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The purpose of this paper is to describe service users’ perspectives on the difference between high secure long-stay forensic psychiatric services in the Netherlands and high secure forensic psychiatric care in England. These perspectives are relevant in considering the benefits of a similar long-stay service in England.

A current in-patient detained in a high secure hospital in England and other mental health service users and carers with experience in forensic-psychiatric settings were asked to watch a documentary on a Dutch high secure long-stay service. Then they were invited to make comparisons between this service and high secure care in England. These perspectives were gained in the context of their membership of the Service User Reference Group of an externally funded study on long-stay in forensic-psychiatric settings in England.

The small group of participants highlighted the importance of relational security, meaningful occupation, autonomy, positive therapeutic relationships with staff and a homely environment for those with lengthy admissions and perceived these to be better met in the Dutch service. These factors might contribute to improved quality of life that services should strive to achieve, especially for those with prolonged admissions.

Perspectives of service users with lived experience of long-stay in forensic settings are important in informing service developments. Lessons can be learnt from initiatives to improve the quality of life in long-stay services in other countries and consideration be given on how to best manage this unique group.

To the authors’ knowledge this is the first study asking service users about their view on forensic services in other countries. The findings suggest that service users have valuable contributions to make to aid service developments and should be involved in similar such exercises in the future.

This paper aims to empirically analyze tourists' length of stay in Istanbul, an important cultural destination. The objective of the study is twofold: (1) uncovering the qualitative difference between one-night visitors and longer stay visitors and (2) for those visitors staying longer at the tourism destination, investigating the key determinants of length of stay.

This research was carried out using a self-administered questionnaire distributed to international tourists who were about to leave the destination. To perform the analysis, we applied a two-step approach: first, we opted for classical binary logit to investigate the tourists' group membership (one-night vs longer stays); second, we applied a zero-truncated Poisson model for uncovering the drivers of length of stay for longer stay visitors.

The results confirmed the structural difference between the two visitor subgroups. Moreover, we found a positive impact of cultural attributes on tourists' length of stay.

The findings provide useful information for destination managers and planners, highlighting the importance of designing different tourism policies in light of tourists' heterogeneity. Moreover, the results confirmed the importance of the preservation and promotion of cultural attributes, given that these are a key factor in determining the success of a destination.

The importance and originality of this study are that it explores the impact of cultural/heritage attributes of the destination on tourists' length of stay. Moreover, it sheds light on the qualitative difference between short- and long-stay visitors.

This paper seeks to demonstrate the critical importance of the destination's decisions made in relation to market positioning in understanding the competitive performance achieved by local businesses.

The aim of the empirical research was to check the following hypotheses: clear positioning on longer‐stay customers makes it more likely that the conduct of ski corporations and hotel businesses will complement each other; clear positioning on longer‐stay customers improves the competitive performance of ski corporations; and clear positioning on longer‐stay customers improves the competitive performance of tourist hospitality businesses. The multiple case study methodology was adopted as a means of refuting or confirming these hypotheses, with the use of a combination of qualitative and quantitative data, although giving greater weight to the quantitative sources.

Two dimensions are of decisive importance: the commercial mix of customers attracted to the destination (distinguishing between day‐trippers and longer‐stay customers), and the structural mix of plant capacity and high turnover tourist accommodation facilities.

The case studies chosen have made it possible to test three hypotheses according to which a clear positioning on longer‐stay customers: increases the extent to which the behaviour of ski corporations and hotel structures complement each other; improves the competitive performance of the ski corporations; and improves the competitive performance of the businesses offering tourist accommodation.

The pricing of parking is a common tool used by governments to facilitate the efficient movement of traffic, raise revenue and, more recently, influence travel behaviour. An important and under-researched by-product of parking pricing schemes is the impact of these schemes on parking supply.

This chapter offers a review of prior research and literature, and explores: who pays the parking levy, the impact of the Congestion Levy on the provision of parking and an overview of the transport impacts of the levy.

The direction of the levy at parking operators and owners rather than the vehicle drivers does not provide a direct link between users and the levy and results in many parking providers not passing the levy onto commuters. The study of parking supply impact shows that, since the introduction of the levy, the supply of commercial off-street parking spaces has declined while the growth in private, non-residential, parking spaces has slowed. Over the same period, there has been a decrease in the number of parking spaces provided for long-stay parking (which attract the parking levy), and an increase in the number of spaces provided for other uses. Understanding these parking supply impacts are important, not only because a reduction in the number of long-stay car parking spaces is an objective of the levy, but also because any such reduction could magnify the travel behaviour impacts that may have occurred solely as a result of an increase in parking price. Investigation of the overall transport impacts of the levy indicate that the parking levy did have an impact on mode choice. However the extent of this impact was not clear due to a large number of associated changes in policy and economic conditions that took place at the same time as the levy.

The chapter shows that the parking levy was positive in its impact on transport use, however there were a number of improvements that could be made to the way the levy was implemented that could improve these. Interestingly, there have been a number of recent changes in the implementation of the levy that address some of these issues. Most importantly, following its own investigation into the impact of the levy, from January 2014 the cost of the levy was increased by 40% to $1,300 per annum, and its coverage extended (Victorian State Revenue Office, 2013). The impact of this change has not been considered in this research.

The uniqueness of the chapter lies in its exploration of how increased prices of parking has influenced supply and how the levy, as a new form of congestion pricing, has influenced the supply of parking in the context of the case study of the Melbourne parking levy in Australia.

This study aims to explore the impact of decentralized long-term rental apartments on the value of in-community housing from two perspectives of housing price and rent.

This study uses the hedonic model to identify the factors affecting the housing value, and the influence of distributed long-rented apartments on the housing value in the community is analyzed from two aspects of housing price and rent by using the ordinary least square method and propensity score matching method.

The primary finding indicates that decentralized long-term rental apartments increase housing prices while decreasing general rental housing rents in the community, with the average degree of increase ranging from 0.93% to 2.59% and the average degree of decrease ranging from 2.23% to 4.34%. According to additional research, the prices of houses within communities rise by 0.042% for every 1% increase in the share of decentralized long-term rentals, while the rents for other types of rental property fall by 0.162%.

The government can regulate the housing market by regulating the access and layout of distributed long-rent apartments.

The findings of this study indicate that the existence and share of distributed long-rent apartments have a heterogeneous impact on the housing price and rent in the community, respectively.

Explores the value of focus group methodology in a patient satisfaction initiative intended to identify quality improvement targets in a large general hospital. Analyses the transcripts from ten focus groups selected from hospital discharge lists, using simple cut‐and‐paste techniques. Finds that the focus group methodology was successful in amplifying feedback previously gleaned from surveys. Highlights patients’ tolerance of shortcomings and their appreciation of staff providing high quality care while under pressure. Concludes that patients regard the National Health Service as deteriorating generally.

Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia).

The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination.

The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia.

This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration.

This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

This research attempts to explore in‐patient perspectives on the long‐stay, medium‐secure hospital wards in which they are based. Semi‐structured interviews focused on the care provided by the hospital in comparison with the high‐secure hospitals from which the participants had been transferred. Questions focused on the participants' perceptions of recovery and the scope for it at both sites.The data suggests that participants identify increased scope for recovery at the long‐stay, medium‐secure facility, and that this is promoted by increased flexibility due to less emphasis on security. Important factors discussed by participants were increased access to a range of activities, graded access into the community, the different atmosphere in the hospital sites and the differences in potential for developing trusting relationships with staff and fellow in‐patients.

Studies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online customer reviews, this study aims to fill this gap in the extant literature on the relationship between length of stay and customer satisfaction level.

The authors collected and used online review data on hotels in London for this study. A series of linear regression analyses were conducted to examine the effect of length of stay on customer satisfaction as measured by review ratings. The authors used the Mahalanobis matching approach to confirm the empirical findings.

This analysis shows that length of stay is negatively associated with customer satisfaction. Additionally, the authors find that this negative relationship is stronger in high-end hotels than in low-end hotels.

The research findings contribute to the literature by shedding light on a new stream of research, namely, length of stay. Additionally, the research findings offer novel insights that could help hotel managers understand the trade-off between longer stays and customer satisfaction.

To the best of the authors’ knowledge, this is one of the first few studies to show the systematic impact of length of stay on the valence of online review ratings, as well as the moderating effect of hotel levels by analyzing customer online reviews on hotel experiences.

The aim of the project was to evaluate the short‐term treatment outcomes of patients treated in a medium secure service for people with intellectual disability. A total of 138 patients, 77 discharged and 61 current inpatients, treated over a six‐year period were included in the audit. Information on demographic and clinical variables was collected on a pre‐designed data collection tool and analysed using appropriate statistical methods. The median length of stay for the discharged group was 2.8 years. About 90% of this group were discharged to lower levels of security and about a third went directly to community placements. None of the clinical and forensic factors examined was significantly associated with length of stay for this group. There was a ‘difficult to discharge long‐stay’ group which had more patients with criminal sections, restriction orders, history of abuse, fire setting, personality disorders and substance misuse. However, when regression analysis was done, most of these factors were not predictive of the length of stay. Clinical diagnosis or offending behaviour categories are poor predictors of length of hospital stay, and there is a need to identify empirically derived patient clusters using a variety of clinical and forensic variables. Common datasets and multi‐centre audits are needed to drive this.