La empresa MOK Perú es subsidiaria de MOK Chile y brinda servicios que cubren distintas actividades relacionadas con la asistencia médica y servicios de extensión de garantías de productos, tanto a personas como a empresas. El objetivo principal de este trabajo de investigación, es desarrollar una propuesta de mejora en el proceso de atención de servicios médicos para los subprocesos de servicios de llamadas para consultas médicas, atención médica a domicilio y servicio de ambulancias para la empresa MOK Perú, utilizando soluciones de inteligencia artificial para mejorar el servicio a través de la eficiencia del proceso. Como resultado del estudio realizado para el presente trabajo, hemos identificado que los servicios médicos representan, el 43 % de ingresos del total MOK Perú. Los problemas críticos en la atención a clientes en el call center de los servicios médicos de la empresa se encuentran principalmente en atender las llamadas para el uso de los servicios médicos, las cuales tienen un nivel de 34% de llamadas no atendidas. Se analizó el escenario actual y el escenario con la incorporación de la tecnología del chatbot, el cual nos muestra que si se realiza una inversión de S/. 255,000.00 se estima un crecimiento de rentabilidad del 5% para el quinto año, así como cumplir con los indicadores de gestión de la empresa: disminuir el tiempo promedio de espera de 22 a 2 segundos, tiempo promedio de operación de 11.8 minutos a 7.5 minutos y la tasa de llamadas no atendidas del 34% al 16%. / The company MOK Peru is a subsidiary of MOK Chile and provides services that cover different activities related to medical assistance and product warranty extension services, both to individuals and companies. The main objective of this research work is to develop a proposal for improvement in the process of medical services for the subprocesses of call services for medical consultations, home health care and ambulance service for the company MOK Peru, using solutions of artificial intelligence to improve the service through the efficiency of the process. As a result of the study conducted for the present work, we have identified that medical services represent 43% of total MOK Peru income. The critical problems in customer service in the call center of medical services of the company are mainly in answering calls for the use of medical services, which have a level of 34% of unanswered calls. The current scenario and the scenario were analyzed with the incorporation of the chatbot technology, which shows us that if an investment of S /. 255,000.00 a 5% profitability growth is estimated for the fifth year, as well as comply with the company's management indicators: decrease the average waiting time from 22 to 2 seconds, average operating time from 11.8 minutes to 7.5 minutes and the rate of unattended calls from 34% to 16%. / Trabajo de investigación

The digitalization of society is a major trend that is currently transforming the businesslandscape (Parviainen et al., 2017, p. 63). Within this digital era, the AI chatbot hasemerged (Hsu & Lin, 2023, p. 2), enabling companies to provide high-quality customerservice 24/7. AI chatbots offer a range of benefits and possibilities that can be utilized invarious industries. There is evidence that AI chatbots have the potential to create loyalcustomers, making it a valuable tool for businesses. Therefore, this thesis investigateswhat AI chatbot attributes can relate to the two dimensions of customer loyalty amongGen Z.The purpose of this research is to help organizations to build relationships with customersand provide insight into which attributes of an AI chatbot can be used by companies tocreate value for customers. This to further facilitate their work of building customerloyalty. In this study, we utilize a framework to quantitatively measure AI chatbotsthrough various attributes, including Availability, Ease of Use, Accuracy of Response,Responsiveness, Assurance, and Empathy. Then the relationship of the attributes washypothesized together with the two dimensions of customer loyalty, attitudinal andbehavior loyalty. The questionnaires was distributed to Gen Z through snowball and self-selection sampling techniques. Through the online survey, 73 responses were collected totest the hypothesis. It was found that empathy has a significant relationship with bothdimensions of customer loyalty, and assurance has a significant relationship withattitudinal loyalty.We are convinced that this strengthens and reinforces the ongoing research on customerloyalty. Moreover, the study expands the quantitative work on AI chatbots by introducingand utilizing a framework to examine the attributes of AI chatbots in a general context.Furthermore, the thesis provides knowledge to companies that are currently working onor developing AI chatbots, of which attributes that should be prioritized to enhancecustomer loyalty. Our findings suggest that these attributes are assurance and empathy.

Problem formulation: The problem that the thesis research relates to is the limitations of artificially intelligent chatbots as interlocutors. The emotional component of communication plays an essential role in the customer experience, but many users have a negative attitude toward chatbots due to their lack of humanity and empathy. The potential of the new ChatGPT in changing user attitudes toward chatbots is also being explored. However, the limited data available on recent versions of ChatGPT presents an additional challenge for research in this area.  Purpose: Our study aims to study people's emotional responses to human-like chatbots and their impact on user satisfaction. We also explore whether human likeness is a crucial driver of chatbot preference and how the new ChatGPT can change user attitudes toward them in a positive way.  Theoretical framework: The study's theoretical framework considers various aspects of using chatbots based on artificial intelligence (AI) in marketing. In this context, we observe ChatGPT as a revolutionary breakthrough in customer service, capable of improving customer experience and interaction with customer. We emphasise the emotional component of human-chatbot interactions, investigating customer emotions, attitudes, and trust, as well as the chatbot's capacity for empathy and human-like characteristics. Drawing from this theoretical exploration, we formulate four hypotheses to guide our research. Methodology: This quantitative study involves 79 respondents aged 18 years and over. The online survey was conducted using social media for dissemination. The empirical data obtained were coded and analysed using the SPSS program.  Empirical findings: Our study confirms the hypothesis of diverse emotional responses (H4) and a generally neutral emotional response during chatbot interactions (H3). We also find partial support for the presence of negative emotions (H2), but not for consistent positive emotions (H1). The data indicate a range of emotional responses, highlighting the complexity of human reactions to chatbots. Conclusion: Our research provides an overall picture of users' emotional responses to interactions with chatbots. Users show a variety of emotions, mostly neutral, which can change depending on the interaction. We also discovered the potential of the new ChatGPT in changing user attitudes towards chatbots in a more positive or neutral direction. The study also uncovers factors influencing users' emotional responses, such as age, attitudes, and past experiences. The results can be used to develop more effective marketing and business strategies for interacting with chatbots.

The success of software development projects heavily depends on effectively capturing and meeting stakeholders' requirements. However, involving multiple stakeholders with diverse backgrounds and objectives often leads to conflicts among these requirements. These conflicts represent inconsistencies in the system design, resulting in various challenges, including project delays, increased costs, and potential system failures. Previous research has primarily focused on identifying conflicts with algorithms or negotiation, while conversational AI's potential to detect conflicts in real-time has been neglected. This thesis study addresses the challenge of requirement conflicts by proposing a novel approach that leverages conversational AI in the form of a chatbot. The chatbot, developed using the Rasa platform, enables real-time detection of conflicts, focusing on three general types: duplicated (similar), incompatible, and contradictory requirements. During the study, the design science research method is employed to guide the chatbot's development. Further, an experiment is applied to evaluate the chatbot's performance compared with domain experts using four different datasets. The experiment results are presented using F1 scores, which calculate precision and recall for both the chatbot and the experts on each dataset. Overall, the chatbot scored 0.8, while the experts achieved a slightly higher score of 0.86. To determine if there was a statistically significant difference between the two performances, a Wilcoxon signed-rank test was conducted on the results. The analysis showed no significant difference in the F1 score between the chatbot and the experts, indicating the chatbot's feasibility and effectiveness in detecting conflicts. The contribution of this thesis study can advance requirements engineering by providing a user-friendly and efficient method for real-time conflict detection, enhancing the quality and overall success of software development projects.

Till följd av den snabbt växande AI-tekonologin syftar denna studie till att fylla det forskningsgap som finns kring hur AI används inom undervisning. Detta har gjorts genom en kvalitativ studie, baserad på intervjuer och kartläggning, som undersökt hur studenter använder sig av chatboten ChatGPT. ChatGPT är en AI-robot som fungerar på ett konversationsliknande sätt för att härma mänsklig interaktion. Detta möjliggör för ChatGPT att vara ett hjälpmedel och komplement för studenter och kan både skriva och bearbeta texter, svara på frågor och generera sammanfattningar. Problemet kring ChatGPT är att det är ett nytt, obeforskat fenomen som på kort tid tagit stor plats inom utbildning, det finns här ett stort forskningsgap som bör fyllas. Studien ger en bra grund för hur AI inom undervisning bör hanteras genom att svara på forskningsfrågan; Vilka är de huvudsakliga användningsområdena med ChatGPT? samt tillhörande underfrågor; Vilka värden och utmaningar som upplevs i samband med ChatGPT? Vilka AI-baserade verktyg i funktionaliteter studenter önskar ha i lärplattformar baserat på ChatGPT teknologi? Studiens resultat visar att studenter i stor utsträckning använder ChatGPT som ett hjälpmedel som skapar tidsbesparing, underlättar studierna och fungerar som en extra lärare. Studien visar också på upplevda utmaningar som den lathet som användandet leder till samt den begränsade pålitlighet som finns kring ChatGPTs svar. De främsta önskemål om funktionaliteter i lärplattformar som studien resulterat i är att de ska inkludera en sökfunktion, samt en integrerad plattform för informationsåtkomst. Hur studenter i denna studie använder sig av ChatGPT står till viss del till kontrast till vad den begränsade, befintliga forskningen inom området uppmärksammar. Således bidrar studien med en nyanserad bild av användningen av ChatGPT som kan användas som grund för vidare forskning inom området / Due to the rapidly growing AI technology, this study aims to fill the research gap in the field of AI in education. This has been done by examining how students use the chatbot ChatGPT through a qualitative study based on mapping and interviews. ChatGPT is an AI robot that mimics human interaction, enabling it to be a tool and complement for students. It can write and process texts, answer questions, and generate summaries. The problem with ChatGPT is that it is a new, unresearched phenomenon that has in a short time taken a huge part in education, this research gap needs to be filled. By illustrating how students use ChatGPT in terms of; What its main area of use is? What values and challenges are experienced? and what AI-based tools and functionalities students wish to have in learning platforms based on ChatGPT technology, the study provides a good foundation for how AI in education should be handled. The study's results show that students primarily use ChatGPT as a tool that saves time, facilitates studies, and serves as an extra teacher. The study also indicates challenges, such as the laziness that the use leads to and the limited reliability of ChatGPT's answers. The main requests for functionalities in learning platforms resulting from the study are a search function and an integrated platform for information access. How students use ChatGPT in this study stands in contrast to what the limited existing research in the field emphasizes. Thus, the study contributes to a nuanced picture of the use of ChatGPT that can be used as a basis for further research in the area.

Chatbots are becoming more common in modern society, but there are almost no studies that explore both the differences and causes that divides human communication from communication with a chatbot. The aim of this thesis was to explore different recipient design people take when communicating with a human and a chatbot. A chatbot was built and an experiment was conducted that measured the performance and experience of the participants. A thematic analysis then found out causes for these experiences. The study focused on finding new differences in addition to exploring people’s boredom, frustration, understanding, repetition, and performance in a task. The study found differences and causes in people’s recipient design when communicating with a human compared to a chatbot, as well as differences in the performance of a task. Hopefully, this will help future research figure out solutions for the differences found.

Since the coronavirus pandemic in 2019, millions of people started to use online streaming and entertainment services during the lockdown. Streaming solutions such as OTT and IPTV has grown and boosted the live streaming and non-linear streaming revenue, by using new technologies and innovation such as Artificial Intelligence (AI) to improve the video quality, performance, and security of video streaming services. However, QoS and QoE guarantee and traffic management is still a challenge to many multimedia services and systems provided over the Internet. In this paper the analysis of a proposed user-friendly design will be discussed, describing how it can improve the management and troubleshooting experience. And in what way an AI knowledge-based agent can be used to improve and support the operators daily tasks and challenges. With an usability test, the results showed that the combination of an user-centered interface and AI-technology can improve the productivity in video troubleshooting and prevention of common issues. The user-centered design focuses on the user’s goal and needs, using interactive products to help the user achieve their goal. And the AI would assist the operators to identifying network congestion, stream quality, and much more.

Artificial intelligence (AI) is quickly transforming business operations and society, as AI capabilities are incorporated into applications ranging from mobile personal assistants to self-driving cars. The potentially disruptive nature of AI calls for an extensive investigation into all aspects of AI-human interactions at individual, group, organizational and market levels. However, there is paucity of academic information systems (IS) research in this area that goes beyond the development and testing of specific narrow AI capabilities. AI represents an important opportunity for organizational and behavioral IS researchers, but also presents challenges associated with the underlying complexity of AI technologies and the diversity of AI applications. Understanding how existing AI research and business practice relate to traditional areas of IS research is an important step towards creating a comprehensive behavioral and organizational AI research agenda. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to understand the current state of business AI research and practice in business through a quantitative literature review, relate the findings to traditional IS research areas, and identify potentially fruitful research areas for AI-focused IS research. Essays 2 and 3 seek to address specific research questions related to one of such research areas, namely, human interactions with AI enabled applications. Essay 2 focus on user experience with a chatbot, a popular AI application, and Essay 3 explores how user experiences with AI assistant apps differ from their interactions with more traditional IT artifacts.

This thesis investigates the impact of the voice modality on user trust in conversational chatbot interfaces. The assumption is that trust can be increased by adding voice output to a chatbot and by a higher quality of a used text-to-speech synthesis. The thesis first introduces chatbots and the concept of conversational interfaces then defines trust in an online context. Based on this, a model for trust and perceiving factors for credibility, ease of use and risk is defined. An online experiment is conducted where participants run through conversational scenarios with a chatbot while varying the voice output. Followed by a survey to collect data about the perception of the trust factors for a scenario with no voice and two scenarios with different speech synthesis qualities. To analyse the ordinal data from the survey the ”Wilcoxon signed- rank test”, a nonparametric statistical test, is conducted to compare trust for the voice output types. Results show that adding the voice output modality to a conversational chatbot interface increases the user trust towards the service. Furthermore, the assumption that synthesis quality has an effect on trust could not hold true because the results are not statistically significant. On this basis, the limitations of the used methods are discussed and suggestions for further research are proposed. / Detta examensarbete undersöker den inverkan som röstmodaliteten har på användarförtroende i konversationsbaserade gränssnitt för chatbottar. Antagandet är att förtroendet kan ökas, dels genom att addera röstutmatning till chatbotten och dels genom att text-till-röst-syntesen ges hög kvalité. Först introduceras chatbottar och konceptet konversationsbaserade gränssnitt. Därefter definieras örtroende i en online-kontext. Baserat på detta definieras en modell för förtroende samt uppfattningsfaktorer för trovärdighet, lättanvändhet och risk. Ett onlineexperiment utfördes, där deltagare går igenom konversationscenarion med en chatbot medan röstutmatningen varieras. Därefter följde en undersökning ämnad att samla in data om uppfattningen om förtroendefaktorerna för ett scenario utan röst och två scenarion med olika talsyntes-kvalitéer. För att analysera den ordnade datan från undersökningen genomfördes Wilcoxon signedrank-testet, ett ickeparametriskt statistiskt test, för att jämföra förtroenden för de olika röstutmatningstyperna. Resultaten visar att addering av röstutmatningsmodalitet till ett konversationsbaserat chatbotsgränsnitt ökar användares förtroende för tjänsten. Vidare, antagandet att synteskvalitén har effekt på förtroendet kunde inte verifieras därför att resultaten inte är statistiskt signifikanta. Begränsningarna hos de använda metoderna diskuteras och förslag för framtida forskning läggs fram.

Since the first implementation of a simple chatbot was made in1964, countless research and development have been made to makethe fascinating idea of talking to a computer a reality. But not untilrecently, have chatbots started to make an appearance in everydaylives amongst a broader audience. As the popularity of chatbotsincreases, the demands and functionality of the chatbots rises whichconsequently expands the size and complexity of the chatbot. Theconversational data from a chatbot can become very complex andhard to understand. Therefore, to ensure the continuousadvancement of features in chatbots, the developer needs tools andinstruments to compete in the growing market. Through a prototype based design process, a problem amongstdevelopers to visualize and understand the conversational data froma chatbot is first identified and addressed. A Conversational DataVisualization (CDV) prototype in virtual reality is then developedwith the intention to help developers understand and explore theconversational data from the chatbot they are working on. Thedesign of the CDV is based on theories about key features ofvisualizations in 3D and related work that study visualizations withsimilar data structures as the conversational data from chatbots.Furthermore, the features of the CDV is based on the identifiedproblem of visualizing conversational data amongst developers.Due to the importance of participatory design in a design process,an exploratory usability test of the CDV prototype was conductedto further explore the design choices regarding the identifiedproblem. The conversational data is visualized with tree structures in acircular formation to allow for visualization of links betweendifferent conversations. Results from the explorative usability testindicates that the visualization gave the users of the CDV anunderstandable overview of the conversational data. However,finding specific stories and nodes in the conversational data wasidentified as a problem due to inadequate information in theoverview of the visualization. / Sedan den första implementationen av en chatbot år 1964, har en stor mängd forskning och utveckling skett för att göra den fascinerandeidén att prata med en dator verklighet. Det är inte förens på senare tid som chatbot har spridit sig till det vardagliga livet hos den stora massan.Samtidigt som utspridningen av chatbotar ökar så höjs kraven på funktionalitet vilket i sin tur utökar storleken och komplexiteten hoschatboten. Konversionsdata hos en chatbot kan bli väldigt komplex och svår att förstå. För att säkerställa den fortsatta utvecklingen avchatbotar behövs därför verktyg och instrument utvecklas för att hjälpa utvecklare av chatbotar. Genom en prototypbaserad designprocess identifieras ett problem hos utvecklare att visualisera och förstå konversationsdata från en chatbot.En prototyp av en konversationdata-visualisering (KDV) är sedan utvecklad med syftet att hjälpa utvecklare förstå och utforskakonversationsdata från chatbotar de jobbar på. Designen på KDV är baserad på teorier angående nyckelområden inom 3D-visualisering ochrelaterade forskningsarbeten som studerar visualiseringar med data liknande konversationsdata från chatbotar. Designen av KDV är ocksåbaserad på problem som identifieras hos utvecklare av chatbotar. På grund av hur viktigt det är att inkludera användaren i designprocesser såutförs en utforskande användbarhetsstudie på KDV för att utforska implementeringarna av dem identifierade designbesluten angående detidentifierade problemen hos utvecklare. Konversationsdata är visualiserad med trädstrukturer i en cirkulär formation för att tillåta visualisering av länkar mellan olika konversationer.Resultat från den utforskande användarbarhetsstudien indikerar att KDV är en visualisering som förstås av användarna. Dock så identifieradesett problem med att hitta specifika noder i konversationsdata eftersom översikten av visualisering inte innehöll tillräckligt med information.