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The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

The purpose of this study is to investigate the factors affecting behaviour intention (BI) to use and actual usages of investment-related FinTech services among the zoomers (Gen Z) and millennials (Gen M) retail investors of India.

The study explores the predictive relevance of actual adoption behaviour among the two different age categories of Indian retail investors. It uses the Unified Theory of Acceptance and Use of Technology-2 and the prospect theory framework as guiding frameworks. Data has been collected from 294 retail investors, actively engaged in the investment-related FinTech services. The multi-group analysis using variance-based partial least square structured equation modelling has been used to compare the two groups. The invariance between the two groups was achieved through measurement invariance assessment.

The study reveals distinct factors significantly affecting BI to use investment-related FinTech services among Gen Z and Gen M retail investors are performance expectancy (PE) to BI, perceived risk (PR) to BI, price value (PV) to BI and PR to service trust (ST).

This study provides insights for financial providers and policymakers, emphasizing different factors influencing BI to use investment-related FinTech services in both age groups. Notably, habit emerges as a common factor influencing the actual usage of investment-related FinTech services across Gen M and Gen Z retail investors in India.

This study explores the heterogeneous behaviour of the heterogenous population in the domain of technological adoption of investment-related FinTech services in India.

A pronounced rise in postpandemic immigration is creating consumption opportunities and challenges for countries worldwide. Past research has shown that immigrant homeownership indicates advanced consumer acculturation. However, critical factors which differentiate immigrant decisions to purchase a home remain underexplored. This study aims to examine the importance of different identity resources in determining homeownership gaps between immigrant groups in Spain during a dynamic decade.

A mixed methods research design with triangulation was used. First, the critical “historical research method” is used to empirically assess 15,465 household-level microdata files from the National Immigrant Survey of Spain. Second, the analysis is corroborated through informant interviews, an evaluation of digital news archives and other historical traces such as relevant advertisements in Spain from 2000 to 2009.

Results provided an account of immigrant homeownership whereby foreign-born consumers leveraged resources to promote social identities aligned with an advanced level of acculturation through housing investment during this period. Furthermore, marketing focused on specific targets of ethnic minority consumers coupled with government policies to promote immigrant homeownership reinforced the “Spanish Dream” as a new paradigm for housing market integration.

Spain provides an unprecedented historical context to explain marketing-related phenomena due to a perfect storm of immigration, job availability and integration supports. Contrary to popular wisdom, immigrant consumer homeownership gaps are not solely a result of differences in income and economic mobility, but rather an advanced acculturation outcome driven by personal and social investments in resources that lead to consumer identities.

This study aims to address existing gaps in the literature and theories by investigating the influence of responsible leadership traits on employees’ sustainable performance (E-SuPer) in the Malaysian service sector. Specifically, the authors focus on three key responsible leadership traits: relationship building, relational governance and sharing orientation. Additionally, the authors explore how these traits interact with leader-member exchange (LMX) and whether gender plays a role in this relationship.

A total of 235 usable responses were analysed using partial least squares structural equation modelling. Multi-group analysis (MGA) was employed to examine the moderating impact of gender.

The results showed that both relationship building and relational governance significantly affect E-SuPer among organisations in the service industry. LMX was found to be a significant moderating condition influencing the association between responsible leaders’ sharing orientation and E-SuPer. Interestingly, the MGA results suggest that the effect on male employees was greater than on female employees across the relationships examined. The findings suggest that responsible leadership traits are essential for sustainable employee performance, but there is room for improvement in how these traits are perceived by female employees.

The present study contributes to gender equality agenda, supports the sustainable development goals, adds to the growing body of knowledge on the relationship between responsible leadership traits and E-SuPer within one of the most important economic sectors in Malaysia and sheds lights on the moderating effect of LMX.

This study investigates how responsible leadership traits affect E-SuPer in the service industry, particularly among male and female employees. Moreover, this study is one of the early investigations into the significance of responsible leadership within Malaysian service sector and offers valuable information for industry actors to improve their management approaches.

Analysis of architectural space is commonly conducted by examining architectural drawings that project spatial information by means of walls and partitions. To capture the lived experience of space, which is richer than what we can see from drawings, a new method is proposed to quantify the cognitive dimension of space and re-present it as an audible format.

Using an urban vernacular house in Seoul as a case study, this research takes a syntactic approach to quantify one's changing perception through their movement from the main gate to the most private reception room. Based on Luigi Moretti's theory of hollow space, a new method is proposed to measure the level of spatial pressure exerted on a navigating body. The numerical data of spatial pressure are then converted to a sound using musical techniques of the chromatic scale and chorale textures.

Building on Moretti's abstract concept, it has been shown that a rule-based quantification of users' spatial perception is possible. In addition, unlike conventional approaches of treating architecture as a static entity, this study showed an alternative approach to represent it as a sequence of sensorial experience that can be readily converted to a sound of music.

This research developed a quantification method to measure the perception of pressure inside buildings by revisiting Luigi Moretti's theory proposed in 1952. It has been also demonstrated that the visual stimuli in space can be translated into an audible experience. This new method is applicable to a wide range of buildings including important historic architecture.

The study examines the residential facades of the Al-Jadida neighborhood, focusing on the use of geometry and proportion in Aleppo's architectural history during the late Ottoman era. The analysis sheds light on the city's past and provides insights into the way residents utilized space and interacted with their surroundings.

The study involves collecting primary and secondary sources, including historical documents, photographs, and drawings. Visual analysis is employed to examine the facades overlooking the courtyard, with a focus on windows, doors, balconies, and other distinctive features that contribute to the overall courtyard design.

The findings reveal that traditional Aleppine architecture is centered around the courtyard and incorporates decorative openings and windows reflecting Islamic principles. Stone decorations are used with unique designs based on geometry and composition, contributing to Aleppo's cultural identity.

Including more samples for studying facades allows for the identification of changes in architectural styles and the influence of different cultural influences on the city's architecture over time. Moreover, conducting further studies is crucial for preserving this important part of Aleppo's history for future generations.

This research analyzes architectural facades in late Ottoman Aleppo, offering insights for future studies and understanding architectural design development. It also informs preservation efforts for historic buildings, enhancing understanding of architectural features and characteristics.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

Front-line lean leadership is critical for implementing and sustaining lean production systems (LPS). The purpose of this paper is to analyze the relationships between front-line lean leader (FLL) capacities (cognitive, social, motivational, knowledge and experience), lean leader practices (developing people and supporting daily kaizen) and the degree of implementation of lean tools (pull system, involvement of employees and process control) in manufacturing companies.

A survey was conducted with FLLs from large Brazilian manufacturing companies. The survey collected 103 responses, 99 of which were validated. Data were analyzed using partial least squares structural equation modeling.

There was a positive, significant and direct relationship between FLL capacities, leadership practices and a degree of implementation of LPS tools on the shop floor. The validated model is a reference base for planning FLL capacities and practices that result in more effectively implementing LPS on the shop floor.

The findings provide managers with a new perspective on the importance of the development and training of FLLs focusing on leadership capacities. As decisions about developing lean capabilities impact the application of Lean leadership practices and the use of lean tools, they are also related to day-to-day lean activities and improved operational results. Additionally, the proposed model can be used by managers as a basis to diagnose, develop and select lean leaders.

This study seeks to fill a theoretical gap of knowledge on front-line lean leadership as it jointly addresses and empirically analyzes the existing relationships between lean leadership capacities, encompassing the perspective of psychology, lean practices and tools on the shop floor.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.