Cited by Conclusions and Future Perspectives

Svetlana Bialkova

Опубликована: Янв. 1, 2024

The factors hypothesised in the conceptual model on chatbotChatbot agency are tested an empirical study hereby. We have invited consumers who had used a at least once their daily life to complete survey, sharing opinion about experienceExperience they concerning agency. chatbotsChatbot were contact customer services 91% of cases, demonstrating increasing role agents as front service line providers. results from regression modelling clear showing that: (1) InformativenessInformativeness and accuracyAccuracy predetermine functionalityFunctionality perception. (2) higher social presenceSocial presence was perceived be, enjoymentEnjoyment interacting with chatbotChatbot. (3) FunctionalityFunctionality positive impact ease useEase use qualityQuality perception, thus, satisfactionSatisfaction. (4) greater satisfactionSatisfaction was, brand loyalty intention future. Interestingly, however, personal carePersonal care did not play hereby, opposite proposition we had. CompetenceCompetence load functionalityFunctionality, but modulated enjoymentEnjoyment. Given, asked userUser after actual situation, reasonable question arising hereby is whether currently available market offer desired competenceCompetence. This serious challenging UXUser Experience (UX) design reconsider contemporary AIArtificial Intelligence (AI) systems, ensure that these provide agency, distinguishable intelligence, empathyEmpathy, interaction.

Язык: Английский

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Core Theories Applied in Chatbot Context DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

Despite the enormous effort to understand factors driving chatbotChatbot effectiveness, researchers are not univocal. The profound literature audit we performed demonstrated that various theories have been employed in order identify key drivers of efficiency. Utilitarian (i.e., cognitive related), hedonic (emotion and social components emerged shape performance evaluation, as summarised thematic map taxonomy developed (see Chap. 2 ). core theoretical notions organised around three main pillars: acceptanceAcceptance models, behavioural theories, influenceSocial influence theories. models included are: TAMTechnology Acceptance Model (TAM), UTAU, Diffusion InnovationDiffusion Innovation (DOI), Gratification theoryGratification theory, Uncanny Valley theoryUncanny valley theory. Frameworks like Planned behaviour, Reasoned action, Self-determination, Motivation, Big five among most frequently cited papers audited thus discussed hereby detail. In addition these anthropomorphismAnthropomorphism, agency, presenceSocial presence, response, parasocial interaction, CASAComputers Social Actors (CASA) brought table. Fundamental paradigms, with relevant examples related papers, applied context Fig. 3.1) presented detail below.

Язык: Английский

Процитировано

Shaping Chatbot Efficiency—How to Build Better Systems? DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

Various types of AIArtificial Intelligence (AI) systems are distinguished based on the algorithms deployed, technical features, and devices integrated into different applications. The puzzling question hereby is whether these provide desired experienceExperience satisfactionSatisfaction to userUser in regard efficiency chatbotsChatbot currently available market. As seen from marketingMarketing examples profound literature audit reported previous chapter, chatbotChatbot perception thus system adoption use very sensitive needs demand for a satisfactory experienceExperience. well known behaviour theories, fosters positive attitudesAttitudes great willingness product. From UXUser Experience (UX), we also informed that crucial inspiring new computational design frameworks (AI). Therefore, challenging fundamental assumptions factors driving satisfactionSatisfaction, aim much-needed understanding how build better AI chatbotAI chatbots implementation. In particular, qualityQuality ease useEase discussed as core parameters loading way evaluated. We further look at shaping interactivityInteractivity. Both cognition emotion turn play role. functionalityFunctionality (cognitive) enjoymentEnjoyment (emotional components) have emerged most frequently explored various HCI, studies, focus their antecedents. While research abovementioned issues been addressed separate often isolation, combine cognitive affective components conceptual model will be tested empirical described detail below.

Язык: Английский

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Explainable AI (XAI) DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

The new generation of AIArtificial Intelligence (AI) technology should enable creation explainable systems that usersUser can understand. Although the behaviour and thus output AI might be affected by various factors, such as algorithms, architecture, training, data, ultimate goal is to guarantee a transparent human-centred approach. In this respect, characteristics emerging hereby crucial in chatbotChatbot efficiency agency applied lifting capacity. present chapter further discusses continuous improvement (XAI)Explainable (XAI) possibility enhancing software testing approaches. It important manage machine so human–computer interaction (HCI) design delivered through informed solutions. distinctive constellation cognitive, emotional, social aspects suggested current work prerequisite for providing desired human–AI interaction. Moreover, offering right unique selling points (USPs)Unique Selling Point (USP) will facilitate experienceExperience bringing customers journey beyond traditional market space extraordinary life activities.

Язык: Английский

Процитировано

Anthropomorphism—What Is Crucial? DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

DespiteAnthropomorphism the recognised need for human-centred design and human-like features to be assigned chatbotChatbot AIArtificial Intelligence (AI) systems, practice is scarce on working technological solutions that incorporate anthropomorphic interface. Such lack of anthropomorphismAnthropomorphism will inevitably lead failures in usabilityUsability perception thus adoption chatbotsChatbot, AI systems general. To anticipate this disruption, there an emergent call provide a better understanding factors determining structures, i.e., what crucial chatbotsChatbot are well accepted by consumers. The current chapter addresses challenge testing framework agency. Parallel cognitive emotional components, have emerged book as key drivers efficiency, we focus our exploration social aspects. expected shed light applications intelligent not only algorithmic thinking, but also empathicEmpathic interaction. Special attention dedicated presenceSocial presence personal carePersonal care, distinguished pivotal from literature audit reported hereby. Social associated with sense human contact, warmth, sociability has facilitating role when interacting others. Personal care seeking attention, understanding, empathyEmpathy might enhance consumer satisfactionSatisfaction. Translating above parameters agency, aim map essential requirements interactivityInteractivity, advising space appropriately meet userUser demand. agency presented detail below.

Язык: Английский

Процитировано

Introduction to Chatbot AI Applications DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

Artificial intelligence (AI)Artificial Intelligence (AI) applicationsChatbot are expected to revolutionise the traditional marketingMarketing space, tremendously changing business, and social life. ChatbotsChatbot as one of these applications forecasted generate significant profit by substituting manpower thus being increasingly implemented speed up various business operations, facilitate service provided, sales activities, processes. Despite recognised benefits market profit, consumer resistance toward AIArtificial systems questions ability chatbotsChatbot justify term intelligence. This is a challenging question inviting further investigation. The current chapter embraces this challenge providing an overview how AI, in particular change landscape. Defining state art, we look at parameters characterising chatbotChatbot efficiency. We zoom-in into potential factors determining consumers currently available on market. Our investigation reports emergent demand understand, create, communicate way humans do, i.e., still prefer human agent front chatbotsChatbot. Such outcome warning call for human–computer interaction (HCI) research UserUser experienceExperience (UX)User Experience (UX) design join efforts with psychology, consumer, expertise, order implement going beyond algorithmic explanation. Efficient AI needed that appropriately meet userUser request high-qualityQuality satisfying their enjoyable functional interaction, offering enhanced journey.

Язык: Английский

Процитировано

Conclusions and Future Perspectives DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

The rise of artificial intelligence (AI)Artificial Intelligence (AI) applications has inspired the scientific community to perform in-depth investigations and looking for explanations underlying mechanisms AIArtificial behaviour. Becoming increasingly interested in impact AI systems may have on individuals society, researchers from different disciplines pursue avenues developing new, smarter, superintelligent systems. Examining state art, current book provides an overview perspective (AI), UXUser Experience (UX), HCI, computer cognitive sciences, psychology, consumer behaviour, marketingMarketing attempt provide much-needed understanding explainable (XAI)Explainable (XAI).

Язык: Английский

Процитировано