Cited by Conclusions and Future Perspectives

AI Transforming Business and Everyday Life DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

The aim of this chapter is to discuss the benefits AIArtificial Intelligence (AI) systems that foster fundamental business transformation. effects emerging from literature audit and substantiated in model testing hereby demonstrate power AI equip companies with tools needed manage their relationships customers an economically feasible manner. In brought table, we see a profound discussion on expected development systems, possibility replacing humans near future. Large language (LLM)Large Language Model (LLM) incorporating machine learning (ML)Machine Learning (ML), deep (DL)Deep (DL), natural processing (NLP)Natural Processing (NLP) techniques can aid training how collect handle large amounts data. Managing such data quickly, correctly, securely, could generate market intelligence boost investments revenues, which any company wants achieve. may encourage more accurate, distinctive, scalable marketingMarketing, personalised businesses (plans) tailored specific userUser demands. Having access vast array customer data, refine browsing history strategies for better targeting, resonating individual(s) way, it possible differentiate not only between customers, but also companies, by offering unique selling point, USPUnique Selling Point (USP). Elevating chatbotChatbot capacity characteristics as be crucial efficiency agency, prerequisite delivering desired USP, create experienceExperience brings journey beyond traditional space. However, there are some challenges, riskRisk, privacyPrivacy, ethics, need further attention. Moreover, biasBias, believability, authenticity information exchange invite exploration. Although, recently developed implemented high volume diverse content. It turns out content fabricated necessarily reflect real facts This serious issue worth explanation, given impact have scaling up shaping everyday life, discussed detail below.

Язык: Английский

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Chatbot Agency—Model Testing DOI

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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Audit of Literature on Chatbot Applications DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

Making sophisticated software applications economically feasible does not necessarily mean that userUser needs and demands are[aut]Bialkova, S. met in regard to chatbotsChatbot (Bialkova 2021, 2022a). Creating consumers are willing use is an easy task. In particular, understanding the key drivers of chatbotChatbot efficiency, reflecting consumer satisfactionSatisfaction, attitudesAttitudes, use, recommendationRecommendation a chatbotChatbot, calls further investigation. The current chapter aims provide profound literature audit order identify efficiency. First, evolution research on discussed, line with different industries contexts, ranging from banking, telecommunications, retail, travel, tourism, education health care. main emerging trends summarised thematic map, raising fundaments build our theoretical framework. encompassed human–computer interaction usabilityUsability, cognitive science psychology, as well behaviour marketingMarketing papers. This multidisciplinary approach provides opportunity generate overarching picture could be used better understand what ingredients needed efficient AIArtificial Intelligence (AI) applications. core notions organised around three pillars: acceptanceAcceptance models, behavioural theories, social influenceSocial influence theories. Fundamental concepts (e.g., qualityQuality, functionalityFunctionality), affective enjoymentEnjoyment), personal carePersonal care, presenceSocial presence) perspectives presented holistic

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

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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

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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

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AI Connecting Business and Consumers DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

AIArtificial Intelligence (AI) technology has enabled new roles for machines and enhanced the processing of information by fuelling autonomous characteristics. Not surprisingly then, potential AI agentsAutonomous agents is embraced brands to easily connect with consumers, speed up management operation processing. Entering transformational era from conventional HCI systems focusing on human interaction non-AIArtificial computing systems, companies could see a real upscale. Doing business facilitated substituting various manpower activities human–machine mirroring agency. agentsAI are developed exhibit unique behaviour, as well demonstrate autonomyAutonomy certain levels human-like intelligence abilities. The development usable explainable demonstrated in two models suggested hereby (Chaps. 5 7 ) spark launch applications that appropriately meet consumer needs market demand. By assembling machine intelligence, may augment capabilities integrating into systems. Introducing channels, however, fosters some challenges, thus requires further exploration. For example, understanding under which conditions mutual trustTrust between humans will be established essential. Another puzzling question whether an agentAI would able take over controlControl system specific domains activities. In this respect, it important look at open dialogue generative pre-trained transformer (GPT)Generative Pre-trained Transformer (GPT). Gaining enormous popularity, perhaps most frequently used conversational natural language generation. current chapter address several these application challenges attempt recommend channel deployment integrates decision-making encompassing interpretable primitives. These should describe steps human-understandable manner. Furthermore, human-driven decision making guaranteed. It recognised success factor implementing human-centred design processes discussed detail below.

Язык: Английский

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Data Management DOI

Svetlana Bialkova

Опубликована: Янв. 1, 2024

The speed and accuracyAccuracy of data management are essential advantages offered by AIArtificial Intelligence (AI) systems. A further advantage could be if the transformed to insightful solutions facilitating business performance end-userUser applications. current chapter addresses how possibly generate such solutions, translating userUser needs into explainable architectures. Understanding generate, train, test, optimise AI-generated behaviour is also in focus hereby. Machine navigated exposing systems specific training data. While substantial human effort was needed annotate, characterise interpret information, enhancement autonomous capabilities mark a new era management. In this respect, classification algorithms for text, voice, images trained on set human-labelled datasets. Most importantly, selection, labelling, particular dataset chosen features can reshape not only an AI system. Rather, modified way system trained. However, may experienceExperience some biasBias, but call rethink systems, order preclude biased responses. We recommend remediation currently available market. As seen from outcomes field studies reported hereby, informativenessInformativeness, accuracyAccuracy, competenceCompetence crucial parameters determining proper functioning, thus its adoption usersUser. Therefore, fine-tuning architectures, effective approach expected created appropriately meet expectations demands transformational solutions.

Язык: Английский

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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