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This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study.

This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding.

Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence.

Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.

This study aims to assess the usability of augmented reality (AR) based mobile app for excavation and earthmoving processes using a novel tool entitled Excavator Augmented Reality (EAR).

A mixed-methods research approach was used through conducting experimentation to collect qualitative and quantitative data collected from the Sri Lankan construction sector. EAR app was used for experimentation in outdoor areas examining how a 360° tracked hydraulic excavator can be navigated in different physical environments similar to the real prospected job.

The findings reveal that EAR could make a considerable impact on enhancing productivity, safety and training processes. However, the developed EAR App subjected to assessment demonstrated the highest satisfaction gap for the auditory aspects. Among the remaining criterion, the satisfaction met user expectations for comfortability and no-risk practice. An analysis of strengths, weaknesses, opportunities and threats (SWOT analysis) conducted revealed that visualising the excavator activities and the requirements of improved features were the highest agreed strengths and weaknesses of the EAR. Among the opportunities for improvement, the necessity of improving emergency and safety reached the highest agreement. Moreover, the study presented the challenges in introducing mobile augmented reality (MAR) to the construction sector under the political, economic, sociocultural, technological, environmental and legal (PESTEL) model along with solutions to be taken.

This study provides a novel approach to addressing the safety, productivity and training concerns in heavy mobile plants and machinery on construction sites which remains to be unexplored to this end.

The study aims to develop and test a supply chain wide green product development framework of focal firms and their major suppliers, in the context of the Chinese automotive industry.

An in-depth case studies approach is adopted for this study. Three automotive sector upstream supply chains involving 17 firms and 51 experts as respondents were interviewed on the importance and implementation effectiveness of 6Rs (reduce, redesign, recover, remanufacture, reuse and recycle) across the manufacturer and their respective tier 1 and tier 2 suppliers.

The results indicate that the Chinese automotive sector supply chains are mainly focused on “reduce” practices with immediate environmental and economic benefits. The investigated firms however had future implementation plans for “redesign” and “recovery” practices to become comprehensive in green product development (GPD).

The study facilitates automotive firms, industry policymakers and researchers the understanding of incorporating comprehensive GSCM practices across the upstream supply chain to achieve circularity. The study focused on upstream supply chain due to the concentration of major production practices in this section of the supply chain. However, the downstream supply chain equally deserve attention as well as the need to understand the mediating and moderating roles of the different Rs to tease out the pros and cons of achieving overall environmental sustainability.

There are very limited studies on comprehensive GPD for achieving optimal GSCM and sustainability. By simultaneous looking at a focal firm and its upstream supply chains GSCM practices, this study addresses a system-wide comprehensive GPD issues from implementation of 6Rs perspectives in the supply chain.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

Effective management of risk and knowledge is critical to ensure the success of industry–university collaboration (IUC) projects. However, the intricate dynamics through which these factors influence the performance of IUC projects have yet to be fully investigated. The purpose of this study is to explore the interplay between risk management and knowledge management capabilities and their impact on IUC project performance.

A model was constructed and evaluated through the examination of a sample of 188 collaborative innovation projects located in the United Arab Emirates (UAE), utilizing structural equation models (SEM) and hierarchical regression analysis.

The findings indicate that social system risk, technical system risk and project management risk have a negative impact on the performance of university–industry collaboration (UIC) projects, while cultural, technical and structural knowledge management capabilities can mitigate the negative impact of these risks on the performance of IUC projects.

The study concludes with three recommendations aimed at improving the management of UIC projects, including the establishment of a distinct and precise management strategy, the deployment of a comprehensive and systematized management methodology and the adoption of a balanced management framework.

The originality and value of this study lie in its exploration of the interplay between risk management and knowledge management capabilities in IUC projects. While previous studies have examined either risk management or knowledge management in IUC projects separately, this study provides a comprehensive analysis of both factors and their combined impact on project performance. The study also contributes to the literature by highlighting the specific risks and knowledge management capabilities that are most relevant to the context of IUC projects in the UAE. The practical recommendations offered by the study can help project managers and stakeholders to improve the success of collaborative innovation projects.

This study aims to identify in literature the role of construction stakeholders in the implementation of circular economy (CE) in the construction industry (CI). This study also sought to identify the status quo and research trend, “hot themes”, areas requiring more research and future research direction of stakeholders’ role in CE implementation in the CI.

A literature search was conducted using the Scopus search engine, augmented by Google Scholar to produce 50 relevant articles for the identification of stakeholders’ roles using content analysis. The scientometric study was undertaken using VOSviewer on 42 articles obtained from Scopus search to identify the status quo, “hot themes”, knowledge gaps and future research direction of the role of stakeholders in CE implementation.

Through content analysis, the identified roles of government as a stakeholder are to develop efficient policy instruments, legislation support and financial investment in research and development activities that will promote the smooth implementation of CE. The role of academia/research is to engage in research aimed at investigating both the short-term behaviour and the durability of recycled materials and products. In addition, the roles of professionals in the consultant/design/procurement team are to design with life cycle assessment and reused materials, design for deconstruction and disassembly, etc. The role of the user/consumer is to ensure knowledge diffusion and awareness exchange that recognize the available opportunities for long-term material usage (including repair, reuse, refurbishment and recycling). The research status quo identified from the scientometric analysis include how stakeholders can come up with innovative ways in demolition that will ensure that more of the materials can be reused. Stakeholders should focus on enacting policies and laws that can promote the use of recycled materials in buildings, etc. Research that focuses on how to engage the various construction stakeholders to come up with specific public policy that will promote CE implementation, identifying the specific roles to be played by stakeholders during policy enactment as well as managing the environment, laws and legislations towards CE implementation. The role of stakeholders in the extension of the life span of projects, the use of strategies to minimize waste generation and standardization of building materials, components and connectors with the purpose of simplifying disassembly are the research gaps and areas for future research studies. Some of the research “hot themes” include “circular business models in the CI”, “environmental sustainability policy implementation”, “environmental, economic and social sustainability”, “policy, laws and legislations on waste management” and “environmental and industrial economics” towards CE implementation.

The findings from this study serve as a valuable reference for construction stakeholders to assess their specific roles as stakeholders and to identify the status quo and research trend, “hot themes”, areas requiring more research and future research directions on stakeholders’ roles in CE implementation in the CI.

Identifying the role of stakeholders for CE implementation is one bold step towards the achievement of CE implementation in the CI.

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.

Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

The purpose of this study is to draw from sustainable social innovation theory and the Consumer styles inventory (CSI) instrument to examine secondhand clothing consumption habits for Russia. The secondhand market is the logical outcome of efforts to adopt resale as a facet of sustainable social innovation, with which to drive sustainable decision-making and socially responsible marketing in the secondhand high-end clothing market. Resale represents the cornerstone of business model innovation (BMi) for the retail sector, offering substantial opportunities for retailers who understand changes in consumption behaviour. More cost-effective and arguably greener, the sale of secondhand clothing is expected to be double the volume of fast fashion by 2030 but it remains an understudied field of research in the literature relating to the fashion industry.

Hypotheses are developed from the existing literature. Survey methodology is used to collect data from 250 Russian consumers in the city of Petersburg. The objective is to test the veracity of CSI shopper orientations, focusing on nascent motivations for high-end branded secondhand clothing, to examine sustainable social innovation and resale in an emerging market.

Four new shopping orientations are identified. Based on the empirical data, the authors suggest that five (out of the original eight CSI shopping orientations) are of internal statistical relevance, and that our new orientations are relevant for not only this market, but for neighbouring Eastern European countries too. The findings reflect postmodern evolution in behavioural motivations for Russian consumers, that can inform retail strategy in terms of BMi consumer for harnessing opportunities offered by sustainable social innovation and resale.

Whilst the CSI has been widely used, research for Eastern Europe is limited. Understanding the shopping orientations for sustainable alternatives to newly produced clothing has theoretical and practical implications for improving circularity, post-war entry strategies and countries facing economic downturn. This study contributes novel insights by examining consumer decision-making and shopping orientations in an emerging market.