Bitcoin is gaining ground in recent years. In the Bitcoin system, miners provide computing power to confirm transactions in pursuit of transaction fees, while users compete by bidding transaction fees for faster confirmation. This process is in essence analogous to online ad auctions, where advertisers bid for more prominent ad slots. Therefore, inspired by ad auction research, we propose to apply the Generalized Second Price (GSP) auction mechanism in the dynamic confirmation game on Bitcoin transactions. Our model is targeted to deal with the problems caused by instability and low efficiency in the currently-adopted Generalized First Price (GFP) auction model in Bitcoin confirmation games. Besides, we use the “rank-by-cost” rule to replace the “rank-by-fee” rule, where each transaction’s cost is calculated by the user-submitted fee and the waiting time. Aiming to probe users’ equilibrium strategy, we first discuss the GSP game with complete information under synchronous submissions, and show that it has the Locally Envy-Free equilibrium. Then, we study the GSP game with incomplete information under asynchronous submissions, and define two types of strategies, i.e., the Farsighted Balanced (FB) strategy and the Instant Balanced (IB) strategy. The FB strategy is in line with users’ practical needs of determining fees so as to maximize the long-term payoffs; however it cannot generate a stable equilibrium. Alternatively, the IB strategy focuses on the instant payoff maximization, and if all users follow the IB strategy, their equilibrium fees can finally converge to a stable profile. Finally, we design computational experiments to validate our theoretical models and analysis. Our research findings indicate that this novel GSP mechanism is superior to the currently adopted GFP mechanism. Besides, the convergence of the GSP game under the IB strategy has also been illustrated by the computational experiments.

In recent years, the rapid development of cryptocurrencies and their underlying blockchain technology has revived Szabo’s original idea of smart contracts, i.e., computer protocols that are designed to automatically facilitate, verify, and enforce the negotiation and implementation of digital contracts without central authorities. Smart contracts can find a wide spectrum of potential application scenarios in the digital economy and intelligent industries, including financial services, management, healthcare, and Internet of Things, among others, and also have been integrated into the mainstream blockchain-based development platforms, such as Ethereum and Hyperledger. However, smart contracts are still far from mature, and major technical challenges such as security and privacy issues are still awaiting further research efforts. For instance, the most notorious case might be “The DAO Attack” in June 2016, which led to more than $50 million Ether transferred into an adversary’s account. In this paper, we strive to present a systematic and comprehensive overview of blockchain-enabled smart contracts, aiming at stimulating further research toward this emerging research area. We first introduced the operating mechanism and mainstream platforms of blockchain-enabled smart contracts, and proposed a research framework for smart contracts based on a novel six-layer architecture. Second, both the technical and legal challenges, as well as the recent research progresses, are listed. Third, we presented several typical application scenarios. Toward the end, we discussed the future development trends of smart contracts. This paper is aimed at providing helpful guidance and reference for future research efforts.

With the rapid development of social media, fast transition of organizational management modes, as well as the increasing security risks caused by human intervention and operations, the existing organizational management systems suffer a severe problem of lowered efficiency and security. This motivates our research. In this paper, we strive to design a new solution to this problem, via instantiating the traditional PDCA cycle theory and using software robots powered by blockchain and smart contracts. In our framework, blockchain and smart contracts can guarantee privacy-protected, traceable, and tamper-resistant data access, and can also help reduce the human intervention. Based on the PDCA cycle theory, we propose the PDCA closed-loop ecological chain on the basis of blockchain. We also validate our work in a real-world scenario, and the results can prove the effectiveness of our model.

Taking Ei Compendex (EI) and China National Knowledge Infrastructure (CNKI) databases as the literature sources, this paper presented a bibliographic analysis of the blockchain-related literature between January 2011 and September 2017. For each literature source, we built a separate dataset. Authors' productivity and collaboration, affiliation of authors and collaboration amongst institutions were analyzed using techniques of social networks analysis on both datasets. According to the results, the CNKI authors/institutes raised their productivity and outperformed the EI authors/institutes since 2016. However, the EI authors/institutes show a higher level than the CNKI authors/institutes in collaboration. We also summarized the hot topics on the EI dataset using textual analysis and discovered researchers have shifted their attention from Bitcoin itself to the blockchain technology underlying it.

In recent years, social media platforms and instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. The online behaviors of employees rely heavily on the policies or mechanisms formulated for the cyber-space, and are expected to impose a significant influence on their performance appraisal in the physical world. In this paper, we focus on the typical online reporting behavior, and empirically study how the work reporting policies formulated by supervisors influence the employees' strategies and in turn their performance, using a unique real-world dataset collected from the Chinese social media platform called WeChat. We consider two types of work reporting policies: the one is concerned with the reporting time and the other with the duplication degree of reporting contents. First, we establish an optimization model, taking both the reputation-based and performance-related utilities into consideration, so as to examine the employees' reporting strategies and their actual work performance. On this basis, we make further discussions about the reporting policy optimization from the perspective of organization supervisors. Then, an empirical study on a median-size organization in China is conducted to validate our proposed model and analysis. The results prove our conclusion that the reporting policy will influence not only employees' reporting strategies, but also their actual work performance.

In recent years, social media platforms and, especially, instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. In this paper, we collect the unique real-world data set from a WeChat-based work performance evaluation system built and used by a medium-sized organization in China, in order to study the optimization of the employees’ work strategies, especially the work time determination, with the purpose of maximizing the total utility comprised of the work and nonwork utilities. In the system, the performance evaluation is completely based on the employees’ work reports and includes three major measures including score, level, and rank. First, we formulate a dynamic optimization model, incorporating the work utility into the reputational and the substantive utility, so as to examine the employees’ work strategy. Then, we conduct both empirical studies and computational experiments to make an in-depth analysis to probe the employees’ optimal work strategies. The main results include: 1) the optimal work time is greatly affected by the weight of the reputational utility; 2) the optimal work time is always the threshold time to achieve a certain level or a certain rank; 3) the total utility is with the trend of decreasing after increasing with the growing work time; and 4) the optimal work time does not always make the employee achieve a good work performance.

With the application of big data analytics in online marketing, real-time bidding (RTB) has developed to be the primary business model and also the major online advertising channel. Due to the precise analysis of Web Cookies, RTB platforms can target the visiting audiences and then forward their generated ad impressions to demanding advertisers who bid on the best-matched audience in a real-time fashion. In RTB markets, the reserve price plays the vital role as a tuner to exclude over-low bids, and hence guarantee the desirable sales prices and revenues for publishers from ad impression sales. In this paper, we strive to study the publisher's strategy on the reserve price and probe its impact on his/her revenues. We first discuss the reserve price of ad impression in a single-channel sales model, including the online RTB channel or the off-line direct channel, aimed to study its impact on the publisher' revenue. Then, we further analyze the impact of the reserve price in the multi-channel settings. Finally, we conduct experiments using empirical log data collected from real-world RTB markets to validate our models and analyses, and the experimental results indicate that: 1) in the single-channel sales model, publishers should set the reserve price for only the online-channel ad impressions while not for the off-line-channel ones and 2) in the multi-channel ad impression sales, publishers should set both off-line and online reserve prices for revenue maximization.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years, and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the generated ad impressions to competing advertisers who submit bids for their best-matched audience in real-time ad auctions. In RTB markets, reserve price serves as an important tuner to exclude advertisers with low estimated values, and hence can guarantee a desirable result for the publisher from ad impression auctions. In this paper, we strive to study publishers' strategy on the reserve price, and probe the impact of reserve price on their revenues. We first analyze the ad impression auction under a direct auction mechanism. We then introduce the reserve price and study its impact on publishers' revenues under an indirect auction mechanism, and our research findings indicate that a rational positive reserve price will always improve publishers' revenues even if it is not optimal. Also, the optimal reserve price is figured out based on the advertisers' bid distributions for publishers' revenue maximization. Finally, experiments using empirical log data from real-world RTB markets are designed to validate our model and analysis, and the results provide strong support to our theoretical analysis. The experimental results also indicate that although the number of bids does not impose any influence on the optimal reserve price, it has significant impacts on publishers' revenues.

Real time bidding (RTB) has become an emerging online advertising with the development of Internet big data in recent years. In the whole RTB ecosystem, the Demand Side Platform (DSP) plays a central role, and it realizes the programmatic and accurate buying of the advertisements for the advertisers via a two-stage auction. In RTB business logics, DSP plays as an intermediary between the advertisers and the center platform. Due to the principle-agent relationship between the advertisers and the DSP, the DSP aims not only to maximize the revenue for the advertisers, but also gain its revenue in this process. So far there are two revenue modes for DSP, namely the two-stage resale model and the commission model, respectively. In this paper, we mainly consider the revenue model for DSP in RTB advertising market. We aim to study the properties of the two revenue models, and compare the revenues for the DSP and the advertisers under these two models. We also provide an example to illustrate our proposed models and their properties. The results show that under small ratio of the commission, the advertisers are more likely to choose the commission model, but the DSP is more likely to choose the two-stage resale model and set a larger weight, while under large ratio of the commission, the advertisers are more likely to choose the two-stage resale model, but the DSP is more likely to choose the commission model. Our research work highlights the importance of the revenue model on the revenues of the advertisers and the DSP, and is intended to provide a useful reference for DSPs in RTB advertising markets.

In social marketing practice, it is usually important to anticipate the long-term impact of the target application to maintain a long-lasting marketing effect, whereas a new product or technology should spread as quickly as possible to establish a competitive advantage. To find a balance between them, we tackle this challenge by modelling the problem as an issue of time-discounted influential sustainability. Given a threshold μ, the goal of the problem is finding a small subset of nodes as seeds and deciding the optimal timing to activate each seed that could maximize the time-discounted number of iterations, each of which actives more than μ nodes. We prove that solving the problem is NP-hard and the objective function is non-negative, non-monotonic, and non-submodular. Therefore we propose a greedy approach to approximately solve this problem. Our experimental results demonstrate that our solution outperforms two baseline algorithms. In order to provide meaningful advices for advertisers on selecting proper initial seed users, we further analyze and compare the performance of four seeding strategies on three typical types of social networks.

With the rapid development of big data analytics in online marketing, real-time bidding (RTB) has emerged as a promising business model in recent years and now becomes one of the major online advertising channels. Based on analysis of Web Cookies, RTB platforms are able to precisely identify the features and preferences of target audiences visiting publishers' websites, and forward the information to competing advertisers submitting bids for their best-matched audience in real-time ad auctions. As the supplier of ad impressions, publishers typically have multiple channels to sell their ad impressions (i.e., ad inventory), making their strategies for allocating ad inventory one of the most critical research problems. In this paper, we strive to study publishers' optimal strategy of allocating ad inventory across online channel of RTB-based auctions and offline channel prevailingly realized in the form of guaranteed contracts. Considering the ad reserve price as the control variable, we establish the optimization model. We also explicitly take the default penalty in offline channels into consideration, so as to balance the short-term online revenue and long-term offline revenue. In our work, we analyze altogether three kinds of strategies for publishers to allocate their ad inventory in pursuit of the optimal strategy, and validate our model and analysis via computational experiments. We find that there is no dominant strategy that can outperform others in all cases, and interestingly, publishers using the hybrid-channel strategy do not always gain more revenues than those using the single-channel strategy.