Search results

Tourism value chain (TVC) analysis has been widely used as an alternative approach for designing sustainable and inclusive programs for tourism development in Africa, Asia, and the Pacific. This chapter looks at the experiences of three sustainable tourism initiatives in the Philippines to assess the strengths and limitations of the value chain as a framework for destination development. Short case studies describe the experiences of stakeholders in two popular destinations in the Philippines: the surfing town of San Juan, La Union, and the resort-island of Panglao, Bohol. The third case study shares the perspective of the Transforming the Tourism Value Chain (TTVC) project, a national campaign for more sustainable hotels, resort as well as meetings, conventions, incentives, and event (MICE) facilities in various destinations around the country. Stakeholders deeply involved in implementing these projects share their insights on the successes, challenges, and limitations of TVC-based approaches to resilient and sustainable destination development. The destinations' experiences suggest that the advocacy for sustainable management and operations may be comparable to a process of technology adoption or acceptance along the TVC. Practical and theoretical recommendations for leveraging the TVC toward more holistic and sustainable visitor economies are given at the end of this chapter.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

The provision of online information through the use of digital platforms (websites and social networks) can be helpful for public sector organisations to promote transparency and accountability, thereby bringing it closer to citizens, more accessible and with higher added value for society. Whereas the call for government transparency is manifest and seems to be equal for all parts of government, this paper investigates whether small government entities are able to cope with this demand. In addition, the drivers of digital platforms are investigated in the context of Portuguese parishes.

The paper follows a quantitative research approach based on an original data set with more than 3,000 observations collected in two different time periods (2015 and 2020), which looks at the parishes’ use of website and Facebook.

Findings show that about half of Portuguese parishes, which are mostly small entities with on average fewer than 3,500 inhabitants, have an institutional website, whereas almost three out of four have Facebook. The empirical analyses reveal that a lack of organisational capacity of small entities can be an obstacle for the use of an institutional website and of Facebook. Furthermore, factors from the provider side (e.g. being part of a merger, participation in previous elections) and the user side (e.g. having access to broadband internet) are also found to have an influence on the adoption, use, and dropping of a digital platform.

The use of an original data set with more than 3,000 observations, and the research of the influence of size as a proxy of organisational capacity on the presence of parishes on the internet and Facebook, are innovative elements of this paper. In addition, this study adds to the knowledge about the influence of organisational, political, and socio-demographic characteristics on the use of digital platforms by small government entities.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0171

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Based on imprinting theory and episodic future thinking, this paper aims to study how CEOs’ attributes and experiences inform innovation in tourism and hospitality businesses. It also explores ways to quantify innovation in this sector.

The authors quantitatively analysed innovation in tourism and hospitality using extensive data from companies’ annual reports. They further adopted multivariate regression to test how CEOs’ experience affects enterprise innovation.

Results demonstrate that CEOs’ academic education and rich work experience can promote corporate innovation. The authors also identified a mediating role of the tone of narrative disclosure in annual reports between CEOs’ academic education and corporate innovation. The imprinting effects of career experience and educational experience appear both independent and interactive.

CEOs are more inclined to engage in corporate innovation when influenced by the combined imprinting effects of strategic management training and work experience. Additionally, leaders should consider how communication styles indirectly influence innovation activities.

This paper introduces an integrated perspective that blends imprinting theory and episodic future thinking to bridge knowledge gaps regarding the interaction of CEOs’ past experiences. This work enhances understanding of how CEOs’ imprinted experiences, together with their capacity for envisioning future scenarios, can drive corporate innovation.

This study aimed to determine the effect of age, complexity, internationalization, educational background of the vice rector for finance and the presence of professors on intellectual capital disclosure (ICD) in the official websites of Indonesian higher education institution (HEI). It also proved whether there was a difference between ICD in the three types of HEI based on autonomy.

The intellectual capital (IC) instrument used was adopted from Nicolo et al. (2021) and subsequently analyzed 78 HEIs in Indonesia, namely PTNBH, PTNBLU and PTS accredited “Excellent.” The content analysis method and multiple linear regression models were used to test the impact of independent variables, while Kruskal–Wallis was used to conduct a t-test.

The empirical results showed that complexity, internationalization and the presence of HEI professors had a positive effect, while age and educational background of the vice rector for finance showed an insignificant effect on ICD. The t-test showed there was a difference in ICD among the three types of HEI.

This study provides new evidence related to differences in ICD practices in three types of HEIs in Indonesia. The research findings are expected to encourage cooperation between the government and HEI to improve regulations for PTNBLU and PTS by referring to regulations that have been applied to PTNBH to improve the quality of universities in Indonesia through increasing international accreditation and the number of professors in HEI.

This study was the first to compare ICD among the three types of Indonesian HEI with new variables, namely the educational background of the vice rector for finance and the presence of professors.

Carbon emissions commonly serve as an indicator for environmental friendliness, and so more and more carbon emission calculators (CECs) are offered that allow an estimation of the environmental footprint of freight transport operations. Unfortunately, their exact measurement is challenging due to the availability or poor quality of necessary input data and a multitude of possible calculation methods that may result in highly inaccurate to very misleading figures.

A structured online search was conducted to identify suitable online carbon emission calculators (OCECs) for further assessment in the form of a benchmark case that includes different modes of transport from road and rail to air and sea between China and Europe. Further comparison resulted in a ranking of OCECs along the categories of transparency (routing system, data sources and calculation method), completeness (input options) and accuracy (data output).

Different predefined inputs and calculation methods employed by the OCECs assessed inevitably result in a wide spread of more or less reliable carbon footprint measurement results.

All potential users of CECs, including policymakers, actors from the transport industry and other stakeholders, are well advised to question greenhouse gas (GHG) emission statements that are not backed by transparent procedures and internationally recognized calculation standards.

This study, including a benchmark case and a ranking, offers a guideline for potential users of CEC to avoid major pitfalls coming along with the present carbon footprint measurement of freight transport operations.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This study aims to deepen the knowledge concerning the metal fused filament fabrication technology through an analysis of the printing parameters of a commercial 316L stainless steel filament and their influence on the porosity and mechanical properties of the printed parts. It also investigates the feasibility of manufacturing complex geometries, including strut-and-node and triply periodic minimal surface lattices.

A three-step experimental campaign was carried out. Firstly, the printing parameters were evaluated by analysing the green parts: porosity and density measurements were used to define the best printing profile. Then, the microstructure and porosity of the sintered parts were investigated using light optical and scanning electron microscopy, while their mechanical properties were obtained through tensile tests. Finally, manufacturability limits were explored with reference samples and cellular structures having different topologies.

The choice of printing parameters drastically influences the porosity of green parts. A printing profile which enables reaching a relative density above 99% has been identified. However, voids characterise the sintered components in parallel planes at the interfaces between layers, which inevitably affect their mechanical properties. Lattice structures and complex geometries can be effectively printed, debinded, and sintered if properly dimensioned to fulfil printing constraints.

This study provides an extensive analysis of the printing parameters for the 316L filament used and an in-depth investigation of the potential of the metal fused filament fabrication technology in printing lightweight structures.

Among the topmost challenges, limiting the transformation of conventional public–private partnership (PPP) projects to meet net-zero targets is financial risk. This challenge is more prevalent in PPP projects in developing economies like Ghana, where financial investments have dwindled due to the recent COVID-19 recession. This paper aims to assess the key financial challenges in transitioning to net-zero PPP projects in Ghana.

The research method process was set as follows. First, a review of the literature to identify the major financial risks from journal articles, project reports and documents was undertaken, followed by questionnaire development and collection of data. Finally, the analysis of 134 questionnaire data was examined with the fuzzy synthetic evaluation.

The results indicate that the following financial challenges could hinder the transition to net-zero PPP projects in the country: increasing borrowing charges to build net-zero PPP projects due to the global covid-economic recession, poor project financial management, unstable local capital market and excessive labour, health and safety costs.

Although, the study was conducted in Ghana, a country in the Sub-Saharan African region, the outcomes have significant impacts for similar developing countries in research investigations into the problem.

Assistance is provided in this study for PPP project practitioners in identifying the key financial challenges and possible strategies to mitigate them.

Towards net-zero sustainability, this study highlights the crucial financial barriers to overcome in the rapid transition to climate change and zero carbon solutions in PPP projects.