35 years of research on business intelligence process : a synthesis of a fragmented literature

Purpose: The business intelligence (BI) research witnessed a proliferation of contributions during the past three decades, yet the knowledge about the interdependencies between the BI process and organizational context is scant. This has resulted in a proliferation of fragmented literature duplicating identical endeavors. Although such pluralism expands the understanding of the idiosyncrasies of BI conceptualizations, attributes and characteristics, it cannot cumulate existing contributions to better advance the BI body of knowledge. In response, this study aims to provide an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones. Design/methodology/approach: This paper reviews 120 articles spanning the course of 35 years of research on BI process, antecedents and outcomes published in top tier ABS ranked journals. Findings: Building on a process framework, this review identifies major patterns and contradictions across eight dimensions, namely, environmental antecedents; organizational antecedents; managerial and individual antecedents; BI process; strategic outcomes; firm performance outcomes; decision-making; and organizational intelligence. Finally, the review pinpoints to gaps in linkages across the BI process, its antecedents and outcomes for future researchers to build upon. Practical implications: This review carries some implications for practitioners and particularly the role they ought to play should they seek actionable intelligence as an outcome of the BI process. Across the studies this review examined, managerial reluctance to open their intelligence practices to close examination was omnipresent. Although their apathy is understandable, due to their frustration regarding the lack of measurability of intelligence constructs, managers manifestly share a significant amount of responsibility in turning out explorative and descriptive studies partly due to their defensive managerial participation. Interestingly, managers would rather keep an ineffective BI unit confidential than open it for assessment in fear of competition or bad publicity. Therefore, this review highlights the value open participation of managers in longitudinal studies could bring to the BI research and by extent the new open intelligence culture across their organizations where knowledge is overt, intelligence is participative, not selective and where double loop learning alongside scholars is continuous. Their commitment to open participation and longitudinal studies will help generate new research that better integrates the BI process within its context and fosters new measures for intelligence performance. Originality/value: This study provides an integrative framework that integrates the interrelationships across the BI process and its organizational context and outlines the covered research areas and the underexplored ones. By so doing, the developed framework sets the ground for scholars to further develop insights within each dimension and across their interrelationships.© Yassine Talaoui and Marko Kohtamaki. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcodefi=vertaisarvioitu|en=peerReviewed

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Intelligent student engagement management : applying business intelligence in higher education

Advances in emerging ICT have enabled organisations to develop innovative ways to intelligently collect data that may not be possible before. However, this leads to the explosion of data and unprecedented challenges in making strategic and effective use of available data. This research-in-progress paper presents an action research focusing on applying business intelligence (BI) in a UK higher education institution that has developed a student engagement tracking system (SES) for student engagement management. The current system serves merely as a data collection and processing system, which needs significant enhancement for better decision support. This action research aims to enhance the current SETS with BI solutions and explore its strategic use. The research attempts to follow socio-technical approach in its effort to make the BI application a success. Progress and experience so far has revealed interesting findings on advancing our understanding and research in organisation-wide BI for better decision-making

Big data in higher education: an action research on managing student engagement with business intelligence

This research aims to explore the value of Big Data in student engagement management. It presents an action research on applying BI in a UK higher education institution that has developed and implemented a student engagement tracking system (SES) for better student engagement management. The SES collects data from various sources, including RFID tracking devices across many locations in the campus and student online activities. This public funded research project has enhanced the current SES with BI solutions and raised awareness on the value of the Big Data in improving student experience. The action research concerns with the organizational wide development and deployment of Intelligent Student Engagement System involving a diverse range of stakeholders. The activities undertaken to date have revealed interesting findings and implications for advancing our understanding and research in leveraging the benefit of the Big Data in Higher Education from a socio-technical perspective

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

A Tale of Evaluation and Reporting in UK Smart Cities

Global trends towards urbanisation are associated with wide-ranging challenges and opportunities for cities. Smart technologies create new opportunities for a range of smart city development and regeneration programmes designed to address the environmental, economic and social challenges concentrated in cities. Whilst smart city programmes have received much publicity, there has been much less discussion about evaluation of smart city programmes and the measurement of their outcomes for cities. Existing evaluation approaches have been criticised as non-standard and inadequate, focusing more on implementation processes and investment metrics than on the impacts of smart city programmes on strategic city outcomes and progress. To examine this, the SmartDframe project conducted research on city approaches to the evaluation of smart city projects and programmes, and reporting of impacts on city outcomes. This included the ‘smarter’ UK cities of Birmingham, Bristol, Manchester, Milton Keynes and Peterborough. City reports and interviews with representative local government authorities informed the case study analysis. The report provides a series of smart city case studies that exemplify contemporary city practices, offering a timely, insightful contribution to city discourse about best practice approaches to evaluation and reporting of complex smart city projects and programmes

Business intelligence systems and user's parameters: an application to a documents' database

This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in Information systems that are used in Business Intelligence systems in order to integrate a better response from such systems. We identified two main types of attributes that can be missing from a base and we showed why they needed to be included. A user model that is based on a cognitive user evolution is presented. This model when used together with a good definition of the information needs of the user (decision maker) will accelerate his decision making process

Cultivating Empathy: New Perspectives on Educating Business Leaders

Beyond rules, procedures, and manuals lie relationships. Jettisoning a formal hierarchical company structure allows all levels of management and employees to positively interact – this is where the key driver of “empathy” is so critical to continue building these relationships and molding a common organizational purpose