Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions

Qin, Min; Zhu, Wei; Zhao, Shiyue; Yu, Zhao

doi:10.3390/su14073974

Cited by 18 publications

(5 citation statements)

References 57 publications

(92 reference statements)

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“…In their study, I. M. D. Andrade and C. Tumelero [177] used data content analysis with Atlas.ti software to analyze how AI can improve customer service efficiency and provide insights on the potential benefits of AI in enhancing customer experience and driving innovation in service processes and the economy. Meanwhile, M. Qin et al [178] offer valuable insights for AI developers and e-commerce platforms aiming to improve online customer service strategies. Finally, to enhance their resilience and adapt to changing circumstances, companies should consider AI and other emerging technologies, as suggested by P. Agarwal et al [179], who examined the effects of COVID-19 on these industries and discussed how information technology could be used to implement business strategies to transform businesses and incentivize the implementation of these technologies during emergencies and suggested that companies should consider AI and other emerging technologies to enhance their resilience and adapt to changing circumstances in the future.…”

Section: Smart Economymentioning

confidence: 99%

“…The e-VITA EU-Japan Virtual Coach for Smart Aging project aimed to prototype virtual coaches for smart environments, develop standards for component interoperability, and conduct a study of "proof-of-concept" for assessing user acceptance. N. Bagaria et al [178] reviewed existing literature on Health 4.0 Using Digital Twins for maintaining personal health and wellness, which involves essential terms, uses, and research gaps. The aim is to give a summary of the possible advantages and difficulties associated with the utilization of Digital Twins in the fields of healthcare and well-being.…”

Section: Smart Livingmentioning

confidence: 99%

See 1 more Smart Citation

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

143

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As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

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Section: Smart Economymentioning

confidence: 99%

Section: Smart Livingmentioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

143

View full text Add to dashboard Cite

show abstract

“…In the online shopping environment, consumer purchase decision behavior is differentially influenced by the moderating effects of product type and consumer characteristics ( 68 ). In the online health field, the factors influencing patients' healthcare selection behavior can also be influenced by the type of disease and the psychological characteristics of the patient.…”

Section: Research Hypotheses and Research Modelmentioning

confidence: 99%

Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model

Qin

Zhu

You

et al. 2022

Front. Public Health

Self Cite

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BackgroundWith the rapid development of “Internet + medicine” and the impact of the COVID-19 epidemic, online health communities have become an important way for patients to seek medical treatment. However, the mistrust between physicians and patients in online health communities has long existed and continues to impact the decision-making behavior of patients. The purpose of this article is to explore the influencing factors of patient decision-making in online health communities by identifying the relationship between physicians' online information and patients' selection behavior.MethodsIn this study, we selected China's Good Doctor (www.haodf.com) as the source of data, scrapped 10,446 physician data from December 2020 to June 2021 to construct a logit model of online patients' selection behavior, and used regression analysis to test the hypotheses.ResultsThe number of types of services, number of scientific articles, and avatar in physicians' personal information all has a positive effect on patients' selection behavior, while the title and personal introduction hurt patients' selection behavior. Online word-of-mouth positively affected patients' selection behavior and disease risk had a moderating effect.ConclusionFocusing on physician-presented information, this article organically combines the Elaboration likelihood model with trust source theory and online word-of-mouth from the perspective of the trusted party–physician, providing new ideas for the study of factors influencing patients' selection behavior in online health communities. The findings provide useful insights for patients, physicians, and community managers about the relationship between physician information and patients' selection behavior.

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“…The national e-commerce transaction volume almost reached 43 trillion by 2022. The number of inquiries handled by intelligent customer service reached 3-5 million person-times per day 1 . A large amount of conversation information between intelligent customer service and customers can truly reflect the customers' attitudes and opinions towards products and services.…”

Section: Introductionmentioning

confidence: 99%

“…Customer service helps consumer handle routine problems in the service process and undertakes order acceptance, in-sale consultation, and after-sales service in a targeted and efficient manner. The existing research suggests that the perceived service quality of customer service is positively related to customer purchase intention [1][2] and customer satisfaction [3]. Traditional customer service relies on humans to provide corresponding consulting and business services, mainly based on the "huge-crowd strategy".…”

Section: Introductionmentioning

confidence: 99%

Sentiments Analysis for intelligent customer service Dialogue Using Hybrid Word Embedding and Stacking Ensemble

duan,

Zhengwei,

Jintao

et al. 2023

Preprint

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With the development of e-commerce, the national e-commerce transaction volume sprung up. An increasing number of customers have made it a habit to contact online customer service when they run into difficulties while shopping online. These conversation texts include a large number of emotional words and tone particles, which can intuitively reflect customers' attitudes toward any situation, product, or service. The intelligent customer service conversation texts have the characteristics of serious colloquialism, high diversity of words, and short text length. Traditional sentiment analysis algorithms are not suitable for dialogue information. Besides, coarse-grained sentiment analysis could not fully display the text information of users in the dialogue, resulting in poor performance of sentiment classification prediction. In this paper, we propose a hybrid word embedding method based on Gaussian distribution to leverage the emotional syntactic and semantic richness of the two distributed word representations. Furthermore, this study utilizes a stacked ensemble method by combining the outputs obtained from three deep learning models (i.e., CNN, LSTM, and GRU) for simultaneously predicting coarse-grained and fine-grained sentiment analysis in the customer service conversation domain. The results show that hybrid word embedding (HWE) can assist us in comprehending our word representations in context more effectively. In comparison to the current state-of-the-art models, our proposed ensemble model significantly enhances sentiment classification performance.

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Is Artificial Intelligence Better than Manpower? The Effects of Different Types of Online Customer Services on Customer Purchase Intentions

Cited by 18 publications

References 57 publications

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model

Sentiments Analysis for intelligent customer service Dialogue Using Hybrid Word Embedding and Stacking Ensemble

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