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Triangulation of research methods is crucial to thoroughly explore how tourism can be better linked to the local economy in the Pacific's ‘Small Island Developing States’ (SIDS) because it includes the use of multiple data collections, analytical methods, data sources and theories or perspectives (Rocco et al., 2003). The exploration of the interactions between the various stakeholders in tourism and the wider economy will help linkages to be understood and enhanced. The research focuses on the following stakeholders: tourists, growers, small and medium tourism enterprises (SMTEs), government officials and village councils. The study explores the ways in which each of these stakeholder groups interacts with each other and their perspectives on the issues surrounding the linkages between tourism and agriculture.

The purpose of this chapter is to demonstrate the use of a case study of Niue and multiple data-gathering techniques to collect critical information on the linkages between tourism and agriculture in Pacific SIDS. The findings and lessons learned from a single case study of Niue using a mixed-methods approach potentially benefit other island nations in the region. This chapter begins with a discussion on the usefulness of case study research and then justifies the use of a mixed-methods approach and multiple stakeholders to better understand the linkages between tourism and agriculture in SIDS. The complexities of the inter-sectoral analysis being undertaken and the lack of prior data in this area necessitated a mixed-methods approach to the research. The chapter thoroughly discusses the research process and participants, including the design of research tools and the conduct of field work. Then the chapter focuses on research findings and concludes by reviewing the lessons learned from this research approach and its use of a case study and mixed methods to gain a holistic insight into the potential for enhancing the linkages between tourism and agriculture on Niue.

Maria Amoamo is a post-doctoral fellow in Te Tumu, the School of Māori Pacific and Indigenous Studies at University of Otago in New Zealand. Maria's research interests include the representation of indigenous, cultural and heritage tourism. Her PhD thesis examined the issue of identity in relation to Māori regional tourism within a post-colonial framework. She is currently examining the economic value of identity in relation to determining ‘what is the profile of Māori tourism in Dunedin?’ Maria is also examining the issue of social vulnerability and resilience of Pacific Island communities in relation to tourism.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.

The aim of this paper is to examine the effect of talent management (TM) practices on employee performance (EP) among academic staff of Malaysian private universities (MPU) through employee engagement (EE).

This paper used both descriptive and quantitative approaches, while the research unit of analysis consists of MPU academic staff. A simple random and stratified sampling approach was utilized in this study while, the research sample consists of 314 MPU academic staff. A questionnaire was used to collect data from the target population, while partial least squares–structural equation modeling was used to evaluate the study hypotheses through a bootstrapping approach.

This paper results demonstrated that TM practices (succession planning practice, promotion practice and performance appraisal practice) have a significant effect on EP, while EE mediates the relationship between TM practices and EP in MPU.

This paper encourages university management to adopt and invest in TM practices for effective EE to achieve and sustain EP.

This paper has made a significant contribution to knowledge and to the operationalization of EE, EP and TM practices literature, which could help to develop theory, model, practice and research in areas of work performance.

This study aims to identify factors impacting female underrepresentation among cybersecurity professionals in Sri Lanka.

The study is based on survey data from 75 female professionals working in the cybersecurity sector of Sri Lanka. Partial least squares structural equation modelling was used to analyse the data.

Results showed that female self-efficacy on their capabilities, family, organisational culture, mentors and role model act as antecedents for women’s perceived motivation to select cybersecurity as a career option.

The study advances the literature on workforce gaps in the cybersecurity sector and claims that there is no single factor causing significant female underrepresentation in the cybersecurity industry. While clarifying the complexity of such factors, the study presents how such factors can systematise to attract females into the cybersecurity field.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.