﻿<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:sciepub="http://www.sciepub.com" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:prism="http://prismstandard.org/namespaces/basic/1.2/">
  <channel rdf:about="http://www.sciepub.com/portal/Journals">
    <title>American Journal of Information Systems</title>
    <link>http://www.sciepub.com/journal/AJIS</link>
    <description>American Journal of Information Systems is a peer-reviewed, open access journal that provides rapid publication of articles in all areas of information systems. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of information systems.</description>
    <dc:publisher>Science and Education Publishing</dc:publisher>
		<dc:language>en</dc:language>
		<dc:rights>2013 Science and Education Publishing Co. Ltd All rights reserved.</dc:rights>
		<prism:publicationName>American Journal of Information Systems</prism:publicationName>
		9
		1
		January 2024
		<prism:copyright>2013 Science and Education Publishing Co. Ltd All rights reserved.</prism:copyright>
    <items>
      <rdf:Seq>
        <rdf:li resource="http://pubs.sciepub.com/ajis/9/1/1"/>
<rdf:li resource="http://pubs.sciepub.com/ajis/9/1/2"/>
      </rdf:Seq>
    </items>
  </channel>
  <item rdf:about="http://pubs.sciepub.com/ajis/9/1/1">
<title>
Use of Conventional Business Intelligence (BI) Systems as the Future of Big Data Analysis
</title>
<link>http://pubs.sciepub.com/ajis/9/1/1</link>
<description>
<![CDATA[Traditional Business Intelligence (BI) systems employ a combination of source systems, databases, data repositories, data warehouses, and analytical tools to gain insights into business operations and chart future organizational strategies. These BI systems typically rely on structured data extracted from the underlying source system databases. However, organizations are increasingly harnessing vast amounts of big data from diverse sources, which often include semi-structured and unstructured data. The BI systems currently in use were initially designed with structured organizational datasets in mind. As the volume of big data required for informed decision-making continues to grow, a pressing question arises: can the existing BI systems effectively analyse this diverse and expansive data landscape? This research seeks to assess the adaptability of current BI systems to analyse big data and presents potential strategies for addressing this evolving data landscape.]]>
</description>
<dc:creator>
Molla  Ehsanul Majid, Dora  Marinova, Amzad  Hossain, Muhammad  E. H. Chowdhury, Farah  Rummani
</dc:creator>
<dc:date>2024-07-04</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2024-07-04</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>9</prism:volume>
<prism:startingPage>1</prism:startingPage>
<prism:endingPage>10</prism:endingPage>
<prism:doi>10.12691/ajis-9-1-1</prism:doi>
</item>
<item rdf:about="http://pubs.sciepub.com/ajis/9/1/2">
<title>
Creating and Verifying Empirical Evidence for Information Technology Acceptance
</title>
<link>http://pubs.sciepub.com/ajis/9/1/2</link>
<description>
<![CDATA[This paper explores the IT acceptance constructs with an emphasis on extending and testing new IT acceptance constructs that can complement existing models such as the TAM and UTAUT. Historical structures, however, remain inadequate, especially considering the technological innovations and organizational peculiarities of sub-sectors like health, commerce, and civil service. The paper points out those existing models often incorporate end-of-the-pipe technologies that must align with today's IT systems, such as cloud computing, mobile applications, and artificial intelligence. Because IT is gradually expanding its penetration into various sectors of the economy and society, the factors that contribute to acceptance become more specific to the context of the process, which re-examines and improves traditional IT acceptance models. The research proposes advanced constructs to overcome these challenges that consider current technology vulnerabilities. Pioneers have created these constructs to capture the modern technological context and address data privacy, cyber security, and user agency issues. Hence, this research uses empirical evidence to assess the validity of these new constructs with the different industries and technologies. This study uses a deductive research approach to test theories derived from questionnaire data. Based on this, the sample size of the 150 participants is considered sufficient to guarantee the reliability of conclusions and measures' validity. Measurement data is performed using structured questionnaires, emphasizing key factors such as perceived usefulness, perceived ease of use, perceived behavioural control and perceived social pressure. The research works concerning early results show an inefficacy of the traditional models in depicting the present-day advertisement of IT infrastructure. Several newly developed concepts relevant to data privacy and cyber security become crucial antecedents of IT acceptance, contribution, and usage. The validity of these constructs is established by conducting a statistical analysis of the constructed models that can be used to explain behavior in different industry contexts. The study's contributions are twofold: It contributes to theoretical knowledge of IT acceptance by proposing and establishing new variables for consideration, Moreover, it enlightens practitioners of organizations that are seeking to increase the rate of end user adoption of embraced technologies. It focuses on the issues unaddressed by current models and casts light on the modern technological factors influencing IT acceptance in the modern rapidly advancing technological environment.]]>
</description>
<dc:creator>
Aysha  Siddiky Pinky, Tughlok  Talukder, Saddam  Nasir Chowdhury, Ashrafuzzaman  Hera, Dr.  Nure Alam Khan, Md  Omar Faruque, Prof.  Dr. Mohammed Julfikar Ali
</dc:creator>
<dc:date>2024-10-28</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2024-10-28</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>9</prism:volume>
<prism:startingPage>11</prism:startingPage>
<prism:endingPage>18</prism:endingPage>
<prism:doi>10.12691/ajis-9-1-2</prism:doi>
</item>
</rdf:RDF>