The self-disclosure of personal information by users on social network sites (SNSs) play a vital role in the self-sustainability of online social networking service provider platforms. However, people’s levels of privacy concern increases as a direct result of unauthorized procurement and exploitation of personal information from the use of social networks which in turn discourages users from disclosing their information or encourages users to submit fake information online. After a review of the Theory of Planned Behavior (TPB) and the privacy calculus model, an integrated model is proposed to explain privacy disclosure behaviors on social network sites. Thus, the aim of this paper is to find the key factors affecting users’ self-disclosure of personal information. Using privacy calculus, the perceived benefit was combined into the Theory of Planned Behavior, and after some modifications, an integrated model was prescribed specifically for the context of social network sites. The constructs of information sensitivity and perceived benefit were redefined after reviewing the literature. Through a study on the constructs of privacy concern and self-disclosure, this article aims at reducing the levels of privacy concern, while sustaining online transactions and further stimulating the development of social network sites.

Successfully developing new products is critical to an entrepreneurial firm’s continued success. Based on the resource management model, this study aims to answer the key research question: how entrepreneurial firms leverage network competence and technological capability to enhance their new product development (NPD) performance in a turbulent environment. Using data collected from 134 entrepreneurial firms in China, we investigate the performance effects of network competence and technological capability, and the moderating effects of technological turbulence and market turbulence. Our findings show that network competence has a positive impact on NPD performance and technological capability plays a mediating role between network competence and NPD performance. Technological turbulence enhances the performance effects of network competence and technological capability; market turbulence advances the performance effect of network competence, but fails to exert significant negative impact on that of technological capability. We discuss managerial implications of our findings and offer directions for future research.

Through moving a significant fraction of uploading from the servers to the peers, using P2P approach can effectively alleviate server loading in VoD deployments. However, users’ asynchronism dilutes their ability to assist each other. We develop a theoretical model to conduct an in-depth study of average server loading in large-scale P2P-VoD systems. This model captures many of the essential features of average server loading in large-scale P2P-VoD systems. Based on the in-depth study on average server loading, we introduce actively collaborative helpers into systems for alleviating server loading. The model of peers is further developed to a hybrid model for studying the optimal download amount of helpers from perspective of the entire network including peers and helpers. We show there exactly exists the sweet point of download amount of homogeneous helpers. With downloading the sweet amount, helpers can contribute the bandwidth around twice than they consume, when they do not assist each other, and even approximately 4 times when they assist each other.

This paper establishes three-sector general equilibrium models and separately investigates how sector-biased corruption influences the wage rates of skilled and unskilled workers, the wage inequality, and the amount of unskilled rural–urban migrants in developing countries. Corrupt activities are introduced in our theoretical models as transaction costs. We find that the reductions in different sector-biased corrupt behaviors exert different impacts through various economic mechanisms. In addition, the change in urban unskilled unemployment due to the decrease in the degree of sector-biased corruption is also taken into account.

Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound.

We study claims problems with indivisible goods. Due to indivisibilities, in certain situations, two agents with equal claims may have to receive unequal amounts. Our main goal is to find rules that deal with these situations in a consistent way. We propose three “systematic favorability” properties. We define a subfamily of up (down) rules studied in Moulin and Stong (Math Oper Res 27:1–30, 2002). We show that the subfamily are the only rules satisfying our first systematic favorability property, composition up, and bilateral consistency. Another family of rules we study are the sequential priority rules. Given a priority order over agents, we satisfy their claims one agent at a time until the resource runs out. These rules are the only ones that satisfy our second systematic favorability property, composition down, and bilateral consistency. Using duality, we also provide another characterization of the sequential priority rules, with our third systematic favorability property. Besides, we provide an alternative characterization of the rules studied in Herrero and Martinez (Soc Choice Welf 30:603–617, 2008).

This paper aims at showing that the class of augmented Lagrangian functions for nonlinear semidefinite programming problems can be derived, as a particular case, from a nonlinear separation scheme in the image space associated with the given problem. By means of the image space analysis, a global saddle point condition for the augmented Lagrangian function is investigated. It is shown that the existence of a saddle point is equivalent to a regular nonlinear separation of two suitable subsets of the image space. Without requiring the strict complementarity, it is proved that, under second order sufficiency conditions, the augmented Lagrangian function admits a local saddle point. The existence of global saddle points is then obtained under additional assumptions that do not require the compactness of the feasible set. Motivated by the result on global saddle points, we propose two modified primal-dual methods based on the augmented Lagrangian using different strategies and prove their convergence to a global solution and the optimal value of the original problem without requiring the boundedness condition of the multiplier sequence.

In the existing economy based models of grid resource allocation and management, just as commodity market model and posted price model, sharing resource is based on negotiating about the usage duration or time, the usage fee, QoS (Quality of Service) and some other items between the owner or his broker and the consumer of grid resource. That will cost so much time for a great deal of grid users no matter whether they are grid resource owners or grid resource consumers, which reduced the sharing efficiency in the grid environment and some time is even unacceptable. In this paper, for solving the problem as described above, we present a scheme which is combined by 3 aspects: one is GRS (Grid Resource Supermarket), the second one is posted price model based on GRS, the third one is optimization based on MOGAP (Multi-Objects Generalized Assignment Problem). In the end, three examples are given to show the feasibility of our scheme.

In this paper, a full-Newton step feasible interior-point algorithm is proposed for solving $$P_*(\kappa )$$ -linear complementarity problems. We prove that the full-Newton step to the central path is local quadratically convergent and the proposed algorithm has polynomial iteration complexity, namely, $$O\left( (1+4\kappa )\sqrt{n}\log {\frac{n}{\varepsilon }}\right) $$ , which matches the currently best known iteration bound for $$P_*(\kappa )$$ -linear complementarity problems. Some preliminary numerical results are provided to demonstrate the computational performance of the proposed algorithm.