The role of motivation in regulating the extent to which data visualisation literacy influences business intelligence and analytics use in organisations

Dissertation (MCom (Informatics))--University of Pretoria 2022.The ability to read and interpret visualised data is a critical skill to have in this information age where business intelligence and analytics (BI&A) systems are increasingly used to support decision-making. Data visualisation literacy is seen as the foundation of analytics. Moreover, there is great hype about data-driven analytical culture and data democratisation, where users are encouraged to have wide access to data and fully use BI&A to reap the benefits. Motivation is a stimulant to the richer use of any information system (IS), yet literature provides a limited understanding of the evaluation of data visualisation literacy and the effect of motivation in the BI&A context. Thus, this study aims to explain the role of motivation in regulating the extent to which data visualisation literacy influences BI&A’s exploitative and explorative use in organisations. Data visualisation literacy is measured using six data visualisations that focus on the five cognitive basic intelligent analytical tasks that assess the user's ability to read and interpret visualised data. Two types of motivations are assessed using perceived enjoyment as an intrinsic motivator and perceived usefulness as an extrinsic motivator. The model is tested using quantitative data collected from 111 users, applying Structural Equation Modelling (SEM). The results indicate that intrinsic motivation exerts a positive effect on BI&A exploitative and explorative use while extrinsic motivation has a positive effect on BI&A exploitative use but weakens innovation with a negative effect on explorative use. The results further show an indirect relationship between data visualisation literacy with BI&A use through motivation. In addition, exploitation leads to creativity with exploitation positively being associated with exploration.InformaticsMCom (Informatics)Unrestricte

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Impact of Business Intelligence (BI) Systems Use on Process Level Performance

This study investigated the relationship between BI systems use, IT infrastructure capability, and firm performance at the process level. Even though a majority of past studies considered BI system usage as a single item variable, this study has taken a broader view of the BI system use and measured it with reference to four aspects – extent of use of various applications/business processes a BI system supports, extent of use of several technology components of a BI system, level of management/governance of BI system use and time since the adoption of a BI system. Based on the survey data collected from 128 business intelligence (BI) users in Australia, this study observed that the firms are using BI software solutions for more than 5 years and mostly using them to support financial and performance reporting processes. In spite of reporting satisfaction with the capabilities of the BI system, a majority of firms continue to use Excel for reporting and analysis functions bypassing the BI system. We found that poor management of access, rigidity of the BI system in its inability to meet dynamic changing business requirements, inadequacy of user training, poor data handling procedures, and absence of data governance are some of the challenges identified in the management of BI system usage. This study confirmed the enabling role of IT infrastructure capability in improving firm performance, and found this to be a significant predictor of process level performance. Firm size, measured by gross revenue and employee strength, had no influence on process level performance, the study found. Effective usage of the applications that support specific business processes and good management of use tend to deliver performance benefits to organizations, rather than just the deployment of technology tools. This study demonstrates the complementarity between BI systems use, IT infrastructure and business processes and highlights the importance of ‘BI use’ and its governance

Research Evidence on the Use of Learning Analytics: Implications for Education Policy

The evidence shows that the use of learning analytics to improve and to innovate learning and teaching in Europe is still in its infancy. The high expectations have not yet been realised. Though early adopters are already taking a lead in research and development, the evidence on practice and successful implementation is still scarce. Furthermore, though the work across Europe on learning analytics is promising, it is currently fragmented. This underlines the need for a careful build-up of research and experimentation, with both practice and policies that have a unified European vision. Therefore, the study suggests that work is needed to make links between learning analytics, the beliefs and values that underpin this field, and European priority areas for education and training 2020. As a way of guiding the discussion about further development in this area, the Action List for Learning Analytics is proposed. The Action List for Learning Analytics focuses on seven areas of activity. It outlines a set of actions for educators, researchers, developers and policymakers in which learning analytics are used to drive work in Europe’s priority areas for education and training. Strategic work should take place to ensure that each area is covered, that there is no duplication of effort, that teams are working on all actions and that their work proceeds in parallel. Policy leadership and governance practices •Develop common visions of learning analytics that address strategic objectives and priorities •Develop a roadmap for learning analytics within Europe •Align learning analytics work with different sectors of education •Develop frameworks that enable the development of analytics •Assign responsibility for the development of learning analytics within Europe •Continuously work on reaching common understanding and developing new priorities Institutional leadership and governance practices •Create organisational structures to support the use of learning analytics and help educational leaders to implement these changes •Develop practices that are appropriate to different contexts •Develop and employ ethical standards, including data protection Collaboration and networking •Identify and build on work in related areas and other countries •Engage stakeholders throughout the process to create learning analytics that have useful features •Support collaboration with commercial organisations Teaching and learning practices •Develop learning analytics that makes good use of pedagogy •Align analytics with assessment practices Quality assessment and assurance practices •Develop a robust quality assurance process to ensure the validity and reliability of tools •Develop evaluation checklists for learning analytics tools Capacity building •Identify the skills required in different areas •Train and support researchers and developers to work in this field •Train and support educators to use analytics to support achievement Infrastructure •Develop technologies that enable development of analytics •Adapt and employ interoperability standard

Framework for Security Transparency in Cloud Computing

The migration of sensitive data and applications from the on-premise data centre to a cloud environment increases cyber risks to users, mainly because the cloud environment is managed and maintained by a third-party. In particular, the partial surrender of sensitive data and application to a cloud environment creates numerous concerns that are related to a lack of security transparency. Security transparency involves the disclosure of information by cloud service providers about the security measures being put in place to protect assets and meet the expectations of customers. It establishes trust in service relationship between cloud service providers and customers, and without evidence of continuous transparency, trust and confidence are affected and are likely to hinder extensive usage of cloud services. Also, insufficient security transparency is considered as an added level of risk and increases the difficulty of demonstrating conformance to customer requirements and ensuring that the cloud service providers adequately implement security obligations. The research community have acknowledged the pressing need to address security transparency concerns, and although technical aspects for ensuring security and privacy have been researched widely, the focus on security transparency is still scarce. The relatively few literature mostly approach the issue of security transparency from cloud providers’ perspective, while other works have contributed feasible techniques for comparison and selection of cloud service providers using metrics such as transparency and trustworthiness. However, there is still a shortage of research that focuses on improving security transparency from cloud users’ point of view. In particular, there is still a gap in the literature that (i) dissects security transparency from the lens of conceptual knowledge up to implementation from organizational and technical perspectives and; (ii) support continuous transparency by enabling the vetting and probing of cloud service providers’ conformity to specific customer requirements. The significant growth in moving business to the cloud – due to its scalability and perceived effectiveness – underlines the dire need for research in this area. This thesis presents a framework that comprises the core conceptual elements that constitute security transparency in cloud computing. It contributes to the knowledge domain of security transparency in cloud computing by proposing the following. Firstly, the research analyses the basics of cloud security transparency by exploring the notion and foundational concepts that constitute security transparency. Secondly, it proposes a framework which integrates various concepts from requirement engineering domain and an accompanying process that could be followed to implement the framework. The framework and its process provide an essential set of conceptual ideas, activities and steps that can be followed at an organizational level to attain security transparency, which are based on the principles of industry standards and best practices. Thirdly, for ensuring continuous transparency, the thesis proposes an essential tool that supports the collection and assessment of evidence from cloud providers, including the establishment of remedial actions for redressing deficiencies in cloud provider practices. The tool serves as a supplementary component of the proposed framework that enables continuous inspection of how predefined customer requirements are being satisfied. The thesis also validates the proposed security transparency framework and tool in terms of validity, applicability, adaptability, and acceptability using two different case studies. Feedbacks are collected from stakeholders and analysed using essential criteria such as ease of use, relevance, usability, etc. The result of the analysis illustrates the validity and acceptability of both the framework and tool in enhancing security transparency in a real-world environment

Supporting decision making process with "Ideal" software agents: what do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation

Utilisation of building information modelling in facilities management: a South African case study

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Building, 2018Facilities management is longest phase in the life cycle of a facility. To effectively manage a facilities’ electronic information is needed. An integrated information management system such as Building Information Modelling (BIM) can be utilised to support data at any given phase of a building life cycle. Literature review shows that there are benefits to using BIM in Facilities Management. However, there is insufficient research regarding the use of BIM in facilities management in South Africa. The purpose of this research was to investigate the extent to which BIM is utilised in the South African Facilities Management sector and identifies the challenges faced by Facilities Management personnel while using BIM. Data was obtained through interviews and an online survey. The interviews were used to gather information from a small sample, while the survey was used to understand a larger sample. Both qualitative and quantitative data analysis techniques were used to analyse the data. The research was limited to international BIM standards, as BIM is a new concept in South Africa and there is scarcity of relevant literature in the context of South Africa. The findings reveal that majority of Facilities Management practitioners are not utilising BIM, due to factors relating to cost and week support organisations. Those who use BIM believe that the model does not have enough information to carry out all Facilities Management activities.XL201

Antecedents of business intelligence system use

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees. This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwait’s telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis. The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI user’s self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture. The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employees’ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUT’s facilitating conditions and the limited variance of respondent demographics

Information Visualisation for Project Management: Case Study of Bath Formula Student Project

This paper contributes to a better understanding and design of dashboards for monitoring of engineering projects based on the projects’ digital footprint and user-centered design approach. The paper presents an explicit insight-based framework for the evaluation of dashboard visualisations and compares the performance of two groups of student engineering project managers against the framework: a group with the dashboard visualisations and a group without the dashboard. The results of our exploratory study demonstrate that student project managers who used the dashboard generated more useful information and exhibited more complex reasoning on the project progress, thus informing knowledge of the provision of information to engineers in support of their project understanding