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Based on social exchange theory, we propose and test the mediating role of formal and informal contracts in linking cooperation characteristics of prior ties and shared values among partners to cooperation success in the public–private partnership context. Results from a survey of 244 partners in public–private partnerships in the medical and healthcare fields in China revealed that both formal and informal contracts mediate the relationship between cooperation characteristics and cooperation success. We discuss the theoretical and managerial implications for public–private partnership research and practice with a specific focus on the Chinese context.

The relationship between gender and language has been studied with main focus on differences between the language of male and female from different angles with different methodologies. The research findings lay different emphasis on the differences, but there are some problems in the researches. This paper will review the previous researches into gender differences, then point out the problems existing in methodology and research findings, and finally propose that researchers should pay more attention to the similarities between the language of both genders, the similarities play the same important part as well as differences.

In the mobile Internet era, mobile payment service becomes the foundation of inclusive finance, which brings convenience and security to people. Various marketing strategies are designed to encourage mobile payment activities by allocating incentives such as coupons or commissions to customers or merchants. We summary two significant issues. First, there is a phenomenon of mutual influence between merchants and customers, i.e., bipartite influence issue, thus making the independent optimization of customers and merchants non-optimal. Second, the redemptions of coupons are partially observed, as we can only observe that the customer redeems the coupon or not at a specific incentive value, but cannot observe that at other incentive value, i.e., data censorship issue. In this paper, we propose a novel joint incentive optimization framework to address the above two issues. We propose to use a graph neural network to represent customers and merchants jointly by modeling the underlying bipartite influences. We then formulate the response model under the hazard regression setting and model the hazard rate with a piecewise nonlinear function to capture the changes of responses to different incentive values. Finally, we propose a linear programming method to allocate approximated optimal incentive values to customers and merchants in real-time. Extensive offline and online experimental results demonstrate the effectiveness of our proposed approach.

The improvement of the primary energy structure has been considered as one of the important measures to achieve the carbon emissions reduction targets in China. This current paper constructed a Markov chain model, which was used to forecast the transition of primary energy structure. GM (1, 1) model and a linear regression model were used to predict the total energy consumption in 2020 and 2030. Then, the CO2emissions intensity was calculated, and the realization of carbon emissions reduction targets in China was analyzed. The findings indicated that (1) China’s nonfossil energy share in primary energy cannot be achieved naturally. (2) Part of the carbon emissions intensity in China’s commitments was not binding actually. (3) The realization of the carbon emissions peak and the reduction target of carbon emissions intensity in 2030 would need the policy intervention. In the last part of this paper, policy recommendations on carbon emissions reduction in China were provided.

Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign. CCS CONCEPTS• Applied computing → Marketing.

This paper is to explore the critical success factors for the websites for Chinese migrant farmer workers. A theoretical framework was put forward according to the theories of usability, information architecture, webometrics and business model. A multi-case study on 10 websites was conducted for testifying 7 hypotheses. Business model and a few technical properties were found to be the critical factors that influence the effect and sustainable development of these websites. The originality of this paper is that it adopted a managerial-technical perspective to explore the critical success factors of the MFW websites.

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tensof-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign. CCS CONCEPTS• Computing methodologies → Sequential decision making.