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Marine plastic pollution (MPP) is increasing in recent times because of the high usage of plastic products. Green consumption behaviour (GCB) gaining attention as effective approach to achieving sustainable source reduction of plastic pollution, which negatively affects both human pollution and marine biodiversity and ecosystem. Although, Higher Education (HE) students are key stakeholders in addressing environmental issues, including MPP, there is limited empirical research in Ghana on factors influencing HE students’ GCB. This study, in an endeavour to bridge the gap, used the revised theory of planned behaviour (TPB) framework to investigate the factors influencing higher-education students’ green consumption behaviour in the Ghanaian context. Specifically, the purpose of the study is to examine the interplay of consumer novelty seeking (CNS), environmental concern (EC), perceived behavioural control and social influence on green consumption behaviour among higher-education students in Ghana. The study also explored the moderating role of gender in the relationship between CNS and green consumption behaviour.

This study used quantitative approach to obtain data from a sample of 233 students at the University of Cape Coast and used the partial least squares structural equation modelling approach for the data analysis.

The findings provide valuable insights, highlighting the important role of CNS and ECs in driving higher education students’ green consumption behaviour in Ghana. This study also found a revealing role for gender as a moderator in the relationship between CNS and green consumption behaviour, with females exhibiting a more pronounced response to CNS in influencing green consumption behaviour. On the contrary, the authors found a non-significant impact of perceived behavioural control and social influence.

Although this study presents results that provide valuable insights for policy and practical implications, it has some limitations worth mentioning for future research directions. Firstly, the participants sampled for this study comprised only higher education students from the University of Cape Coast in Ghana, which may limit the applicability of the findings to other student populations at various universities in Ghana and beyond. Moreover, the exclusion of non-students who are considered as “Generation Z” (i.e. born within 1995–2010) may narrow the scope of generalisability in the context of young consumers’ green consumption behaviour in Ghana. To enhance the generalisability of future studies, it is recommended that the scope of this study be extended. Furthermore, it should be noted that this study primarily measured higher education students’ green consumption behaviour based on self-reported data. Therefore, future research could adopt alternative approaches, such as non-self-reported measures or experimental data so to reduce the complexities and the gap that may exist between attitudes and behaviour.

These results provide valuable insights for policymakers, educators and environmental advocates to develop targeted initiatives that resonate with Ghanaian higher education students to foster green consumption practices and contribute to global efforts against marine plastic pollution.

The novelty of this study lies in the decision to propose a TPB model by including variables like CNS and EC that are believed to positively shape attitudes towards green consumption behaviour. The rationale for examining these variables is grounded in the belief that they are appropriate factors that may predict students’ green consumer behaviour, which may serve as a potential solution to marine plastic pollution.

Infrastructure projects are complex, task-based endeavours; the reliability of project schedules is often low, as delays occur. As a result, the infrastructure sector is constantly searching for innovative practices that might improve project performance. This study aims to pursue one research question: what are the benefits and challenges to last planner system (LPS) implementation in Australia’s infrastructure industry? It addresses this question through an examination of two rail infrastructure projects in Victoria, Australia.

The state of Victoria in Australia provides an excellent context for this study. The study was set in one of the programme alliances which was contracted to carry out five level-crossing removal projects. This study uses a combination of qualitative and quantitative research techniques, including surveys, interviews and site visits. The authors collected 43 valid survey responses. Additionally, 19 participants from the alliance programme’s two projects (Projects A and B) took part in the interviews. Extensive empirical evidence has been triangulated to achieve the research goals.

The findings show through the questionnaire survey that the greatest benefits that digital LPS brings are primarily associated with process improvement and social benefits. The interview results acknowledged that the roll-out of digital LPS had a positive impact on schedules, cost, occupational health and safety (OHS) and quality performance. The research also noted a few challenges in the introduction of digital LPS. Overall, the finding presents strong evidence that the introduction of digital LPS has been a clear success in the case company.

This is the first piece of research to fill this major gap in the LPS literature by exploring the issue of digital LPS adoption in Australia’s infrastructure sector. This study has thus laid the foundation for future work on LPS research in the Australian context. The authors are optimistic that this exploratory work will generate interest across other alliances, and perhaps extend into Australia’s construction sector.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

The lack of knowledge has hindered the successful implementation of lean in the construction industry. This has alarmed the need for lean learning practices. Out of numerous models, the learning-to-learn sand cone model received a wider acknowledgment for learning practices. Thus, this study aims to propose a learning-to-learn sand cone model integrated lean learning framework for the construction industry.

The research adopted an interpretivism stance. A qualitative research approach was adopted for the study. Consequently, fifteen (15) semi-structured interviews and document reviews were carried out to collect data in three (3) cases selected through purposive sampling. Code-based content analysis was used to analyse the data.

Fifty-two (52) sub-activities pertaining to nine lean learners at each stage of the lean learning procedure were identified. The most significant practices in the lean learning procedure to continuously improve lean learning in the organisation were maintaining records, providing a performance update to senior management and preparing and distributing several hierarchical manuals for all levels of staff to aid in the implementation of lean approaches.

The findings of the research can be aided to successfully implement lean by following the identified sub-activities via various parties within the organisation. The proposed lean learning framework opens several research areas on lean learning in the construction industry. This is the first research to uncover a lean learning framework in the construction industry rather than at the educational institute level.

This study aims to explore the influence of perceived value, enjoyment and novelty of fast-food chains’ smart retailing technology (SRT) on Filipino consumers’ attitude and loyalty.

Purposive sampling was used in identifying the respondents (N = 343). The participants were composed of consumers of the leading fast-food chains in the Philippines, where SRT using self-service ordering kiosks is being implemented. The hypotheses were explored using partial least squares path modeling, and predictive-causal was the study’s research design.

The results reveal that, among the factors, perceived enjoyment substantially contributes to the formation of favorable consumers’ attitude toward SRT. Moreover, perceived value was found to have a moderate effect on attitude while perceived novelty showed small impact. In terms of consumers’ attitude and loyalty, the two variables were found to have large positive and significant relationship. The moderation analysis shows that consumers’ attitude toward SRT has medium indirect effect on the relationship between perceived enjoyment and loyalty, while there is small indirect influence on the links between perceived value and loyalty, and between perceived novelty and loyalty.

As more and more fast-food establishments are adopting the use of SRT via self-ordering kiosks, the present study is the only study in the Philippine context that explores how perceived value, enjoyment and novelty affect consumers’ attitude and loyalty.

The study aims to identify the determinants of customer satisfaction in the electronic commerce (e-commerce) industry in Bangladesh. It also investigates whether acceptance of IT innovation mediates the relationship between the determinants of e-commerce and customer satisfaction.

A survey questionnaire had been designed and distributed among the customers of e-commerce businesses. Data were collected from 408 respondents, who were mostly from urban areas of the country. The collected data had been analysed with the application of the partial least square approach using SmartPLS4. First, the measurement model was applied to determine the validity and reliability of the dataset. Then, the structural model was utilized to justify the hypothesis.

The results showed that customer service, perceived ease of use and customer trust in e-commerce services have a significant positive impact on customer satisfaction. The acceptance of IT innovation, which showed a positive influence on customer satisfaction, enhanced customer satisfaction when accompanied by perceived ease of use and digital literacy.

The results would have valuable insight for the e-commerce business in designing their products and services and taking policies to achieve long-term customer loyalty.

This is the first study that incorporates IT innovation acceptance as a mediating variable. Although a number of factors have been identified as the determinants of customer satisfaction, the specific mechanism of IT innovation acceptance as a mediator between predictors and customer satisfaction is unique in this study.

To develop a fuzzy causal model of enterprise flexibility dimensions in a case study of Indian pharmaceutical industry.

The eight dimensions of enterprise flexibility were identified based on literature review. Fermatean fuzzy decision-making trail and evaluation laboratory (FF-DEMATEL) technique is applied to develop the cause-and-effect interrelationship model among various enterprise flexibility dimensions.

The information technology flexibility, supply chain flexibility, technical flexibility and marketing flexibility are found to be causing/influencing other flexibilities and contributing to overall enterprise flexibilities. Therefore, more attention needs to be paid to develop and sustain them for competitive advantage.

Fermatean fuzzy sets offer more flexibility and more accurate handling complex uncertain group decision making. FF-DEMATEL is a more accurate method to develop inter-dependencies and causal model than ISM, TISM. Ratings from the limited number of decision experts (DEs) from few pharmaceutical firms were done. Future study should take bigger sample of firms and more number of DEs to generalize the findings.

The model will help managers in pharmaceutical industry to prioritize the dimensions of enterprise flexibility to achieve agility, responsiveness, resilience and competitive advantage.

To the best knowledge of the authors, causal modeling enterprise flexibility dimensions using FF-DEMATEL has been studied for the first time in a developing economy context.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

This study aims to investigate the morphology and physicochemical properties of BiOBr/Polyvinylidene fluoride (PVDF) composite membranes and the differences in the properties of BiOBr/PVDF composite membranes made by adding different precursor ratios during the casting process.

In this paper, sodium bromide and Bi(NO3)3 were used as precursors for the preparation of BiOBr photocatalysts, and PVDF membranes were modified by using the phase conversion method in conjunction with the in situ deposition method to produce BiOBr/PVDF hydrophilic composite membranes with both membrane separation and photocatalytic capabilities.

The characterization results confirmed that the composites were successfully and homogeneously co-mingled in the PVDF membranes. The related performance of the composite membrane was tested, and it was found that the composite membrane with the optimal precursor incorporation ratio had good photocatalytic efficiency and antipollution ability; the removal efficiencies of methyl orange, rhodamine B and methylene blue were 80.43%, 85.02% and 86.94%, respectively, in 2.5 h. The photocatalytic efficiency of composite membranes with different precursor ratios increased and then decreased with the increase of the precursor addition ratio.

The composite membrane is prepared by phase conversion method with in situ deposition method, and the BiOBr material has unique advantages for the degradation of organic dyes. The comprehensive experimental data can be known that the composite membrane prepared in this paper has high degradation efficiency and good durability for organic dyes.