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To develop a conceptual framework for temporal and spatial e‐service value.

In the empirical study, the temporal and spatial value of e‐services is qualitatively explored. Positioned within service research, a conceptualisation of customer perceived value based on benefit and sacrifice of technical, functional, temporal and spatial dimensions is used.

The qualitative study identifies subdimensions of temporal and spatial value. In addition to benefits such as access and flexibility, these subdimensions also involve aspects related to temporal and spatial sacrifice. The subdimensions indicate the versatility of the dimensions. Another finding is the interdependence between the benefit and sacrifice of service value.

Extends prior research by qualitatively describing benefit and sacrifice of temporal and spatial value. Presents a conceptual model of temporal and spatial e‐service value. Future research needs to quantitatively validate the subdimensions of the temporal and spatial dimensions across different contexts.

Shows what temporal and spatial aspects in services create value for customers, especially in an e‐service context. Particularly, it identifies aspects that decrease the perceived service value. An important marketing challenge is to emphasize and develop the value‐increasing parts of a service while understanding and reducing its value‐decreasing components.

Contributes to marketing research and practice with its conceptualisation of temporal and spatial e‐service value. The importance of time and location in creating customer perceived value is emphasised.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

This paper presents a procedure for considering interactions of neighbourhood quality and property specifics within hedonic models of housing price. It handles interactions between geographical factors and the marginal contribution of each property attribute for enhancing values assessment. Making use of simulation procedures, it is combining GIS technology and spatial statistics to define principal components of accessibility and socio‐economic census related to transaction prices of single‐family homes. An application to the housing market of the Quebec Urban Community (more than 3,600 bungalows transacted in 1990 and 1991) illustrates its usefulness for building spatial hedonic models, while controlling for multicollinearity, spatial autocorrelation and heteroskedasticity. Distance‐weighted averages of each property attribute in the neighbourhood and interactions of property attributes with each principal component are used to detect any spatial effect on sale price variations. This first‐stage spatial hedonic model approximates market prices, which are then used in order to compare “expected” and actual property tax amounts, which are added to obtain a second‐stage model incorporating fiscal effects on house values. Interactions between geographical factors and property specifics are computed using formulae avoiding multicollinearity problems, while considering several processes responsible for spatial variability. For each property attribute, they define sub‐models which can be used to map variations, across the city, of its marginal value, assessing the cross‐effect of geographical location (in terms of neighbourhood profiles and accessibility to services) and its own valuation parameters. Moreover, this procedure distinguishes property attributes, exerting a stable contribution to value (constant over the entire region) from those whose implicit price significantly varies over space.

This study explores the characteristics of female space evolution in Korean vernacular houses in the Yanbian region of China. In addition, it discusses the influence of social logic on the evolution of female spaces.

This study utilises space syntax methodology to examine the evolution of female spaces in Korean vernacular houses in terms of connectivity value, step depth and integration value. Furthermore, it conducts an analytical exploration of social logic based on the evolutionary characteristics of female space.

The findings elucidate the evolutionary characteristics of the spatial configuration of female spaces in Korean vernacular houses, with differential changes in connectivity, a gradual tendency towards openness and simplicity and increased accessibility and centrality. This reflects the changing spatial needs of Korean women brought about by changes in lifestyle, consciousness, social status and family structure.

This study provides perspectives and insights into the vernacular architecture and architectural sociology of ethnic minorities in regions of China and Asia. Furthermore, it can provide relevant construction organisations with a more intuitive understanding of Korean vernacular houses and a reference for future house renewal and construction in the Yanbian region.

Although many studies have investigated various aspects of Korean vernacular houses and female spaces, none have examined the influence of social logical changes on the evolution of female spaces in Korean vernacular houses. Thus, this study is valuable and novel.

Smart tourism has become an inevitable trend in future tourism development. However, despite significant investment in its technological foundation, little is known about whether and when tourists are willing to be involved in smart tourism. This study explores tourists' willingness to contribute to smart tourism development by empirically examining their intention to share personal information and use smart technology.

Based on construal level theory (CLT), a 2 (far/near spatial distance) × 2 (gain/loss persuasive information frame) × 2 (altruistic/egoistic value orientation) laboratory experiment with different contextual features was designed to examine tourists' willingness to contribute to smart tourism.

Tourists are most willing to share personal information and use smart technologies when spatial distance aligns with information framing, spatial distance aligns with value orientation and information framing aligns with value orientation.

This study provides essential insights for destination management organizations (DMOs) about tourists' perceptions of smart tourism, enabling DMOs to develop more precise marketing strategies to encourage tourists to contribute to smart tourism development and enrich tourists' travel experiences.

This study enriches theoretical knowledge of DMOs' boundaries in encouraging tourists to contribute to smart tourism and provides critical insights into future smart tourism development for researchers and practitioners.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

The construction industry accounts for a large proportion of the economy of developing countries, but the connotation and influencing factors of high-quality development (HQD) are still unclear. This study aims to gain a more comprehensive insight into the current development status of the regional construction industry under China's HQD orientation and the obstructive factors affecting its development and to provide informative suggestions for its HQD prospects.

In this study, the construction industry of 16 cities in the Chengdu-Chongqing economic circle (CCEC), a new region in southwest China, was used as the research object to collect data from the 2006–2019 yearbooks, construct an evaluation index system for HQD of the construction industry, derive the development level of the construction industry using the entropy value method and spatial autocorrelation method and then apply the barrier Diagnostic model was used to compare and analyze the impact level of each index.

In terms of the time dimension, the development of the construction industry in CCEC is characterized by “high in the twin core and low in the surrounding area”, with unbalanced and insufficient development; in terms of spatial correlation, some factors have positive aggregation in spatial distribution, but the peripheral linkage decreases; through barrier analysis, the impact of different barrier factors is different.

This paper will help governments and enterprises in developing countries to make urban planning and management policies to fundamentally improve the development of the construction industry in underdeveloped regions.

The purpose of this article is to explore the relationship between measures of the plan configuration of buildings (in this case multi‐bed wards), and subjective judgements on spatial locations for privacy.

Measures of plan configuration from six generic ward designs have been made using space syntax software (visibility graph analysis and depth map). Subjective judgements have been assessed by means of a questionnaire.

Participants' chosen locations for privacy have been shown to have a systematic relationship with spatial properties of the ward plans. At a ward level the designs with low integration and high control were chosen. At the bed location lower values of integration and control were selected.

In this study one facet of privacy (i.e. spatial location) has been investigated. These findings now need to be extended to studies involving environmental simulations, visibility and three‐dimensional awareness of spaces as experienced by hospital users. In addition further analysis will be carried out at an individual design level and the possibility of subgroups of people with different preferences will be explored.

Space syntax has a complicated theoretical and methodological approach to spatial measures. Many designers or architects may not be prepared to try to understand the implications.

At a general level there is little in the literature on the implications of plan form for the subjective experiences of people in buildings. At a specific level about privacy in wards, no evidence could be found that these systematic findings have been reported before.

Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and investment activity within the private rental sector. The primary purpose of this paper aims to analyse the local variation and spatial heterogeneity in residential rental prices in a large urban market in the UK using various geo-statistical approaches.

Applying achieved price data derived from a leading internet-based rental agency for Belfast Northern Ireland is analysed in a number of spatially based modelling frameworks encompassing more traditional approaches such as hedonic regressive models to more complex spatial filtering methods to estimate rental values as a function of the properties implicit characteristics and spatial measures.

The principal findings show the efficacy of the geographically weighted regression (GWR) technique as it provides increased accuracy in predicting marginal price estimates relative to other spatial techniques. The results reveal complex spatial non-stationarity across the Belfast metropole emphasizing the premise of location in determining and understanding rental market performance. A key finding emanating from the research is that the high level of segmentation across localised pockets of the Belfast market, as a consequence of socio-political conflict and ethno-religious territoriality segregation, requires further analytical insight and model specification in order to understand the exogenous spatial and societal effects/implications for rental value.

This study is one of only a few investigations of spatial residential rent price variation applying the GWR methodology, spatial filtering and other spatial techniques within the confines of a UK housing market. In the context of residential rent prices, the research highlights that a soft segmentation modelling approaches are essential for understanding rental gradients in a polarised ethnocratic city.

The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.

In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.

The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.

Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.