoretical foundation built on traditional advertising, hence, not studying the subject on its own. It is therefore relevant to study the topic from the ground up, exploring users perspective on main factors driving their attitudes towards mobile advertising.Purpose: To explore consumer attitudes toward mobile advertising.Methodology: A qualitative exploratory study based on 4 focus groups, sampled through convenience sampling and analysed using direct content analysis.Conclusion: Findings indicate that, mobile advertising lack credibility, which drives negative attitudes and that entertainment was non-present in mobile advertising. Perceptions expressed a vast element of irritation and that informativeness depends on the relevance of ads forming the outcome of attitude. Additionally, personalization emerged as a component influencing the majority of the studied factors, and consequently suggested to be further studied as a factor on its own.

Ο μεγάλος όγκος σελίδων και υπηρεσιών στο Διαδίκτυο αρκετές φορές δημιουργεί προβλήματα πλοήγησης με αποτέλεσμα η αναζήτηση εγγράφων και πληροφοριών να είναι μια εξαιρετικά χρονοβόρα και δύσκολη διαδικασία. Για το λόγο αυτό είναι απαραίτητη η πρόβλεψη των αναγκών των χρηστών με στόχο τη βελτίωση της χρηστικότητας του Διαδικτύου αλλά και της παραμονής του χρήστη σε έναν δικτυακό τόπο. Ο στόχος αυτής της διπλωματικής εργασίας είναι αρχικά να παρουσιάσει μεθόδους και τεχνικές που χρησιμοποιούνται για την εξατομίκευση και προσαρμογή στα ενδιαφέροντα του χρήστη, δικτυακών τόπων. Η εξατομίκευση περιλαμβάνει τη χρήση πληροφοριών που προέρχονται από τα ενδιαφέρονται και τη συμπεριφορά πλοήγησης του χρήστη σε συνδυασμό με το περιεχόμενο και τη δομή του δικτυακού τόπου. Στη συνέχεια παρουσιάζεται ένα σύστημα αναδιοργάνωσης της δομής ενός δικτυακού τόπου, του οποίου η υλοποίηση βασίστηκε στη δημοτικότητα των σελίδων για κάθε χρήστη όπως αυτή προκύπτει από τα log αρχεία που διατηρεί ο server του δικτυακού τόπου. Τέλος, το σύστημα αυτό εφαρμόζεται σε έναν πειραματικό δικτυακό τόπο και γίνεται αξιολόγηση των αποτελεσμάτων εφαρμογής του. / The large number of web pages on many Web sites has raised navigation problems. As a result, users often miss the goal of their inquiry, or receive ambiguous results when they try to navigate through them. Therefore, the requirement for predicting user needs in order to improve the usability and user retention of a Web Site is more than ever, indispensable. The primary purpose of this thesis is to explore methods and techniques for improving or “personalizing” Web Sites. Web personalization includes any action that adapts the information or services provided by a Web site to the needs of a particular user or a set of users, taking advantage of the knowledge gained from the users’ navigation behavior and interests in combination with the content and structure of the Web Site. Secondly, this thesis describes the implementation of a tool (reorganization software) which parses log files and uses specific metrics related to web page accesses, in order to reorganize the structure of a web site according to its users’ preferences. Finally, the tool is applied in an experimental Web Site and the results of this reorganization process are evaluated.

One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

Recommender systems are ubiquitously used by online vendors as profitable tools to boost sales and enhance the purchase experience of their consumers. In recent literature, the value created by recommender systems are discussed extensively. In contrast, few researchers look at the negative side of the recommender systems from the viewpoint of policymakers. To fill this gap, I critically investigate the welfare impact of recommender systems (RSs) during my Ph.D. study. The main focus of my Ph.D. dissertation is analyzing whether there exists a conflict of interest between the recommendations provider and its consumers in the electronic marketplace. My dissertation is composed of three parts. In Part I, I evaluate empirically whether in the real world, the profit-driven firm will choose a recommendation mechanism that hurts or is suboptimal to its consumers. In Part II, I analyze the role of personalization technology in the RSs from a unique perspective of how personalization resembles price discrimination as a profitable tool to exploit consumer surplus. In part III, I investigate the vendor’s motivation to increase the level of personalization in two-period transactions. As the RSs are designed by the firm, and the firm’s objective is to maximize profits, the RSs might not maximize consumers’ welfare. In Part I of my thesis work, I test the existence of such a conflict of interest between the firm and its consumers. I explore this question empirically with a concrete RS created by our industry collaborator for their Video-on-Demand (VoD) system. Using a large-scale dataset (300,000 users) from a randomized experiment on the VoD platform, I simulate seven RSs based on an exponential demand model with listed movie orders and prices as key inputs, estimated from the experimental dataset. The seven simulated RSs differ by the assignments of listed orders for selected recommended movies. Specifically, assignments are chosen to maximize profits, consumer surplus, social welfare, popularity (IMDB votes and IMDB ratings), and previous sales, as well as random assignments. As a result, the profit-driven recommender system generates 8% less consumer surplus than the consumer-driven RSs, providing evidence for a conflict of interest between the vendor and its consumers. Major e-vendors personalize recommendations by different algorithms that depend on how much and types of consumer information obtained. Therefore, the welfare evaluations of personalized recommendation strategies by empirical methods are hard to generalize. In Part II of my thesis, I base my analysis of personalization in RSs on a conceptual approach. Under an analytic framework of horizontal product differentiation and heterogenous consumer preferences, the resemblance of personalization to price discrimination in welfare properties is presented. Personalization is beneficial to consumers when more personalization leads to more adoption of recommendations, since it decreases search costs for more consumers. However, when the level surpasses a threshold when all consumers adopt, a more personalized RS decreases consumer surplus and only helps the firm to exploit surplus from consumers. The extreme case of perfect personalization generates the same welfare results as first-degree price discrimination where consumers get perfectly fit recommendations but are charged their willingness-to-pay. As shown in Part II, personalization is always profitable for the monopoly seller. In Part III, I investigate the vendor’s motivation to increase the level of personalization in a two-period transactions. In the first period, consumers do not observe the true quality of the recommendations and choose to accept recommended products or not based on their initial guesses. In the second period, consumers fully learn the quality. The settings of consumer uncertainty and consumer learning incentivize the firm to charge lower-than-exploiting price for recommendations to ensure consumers’ first-period adoptions of the RS. Therefore, uncertainties mediate the conflicts of interest from the vendor’s exploitive behavior even though the vendor might strategically elevate consumers’ initial evaluation to reduce such effect.

Many argue that personalization is needed in a modern marketing strategy. Whilst there are several positive aspects of personalization, e.g. improved customer satisfaction rates, it can also lead to firms being perceived as intrusive and elicit privacy concerns. This dilemma describes the personalization paradox, which refers to the two-sided results of using personalized communication by collecting and analyzing consumer data. To address the issue of how firms balance the need for personalization while still respecting consumers’ privacy, previous researchers have mainly investigated the issue from the consumer perspective. However, the consumer is believed to display a paradoxical behavior in regards to personalization. Therefore, we have addressed this issue through interviewing 12 company representatives from 7 companies. Our findings indicate that companies are mindful when creating personalized content and do acknowledge the issues with privacy and the risk of being perceived as intrusive. To overcome the personalization paradox, firms are not explicit about their data analysis in their personalized communication as this can lead to consumers feeling discomfort. Finally, an essential way that firms can prevent privacy concerns is to create relevant content as this outweighs feelings of discomfort. These findings to a certain extent do not reflect the empirical research on the topic, however the discrepancies may exist as previous studies were conducted from the consumer side.