Introduction to Decision Support for Smart City and Digital Services Minitrack

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Exploring Trust in Online Ride-sharing Platform in China: A Perspective of Time and Location

Trust is a key issue to be considered deliberately in the online ride-sharing platform to reduce risk and ensure transactions. In this paper, trust-in-platform is explored from these two perspectives to fill the research gaps. A ride-sharing platform in China was investigated. Results show that trust-in-platform in economically developing districts is slightly higher than that in economically developed districts. At the same time, trust-in-platform level differs in time, trust-in-platform levels are obviously lower between 19’o clock and 23’o clock. Moreover, machine learning is employed to predict the relationships between time/location and trust-in-platform. The result is that recall is 78.3%, precision is 57.3%, and F1 is 66.2%. The result shows trust-in-platform has an obvious correlation with time and location, thus further consolidates the findings. This study contributes to the existing knowledge on trust in the ride-sharing platforms and has practical implications for platform operators

Understanding the Role of Virtual Anchor-Brand Image Fit in Virtual Live Streaming

We explore the influence of matching the virtual anchor’s image, voice, and language style with brand image on consumers’ purchase intentions. Furthermore, we identify two key mediators (i.e., processing fluency and perceived affinity) that impact the relationship between the virtual anchor-brand image fit and purchase intentions. We find that virtual anchor image and brand image fit, virtual anchor voice and brand image fit both have a positive influence on purchase intentions. Figurative language causes a higher purchase intention for a warm brand image, while literal language does not lead to a higher purchase intention for a competent brand image. Processing fluency and perceived affinity mediate the relationship between virtual anchor image and brand image fit, virtual anchor voice and brand image fit, and purchase intentions. Perceived affinity mediates the effects of language style and brand image fit on purchase intention, whereas no mediating effect of processing fluency is found

Operational Mechanism of Digital Humanistic Crowdsourcing Project Based on Actor Network Theory

This article is to promote the development of digital humanity-related crowdsourcing projects based on actor network theory (ANT). A case study on Shengxuanhuai Documents from Shanghai Library is selected as our research object. The article employs qualitative research approach to investigate core concepts, namely Problematization, Obligatory Passage Point, Interestment, and Mobilisation involved in the underway of the digital humanity-related crowdsourcing project. This study conducts interviews with 32 respondents, including the 10 contractees and 22 users. The crowdsourcing actors in humanity-related projects are mainly the organizers from public libraries, museums, archives, and other digital humanity institutions. Based on the project development documents and semi-structured interview data, we find that the main obstacles to prevent actors engaging in crowdsourcing projects include task guidance, user motivations, platform designs, and competition evaluations. The paper demonstrates the usefulness of ANT’s concepts and explores the contribution of each ANT analytical concept

IEEE Access Special Section Editorial: Artificial Intelligence and Cognitive Computing for Communication and Network

With the rapid development of communication and network technologies, novel information services and applications are rapidly growing worldwide. Advanced communications and networks greatly enhance the user experience, and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. Although advanced techniques have extensively improved users' quality of experience (QoE), they are not adequate to meet the various requirements of seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and other scenarios. Therefore, it is a great challenge to develop smart communications and networks that support optimized management, dynamic configuration, and feasible services

Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation

Learning rate plays an important role in separating a set of mixed signals through the training of an unmixing matrix, to recover an approximation of the source signals in blind source separation (BSS). To improve the algorithm in speed and exactness, a sampling adaptive learning algorithm is proposed to calculate the adaptive learning rate in a sampling way. The connection for the sampled optimal points is described through a smoothing equation. The simulation result shows that the performance of the proposed algorithm has similar Mean Square Error (MSE) to that of adaptive learning algorithm but is less time consuming