Many businesses are now moving to e-business and implementing computerized accounting information systems. This phenomenon has given impact to audit profession in performing IT audit, financial reports audit and tracing electronic source documents. Computer-Assisted-Auditing Techniques and Tools (CAATTs) are audit technologies that allow IT audit work to be performed efficiently, effectively and reduce audit time. However, little is known about CAATTs adoption by public audit firms. This paper presents a new paradigm of Individual-Technology-OrganizationEnvironment (I-TOE) to investigate the acceptance of CAATTs in audit firms. There are gaps that exist in prior literatures which studied CAATTs acceptance from only individual auditor views and did not deliberate on issues from both organizational and individual perspectives. Consequently, this paper contributes to extend the literature by providing a better understanding on relationship of both organizational and individual factors in foreseeing CAATTs adoption and investment. A combination of Unified Theory of Acceptance and Use of Technology 2, and TechnologyOrganization-Environment framework are used as the underlying theories. In addition to that, this paper complements the framework with new variables of technology risk, technology task fit, organization readiness and top management commitment. I-TOE framework contributes to professional audit firms that need to measure CAATTs acceptance for the advancement of audit profession. Future experimental studies may be done to provide evidence and empirically validate I-TOE framework in other domain.

Literature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

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The purpose of this paper is to use the Technology, Organisation and Environment (TOE) framework to understand the audit technology adoption in audit firms. Previous studies have only looked from the viewpoint of individual auditors and do not use a framework in which to understand the audit technology adoption. The audit technology differs from other information technology adoption because audit tools change the way in which auditors carry their tasks. One of the major contributions in this study is to use the TOE framework to analyse the factors in organisation adoption. Data of this study were gathered through questionnaire surveys that were self-administered to 1,367 audit firms registered in the Malaysian Institute of Accountants directory. Our findings indicate that although firms generally acknowledge that there are advantages of audit technology implementation and the benefits outweigh the costs, the firms also recognise that their organisations are only somewhat ready to adopt and their staff's competency are only at a moderate level to be able to use the audit technology. Our survey also indicates that the role played by professional body support is important to increase its adoption.

Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in a broad range of applications such as understanding event chains and learning event-event relations, which can then be used for commodity price prediction. The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods. This paper proposes an effective use of Graph Convolutional Networks (GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event extraction in commodity news. The event extraction model is trained using feature embeddings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus. Experimental results show the efficiency of the proposed solution, which outperforms existing methods with F1 scores as high as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification. For the goal of reproducibility, the code and trained models are made publicly available 1 .