Search results

Across the world, many universities are dealing with a pressure on resources, caused by both organisational developments and ageing campuses. Space utilization studies have a strategic role, providing information on how space is being used, thereby informing decisions about the type and scale of facilities that are needed.

This study reports on the space use measurements conducted at TU Delft over the past five years, complemented by their use to make decisions about the university's real estate portfolio.

The education spaces of the university are found to perform well in terms of frequency rates and can be improved in terms of occupancy rates. The information helped to support short- and long-term decision-making. The study places of the university have a satisfactory occupancy in some types of study places, while in others there is room for improvement. More research is needed here to understand the relationship between space norms and space use.

The space utilization studies have supported discussions with the student council and decision makers on which interventions are required and which current facilities meet students' needs best.

Not much space utilisation studies are reported in the academic literature, and those that do have several limitations. This study may serve as a best practice for benchmarking by other universities and as evidence in other research for the planned and actual use of university facilities.

This study aims to optimize the traffic capacity allocation to solve the problem of low share of public transit in the landside system so as to get rid of the congestion trouble in landside traffic. The optimal timetable for airport buses can be searched by changing the departure interval of each line and evaluating the corresponding performance continuously.

This paper constructs a simulation model based on the real-world situation in Beijing Capital International Airport (BCIA), which simulates the whole process of airport bus schedules and analyzes the connections among multiple steps for transferring. The evaluation system is constructed by considering the benefits of passengers, airports and companies comprehensively. The optimal timetable for airport buses can be searched by changing the departure interval of each line and evaluating the corresponding performance continuously.

According to the experimental results, an excellent evacuation effect can only be achieved when the majority of departure intervals of airport buses are shortened to 50% of their original values, and some busy routes such as the Beijing Station line are supposed to be reduced to one-third of their original fixed intervals. As the airport bus passenger flow presents an obviously periodic variation over days, the timetable of the airport bus is supposed to be redesigned every day. A flexible bus timetable can not only meet the dynamic passenger flow but also enhance the attractiveness of public transit.

This paper constructs a simulation model based on the real-world situation in BCIA, which can not only model the complex scenes in the whole process of airport bus schedules but also reflect the intricate interaction between transferring passengers and vehicles caused by dense streamlines.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified.

The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.

This paper aims to address three research questions (1) Who is the main stakeholder that shapes Airbnb experience, (2) Does Airbnb offers an authentic travel experience? and (3) What should be the future research trends in Airbnb?

This paper uses the systematic literature review (SLR) with a well-defined protocol, research strategy and methods to answer the research questions.

The review revealed that while Airbnb plays a significant role as the platform provider, the stakeholders influencing the experiences are multifaceted. Hosts, guests, local communities and even regulatory bodies all contribute to shaping the overall Airbnb Experience ecosystem. Hosts, in particular, have a crucial role in curating and delivering unique experiences, which significantly impacts the quality and authenticity of the offerings. On the question of whether Airbnb offers an authentic travel experience, the review uncovered mixed findings. For examples, some studies emphasized the potential for Airbnb to provide authentic and local experiences, allowing travelers to engage with the community and cultural aspects of a destination. However, other studies raised concerns about the commodification and standardization of experiences, leading to a potential loss of authenticity.

This paper is different from previous SLR where previous research systematically reviewed; motivations to use and choose Airbnb, institutionalization of Airbnb, stakeholders of Airbnb. This paper addresses authentic experience as a factor that influences activity participation.

This study aims to provide more insight into how customer voice is captured and used in managerial decision-making at the marketing-finance interface. This study’s focus is on understanding how the customer voice, often communicated through online and social media platforms, is used in high-performing hotels.

This research is based on a case study of four high-performing Irish hotels. For each case, multiple informants, including marketing managers, general managers and finance managers, were interviewed and shadowed. Twenty seven decisions across the four cases were analysed to assess the use of customer voice in managerial decision-making.

Social media provides a stage that has empowered the customer voice because of the public nature of the interaction and the network effect. Customer voice is incorporated in managerial decision-making in three distinct ways – symbolically as part of an early warning system, for action-oriented operational decisions and to some extent in the knowledge-enhancing role for tactical decisions. While there is a greater appreciation among senior managers and the finance and accounting managers of the importance of customer voice, this study finds clear limits in its utilisation and more reliance on traditional finance and accounting data, especially in strategic decision-making.

The cases belong to a highly visible open environment of hotels in an industry where customer voice has immediate and strong effects. The findings may not directly apply to industries characterised by a relatively more closed context such as banking or insurance. Moreover, the findings reflect the practices of high-performing hotels and do not necessarily capture the practices used in less successfully operating hotels.

While marketers need to enhance their ability to create a narrative that links the customer voice to revenue generation, finance managers also need to develop a skillset and adopt a mindset that appropriately reflects the influential role for customer voice in managerial decision-making.

Despite the linkage of marketing performance to business performance, there is limited research on the impact of customer information on managerial decision-making. This research provides insight into how customer voice is considered at the critical marketing-finance interface.

This study aims to analyze how different room-committing practices affect the occupancy and profitability of hotels and it critically reviews the role of minimum-length-of-stay (MLOS) requirements given these findings.

The approach uses statistical analysis of simplified contexts to develop understanding, and simulations of more complex situations to confirm the relevance in realistic contexts.

The study demonstrates that proper solutions of the room-committing problem improve occupancy and profitability, in particular, for hotels working in high-season and high-occupancy situations. Smart committing algorithms diminish the role of MLOS requirements. More demand can be accepted without sacrificing late-arriving long reservations.

To the best of the authors’ knowledge, this work, building upon a previous one cited in this paper, is the first to rigorously study the room-committing problem and to demonstrate its relevance in practical situations and its implications on MLOS rules.

Focusing on two beer festivals held in Nottingham, England, this study aims to evaluate their indirect impact on the performance of city hotels. This study builds on theoretical insights from the revenue management literature to shed empirical light on the potentially beneficial effects of events on the hotels’ performance. This study investigates the impact of the differential support offered by the destination management organisation (DMO) over two years.

Using online prices posted in advance of the events on an online travel agent, the authors assess hotel performance for each day of the events relative to the same day of the week in a week with no event. A similar comparison is made to assess the impact across two different years. In both cases, an ordinary least squares methodology was used.

Both events appear not to have had a strong impact on hotel prices and occupancy in 2016, i.e. when the DMO’s promotional effort was more proactive. Instead, in 2017, one event registered higher hotel prices and occupancy both relative to the year before and to the “business as usual” week.

The study identifies the existence of an indirect positive economic impact of the events on the hospitality sector.

The investigation adopts a more naturalistic experimental design to collect the data, which allows the authors to control for both the impact on prices and occupancy at the level of the single hotel. The evidence is therefore micro-founded. Moreover, results shed light on the role played by the DMO.

This study aims to provide a cutting-edge evaluation of the sharing economy's impact within the realm of tourism and hospitality. The primary objectives guiding this research are as follows: to uncover the prevalent discussions and debates within the tourism and hospitality sector concerning the implications and effects of the sharing economy on urban destinations; and to analyse how scholarly inquiries and empirical investigations have contributed to a comprehensive comprehension of the intricate theoretical foundations and practical intricacies inherent in the sharing economy. This exploration takes place within the extensive expanse of existing literature.

The study used the non-conventional method for data mining. An artificial intelligence (AI) tool called www.dimensions.ai was used to mine data between the year 2002 and 2021. After which the data was analysed, using Citespace software that assisted in building themes for answering the research questions.

The sharing economy has multifaceted implications for rural and urban destinations. For instance, the findings demonstrated that emotional solidarity fosters community bonds between tourists and residents, enhancing authenticity. While, management firms optimise short-term rentals, boosting revenue and occupancy rates despite capped at 20%. It further demonstrated that the sharing economy disrupts traditional accommodations, especially hotels, impacting rural and urban destinations differently based on location and regulatory flexibility. Technological advancements would shape the digital future, transforming the resource in sharing and connectivity in urban settings.

Management firms or agents significantly enhance property facilities, revenue and occupancy rates. Properties managed by professionals perform better in terms of revenue and occupancy; furthermore, traditional accommodations need innovative strategies to compete with sharing economy platforms. Policymakers must consider location-specific regulations to balance sharing economy impacts. Embracing technological advancements ensures urban destinations stay relevant and competitive.

Emotional solidarity fosters bonds between residents and tourists, contributing to a sense of community. Management firms contribute to local economies and stability. However, Airbnb's impact on traditional accommodations raises concerns about the effect on residents and communities.

The study incorporates classical sociology theory to understand emotional solidarity and extends the concept of moral economy to guide economic behaviour in the sharing economy. The analysis also underscores the influence of technological trends such as mobile technology, Internet of Things, AI and blockchain on sharing practices in reshaping existing theoretical frameworks in the sharing atmosphere. Furthermore, the co-creation of value theory highlights collaborative interactions between hosts and guests, shaping the sharing economy experience. Consumer segmentation and choice theories shed light on sharing economy dynamics. Institutional and location-based theories provide insights into regulatory and location-specific impacts.

This research contributes by comprehensively exploring the multifaceted implications of the sharing economy on a tourist destination. It delves into emotional solidarity, management firm roles and location-specific impacts, enriching the understanding of the sharing economy's effects. The application of co-creation of value theory and examination of platform technologies offer fresh perspectives on value creation and user engagement. The study's focus on practical dimensions guides stakeholders in optimising the benefits and addressing challenges posed by the sharing economy in urban contexts. The exploration of moral economy and its relevance to the sharing economy provides a novel perspective, while the examination of technological influences on sharing practices contributes to understanding the digital future of the sharing economy.