Australian Computer Society: ACS Digital Library
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“Power to the People!”: Social Media Discourse on Regional Energy Issues in Australia
Get PDFSocial media provides people from all socio-economic sectors with the opportunity to voice their opinions. Platforms such as Twitter provide the means to share one’s opinion with little effort and cost. But do these media empower everyday people to make their voice heard? In this research, we introduce a novel approach for investigating the voice of different Twitter groups on social media platforms, by combining text clustering and an analysis of cliques in the resulting network. We focus on a case study using Twitter interactions with respect to energy issues, in particular the closure of coal-fired power stations such as Hazelwood. Implications from this study will benefit stakeholders from governments to industry to the ‘common man’, in understanding how discourse on social media reflects public consumer sentiment
Revisiting Concepts and Theories in Information Systems and Technology
Get PDFA Preface to the Research Notes Special Section on Revisiting Concepts and Theories in Information Systems and Technolog
A Post Publication Review of “Threats to Autonomy from Emerging ICTs"
No full textA short post publication review of a recent AJIS paper
Typology and Hierarchy of Students’ Motivations to Use Technology in Learning
Get PDFConsiderable discussion has taken place in practice and academe regarding the need for changes to the educational system to better suit current student’s approaches and preferences for technology use in learning. Much of this discussion involves assumptions about the current students (referred to by some as ‘digital natives’) preference for independent learning and that students are motivated in similar ways to use technology to achieve and support their preferred learning style. This study sought to better understand student’s motivations for technology use in learning and whether assumptions about the homogeneity of motivations are warranted. We sought to identify students’ motivation typology and any groupings within these typologies, and understand the inter-relationship between motivations. Using data collected from 16 Information Systems (IS) students via the Repertory Grid Interview technique (RGT), a cluster analysis segmented respondents into two distinct groups: ‘Independent Learners’ and ‘Traditional Learners’. A hierarchical framework of technology use motivations was developed for each group using Interpretive Structural Modelling (ISM) and Cross-impact Matrix Multiplication Applied to Classification (MICMAC) was used to categorise each group’s motivation factors. Results show that the two groups were driven to achieve the same learning goals by different paths and hence questioning the assumption of homogeneity in technology use motivations among the current student cohort
Using Social Media to Enable Staff Knowledge Sharing in Higher Education Institutions
Get PDFHigher education institutions (HEIs) are knowledge intensive environments by nature. However, the management of organisational knowledge and the promotion of staff knowledge sharing is largely neglected in these institutions. This study examines how enterprise social networks can enable staff knowledge sharing in communities of practice in that context. The study is framed as an Action Research project, covering three cycles over a 12 month period. A conceptual model was developed for empirical testing and data was collected through focus groups and interviews, supplemented by reflective journaling and content analysis. The findings support the conceptual model and provide insight into the antecedents necessary for the creation of an enterprise social network enabled knowledge sharing environment, the motivators for and barriers to participation, and the perceived organisational and individual benefits of increased staff knowledge sharing activity. The findings indicate that the barriers to participation are influenced by the prevalent organisation structure and culture, and a divide between faculty and other staff. However, individual benefits that accrue from participation may influence greater participation, and organisational benefits that accrue may influence organisational strategies that drive change in structure and culture to promote the development of the knowledge sharing environment. A number of findings have practical implications for the management of higher education institutions, such as the evidence of a divide between faculty and other staff, and the perceived existence of an organisational culture that inhibits staff communication, interaction and collaboration. In general, the study findings provide an opportunity for educationalists to better understand the scope and impact of employing social media platforms for knowledge sharing. This study adds to the growing body of work on organisational implementations of social media, and should be of interest to practitioners and researchers undertaking similar projects
A Comprehensive Review and Meta-Analysis on Applications of Machine Learning Techniques in Intrusion Detection
Get PDFSecuring a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for the changing usage pattern by comparing the different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies
Prescriptive Training Courseware: IS-Design Methodology
Get PDFInformation systems (IS) research is found in many diverse communities. This paper explores the human-dimension of human-computer interaction (HCI) to present IS-design practice in the light of courseware development. Assumptions are made that online courseware provides the perfect solution for maintaining a knowledgeable, well skilled workforce. However, empirical investigations into the effectiveness of information technology (IT)-induced training solutions are scarce. Contemporary research concentrates on information communications technology (ICT) training tools without considering their effectiveness. This paper offers a prescriptive IS-design methodology for managing the requirements for efficient and effective courseware development. To develop the methodology, we examined the main instructional design (ID) factors that affect the design of IT-induced training programs. We also examined the tension between maintaining a well-skilled workforce and effective instructional systems design (ISD) practice by probing the current ID models used by courseware developers since 1990. An empirical research project, which utilized this IS-design methodology investigated the effectiveness of using IT to train government employees in introductory ethics; this was a study that operationalized the interactive effect of cognitive preference and instructional format on training performance outcomes. The data was analysed using Rasch item response theory (IRT) that models the discrimination of people’s performance relative to each other’s performance and the test-items’ difficulty relative to each test-item on the same logit scale. The findings revealed that IS training solutions developed using this IS-design methodology can be adapted to provide trainees with their preferred instructional mode and facilitate cost effective eTraining outcomes.Supplementary files: Please note this article has supplementary files accessible via the Article Tools menu to the right
The Mandated Adoption and Implementation of an Academic Information System: Empirical Evidence from an Indonesian University
Get PDFUnder the scenario of contingent authority innovation-decision, organisation managers make the initial decision to adopt an innovation and mandate its use to the employees. Although accelerating adoption by the employees, the ensuing stages of implementation are often problematic partly due to its non-voluntary nature. Utilising an interpretive case study, this research aimed to explore the nature of the mandated adoption and implementation of an Academic Information System (AIS) for academics in an Indonesian University. Gallivan’s (2001b) framework for innovation adoption and implementation was modified and then applied as a lens to investigate the case. The results indicated that the mediating factors (i.e., managerial interventions, subjective norms, and facilitating conditions) played a vital role in reducing the resistance resulting from the authoritarian approach to mandating usage. Based on the findings, contributions were made by extending the existing framework and providing insights for the university executives regarding the pre- and post- implementation managerial interventions
A Post Publication Review of “An investigation into failure of Internet firms: Towards development of a conceptual model."
No full textA short post publication review of a recent AJIS paper
ForEx++: A New Framework for Knowledge Discovery from Decision Forests
Get PDFDecision trees are popularly used in a wide range of real world problems for both prediction and classification (logic) rules discovery. A decision forest is an ensemble of decision trees and it is often built for achieving better predictive performance compared to a single decision tree. Besides improving predictive performance, a decision forest can be seen as a pool of logic rules (rules) with great potential for knowledge discovery. However, a standard-sized decision forest usually generates a large number of rules that a user may not able to manage for effective knowledge analysis. In this paper, we propose a new, data set independent framework for extracting those rules that are comparatively more accurate, generalized and concise than others. We apply the proposed framework on rules generated by two different decision forest algorithms from some publicly available medical related data sets on dementia and heart disease. We then compare the quality of rules extracted by the proposed framework with rules generated from a single J48 decision tree and rules extracted by another recent method. The results reported in this paper demonstrate the effectiveness of the proposed framework