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The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

Pre-construction of a project comprises stages that are pivotal for the procurement performance. It is defined as the duration from the project's initiation to construction. However, Private Public Partnerships (PPPs) have been subjected to a long pre-construction, thereby leading to an inefficient development process. Therefore, the purpose of this paper is to pay attention to the influencing factors elongating the pre-construction duration.

Based on data of 5,677 PPP projects between 2009 and 2021 in China, the authors adopt the Accelerated Failure Time (AFT) model in duration analysis to empirically analyze the following underlying dynamics determining the duration of PPP pre-construction stages: (1) policy uncertainty; (2) corruption; and (3) procurement method selection. To observe the influencing paths more specifically, the authors divided the pre-construction duration into the pre-tendering period and tendering period and regressed them separately.

The results indicate that the pre-construction duration is significantly prolonged with increased policy uncertainty and corruption degree as well as the use of tendering methods. Meanwhile, the above factors have a greater impact on the pre-tendering period than the tendering period.

The contribution of this study is twofold: (1) theoretically, this paper provides new evidence on the impact of PPP policy uncertainty, corruption and procurement method selection on the pre-construction duration. It complements empirical studies on the factors elongating the time efficiency of PPPs projects. (2) In practice, it provides a specific path for the government to improve the time efficiency of PPPs.

This study investigated the relationship between principals' abusive leadership and teachers' intrinsic and extrinsic motivations, exploring the potential moderating effects of the duration of the relationship and group size within educational settings.

Data were collected from a sample of teachers in Bahrain, using self-report measures. The data were analyzed using regression analyses.

The findings reveal a significant negative relationship between principals' abusive leadership and teachers' intrinsic and extrinsic motivations. The results also show that the duration of the relationship moderates the correlation between abusive leadership and teachers' extrinsic motivation, with teachers who have been in longer relationships with their principals showing greater resistance to the detrimental effects of abusive leadership on their extrinsic motivation. Group size was found to moderate this correlation, with larger groups exhibiting stronger buffering against the negative effect of abusive leadership.

The findings provide valuable insights into the dynamics of abusive leadership in educational settings and the potential moderating factors that can help alleviate its detrimental effects on teachers' motivations.

Previous research has identified factors that enable lean change to be sustained. What remains unknown is how the interaction effects amongst these factors vary as lean change programmes mature. When are particular factors at their most influential?

Using a data and investigator triangulated qualitative research strategy, this paper tests an a priori model of change sustainability factors. In phase one, the research reveals the influence and significance of the model's change sustainability factors within 13 manufacturers. In phase two, four factors (Leadership, Political, Individual and Managerial) were selected for in-depth case study analyses in three manufacturers.

These point to when in the lean change, certain factors have the most influence on its sustainability. The Leadership factor and political factor are essential at the beginning and remain influential throughout. Employees' individual commitment (Individual factor) is significant in sustaining the change but it is at its most influential in the later stages. The Managerial factor (management approach) is influential in the mature stages of the programme.

Recognising where to put maximum focus during a lean change programme as it matures is crucial for management.

Sustaining lean change has not been studied from the perspective of what factors need to be emphasised at different stages in the programme for successful maturity to occur. Through empirical validation, this study helps address this knowledge gap.

Interesting because – Researchers have found that the majority of lean implementations fail – changes to structures and working practices are made only to see the gains dissipate. Previous research has suggested that multiple factors including leadership, culture and politics influence the change towards a lean organisation. While studies have shown that such factors play a role, what has not been studied is the time, that is, when the factors influence the change process. This study investigated when 11 factors have the most influence on lean implementation. Theoretical value – The study extends the state-of-the-art understanding of implementation of lean in organisations by adding a time element. It is found that in order to have a change that is sustainable, that is, lasting at least 18 months, factors that indicate the importance to the organisation are influential during the entire implementation process. This includes how central the change is to the organisation, the influence of leadership that sets vision and goals, and the implementation methods. Factors that can set things in motion such as the influence of important stakeholders are important at the beginning of the implementation process but then decline in influence over time. While factors that seem to have to do more with how companies operate have less influence at the start but become more influential over time. These include employees' commitment, the managerial style and approach, the organisational policies and structure, and the organisational culture. Practical value – Introducing lean into an organisation and gaining its sustained benefits is often not successful. While factors have previously been identified that influence the success of lean implementation, this study provides additional practical insight. It helps manufacturers be more effective by pinpointing which factors should be focused on during the various stages of the implementation process.

Despite the growing popularity of online secondhand platforms globally, there is a lack of studies exploring how consumers worldwide perceive contamination and the use of secondhand goods differently according to the culture. Based on the consumer contamination theory, this study aims to investigate the cultural differences of South Koreans and Americans by examining three variables (e.g. transaction type, ownership duration and physical attractiveness) related to consumers' perception of contamination and purchase intentions for a secondhand apparel item.

Data were collected from 422 US and South Korean female consumers who were assigned to an experimental scenario, and their secondhand purchase intentions and perceived contamination were compared through independent t-tests and moderated regression analyses.

Consumers' purchase intentions increased, and perceived contamination decreased when the transaction type was business-to-consumer (vs consumer-to-consumer), when the item had been owned for a shorter period of time and when the item was sold by an attractive seller. Such effect was more pronounced for South Korean consumers than the US consumers in the negative contamination contexts (i.e. transaction type, ownership duration), but not in the positive contamination context (i.e. attractiveness).

The findings of the study add to the literature on consumer contamination theory through an examination of several negative and positive contamination factors in retail contexts and highlight the role of culture as a critical moderator.

Due to the high rate of failure of most crowdfunding projects, knowing the most essential factor to obtain funding success on the crowdfunding platform is of great importance for fund seekers on the crowdfunding platform. The purpose of this study is to explore crowdfunding success factors to know the most essential success factor for stakeholders of the crowdfunding platform to make the best decision when seeking funds on the crowdfunding platform. This study identified and ranked crowdfunding success factors for stakeholders of crowdfunding platforms. Sixteen factors were identified and categorized under five broad headings. These were; project ideas, target capital, track records, geographical proximity and equity.

To rank the identified crowdfunding success factors and subfactors, this study used the Multi-Objective Optimization Based on Ratio Analysis (MULTIMOORA) integrated with the Evaluation based on Distance from Average Solutions (EDAS).

Target capital ranked first among the five categories—while duration involved in raising funds ranked first among the sixteen subfactors. An approach for analyzing how each success factor enhances a crowdfunding campaign was developed in this study. This study provides valuable insight to fund seekers on the crowdfunding platform on how funding success can be achieved by knowing which factor to consider essential when seeking funds on the crowdfunding platform.

This is the first study to explore crowdfunding success factors using the MULTIMOORA-EDAS method. The use of this method will help fund seekers on the crowdfunding platform to know which crowdfunding success factor is essential, thereby aiding fund seekers to make the best decision when seeking funds on the crowdfunding platform. Also, this study is particularly helpful for business owners, platform operators and policymakers when deciding how to allocate resources, plan campaigns and implement regulations.

The Spain–Portugal Cross-Border Cooperation Program launched the “Espomar” project in 2017, with the aim of designing a sustainable and transboundary maritime transport system in the Gulf of Cádiz, which includes the Spanish provinces of Cádiz (its Atlantic coast) and Huelva, together with the Portuguese region of the Algarve. This chapter limits the scope to the Algarve-Huelva connection and the resident population demand. The findings suggest a maritime connection defined and marketed as part of leisure activities. The demand for potential tourism packages created around boat trips appears to be promising. The sun beach, gastronomy wine and culture landscape vectors are the main attractors.

This research is aimed to mainly be applicable to expediting engineering projects, uses the method of inverse optimization and the double-layer nested genetic algorithm combined with nonlinear programming algorithm, study how to schedule the number of labor in each process at the minimum cost to achieve an extremely short construction period goal.

The method of inverse optimization is mainly used in this study. In the first phase, establish a positive optimization model, according to the existing labor constraints, aiming at the shortest construction period. In the second phase, under the condition that the expected shortest construction period is known, on the basis of the positive optimization model, the inverse optimization method is used to establish the inverse optimization model aiming at the minimum change of the number of workers, and finally the optimal labor allocation scheme that meets the conditions is obtained. Finally, use algorithm to solve and prove with a case.

The case study shows that this method can effectively achieve the extremely short duration goal of the engineering project at the minimum cost, and provide the basis for the decision-making of the engineering project.

The contribution of this paper to the existing knowledge is to carry out a preliminary study on the relatively blank field of the current engineering project with a very short construction period, and provide a path for the vast number of engineering projects with strict requirements on the construction period to achieve a very short construction period, and apply the inverse optimization method to the engineering field. Furthermore, a double-nested genetic algorithm and nonlinear programming algorithm are designed. It can effectively solve various optimization problems.

This paper aims to deepen the current knowledge of seasonality by investigating visitors’ intentional and behavioural patterns during peak and off-peak seasons. It compares the variation in several key behavioural factors, namely, duration of stay, party size, revisit intention, spending and breakdown of spending in different sectors in hospitality and tourism including entertainment, restaurant, accommodation and transportation. Moreover, this research expands the understanding by examining the effectiveness of two innovative strategies of offering a digital app and organising a unique event to tackle seasonal imbalances through stimulating visitors’ intention to change their timing of visit from peak to off-peak periods.

The author initially used a Delphi approach to gather experts’ opinion on the two scenario settings: event organisation and a trip planner app. The scenarios aimed to potentially encourage visitors to change their visit time to off-peak seasons. Then, using a quantitative survey, the travel habits and spending behaviours of 310 participants were captured. Furthermore, the survey assessed their intention to travel during off-peak seasons in response to the implementation of the two innovative strategies.

The results revealed that although the number of visitors who travel in off-peak seasons may be lower, their daily spending is higher than peak season visitors. In addition to total spending per day, the duration of stay, part size, quality of accommodation and re-visit intention of visitors indicated significant variation between peak and off-peak seasons. According to the statistical analysis’ results, organising events (including festivals) proves more effective in encouraging visitors to travel during off-peak seasons compared to digital innovation (i.e. a trip planner app). This finding is in line with the tenets of the Jobs-to-be-Done Theory of innovation.

This study contributes by conceptualising the mechanism of seasonality and its impacts on subsectors of tourism and hospitality. To the best of the author’s knowledge, this is one of the few empirical research that compares the behavioural patterns of visitors including their average spending per day between peak and off-peak seasons. Previous studies focused on specific regions or sectors, whereas this research investigates visitors’ behaviour on a broader scale to provide more comprehensive view. Furthermore, this study is novel due to practising an outside-in approach through investigating the effectiveness of the two innovative strategies aimed at addressing seasonality in the hospitality and tourism industry from visitors’ point of view.