Buying Through Social Platforms

Martínez–López, Francisco J.; Li, Yangchun; Feng, Changyuan; López-López, David

doi:10.4018/joeuc.20210701.oa4

Cited by 14 publications

(4 citation statements)

References 72 publications

(99 reference statements)

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“…Enterprise Risk Management [8,9]. Risk management must run through all aspects of the enterprise, involve all employees of the enterprise, and be based on effective communication and docking.…”

Section: Application Methods Of Machine Learning Inmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.

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Section: Application Methods Of Machine Learning Inmentioning

confidence: 99%

Application of Machine Learning in Enterprise Risk Management

Jia

2022

Security and Communication Networks

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“…These topics converged into social commerce because social media platforms have become a significant factor in the shopping experience, allowing consumers to search for and purchase products through social channels. Social commerce studies in TS3 examined the factors influencing consumers' trust and purchase intention in social commerce (Martínez‐López et al, 2021), including the effects of live streaming (Chandrruangphen et al, 2022). Likewise, Khan et al (2022) identified the reasons for social commerce advertising avoidance and shopping cart abandonment.…”

Section: Science Mapping Results and Discussionmentioning

confidence: 99%

“…Social commerce is categorized as a niche topic, fully developed but less critical for the domain. This is because studies on this cluster focused mainly on its advertising and live‐streaming effects on shopping intention (Chandrruangphen et al, 2022; Khan et al, 2022; Martínez‐López et al, 2021). We believe that social commerce studies should also be examined in the contexts of showrooming, webrooming, and channel shopping behaviors.…”

Section: Science Mapping Results and Discussionmentioning

confidence: 99%

Developing trends in showrooming, webrooming, and omnichannel shopping behaviors: Performance analysis, conceptual mapping, and future directions

Halibas

Nguyen

Akbari

et al. 2023

J of Consumer Behaviour

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In an omnichannel era, businesses and marketers need insights into the dynamics of customer shopping behaviors, particularly the interplay between omnichannel, showrooming, and webrooming behaviors. This study investigates the evolution and trends of the research and channel shopping behaviors (RCSB) domain, spanning from 1998 to 2022, including the Covid‐19 era. The study performed a bibliometric review of 500 papers in the Scopus database. The performance analysis reveals an annual growth rate of nearly 16%, with average citations per document of 44, indicating sustained and growing research interest. Science mapping revealed five distinct cluster themes, including showrooming and webrooming in multi‐ and omni‐channel contexts; consumer behavior in online retail and shopping; customer satisfaction and trust in multi‐channel retailing; mobile commerce in a multi‐channel environment; and the interplay between online shopping, channel choice, and supply chain management. Furthermore, topics, such as showrooming, e‐commerce, retailing, and omnichannel retailing, remain popular before and during the pandemic, as seen in the thematic evolution. Our examination of the thematic maps revealed various topics that gained significance during the pandemic, such as multichannel, channel choice, customer experience, social commerce, purchase behavior, and covid‐19. Among these, the thematic maps indicate that customer experience, channel choice, multichannel, and covid‐19 are emerging and basic topics. These topics can steer research directions in the RCSB domain toward examining customer experiences using digital innovations, e‐commerce (including mobile and social commerce), and omnichannel strategy and management.

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“…Perceived risk as the level of uncertainty experienced by consumers regarding their personal purchase or usage of a product or after using the purchased products (Chao et al 2016). When using a new tool for transactions, most consumers first consider whether the tool is trustworthy and the risks that exist (Martínez-López, et al, 2021). Artificial intelligence as a complex new technology, human-AI interaction in which humans may perceive risks (Glikson, E. 2020).…”

Section: Perceived Risk and Consumer Trust In Ai Chatbotsmentioning

confidence: 99%

Determinants Affecting Consumer Trust in Communication With AI Chatbots

Li,

Wu,

et al. 2023

Journal of Organizational and End User Computing

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This paper summarized the factors that influence consumers' trust in AI chatbots and divided it into chatbot-related factors (expertise, anthropomorphism, responsiveness, and ease of use), company-related factors (perceived risk, brand trust, human support), and consumer-related factors (privacy concerns). This research attempts to explore the mechanism of human-AI chatbots trust formation and answer the question of how to promote consumers' trust in AI chatbots. The results found that the chatbot-related factors (expertise, responsiveness, and anthropomorphism) positively affect consumers' trust in chatbots. The company-related factor (brand trust) positively affects consumers' trust in chatbots, and perceived risk negatively affect consumers' trust in chatbots. Privacy concerns have a moderating effect on company-related factors. This study helps deepen the understanding of human-AI chatbots communication trust, constructs a basic model of human-AI chatbots trust, and provides insights for e-commerce enterprises to improve chatbots and enhance consumer trust.

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Buying Through Social Platforms

Cited by 14 publications

References 72 publications

Application of Machine Learning in Enterprise Risk Management

Application of Machine Learning in Enterprise Risk Management

Developing trends in showrooming, webrooming, and omnichannel shopping behaviors: Performance analysis, conceptual mapping, and future directions

Determinants Affecting Consumer Trust in Communication With AI Chatbots

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