The Moderation Influence of Power Distance on the Relationship Between Technological Factors and the Successful Implementation of Citizen Relationship Management in the Public Sector

Abstract

The revolution of information and communication technology and the widespread use of it in all aspects of daily life, whether on a personal or institutional level, leads to the increase demand of citizens to get advanced and integrated electronic services. This has pushed many government institutions to implement Citizen Relationship Management systems to improve their performance and provide more sophisticated, efficient and effective services to citizens. However, previous studies have revealed the high rates of failure of such systems, and institutions have suffered significant financial losses. Studies on successful implementation of these systems in the public sector are rare, leaving us a nearly unexplored field of research. This study reinforced the body of knowledge on the successful implementation of Citizen Relationship Management by attempting to bridge the gap in previous studies and propose a comprehensive, unique and innovative theoretical model combining the technological context of Technological, Organizational, and Environmental (TOE) theory, and the power distance factor of Hofstede's national cultural model as a moderator. This study aims to provide new insights into the factors that affect the successful implementation of Citizen Relationship management. The model was examined empirically through a quantitative study conducted on one of the Omani government institutions. The theoretical framework was verified using the structural equation modeling (SEM) approach using SmartPLS software. IT infrastructure and system quality found to be significant technological factors affecting the successful implementation of Citizen Relationship Management. Further, the power distance found to be significant moderator.

Identification of End-User Economical Relationship Graph Using Lightweight Blockchain-Based BERT Model

“…Potential nancial activity risks and the ght against economic crime are also signi cant. e key to building a personnel relationship graph in thenancial sector is extracting personnel-related entities, attributes, and events from unstructured economic announcements, which entails tasks such as named entity recognition [1,2], relationship extraction, and event extraction [3]. In recent years, as computer power has increased, deep learning technology based on neural networks has increasingly become the de facto standard way for named entity recognition.…”