Research in Transportation Business and Management最新文献

The trucking industry holds most of the Indonesian transportation market share, and COVID-19 tremendously impacts this sector. However, not many scholars have researched the impacts of COVID-19 on the trucking business. This study identifies the differences between high-, medium-, and low-performing trucking companies by investigating the relationships between resilience factors and performance and differentiating survivability and high-performance framework. Using partial least squares structural equation modeling and gap analysis, we identify key resilience factors that differentiate these companies. High performers prioritize resilient leadership, innovation digitalization, adaptability, and risk management, while medium and low performers focus primarily on finances. Our research extends the existing literature by highlighting strategies for survival and high performance during the pandemic, showcasing the importance of broader resilience factors for top performers.

Growing global research utilizes user acceptance models to investigate the public acceptance of automated vehicles (AVs). A growing body of literature suggests it is essential to recognize cultural differences that may influence people's decisions and the intention to use (AVs). While the influence of perceived safety on AVs adoption has been examined globally, it has often been overlooked in Australia. To address this knowledge gap, this study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) model by incorporating perceived safety and socio-demographic factors in assessing behavioral intention for fully AVs in Australia. This study is the first in Australia to include perceived safety in the UTAUT model and look at how different factors like age, gender, experience, income, education, and travel habits affect people's intention to use technology. The model was evaluated with Structural Equation Modelling using a dataset of 804 respondents from Australia.

Perceived Safety (PS) holds comparable importance to Social Influence (SI) and Facilitating Conditions (FC). Our analysis revealed that younger age groups exhibit a more substantial positive correlation between Performance Expectancy (PE) and Behavioral Intention (BI) compared to older age groups. Notably, there are significant distinctions in the impact of PS on BI between older and younger age groups, as well as between those with and without prior experience with AVs. Moreover, gender has a moderating effect akin to age in the PE-BI relationship. Our findings also reveal that age moderates the relationship between PE and BI, with younger individuals exhibiting less susceptibility to social influence compared to older counterparts. Gender also emerges as a moderator, affecting the relationship between FC and BI. Additionally, income moderates the relationships between both EE (Effort Expectancy) and FC with BI. However, qualifications do not significantly moderate the relationships between latent variables and BI.

The multigroup analysis highlights a significant divergence in the influence of PE on BI between groups with no experience and experienced people. Additionally, the study shows that the higher-income group displays a lower coefficient of FC towards BI, potentially due to their pre-existing knowledge base. The findings from this study assist decision-makers by providing insights into public attitudes towards AVs by revealing the key factors influencing public acceptance.

Railroads are generally the primary public transportation system connecting the region. However, when a railway crosses a city, it divides it into north-south (east-west), creating the front and rear sides of the city. In Japan, grade separation projects elevate the existing railway lines. The primary purpose is to relieve traffic congestion caused by railroad crossings. However, integrating the north–south axis has also been highlighted. In addition, creating a new centrality to station spheres in mature cities will be an essential guideline for the future formation of an urban structure centered on station spheres by transit-oriented development. This study focused on 12 cases in which an elevated railway line in the Kyoto-Osaka-Kobe conurbation of Japan was combined with an urban redevelopment project. We investigated changes in land use and land prices in the surrounding area. The study results confirmed that the disparity between the front and rear sides tended to shrink. In addition, based on the station's characteristics and area to be redeveloped, a policy for more effective integrated redevelopment and elevation projects was identified. The findings of this study will assist in reorganizing cities into a more sustainable urban structure by eliminating urban fragmentation centered on railway stations.

The purpose of this study is to investigate the on-demand food delivery (ODFD) riders' safety and delivery efficiency under the influence of individual well-being. Applying the conservation of resources (COR) theory, we develop our direct, mediation, and moderation hypotheses. Structural equation modeling with partial least square analysis is used to test the hypotheses. We find that stress mediates the relationship between well-being and risky driving behaviors as well as delivery efficiency. Regarding the monitoring algorithms, interactional monitoring is found to strengthen the effect of stress on risky driving behaviors and delivery efficiency. On the other hand, observational monitoring is found to reduce the effect of stress on risky driving behaviors. This study offers practical insights into ODFD companies that riders' well-being is influential on their risky driving behaviors and delivery efficiency. ODFD firms can manage observational monitoring to reduce risky driving behaviors but should be concerned about the role of interactional monitoring.

The use of autonomous delivery robots (ADRs) in last-mile delivery, as an emerging innovation, could have drastic implications for businesses and customers. This study adapts and extends the UTAUT2 (Unified Theory of Acceptance and Use of Technology) model to investigate the determinants of acceptance for autonomous food delivery robots. We examine the impacts of privacy and security concerns on the relationship between social influence and adoption behaviour. In addition, the relationship between environmental friendliness and behavioural intention toward adoption is examined. These factors are fairly unexplored in previous studies in the context of food ADRs. In doing so, the study collected 590 online responses from potential customers using a random sampling technique. The results showed that both moderation effects of privacy concerns and security concerns are supported and both variables deteriorated the social influence of adoption. In addition, the negative relationship between contactless delivery and the adoption of ADRs was confirmed in this study. Furthermore, the results confirmed the positive impact of environmental friendliness on the adoption of food ADRs. The findings offer valuable insights into the effective deployment strategies of ADRs in food delivery.

Transportation is a critical contributor to carbon emissions, with road transportation playing a dominant role due to its dense network and versatility. However, the overreliance on road transportation has led to congestion, impacting reliability. As international trade grows, the demand for sustainable logistics practices intensifies. Intermodal transportation systems have emerged as a promising solution, harnessing different modes to reduce emissions and environmental impact while optimizing costs. It is important to underscore the significance of mode combinations in achieving environmental goals, aligning with the broader concept of environmental sustainability that encompasses economic and social dimensions. This article contributes to this evolving landscape by presenting a bi-objective intermodal transportation problem focusing on carbon emission reduction. Leveraging machine learning algorithms, including multiple linear regression, support vector regression, decision tree, and random forest, we predict transportation-based CO₂ emissions, offering environmentally friendly logistics plans. Our research responds to the call for green intermodal transportation, addresses financial incentives, emphasizes profit maximization, and reflects the growing influence of government policies. This paper outlines our methodology, presents a real-world case study, and offers computational results, underscoring the significance of sustainable intermodal transportation in the context of global climate goals and government initiatives.

This paper explores factors that affected the passenger load factors (PLF) in post-pandemic international flights from Taiwan to leading travel destinations, including Asia (Japan, China, Vietnam, South Korea, Thailand, and Hong Kong) and the Americas (the United States and Canada). Monthly data for outbound flights from 43 airlines were collected between January 2022 and May 2023. A hybrid model combining latent class clustering analysis with multinomial logit models was developed to identify unobserved heterogeneity in variables affecting the PLF. In addition to investigating the impact of factors such as flight frequencies, flight length, aircraft sizes, airline types, alliance members, jet fuel prices, seasonality, travel restrictions, and public health events, findings also revealed other significant impacts on the PLF including arranging connecting flights for transfer at the airport, offering promotional fares, and policy of free baggage allowances using the piece concept. Furthermore, a comparison of flight operations in pre- and post-pandemic found changes in adjusting operating strategies, including legacy airlines using narrow-body aircraft for short-haul flights and offering airfare promotions on long-haul flights are more likely to increase the PLF in the post-COVID-19 recovery phase.

This paper constructs a comprehensive evaluation system for urban low-carbon passenger transportation development based on the VPOSR model. First, the game theory combination G1 and CRITIC-entropy weight methods are used to obtain the indicator weights. Secondly, the TOPSIS method was combined to calculate and get the comprehensive value index for 2007–2021 and select the data of Tianjin from 2017 to 2021 to analyze the composite value index of the guideline level. Finally, the obstacle factors hindering the invention of low-carbon transportation in different periods are identified, and Tianjin is taken as an example for analysis and corresponding countermeasures. The results show that Tianjin's urban low-carbon passenger transportation structure improved significantly from 2007 to 2021, and the comprehensive value index increased from 0.357 to 0.558, but still at the intermediate level. The number of new energy vehicles is a key influencing factor for optimizing urban passenger transport structure in Tianjin.

Maritime clusters play a crucial role in the economic development of countries, encompassing seaports, maritime businesses, universities, and government agencies. South Asia, although lacking established maritime clusters, exhibits potential for the emergence of such clusters in its seaports. Evaluating the competitiveness of these emerging maritime port clusters is vital as it directly impacts their performance and future potential as world-class maritime clusters. However, no previous attempts have been made to measure the competitiveness of these emerging port clusters. This pioneering study addresses this gap by assessing the level of competitiveness in the emerging South Asian maritime port clusters. The research develops a competitive index using the Multi-Attribute Utility Theory (MAUT) approach, identifying key factors that enhance cluster competitiveness and evaluating the competence of each port cluster across these factors. The findings indicate that Jawaharlal Nehru Port Trust (JNPT) near Mumbai ranks as the top-indexed port cluster, followed by the cluster of Colombo and Male. This study provides a comprehensive assessment tool, identifies strengths and weaknesses, and provides actionable policy implications to enhance the competitiveness of maritime port clusters in South Asia and serves as a foundation for future research and policymaking in this field.

The rapid development of the railway transport network and the improvement of transport accessibility (TA) have the potential to facilitate the regional tourism industries and tourism economic connection (TEC). Quantifying the mutual interaction between TA and TEC is crucial for promoting coordinated and sustainable development. Our study focused on 41 cities in the Yangtze River Delta (YRD) region. By utilizing train schedule data from the 12306.cn website and statistics for 2010 and 2019, the weighted average travel time for each city was calculated to represent the regional railway TA. The modified gravity model incorporating average travel distances was then introduced to estimate the intensity of TEC between cities. Finally, a coupling coordination model was constructed to quantify the coupling correlation degree (CCD) between railway TA and TEC. The comparison results for 2019 and 2010 revealed an improved TA and its overall equilibrium in the YRD region, with a “core-periphery” pattern. The trend towards a spatially networked intensity of regional TEC became noticeable, with key railways as the axes. The CCD between regional railway TA and TEC showed an overall upward trend, where about 87% of cities improved at least one CCD grade. The regional spatial imbalances decreased but still existed, where southern and eastern cities were more coordinated. The reliability of proposed methods was also confirmed by correlations with socioeconomic indicators and population flow data. This study can provide fundamental support for the coordinated development of regional tourism and transport system, and integrated development strategy of the YRD region.