Smart and sustainable agricultural practices are more complex than other industries as the production depends on many pre- and post-harvesting factors which are difficult to predict and control. Previous studies have shown that technologies such as blockchain along with sustainable practices can achieve smart and sustainable agriculture. These studies state that there is a need for a reliable and trustworthy environment among the intermediaries throughout the agrifood supply chain to achieve sustainability. However, there are limited studies on blockchain technology adoption for smart and sustainable agriculture. Therefore, this systematic review uses the PRISMA technique to explore the barriers and enablers of blockchain adoption for smart and sustainable agriculture. Data was collected using exhaustive selection criteria and filters to evaluate the barriers and enablers of blockchain technology for smart and sustainable agriculture. The results provide on the one hand adoption enablers such as stakeholder collaboration, enhance customer trust, and democratization, and, on the other hand, barriers such as lack of global standards, industry level best practices and policies for blockchain adoption in the agrifood sector. The outcome of this review highlights the adoption barriers over enablers of blockchain technology for smart and sustainable agriculture. Furthermore, several recommendations and implications are presented for addressing knowledge gaps for successful implementation.

The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R²) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

Purpose: – Opportunities for malicious cyber activities have expanded with the globalisation and advancements in information and communication technology. Such activities will increasingly affect the security of businesses with online presence and/or connected to the internet. Although the real estate sector is a potential attack vector for and target of malicious cyber activities, it is an understudied industry. This paper aims to contribute to a better understanding of the information security threats, awareness, and risk management standards currently employed by the real estate sector in South Australia. Design/methodology/approach: – The current study comprises both quantitative and qualitative methodologies, which include 20 survey questionnaires and 20 face-to-face interviews conducted in South Australia. Findings: – There is a lack of understanding about the true magnitude of malicious cyber activities and its impact on the real estate sector, as illustrated in the findings of 40 real estate organisations in South Australia. The findings and the escalating complexities of the online environment underscore the need for regular ongoing training programs for basic online security (including new cybercrime trends) and the promotion of a culture of information security (e.g. when using smart mobile devices to store and access sensitive data) among staff. Such initiatives will enable staff employed in the (South Australian) real estate sector to maintain the current knowledge of the latest cybercrime activities and the best cyber security protection measures available. Originality/value: – This is the first academic study focusing on the real estate organisations in South Australia. The findings will contribute to the evidence on the information security threats faced by the sector as well as in develop sector-specific information security risk management guidelines.

Studies have been conducted on university students’ acceptance of e-learning systems during COVID-19. However, less attention has been paid to students’ use of e-learning post-pandemic. This research provides a more comprehensive framework to investigate the effects of e-learning students’ various quality perceptions on attitude, learning engagement, and stickiness toward e-learning platforms. A survey-based quantitative method is adopted by this study in which sample data are collected from students in Australian universities. A total of 403 valid samples were analysed using covariance-based structural equation modelling. This study found that students’ perceived educational quality, service quality, information quality, and technical system quality play different roles in their attitudes and behaviours towards e-learning. It expands the information system success model by comparing the effects of students’ various perceived qualities on their ongoing commitment to e-learning. It provides insights to e-learning providers in pursuing better designs and more sustainable development of educational information systems.

Information technology outsourcing (ITO) is a USD multi-trillion industry. There is growing competition among ITO service providers to improve their service deliveries. Natural language processing (NLP) is a technique, which can be leveraged to gain a competitive advantage in the ITO industry. This paper explores the information security implications of using NLP in ITO. First, it explores the use of NLP to enhance information security risk management (ISRM) in ITO. Then, it delves into the information security risks (ISRs) that may arise from the use of NLP in ITO. Finally, it proposes possible ISRM approaches to address those ISRs in ITO from the use of NLP. The study follows a qualitative approach using the case study method. Nine participants from three organisations (an ITO client, service provider and sub-contractor) engaged in an ITO relationship in the ICT industry were interviewed through a semi-structured questionnaire. The research findings were verified through a focus group. Case study scenarios are provided for a clear understanding of the findings. To the best of our knowledge, it is the first study to investigate the information security implications of the use of NLP in ITO.