Jun Chen

Consumers' decisions in social commerce context

With the popularity and growth of social networking, consumers often rely on the advice and recommendations from online friends when making purchase decisions. Social commerce in this regard represents a shift in consumers thinking from inefficient individual-based consumption decisions to collaborative sharing and social shopping. In this study, we investigate social commerce from two different but interrelated angles (i.e., social shopping and social sharing). Built on the literature of social support, commitment-trust theory, and trust transfer theory, a research model was developed and empirically examined. The findings of this study demonstrated that both emotional and informational social support significantly affected consumers trust and community commitment, which in turn exerted profound impacts on both social shopping and social sharing intention. Trust toward members also can be transferred into trust toward community, which further led to users community commitment. Limitations and implications for both research and practice are discussed. Consumers decisions in social commerce context are examined with insights from both social sharing and social shopping intentions.Relational factors (i.e., community commitment, trust toward community and members) together explained 44.4% of the variance in social shopping intention, and 31.8% of the variance in social sharing intention.The effect of trust toward members on social sharing intention is fully mediated by users trust toward social commerce community.Both emotional and informational social support significantly affected consumers trust and the community commitment.

Temporal Data Dissemination in Vehicular Cyber–Physical Systems

Efficient data dissemination is one of the fundamental requirements to enable emerging applications in vehicular cyber-physical systems. In this paper, we present the first study on real-time data services via roadside-to-vehicle communication by considering both the time constraint of data dissemination and the freshness of data items. Passing vehicles can submit their requests to the server, and the server disseminates data items accordingly to serve the vehicles within its coverage. Data items maintained in the database are periodically updated to keep the information up-to-date. We present the system model and analyze challenges on data dissemination by considering both application requirements and communication characteristics. On this basis, we formulate the temporal data dissemination (TDD) problem by introducing the snapshot consistency requirement on serving real-time requests for temporal data items. We prove that TDD is NP-hard by constructing a polynomial-time reduction from the Clique problem. Based on the analysis of the time bound on serving requests, we propose a heuristic scheduling algorithm, which considers the request characteristics of productivity, status, and urgency in scheduling. An extensive performance evaluation demonstrates that the proposed algorithm is able to effectively exploit the broadcast effect, improve the bandwidth efficiency, and enhance the request service chance.

On the performance of real-time multi-item request scheduling in data broadcast environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were conducted under the assumption that each client requests only one data item at a time. With the rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this paper, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration. Based on our analysis, a novel algorithm that considers both request popularity and request timing requirement is proposed. The performance results of our simulation study show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances.

Efficient processing of requests with network coding in on-demand data broadcast environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and the ability to handle dynamic user access patterns. In traditional on-demand broadcast, only one data item can be retrieved by mobile clients during the course of each broadcast, which limits bandwidth utilization and throughput. In this paper, we consider data broadcast with network coding in on-demand broadcast environments. We analyze the coding problem in on-demand broadcast and transform it into the problem of finding the maximum clique in graph theory. Based on our analysis, we first propose a new coding strategy called AC, which exploits the cached information related to clients and data items requested by them, to implement a flexible coding mechanism. Then, based on AC, we propose two novel coding assisted algorithms called ADC-1 and ADC-2 which consider data scheduling, in addition to network coding. In ADC-1 data scheduling and coding are considered separately, while these two factors are fully integrated in ADC-2. The performance gain of our proposed algorithms over traditional and other coding assisted broadcast algorithms is demonstrated through simulation results. Our algorithms not only reduce request response time but also utilize broadcast channel bandwidth more efficiently.

Efficient Processing of Real-Time Multi-item Requests with Network Coding in On-demand Broadcast Environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Traditional on-demand broadcast is under the assumption that only one data item can be retrieved by mobile clients in each time unit. However, the above constraint limits bandwidth utilization and throughput of broadcast systems. In this paper, we consider data broadcast with network coding in real-time on-demand broadcast environments. We analyze the coding problem in on-demand broadcast and transform it into the problem of finding the maximum clique in graph theory. Based on our analysis, a novel algorithm called ADC is proposed. ADC considers both request overlapping and request timing requirement in request scheduling and fully exploits information about clients cached and requested data items to implement a flexible coding mechanism. The advantages of our proposed algorithm over other traditional and coding assisted broadcast algorithms are shown through simulation results. Our algorithm not only reduces deadline miss ratio of requests, but also utilizes broadcast channel bandwidth efficiently.

Scheduling algorithm for multi-item requests with time constraints in mobile computing environments

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. Little work, however, has considered the on- demand broadcast with time-critical multi-item requests. In this paper, we study the problem arising in this new environment and observe that existing single item based scheduling algorithms are unable to manage multi-item requests efficiently. Thus, a new scheduling algorithm that combines the benefit of data item scheduling and request scheduling is proposed. The performance results show that the proposed algorithm is superior to other classical algorithms under a variety of factors. Our algorithm not only reduces deadline-missing ratio of requests, but also saves broadcast channel bandwidth.

Understanding Social Commerce Intention: A Relational View

With the growing popularity of social media, consumers often rely on the recommendations obtained from online sources when making the purchase decision. Social commerce in this regard represents a shift in consumers thinking from inefficient individual consumption to collaborative sharing and shopping. In this study, we investigate social commerce intention from two different but interrelated angles, i.e., social shopping and social sharing. Built on commitment-trust theory and trust transfer theory, a research model was developed and empirically examined. The results demonstrated that community commitment and trust towards community exerted significant impacts on both social shopping and social sharing intention. Trust towards members can be transferred to trust towards community, which in turn leads to community commitment. In addition, trust towards members posits a direct effect on social shopping, and an indirect effect on social sharing via trust towards community. Limitations and implications for both research and practice are discussed.

Scheduling Real-Time Multi-item Requests in On-Demand Broadcast

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has considered on-demand broadcast environment with time-critical multi-item requests. In this paper, we investigate the scheduling problem arising in this new environment and observe that existing single item request based algorithms are unable to manage multi-item requests efficiently. Thus, an innovative algorithm that combines the strengths of data item scheduling and request scheduling is proposed. The performance results of our simulation show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances. Our algorithm not only reduces deadline miss ratio of requests, but also saves broadcast channel bandwidth.

Admission Control-Based Multichannel Data Broadcasting for Real-Time Multi-Item Queries

Owing to its potential to satisfy all outstanding queries for the same data item with a single response, on-demand data broadcast becomes a widely accepted approach to dynamic and scalable wireless information dissemination. In some emerging applications, such as road traffic navigation system, users may query multiple data items which have to be received before a deadline. However, in existing works, a client only knows that its query is satisfied when it receives all the required data items or not satisfied when the deadline expires. In this paper, admission control is introduced in data broadcast systems such that clients can be informed in a timely manner. On the one hand, when a query has no hope to be satisfied, it is a waste of time and resources for the client listening to the channels. Instead, an early notification allows the client to take prompt remedial actions to recover the situation. On the other hand, when a query has a very high chance to be served before its deadline, an early guarantee provides a better quality of service to the client. Furthermore, a matching-based allocation scheme is proposed to maximize data sharing among queries and minimize switching among channels in multichannel architectures. Extensive simulations are performed to analyze the validity and efficiency of the proposed admission control and channel allocation schemes on existing scheduling algorithms in a wide range of circumstances.

Scheduling real-time multi-item requests in wireless on-demand broadcast networks

On-demand broadcast is an effective data dissemination approach for mobile computing and wireless communication. Previous studies on real-time data scheduling in on-demand broadcast focus on single item requests. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support real-time multi-item requests. However, we observe that scheduling algorithms for single item requests are unable to manage multi-item requests efficiently. In this paper we study the problem arising in on-demand broadcast with time-critical multi-item requests and propose an innovative algorithm that integrates both request and data scheduling. The performance of our simulation shows that the new algorithm is superior to the classical algorithms designed for single item requests. Our algorithm not only reduces deadline miss rate of requests, but also saves broadcast channel bandwidth.