The 21st century has seen a significant increase in the global prevalence of mental health problems, affecting almost a billion people. These conditions not only reduce the quality of life for individuals but also lead to stigmatization, discrimination, and social isolation. The COVID-19 pandemic has further exacerbated mental health issues, with depression and anxiety becoming the most common mental disorders among young workers. However, encouraging young adults to seek help for mental health is challenging.

To address this issue, the concept of digitalizing psychotherapies through mental health chatbot services is proposed as a potential solution. Giving the chatbot a voice to communicate is suggested to increase perceived trust and intention to use among users. A research question is posed: "How does vocal communication and its personalization in chatbots affect the intention to use of young adults with depression?"

The study conducted an online survey using Qualtrics with three groups of participants: the control group (text-based communication) and two treatment groups (vocal communication, and personalized vocal communication). The chatbot followed a rigid script based on a psychotherapy protocol called behavioral activation. Data analysis involved Mann-Whitney U tests, ordinal logistic regression, and one-way ANOVA for randomization check.

Findings from the study indicated that chatbots communicating through speech did not significantly influence trust compared to text-based chatbots. Personalization of the voice did not increase trust based on emotions and even negatively affected cognitive trust. Both cognitive and affective trust were positively associated with the intention to use chatbots. Cognitive trust indirectly mediated the relationship between personalized vocal communication and intention to use.

The study contributed to existing literature by exploring the adoption of conversational agents for mental health support, focusing on young adults with depression. It also provided insights into the use of speech in chatbots and the impact of personalization on trust and chatbot adoption. Additionally, the study shed light on the adoption of mental health chatbots in developing countries, which lack access to healthcare.

From a managerial and societal standpoint, the study's findings have implications for companies developing chatbots for mental healthcare. The insights gained can improve the design of mental health chatbots, potentially easing the burden on mental health professionals and contributing to long-term societal well-being. Moreover, the study highlights the potential benefits of chatbot accessibility for users in developing countries with limited access to healthcare.

Overall, the research provides valuable knowledge about the adoption of chatbots for mental health support, voice communication, personalization, and its implications for various demographics and regions. It also raises ethical considerations, particularly regarding gender-based biases when using gender-specific voices for chatbots.

Global mental healthcare confronts daunting challenges, notably posttraumatic stress disorder (PTSD), necessitating immediate attention and innovative solutions. Many individuals requiring mental health support face various barriers, including social stigma, low perceived need, and restricted access to care providers — especially prevalent in certain regions — which impedes their quest for professional assistance. Amidst these obstacles, artificial intelligence (AI) emerges as a promising instrument, ushering in its unique challenges and opportunities. This thesis delves into these intricacies, aiming to develop comprehensive and pioneering design guidelines for AI applications within mental healthcare. The research focuses on three pivotal areas: identifying the distinct challenges and opportunities of implementing AI in mental healthcare; adapting existing AI design principles to fit the mental health landscape; and understanding the crucial role of multidisciplinary collaboration and user-centered design in this context. The primary objective is to devise guidelines that address inherent difficulties in mental healthcare, such as stigma and complexity of disorders, while leveraging potential benefits like early support intervention and expanded access to mental healthcare services. The suggested design guidelines embrace a systematic approach, encapsulating problem definition, stakeholder engagement, data acquisition, ethical and legal considerations, model design, system deployment, usage, maintenance, and iterative improvements based on feedback. Grounding these guidelines in a practical context, the thesis introduces AnchorAid, a tool that is designed theoretically and remains hypothetical. This virtual assistant provides post-trauma recovery support by gathering data, generating personalized feedback, recommending symptom management strategies, and assisting clinicians in the patient management processes. Through design guidelines establishment and their implementation via AnchorAid, this thesis lays a solid foundation for AI integration into mental healthcare.

Artificial Intelligence technology offers computational, decision-making, and optimizing abilities that surpass every previously established traditional computation method. By being able to navigate across large amounts of data, the realized solutions learn on their own and provide results that would be unattainable with other ways. The complex nature of the AI makes the solutions difficult to define. The potential is left untapped, as the solution developers struggle to define product features that would correlate with the user needs. On the other hand, the the potential AI solution users may not be aware of the AI capabilities, possibilities of integration, and automatization of the operation processes.This creates situation, in which the potential of the AI is not used effectively, and areas that could benefit from it are not able to integrate AI systems in their operation.
This research looks at this issue from the perspective of the value proposition design process. This process assumes a definition of potential benefits and uses on the side of the developer, aligning, and communicating them with the potential users. Specifically, the Value Proposition Creation model is used, as it is a method often used by AI startups and university spinoffs. The main question this research asks is “What are the factors influencing the specific value proposition of innovative AI solutions”. By answering this question, the paper hopes to establish a method for understanding the specific aspects of the AI solutions that should be taken into consideration while designing an effective value proposition and communicating it with the potential user.
To answer the research question, an in-depth look is taken at the Artificial Intelligence adoption processes. First, a literature study helps define the factors involved in the AI adoption. A conceptual model is created, which is then evaluated via a series of semi-structured interviews with AI research and development experts. By doing so, the relationships between the factors are obtained and a general impact on the adoption process is understood. This allows for a formulation of the relationship of said factors with the Value Proposition Canvas. Additionally, literature research is conducted on the value proposition frameworks, which allows for a definition of good practices in value proposition design in relation to the Artificial Intelligence solutions. The obtained frameworks are then tested in a single exploratory case study, which looks specifically at the idea of Deep Reinforcement Learning algorithms used as a decision-making method in the context of road maintenance planning.
The research provides an overlay on the Value Proposition Canvas, obtained through the evaluation of the AI adoption process. Such approach creates a framework, which then can be used to effectively define and clarify the individual values, benefits, gains, and features of an AI product. Moreover, by providing the interrelationships between the adoption factors, understanding of the internal dependencies of factors necessary in the product development process can be obtained. The framework can be used by developers in the field of Artificial Intelligence to assess the necessary requirements of the solution, highlight the key areas that have to be researched, as well as help in communicating of the crucial solution aspects with the potential clients and users. The framework can be best used in the context of an AI startup or a university spinoff, because of its generalist approach. It assumes a fast development of a value proposition, which is necessary in the context of a Minimum Viable Product definition.
The results of the research combine the adoption processes of the AI solution and the method for their value proposition, realized with the VPC. It has been shown that the adoption factors may play a relevant role in the value definition of the potential solutions. Because the adoption models may not be used directly in the VP processes, a framework of questions has been setup for the solution developers to reflect upon during the Value Proposition Creation. Such approach is in line with the general methodology of the VPC, as it uses questions to define the relevant fields of the canvas. The goal of the research was not to modify the canvas itself, as its intrinsic agility and simplicity is the core strength. Moreover, by looking at the common practices seen in other Value Proposition frameworks, good practices have been defined for the VPC in relation specifically to the Artificial Intelligence solutions. These results have been afterwards validated in the exploratory case study for the Deep Reinforcement Leaning decision-making system in the field of road maintenance, indicating the specific steps that the solution developer must undertake and creating an example Value Proposition Canvas for this specific case.

The applications of artificial intelligence (AI) are significant on a social and economic scale, and they can offer businesses great value and opportunities. However, because of AI's varied application areas, its inherent complexity, and the new organizational requirements that result from AI adoption, companies encounter pitfalls when deploying the technology. Potential implementation scenarios are not always clear. Understanding the AI readiness on an organizational level, which shows “the extent to which an organization has the ability to reap the benefits of AI” can improve the chances of effective AI deployment. This study aims to bridge the gap between academia and practice as most attention to artificial intelligence was paid to modeling steps in academia. Research on applying AI models to real-life problems and realizing business value has received insufficient attention. The current frameworks do not incorporate context-specific considerations for organizational readiness. This research investigates the organizational readiness of AI in the semiconductor industry. It is a critical step in avoiding costly failures, considering the capital-intensive characteristic of the semiconductor industry. A literature study has been conducted at first to review the existing AI organizational readiness framework and typical readiness factors. Then eight industry expert interviews are conducted to give a more holistic view of AI use cases across the semiconductor value chain as identifying the opportunity is the first step to establish AI readiness. Three challenges of AI deployment are summarized and the potential AI organizational readiness factors are listed to guide the case study interviews. The case study is carried out in ASML, one of the leading producers of chip-making equipment in the world. Through fourteen case study interviews, this research proposes an AI organizational readiness framework with six dimensions and conceptualizes twenty readiness factors. In the strategic alignment dimension, there are needs and added-value assessment, bottom-up proposal/innovation lab, top management support, business model innovation. In the resource dimension, there are talents, financial budget, IT infrastructure, competence center. In the process dimension, there are multidisciplinary team/collaboration, agile way of working, employee training, business process standardization. Regarding data dimension, there are data availability, data governance, data platform. In the AI model cluster, there are explainable AI with domain experts, context-aware modeling, model operation. In the external business environment, there are peers/competitors/software vendors and customer demand. Furthermore, twenty propositions on AI readiness in semiconductor organizations are given indicating their positive or negative influence on AI organizational readiness. This study contributes to the emerging literature on AI organizational readiness with the developed multi-dimension framework. It identifies AI-specific readiness factors under the dimensions of "Data" and "AI model". Moreover, among 20 readiness factors, 10 readiness factors are newly identified such as the agile way of working, competence center, context-aware modeling. Companies that seek to implement AI can use the proposed framework with readiness factors as a tool for assessment to help decision-makers, managers, and project teams to develop and deploy AI faster and more effectively.