Connecting artificial intelligence to value creation in services: mechanism and implications

Service Business, Journal Year: 2023, Volume and Issue: 17(4), P. 851 - 878

Published: Oct. 31, 2023

Abstract Artificial intelligence (AI) is transforming services by providing personalized solutions, enhancing customer experience, and reducing operational costs. To tackle the challenges posed extensive diverse literature on AI services, a comprehensive review was conducted using text mining techniques journal articles. Twelve key research topics were identified, enabler–interface–business framework developed. In addition, value creation mechanism for consisting of 6Cs (i.e., connection, collection, computation, communication, control, co-creation) proposed. The study provides complete overview facilitating academic discussion industrial transformation.

Language: Английский

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Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology

Applied Artificial Intelligence, Journal Year: 2024, Volume and Issue: 38(1)

Published: March 19, 2024

Consumers have begun to move their attention away from product functioning and toward value probably extracted items. Companies use customer service systems (CSS) in response this trend, which are business models that give clients with not solitary tangible items as well intangible facilities. Even substantial investigation on Smart CSS frameworks, rare of frameworks considered customers active data producers actively creating for the CSS. Furthermore, majority them offered a generic remedy rather than personalized one. To classify systems, performance metrics, such precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, RoC considered. The AETGCN-NGOA-CSS approach attains 19.11%, 24.12% 28.13% high specificity, 24.93%, 23.04%, 9.51% lower computation 15.2%, 25.45% 13.91% higher ROC 8.45%, 20.98%, 27.55% accuracy compared existing methods, developing recommendation system smart depend unsupervised learning model (CSS-BERT), Cognitive Decision-Making approaches Data-driven Retail Intelligence: Consumer Sentiments, Choices, Shopping Behaviors (CSS-CDMA), e-Commerce Online Intelligent Customer Service System under Fuzzy Control (CSS-FFNN), respectively.

Language: Английский

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Service design: a critical examination and future research directions in servitization literature

Journal of Business and Industrial Marketing, Journal Year: 2024, Volume and Issue: 39(9), P. 1950 - 1968

Published: June 11, 2024

Purpose This paper aims to deepen the understanding of service design concept by critically analyzing existing servitization literature. The paper’s main purpose is structure and offer a clear how it should be applied. Design/methodology/approach A systematic literature review was conducted within understand evolution concept. authors use pillars (i.e. user-centered, co-creative, sequencing, evidencing holistic) as theoretical framework explain effectively incorporated into journey. Findings findings expose discordant interpretation underpinning design, revealing paradoxical comprehension that jeopardizes its practical advancement propose first seen holistically, then target user-centered practices for sequencing development steps, finally, co-creating with partners make evident users. Furthermore, contextualize contemporary traditional service-related issues such innovation, customer experience, service-dominant logic, ecosystems digital transformation. Originality/value research pinpoints concept’s shortcomings in study promotes critical reflection on current application, providing avenues future research.

Language: Английский

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An NLP-based approach to assessing a company’s maturity level in the digital era

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124292 - 124292

Published: May 27, 2024

Conducting a maturity assessment allows companies to measure their readiness in implementing novel technologies. However, this task is challenging due the multidimensional, complex, unpredictable, and non-linear nature of innovation. In paper, we introduce an innovative approach that enables both intra- inter-company analysis. Our evaluates company's absolute score concerning specific technology or area. By leveraging Natural Language Processing pipeline applied semi-structured questionnaire extract popular concepts from answers present them human expert for The can refine analysis by adding removing as needed. Subsequently, compute similarity metric each answer determine concepts. output our presented through human-readable plots, offering clear insights into internal level company allowing comparison with competitors across chosen To demonstrate capabilities method, provide running example showcasing quantitative qualitative results demonstrates efficiency, preprocessing completed 1.967±0.758 s, information extraction 0.074±0.017 s on average, excluding intervention time, requiring low hardware resources.

Language: Английский

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Industry 4.0 Technologies in Support of Circular Economy: A 10r-Based Integration Framework

Published: Jan. 1, 2024

Language: Английский

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