Identifying Facilitators and Challenges for IT Adoption at a Local Malaysian Retail Company: The IT Management Perspective

Substantial research that examines the adoption of IT-enabled initiatives by organisations has been conducted in the context of western countries. Existing research shows that IT systems and their adoption are largely shaped by the characteristics of organisations, organisational culture, and business environment. To address the limited understanding of IT adoption practices in developing countries, this paper investigates facilitators and challenges related to technological, organisational and environmental contexts for IT adoption. For this purpose, a case study was conducted with a local Malaysian retail company which experiences enormous competitive pressure from influential international retail giants operating in Malaysia. The findings of the study offer important lessons for organisations operating in developing countries

Opportunities, challenges, and benefits of AI innovation in government services: a review

Abstract Artificial intelligence (AI) has emerged as an excellent tool across multiple industries and holds great promise for the government, society, and economy. However, the absence of a distinct consensus regarding the definition and scope of artificial intelligence hinders its practical implementation in government settings. This article examines the various methodologies, emphases, and goals within artificial intelligence, emphasizing its ability to enhance human capabilities in critical situations. Considering the present advantages and enhanced productivity brought about by AI adoption in trailblazing government departments, this study explores the possible benefits and limitations of AI usage in the public sector. By looking at the cross-disciplinary difficulties of public AI applications, such as language hurdles and service delays, this study highlights the necessity for a thorough knowledge of the risks, impediments, and incentives of employing AI for government services. The study hopes to provide insight into AI research's ultimate aims, including object manipulation, natural language processing, and reasoning. This study emphasizes the potential for greater productivity, simplified procedures, and reduced obligations by analyzing the pros and cons of using AI in the public sector. Further, organizational theory is considered a tool for figuring out how to deal with challenges and maximize possibilities associated with AI deployment. The theory is used as the conceptual framework to understand the benefits, opportunities, and challenges involved in using AI when providing government services. The results of this research help us better understand how AI may revolutionize public service delivery by stimulating new ideas and improving efficiency. This study covers critical questions about organizational theory's role in improving government AI adoption, the challenges governments have in adopting AI, and the potential benefits AI might offer public service delivery. The research recommends a strategic approach to AI adoption in the public sector, considering organizational, ethical, and societal implications while recognizing the possibility of AI's transformative impacts on governments' service provision

Towards Improving Meta-Search through Exploiting an Integrated Search Model

Meta-search engines are created to reduce the burden on the user by dispatching queries to multiple search engines in parallel. Decisions on how to rank the returned results are made based on the query's keywords. Although keyword-based search model produces good results, better results can be obtained by integrating semantic and statistical based relatedness measures into this model. Such integration allows the meta-search engine to search by meanings rather than only by literal strings. In this article, we present Multi-Search+, the next generation of Multi-Search general-purpose meta-search engine. The extended version of the system employs additional knowledge represented by multiple domain-specific ontologies to enhance both the query processing and the returned results merging. In addition, new general-purpose search engines are plugged-in to its architecture. Experimental results demonstrate that our integrated search model obtained significant improvement in the quality of the produced search results.Meta-search, ontology, natural language query understanding, semantic and statistical-based relatedness measures, collection fusion, experimental validation