Recent public artificial intelligence (AI) advancements, particularly ChatGPT, are predicted to transform whole industries, work roles and organisational structures, leading to some jobs becoming obsolete while also creating new opportunities. This qualitative research explores the effects of ChatGPT on work roles and organisations in the information technology (IT) industry, more specifically, the effects on skills, competence, and organisational processes such as the automation of routine and non-routine tasks. The aim is to fill the gap in how ChatGPT affects the IT industry and to provide recommendations for policy makers, companies, and workers to address these challenges. Two research questions were formulated: “How does the increasing adoption of ChatGPT in internal work processes of businesses in the IT industry change work roles” and “impact the organisation and what are the potential implications for changes in work roles due to ChatGPT?”. To explore and answer these questions two data collection methods were used such as semi-structured interviews and qualitative questionnaires, with a combined sample size of 14 participants. The data was analysed using thematic as well as content analysis and the theoretical framework. The findings suggest that adopting ChatGPT is indeed transforming work roles and organisations by automating routine and non-routine tasks, leading to efficiency and cost savings. While some roles and skills change, others become entirely obsolete. The impact varies based on organisational factors, the nature of work and adaptability to new technologies, leading to the emergence of new opportunities in AI management and big data. Smaller companies in particular benefit from implementing ChatGPT, allowing focus on other tasks such as for example strategic development. Organisational challenges include training employees and adapting to new technology as well as concerns for job loss.

Artificial Intelligence (AI) is adopted fast and wide across consumer industries and digital marketing. This new technology has the potential to enhance online customer experience and outcomes of customer experience. However, research relating to the impact of AI is still developing and empirical evidence sparse. Taking a consumercentred approach and by adopting Social Response Theory as theoretical lens, this research addresses an overall research question pertaining to the implications of online customer experience with AI on consumer behaviour. A quantitative research strategy with positivist approach is adopted to gather a large sample (n= 489) of online consumers who have previously interacted with AI-enabled technology. The collected data is analysed statistically utilising Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). Empirical findings show strong positive effects of anthropomorphism of AI, para-social interaction with AI, and performance expectancy of AI on all three customer experience dimensions of informativeness, entertainment and social presence. Additionally, there is strong statistical support for the positive effect of informativeness and social presence on continued purchase intentions (β= .379 and β= .315), while the effects of entertainment are less strong. The mediating effects of customer experience have been assessed, highlighting social presence as most important mediator. This research contributes to knowledge by extending previous customer experience theory and quantifying the influence of online customer experience with AI on purchase intentions and eWOM. The theoretical insights also translate into direct implications for marketing practice relating to the design, integration, and implementation of more consumer- and outcome-oriented AI applications. / Faculty of Management, Law and Social Sciences studentship

Electronic Health Record (EHR) creates a huge number of databases which are being updated dynamically. Major goal of interoperability in healthcare is to facilitate the seamless exchange of healthcare related data and an environment to supports interoperability and secure transfer of data. The health care organisations face difficulties in exchanging patient’s health care information and laboratory reports etc. due to a lack of semantic interoperability. Hence, there is a need of semantic web technologies for addressing healthcare interoperability problems by enabling various healthcare standards from various healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its semantics which can be understood by both machines and humans. Thus, a framework with a similarity analyser has been proposed in the thesis that dealt with semantic interoperability. While dealing with semantic interoperability, another consideration was the use of word embedding and ontology for knowledge discovery. In medical domain, the main challenge for medical information extraction system is to find the required information by considering explicit and implicit clinical context with high degree of precision and accuracy. For semantic similarity of medical text at different levels (conceptual, sentence and document level), different methods and techniques have been widely presented, but I made sure that the semantic content of a text that is presented includes the correct meaning of words and sentences. A comparative analysis of approaches included ontology followed by word embedding or vice-versa have been applied to explore the methodology to define which approach gives better results for gaining higher semantic similarity. Selecting the Kidney Cancer dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word embedding to enrich data first has shown better results. Apart from enriching the EHR, extracting relevant information is also challenging. To solve this challenge, the concept of analogy has been applied to explain similarities between two different contents as analogies play a significant role in understanding new concepts. The concept of analogy helps healthcare professionals to communicate with patients effectively and help them understand their disease and treatment. So, I utilised analogies in this thesis to support the extraction of relevant information from the medical text. Since accessing EHR has been challenging, tweets text is used as an alternative for EHR as social media has appeared as a relevant data source in recent years. An algorithm has been proposed to analyse medical tweets based on analogous words. The results have been used to validate the proposed methods. Two experts from medical domain have given their views on the proposed methods in comparison with the similar method named as SemDeep. The quantitative and qualitative results have shown that the proposed analogy-based method bring diversity and are helpful in analysing the specific disease or in text classification.

Our society is becoming more and more digital and the outbreak of COVID-19 has stressed this process even more. Thus, the need for online teaching and learning has increased and many new advancements in technology have been made. These advancements have enabled the use of Artificial Intelligence (AI), and recent years have consequently witnessed increased attention to the use of AI for educational experiences, not least in K-12 schools. However, today little is known about how students perceive AI-based teaching which makes this area interesting to explore further. This study investigated childrens’ perception and learning experience of videos of a human-looking AI instructor, in comparison to videos of a human instructor. If there would appear to be no negative effects on childrens’ learning experience of an AI instructor, this could be used as a basis for developing the growing field of online education applications for children. The study has undertaken a case study approach. Data were collected through user tests and interviews with six children. The respondents were given video instructions by an AI generated instructor and a human instructor in order to evaluate how their perceived learning experience differs between these two. Primary findings indicate that the respondents notice only small differences between the two instructors. However, the answers from the respondents were very diverse, indicating that some respondents preferred the AI instructor while some preferred the human instructor. A lot of interesting findings, such as that children do not seem to be very observant with small malfunctions, are being discussed, indicating that children's learning experience might at least not be negatively affected by an human-looking AI instructor instead of a human instructor. / Vårt samhälle blir allt mer digitaliserat och utbrottet av COVID-19 har påskyndat denna utveckling ännu mer. Således har behovet av onlineundervisning ökat, och likaså har många nya framsteg inom tekniken gjorts. Dessa framsteg har sedan möjliggjort användningen av artificiell intelligens (AI), och följaktligen har man på senare år sett en ökad uppmärksamhet kring användningen av AI inom utbildning, särskilt i grundskolor. Idag är det dock relativt outforskat hur elever uppfattar AI-baserad undervisning vilket gör detta område intressant att utforska vidare I denna studie undersöks barns uppfattning och lärandeupplevelse av videor med en AI-instruktör med mänskligt utseende i jämförelse med videor med en riktig mänsklig instruktör. Om det inte verkar finnas några negativa effekter på barns lärandeupplevelse av en AI-instruktör kan detta användas som en grund för att utveckla det växande området för onlineutbildning applikationer för barn. Studien genomfördes genom en fallstudie. Data samlades in genom användartester och intervjuer med sex barn. Respondenterna fick videoinstruktioner av en AI-genererad instruktör och en mänsklig instruktör för att utvärdera hur deras upplevda lärandeupplevelse skiljer sig mellan dessa två. Resultatet visade på att deltagarna märkte även små skillnader mellan de två instruktörerna. Svaren från deltagarna varierade dock mycket och antyder att vissa deltagare föredrog AI-instruktören medan andra föredrog mänskliga instruktören. Många intressanta insikter diskuteras i denna studie, t.ex., att barn tenderar att inte vara så observanta på små felfunktioner, vilket indikerar på att barns lärandeupplevelse åtminstone inte verkar påverkas negativt av en AI-instruktör med mänskligt utseende istället för en människa.

The study examines how explainable AI (XAI), and transparency can increase trust and facilitate the adoption of AI technologies within small and medium-sized enterprises (SMEs). These businesses face significant challenges in integrating AI due to limited technical expertise and resources. The purpose of the study is to explore how XAI could bridge the gap between complex AI models and human understanding, thereby enhancing trust and operational efficiency.   The research methodology includes a case study with a literature review and expert interviews. The literature review provides background and context for the research question, while the expert interviews gather insights from employees in various roles and with different levels of experience within the participating SMEs. This approach offers a comprehensive understanding of the current state of AI adoption and the perceived importance of XAI and transparency.   The results indicate a significant knowledge gap among SME employees regarding AI technologies, with many expressing a lack of familiarity and trust. However, there is strong consensus on the importance of transparency and explainability in AI systems. Participants noted that XAI could significantly improve trust and acceptance of AI technologies by making AI decisions more understandable and transparent. Specific benefits identified include better decision support, increased operational efficiency, and enhanced customer confidence.   The study concludes that XAI and transparency are crucial for building trust and facilitating the adoption of AI technologies in SMEs. By making AI systems more comprehensible, XAI addresses the challenges posed by limited technical expertise and promotes broader acceptance of AI. The research emphasizes the need for continuous education and clear communication strategies to improve AI understanding among stakeholders within SMEs.   To enhance transparency and user trust in AI systems, SMEs should prioritize the integration of XAI frameworks. It is essential to develop user-centered tools that provide clear explanations of AI decisions and to invest in ongoing education and training programs. Additionally, a company culture that values transparency and ethical AI practices would further support the successful adoption of AI technologies. The study contributes to the ongoing discourse on AI adoption in SMEs by providing empirical evidence on the role of XAI in building trust and improving transparency. It offers practical recommendations for SMEs to effectively leverage AI technologies while ensuring ethical and transparent AI practices in line with regulatory requirements and societal expectations. / Studien undersöker hur förklarbar AI (XAI) och transparens kan öka förtroendet och underlätta införandet av AI-teknologier inom små och medelstora företag (SME). Dessa företag står inför betydande utmaningar vid integrationen av AI på grund av begränsad teknisk expertis och resurser. Syftet med studien är att undersöka hur XAI kan överbrygga klyftan mellan komplexa AI-modeller och mänsklig förståelse, vilket i sin tur främjar förtroende och operationell effektivitet.   Forskningsmetodiken inkluderar en fallstudie med en litteraturöversikt och expertintervjuer. Litteraturöversikten ger bakgrund och kontext till forskningsfrågan, medan expertintervjuerna samlar insikter från anställda i olika roller och med olika erfarenhetsnivåer i de deltagande SMEs. Detta tillvägagångssätt gav en omfattande förståelse av det nuvarande tillståndet för AI adoption och den upplevda vikten av XAI och transparens.   Resultaten visar på en betydande kunskapslucka bland SME-anställda när det gäller AI teknologier, med många som uttrycker en brist på bekantskap och förtroende. Det råder dock stark enighet om vikten av transparens och förklarbarhet i AI-system. Deltagarna angav att XAI avsevärt kunde förbättra förtroendet och acceptansen av AI-teknologier genom att göra AI beslut mer förståeliga och transparenta. Specifika fördelar som identifierades inkluderar bättre beslutsstöd, ökad operationell effektivitet och ökat kundförtroende.   Studien drar slutsatsen att XAI och transparens är avgörande för att skapa förtroende och underlätta införandet av AI-teknologier i SME. Genom att göra AI-system mer förståeliga adresserar XAI utmaningarna med begränsad teknisk expertis och främjar en bredare acceptans av AI. Forskningen understryker behovet av kontinuerlig utbildning och tydliga kommunikationsstrategier för att förbättra AI-förståelsen bland intressenter inom SME.   För att öka transparensen och användarförtroendet i AI-system bör SME prioritera integrationen av XAI-ramverk. Det är viktigt att utveckla användarcentrerade verktyg som ger tydliga förklaringar av AI-beslut och att investera i kontinuerliga utbildnings- och träningsprogram. Dessutom kommer en organisationskultur som värderar transparens och etiska AI-praktiker ytterligare stödja det framgångsrika införandet av AI-teknologier. Studien bidrar till den pågående diskursen om AI-adoption i SME genom att tillhandahålla empiriska bevis på rollenav XAI i att bygga förtroende och förbättra transparens. Den erbjuder praktiska rekommendationer för SME att effektivt utnyttja AI-teknologier, och säkerställa etiska och transparenta AI-praktiker som är i linje med regulatoriska krav och samhälleliga förväntningar.

This study examined how Swedish digital marketers use AI and its impact on their workflow, as well as the limitations of adopting AI for digital marketing. Semi-structured interviews were performed with Swedish digital marketing professionals, and a thematic analysis was conducted to identify themes and patterns that appeared in the data collection.  The software used by participants varied, but all seven utilized ChatGPT. Five used Google Ads and Google Analytics, three used Adobe software (including Adobe Firefly and Photoshop), and two used Midjourney. Other software was also used independently by participants. The findings indicate that AI has enhanced perceived productivity and proved valuable to marketers. Those who employed AI technology reported more effective work sessions and shorter work completion times. However, the study also identified significant limitations of AI in digital marketing. These limitations include AI's inability to match human creativity, which limits the development of creative brand storylines, campaign designs, and content production. Additionally, issues with the tone and accuracy of generative AI content highlight the need to maintain authenticity and reliability in marketing communications. Marketers expressed concerns about the accuracy and quality of AI-generated information and sought clearer guidelines and regulations regarding the use of AI tools.

Yes / This chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.

Artificial Intelligence is a fuzzy concept. My role, as I see it, is to put down a working definition, a criterion, and a set of assumptions to set up equations for a workable methodology. This research introduces the notion of Artificial Intelligent Agency, denoting the application of Artificial General Intelligence. The problem being handled by mathematics and logic, and only thereafter semantics, is Self-Supervised Machine Learning (SSML) towards Intuitive Vehicle Health Management, in the domain of cybernetic-physical science. The present work stems from a broader engagement with a major multinational automotive OEM, where Intelligent Vehicle Health Management will dynamically choose suitable variants only to realise predefined variation points. Physics-based models infer properties of a model of the system, not properties of the implemented system itself. The validity of their inference depends on the models’ degree of fidelity, which is always an approximate localised engineering abstraction. In sum, people are not very good at establishing causality. To deduce new truths from implicit patterns in the data about the physical processes that generate the data, the kernel of this transformative technology is the intersystem architecture, occurring in-between and involving the physical and engineered system and the construct thereof, through the communication core at their interface. In this thesis it is shown that the most practicable way to establish causality is by transforming application models into actual implementation. The hypothesis being that the ideal source of training data for SSML, is an isomorphic monoid of indexical facts, trace-preserving events of natural kind.

The pandemic produced by the COVID-19 virus has resulted in an estimated 6.4 million deaths worldwide and a rise in unemployment rates, notably in the UK. Healthcare monitoring systems encounter several obstacles when regulating and anticipating epidemics. The study aims to present the AF-HIDOP model, an artificial neural network Fast Fourier Transform hybrid technique, for the early identification and prediction of the risk of Covid-19 spreading within a specific time and region. The model consists of the following five stages: 1) Data collection and preprocessing from reliable sources; 2) Optimal machine learning algorithm selection, with the Random Forest tree (RF) classifier achieving 94.4% accuracy; 3) Dimensionality reduction utilising principal components analysis (PCA) to optimise the impact of the data volume; 4) Predicting case numbers utilising an artificial neural network model, with 52% accuracy; 5) Enhancing accuracy by incorporating Fast Fourier Transform (FFT) feature extraction and ANN, resulting in 91% accuracy for multi-level spread risk classification. The AF-HIDOP model provides prediction accuracy ranging from moderate to high, addressing issues in healthcare-based datasets and costs of computing, and may have potential uses in monitoring and managing infectious disease epidemics.

In healthcare, the rising demand for medical services, compounded by a shortage of professionals, presents significant challenges. To address these issues, the healthcare industry has turned to artificial intelligence (AI) to enhance various services such as disease diagnosis, medical imaging interpretation, clinical laboratory tasks, screenings, and health communications. By offering real-time, human-like interactions, AI-driven chatbots facilitate access to healthcare information and services, aiding symptom analysis and providing preliminary disease information before professional consultations. This initiative aims not only to reduce healthcare costs but also to enhance patient access to medical data. Despite their growing popularity, AI-enabled chatbots or conversational agents chatbots in the healthcare disease diagnosis domain continue to encounter obstacles such as a limited user adoption and integration into healthcare systems. This study addresses a gap in the existing literature on the adoption of AI enabled healthcare disease diagnosis chatbots by analysing the elements that influence users' behavioural intention to utilize AI-enabled disease diagnosis chatbots. Employing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical framework, this quantitative study began with exploratory research to define its scope and context, followed by a survey of 130 participants. The study utilized multiple linear regression and Pearson correlation analysis to evaluate the data. The outcomes suggest that performance expectancy, habits, social influence, and trust significantly associated with the individuals’ behavioural intentions to use AI-enabled chatbots for disease diagnosis. The results of this study reveal that performance expectancy, habits, social influence, and trust significant association with intention to use AI-enabled chatbots for disease diagnosis. The outcomes of this study contribute to existing knowledge in information systems, particularly identifying key factors that boost user adoption of AI-enabled chatbot applications for disease diagnosis. These insights can guide system designers, developers, marketers, and promotors involved in developing, revamping, and promoting chatbot applications, considering the influential factors discovered in this research, thereby increasing the usage of chatbot apps. Furthermore, the research model developed here could serve as a valuable model for future studies on disease diagnostic chatbot applications.