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Empirical literature is the primary source of understanding how policing can effectively reduce criminal activities. Spatial analyses can identify particular effects that can explain and assist in constructing appropriate regional strategies and policies; nevertheless, studies that use spatial regression methods are more limited and can provide a perspective on specific effects in a more disaggregated regional context.

This research aims to conduct a systematic literature review (SLR) to understand the relationship between crime indicators and police production using spatial regression models. We consider a combination of Kitchenham and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols as a methodological strategy in five bibliographic databases for collecting scientific articles.

The SLR suggests a limited amount of evidence that meets the criteria defined in the research strategy. Several particularities are observed regarding police and criminal production metrics, either in terms of aggregation level, indicator transformations or scope of analysis. A broader time perspective did not necessarily indicate statistical significance compared to models with a single-period sample.

The findings suggest the possibility of expanding efforts by the public sector to provide policing data with the intention of conducting appropriate research using spatial analysis. This step could allow for a more robust integration between the public sector and researchers, strengthening policing strategies, evaluating the effectiveness of public security policies and assisting in the development of strategies for future policy actions.

Limited empirical evidence meets the criteria of spatial regression models with temporal components considering police production and criminality indicators. Constructing an SLR with this scope is an unprecedented contribution to the literature. The discussion can enhance the understanding of approaches for studying the relationship between police efforts and crime prevention.

Well-constructed transportation infrastructure may effectively decrease barriers to the flow of innovative human resources and inventive elements, accelerating enterprise innovation activities. This study will explore how HSR helps enterprises achieve ambidextrous innovation, including the mediating mechanism of absorbed slack resources, innovative talents, and the heterogeneous effects of management shareholding ratio and financing constraints.

Based on resource dependence theory and social network theory, this study employs a quasi-natural experiment of China’s high-speed railway and builds a multi-time point DID model to investigate its influence on enterprise ambidextrous innovation.

Results suggest that the HSR positively influences both exploitative and exploratory innovation, and the influence is more substantial on exploitative innovation. Further analysis finds two influencing channels through which HSR influences enterprise ambidextrous innovation: providing redundant resources and attracting innovative talents. Heterogeneity analysis indicates that HSR has a more significant positive effect on exploratory innovation for enterprises with high management shareholding. In the low financing constraint group, the HSR opening has a more significant impact on ambidextrous innovation.

In ambidextrous innovation, enterprises should rationalize the allocation of resources, attach importance to the innovative talent introduction, and choose differentiated paths based on intrinsic characteristics. Meanwhile, the government should actively improve the HSR routes and continuously improve the innovative environment.

This study enriches the theoretical research framework of HSR and ambidextrous innovation by identifying the channel mechanisms and boundary conditions through which HSR affects ambidextrous innovation and expands the consequences of HSR and the antecedents of ambidextrous.

This paper aims to investigate place marketing and branding at the micro-place scale through the case study of St Christopher’s Place in London (UK). This study illustrates the distinctive differences of micro-place marketing, in comparison to city and country levels.

An exploratory case study was conducted through a sequential mixed methods approach involving direct observation, semi-structured interviews, questionnaires and social media analysis. Analysis of data was performed by using thematic analysis and triangulation of quantitative measures collected through the questionnaire and social media analysis.

Analysis of data illustrated noticeable differences of place management at the micro-place level compared to city or country scale of place marketing and branding. The function of emotional marketing leading to value co-creation is more effective at this level, establishing close and personal ties between occupiers and customers. Yet, measurement of micro-place marketing and branding value creation is difficult to achieve.

This study draws attention to the unique value and benefits of place branding at smaller spatial scales. Findings contribute to the place micro-brand concept by adding knowledge of micro-places through place management activities comparing them with city and country scales, and emotional marketing value co-creation practices, including challenges relating to measurement.

This paper examines the relationship between open innovation (measured by exploratory and exploitative linkages) and firm-level innovative activity in the offshore renewable energy (ORE) sector.

A unique, purpose-built survey that targeted firms operating in the ORE sector and its supply chain was used. The data provides novel insights into the research activities and networking capabilities of an industry in its infant stages of development. Regression models are used to estimate the relationship between firm-level external linkages and innovative activity.

Exploratory linkages are positively related to more innovative activity. This relationship is subject to diminishing returns, distinguishing the ORE sector from other sectors. Collaborating with suppliers and accessing scientific journals are conducive to research and development (R&D) activity and process innovation, whilst collaborating with customers is associated with the decision to introduce new products and processes.

This study provides evidence of a positive, but curvilinear, relationship between external knowledge linkages and innovative activity, adding novel insights into the relationship between open innovation (OI) strategies, research and innovation outcomes for firms predominantly in the introductory stages of the technological life cycle with limited commercialisation experience. The nuanced finding that specific linkages matter for certain research and innovation (R&I) outcomes adds deeper complexity to March’s (1991) framework, where tailoring certain exploratory or exploitative linkages to specific innovation activities is important.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.

Given the inevitable transition to renewable resource utilization and the urgent need to reduce carbon emissions, this study conducted quasi natural experiments to assess the impact of renewable resource utilization on carbon emissions based on the national “urban mining” demonstration bases (NUMDB).

This study uses panel data from 275 prefecture-level cities in China from 2006 to 2019. The paper selects NUMDB as the proxy variable and conducts a quasi-natural experiment using a multi-period differences-in-differences model. We examine the impact of NUMDB on reducing carbon emissions, and then deeply explore its mechanism and spatial spillover effect.

This study found that: (1) the construction of NUMDB can significantly decrease the carbon emission in the host cities; (2) NUMDB’s construction has more significantly reduced the carbon emission in regions with higher levels of circular economy development, green technology innovation, regional environmental pollution, digital economy development and financial development; (3) by means of green technology innovation, optimized energy structure, and high-quality talent aggregation, NUMDB reduces urban carbon emissions; (4) NUMDB construction positively affects the carbon reduction efficiency of neighboring regions.

We propose corresponding policy suggestions to further promote the carbon emission reduction effect of NUMDB and develop the renewable resources industry in China based on the research findings.

The contributions of this paper are as follows. Our study contributes to expanding the research scope on the environmental impact of the renewable resource industry, as there are few quantitative studies in this area.

We further consider the spatial heterogeneity of policies and analyze the carbon reduction effect of the NUMDB from the city level, which is beneficial to exploring more targeted and operable carbon reduction paths.

This study on identifying the causal relationship between renewable resource utilization and carbon emission reduction helps to explore the sustainable development path of renewable resource more comprehensively. Meanwhile, this paper provides a reference for other countries to improve the utilization of renewable resource and effectively reduce carbon emissions.

This research investigates the capabilities of Cloud-based Building Information Modelling (CBIM) in managing quality asset information, drawing upon software engineers' perspectives. Compelling statistics highlight the relationship between building information and environmental sustainability. However, despite the growing utilisation of CBIM in the Architecture, Engineering and Construction (AEC) industry, a significant knowledge gap remains concerning its effectiveness in maintaining quality asset information.

This study employed an exploratory qualitative approach, utilising semi-structured interviews with thirteen software engineers actively developing technological solutions for the AEC industry. Following thematic analysis, the findings are categorised into four dimensions: strengths, weaknesses, opportunities and technological limitations. Subsequently, these findings are analysed in relation to previously identified information quality problems.

This research reveals that while CBIM improves project coordination and information accessibility, its effectiveness is challenged by the need for manual updates, vulnerability to human errors and dependency on network services. Technological limitations, notably the absence of automated updates for as-built drawings and the risk of data loss during file conversions in the design phase, coupled with its reduced capability to validate context-specific information from the user's viewpoint, emphasise the urgent need for managerial strategies to maximise CBIM's capabilities in addressing information quality problems.

This study augments the understanding of CBIM, highlighting the managerial implications of a robust information management process to safeguard information integrity. This approach fosters sustainable practices anchored in reliable information essential for achieving desired outcomes. The findings also have broader managerial implications, especially for sectors that employ CBIM as an instrumental tool.

User-generated content was explored to understand the public discourse surrounding the Vessel, a star architecture in New York. Through text analytics, the study aims to uncover topics, sentiments and themes in public opinion regarding this controversial building from social media data.

This study utilized a big data and text analytics approach, employing topic modeling with the BERTopic technique, sentiment analysis with roBERTa and thematic analysis on 10,259 Reddit comments pertaining to the Vessel.

The comments were grouped into 20 topics and seven themes, shedding light on discussions regarding the Vessel’s philosophy of existence, critiques of the architect’s approach, evaluations of project success or failure and considerations of the project’s future. Negative sentiments dominate the discourse, reflecting widespread criticism and skepticism towards the project.

The manual data collection method, due to API restrictions, precluded tracking evolving trends over time. Nevertheless, the study provides insights for architects, urban planners, policymakers and stakeholders involved in public space design and management, highlighting the importance of considering user feedback from social media platforms.

This study enriches our comprehension of how users perceive star architecture in the age of social media, focusing on hidden layers of discourse surrounding a controversial iconic building. By combining topic modeling and sentiment analysis, the study offers a novel approach to analyzing architectural public debates on social media platforms like Reddit.

This study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives.

NLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA).

The researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators.

This case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.

This study aims to develop an all-encompassing model to analyse various aspects of atmospherics, including components of the museum space and its physical surroundings. Moreover, it evaluates whether the identified attributes of the “museumscape” affect the positive word of mouth of museum visitors.

This exploratory study adopts a quantitative methodology. Data were collected through direct interviews with visitors at three Italian art museums and through a structured questionnaire. All dimensions were measured with multiple items on a five-point Likert scale. To assess the influence of the museumscape attributes on positive word of mouth, a structural equation model is performed adopting the two-stage testing procedure estimating the measurement model in the first stage and running a confirmatory factor analysis to assess reliability and demonstrate convergent and discriminant validity for all multi-item measures.

Six attributes of the museumscape are delineated (ambient conditions; facilities and convenience; signs and signage; staff behaviour; art gallery quality; exhibition space aesthetics). The latter three positively influence visitors' positive word of mouth.

The study expands frameworks from previous service museum marketing research in general and service museum research on atmospherics in particular. The framework developed here identifies the direct predictive power of museumscape cues on positive museum visitor's word of mouth, thus increasing knowledge of the customer service experience and service quality and atmospherics management.

The constructs discovered here may help museum managers to carefully design and manage the museumscape to enhance visitors' satisfaction and loyalty.

This study is the first application of servicescape theory in the museum context; previous applications focus on for-profit sectors.