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Encouraging socio‐economic development in developing countries has resulted in many changes in the lifestyle of communities. Changes in dietary patterns are one of the main outcomes from the rapid socio‐economics advancement, for example excessive intake of fat, high‐protein diet (animal protein), salt and preservatives. Chronic diseases such as diabetes, coronary artery disease, hypertension and cancer are mostly related to diet. With the community becoming more nutrition and health conscious, one of the challenges faced is to make sure that the information and knowledge on diet and healthy lifestyle gets across to the community. This paper presents a model of web‐based diet system (WebDIET) that attempts to make diet information and menu plans that are customised to local preference more accessible via the use of Internet. The system is to be used by dieticians who serve as administrators and the public who are the end users. The dietary standard adapted in developing the system is Recommended Dietary Allowances (RDA) for Malaysia. The Malaysian Dietary Guidelines was also referred as it emphasises on Malaysian diet. The system consists of six modules namely Authentication Module, Menu Plan Module, Diabetic Menu Plan Module, Food Selection Module, Disease Info Module and Feedback Module. Diabetic menu plan module models the reasoning process employed by dieticians in suggesting menu plans. The planning task is solved using an artificial intelligence technique through the case‐based reasoning (CBR) approach. CBR, generally describes, the process of solving the current problem based on the proposed solution of similar problems in the past. Nearest Neighbour Algorithm was used to compute the similarities in weighted average. Tools used for the development of the system are Microsoft Visual Interdev, Microsoft FrontPage 2000, while HTML, VBScript and JavaScript are the scripting languages used to develop the system.

The purpose of this paper is to propose a method to have more accurate results in comparing performance of the paired information retrieval (IR) systems with reference to the current method, which is based on the mean effectiveness scores of the systems across a set of identified topics/queries.

Based on the proposed approach, instead of the classic method of using a set of topic scores, the documents level scores are considered as the evaluation unit. These document scores are the defined document’s weight, which play the role of the mean average precision (MAP) score of the systems as a significance test’s statics. The experiments were conducted using the TREC 9 Web track collection.

The p-values generated through the two types of significance tests, namely the Student’s t-test and Mann-Whitney show that by using the document level scores as an evaluation unit, the difference between IR systems is more significant compared with utilizing topic scores.

Utilizing a suitable test collection is a primary prerequisite for IR systems comparative evaluation. However, in addition to reusable test collections, having an accurate statistical testing is a necessity for these evaluations. The findings of this study will assist IR researchers to evaluate their retrieval systems and algorithms more accurately.

In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues.

This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods.

The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments.

Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.

The construction industry is a major generator of waste, which has a high potential to yield a substantial amount of waste into the economy as a valuable resource. Waste trading (WT) is a sustainable strategy for improving resource utilisation and transitioning the construction industry towards the circular economy. However, resource recovery through WT is greatly impeded by several barriers which have not been highlighted in previous research. This paper aims to determine the barriers for implementing effective WT practices in the Australian construction and demolition (C&D) sector.

To achieve the aim of this research, a triangulation approach of quantitative and qualitative methods has been used. This mixed-method approach combines a comprehensive literature review, a questionnaire survey using an expert forum and semi-structured interviews with industry experts.

This study has explored a wide range of barriers to the practices of WT in the C&D sector, which have been categorised based on six perspectives. From a collective perspective, the technical barriers were found to be most important among other categories. The key individual barriers found in this study include the following: the high cost associated with sorting and processing of waste on-site, lack of consistent waste data and reporting system at project, industry and national level, insufficient secured and established market for reusable/recycled waste materials, lack of communication and coordination among stakeholders, lack of user-friendly and active web-based waste exchange systems (with reliable waste information) and lack of incentives from the government to encourage market development. Overcoming these barriers collectively would enable the wide application of WT, which in turn, would have a positive impact on the economy, environment and efficiency of the industry.

The outcomes of this study are based on the data collected only in the state of New South Wales (NSW) in Australia which is considered as the limitation of this study.

This study contributes to the body of knowledge of C&D waste management (WM) by providing the theoretical and practical implications of closing the loop material cycle by highlighting the importance of economic and environmental benefits of WT. In this study, WT has been recognised as a sustainable strategy to manage waste by identifying the barriers impeding the wider application of effective trading practices in the C&D sector. The findings are useful to WM businesses engaged to establish new circular business models and to government/regulatory bodies in developing initiatives and incentives aiming to promote WT strategies and market platforms. Further research is suggested to test and validate the findings from other jurisdictions of Australia.

Online images can convey sensory-based elements affecting digital users' emotions and digital engagement. The purpose of this study is to investigate which image-based features are more effective in conveying and stimulating particular emotions and engagement towards organizations operating in the food industry.

An online experimental survey was implemented. Two image-based features, narrativity and dynamism were chosen. The stimuli comprise four images, one with high and one with low level of narrativity, and one with high and one with low dynamism, published by a food company on its official Instagram account. Food-identity, emotional appeals and digital visual engagement behaviours were measured. A total of 141 students between 19 and 25 years old of a European University completed the questionnaire. Data was analysed through SPSS software using t-test analysis.

Results show that both narrativity and dynamism impact digital users' emotions and it was found to impact digital visual engagement attitude. Food involvement was measured in terms of food identity impact the effects of specific image-based features on emotions and visual engagement.

The study focuses on only two visual social semiotics features – narrativity and dynamism – and therefore, only partially captures the potentialities of images in digital communications.

This study provides professionals with empirical evidence and insights for effectively planning a visual social media strategy.

This paper contributes to the stream of research in social media communications by investigating the visual social semiotic features of images published online by a food company.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

This paper aims to investigate the relationship between a sustainable university brand and the intention of international students to study at Universiti Sains Malaysia (USM), one of Malaysia’s premier universities. Moreover, the study explored the moderating effect of opinion leaders on the intention of international students to study at USM.

A survey involving 391 international students was conducted using a self-assessment questionnaire, data from which were analysed using partial least squares structural equation modelling.

Empirical data show that USM’s sustainability brand had a positive impact on international students’ intention to study at the university, but opinion leaders had no significant sway in influencing this decision. This finding could be attributed to USM’s established reputation as a sustainable university, which helps cement its standing as the top choice for international students.

This research only focussed on international students at one Malaysian university. Hence, the findings are not generalisable, in particular, to illuminate the experiences of students at non-Malaysian institutions, whose contexts are inevitably different than Malaysia’s.

This study offered a dimensional insight into the university management on the pivotal branding of sustainability as one of the important tools for attracting international students to study at the university. In light of the findings, it is suggested that universities magnify their efforts to support the sustainable agenda, to help create a sustainable university brand that adds value to the interests of stakeholders.

Universities are continuously faced with challenges in terms of branding. Besides, not many universities are branded as sustainable universities despite the high involvement in sustainability-focused activities. Research has scarcely focused on the influence of the “sustainable university brand” on the marketing effort of the university to international students. In studies where this topic was highlighted, they focused on the opinion leader as the moderating influence of the choice of university amongst international students.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

Social media celebrities are getting popular in promotions, and more people have experienced social media to receive information on diet and health tips. This research presents a study showing how social media can influence young people's diet behavior through collaboration with social media celebrities.

Through recruiting on various social media and online education forums, this research recruited 196 young Hongkongers to participate in an online survey developed based on the AIDA (Action, Interest, Desire, Attention) marketing communication model on how youths access diet information from social media celebrities in Hong Kong.

Hong Kong youths consume diet information from social media celebrities through instant messaging systems, social networking sites and online videos, and, in particular, information on food calories and nutrition. However, sponsorship from vendors would decrease their desire to agree with the messages from social media celebrities. After receiving this information, some participants would follow tips and guides from social media celebrities. However, they seldom share such information. Interestingly, males are more willing to follow these tips and guides.

First, this study fills the gap of prior research, which did not study much on how social media celebrities contribute to diet promotion to youths in Asia. Second, through the AIDA Model, this study shows how social media can affect the awareness and accessibility of diet information by young Hongkongers, followed by initiating their interest in this topic and retrieving more relevant information. Furthermore, the authors further understand their desire to follow and improve their behavior as promoted by social media influencers and how they practice such behavior. Based on these findings, health-related brands could consider using social media influencers helping to promote their products and services, and these brands could further use social media to secure customer engagement.

Managing the green (environmentally sustainable) supply chain is an important issue for industry. This paper aims to provide a framework to understand and appreciate the relationships of various research streams and topics in this field. Utilizing this framework, emergent research directions to advance the field are also presented.

Published research in peer‐reviewed journals is evaluated using a new framework of nine non‐exclusive, interrelated boundaries and five flows of resources related to green supply chains and supply chain management.

The research literature can be integrated into these comprehensive multidimensional frameworks, which also provide opportunities as vehicles for future research. Research directions are described utilizing the framework presented in this paper.

This work presents one potential set of frameworks. Insights relating to other potential frameworks are additional areas of investigation and not presented in this study. The literature reviewed in this paper focuses almost exclusively on peer reviewed journals. Emergent research in this area may also appear in books and conference papers. The frameworks provide guidance for various research streams.

The frameworks and review also provide opportunity for managers and organizations to more comprehensively understand issues underlying green supply chain management.

Greening supply chains has become a necessity as environmental concerns have remained at the forefront of the debate of global and local social interests.

The comprehensive boundaries and flows framework can be valuable for identifying barriers to study and implementation of the interdisciplinary green supply chain management topic based on recent published literature. They also provide insights into research streams and practice. The research questions provide some further direction for those wishing to investigate this field.