Background: As the landscape of the fashion industry is challenged by the emergence of big data and high sustainability demands, efficient solutions for product innovation and development are required. Artificial Intelligence (AI) is generating organizational shifts in various industries, but the fashion industry is still very early in its adoption. AI shows abilities to facilitate the challenges of the industry, and its application in creative design and product development processes is estimated to hold potential. Problem: As the fashion industry is characterized by creativity and human ideation, there is a need to evaluate if AI is compatible with the values of the industry. Management’s attitudes are proven to influence the adoption of digital technologies, leaving implications to study the attitudes of professionals in design and product development towards AI as well. Further, it is relevant to understand the possibilities and limitations of utilizing generative AI in creative processes, to ensure a successful implementation. Purpose: This thesis aims to investigate the implementation of AI in creative ideation and product development within the fashion industry, particularly exploring the attitudes of fashion professionals toward the relationship between human ideation and AI to determine the industry’s current position. Method: This study utilized qualitative research design by conducting 10 semi-structured interviews with professionals working in the fields of fashion design, product development, and AI. Conclusion: The results show that AI is currently not implemented within fashion, among the interviewees. The study identified determinants, such as awareness, attitudes, data, knowledge, objectives, and competencies that influence the adoption of AI, in the early stages. The attitudes toward AI are an essential factor in the early stages of adoption.

The study aims to investigate user engagement in the Generated Artificial Intelligence (GAI) marketing content within social media marketing. The increasing prominence of GAI content has sparked diverse reactions, particularly concerning the safety of personalized content and individuals' awareness of their data being utilized for customization. The main goal of this research is to understand the impact of GAI on user engagement in social media marketing, focusing on the utilization of customer data and preferences on social media platforms. The research unfolds as an empirical exploration, employing a quantitative approach and a structured questionnaire. In conclusion, the research implications emphasize the significance of transparent communication, strategic content creation, and a nuanced understanding of the relationship between awareness, personalization, and consumer engagement in the context of AI-generated content. Marketers can leverage these insights to refine their strategies, enhance user experiences, and build trust in the era of AI-driven marketing.

The purpose of the study was to examine whether it was possible to discern any effect on the perception of the content of a copytext in terms of credibility and conviction, depending on whether the writer was ChatGPT or a human copywriter, and whether it was possible to discern any effect on the perception of the writer in terms of creativity and professionalism. To achieve the purpose of the study, the following questions were formulated: 1. How credible is an AI-generated text perceived compared to a text written by a human copywriter? 2. How convincing is an AI-generated text compared to a text written by a human copywriter? 3. How credible is ChatGPT compared to a human copywriter? 4. Is there a connection between the attitude towards AI and the perception of the content? The theoretical framework of the study consisted of theory and previous research regarding generative artificial intelligence (GAI), recommendations for writing a prompt, attitudes towards AI, the copywriting competence and recommendations for writing an effective copytext. The theoretical concepts of credibility and persuasion are two central concepts of the study that have been operationalised into competencies and recommended content as well as unique selling proposition (USP), call to action (CTA), rhetoric and storytelling. The results show that those who read the AI-generated copy text had a more positive perception of the content and the writer than those who read the human-written copy text. However, the results show that the creativity of the writer was perceived equally by both experimental groups. Overall, there were no statistically significant relationships between the perception of the content and the attitude towards AI. Based on the significance of the results and their mean values, the study's conclusions are that the text written by ChatGPT is perceived to be more credible in terms of competence and recommended content than the text written by the human copywriter and that the text written by ChatGPT is perceived to be more convincing in terms of USP, CTA, rhetoric and storytelling than the text written by the human copywriter. We can also conclude that ChatGPT is perceived to be more credible in terms of competence operationalised to professionalism, than the text written by the human copywriter, which, however, could be due to chance as the result was not statistically significant. The result of the study shows that ChatGPT threatens copywriting skills. However, it is not the AI tool itself that threaten the professional role, as it currently still requires human involvement. Instead, it is the competence in prompt engineering that threatens the profession, as this knowledge, according to the study, is crucial for achieving a credible and convincing AI-generated copytext. / Syftet med studien var att undersöka om det gick att urskilja någon effekt på upplevelsen av innehållet i en copytext i form av trovärdighet och övertygelse, beroende på om skribenten var ChatGPT eller en mänsklig copywriter, samt om det gick att urskilja någon effekt på upplevelsen av skribenten i form av kreativitet och professionalitet. För att uppnå studiens syfte formulerades följande frågeställningar: 1. Hur trovärdig upplevs en AI-genererad text jämfört med en text skriven av en mänsklig copywriter? 2. Hur övertygande upplevs en AI-genererad text jämfört med en text skriven av en mänsklig copywriter? 3. Hur trovärdig upplevs ChatGPT jämfört med en mänsklig copywriter? 4. Finns det ett samband mellan attityden till AI och upplevelsen av innehållet? Studiens teoretiska ramverk bestod av teori och tidigare forskning gällande generativ artificiell intelligens (GAI), rekommendationer för att skriva en prompt, attityder till AI, copywritingkompetensen och rekommendationer för att skriva en effektiv copytext. De teoretiska begreppen trovärdighet och övertygelse är två centrala begrepp för studien som har operationaliserats till kompetens och rekommenderat innehåll samt unique selling proposition (USP), call to action (CTA), retorik och historieberättande. Resultaten visar att de som fick läsa den AI-genererade copytexten hade en mer positiv upplevelse av innehållet och skribenten än de som fick läsa den mänskligt skrivna copytexten. Resultaten visar däremot att kreativiteten hos skribenten upplevdes lika hög av de båda experimentgrupperna. Det fanns överlag inga statistiskt signifikanta samband mellan upplevelsen av innehållet och attityden till AI. Utifrån resultatens signifikans och dess medelvärden landar studiens slutsatser i att texten skriven av ChatGPT upplevs vara mer trovärdig i form av kompetens och rekommenderat innehåll, än texten skriven av den mänskliga copywritern och att texten skriven av ChatGPT upplevs vara mer övertygande i form av USP, CTA, retorik samt historieberättande, än texten skriven av den mänskliga copywritern. Vi kan även konstatera att ChatGPT upplevs vara mer trovärdig i form av kompetens som operationaliserats till professionalitet, än texten skriven av den mänskliga copywritern, vilket däremot skulle kunna bero på slumpen då resultatet inte var statistiskt signifikant. Sammanfattningsvis kan vi utifrån studiens resultat dra slutsatsen att ChatGPT hotar copywritingkompetensen. Däremot är det inte AI-verktyget i sig som hotar yrkesrollen, då det i nuläget fortfarande kräver mänsklig involvering. Det är i stället kompetensen inom prompt engineering som hotar yrkesrollen, då denna kunskap enligt studien är avgörande för att åstadkomma en trovärdig och övertygande AI-genererad copytext.

This thesis investigates the impact of generative AI technologies on deliberative democracy in Latin America, focusing on the cases of Peru and Brazil. Through qualitative interviews with experts from both countries, this research uses several techniques to process the data collected and intends to draw conclusions from it. The study found that unregulated generative AI is a source of concern for Peruvian and Brazilian participants. Another key finding was that these two countries are not keen on collaborating with other countries in the region to establish a regulatory framework on generative AI. Through the analysis of the five principles of deliberative democracy: citizen participation, inclusivity, accessibility, accountability, and quality of deliberation, this project was able to draw conclusions on which of these dimensions could be impacted by generative AI. The results of the study show that in the case of  Peru, generative AI technologies impact citizen participation and accountability the most.  In the case of Brazil generative AI technologies impact citizen participation, inclusivity, and transparency the most. The most important finding, however, is the commonality shared by Peru and Brazil which signifies that in both countries citizen participation will be impacted by generative AI. The question of how citizen participation will be impacted requires a deeper analysis of the responses of the interviewees as they have both positive and negative perspectives on the issue.

Condition assessment of civil engineering infrastructure systems is of growing importance as they face aging and degradation due to both human-made activities and environmental factors. Nevertheless, challenges persist in data collection, leading to "data scarcity", and the need for frequent site visits in inspections, presenting significant obstacles in the assessment of the civil infrastructure systems. This dissertation aims to overcome these challenges by exploring the potential of two emerging technologies: Generative Artificial Intelligence (AI) and Virtual Reality (VR). In tackling the issue of data scarcity, the research question revolves around how Generative AI can be utilized to mitigate data collection-related constraints and increase data availability, thus facilitating health monitoring applications of infrastructure systems. For that, using various Generative AI models, the dissertation works on acceleration response data generation, including data augmentation and domain translation applications on different structures. In addressing the site visit challenge, the dissertation focuses on the use of VR to bring the infrastructure to the experts in a single collaborative immersive environment and investigate its impact on decision-making in inspections. For that, using VR technology, the dissertation develops a Virtual Meeting Environment (VME) integrated with the infrastructure data and models presented through novel immersive visualization techniques. The dissertation further investigates the impact of VME on decision-making in infrastructure inspections through experimentation with engineers. These investigations of the use of Generative AI and VR demonstrate various contributions. Generative AI effectively tackles the need for vast datasets in data-intensive damage detection applications. It also demonstrates its potential in estimating representative response data for various structural conditions across dissimilar infrastructures. VME, on the other hand, offers an increased understanding of the material along with a safer, practical, and cost-effective complementing alternative to traditional in-person site visits. It further reveals how VME improves decision-making in infrastructure inspections.

The marketing process has undergone significant changes over the years, due to new technologies. Among these advancements, artificial intelligence (AI) has been increasingly used to generate advertising messages and images. This study examines consumers' perceptions of AI-generated advertisements, with a focus on the influence of age. Participants were divided into two groups, with one group believing the ads were human-created and the other assuming they were AI-generated. Results indicated that perceived ad falsity led to more negative perceptions. However, younger individuals exhibited more favorable attitudes toward AI-generated ads compared to older individuals. Despite an overall negative bias towards AI-generated ads, one AI-generated ad was received as most positively across all age groups, supporting prior research that AI-generated content can be well-received. A theoretical model was built and tested to explore the relationship between age and ad perception, suggesting that prior experience with AI, attitudes towards AI, and AI credibility sequentially mediate this relationship. While the effect of machine heuristics was found to be nonsignificant, it did influence AI credibility, indicating potential avenues for future research.

This research thesis aims to explore the interplay between the recent introduction of generative artificial intelligence at work and professional role identity. As the public introduction of generative artificial intelligence shook up people’s lives in November 2022, we have reasons to think that an exploration of workers’ professional role identity regardinggenerative artificial intelligence is relevant. Due to the recent studies related to the massive introduction of artificial intelligence in the content creators’ field, we chose to explore how their role and their identity are evolving. Organizations face challenges in managing artificial intelligence systems and their impact on professional role identity, which, however, has received limited scholarly attention and is not well-understood yet. While prior research has studied various aspects related to artificial intelligence and has suggested that artificial intelligence can enhance productivity, efficiency, and prosperity, it has to a large extent neglected the influence of artificial intelligence on professional role identities. Therefore, this research aims to contribute to artificial intelligence literature by providing empirical insights into the evolution of work and identity.  To address the abovementioned gap, we build on previous research and conduct a qualitative study. We gathered important insights through interviews with several content creators, including photographers and designers from different countries, to discuss their experiences and elaborate a more comprehensive approach to the potential consequences of the introduction of generative artificial intelligence at work. Taking inspiration from a phenomenological approach, clarifications have been brought forward on the reasons exposed by content creators to introduce generative artificial intelligence in their work process and the consequences of such a choice. A broader perspective has been borrowed in order to question the legitimacy of clients and peers regarding the integration of generative artificial intelligence at work. More than adding a layer on the benefits of the introduction of generative artificial intelligence at work, our thesis sheds light on what we call “a dual motion” for workers’role identity, highlighting both interactions between generative artificial intelligence at work and professional role identity, challenging and enhancing each other. Additionally, this research explores manners content creators can enrich their work with generative artificial intelligence. This thesis gains perspective on this subject and aims to expose practical implications for workers to inform and broaden people’s minds about generative artificial intelligence.

Syftet med studien är att skapa en djupare förståelse för hur marknadsförare upplever att generativ artificiell intelligens förändrar marknadsföringsarbetet samt dess kreativa processer.  Studien antar en kvalitativ forskningsmetod. Sju stycken semistrukturerade intervjuer genomfördes och detta följdes av en tematisk analys. Den teoretiska referensramen baseras på tidigare studier. Resultatet påvisar att marknadsförare upplever att generativ AI (GAI) har en betydande påverkan på marknadsförares arbetsuppgifter och på marknadsföringsbranschen. Denna påverkan uppfattas mestadels som positiv. Trots fördelarna som GAI bringar, finns det utmaningar vid implementeringen, särskilt etiska frågor är en utmaning då det ännu inte finns tydliga riktlinjer och lagar för användandet. GAI kommer inte att ersätta marknadsförare helt, men teknologin tar över allt fler uppgifter och effektiviserar många processer, såsom skapande av innehåll, idéer, insikter samt personalisering av innehåll. Människor har en viktig roll i att ställa rätt frågor till GAI, kontrollera, redigera samt utveckla det som genereras. Det är tvetydigt huruvida GAI kan generera kreativt innehåll autonomt, då det framgår att modellerna syntetiserar sin träningsdata snarare än genererar nytt och unikt innehåll. Det är däremot tydligt att kreativitet kan ta sig uttryck på olika sätt, och att GAI effektivt kan stötta marknadsförare i att uppnå kreativa resultat. Denna stöttning kan ske på flera olika sätt.  Studien bidrar med en ökad förståelse för hur marknadsförare upplever att GAI förändrar marknadsföringsarbetet, kreativa processer och möjligheten att nå kreativa resultat. Studien bidrar utöver det med praktiska bidrag till företag. Det föreslås att forska vidare kring samma frågor i takt med att teknologin utvecklas. Vidare föreslås en studie där urvalet avgränsas mer, i syfte att skapa djupare förståelse för ett specifikt område. Utöver det föreslås en kvantitativ studie för att fånga in en mer representativ grupp och därmed kunna generalisera resultatet mer. Avslutningsvis föreslås en kvalitativ studie kring hur lagstiftning kan utformas. / The purpose of this study is to gain a deeper understanding of how marketers perceive the impact of generative artificial intelligence on marketing work and its creative processes. The study adopts a qualitative research method. Seven semi-structured interviews were conducted followed by a thematic analysis. The theoretical framework is based on previous studies. The results indicate that marketers perceive generative AI (GAI) as having a significant impact on their tasks and the marketing industry, the impact is largely perceived as positive. Although GAI offers advantages, challenges exist in its implementation, particularly regarding ethical issues, as clear guidelines and regulations are still lacking. While GAI will not completely replace marketers, it is increasingly taking over tasks and streamlining various processes, such as content creation, idea generation, insights, and content personalization. Humans play a crucial role in posing the right questions to GAI, verifying, editing, and refining the generated content. It remains ambiguous whether GAI can autonomously generate creative content, as it appears that the models synthesize training data rather than produce entirely new and unique content. Nonetheless, it is evident that creativity can manifest in diverse ways, and GAI can effectively support marketers in achieving creative outcomes in various capacities. The study contributes to an enhanced understanding of how marketers perceive that GAI changes marketing work, creative processes, and the ability to achieve creative outcomes. Additionally, the study provides practical insights and contributions for businesses. It is proposed that further research be conducted on the same questions as technology advances. Additionally, a study with a more narrowly defined sample is suggested to gain a deeper understanding of a specific area. Furthermore, a quantitative study is recommended to capture a more representative group, thereby allowing for greater generalization of the results. Finally, a qualitative study on how legislation can be formulated is proposed.

Text-to-image generation is one of the most significant technological advancements in artificial intelligence (AI) during recent years, with the software Midjourney being amongst the most popular tools for this purpose. There is however some uncertainty about how tools like Midjourney will impact and integrate with creative professions, such as graphic design. To successfully integrate graphic designers with image-generating software, it is necessary for the tools to be designed according to the needs of their design process. This study, therefore, examines the requirements graphic designers have for Midjourney to be integrated into the idea generation phase of their design process. Requirements were gathered through interviews, which were then prioritized through surveys. A comprehensive list of requirements was formulated based on these findings. An analysis of the results was also conducted to analyze the prioritization of each individual requirement. The results highlight areas of improvement in Midjourney, with a focus on control and usability. The discussion explores factors that could have influenced the validity of the results, along with a reflection on the study's implementation. Finally, the conclusion of the study outlines the key improvements needed for Midjourney according to the results and suggests directions for future research within the research area.

Deep learning methods applied to chemistry can speed the discovery of novel compounds and facilitate the design of highly complex structures that are both valid and have important societal applications. Here, we present a pioneering exploration into the use of Generative Artificial Intelligence (GenAI) to design coordination cages within the field of supramolecular chemistry. Specifically, the study leverages GraphINVENT, a graph-based deep generative model, to facilitate the automated generation of tetrahedral coordination cages. Through a combination of computational tools and cheminformatics, the research aims to extend the capabilities of GenAI, traditionally applied in simpler chemical contexts, to the complex and nuanced arena of coordination cages. The approach involves a variety of training strategies, including initial pre-training on a large dataset (GDB-13) followed by transfer learning targeted at generating specific coordination cage structures. Data augmentation techniques were also applied to enrich training but did not yield successful outcomes. Several other strategies were employed, including focusing on single metal ion structures to enhance model familiarity with Fe-based cages and extending training datasets with diverse molecular examples from the ChEMBL database. Despite these strategies, the models struggled to capture the complex interactions required for successful cage generation, indicating potential limitations with both the diversity of the training datasets and the model’s architectural capacity to handle the intricate chemistry of coordination cages. However, training on the organic ligands (linkers) yielded successful results, emphasizing the benefits of focusing on smaller building blocks. The lessons learned from this project are substantial. Firstly, the knowledge acquired about generative models and the complex world of supramolecular chemistry has provided a unique opportunity to understand the challenges and possibilities of applying GenAI to such a complicated field. The results obtained in this project have highlighted the need for further refinement of data handling and model training techniques, paving the way for more advanced applications in the future. Finally, this project has not only raised our understanding of the capabilities and limitations of GenAI in coordination cages, but also set a foundation for future research that could eventually lead to breakthroughs in designing novel cage structures. Further study could concentrate on learning from the linkers in future data-driven cage design projects. / Deep learning-metoder (djup lärande metoder) som tillämpas på kemi kan påskynda upptäckten av nya molekyler och underlätta utformningen av mycket komplexa strukturer som både är giltiga och har viktiga samhällstillämpningar. Här presenterar vi en banbrytande undersökning av användningen av generativ artificiell intelligens (GenAI) för att designa koordinationsburar inom supramolekylär kemi. Specifikt utnyttjar studien GraphINVENT, en grafbaserad djup generativ modell, för att underlätta den automatiska genereringen av tetraedriska koordinationsburar. Genom en kombination av beräkningsverktyg och kemiinformatik syftar forskningen till att utöka kapaciteten hos GenAI, som traditionellt tillämpas i enklare kemiska sammanhang, till den komplexa och nyanserade arenan för koordinationsburar. Metoden innebar inledande förträning på ett brett dataset (GDB-13) följt av transferinlärning inriktad på att generera specifika koordinationsburstrukturer. Dataförstärkningstekniker användes också för att berika träningen men gav inte några lyckade resultat. Flera strategier användes, inklusive fokusering på enstaka metalljonsystem för att förbättra modellens förtrogenhet med Fe-baserade burar och utöka träningsdataset med olika molekylära exempel från ChEMBL-databasen. Trots dessa strategier hade modellerna svårt att fånga de komplexa interaktioner som krävs för framgångsrik generering av burar, vilket indikerar potentiella begränsningar inom både mångfalden av träningsdataset och modellens arkitektoniska kapacitet att hantera den invecklade kemin i koordinationsburar. Däremot var träningen på de organiska liganderna (länkarna) framgångsrik, vilket betonar fördelarna med att fokusera på mindre byggstenar. Dock är fördelarna med detta projekt betydande. Den kunskap som förvärvats om hur generativa modeller fungerar och den komplexa världen av supramolekylär kemi har gett en unik möjlighet att förstå utmaningarna och möjligheterna med att tillämpa GenAI på ett så komplicerat område. Erfarenheterna har visat på behovet av ytterligare förfining av datahantering och modellträningstekniker, vilket banar väg för mer avancerade tillämpningar i framtiden. Det här projektet har inte bara ökat vår förståelse för GenAI:s möjligheter och begränsningar i koordinationsburar utan också lagt grunden för framtida forskning som i slutändan kan leda till banbrytande upptäckter i utformningen av nya burstrukturer. Ytterligare studier skulle kunna fokusera på att lära sig från länkarna för att hjälpa framtida datadrivna projekt för burdesign.