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The study aims to draw on the self-congruity theory to investigate the relationship among destination personality (DP), self-congruity and tourists’ pro-environmental behavioral intention (BI) among Guangzhou citizens who have experienced nature-based tourism (NBT).

The survey-based quantitative research was divided into two rounds, namely, a preliminary study exploring the dimensions of DP and the verification of whether the DP dimensions that significantly affect pro-environmental BI in step one would be selected for the main research to validate the conceptual model.

The results suggest that wholesome, one of the destination personalities, strongly predicts tourists’ pro-environmental BI, while actual self-congruity plays a mediating role between sincere, another DP, and tourists’ pro-environmental BI.

In practice, it offers multidimensional knowledge and robust evidence-based recommendations for the sustainable development and destination branding of NBT destinations in the post-epidemic era.

The study presents pioneering work that reveals previously underestimated factors influencing pro-environmental BI.

Housing market is predominantly driven by supply and demand, and the measurement of housing supply plays a crucial role in understanding market dynamics. One such measure is the number of building permits (BPs) issued. Despite the importance of BPs as an economic indicator, direct links have yet to be drawn between BP and housing value index (HVI). The purpose of this paper is to establish links between HVI and BP.

Trials were conducted using data at the national, state and metropolitan statistical area (MSA) levels. For each trial, the Granger causality test was used first to identify causal relationships between HVI and BP. Subsequently, the vector autoregression model was implemented in an attempt to observe impulse–response relationships and to create a forecast for HVI.

Bidirectional causal relationships were observed between HVI and BP at the national, state and MSA levels. The number of issued BPs proves to be an indicator for HVI. Impulse response functions indicate that HVI responds negatively to an increase in BP in the short term of 4–7 months but positively to an increase in BP with a lag of 10–12 months.

To the best of the authors’ knowledge, this paper is the first in the body of knowledge that establishes the number of issued BPs as an indicator for housing value. The results drawn using impulse–response function are also novel and had not been observed in previous studies.

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Islamic home financing products with Murabaha contracts are widely favored among the Muslim community in Indonesia, given that the country has a population of over 230 million Muslims. To facilitate the development of products and enhance public interest, it is important for Islamic banking institutions to comprehend the elements that may impact the intents of Muslim communities in Indonesia when selecting Islamic home financing products with Murabaha contracts. The purpose of this study is to ascertain the many aspects that may have an impact on the decision-making process of Muslim communities in Indonesia when selecting Islamic home financing product that use Murabaha contracts.

The partial least square-structural equation modeling data processing techniques will be used to process and evaluate these components. The data used in this study was acquired by administering questionnaires to a sample of 298 Muslim communities, which were randomly selected from a pool of 301 possible customers of Islamic house finance in Indonesia.

The results of this research show that attitude, subjective norms and perceived behavioral control have positive influence on intention to choose an Islamic home financing scheme with Murabaha agreement, while price fairness of Islamic home financing and Islamic altruism have direct and indirect influence on intention to choose Islamic home financing.

Analyzing factors that affect intention to choose Islamic home financing product under Murabaha contract is essential. Future study is required to analyze other Islamic home financing products, such as istisna, ijarah muntahia bi tamlik and diminishing musharakah. This study only serves as a foundation for further investigations into conventional approaches to home financing in emerging nations. The areas can be expanded to be implemented in other countries.

It is anticipated that Islamic banks have the capacity to cultivate a favorable and constructive perception, hence fostering a positive disposition among the Muslim populace in Indonesia. Furthermore, it is essential for Islamic banks to guarantee that all stakeholders within the sharia-compliant institution, particularly the frontline staff, have enough expertise and understanding of the intricacies of Islamic home financing products including Murabaha contracts, which are intended for prospective customers. In the foreseeable future, it is anticipated that the Muslim population in Indonesia would exhibit a greater intention toward the use of Islamic home financing solutions that use Murabaha contracts, facilitated by the establishment of a conducive environment.

This research integrates the impacts of pricing fairness and Islamic charity as a modified model, alongside the theory of planned behavior model, to examine the influence of these factors on individuals’ intentions to use Islamic home financing in Indonesia.

Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

The rapid development of urban areas in Sleman District, Indonesia, has created new challenges for firefighting response services. One of the primary challenges is to identify the optimal locations for new fire stations, to improve service quality and maximize service coverage within the specified time.

This paper proposes a method for precisely calculating travel time that integrates delay time caused by traffic lights, intersections and congestion. The study highlights the importance of precise calculation of travel time in order to provide a more accurate understanding of the service area covered by the fire stations. The proposed method utilizes network analysis in ArcGIS, the analytical hierarchy process (AHP) and simple additive weighting (SAW) to accurately calculate travel time and to identify the best locations for new fire stations. The identification of new site was based on service safety, service quality, service costs and demographic factors and applied to the Sleman district in Indonesia.

The results showed that the total area covered by old and new fire stations decreased from 61% to 31.8% of the study area when the adjusted default speed scenario was implemented.

The results indicated that the default speed scenario could provide misleading information about the service area, while the adjusted default speed scenario improved service quality and maximized service coverage.

The proposed method provides decision-makers with an effective tool to make informed decisions on optimal locations for new fire stations and thus enhance emergency response and public safety.

This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

This study aims to identify the regions with a high risk of natural disaster, estimate the proportion of households potentially participating in natural disaster insurance and analyze the relationship between disaster risk index and proportion of household potentially participating in natural disaster insurance.

Descriptive and quadrant analysis was applied on the 2019 Indonesia Disaster Risk Index (IRBI) scores and the 2019 National Socio-Economic Survey data.

The results showed there are only two categories of disaster risks in Indonesia based on IRBI categorization: “Medium” and “High.” Some districts in Aceh Province such as Simeuleu, Pidie Jaya and Banda Aceh City were observed to have a high proportion of households potentially participating in the natural disaster insurance while some in Jawa Tengah provinces have fairly low level even though they were categorized as high disaster-prone areas. Moreover, the quadrant analysis showed that 43 districts have high IRBI scores but low insurance participation rates with most discovered to be in Jawa Barat and Sumatera Selatan provinces.

Indonesia does not have a financial mitigation program up to the present time because almost all disaster resolutions are formulated based on emergency funds from the state budget even though it is important to use insurance schemes in all stages of disaster management. To the best of the authors’ knowledge, this study is the first attempt to identify households potentially participating in natural disaster insurance through the National Socio-Economic Survey in Indonesia.