A comprehensive investigation on the relationship between information technology investments and firm diversification

This paper empirically investigates the role played by information technology in diversified firms by building a demand function for IT investments. First by reviewing the management literature, we briefly examine different types of diversification, including related diversification, unrelated diversification, and geographic diversification. After carefully developing the theoretical arguments we empirically test the relationship between IT investments and different types of diversification. We find that in general diversified firms demand more investments in information technology, but the positive relationship may also depend on the extent to which firms diversify. Our findings show that firms with diversified structures that increase the complexities of coordination and control, e.g. unrelated diversification or extensive geographic diversification, would face a lesser demand for IT investments because of the increased use of financial controls instead of strategic controls by these firms. Overall, we find that information technology can serve as an effective coordination and control mechanism for moderate levels of diversification whereas non-IT mechanisms for coordination and control becomes more suitable in a context of higher levels of diversification. The implications of these findings for research and practice are discussed.

Relative importance,specific investment and ownership in interorganizational systems

Implementation and maintenance of interorganizational systems (IOS) require investments by all the participating firms. Compared with intraorganizational systems, however, there are additional uncertainties and risks. This is because the benefits of IOS investment depend not only on a firm’s own decisions, but also on those of its business partners. Without appropriate levels of investment by all the firms participating in an IOS, they cannot reap the full benefits. Drawing upon the literature in institutional economics, we examine IOS ownership as a means to induce value-maximizing noncontractible investments. We model the impact of two factors derived from the theory of incomplete contracts and transaction cost economics: relative importance of investments and specificity of investments. We apply the model to a vendor-managed inventory system (VMI) in a supply chain setting. We show that when the specificity of investments is high, this is a more critical determinant of optimal ownership structure than the relative importance of investments. As technologies used in IOS become increasingly redeployable and reusable, and less specific, the relative importance of investments becomes a dominant factor. We also show that the bargaining mechanism—or the agreed upon approach to splitting the incremental payoffs—that is used affects the relationship between these factors in determining the optimal ownership structure of an IOS.

A Low-Cost Checkpointing Technique for Distributed Databases

For distributed databases, checkpointing is used to ensure an efficient way to perform global reconstruction. However, the need for global reconstruction is infrequent. Most current checkpointing approaches for distributed databases are too expensive during run time. Some of them allow the checkpointing process to run in parallel with normal transactions at the cost of more data and resource contention, which in turn causes longer response time for normal transactions. Thus, an efficient way to checkpoint distributed databases is needed to avoid degrading the system performance. This paper presents a low-cost solution, called Loosely Synchronized Local Fuzzy Checkpointing (LSLFC), to these problems. LSLFC supports global reconstruction, and our performance study shows that LSLFC has little overhead during run time.     

Novel criteria for global robust exponential stability of neural networks with time-varying delays via LMI approach

The article addresses the problem of global robust exponential stability of interval neural networks with time-varying delays. On the basis of linear matrix inequality technique and M-matrix theory, some novel sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point for delayed interval neural networks are presented. It is shown that our results improve and generalize some previously published ones. Some numerical examples and simulations are given to show the effectiveness of the obtained results.     

A new semismooth Newton method for NCPs based on the penalized KK function

In this paper, based on the Kanzow-Kleinmichel (KK) function, we introduce a new nonlinear complementarity problem (NCP) function: penalized KK function. We show that the function possesses desirable properties similar to those of the KK function. Based on this new NCP function, we reformulate the NCP to a system of semismooth equations. We also propose a new semismooth Levenberg–Marquardt method to solve the system of semismooth equations that employs both trust region techniques and line searches. The global and quadratic convergence properties can be established under very mild assumptions. Numerical results show the effectiveness of the proposed algorithm and also indicate that superior behaviour of the proposed new NCP function.     

A non-monotone inexact regularized smoothing Newton method for solving nonlinear complementarity problems

In the paper [S.P. Rui and C.X. Xu, A smoothing inexact Newton method for nonlinear complementarity problems, J. Comput. Appl. Math. 233 (2010), pp. 2332–2338], the authors proposed an inexact smoothing Newton method for nonlinear complementarity problems (NCP) with the assumption that F is a uniform P function. In this paper, we present a non-monotone inexact regularized smoothing Newton method for solving the NCP which is based on Fischer–Burmeister smoothing function. We show that the proposed algorithm is globally convergent and has a locally superlinear convergence rate under the weaker condition that F is a P ₀ function and the solution of NCP is non-empty and bounded. Numerical results are also reported for the test problems, which show the effectiveness of the proposed algorithm.     

Convergence of memory gradient methods

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases.     

A new SQP approach for nonlinear complementarity problems

Sequential quadratic programming (SQP) methods have been extensively studied to handle nonlinear programming problems. In this paper, a new SQP approach is employed to tackle nonlinear complementarity problems (NCPs). At each iterate, NCP conditions are divided into two parts. The inequalities and equations in NCP conditions, which are violated in the current iterate, are treated as the objective function, and the others act as constraints, which avoids finding a feasible initial point and feasible iterate points. NCP conditions are consequently transformed into a feasible nonlinear programming subproblem at each step. New SQP techniques are therefore successful in handling NCPs.