Tapani Aho The Potential of Utilizing Chatbots to Improve Customer Experience of Public Services Vaasa 2025 School of Marketing and Communication Master’s thesis in Marketing Master’s Degree Programme in Marketing Management 2 UNIVERSITY OF VAASA School of Marketing and Communication Author: Tapani Aho Title of the thesis: The Potential of Utilizing Chatbots to Improve Customer Experience of Public Services Degree: Master of Science in Economics and Business Administration Discipline: Marketing Management Supervisor: Catharina von Koskull Year: 2025 Pages: 124 ABSTRACT: Digitalization has transformed organizational service environments by making customer experi- ence a strategic priority. Traditional service models are evolving into digital formats that facili- tate real-time, user-friendly, and time- and location-independent interactions. In this context, chatbot technology presents a promising avenue for innovation by efficiently managing high customer demand and allowing service personnel to focus on more complex and advanced is- sues. The rapid adoption of chatbots across private and public sectors has revolutionized service de- livery and customer experience. As self-service options expand, customers have become active participants in service interactions, empowering them to choose the most suitable communica- tion channels. Consequently, the integration of chatbots increasingly facilitates value creation in digital interactions between organizations and their customers. This study investigates the role of chatbots in enhancing public service delivery. By integrating theories of customer experience and value creation into a comprehensive analytical framework, the research examines the opportunities and challenges associated with adopting chatbot tech- nology in the public sector. Employing a case study methodology, the study draws on semi-struc- tured interviews with organizational representatives to capture practical insights into chatbot utilization in public services. The study’s findings suggest that chatbots can significantly improve public service accessibility, efficiency, and cost-effectiveness. However, as current chatbot solutions primarily rely on providing general advice through predefined responses, it limits their ability to handle complex inquiries. Although advanced chatbots can leverage extensive datasets, the accuracy of their responses is not entirely guaranteed, which is a crucial factor in advisory of public services. The study indicates that future advancements should focus on optimizing user data utilization and enhancing the accuracy of chatbot-generated responses, while ensuring compliance with legal standards for accuracy, reliability, and security. These improvements will help align chatbot- driven customer experiences with established service objectives and support the holistic devel- opment of public services. KEYWORDS: chatbot, digital customer experience, value creation, public service 3 VAASAN YLIOPISTO Markkinoinnin ja viestinnän yksikkö Tekijä: Tapani Aho Tutkielman nimi: Chatbottien hyödyntämisen mahdollisuudet julkisten palvelujen asiakaskokemuksen parantamisessa Tutkinto: Kauppatieteiden maisteri Oppiaine: Markkinoinnin johtaminen Työn ohjaaja: Catharina von Koskull Valmistumisvuosi: 2025 Sivumäärä: 124 TIIVISTELMÄ: Digitalisaatio on tuonut merkittäviä muutoksia organisaatioiden palveluympäristöihin ja nosta- nut asiakaskokemuksen keskeiseksi strategiseksi tekijäksi. Perinteiset asiakaspalvelun vuorovai- kutusmallit ovat digitalisoituneet ja teknologinen kehitys avaa uusia mahdollisuuksia palvelui- den kehittämiselle. Yksi keino uudistaa asiakaspalvelua on chatbot-teknologia, joka mahdollistaa reaaliaikaisen, helpon ja paikasta riippumattoman palvelukokemuksen. Lisäksi chatbotit edistä- vät organisaatioiden resurssi- ja kustannustehokkuutta, sillä ne kykenemään hallitsemaan suurta asiakaskysyntää ja siten vapauttamaan asiakaspalveluhenkilöstöä muihin tehtäviin. Viime vuosina chatbottien yleistyminen niin yksityisillä kuin julkisillakin sektoreilla on muuttanut asiakaspalvelun toteutustapaa ja asiakaskokemuksen muodostumista. Erilaisten itsepalveluka- navien lisääntyessä asiakkaan rooli palvelutilanteissa on kasvamassa: asiakas ei enää ole passii- vinen vastaanottaja, vaan aktiivinen toimija, jolla on valta valita itselleen parhaiten sopivan vuo- rovaikutuskanavan. Näin ollen palvelujen arvonluonti jakautuu vuorovaikutustilanteessa entistä enemmän organisaation ja asiakkaan kesken. Tutkielman tavoitteena on selvittää, miten chatbotit voivat kehittää julkisten palvelujen asiakas- kokemusta ja edistää vuorovaikutustilanteiden arvonluontia. Tutkimus tarkastelee chatbottien käyttöönoton mahdollisuuksia ja haasteita julkisessa palvelukontekstissa siten, että asiakasko- kemuksen ja arvonluonnin teoriat yhdistetään analyysia varten toimivaksi viitekehykseksi. Ta- paustutkimuksena toteutettu tutkimus yhdistää teoreettisen viitekehyksen empiirisiin havain- toihin puolistrukturoitujen teemahaastattelujen avulla, jotka kartoittavat kohdeorganisaation edustajien kokemuksia ja näkemyksiä chatbottien hyödyntämisestä asiakaspalvelussa. Tutkimuksen tulokset osoittavat, että chatbotit voivat merkittävästi parantaa julkisten palvelu- jen saavutettavuutta sekä palvelun nopeutta ja helppoutta. Ne tekevät asiakaspalvelusta tehok- kaampaa ja automatisoidumpaa, mikä johtaa merkittäviin kustannus- ja resurssisäästöihin. Ny- kyisten chatbottien toiminta rajoittuu kuitenkin pääosin yleisneuvontaan ennalta määriteltyjen vastausten avulla, mikä heikentää niiden soveltuvuutta monimutkaisempien kysymysten hoita- miseen. Haasteena kehittyneimmille chatbot-sovelluksille on asiakastietojen ja chatbot-järjes- telmän integrointi toimivaksi kokonaisuudeksi sekä chatbotin luomien generoitujen vastausten oikeellisuuden varmistaminen. Vaikka edistyneet chatbotit kykenevät hyödyntämään laajempaa tietomäärää neuvonnassaan, niiden tuottamien vastausten paikkansapitävyyttä ei aina voida täysin taata. Siten uusinta teknologiaa hyödyntäessä on olennaista varmistaa, että chattibottien neuvonta täyttää julkisille palveluille asetetut lainsäädännölliset vaatimukset oikeellisuuden, luotettavuuden ja turvallisuuden osalta. Vain tällöin voidaan saavuttaa se, että chattibottien käytöstä muodostuva asiakaskokemus sekä vuorovaikutustilanteissa syntyvä arvonluonti vas- taavat asetettuja tavoitteita ja tukevat kokonaisvaltaista palvelujen kehittämistä. AVAINSANAT: chatbot, digitaalinen asiakaskokemus, arvonluonti, julkinen palvelu 4 Contents 1 Introduction 7 1.1 Purpose and objectives of the study 11 1.2 Research Approach, Study Structure and Limitations 12 1.3 Definitions of focal concepts of the study 15 2 Digital customer service and value creation in public services 18 2.1 Chatbots in public services 18 2.1.1 Chatbots complementing service personnel in the service encounter 19 2.1.2 Chatbots’ role in enhancing service delivery 22 2.2 Value creation in public services 25 2.2.1 The Organization and Significance of Public Services 25 2.2.2 Evolving Approaches to Public Service Delivery 27 2.3 Chatbots and value creation in public services 29 2.3.1 Enhancing Public Value Creation Through AI-Driven Chatbots 30 2.3.2 Challenges in Value Creation with Chatbot Utilization 33 2.3.3 Public service value for chatbot-mediated service delivery 36 3 Methodology 40 3.1 Research method 40 3.2 A case study approach 41 3.3 Data collection 44 3.4 Data analysis 47 3.5 Reliability and validity of the study 48 3.6 Ethical considerations 51 4 Findings 53 4.1 Facilitating public service value with chatbots 53 4.1.1 Efficiency 58 4.1.2 Accessibility 62 4.1.3 Accuracy and user orientation 64 4.2 Challenges with creating value with chatbots 68 5 4.2.1 Lack of human touch 68 4.2.2 Resource issues 71 4.2.3 Reliability and trust 73 4.2.4 Technical and regulatory challenges 76 5 Discussion and conclusions 83 5.1 Conclusion 83 5.2 Theoretical and Practical Implications 84 5.3 Managerial implications 87 5.4 Limitations 89 5.5 Further Research 90 References 92 Appendices 123 Appendix 1. Semi-structured thematic interview 123 6 Figures Figure 1. A typology of technology infusions into customers’ service frontline experiences (Van Doorn et al., 2017). 21 Figure 2. Chatbot-mediated public service delivery levels (Makasi et al., 2020). 24 Tables Table 1. List of public service values defined in the context of chatbot-supported public service delivery (adapted from Makasi et al., 2020). 37 Table 2. Research Participants. 47 7 1 Introduction Digitalization and technological advancements have strongly impacted organizations, particularly in the evolution of their service offerings. As Kim (2023) suggests, techno- logical advancements transform service characteristics, enhance customer experience, redefine service provider functions, and shift the dynamics between customers and or- ganizations. Kim (2023) further contends that the emergence of artificial intelligence (AI) has driven innovation and initiated a transformative shift within the service industry. Consequently, rapid technological advancements compel organizations to adapt and en- hance their operations to remain aligned with an increasingly dynamic environment. In this context, Eskola (2018) emphasizes the vital importance of agility and adaptability, arguing that organizations must proactively respond to industry disruptions and global changes, as responsiveness is crucial for sustained success in a continuously evolving landscape. Digital transformation has significantly revised many daily routines, progressively shift- ing service interactions between organizations and customers to digital platforms. As a result, organizations are increasingly enabling service experiences across omnichannel platforms. Alongside traditional service encounters, digital customer interactions have experienced significant growth. This shift has led to what is known as the 'immediacy crisis,' marked by customers’ heightened expectations for instant access to information and support at any time and from any location (Parise et al., 2016). As people spend more time online, their demand for organizations to be accessible through digital chan- nels has grown substantially. Consequently, the nature of interactions between organi- zations and customers has undergone a fundamental transformation (Parasuraman & Grewal, 2000), with traditional face-to-face encounters being progressively replaced by self-service interactions in online environments. As digital technologies continue to evolve, they have profoundly transformed service dy- namics, particularly in the interactions between organizations and customers. Despite these advancements, research highlights the ongoing difficulty of translating traditional 8 service attributes such as personalization and social presence into digital environments (Verhagen et al., 2014). As a result, organizations frequently face challenges in effectively engaging customers through emerging digital service channels (Zeithaml et al., 2022). Autor and Dorn (2013) argue that the automation of customer service has historically been considered complex, largely because it requires human-like capabilities such as flexible interpersonal communication and physical proximity. However, recent techno- logical developments have raised customer expectations for seamless and intuitive digi- tal service experience. In turn, organizations must continuously adapt to changing cus- tomer needs, with an increased focus on improving digital engagement and the overall quality of the customer experience. The rapid advancement of digital transformation has significantly accelerated the inte- gration of AI into service production by enhancing operational frameworks and improv- ing service efficiency. The rapid advancement of AI models and applications has become one of the most prominent global megatrends in recent years (Närhi, 2024). This pro- gress has opened numerous opportunities and benefits for organizations to facilitate vir- tual interactions in customer service, particularly through chatbot applications. Chatbots, also known as conversational agents, are computer programs designed to process and understand language in both text and speech formats and generate appropriate re- sponses (van Noordt & Misuraca, 2019, p. 51). Fundamentally, chatbots analyze user in- puts and generate contextually relevant responses by retrieving information from their underlying databases (Zumstein & Hundertmark, 2017). Voutilainen (2018, p. 907) emphasizes that recent advancements in chatbot technology, driven by AI, enable these systems to expand their knowledge base through user inter- actions and apply the acquired insights to future applications. He further notes that chat- bot technology has significantly improved in simulating natural human language, making interactions between users and chatbots increasingly resemble human-to-human com- munication in digital environments. As people spend more time on messaging platforms, 9 chatbots have become a vital and easily adoptable tool for organizations to enhance cus- tomer engagement (Brandtzaeg & Følstad, 2018). Chatbots can enhance organizational efficiency by facilitating interactive customer en- gagement, thereby reducing the strain on human resources in customer service opera- tions. Simultaneously, this shift in service interactions opens new avenues for organiza- tions to co-create value with their customers. For instance, chatbot technology facilitates automated service encounters, ensuring customers receive prompt and real-time re- sponses to their inquiries, while allowing organizations to allocate human resources to more complex tasks (Brill et al., 2019). Furthermore, digital technologies offer organiza- tions valuable insights into customer needs during service interactions, ultimately con- tributing to enhanced customer experience (Westerman et al., 2011). Organizations have increasingly recognized the strategic opportunities presented by chatbot technology. Some studies indicate that organizations have a strong interest in chatbots, as their use has proven highly beneficial in enhancing customer service. Ac- cording to Gartner (2022), by 2027, chatbots are projected to become the primary cus- tomer service channel for approximately 25 percent of organizations, highlighting their increasing prominence. Similarly, a study by IBM (2021) underscores the substantial ben- efits of AI-driven virtual agents across various sectors, reporting that 99 percent of or- ganizations using these technologies experienced enhanced customer satisfaction, while 96 percent of early adopters achieved or exceeded their anticipated return on invest- ment. While chatbots have been widely adopted to enhance customer service in the private sector (Chen et al., 2023), public organizations are increasingly exploring their potential to improve service delivery (van Noordt & Misuraca, 2019, p. 52). Consequently, chat- bots are playing an expanding role in public sector processes and functions (Cortés- Cediel et al., 2023). They are now recognized as one of the most prominent applications 10 of AI in the public sector (van Noordt & Misuraca, 2022) and are anticipated to become a transformative force in public service delivery in the coming years (Moore, 2019). The adoption of chatbots has already demonstrated significant potential in enhancing the efficiency and accessibility of public services worldwide. For instance, Tolentino (2024) reports that the chatbot used by U.S. Citizenship and Immigration Services pro- cesses over one million inquiries per month, generating annual savings exceeding $12 million by automating routine requests. Similarly, Singapore’s government chatbot, launched in 2019 across more than 20 government agencies, handled over three million citizen inquiries within its first two years (Tolentino, 2024). These examples highlight how chatbot integration can optimize operations while improving public access to essential services and information. Although chatbots are generally implemented as complemen- tary service delivery channels, their growing adoption suggests they have the potential to fundamentally reshape how public organizations interact with citizens and manage service delivery (Vassilakopoulou et al., 2022). Therefore, the increasing importance of chatbots in public services is particularly evident, as public organizations are often legally obligated to provide advisory services within their respective domains. This requirement demands both effective customer support and the efficient fulfillment of advisory responsibilities. As a result, chatbots in public services serve as supplementary tools for creating public value, with significant implica- tions for service quality and the interactions between customers and public organiza- tions (Larsen & Følstad, 2024). As digital interactions between public organizations and citizens continue to expand, it is essential to examine value creation from a perspective that integrates both human and technological elements (Kaartemo & Helkkula, 2018). However, some studies indi- cate that many public organizations remain unfamiliar with best practices for chatbot implementation or have yet to fully leverage their potential in service delivery (e.g., Alila et al., 2022). As a result, there is a growing need to develop new insights into the role of 11 chatbots in value creation, particularly in supporting various value-creating processes within public services (Riikkinen et al., 2018, p. 1146). 1.1 Purpose and objectives of the study In recent years, public organizations have strategically invested resources in adopting emerging AI technologies for their operations (de Sousa et al., 2019). However, Wirtz et al. (2019, p. 597) argue that despite growing investments and research in AI, its applica- tion in the public sector is still emerging and lacks comprehensive analysis of its benefits and challenges. While some studies have examined AI adoption in public services, most research has primarily focused on private-sector implementations. Sun and Medaglia (2019, p. 369) emphasize that although discussions on the potential and challenges of adopting AI in public services are increasing, empirical research validating these con- cerns or providing sector-specific guidelines have remained limited. Sun and Medaglia (2019, p. 369) further emphasize that research interest has predomi- nantly been directed toward the commercial applications of AI. They note that AI's im- pact has primarily been examined in industries such as high technology, automotive, fi- nancial services, retail, media, education, and travel. Although AI-driven initiatives are increasingly being implemented in public services, including healthcare, law enforce- ment, and tax administration, empirical research on AI applications in the public sector remains relatively limited (Sun & Medaglia (2019, p. 369). Given that the nature of the public sector directly influences service delivery, this study aims to contribute a new per- spective to AI research by examining how AI is perceived from the perspective of cus- tomer experience and value creation in public services. The digital customer experience is shaped by multiple factors at various levels. As previ- ously discussed, AI is playing an increasingly significant role in modern service interac- tions. Therefore, this study examines the role of artificial intelligence in customer inter- actions, focusing on how public organizations can enhance value creation and improve 12 the overall customer experience in digital services. It explores the key factors that define a distinctive digital service experience in public services. Specifically, it analyzes chatbots as a key AI application, examining their opportunities for service delivery, their impact on customer experience and value creation, and the challenges of their implementation from a service provider’s perspective. The research questions addressed in this thesis are as follows: 1. What are the unique elements of digital customer experience and perceived value in public services, and how can AI enhance this experience? 2. From the perspective of public service providers, what are the potential benefits and challenges of implementing chatbots to enhance customer experience and facilitate value creation? Building on this, the study further explores the role of chatbots, as a key AI-driven tech- nology, in value creation within public services from the perspective of service providers. It examines the AI attributes that enable organizations to enhance customer experience and assesses how these technologies are utilized to optimize service interactions. Addi- tionally, the study investigates the opportunities and challenges associated with AI- driven service delivery. By deepening the understanding of chatbot capabilities, this study aims to provide valuable insights to help public organizations design AI-powered services that promote value creation and strengthen customer engagement. 1.2 Research Approach, Study Structure and Limitations In line with the objectives of this thesis, the study aims to deepen the understanding of how public organizations can enhance customer experience and value creation in digital services, particularly through the utilization of chatbots. It explores the key factors that influence customer interactions with these technologies, emphasizing an organizational perspective. To capture these insights, a qualitative research approach is adopted to 13 examine how public organizations perceive, implement, and optimize chatbot-driven customer interactions. A qualitative approach is particularly well-suited for this study, as it facilitates an in-depth exploration of organizational viewpoints, decision-making processes, and strategic con- siderations related to chatbot adoption from the perspectives of organizational repre- sentatives. As Puusa et al. (2020) highlight, qualitative research focuses on individuals' subjective experiences and interpretations. In this context, it provides a deeper under- standing of how public sector organizations navigate the opportunities and challenges associated with digital service transformation. From an empirical research perspective, this study adopts a hermeneutic approach to explore research participants' experiences, emphasizing the significance of interpreta- tion and understanding in the research process (Eriksson & Kovalainen, 2016, pp. 21– 22). The research follows an explanatory case study methodology, which aims to uncover the reasons behind the current state of AI adoption in public services. In this approach, the study focuses on analyzing the interconnections and mechanisms (Eriksson & Koist- inen, 2014, p. 13) that shape AI-driven service delivery. To gain a deeper understanding of how chatbots can enhance public service delivery, this case study examines the Finn- ish Tax Administration's integration of chatbots into its service offerings as an illustrative example. The empirical data for this thesis is collected through thematic interviews. In this method, participants are presumed to have relevant experience with the subject matter. A defin- ing characteristic of thematic interviews is the establishment of predetermined themes that guide the conversation while allowing for flexibility in discussion (Puusa et al., 2020). In this study, chatbot specialists are interviewed to provide insights into their perspec- tives, challenges, and experiences regarding AI in public service interactions. To ensure high-quality interviews, the researcher must possess a thorough understanding of the subject, enabling meaningful engagement and the extraction of valuable insights. 14 This thesis is structured into five distinct chapters. The first chapter serves as an intro- duction, outlining the purpose and objectives of the study, the chosen research meth- odology and approach, key concepts, and the study's framework and limitations. Addi- tionally, this section highlights the rationale for the study and its significance. The second chapter presents the theoretical framework, structured around the study's research ob- jectives. The literature review begins with an exploration of digital customer service and value creation in public services, providing a comprehensive understanding of their sig- nificance and discussing the associated benefits from an organizational perspective. Spe- cifically, the study focuses on the role of digitalization and chatbot utilization through the lens of value creation. The theoretical foundation is grounded in literature related to customer experience, value creation, and the strategic management of AI applications within public services. Following the theoretical background, the third chapter focuses on the research meth- odology, discussing the study's approach, design, and strategy. It details the data collec- tion process and addresses the validity and reliability of the research. This chapter also explains how the research material was gathered and analyzed. The fourth chapter pre- sents the empirical findings of the study and offers a comprehensive analysis of the col- lected data. The final chapter integrates the insights gained from the preceding chapters, providing a comprehensive review of the research objectives and their conclusions. It provides final conclusions based on both theoretical and empirical findings and discusses their implications. This chapter also outlines managerial implications, acknowledges the study's limitations and suggests directions for future research. This study specifically examines the application of AI in public services, ensuring a fo- cused and in-depth analysis of the case organization while contextualizing the role of AI within the public sector. The research explores customer experience, value creation, and AI development from the perspective of public service delivery. Given that the empirical 15 study is based on a single case organization, the findings and insights presented in this thesis are inherently limited to that specific entity. 1.3 Definitions of focal concepts of the study This thesis focuses on the following key concepts: customer experience, digital customer experience, service, artificial intelligence, and chatbots. The following section provides detailed definitions of these concepts, drawing on a range of academic sources to estab- lish a foundational understanding for the reader. These concepts are also intended to provide an introductory framework for the thesis topic. Customer experience is broadly defined as the "set of interactions between a customer and an organization" (LaSalle & Britton, 2003) and is shaped by an "individual interpre- tation of events" (Pine & Gilmore, 1999). Ahvenainen et al. (2017) further characterize customer experience as the perceptions and emotions that customers form when en- gaging with an organization through different touchpoints. Lemon and Verhoef (2016) highlight its multidimensional nature, encompassing cognitive, emotional, behavioral, sensory, and social components. Johnston and Kong (2011, p. 7) assert that every service interaction results in an experience—whether positive, negative, or neutral—providing opportunities for emotional engagement, regardless of the routine nature of the service. Löytänä and Kortesuo (2011) emphasize that customer experience is primarily shaped by emotions and perceptions, highlighting that it is not solely a rational decision-making process but rather an experiential phenomenon. The objective of customer experience management is to generate added value for customers while simultaneously cultivating a competitive advantage for organizations. In the digital age, customers possess greater influence and are increasingly inclined to engage with organizations that offer excep- tional experiences and provide services regardless of time or location (Ahvenainen et al., 2017). 16 Digitalization has transformed and broadened the scope of customer experience. Je- hanne (2023) defines the digital customer experience as the integration of digital tech- nologies and data-driven insights to enhance customer interactions. Alamir (2025) and Jehanne (2023) describe it as the cumulative effect of all online interactions a customer has with an organization. Jehanne (2023) further stresses that digital customer experi- ence extends beyond technology, encompassing a deep understanding of customer needs, preferences, and behaviors in a digital environment. She underscores the im- portance of developing a customer-centric digital strategy that aligns with business ob- jectives while delivering value to customers. Additionally, Jehanne (2023) highlights the necessity of an omnichannel approach in today’s digital landscape, ensuring a seamless and personalized experience across all digital platforms. Johnston and Kong (2011, p. 7) define services as processes in which organizations cre- ate and implement activities that require customer input and participation. In this sense, services are co-created or co-produced through customer involvement. Grönroos (2019, p. 778) argues that the primary purpose of service is to assist individuals in achieving their objectives in a way that adds value. Similarly, Grönroos (2006, p. 324) describes services as processes where a set of an organization’s resources interact with customers to facilitate value creation within the customers’ processes. This definition underscores the idea that value is not merely produced and delivered by organizations but emerges through an understanding of customer needs and experiences, leading to the develop- ment of services that support their value creation activities (Grönroos, 2011). Boucher (2020) references the European Commission's definition of artificial intelli- gence, describing it as a system capable of exhibiting intelligent behavior by analyzing its environment and making autonomous decisions to achieve specific objectives. He fur- ther emphasizes that AI encompasses a wide range of technologies and applications, unified by their perceived intelligence, which remains a highly subjective and open to interpretation concept. Russell and Norvig (2016) expand on this characterization by de- fining AI in terms of its operational attributes, describing it as the capacity of machines, 17 devices, systems, and services to perform tasks, learn, infer, and make predictions in ways that appear intelligent across various contexts. Criddle (2023) defines AI as the capability of machines to perform tasks traditionally car- ried out by humans. She notes that AI leverages computing power to replicate or en- hance human abilities, often surpassing previous levels of speed and precision. Further- more, she explains that AI integrates computer science and data analysis to solve prob- lems and make predictions. A distinguishing characteristic of AI systems is their reliance on algorithms, which are structured sets of rules embedded in computer code that ena- ble automated decision-making (Criddle, 2023). Similarly, Cao (2021) describes AI as a system's capacity to continuously learn from and adapt to new challenges in a dynamic environment. Through ongoing data accumulation, AI systems refine their capabilities to achieve specific objectives. According to the Oxford Dictionary (2023), a chatbot is "a computer program designed to simulate conversation with a human user, usually over the internet, especially as part of an automated service providing information or assistance." Makasi et al. (2022, p. 2334) define chatbots as computer programs that utilize natural language processing to replicate human conversations, generating text- or voice-based responses based on user inquiries and previously gathered data. Følstad and Mærøe (2022) identify chatbots as a technological innovation capable of driving incremental improvements, such as respond- ing to frequently asked questions. Additionally, they highlight the potential of chatbots to deliver disruptive or transformative service experiences to customers, particularly through the provision of personalized services or the facilitation of seamless information retrieval across multiple sources. 18 2 Digital customer service and value creation in public services This chapter examines the role of AI, with a particular focus on chatbots, in the context of customer service. It begins by discussing the transformative impact of chatbots within public services, highlighting key disruptions and technological advancements. The chap- ter then analyzes the ways in which chatbots can enhance public service delivery. Fol- lowing this, it explores the concept of value creation in public services and emphasizes its significance. Finally, the chapter offers an evaluation of chatbots as tools for facilitat- ing value creation, addressing both their potential benefits and the challenges they en- tail. 2.1 Chatbots in public services AI is increasingly recognized as a transformative influence in public service delivery, driv- ing significant improvements in efficiency, accessibility, and citizen engagement. Among AI applications, chatbots are particularly deployed in customer service to automate rou- tine inquiries, provide real-time assistance, and streamline administrative processes (van Noordt & Misuraca, 2019; Cortés-Cediel et al., 2023). In an environment of limited re- sources and rising service demands, these systems enable public organizations to ensure continuous service availability while allowing human resources to devote their expertise to tasks that require critical judgment and evaluation (Brill et al., 2019). The primary motivation for integrating AI into public services is to optimize service op- erations and enhance responsiveness. Chatbots allow public organizations to manage high volumes of service requests and deliver customer support at any time (Vass- ilakopoulou et al., 2022). However, Larsen and Følstad (204) suggest that successful AI implementation requires that these systems embody essential public service values, such as transparency, inclusivity, and trust to sustain public legitimacy and ensure wide- spread social acceptance. Understanding the evolving role of chatbots is therefore criti- cal, as it informs the strategic integration of automated systems with human expertise 19 to enhance digital interactions, improve service quality, and ultimately increase cus- tomer satisfaction. 2.1.1 Chatbots complementing service personnel in the service encounter With the advent of digitalization, the context in which services are designed, delivered, and consumed is evolving at an increasingly rapid pace (Larivière et al., 2017). In the contemporary dynamic environment, organizations continuously seek strategies to en- hance operational efficiency and cost-effectiveness (Barbuceanu et al., 2004). Tradition- ally, customer service has been a central aspect of organizational operations; however, it has experienced a paradigm shift from a personalized service model to a more self- service-oriented approach, primarily driven by technological advancements (Følstad et al., 2018). Barbuceanu et al. (2004) argue that conventional customer service methods, such as face-to-face interactions and telephone-based assistance, are often perceived as expensive, inflexible, and inefficient in addressing customer needs. Consequently, organ- izations have increasingly adopted digital technologies, leveraging information networks and the Internet to achieve cost savings and enhance service delivery (Froehle & Roth, 2004; Larivière et al., 2017). While the initial transformation of service delivery led to the centralization of customer service within contact centers, it enabled customers to engage with organizations through multiple communication channels, including telephone service, messages, and chat services (Kumar & Telang, 2012; Scherer et al., 2015). However, the continuous ad- vancement of AI has further revolutionized customer service provision, particularly within the public sector. AI-powered chatbots, which provide instant and automated re- sponses to frequently asked questions, play a crucial role in improving service efficiency (Makasi et al., 2020). These solutions provide 24/7 service and can lead to significant cost savings for organizations (Gnewuch et al., 2017; Shetty, 2024; Carvalho & Barbosa, 2019; Keyner et al., 2019; Makasi et al., 2022). Furthermore, Borana (2016) emphasizes that AI leverages computational capabilities to deliver responses more rapidly than 20 human agents. By managing routine inquiries, chatbots enable human agents to focus on more complex tasks that require specialized expertise (Maedche et al., 2019; Shetty, 2024), while also allowing them to offer more personalized and in-depth support when needed (Viliavin, 2023). From the customer’s perspective, chatbots can offer several advantages. Følstad et al. (2018) assert that chatbots deliver concise and straightforward responses to simple in- quiries, thereby streamlining customer interactions. Unlike traditional customer service channels, chatbot interactions are not constrained by time and location, which elimi- nates the necessity for customers to wait for assistance (Følstad et al., 2018; Dash & Bakshi, 2019; Shetty, 2024). Moreover, some customers may prefer interacting with chat- bots over human agents, especially when seeking answers to questions they perceive as trivial (Følstad et al., 2018). Nevertheless, some studies suggest that in contexts requir- ing emotional support, customers often perceive chatbots as undesirable in service sit- uations due to their perceived lack of empathy and reliability compared to human agents. Consequently, human interaction remains preferable in situations demanding emotional intelligence and nuanced support (Adam et al., 2020; Nicolescu & Tudorache, 2022; Lei et al., 2021). The adoption of chatbots is frequently linked to factors such as ease of use, perceived usefulness, and user engagement (Belda-Medina & Kokošková, 2023). Additionally, Abu Shawar and Atwell (2016) suggest that customers may find chatbot-based information retrieval more engaging than traditional website search fields. Their study indicates that chatbots are perceived as interactive tools that facilitate natural conversations, with some users even preferring chatbot interactions over human interactions. Furthermore, chatbots can facilitate accessibility for international users by supporting multiple lan- guages and facilitating seamless language integration (Abu Shawar & Atwell, 2016). Moreover, their 24/7 availability ensures continuous interaction between customers and organizations, which is particularly beneficial for users operating across different time zones (Johannsen et al., 2018; Dash & Bakshi, 2019). Beyond their application in 21 customer service, chatbots prove to be valuable tools for enhancing civic engagement and enabling interactions between customers and public organizations, particularly through the collection of customer feedback (Androutsopoulou et al., 2019; Petriv et al., 2020; van Noordt & Misuraca, 2019). In examining technological developments in service interactions, Van Doorn et al. (2017) propose a comprehensive framework for understanding the role of technology in cus- tomer interactions and its impact on perceptions of human social presence. This frame- work is illustrated in Figure 1. The model categorizes technologies based on their degree of automation and social presence. At the lower end of the spectrum are self-service technologies, which operate independently of human intervention. These are distinct from technology-mediated channels, such as live chat services, where technology facili- tates human interaction. The upper section of the framework encompasses technologies designed to emulate human behavior, including chatbots and service robots. While chat- bots can independently manage customer interactions, thereby reducing the necessity for human agents, service robots often function as complementary tools to human em- ployees rather than replacing them entirely (Van Doorn et al., 2017). Figure 1. A typology of technology infusions into customers’ service frontline experiences (Van Doorn et al., 2017). 22 Before implementing new technologies in customer service, organizations must conduct comprehensive assessments of their potential benefits (e.g., Kumar & Telang, 2012). This evaluation should consider both the target audience and the intended outcomes of the implementation. Kumar and Telang (2012) further emphasize that the introduction of a new service channel does not necessarily reduce the usage of existing channels, and or- ganizations should not prioritize direct cost savings as the primary objective in service development. For example, if an online portal is poorly designed or lacks adequate in- formation, it may result in an increased volume of customer inquiries due to user confu- sion (Kumar & Telang, 2012). Additionally, Scherer et al. (2015) highlight that research on self-service channels has often overlooked the role of traditional, personalized service in fostering customer trust, loyalty, and long-term relationships. 2.1.2 Chatbots’ role in enhancing service delivery Chatbots are increasingly being utilized as a digital interface for delivering public services, offering varying degrees of sophistication in service development (Nili et al., 2019; Riik- kinen et al., 2018). Radnor et al. (2023) highlights that effective service delivery often necessitates a reciprocal exchange of information between the user and the service pro- vider. Additionally, from the service perspective, the ability of chatbots to meet custom- ers' needs is inherently linked to their technical capabilities. Studies indicate that chat- bots can interact with customers at multiple levels, each constrained by their technolog- ical limitations. Consequently, the service experience associated with chatbots is contin- gent upon their technical capabilities. To clarify the capabilities of chatbots in service provision, Makasi et al. (2020) identified three distinct levels of chatbot-mediated ser- vice delivery, each defined by specific functionalities and varying levels of complexity. These levels are illustrated in Figure 2. The first level in chatbot-mediated service delivery, information provisioning, involves providing customers with general information and guidance in response to their inquir- ies or search terms, without requiring authentication. At this stage, the chatbot strives 23 to interpret customer inquiries and connect them with relevant information and service resources (Androutsopoulou et al., 2019; Nili et al., 2019). The chatbot's replies are typ- ically generated from predefined templates developed by the organization. The second level, targeted assistance, introduces a higher degree of personalization in chatbot-mediated service delivery. This level entails the collection and analysis of cus- tomer-specific information, which often requires the disclosure of identifying details (Makasi et al., 2020). For instance, user data may be stored in an online profile accessible to the chatbot, allowing for more tailored responses. Additionally, chatbots can retrieve information about service variations and relevant business rules from a separate data- base (Ni et al., 2017; Venkatesan, 2018) to enhance service delivery. While chatbots can autonomously respond to less sensitive service inquiries, Makasi et al. (2020) further observe that for more advanced, complex, or sensitive matters, chatbots may forward these interactions to human agents. In such instances, chatbots can provide real-time and historical data from customer encounters to human agents to support their review and assistance. The third level, service negotiation, involves advanced interactive negotiation and deci- sion-making process between the user and the service provider (Makasi et al., 2020). At this stage, chatbots assist customers in exploring different service outcomes and negoti- ating the most suitable option based on their needs. Customers are presented with var- ious service alternatives, enabling them to engage in a structured dialogue with the chat- bot to determine the best course of action. The conversation progresses through a se- quence of questions and responses, with the flexibility to revisit earlier discussion stages as needed (Androutsopoulou et al., 2019). Additionally, chatbots at this level may incor- porate more advanced customization options to refine the final service selection (Nili et al., 2019; Zumstein & Hundertmark, 2017). 24 Figure 2. Chatbot-mediated public service delivery levels (Makasi et al., 2020). As noted, these chatbot levels clearly illustrate the evolving functionality of public ser- vice delivery. In this context, Makasi et al. (2020) emphasize that many public services require ongoing interaction between customers and public organizations. Consequently, chatbots deployed in this context should ideally facilitate ongoing customer interactions. For instance, as customers’ circumstances evolve, the chatbot could proactively suggest updated service options tailored to their changing needs. However, Makasi et al. (2020) contend that the number of current public-sector chatbots capable of supporting such dynamic interactions remains relatively limited. This limitation can be attributed to sev- eral challenges, including the complexity of implementation, concerns regarding public perception, the financial burden associated with high-cost technological infrastructure, and data privacy considerations (Androutsopoulou et al., 2019). Additionally, legal con- straints governing the use of AI in public service provision present further challenges (Voutilainen, 2018). Nevertheless, as chatbot technology continues to advance, Makasi et al. (2020) anticipate a broader adoption of sophisticated chatbots within public ser- vice delivery. Information provisioning Targeted assistance Service negotiation 25 2.2 Value creation in public services Value creation in public services is primarily focused on meeting societal needs, enhanc- ing citizen well-being, and ensuring efficient and equitable service delivery. Unlike the private sector, where value is often tied to financial gain, public services create public value by addressing collective interests and fostering trust in institutions (Moore, 1995; Osborne, 2021). This value emerges through interactions between service providers, cit- izens, and other stakeholders, emphasizing value co-creation, effectiveness, and the so- cietal impact of service situations (Grönroos, 2019; Virtanen & Jalonen, 2023). The organization and delivery of public services are crucial in shaping their overall value. Traditional bureaucratic models have increasingly been supplanted by approaches that emphasize citizen participation, service ecosystems, and digital innovations aimed at en- hancing responsiveness and efficiency (Osborne, 2018; Taiminen, 2023). As public ser- vice models continue to evolve, it becomes essential to understand how value is created, delivered, and experienced in order to maximize societal benefits. Enhancing value cre- ation through digital solutions requires a comprehensive understanding of both the structural dynamics and societal importance of public services, alongside the transfor- mation of delivery models toward more customer-centric approaches. Moreover, the ongoing evaluation and adaptation of these models are critical to ensuring that public services remain relevant and effective in meeting the changing needs and expectations of society. 2.2.1 The Organization and Significance of Public Services Over time, the organization and delivery of public services have become prominent top- ics of interest in both academic and political discourse. Their role and significance have evolved across different societal models, often framed through debates on the welfare state and its subsequent integration with competition- and knowledge-based paradigms (Mitronen & Rintamäki, 2012, p. 174). The later emergence of the “service state” further 26 advanced this evolution by incorporating elements from both public and private sector service frameworks (e.g., Häyrinen-Alestalo, 2009). This progression has highlighted the growing importance of clearly defining service production processes and establishing quality standards to guide their delivery (Mitronen & Rintamäki, 2012, p. 174). Modern public services increasingly emphasize the value they create for customers. Value creation occurs in both private (e.g., Rintamäki et al., 2007; Vargo & Lusch, 2004) and public services (Osborne, 2021; Grönroos, 2019), although the goals pursued by or- ganizations in these sectors differ significantly. Nonetheless, Grönroos (2019) argues that there are no inherent differences between public and private service organizations that would render public services less efficient or service-oriented than their private coun- terparts. However, some studies suggest that public services are fundamentally distinct from private services (e.g., Hartley & Skelcher, 2008, p. 9). While the private sector pri- marily seeks financial profit, public services aim to create public value, which is often conceptualized as the collective expectations of customers regarding public organiza- tions and services (Moore, 1995). Talbot (2011, p. 28) further defines public value as “the combined view of the public about what they regard as valuable.” Virtanen and Jalonen (2023) argue that the concept of public value, initially articulated by Moore (1995), is grounded in three fundamental dimensions: first, how public activi- ties create value for service users, stakeholders, and citizens; second, the ability of public organizations to attract resources and derive legitimacy from the political sphere; and third, the performance of public administration, organizations, and services in terms of efficiency, effectiveness, service ecosystems, and societal advancement (see McConnell, 2010, pp. 347–348; Criado & Gil-Garcia, 2019; Högström et al., 2016). Moreover, the cli- entele of public services differs substantially from that of the private sector. Public or- ganizations must often cater to diverse customer groups with conflicting needs (Osborne, 2018). Unlike the private sector, where customer retention is highly valued, repeated usage of public services may sometimes even indicate service failure (Virtanen & Jalonen, 2023). Magnussen and Rønning (2021) further note that for-profit organizations 27 generally do not encounter unwilling or coerced customers, as they operate within a voluntary market. In contrast, public organizations frequently serve involuntary clients, which introduces additional complexity to service provision. 2.2.2 Evolving Approaches to Public Service Delivery The complexity of public service delivery has led to growing interest in co-production and co-creation strategies (Brandsen et al., 2018, p. 3), which emphasize customer par- ticipation (Osborne, 2010). These approaches are often seen as responses to customers’ needs, civic engagement, resource efficiency, innovation, and service acceptability (Kir- javainen & Jalonen, 2022). Osborne (2017) notes increasing research attention on value creation, public value, and service delivery. Historically, public organizations often fol- lowed inward-looking traditions that neglected or resisted service orientation (Grönroos, 2019), with traditional public administration viewing citizens as passive recipients with limited input in service provision (Pestoff, 2018). For decades, New Public Management (NPM) theory shaped public sector reforms (Hood, 1991; Lane, 2000), promoting marketization and commercialization to increase effi- ciency (Pestoff, 2018). However, Osborne et al. (2013) argue that NPM failed to make public organizations effective service providers, relying on a manufacturing logic that fo- cused on inputs and outputs rather than service outcomes. In response, New Public Gov- ernance (NPG) emerged, promoting partnerships where citizens co-produce services (Pestoff, 2018). Still, Virtanen and Jalonen (2024) argue that neither NPM nor NPG fully integrated public services into management doctrines in ways that connect service de- livery, co-creation, and value-in-use with public value. To address these gaps in public service delivery, research has turned to service-dominant logic (SDL), a marketing-based approach that emphasizes value co-creation through cus- tomer involvement (Osborne et al., 2015; Grönroos, 2019). SDL suggests that organiza- tions can only make value propositions, which are realized through their interaction with 28 customers and other stakeholders (Vargo & Lusch, 2004; 2008). Complementing SDL, service logic (Grönroos, 2005) highlights direct interactions between service providers and customers, focusing on supporting customers’ processes (Saarijärvi et al., 2013). Ad- aptations of Service-Dominant Logic (SDL) and service logic to the public sector have led to the development of Public Service-Dominant Logic (PSDL) and Public Service Logic (PSL). PSDL highlights the active role of citizens in the co-creation of services, challenging traditional service models that position them as passive recipients (Osborne, 2018). PSL builds on this foundation by emphasizing dynamic relationships and a service-oriented approach to public management (Osborne, 2018). While Virtanen and Jalonen (2023) argue that value in public services is multi-dimen- sional, the perspectives offered by Public Service-Dominant Logic (PSDL) and Public Ser- vice Logic (PSL) particularly emphasize that this value is not inherent but emerges through service use (Osborne et al., 2018; 2022). Central to this view is the concept of value-in-use, which denotes the benefits customers derive from engaging with services (Virtanen & Jalonen, 2023). Accordingly, value is co-created through dynamic interaction and collaboration among the various actors involved, rather than being embedded in the service itself (Grönroos & Voima, 2013; Rossi & Tuurnas, 2021). Despite the significant influence of SDL, some studies argue that this logic remains ab- stract and retains producer-centered elements (Mitronen & Rintamäki, 2012; Heinonen & Strandvik, 2015; Heinonen et al., 2010). In response, Heinonen et al. (2010) have pro- posed Customer-Dominant Logic (CDL), which centers on customer experience and value-in-use in their own context. CDL particularly shifts the focus from co-creation to understanding customers' independent activities and how services fit into their lives, urging organizations to support autonomous value creation (Heinonen et al., 2010). With increasing digitalization, new forms of value creation such as value self-creation have emerged. Taiminen (2023) expands the idea of co-creation to highlight customers' independent roles in value creation. Unlike co-creation, value self-creation relies on self- 29 service technologies and digital platforms (Zainuddin et al., 2016; Taiminen et al., 2018), requiring public service providers to shift from authoritative roles to service facilitators (Taiminen, 2023). Thus, Taiminen (2023) stresses that from value creation perspective, service models must adapt to technology that empowers customers to create value in- dependently. Rather than focusing solely on co-creation, service models should facilitate customer autonomy and trust. Public organizations should ensure that digital services enable rather than hinder value realization. By embracing self-service technologies, pub- lic services can better meet modern expectations for efficiency, accessibility, and mean- ingful value creation. 2.3 Chatbots and value creation in public services Before delving into the specific mechanisms through which chatbots contribute to public value creation, it is important to situate their role within the broader transformation of service delivery in the public sector. Recent advances in conversational artificial intelli- gence have enabled public organizations to complement traditional human-mediated service channels with automated and increasingly sophisticated digital interfaces. As frontline touchpoints in public service interactions, chatbots can rapidly triage inquiries, present contextually relevant information, and guide users through administrative pro- cesses. In doing so, they not only expand service accessibility and enhance organizational responsiveness but also provide valuable data on customers’ needs and behaviors. This data can be reintegrated further into service design and policymaking (Larsen & Følstad, 2024; Riikkinen et al., 2018). Chatbots in public services therefore embody a dual value creation logic: they perform immediate transactional functions while simultaneously strengthening organizational capacities to create public value through customers en- gagement. The following sections explore these dimensions more comprehensively, be- ginning with an examination of how AI-driven chatbots contribute to public value crea- tion. 30 2.3.1 Enhancing Public Value Creation Through AI-Driven Chatbots AI is increasingly recognized for its ability to facilitate value creation activities and cus- tomer engagement (Raimer & Weiß, 2022; Hollebeek & Belk, 2021), as well as to improve customer-organization relationships (Skålén et al., 2015), customer experiences (Puntoni et al., 2021; Haji et al., 2021; Rahman, 2006), and overall satisfaction (Gelbrich et al., 2021; Sweeney et al., 2015). However, the digital transformation of value creation sys- tems necessitates a reconsideration of how creative processes unfold and are perceived by customers (Ramaswamy & Ozcan, 2018; Ostrom et al., 2019). Consequently, Riikkinen et al. (2018) emphasize the importance of recognizing the active role customers play in value creation, which places increased responsibility on organizations to support cus- tomers in integrating their resources effectively. Although services have traditionally been seen as supporting customers in achieving their goals, Riikkinen et al. (2018) emphasize that understanding how customers per- ceive value is essential for service providers to effectively design and deliver their offer- ings. This assessment requires an understanding of how organizations facilitate value creation through their own resources and processes. In this context, chatbots emerge as a novel mechanism for organizations to interact with customers, enhance their under- standing of citizen needs, and improve customer-oriented communication. This, in turn, fosters public value creation by improving information accessibility and service provision (Larsen & Følstad, 2024). Additionally, chatbots offer a cost-effective means of supple- menting organizational resources (Riikkinen et al., 2018, p. 1149). Through these inter- actions, both organizations and customers influence each other's processes, making them active participants in the co-creation of value (Grönroos & Ravald, 2011; see Ny- man, 2013). Larsen and Følstad (2024) emphasize that enhancing public services requires continuous improvements by public organizations, including better service accessibility, improved communication between organizations and customers, and enhanced information dis- semination (Twizeyimana & Andersson, 2019; Androutsopoulou et al., 2019; Cordella & 31 Paletti, 2018; Makasi et al., 2020, 2022; Rose et al., 2015). Implementing and utilizing chatbots in public services can significantly contribute to public value creation by better meeting customers’ information and service needs (Androutsopoulou et al., 2019; Ma- kasi et al., 2020) and facilitating faster, more convenient responses to customers’ inquir- ies (Ostrom et al., 2019). Riikkinen et al. (2018, p. 1149) highlight that a key resource for customer value creation is the information provided by the organization. However, they caution that data alone does not constitute information; rather, information becomes valuable only when it is relevant to the customer. As Drucker (1988, p. 4) states, "information is data endowed with relevance and purpose." Therefore, only relevant information serves as a potential resource for customer value creation (Riikkinen et al., 2018, p. 1149). Larsen and Følstad (2024) further stress that rapid information delivery is particularly valuable for custom- ers, emphasizing their individual service needs and the role of chatbots in providing more efficient access to information (Makasi et al., 2022; Rose et al., 2015). Conse- quently, research suggests that chatbots significantly contribute to public value creation by enhancing efficiency, information availability, and the management and dissemina- tion of organizational information (Larsen & Følstad, 2024). Berryhill et al. (2019) under- score the necessity for organizations to ensure access to high-quality, unbiased data to ethically and effectively leverage these digital tools. Chatbots also contribute to administrative efficiency by reducing workload in customer service (Hilhorst et al., 2022; Makasi et al., 2020; Ranerup & Henriksen, 2019; Rose et al., 2015), lowering service costs, and improving administrative processes and service qual- ity. Furthermore, they enhance open government capabilities, increasing transparency and professionalism in public service operations. This includes upholding key values such as fairness, trustworthiness, and accountability to customers (Twizeyimana & Andersson, 2019; Rose et al., 2015; Cordella & Paletti, 2018; Bannister & Connolly, 2014; Hilhorst et al., 2022; Jørgensen & Bozeman, 2007). Additionally, chatbot implementation must align with administrative law and procedural fairness principles (Henman, 2020; Surden, 2018, 32 2020). While chatbot capabilities define their potential to create public value, their ac- tual impact depends on effective implementation, maintenance, and user adoption (Larsen & Følstad, 2024). Beyond organizational improvements, chatbots can contribute to social value by foster- ing public trust and confidence in organizations (Larsen & Følstad, 2024). Gillath et al. (2021) highlight the importance of trust in human-computer interactions, while Glikson and Woolley (2020) assert that user trust in chatbots is essential for their successful in- tegration into organizations. This trust encompasses organizational responsibilities such as safeguarding customer privacy, managing public resources, and ensuring chatbot in- teractions positively impact customers' lives (Twizeyimana & Andersson, 2019; Aoki, 2020; Bannister & Connolly, 2014; Cordella & Bonina, 2012; Scupola & Mergel, 2022; Jørgensen & Bozeman, 2007). Public service chatbots play a crucial role in shaping cus- tomers’ trust in public organizations while promoting inclusivity and equitable access to services (Larsen & Følstad, 2024). Jiang et al. (2023) emphasize that chatbot design in- fluences trust, with human-like characteristics enhancing perceived social presence (Mo- rana et al., 2020). Additionally, personalized and interactive chatbot experiences simu- late real-life interpersonal communication, further reinforcing customer trust (Chung et al., 2020). Regarding public service value, Larsen and Følstad (2024) suggest that customers gener- ally view chatbot implementation positively, as it demonstrates an organization’s com- mitment to fast and flexible service delivery (Androutsopoulou et al., 2019; Makasi et al., 2022; Ranerup & Henriksen, 2019). By assisting with requests and streamlining infor- mation retrieval, chatbots support value-creating processes (Riikkinen et al., 2018). How- ever, Larsen and Følstad’s (2024) findings indicate that while chatbots enhance service efficiency, they are not perceived as a transformative solution. Instead, they serve as an additional service channel that effectively manages routine inquiries, allowing human resources to focus on more complex tasks. Although chatbots primarily deliver re- quested information, this information is often meaningful and enhances service 33 availability, contributing to overall value creation (Riikkinen et al., 2018; Larsen & Følstad, 2024). While not all customers experience chatbot benefits equally, Larsen and Følstad (2024) note that chatbots can still improve public service delivery, indirectly benefiting other customers by enabling the reallocation of human resources to assist individuals who ei- ther cannot or prefer not to use chatbots for customer service. Additionally, customers associate chatbots with improved accessibility, reinforcing their role in fostering social value and trust in public services (Twizeyimana & Andersson, 2019). However, Aoki (2020) suggests that chatbot accuracy in responding to general inquirires requires further im- provement, and trust in chatbots varies depending on the nature of the inquiries. Fur- thermore, customers anticipate continuous enhancements, reflecting growing expecta- tions for efficient public service delivery as chatbot technology evolves (Larsen & Følstad, 2024). 2.3.2 Challenges in Value Creation with Chatbot Utilization While AI holds considerable potential to enhance value creation in public services, its implementation is accompanied by significant concerns and challenges (Wirtz et al., 2019). In practice, chatbot initiatives within the public sector often fall short of customer expectations due to their simplicity, limited contextual understanding, and a predomi- nant focus on organizational objectives rather than user needs (Larsen & Følstad, 2024). Public service chatbots have primarily been deployed to provide basic advice and infor- mation, confining them to simple text-based interactions and thereby restricting their broader value-creation potential (Cortés-Cediel et al., 2023; Abbas et al., 2023). Conse- quently, although some regard chatbots as transformative enablers of public service de- livery (Androutsopoulou et al., 2019), their impact has largely been characterized as in- cremental rather than revolutionary (van Noordt & Misuraca, 2019). 34 Issues related to AI responsibility, implementation, and broader social and ethical con- siderations present further difficulties to its effective deployment (Purdy & Daugherty, 2016; Quraishi et al., 2017; Ransbotham et al., 2017). Furthermore, optimizing services without sufficient attention to contextual and societal norms can lead to customer con- fusion, discomfort, and uncertainty regarding service provision (Ostrom et al., 2019; Tene & Polonetsky, 2013). Trust is considered as a foundational aspect in online service environments, particularly within self-service channels, where it requires a higher de- gree of validation than in face-to-face interactions (Reichheld et al., 2000). While existing research has begun to identify key features for effective chatbot interactions (Feine et al., 2019), much of it remains software-centric and lacks a strong focus on the user per- spective (Rodríguez Cardona et al., 2019). Moreover, traditional technology adoption theories often fall short in explaining the uptake of AI, as it represents a fundamentally new technological paradigm that extends beyond the scope of conventional models (Sohn & Kwon, 2020). The rollout of these technologies can also worsen privacy concerns, particularly when personal information is required (Reichheld et al., 2000). Growing reliance on online ser- vices increases risks of data protection breaches and broader cybersecurity threats (Hen- man, 2020). Continuous data collection and ambiguity around data usage may erode trust (Nikunen et al., 2024), while inadequate maintenance or updates can cause tech- nical failures that disrupt interactions and harm user experience (Shetty, 2024). Moreo- ver, legislation can often lag behind rapid technological advances, creating ambiguities around customer rights and ethical guidelines (Tene & Polonetsky, 2013). Customer capacity to adopt new technologies is another critical factor in chatbot utiliza- tion. Nonetheless, Ostrom et al. (2019) argue that existing research has largely over- looked the impact of AI on customers in service environments, as well as their adoption of emerging service applications. Studies of self-service technologies however indicate that motivation, ability, and role clarity predict adoption (Meuter et al., 2000), alongside perceived control and uncertainty (Lee & See, 2004; Guo et al., 2016). Yet motivational 35 factors and resistance vary by context (Antioco & Kleijnen, 2010; Heidenreich & Handrich, 2015; Edwards et al., 2016), and customer resistance to digital innovations remains a substantial organizational challenge (Ju & Lee, 2020; Talwar et al., 2020). Although AI can facilitate customer experience, it also carries the risk of suboptimal out- comes: for instance, technical malfunctions may block access entirely (Ostrom et al., 2019), and latency or misinterpretation can frustrate customers (Pillai & Sivathanu, 2020; Kwangsawad & Jattamart, 2022). When chatbots fail to meet service customers’ needs or deliver relevant information, dissatisfaction and subsequent reluctance to re-engage can intensify (Ashfaq et al., 2020; Shumanov & Johnson, 2021; Verne et al., 2022; Huang & Dootson, 2022; Shetty, 2024; Adam et al., 2020). The proliferation of AI in public services also raises concerns about displacement of cus- tomer service personnel (Huang & Rust, 2018; Kaplan & Haenlein, 2019) and reduced opportunities for human interaction (Følstad et al., 2018). As customers often prefer per- sonalized communication, particularly in nuanced contexts, they may worry that service quality will decline if AI wholly replaces human agents (Larsen & Følstad, 2024; Følstad et al., 2018). Nevertheless, AI’s strengths today lie in task augmentation rather than full scale professional replacement, since spontaneous, context rich interactions remain a human forte (Huang & Rust, 2018). Finally, chatbots’ inability to convey genuine emotional intelligence and empathy, which are fundamental to human dignity and the development of societal bonds, significantly limits their effectiveness in sensitive or support-oriented contexts (Pillai & Sivathanu, 2020; Shetty, 2024; Nikunen et al., 2024). For this reason, many studies argue that chat- bots should complement rather than replace human agents (Abu Shawar & Atwell, 2007). Achieving meaningful value creation requires a customer-centric approach that includes clearly defined value propositions and ensures that chatbot functionality aligns with gen- uine user needs (Coniam, 2014; Siggelkow & Terwiesch, 2023). Moreover, since customer experience is a dynamic and multi-level construct that organizations actively shape 36 through innovative engagement strategies (Prahalad & Ramaswamy, 2004; Lemon & Verhoef, 2016; Verhoef et al., 2015), public services must integrate advanced technolo- gies in ways that preserve human-centered values in order to optimize adoption, en- gagement, and overall satisfaction (Gnewuch et al., 2017; Urbani et al., 2024; Nikunen et al., 2024). 2.3.3 Public service value for chatbot-mediated service delivery Wirtz et al. (2019) argue that artificial intelligence in public services remains an emerging field, with limited research addressing its applications and challenges. In response, Ma- kasi et al. (2020) explored public values to identify the key value dimensions relevant to chatbots used by public organizations for service delivery. However, as Rose et al. (2018) highlight, chatbot initiatives in the public sector often struggle to uphold and effectively integrate these public value dimensions. This gap in research is critical, as implementing chatbots without careful consideration of public values risks creating discriminatory ser- vices that selectively benefit specific stakeholder groups (Van den Hoven, 2013). Makasi et al. (2020) observe that existing methods for evaluating chatbots from a cus- tomer perspective primarily focus on individual customer experiences (Fernandes et al., 2020). These assessments typically adopt a human-computer interaction approach, em- phasizing factors such as empathy and ease of use, or an information quality perspective, focusing on the chatbot’s ability to deliver timely and relevant information (Brandtzæg & Følstad, 2017; Chaves & Gerosa, 2021; Dennis et al., 2020; Maniou & Veglis, 2020). Additionally, dependability—defined as the extent to which users feel in control of the interaction—is considered a crucial determinant of user satisfaction (Chaves & Gerosa, 2019; Holmes et al., 2019). While these elements are essential for enhancing individual user experiences, Makasi et al. (2020) argue that they do not fully capture the public service values necessary for effective service delivery. Moreover, Makasi et al. (2022) argue that many chatbot 37 initiatives are primarily designed to meet organizational objectives, which often neglects the expectations of broader public stakeholders. Consequently, they propose assessing chatbots through the lens of public service value, emphasizing the importance of incor- porating diverse stakeholder perspectives in the evaluation process. To address this research gap, Makasi et al. (2020) explored the integration of public ser- vice values into chatbot deployment, proposing a framework that aligns chatbot func- tionalities with these values. The framework consolidates a comprehensive list of public service values, drawing on prior studies examining the role of information and commu- nication technology in public service delivery (e.g., Androutsopoulou et al., 2019; Barth & Arnold, 1999; Valle-Cruz et al., 2019). These values were further contextualized for AI- driven public service chatbots, leading to the identification of 14 core public service val- ues, which are presented in the table below. Table 1. List of public service values defined in the context of chatbot-supported public service delivery (adapted from Makasi et al., 2020). A list of public service values defined in the context of chatbot-assisted public service delivery Public service value Adapted definition of public service value for chatbot-based service delivery Adaptability The degree to which the chatbot adapts to changing conditions (varying non-technical conditions such as changes in business rules and service eligibility and varying technical conditions such as adapting to different de- vices and networks) while providing a service User orientation The chatbot's ability to effectively handle user expressions and needs in case resolution Professionalism The degree to which the chatbot demon- strates principled, competent, honest, re- spectful, consistent, and trustworthy behav- ior when it is used to deliver a service Effectiveness The degree to which the chatbot's effective- ness in achieving its intended outcome is 38 determined by the resources invested in its service delivery Efficiency The degree to which the chatbot facilitates service delivery while reducing costs and re- sources required Fairness The degree to which favoritism and discrimi- nation (based on individual differences) do not exist when a chatbot is used to provide a service Legitimacy The chatbot's compliance with legal and rea- sonable steps and mandates when delivering a service Acceptability The degree to which the chatbot is a viable option for service delivery and beneficial in such a way that the public has a minimal or favorable reaction to using it Openness The degree to which chatbots disclose their identity to users before beginning a service in- teraction and provide rationale (to users or their representatives, such as customer advo- cacy groups) for decision-making when deliv- ering a service Accountability The degree to which the chatbot represents an accountable/responsible channel (includ- ing acknowledgment of limitations) when providing a service Social license The chatbot's continued approval as a viable service delivery channel by the community and other stakeholders Privacy The degree to which the chatbot ensures the protection of user's information during and after being used to deliver a service Trust in public organizations The degree to which a chatbot contributes to the public's intentional sharing of personal in- formation with the organization, regardless of any associated vulnerabilities, such as access to personal information and services recom- mended to the user Collaborative intelligence The degree to which a chatbot collaborates with the user and other service stakeholders, 39 complementing their abilities to meet service needs The value aspects identified in the framework are inherently tied to the service interac- tions between public organizations and their customers. Meier et al. (2024) argue that customers’ willingness to engage with chatbots is significantly shaped by their percep- tions of the value these technologies deliver. While previous studies have often concep- tualized values as distinct and additive (Yang & Lin, 2017; Zhu et al., 2023), Meier et al. (2024) suggest that a greater number of perceived values within a service interaction increases the likelihood of customer engagement with the technology. To investigate how these value dimensions are reflected in the context of chatbot use in public services, insights were gathered through interviews with representatives of the case organization. Insights from these discussions are further analyzed and presented in the research find- ings. 40 3 Methodology This chapter provides a comprehensive examination of the study’s methodological framework. It begins with an in-depth analysis of the chosen research method, followed by a detailed discussion of the overall research approach. Subsequently, it outlines the data collection techniques, and the methods applied for data analysis. The chapter con- cludes with a critical evaluation of the research’s reliability and validity. 3.1 Research method This thesis employs a qualitative approach to comprehensively analyze the role of chat- bots in facilitating value creation for customers in public service interactions. The study includes interviews with employees from the case organization who possess expertise in chatbot operations and their application in facilitating both customer experience and value creation in service interactions. The research primarily adopts an organizational perspective, considering employees as key informants who provide valuable insights into the potential of chatbots for value creation. Qualitative research prioritizes an in-depth understanding of individual phenomena within specific contextual settings. Unlike quantitative studies, which rely heavily on nu- merical data, qualitative research does not aim to establish generalizable, static cause- and-effect relationships (Kaplan & Maxwell, 2005). Instead, it focuses on understanding phenomena, capturing dynamic processes rather than static details, and uncovering in- dividuals’ perceptions within specific contextual settings. In chatbot-related research, particularly within the domains of AI and customer service, the selection of an appropriate research sample is critical. Given that chatbots are a rel- atively recent phenomenon, there remains limited established knowledge about their interactions with users, especially regarding social dynamics and the structure of these interactions. Qualitative research is particularly well-suited to exploring such emerging 41 phenomena, as it does not depend on pre-existing hypotheses; rather, hypotheses are developed inductively throughout the research process (Kaplan & Maxwell, 2005). More- over, Kaplan and Maxwell (2005) emphasize the practical strengths of qualitative meth- ods, including their applicability and ease of implementation in real-world settings. This study adopts a hermeneutic approach, emphasizing the importance of understand- ing and interpreting the research subject (Eriksson & Kovalainen, 2016, pp. 21–22). In the hermeneutic approach, the primary objective is to derive accurate interpretations from interviews, with linguistic expressions often serving as focal points for analysis (Laine, 2010, p. 33). The process of knowledge formation follows a hermeneutic circle, wherein the researcher’s initial understanding of the topic expands over the course of the study. Through an iterative movement between theory and empirical evidence, the researcher gradually develops a holistic comprehension of the subject (Puusa et al., 2020, pp. 73–74). 3.2 A case study approach For this thesis, an explanatory case study approach was adopted. This method aims to understand the underlying reasons for a specific situation or its developmental process. It focuses on exploring relationships and mechanisms between different entities and events (Eriksson & Koistinen, 2014, p. 13). Case studies are widely used across various scientific disciplines because they provide comprehensive and in-depth insights into the phenomenon under investigation, contextualized within specific cases (Eriksson & Ko- valainen, 2016, p. 131). In this thesis, chatbots serve as the research focus, with partic- ular emphasis on their characteristics that influence customer experience and value cre- ation. A case study typically entails a comprehensive and systematic analysis of a single organ- ization, event, or instance, with a focus on the underlying processes, interactions, and contextual factors that influence the phenomenon under investigation. A range of 42 research methods, such as interviews and document analysis, may be employed to gather data. The primary aim is to collect information from real-world settings to en- hance understanding of the phenomenon (Hirsjärvi et al., 2014, pp. 134–135). This thesis examines the Finnish Tax Administration as the case organization. The selec- tion is justified by its prominent role as a public institution committed to improving cus- tomer experience and exploring the use of AI in its services. Notably, the organization has substantial experience in implementing AI-driven solutions in customer service, making it a particularly relevant subject for this study. As such, analyzing the Finnish Tax Administration aligns closely with the research objectives. The research data for this study is derived from the firsthand experiences and perspec- tives of employees at the Finnish Tax Administration. The participating employees were selected based on their expertise and active involvement in customer service, chatbot development, or customer experience enhancement. These insights are influenced by various organizational factors encountered during service development, which this study critically examines. Given the study's focus on chatbots in relation to customer experi- ence and value creation within the case organization, the findings are context-specific and may not be fully generalizable to other settings. The Finnish Tax Administration’s (2024a) has defined its mission to collect the correct amount of tax at the appropriate time to ensure the funding of public services. Its oper- ations are underpinned by a strong customer-oriented approach that emphasizes cus- tomer understanding and guidance. The organization prioritizes the simplification of tax- related transactions while ensuring efficient tax revenue collection (Finnish Tax Admin- istration, 2024a). While customer experience remains a primary focus, the organization has made significant investments in AI to enhance both service quality and operational efficiency. 43 To facilitate operational efficiency and the value provided in tax-related services, the Finnish Tax Administration has strategically incorporated AI into its service delivery. This integration aligns with its broader strategic goals, which include ensuring accurate tax revenue collection, fair tax assessment, and positive customer interactions (Finnish Tax Administration, 2024b). AI facilitates the automation of routine tasks, expediting tax as- sessment processes and minimizing human errors. Furthermore, AI enables the organi- zation to allocate human expertise to more complex cases requiring nuanced decision- making (Finnish Tax Administration, 2024b). In general, AI has primarily been utilized to enhance the efficiency of chat-based cus- tomer service. Initially, the introduction of human-operated chat services within the Finnish Tax Administration (2024b) was met with overwhelming demand that exceeded available human resources. The organization observed that during service hours, cus- tomer service agents were consistently occupied, resulting in extended waiting times and reduced accessibility for customers. Additionally, customers required assistance be- yond regular service hours for using the MyTax online service and managing their tax affairs (Finnish Tax Administration, 2024b). These challenges underscored the necessity of developing customer service solutions that better aligned with customer needs in terms of availability and accessibility. To address these challenges, the Finnish Tax Administration launched Chatbot Virtanen, which is integrated into the MyTax online service and also provides assistance via the organization's website. Chatbot Virtanen utilizes natural language processing to deliver real-time responses to frequently asked questions. Although initially acquired as an off- the-shelf solution, the Tax Administration independently oversees its operation, includ- ing response development, training, and functionality monitoring (Finnish Tax Admin- istration, 2024b). According to the Finnish Tax Administration (2024b), Chatbot Virtanen significantly en- hances customer service by addressing high inquiry volumes and providing 24/7 44 assistance. This enables customers to independently manage their tax-related matters at their convenience. The chatbot’s extensive database currently contains responses to over 4,000 questions (Finnish Tax Administration, 2023). If the chatbot is unable to pro- vide a satisfactory response, it prompts users to rephrase their inquiries or directs them to the Tax Administration’s telephone service for further assistance (Finnish Tax Admin- istration, 2023). The Finnish Tax Administration (2023) stresses that Chatbot Virtanen is designed to han- dle anonymous tax-related inquiries, excluding cases involving personal data. Conse- quently, its application in customer service remains limited to general questions. Never- theless, the Finnish Tax Administration remains committed to continuously improving the chatbot’s response capabilities and comprehension while ensuring ethical and re- sponsible AI use (Finnish Tax Administration, 2019; 2023). According to the Finnish Tax Administration (2024b), the implementation of AI and Chat- bot Virtanen has significantly improved customer service, operational efficiency, and the overall taxpayer experience. The organization views this strategic AI integration as sup- porting its objectives of accurate tax revenue collection, fair tax assessment, and en- hanced customer interactions (Finnish Tax Administration, 2024b). By automating rou- tine inquiries, the chatbot enables the administration to focus on complex cases, thereby reducing the risk of human errors and optimizing resource allocation (Finnish Tax Admin- istration, 2024b). 3.3 Data collection The thesis employed thematic interviews as the primary data collection method, involv- ing interviewees with experience related to the phenomenon under study. This approach allows for flexibility, as initial discussion points guide the interview without overly con- trolling its direction (Puusa et al., 2020). Thematic interviews are widely applicable in research due to their adaptability. Interviewees' active engagement in constructing 45 meanings offers vital insights, enabling the interviewer to conduct a more thorough ex- ploration of the phenomenon (Hirsjärvi & Hurme, 2000, pp. 34–35). The core concept of a thematic interview is to structure the conversation around specific research themes. The objective is to foster a conversational exchange between the in- terviewer and interviewee, enabling a deeper exploration of the subject under study (Hirsjärvi et al., 2015, pp. 203–204). Thematic interviews facilitate free discussion within the framework of these themes. Successful execution of such interviews relies on the interviewer’s solid understanding of the subject matter, enabling the effective use of rel- evant information during the conversation. Interviews can be categorized as structured, semi-structured, or unstructured (Tuomi & Sarajärvi, 2018, p. 87). Structured interviews consist of predefined questions with fixed answer options, allowing interviewees to select the most suitable responses. Conversely, unstructured interviews are more conversational, with no predetermined questions or themes, enabling the subject to be explored freely based on the interviewee’s terms (Puusa et al., 2020, pp. 111–114). Semi-structured thematic interviews fall between these two methods, offering flexibility in the order and depth of exploring questions and themes, and allowing for adaptation as needed (Puusa et al., 2020, pp. 112–113). In this thesis, semi-structured thematic interviews were employed as the primary data collection method. Themes derived from the theoretical framework of the thesis formed the basis for questions and discussion topics. Interviewees addressed questions related to these themes freely. The interviewer’s thorough acquaintance with the chatbot phe- nomenon and its features prior to the interviews allowed for more detailed inquiries if needed. The thematic interview framework (Appendix 1) was developed using the the- oretical framework. The interviews covered two main themes. The interviews began with a discussion of the participants' backgrounds and their experience with chatbots. The interview themes encompassed topics related to digital customer experience, the role 46 of chatbots in public services, and their integration into service development. The inter- viewees addressed these themes in a structured and sequential manner. For this study, interviews were conducted with employees from the Finnish Tax Admin- istration who have expertise in customer service, chatbot implementation, or the devel- opment and analysis of customer experience. Interviewees were required to be familiar with the operation or development of chatbots in customer service. Suitable candidates were identified through a contact person related to the thesis from the Finnish Tax Ad- ministration, and potential interviewees were approached via email. A total of ten indi- viduals working in various roles at the Finnish Tax Administration were interviewed for the study. The participants selected for the thematic interviews needed to have experience related to the subject or phenomenon under study to leverage their subjective experiences and perceptions. The objective was to highlight the interviewees' perspectives for the re- search; therefore, interviewees were chosen through purposive sampling from a broad area to ensure they possessed extensive knowledge of the phenomenon under investi- gation (Hirsjärvi & Hurme, 2000, p. 47; Tuomi & Sarajärvi, 2018, p. 99). This extensive experience and background knowledge were crucial for providing valuable insights and a deeper understanding of the phenomenon, thereby enhancing the reliability and va- lidity of the research findings (Creswell & Poth, 2018). The data collection process for this study involved conducting nine remote interviews and one face-to-face interview. All interviews were recorded, transcribed, and anony- mized prior to the analysis phase. Each interview commenced with a brief discussion of the interviewees' background and their experiences with chatbots, followed by an in- depth examination of the chatbot and its functionalities. The final segment of the inter- views focused on the service experience associated with the chatbot and its potential for value creation in customer service situations. A detailed overview of the interviewees is provided in the table below (Table 2). 47 Table 2. Research Participants. 3.4 Data analysis After gathering and anonymizing the data, thematic analysis was employed during the data analysis phase. In theme development, key themes and perspectives were identi- fied from the material (Tuomi & Sarajärvi, 2018, p. 106). The primary goal of this research was to identify shared characteristics among the interviewees in the data. By synthesiz- ing the data, the aim was to uncover patterns or similarities across different themes (Puusa et al., 2020). The method used in this thesis follows Clarke and Braun’s (2013) six- stage approach to thematic analysis. According to Clarke and Braun (2013), the initial stage of data analysis involves carefully reading through the transcribed data multiple times. Next, preliminary coding identifies repeated patterns and classifies them within the text. In the third phase, these classifi- cations are grouped into larger thematic entities. Subsequently, the resulting theme groups are evaluated against the material to ensure that all created themes are well- Interviewee Position Unit Interviewee 1 Senior Tax Clerk The Incomes Register Unit Interviewee 2 Senior Tax Clerk The Incomes Register Unit Interviewee 3 Product Owner Product Management Unit Interviewee 4 Senior Tax Clerk Taxation Unit Interviewee 5 Procedure Specialist Taxation Unit Interviewee 6 Functional Specialist Product Management Unit Interviewee 7 Account Manager Customer Relations Unit Interviewee 8 Customer Understanding Specialist Customer Relations Unit Interviewee 9 Software Designer Product Management Unit Interviewee 10 Functional Specialist Product Management Unit 48 supported. In the penultimate step, emerging themes are named and defined. Finally, the analysis concludes by presenting material quotations, justifying findings, and com- paring them to the theoretical framework that informs the research. Clarke and Braun (2013) emphasize that thematic analysis is well-suited for small data sets and can be effectively integrated with background theory. These factors justify its application in an- alyzing the study material. Furthermore, thematic analysis stands out as a straightfor- ward method, making it particularly suitable for researchers who lack extensive prior experience in data analysis. 3.5 Reliability and validity of the study According to Hirsjärvi et al. (2015, p. 231), the reliability and validity of research out- comes may vary, making the evaluation of reliability a crucial step in the research process. Tuomi and Sarajärvi (2018, p. 121) emphasize the importance of assessing reliability, highlighting its connection to accuracy, truthfulness, and objectivity. Similarly, Eskola and Suoranta (2009, p. 210) stress that conducting research requires adherence to good sci- entific practices throughout the entire research process. Koskinen et al. (2005, p. 253) state that the terms reliability and validity are frequently used when assessing the trustworthiness of research. They further note that these con- cepts are applied in qualitative research to evaluate the dependability of the study, and the claims made within it. Hirsjärvi et al. (2015, p. 231) define reliability as the con- sistency of measurement results, meaning that research should produce stable and non- arbitrary outcomes. Conversely, validity refers to the extent to which the research accu- rately measures what it is intended to measure (Hirsjärvi et al., 2015, p. 231). However, Tuomi and Sarajärvi (2018, p. 163) note that applying reliability and validity to qualitative research has faced criticism, as these concepts originate from quantitative research and are tailored to its requirements. Koskinen et al. (2005, pp. 185–189) assert that the concepts of reliability and validity are grounded in the notion of an objective 49 truth and reality. In contrast, Hirsjärvi et al. (2015, p. 32) suggest that the unique nature of qualitative research complicates the assessment of its trustworthiness using tradi- tional notions of reliability and validity. Consequently, qualitative research is often eval- uated based on alternative criteria, such as credibility, transferability, dependability, and confirmability (Tuomi & Sarajärvi, 2018, pp. 165–166; Eriksson & Kovalainen, 2016, pp. 305, 307–308). Stenfors et al. (2020, p. 598) define credibility as the extent to which research findings are plausible and trustworthy. Eriksson and Kovalainen (2016, p. 308) emphasize that credibility is based on the researcher’s deep understanding of the subject and their abil- ity to gather sufficient evidence to support their conclusions. Evaluating credibility in- volves assessing whether the research claims are well-founded, whether a strong con-