Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and provide assessment materials for educational purposes. Existing neural question generation models are not sufficient mainly due to their inability to properly model the process of how each word in the question is selected, i.e., whether repeating the given passage or being generated from a vocabulary. In this paper, we propose our Clue Guided Copy Network for Question Generation (CGC-QG), which is a sequence-to-sequence generative model with copying mechanism, yet employing a variety of novel components and techniques to boost the performance of question generation. In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation. Furthermore, our input passage encoder takes as input, among a diverse range of other features, the prediction made by a clue word predictor, which helps identify whether each word in the input passage is a potential clue to be copied into the target question. The clue word predictor is designed based on a novel application of Graph Convolutional Networks onto a syntactic dependency tree representation of each passage, thus being able to predict clue words only based on their context in the passage and their relative positions to the answer in the tree. We jointly train the clue prediction as well as question generation with multi-task learning and a number of practical strategies to reduce the complexity. Extensive evaluations show that our model significantly improves the performance of question generation and out-performs all previous state-of-the-art neural question generation models by a substantial margin.

T his paper presents a case study of the research and development of an RFID-based traceability system in an aircraft engineering company in Hong Kong. We report the system design and implementation, and discuss our experiences and lessons learned. The aim of the RFID system is to effectively support the tracking and tracing of aeroplane repairable items in the company. The study reveals eight critical success factors for the successful implementation of RFID systems, namely, create strong internal and external motivation for improvement, stir up desire to keep abreast of the latest technology for global competitiveness, strive for cross organizational implementation, avoid major process changes/limit process changes, start with a small RFID project scope, facilitate equipment vendor's investment, use cost-effectiveness reusable tags, and transfer RFID skills and knowledge from university to industry. We also summarize 13 lessons learned, including three lessons concerning RFID implementation at strategic level, six lessons at management level, and four lessons at operational level resulting from carrying out this project. Given the contextual details of the study, the lessons learned can help other firms to better anticipate the hurdles they will experience, and make them aware of the possible ways to cope with such difficulties before embarking on the journey of RFID implementation.

Purpose Due to the different institutional pressure such as those from market, regulations and competitors, companies have implemented green supply chain management (GSCM). Unfortunately, tens of GSCM practices exist. Whether all companies should implement GSCM and how to achieve both environmental and economic performance are still not clear for many companies. The purpose of this paper is to develop models that can be helpful for companies to identify right GSCM practices and implement GSCM effectively and efficiently. Design/methodology/approach Based on about 18 years of study on GSCM with four surveys in China in 2001, 2005, 2012 and 2016, as well as numerous site visits and interviews mainly in China but also in Japan, Germany and Canada, this paper explores institutional drivers as well as opportunities and challenges using theoretical analysis and case studies. GSCM is defined considering a product life cycle. A key three-step GSCM approach is theoretically developed considering opportunities and challenges through life cycle analysis (LCA) of a product and position of a company. Findings All companies should implement GSCM practices to avoid risks. To effectively implement GSCM practices, a company should understand the life cycle of its product and its position in the supply chain. A key three-step LCA-based approach can help companies to identify the critical GSCM practices. Originality/value A key three-step LCA-based approach for GSCM implementation is originally developed based on theoretical analysis and eight years of study.

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PurposeDigital transformation (DT) in the semiconductor industry goes beyond traditional business operations and supply chain management (OSCM) to the digital world. Despite significant developments in recent years, blockchain implementations for OSCM remain relatively underdeveloped in the semiconductor industry. Therefore, this research aims to examine the relationships between blockchain visibility, supply chain integration (SCI) and supply chain performance (SCP) in the era of DT in Malaysia's semiconductor industry to shed light on this emerging area.Design/methodology/approachA convenience sampling of 71 operations and supply chain managers attached to semiconductor manufacturing firms in Malaysia were invited to participate in a survey. In assessing blockchain visibility within the industry, key terms namely business intelligence gathering, information exchange, information technology (IT) and knowledge of asset status, were conceptualised from the literature review. The questionnaires developed to collect data were validated by industry and academic experts.FindingsThe results from the analysis confirmed that SCI mediates the link between blockchain visibility (information exchange, business intelligence gathering and knowledge asset status) and SCP. Likewise, the importance-performance matrix analysis (IPMA) outcomes revealed that IT played a minor role. The results suggested that semiconductor manufacturers should pay less attention to IT since this was identified as having the least priority towards improvement.Practical implicationsThe outcomes from this research enable policymakers to strategise and integrate blockchain technology in the era of DT to ensure sustainable SCM in the semiconductor industry in Malaysia.Originality/valueThe research bridge the knowledge gap by revealing the value that blockchain visibility can facilitate SCP and explore SCI as the prevailing factor and demonstrates how Resource-Based Theory and Network Theory can be applied in this study.

Mobile health (mHealth) is considered to be an important means of releasing the aging population problem. The efficiency of mHealth service can be increased by incorporating more elderly users and guaranteeing their continued use. However, limited attention has been directed toward investigating elderly users' continuance intention for mHealth service use. Drawing upon the trust theory, we investigated elderly users' characteristics, i.e. health anxiety and technology anxiety, to explain continuance intention. Survey data were collected comprising 261 valid responses to validate the research model and hypotheses. The results revealed that both cognitive and affective trust enhance continuance intention of mHealth services use. Health anxiety strengthens the effect of cognitive trust, but weakens the effect of affective trust, on the continuance intention. Furthermore, technology anxiety strengthens the effect of affective trust, but not that of cognitive trust, on the continuance intention. The limitations of our study and the theoretical and practical implications are discussed.

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