Background:  Artificial Intelligence (AI) has reshaped marketing, particularly with the advent of Generative AI (GAI), streamlining content creation processes. Highlighted by the European Commission, AI in the Cultural and Creative Sectors (CCS) promises economic and social benefits, potentially enhancing global visibility and marketing strategies. Amid digital transformation, there is a need to develop ways to engage digital audiences with content. Meanwhile, the digital content consumption has made platforms like podcasts popular, blending information and entertainment to attract diverse audiences.   Purpose: This research aims to explore what motivates content creators to engage with GAI in the creative process and how they perceive its influence on their creativity during the interaction.   Method:  The research philosophy of this study is rooted in interpretivism ontology. Given the lack of theories about human creativity and AI, this research employs inductive methods to expand the Componential Model of Creativity (Amabile, 1983). Recognising that creativity is entrenched in social psychology, where interpretations are subjective, this research utilised Interpretative Phenomenological Analysis (IPA) with semi-structured interviews.   Conclusion:              There are several motivations for using GAI in the creative process, including curiosity, its role as a co-creator for in brainstorming, and its ability to enhance efficiency and provide information. However, content creators have concerns that an over-reliance on GAI could replace elements of the creative process and therefore reduce human creativity. Content creators regard human input as a crucial element of the creative process to ensure authentic and genuine innovation.

As artificial intelligence (AI) becomes increasingly integral in various sectors, its role in the design industry, particularly in education, necessitates examination. This thesis explores the integration of AI in design processes from the perspective of designers as users, particularly focusing on Design students in a case study. By adopting a humanistic approach within Human-Computer Interaction (HCI), this research develops the "AI-Designer Collaboration Canvas," a tool designed to facilitate effective collaboration between designers and AI systems. The study addresses how designers can be supported to develop a nuanced view of AI integration in the design process. The developed tool, the "AI collaboration canvas," aims to assist designers in planning and integrating AI into their design processes, aiming to transform perceptions and enhance engagement with AI technologies.

<p dir="ltr">In traditional Software as a Service (SaaS) enterprise applications, there is a need for easy-to-do machine learning (ML) frameworks. Additionally, SaaS applications are closely related when they form an application suite, which brings forth the need for an ML framework that can facilitate the “lift and shift” of ML model code in similar needs in multiple enterprise applications in a suite. To add to this, some SaaS applications are still using legacy infrastructure (on-premise) mandating the need for an ML framework that is backward compatible with coexisting platforms, both cloud and legacy on-premise infrastructure. This study first demonstrated that in SaaS applications, microservices were important ingredients to deploying machine learning (ML) models successfully. In general, microservices can result in efficiencies in software service design, development, and delivery. As they become ubiquitous in the redesign of monolithic software, with the addition of machine learning, the traditional SaaS applications are also becoming increasingly intelligent. Next, the dissertation recommends a portable ML microservice framework Minerva (also known as contAIn—second generation), a Micro-services-based container framework for Applied Machine learning as an efficient way to modularize and deploy intelligent microservices in both traditional “legacy” SaaS application suite and cloud, especially in the enterprise domain. The study also identified and discussed the needs, challenges, and architecture to incorporate ML microservices in such applications. Secondly, the study further identifies that there is an impetus to innovate quickly for machine learning features in enterprise SaaS applications. Minerva’s design for optimal integration with legacy and cloud applications using microservices architecture leveraging lightweight infrastructure accelerates deploying ML models in such applications. The study highlights the real-world implementation of Minerva, doubling innovation speed with the human resources. It evaluates ML model code reusability across applications, resulting in 1.15 to 2X faster adoption compared to previous methods in a marketing application suite. Minerva’s top-tier security encompasses several advanced features designed to protect sensitive data in SaaS marketing applications. It includes end-to-end data encryption, ensuring all data remains secure both in transit and at rest using robust cryptographic algorithms. While a layered design accelerated innovation through porting existing models to related business suites, generative AI methods, while promising, hadn't yielded significant gains with smaller models yet porting over already no code optimized model code.</p>

This research addresses the growing applications and impacts of AI-generated digital human avatars from software suites like HeyGen. By exploring the role of trust in mediating user interaction with such technology, this study establishes a basic hierarchical model which supports some foundational theories of human-computer interaction, while also calling into question some more recent theories and models previously used to evaluate avatar technology. By modeling user behavior and user preference through the lens of trust, this study is able to demonstrate how this emerging technology is similar to its predecessors and their relevant theories, while also establishing this technology as something distinctly new and largely untested. This research serves as an exploratory study, using notions of social presence, anthropomorphic design, social trust, technological trust, and human source-bias to separate this generation of AI Avatar technology from its predecessors, and determine what theories and models govern the use of this new technology. The findings from this study and their impacts on use-cases are then applied, speculating on prosocial as well as potentially unethical uses of such technology. Finally, this study problematizes the loss of “primary trust” that this technology may afford, highlighting the importance not only of continued research, but also rapid oversight in the deployment of this emerging technology.

Abstract: This thesis investigates the capacity of individuals to detect AI-generated text, and the indicators that enable them to do so. This inquiry is situated in the broader theoretical context of digital capital, the digitization of society, deep mediatization, and AI literacy. Using a quantitative correlation approach, the study tested participants’ accuracy in detecting AI content, and shared factors between participants with high scores on this task. Participants were assessed on a number of self-reported demographic, digital capital, and digital society-based benchmarks in conjunction with AI detection accuracy. The study employed a mix of statistical methods, including logistic regression and point-biserial correlation matrices. However, only a few specific questions within the digital capital and digital society framework had a statistically significant impact on a participant being in the high-accuracy group, and these correlations were weak. Furthermore, two aspects of digital capital actually had a negative effect on the odds of scoring high on the text detection task.  The findings reveal that there is room for more research into what indicators influence human AI detection capabilities, and whether these skills are learnable or inherent to certain individuals. Moreover, the research highlights the necessity of fostering AI literacy, particularly if these capabilities improve human AI detection. While AI systems can ‘catch’ AI-generated text, their efficacy is mixed, and producers of AI text and evaluators are constantly locked in a game of cat-and-mouse, using evolving AI to recognize evolving AI. Thus, human skills are pivotal, lest we become even more dependent on technology in our deeply mediatized society.

AI-implementeringar inom organisationer har på den senare tiden växt explosionsartat där chatbotar baserade på generativ AI beräknas bli allt mer populärt. Tidigare forskning har framförallt bedrivit kvantitativa studier som dessutom fokuserat på externa chatbotar, där syftet varit att kommunicera med intressenter utanför organisationen. Den här studien fokuserar därför på interna chatbotar som ämnar stötta de anställda i deras vardagliga arbetsuppgifter, med fokus på hur samspelet ser ut mellan medarbetarna och AI på arbetsplatsen. Med hjälp av att använda en kvalitativ forskningsmetod, i form av tio semistrukturerade intervjuer på managementkonsultföretaget Centigo, var studiens syfte att bidra till en djupare förståelse för hur medarbetare på den utvalda organisationen upplever och skapar mening i förhållande till den AI-baserade chatboten på arbetsplatsen, samt vilken påverkan det har på deras självbild och den kommunikation som de för inom verksamheten. Genom att analysera empirin med en socialkonstruktivistisk utgångspunkt och meningsskapande som huvudsakligt analysverktyg, visade resultatet att chatboten både påverkar medarbetarnas självbild och de kommunikationsprocesser som medarbetarna för inom organisationen. Framförallt i form av att den digitala identiteten riskerar att bli alltmer generisk, vilket gör att de anställdas personlighet och karisma får en större betydelse i det fysiska rummet. Enklare problem tas dessutom numera med chatboten istället för med en kollega, vilket öppnar upp för ett nytt sätt att kommunicera på arbetsplatsen. Sammantaget var det tydligt att de mänskliga relationerna fyller en viktig funktion på arbetsplatsen, där det fanns skepticism mot att AI någonsin kommer kunna ersätta det. / AI implementations within organizations have recently grown exponentially, with chatbots based on generative AI expected to become increasingly popular. Previous research has primarily conducted quantitative studies, focusing on external chatbots aimed at communicating with external stakeholders. This study therefore focuses on internal chatbots intended to support employees in their daily work tasks, with a focus on the interaction between employees and AI in the workplace. By employing a qualitative research method through ten semi-structured interviews at the management consulting firm Centigo, the aim of the study was to contribute to a deeper understanding of how employees in the selected organization perceive and create meaning in relation to the AI-based chatbot in the workplace, and its impact on their self-image and the communication they engage in within the organization. By analyzing the empirical data from a social constructivist perspective and using sensemaking as the main analytical tool, the results showed that the chatbot affects both employees' self-image and the communication processes within the organization. Particularly, the digital identity risks becoming more generic, emphasizing the importance of employees' personality and charisma in physical interactions. Also, simpler issues are now handled with the chatbot rather than with a colleague, which introduces a new way of communicating in the workplace. Overall, it was evident that human relationships serve a vital function in the workplace, with skepticism towards AI ever being able to replace them.

When a production company commits to creating a film based on a novel, it is essential that their team is equipped to manage the extensive responsibilities required to authentically translate the book to the big screen. This study aims to explore and address these challenges by utilizing contemporary Generative Artificial Intelligence technologies, including Large Language Models, Text-To-Speech, and Text-To-Image models. While recent advancements have focused on enhancing these models, there is a gap in research on their practical application and effectiveness in real-world scenarios. This research will detail the steps necessary to deconstruct a novel’s narrative and produce the final cinematic product. Additionally, it will propose novel methods to mitigate errors such as hallucinations generated by Language Models and image models, enhancing the fidelity and quality of the adaptations. iii

Access to the internet is more available than ever before for small children and adolescents, along with an increasing number of channels for using Generative Artificial Intelligence (GAI). For parents of children and teens, this is a new frontier with innovative tools, terminology, and effects that test the integrity of existing parental mediation strategies for modern media. The lack of research aimed at parental awareness of GAI or how this tech can influence children’s well-being led us to fill this current gap and gain valuable insights for future use. The present study explores the current state of parental awareness regarding GAI and its effects on the well-being of children and what mediation strategies parents employ to mitigate these effects. By using the Parental Mediation Theory (PMT) as a theoretical framework, patterns gathered through conducting semi-structured interviews with parents (N=10) are identified with thematic analysis. Through these interviews, themes are uncovered that shed light on how parents perceive GAI in the context of the effects that such technology has on their children, as well as how it could impact their children’s well-being in the future. The conclusion of this study reveals that while most parents know about GAI, many parents are not aware of the less-familiar effects of this technology being used for media manipulation, chatbot companionship or educational assignments that can have a potentially negative impact on the well-being of their children. Stemming from the PMT, a new parental mediation strategy emerged from an analysis of the collected data. This strategy is called ‘planned mediation’ and it serves to proactively protect children from GAI and its less-familiar effects, rather than responding reactively with the mediation strategies that currently exist.

Businesses are increasingly integrating AI to enhance efficiency and remain competitive; it is anticipated that AI will automate up to 50% of jobs by 2033, streamlining activities within marketing and sales teams by enhancing productivity and reducing costs (Frey &amp; Osborne, 2017). However, the application of generative AI in the B2B sector remains underexplored, despite its potential to transform complex, interaction-heavy marketing and sales processes where salespeople are crucial in building trust and long-term relationships through demanding informational exchanges. This research investigates the application of generative AI within B2B marketing and sales operations and how it can enhance firms’ competitive advantage. Utilizing the Situated AI Framework, the study empirically evaluates the relationship between specific AI-driven activities and competitive advantage in B2B settings. Through a qualitative research approach involving three detailed case studies, this research examines the strategic utilization of generative AI, focusing on grounding, bounding, and recasting activities as defined by the framework. Findings indicate that generative AI contributes to competitive advantage by enhancing operational efficiency, improving customer engagement, and enabling strategic data-driven decision-making. Each activity within the framework appears to play a significant role in adapting generative AI technologies to align with specific firm objectives. This adaption potentially enhances more than just task automation, but also possibly contributes to a strategic enhancement of the firms’ competitive advantage. In conclusion, this study encourages further investigation to validate and expand upon the strategic applications of generative AI in B2B marketing and sales operations. Adopting a quantitative approach in future studies could provide more comprehensive insights into how grounding, bounding and recasting activities enhance competitive advantages.

Det är svårt att undgå de återkommande diskussionerna om artificiell intelligens (AI), specifikt generativ AI och dess allt mer utbredda användning. Utvecklingen av exempelvis chatbotar, såsom ChatGPT, markerar en övergång mot teknik som anpassar sig efter människan och inte tvärtom, vilket väckt frågor kring användarupplevelser och design. Därför ämnar den här undersökningen att skapa djupare förståelse för vad som motiverar människor att börja och fortsätta använda ChatGPT, den senaste generationen av chatbot. För att göra det används Uses and gratification theory som ramverk för undersökningen då den kan identifiera bakomliggande motivation och vilka tillfredsställelser användaren får vid användandet av ett visst medium. I motsats till tidigare forskning som huvudsakligen använt enkäter och frågeformulär, använder denna studie semistrukturerade intervjuer för att få insikt i användarnas perspektiv och upplevelser. För att sortera den empiri som samlats in gjordes ett affinitetsdiagram. Den sorterade datan har sedan analyserats genom linsen av Uses and gratification theory.  Resultatet från undersökningen visar att det finns fyra primära motivationer för att börja och fortsätta sin användning av ChatGPT: tidseffektivitet, produktivitet, nyfikenhet och tillit. Det framgår att nyfikenhet initierar användningen medan tidseffektivitet och produktivitet är de primära motivationerna till fortsatt användning. Vidare anses tillit vara den kritiska motivationen som behöver tillfredsställas för att bibehålla användares engagemang. Slutsatsen är att framtida forskning och designprocesser kan med fördel inkludera personer som idag inte använder den här typen av tjänster för att kunna identifiera fler bakomliggande orsaker till bristande tillit och motivation. Det finns även ett behov av att utöka eller ta fram nya ramverk för att undersöka och identifiera nya motivationsfaktorer och tillfredsställelser i det föränderliga landskapet av digitala tjänster. Slutligen anses det kritiskt att den fortsatta utvecklingen av ChatGPT och liknande tjänster antar en användarcentrerad approach för att säkerställa en positiv användarupplevelse. / It is difficult to avoid the recurring discussions about artificial intelligence (AI), specifically generative AI and its increasingly widespread use. The development of chatbots, such as ChatGPT, marks a transition toward technology that adapts to humans rather than the other way around, raising questions about user experiences and design. Therefore, this study aims to create a deeper understanding of what motivates people to start and continue using ChatGPT, the latest generation of chatbots. To achieve this, the Uses and Gratifications Theory is used as the framework for the study, as it can identify underlying motivations and the gratifications users derive from using a particular medium. In contrast to previous research that mainly used surveys and questionnaires, this study employs semi-structured interviews to gain insights into users' perspectives and experiences. Affinity diagrams were used to sort the collected empirical data. The sorted data has then been analyzed through the lens of the Uses and Gratifications Theory. The results of the study show that there are four primary motivations for starting and continuing the use of ChatGPT: time efficiency, productivity, curiosity, and trust. It appears that curiosity initiates usage, while time efficiency and productivity are the primary motivations for continued use. Furthermore, trust is considered the critical motivation that needs to be satisfied to maintain user engagement. The conclusion is that future research and design processes could benefit from including people who do not currently use this type of service to identify more underlying causes of lack of trust and motivation. There is also a need to expand or develop new frameworks to explore and identify new motivational factors and gratifications in the evolving landscape of digital services. Finally, it is deemed critical that the continued development of ChatGPT and similar services adopts a user-centered approach to ensure a positive user experience.