The Moderating Effects of Network Centrality between IT Initiatives and Firm Performance: An Empirical Study

The IS research community has accumulated a criticalmass of studies on IT business value, but the questions of howand why IT creates business values remain understudied. In thisstudy, we focus on the role of network centrality and conjecturethat network centrality moderates the effects of IT initiatives onfirm performance. We collected data of 26 public companiescross 19 industries over a period of 1994-2008 (15 years) andconducted a multiple-level analysis. The results of data analysisshow that IT initiatives are significantly, positively related to firmperformance only in the high network centrality situation

BUSINESS VALUE OF INFORMATION TECHNOLOGY IN NETWORK ENVIRONMENTS

Information Technology (IT) business value research is suggested as fundamental to the contribution of the IS discipline. The IS research community has accumulated a critical mass of IT business value studies, but only limited or mixed results have been found on the direct relationship between IT and firm performance. Extant studies mostly focus on whether IT creates business value and demonstrate indirect relationships between IT and some aspects of firm value; however, the question of why and how IT can do so remains understudied. These limitations lead to the challenge where existing IT business value studies have not done enough on providing feasible, practical guidance for IT practitioners and have had lacking relevance to the business world. In this study I propose the concept of dynamic IT capability (DIC), defined as the ability of a firm to build, integrate, and upgrade IT resources to improve, enhance, and reengineer business processes as responses to rapidly changing environments, and apply it in network environments. Using data of 26 companies over a span of 8 years from a number of secondary sources, I examined the direct link between DIC and firm performance and the indirect link through the mediation of firm innovation, both moderated by network structures. The results of data analysis indicate that DIC is an important indicator of IT business value in network environments. DIC contributes to firm performance directly or indirectly through firm innovation. Also, DIC complements network structures to positively influence firm performance. These findings have important implications for both researchers and practitioners

Neural Networks are Integrable

In this study, we explore the integration of neural networks, a potent class of functions known for their exceptional approximation capabilities. Our primary emphasis is on the integration of multi-layer neural networks, a challenging task within this domain. To tackle this challenge, we introduce a novel numerical method that combines the forward algorithm with a corrective procedure. Our experimental results demonstrate the accuracy achieved through our integration approach

Business Value of Information Technology in A Network Environment

Business value of information technology (IT) continues to be an important issue for both practitioners and academic scholars. Most IT business value literature focuses on the scope of an individual firm and overlooks the impact of the network environment it resides in. On the other hand, interorganizational system (IOS) studies tend to rely on a single information system and fall short on providing a complete picture of IT business value in a network environment. This study extends current IT business value models with explicit inclusion of the network environment factors and examines effects of IT resources on network capabilities and firm performance. Considering theories of dynamic capabilities, flexible specialization, and social network, we propose that IT resources are directly related to both network characteristics and network capabilities. In turn, these network characteristics and capabilities affect firm performance. By referring to different theoretical bases and proposing a nomological model, we advance current IT business value research and provide guides for IT practitioners. This study is planned with both archival and survey data in large, multidivisional and multinational companies in high-tech industries

The Effect of Individual Differences, Tasks, and Decision Models on User Acceptance of Decision Support Systems

Past studies suggested that decision support systems (DSS) must be an “enabling” system aiming to enhance users’ capabilities and to leverage their skills and intelligence. This suggests that users be the center of DSS and users’ characteristics be an important factor of explaining their DSS acceptance behavior. Since DSS are aimed to work in semi-structured and unstructured task environment, perceived task complexity can be used to explain users’ willingness to accept DSS. Further, several studies also used decision models for investigating users’ DSS acceptance behavior. We argue that nature of DSS (based on their underlying decision models) and its interaction with individual differences also play important roles on users’ DSS acceptance behavior. With the conjecture that users’ DSS acceptance behavior directly affects the DSS usage and DSS success, our research question focuses on how do individual differences influence users’ DSS acceptance behavior with consideration of task characteristics and nature of the DSS. The contribution of this paper is multifold. First, we extend the existing understanding of effects of individual differences on users’ DSS acceptance behavior. Second, we extend two major measurements of cognitive styles (GEFT - Group Embedded Figures Test and MBTI - Myers-Briggs Type Indicator) for individual differences in the context of DSS. Third, we investigate multiple task complexities and multiple DSS models. Hypotheses are developed and will be tested with an experiment of 300 plus subjects

A Comprehensive Evaluation of the DFP Method for Geometric Constraint Solving Algorithm Using PlaneGCS

The development of open-source geometric constraint solvers is a pressing research topic, as commercially available solvers may not meet the research requirements. In this paper, we examine the use of numerical methods in PlaneGCS, an open-source geometric constraint solver within the FreeCAD CAD software. Our study focuses on PlaneGCS\u27s constraint solving algorithms and the three built-in single-subsystem solving methods: BFGS, LM, and Dogleg. Based on our research results, the DFP method was implemented in PlaneGCS and was successfully verified in FreeCAD. To evaluate the performance of the algorithms, we used the solving state of the constraint system as a test criterion, and analysed their solving time, adaptability, and number of iterations. Our results highlight the performance differences between the algorithms and provide empirical guidance for selection of constraint solving algorithms and research based on open-source geometric constraint solvers

LU decomposition and Toeplitz decomposition of a neural network

It is well-known that any matrix $A$ has an LU decomposition. Less well-known is the fact that it has a 'Toeplitz decomposition' $A = T_1 T_2 \cdots T_r$ where $T_i$'s are Toeplitz matrices. We will prove that any continuous function $f : \mathbb{R}^n \to \mathbb{R}^m$ has an approximation to arbitrary accuracy by a neural network that takes the form $L_1 \sigma_1 U_1 \sigma_2 L_2 \sigma_3 U_2 \cdots L_r \sigma_{2r-1} U_r$, i.e., where the weight matrices alternate between lower and upper triangular matrices, $\sigma_i(x) := \sigma(x - b_i)$ for some bias vector $b_i$, and the activation $\sigma$ may be chosen to be essentially any uniformly continuous nonpolynomial function. The same result also holds with Toeplitz matrices, i.e., $f \approx T_1 \sigma_1 T_2 \sigma_2 \cdots \sigma_{r-1} T_r$ to arbitrary accuracy, and likewise for Hankel matrices. A consequence of our Toeplitz result is a fixed-width universal approximation theorem for convolutional neural networks, which so far have only arbitrary width versions. Since our results apply in particular to the case when $f$ is a general neural network, we may regard them as LU and Toeplitz decompositions of a neural network. The practical implication of our results is that one may vastly reduce the number of weight parameters in a neural network without sacrificing its power of universal approximation. We will present several experiments on real data sets to show that imposing such structures on the weight matrices sharply reduces the number of training parameters with almost no noticeable effect on test accuracy.Comment: 14 pages, 3 figure