IT-Business strategic alignment in influencing sustainable competitive advantage in Jordan: Structural Equation Modelling (SEM) approach

In many review articles or studies, the researchers have encouraged further exploration on the causal links between Information Technology (IT) investments and a firm’s sustainable competitive advantage.The outcomes of empirical studies have been inconclusive, which is to a certain extent due to the omission of IT-business strategic alignment.Indeed, strategic alignment has emerged as one of the most important issues facing business and IT executives all over the world. This paper reports on the empirical investigation of the success factors, which consist of leadership, structure and process, service quality, and values and beliefs, which are representative of the culture gap between IT strategy and business strategy.A questionnaire survey among 200 IT managers was carried out and 172 data sets were collected.This represented a 86% response rate. After a rigorous data screening process including outliers, normality, reliability and validity, 172 data sets were ready for structural equation modelling (SEM) analysis. Confirmatory Factor Analysis (CFA) was performed to examine the composite reliability, convergent validity and goodness of fit of the individual constructs and measurement models. The revised structural model demonstrates the relationships between all the four exogenous variables and IT-business strategic alignment, and all the four exogenous variables and sustainable competitive advantage. In addition, regarding the revised model there are two mediating effects of strategic alignment in the relationship between leadership, structure and process, service quality, values and beliefs, and sustainable competitive advantage

Effect of material properties on ductility factor of singly rc beam sections

Ductility may be defined as the ability to undergo deformations without a substantial reduction in the flexural capacity of the member. The ductility of reinforced concrete beams depends mainly on the shape of the moment-curvature relationship of the sections. The constituents of reinforced concrete are very complex due to its mechanical properties. The stress-strain behavior of concrete is considered parabolic and that of the steel is elastic plastic. Concrete and reinforcing steel are represented by separate material models that are combined together to describe the behavior of the reinforced concrete sections. The end displacements of the steel element are assumed to be compatible with the boundary displacements of the concrete element which implied perfect bond between them. The curvature ductility factor of singly reinforced concrete rectangular beams is derived taking into account the possible nonlinear behavior of the unconfined compressed concrete and reinforcing steel. Effects of material properties such as concrete compressive strength, reinforcement ratio and yield strength of reinforcement on the curvature ductility factors are derived analytically. From the analyses it is observed that an increasing steel content decreases the curvature ductility of a singly reinforced concrete section and this pattern is valid for any concrete strength. On the other hand, for the same reinforcement content curvature ductility increases as the concrete strength is increased

Engineering the Hardware/Software Interface for Robotic Platforms - A Comparison of Applied Model Checking with Prolog and Alloy

Robotic platforms serve different use cases ranging from experiments for prototyping assistive applications up to embedded systems for realizing cyber-physical systems in various domains. We are using 1:10 scale miniature vehicles as a robotic platform to conduct research in the domain of self-driving cars and collaborative vehicle fleets. Thus, experiments with different sensors like e.g.~ultra-sonic, infrared, and rotary encoders need to be prepared and realized using our vehicle platform. For each setup, we need to configure the hardware/software interface board to handle all sensors and actors. Therefore, we need to find a specific configuration setting for each pin of the interface board that can handle our current hardware setup but which is also flexible enough to support further sensors or actors for future use cases. In this paper, we show how to model the domain of the configuration space for a hardware/software interface board to enable model checking for solving the tasks of finding any, all, and the best possible pin configuration. We present results from a formal experiment applying the declarative languages Alloy and Prolog to guide the process of engineering the hardware/software interface for robotic platforms on the example of a configuration complexity up to ten pins resulting in a configuration space greater than 14.5 million possibilities. Our results show that our domain model in Alloy performs better compared to Prolog to find feasible solutions for larger configurations with an average time of 0.58s. To find the best solution, our model for Prolog performs better taking only 1.38s for the largest desired configuration; however, this important use case is currently not covered by the existing tools for the hardware used as an example in this article.Comment: Presented at DSLRob 2013 (arXiv:cs/1312.5952