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The lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index.

A theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises.

Development of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other.

The development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services.

This system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

This systematic literature review investigates the contribution of design thinking (DT) as a process and tool to drive innovation in a sustainable built environment (SBE) and develops a new model for sustainability research integrating DT and future thinking approaches toward achieving a unified DT and foresight notion for future research and applications.

This review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Open-access English articles published between 2000 and 2022 identified using the EBSCOhost, Emerald Insight, DOJA, JSTOR, Scopus and Taylor and Francis database searches were reviewed. The review framework deploys a previously proposed modified Ansoff matrix with an integrated innovation matrix to identify and analyze the challenges and opportunities for innovation growth in SBE. Additionally, a citation analysis was conducted to explore the impact of DT for innovation in SBE, and a proposed framework based on design by drawing on foresight theory was developed.

Research on DT for innovation in SBE faces the challenge of unanticipated impacts. According to the average number of citations per document, innovation associated with new solutions within a new context seems to become highly influential. Additionally, research gaps exist in the integration of foresight and DT into sustainability research to identify new contexts and solutions to SBE. A model of foresight design thinking (FDT) is proposed to guide future research and support the practical application of DT in sustainability.

This analysis was limited by the selection criteria as only certain keywords were used and English-only articles were selected. Future research should consider the use of DT for innovation in SBE using various important keywords, which would improve research findings and expand the contribution of DT to SBE.

The FDT model offers a new holistic framework for the iterative process of reframing and reperception, focusing on divergent and convergent thinking with the goal of contributing to SBE practices.

The integrated framework of DT and foresight can contribute to the study and development of sustainable innovation and a strategic shift toward a sustainable society.

The integration of DT, foresight and sustainability can broaden the horizons of sustainability research by systematically addressing future challenges related to SBE, which can be translated into feasible and innovative solutions. Thus, the FDT model complements the application of DT in sustainable innovation in this research field.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.