Artificial Intelligence (AI) has emerged from its traditional domain of computer science research to be a management reality. This can be seen in the remarkable increase in the adoption of AI technology in organizations resulting in increased revenue, reduced costs and improved business efficiency [19]. Despite this trend, there are still many organizations that are facing the decision whether to adopt AI. Thus, to evaluate the adoption of AI at organizational-level, we draw on two-grounded theories:Technology-Organizations-Environment (TOE) framework and Diffusion of Innovation theory (DOI) to identify factors that influence the adoption of AI. Survey data collected from 208 large, mediumsized and small organizations in Australia is used to test the proposed framework. We offer a method of how examining AI over a set of organizations. Besides offering several important recommendations for AI adoption future directions for research in this area are also included in this paper.

PurposeThe purpose of the paper is to demonstrate that significant improvements through business process re‐engineering can still be achieved after the implementation of enterprise resource planning systems. While the business process re‐engineering benefits of enterprise resource planning systems have been widely published, the opportunities for process improvement after the installation of integrated systems have not been extensively explored.Design/methodology/approachThis paper results from a case study of the highly successful intervention in the purchasing and accounts payable functions of Mobil Oil Australia Limited undertaken well after the implementation one of the widely used off‐the‐shelf enterprise resource planning systems, SAP (Systems, Applications and Products in Data Processing).FindingsSignificant benefits were achieved in the purchasing and accounts payable functions of Mobil Oil Australia Limited, via a focus on best practice and radical process improvement. Invoices and invoice processing were largely eliminated. Cheque usage was reduced by 87 per cent and the staff paying accounts cut by almost 75 per cent.Originality/valueThe case study demonstrates clearly to companies and practitioners that business process re‐engineering can achieve “dramatic improvements in cost, quality, service and speed” even after an enterprise resource planning solution has been implemented via a focus on best practice benchmarking and using best practice to provide a target for the change team. Companies can leverage existing, often substantial, investments in installed systems to further improve their processes and increase the return on those investments.

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Business process reengineering (BPR) and total quality management (TQM) both emphasise the benefits that a process orientated view of company operations can bring. Acquiring a clear definition of the “as‐is” business process and developing an understanding about how the process may be re‐engineered is a crucial stage in any BPR project. This early phase normally has three objectives: to achieve a full understanding of the process to be re‐engineered so as to clarify its objectives and characteristics; to create a shared vision and understanding among the re‐engineering team; to have a basis for starting the redesign. To support this phase, there is a range of tools: manual, computer supported and computer enabled which are traditionally used to help in the activities of process definition and analysis. This paper describes some of the newer approaches. The product introduction process (PIP) is examined in a general sense, and specific examples from the automotive industry are taken as a basis for evaluation of the available tools and applications. A sample from the process analysis tools identified was used to model the PIP, and from the difficulties and successes an understanding of the attributes required in such an analysis tool was derived.

Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.