1,721,000 research outputs found
How Perceived Intelligence Affects Consumer Adoption of AI-Based Voice Assistants: An Affordance Perspective
Full text linkVoice assistants are artificial intelligence (AI)-based technologies widely adopted by consumers today. However, firms offering business services like voice shopping through voice assistants fail to meet consumer expectations. The theory of affordances highlights perceived intelligence as a crucial AI-specific factor affecting consumer adoption of business services offered through voice assistants. Perceived intelligence is conceptualised and operationalised using four dimensions: perception, comprehension, action, and learning. An instrument was developed to measure each dimension and subdimension of perceived intelligence and their effects on consumer adoption of voice assistants in the voice shopping context. Empirical analyses established item and construct validity of the measurement instrument. Survey data from 278 participants revealed that perception, action, and learning dimensions of perceived intelligence significantly affect consumer adoption of voice assistants. The implications for consumer adoption of business services offered through voice assistants are discussed
Measuring network-driven citations: An adjusted citation count metric
No full textInternational audienceCitation count is extensively used within research systems to measure research performance and relevance. However, high numbers of citations could result from many different factors. This paper proposes an adjusted citation count metric to help identify citations from researchers’ networks. Using data from the Web of Science core collection, this research suggests and illustrates an approach to compute the proportion of citation counts from within and out of author networks, defined as the set of their co-authors. The results reveal the trends and effects of author networks on citation counts. The paper explains how these findings could be used to assess research performance. Its utilisation will depend mainly on the objectives of the assessor and the stage of the researcher’s career. An algorithm is also proposed that could be used to automate the computation of the proposed metric. It could serve as a step towards refining and integrating this metric into existing bibliometric analysis packages built on the R software platform
Measuring Network-Driven Citations: An Adjusted Citation Count Metric
No full textCitation count is extensively used within research systems to measure research performance and relevance. However, high numbers of citations could result from many different factors. This paper proposes an adjusted citation count metric that could help identify citations coming from researchers' networks. Using data from the Web of Science core collection, this research suggests and illustrates an approach to compute the proportion of citation counts from within and out of author networks, defined as the set of their co-authors. The results reveal the trends and effects of author networks on citation counts. The paper explains how these findings could be used to assess research performance. Its utilisation will depend mainly on the objectives of the assessor and the stage of the researcher's career. An algorithm is also proposed that could be used to automate the computation of the proposed metric. It could serve as a step towards refining and integrating this metric into existing bibliometric analysis packages built on the R software platform
Measuring Network-Driven Citations: An Adjusted Citation Count Metric
No full textCitation count is extensively used within research systems to measure research performance and relevance. However, high numbers of citations could result from many different factors. This paper proposes an adjusted citation count metric that could help identify citations coming from researchers' networks. Using data from the Web of Science core collection, this research suggests and illustrates an approach to compute the proportion of citation counts from within and out of author networks, defined as the set of their co-authors. The results reveal the trends and effects of author networks on citation counts. The paper explains how these findings could be used to assess research performance. Its utilisation will depend mainly on the objectives of the assessor and the stage of the researcher's career. An algorithm is also proposed that could be used to automate the computation of the proposed metric. It could serve as a step towards refining and integrating this metric into existing bibliometric analysis packages built on the R software platform
Influencer is the New Recommender: insights for Theorising Social Recommender Systems
No full textInternational audienc
Tirer une valeur commerciale de l'intelligence artificielle dans les entreprises de commerce électronique B2C
No full textCette thèse par articles étudie comment les entreprises peuvent tirer parti de l’intelligence artificielle (l'IA) pour améliorer leurs capacités en matière de commerce électronique. La principale question de recherche à laquelle elle cherche à répondre est la suivante : comment l'IA peut-elle être comprise et exploitée pour améliorer les processus et les connaissances commerciales de l’entreprise, ainsi que l'engagement des clients dans le commerce électronique ? Le sujet de recherche est motivé par (i) le besoin croissant des entreprises de comprendre et d'apprendre sur les moyens de capitaliser sur les investissements en commerce électronique, susceptibles de répondre aux besoins actuels de leurs clients, et (ii) le besoin urgent d'expliquer le concept moderne de l'IA aux gestionnaires et la façon dont les technologies de l’IA peuvent être exploitées pour améliorer leurs capacités de commerce électronique. Cela a conduit à trois questions de recherche spécifiques : (i) comment comprendre l'IA d'une manière qui soit pertinente pour les gestionnaires ? (ii) comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leur connaissance des affaires ? (iii) comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer l'engagement des clients ? En raison de la densité et de la complexité du sujet, cette thèse adopte une approche méthodologique vaste. Elle fait appel à des typologies de recherche exploratoire, explicative et descriptive pour expliquer le quoi, le pourquoi et le comment des facteurs qui affectent la façon dont les entreprises peuvent créer de la valeur en utilisant l'IA dans le commerce électronique. Des stratégies de recherche quantitatives et qualitatives ont été adoptées, en fonction de la nature de la question de recherche. Les résultats de cette thèse sont résumés et présentés par question de recherche. RQ1 : Comment l'IA peut-elle être comprise d'une manière qui soit pertinente pour les managers ? Une perspective de l'IA qui peut être pertinente pour les gestionnaires intègre les dimensions de perception, de compréhension, d'action et d'apprentissage de l'intelligence que l'on retrouve dans l'IA. Dans cette optique, l'IA peut être classée en deux catégories principales : l'une centrée sur les interactions sociales et l'autre centrée sur les données. Utilisées ensemble, ces deux catégories peuvent créer une valeur commerciale importante pour les entreprises. RQ2 : Comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leur connaissance du marché ? Les entreprises peuvent tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leurs connaissances du marché en utilisant l'IA pour l'automatisation intelligente et l'aide à la décision. La capacité d'analyse automatisée (CAA) est une capacité dynamique que les entreprises pourraient développer pour atteindre cet objectif. Elle implique l'utilisation de techniques d'IA avancées pour automatiser les capacités analytiques des entreprises. RQ3 : Comment les entreprises peuvent-elles exploiter l'IA pour améliorer l'engagement des clients ? Les entreprises peuvent tirer parti de l'IA pour améliorer l'engagement des clients par le biais de l’amélioration de l'expérience client. Cette thèse contribue à la recherche sur la valeur commerciale de l'IA qui est un débat passionné dans les forums de chercheurs et de praticiens. Plus précisément, elle contribue aux débats dans le domaine des systèmes d’information sur la valeur commerciale de l'IA du point de vue des gestionnaires et des consommateurs, sur l'utilisation stratégique de l'IA pour créer de la valeur commerciale, et sur la façon dont l'IA peut servir de source de création de valeur par l'engagement des clients et l'automatisation.This thesis by publication investigates how firms can leverage AI to enhance their e-commerce capabilities. The main research question it sought to answer is: how can AI be understood and leveraged to improve business processes, insights, and customer engagement in e-commerce? The research topic is motivated by two main aspects. First, it is motivated by the growing need for firms to understand and learn how to capitalise on e-commerce investments that meet their customers' current needs. Second, it is inspired by the urgent need to explain the modern-day AI concept to managers and how it can be leveraged to enhance their e-commerce capabilities. These motivations led to one main research question: how can AI be understood and leveraged to enhance processes, business insights, and customer engagement in e-commerce? This main question was broken down into three specific research questions: (i) how can managers understand AI in a relevant way for value creation? (ii) how can firms leverage AI to improve their e-commerce processes and business insight? (iii) how can firms leverage AI to enhance customer engagement? This thesis adopts a vast methodological approach due to the topic's density and complexity. It uses exploratory, explanatory, and descriptive research types to explain the what, why, and how factors affecting firms' ability to create value by using AI in e-commerce. Quantitative and qualitative research strategies were adopted, depending on the research question. Documents (literature review data and data from practitioner sources), bibliometric data, interview data, and survey data were used to conduct this research. They were analysed using various techniques: content analysis, bibliometric analysis, PLS-SEM, and FsQCA. Furthermore, several theories were mobilised to explain the results: dynamic capability theory, theory of affordances, Goldberg’s Big Five Factors of personality, and a contextualised theory of reasoned action (TRA-privacy). The results of this thesis are summarised and presented by research question. RQ1: how can managers understand AI in a relevant way for value creation? A perspective of AI that can be relevant to managers incorporates the perception, comprehension, action, and learning dimensions of intelligence found in AI. Using this lens, AI can be categorised into two main categories: one centred around social interactions (frontend AI) and the other that is data-centred (backend AI). Both of them used together can create significant business value for firms. RQ2: How can firms leverage AI to improve their e-commerce processes and business insight? Firms can leverage AI to improve their e-commerce processes and business insights by using (backend) AI for intelligent automation and decision support. Automated analytics capability (AAC) is a dynamic capability that firms could develop to meet this objective. It implies using advanced AI techniques to automate firms' analytics capabilities. RQ3: How can firms leverage AI to enhance customer engagement? Firms can leverage AI to enhance customer engagement by leveraging (frontend) AI to enhance customer experience. This thesis contributes to research on AI's business value, a trending debate in both researcher and practitioner forums. Specifically, it contributes to IS debates on AI's business value from the manager and consumer perspectives, the strategic use of AI to create business value, and how AI can serve as a value creation source through customer engagement and automation. In marketing research, it contributes to debates about AI transforming marketing and retailing processes. It also contributes to understanding and anticipating voice shopping behaviours, experiences, and customers' engagement. Finally, it contributes to business & management research on AI by proposing an alternative interpretation of the concept of AI that could facilitate the use of AI for realistic digital transformation
Deriving Business Value From Artificial Intelligence In Business-To-Consumer E-Commerce Firms
No full textCette thèse par articles étudie comment les entreprises peuvent tirer parti de l’intelligence artificielle (l'IA) pour améliorer leurs capacités en matière de commerce électronique. La principale question de recherche à laquelle elle cherche à répondre est la suivante : comment l'IA peut-elle être comprise et exploitée pour améliorer les processus et les connaissances commerciales de l’entreprise, ainsi que l'engagement des clients dans le commerce électronique ? Le sujet de recherche est motivé par (i) le besoin croissant des entreprises de comprendre et d'apprendre sur les moyens de capitaliser sur les investissements en commerce électronique, susceptibles de répondre aux besoins actuels de leurs clients, et (ii) le besoin urgent d'expliquer le concept moderne de l'IA aux gestionnaires et la façon dont les technologies de l’IA peuvent être exploitées pour améliorer leurs capacités de commerce électronique. Cela a conduit à trois questions de recherche spécifiques : (i) comment comprendre l'IA d'une manière qui soit pertinente pour les gestionnaires ? (ii) comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leur connaissance des affaires ? (iii) comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer l'engagement des clients ? En raison de la densité et de la complexité du sujet, cette thèse adopte une approche méthodologique vaste. Elle fait appel à des typologies de recherche exploratoire, explicative et descriptive pour expliquer le quoi, le pourquoi et le comment des facteurs qui affectent la façon dont les entreprises peuvent créer de la valeur en utilisant l'IA dans le commerce électronique. Des stratégies de recherche quantitatives et qualitatives ont été adoptées, en fonction de la nature de la question de recherche. Les résultats de cette thèse sont résumés et présentés par question de recherche. RQ1 : Comment l'IA peut-elle être comprise d'une manière qui soit pertinente pour les managers ? Une perspective de l'IA qui peut être pertinente pour les gestionnaires intègre les dimensions de perception, de compréhension, d'action et d'apprentissage de l'intelligence que l'on retrouve dans l'IA. Dans cette optique, l'IA peut être classée en deux catégories principales : l'une centrée sur les interactions sociales et l'autre centrée sur les données. Utilisées ensemble, ces deux catégories peuvent créer une valeur commerciale importante pour les entreprises. RQ2 : Comment les entreprises peuvent-elles tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leur connaissance du marché ? Les entreprises peuvent tirer parti de l'IA pour améliorer leurs processus de commerce électronique et leurs connaissances du marché en utilisant l'IA pour l'automatisation intelligente et l'aide à la décision. La capacité d'analyse automatisée (CAA) est une capacité dynamique que les entreprises pourraient développer pour atteindre cet objectif. Elle implique l'utilisation de techniques d'IA avancées pour automatiser les capacités analytiques des entreprises. RQ3 : Comment les entreprises peuvent-elles exploiter l'IA pour améliorer l'engagement des clients ? Les entreprises peuvent tirer parti de l'IA pour améliorer l'engagement des clients par le biais de l’amélioration de l'expérience client. Cette thèse contribue à la recherche sur la valeur commerciale de l'IA qui est un débat passionné dans les forums de chercheurs et de praticiens. Plus précisément, elle contribue aux débats dans le domaine des systèmes d’information sur la valeur commerciale de l'IA du point de vue des gestionnaires et des consommateurs, sur l'utilisation stratégique de l'IA pour créer de la valeur commerciale, et sur la façon dont l'IA peut servir de source de création de valeur par l'engagement des clients et l'automatisation.This thesis by publication investigates how firms can leverage AI to enhance their e-commerce capabilities. The main research question it sought to answer is: how can AI be understood and leveraged to improve business processes, insights, and customer engagement in e-commerce? The research topic is motivated by two main aspects. First, it is motivated by the growing need for firms to understand and learn how to capitalise on e-commerce investments that meet their customers' current needs. Second, it is inspired by the urgent need to explain the modern-day AI concept to managers and how it can be leveraged to enhance their e-commerce capabilities. These motivations led to one main research question: how can AI be understood and leveraged to enhance processes, business insights, and customer engagement in e-commerce? This main question was broken down into three specific research questions: (i) how can managers understand AI in a relevant way for value creation? (ii) how can firms leverage AI to improve their e-commerce processes and business insight? (iii) how can firms leverage AI to enhance customer engagement? This thesis adopts a vast methodological approach due to the topic's density and complexity. It uses exploratory, explanatory, and descriptive research types to explain the what, why, and how factors affecting firms' ability to create value by using AI in e-commerce. Quantitative and qualitative research strategies were adopted, depending on the research question. Documents (literature review data and data from practitioner sources), bibliometric data, interview data, and survey data were used to conduct this research. They were analysed using various techniques: content analysis, bibliometric analysis, PLS-SEM, and FsQCA. Furthermore, several theories were mobilised to explain the results: dynamic capability theory, theory of affordances, Goldberg’s Big Five Factors of personality, and a contextualised theory of reasoned action (TRA-privacy). The results of this thesis are summarised and presented by research question. RQ1: how can managers understand AI in a relevant way for value creation? A perspective of AI that can be relevant to managers incorporates the perception, comprehension, action, and learning dimensions of intelligence found in AI. Using this lens, AI can be categorised into two main categories: one centred around social interactions (frontend AI) and the other that is data-centred (backend AI). Both of them used together can create significant business value for firms. RQ2: How can firms leverage AI to improve their e-commerce processes and business insight? Firms can leverage AI to improve their e-commerce processes and business insights by using (backend) AI for intelligent automation and decision support. Automated analytics capability (AAC) is a dynamic capability that firms could develop to meet this objective. It implies using advanced AI techniques to automate firms' analytics capabilities. RQ3: How can firms leverage AI to enhance customer engagement? Firms can leverage AI to enhance customer engagement by leveraging (frontend) AI to enhance customer experience. This thesis contributes to research on AI's business value, a trending debate in both researcher and practitioner forums. Specifically, it contributes to IS debates on AI's business value from the manager and consumer perspectives, the strategic use of AI to create business value, and how AI can serve as a value creation source through customer engagement and automation. In marketing research, it contributes to debates about AI transforming marketing and retailing processes. It also contributes to understanding and anticipating voice shopping behaviours, experiences, and customers' engagement. Finally, it contributes to business & management research on AI by proposing an alternative interpretation of the concept of AI that could facilitate the use of AI for realistic digital transformation
Consumer Adoption of Artificial Intelligence: A Review of Theories and Antecedents
Full text linkRecently, people are increasingly adopting technologies powered by artificial intelligence (AI) in their everyday lives. Several researchers have investigated this phenomenon using several theoretical perspectives to explain the motivations behind such behaviour. Our paper reviews this body of knowledge to highlight the technologies, theories, and antecedents of AI adoption investigated this far in academic research. By analysing publications found in Harzing\u27s Journal Quality List, this paper identifies 52 publications on user adoption of AI, 198 antecedents, and 36 theoretical perspectives used to explain user adoption of AI. The most widely used theoretical perspectives in this area of research are the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT). Meanwhile, perceived usefulness, perceived ease of use, and trust are the most studied antecedents. Finally, we discuss the implications of these findings for future research on AI adoption by consumers
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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