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Rapid urbanization and development of pilgrimage cities cause significant problems for the environment and society, leading to long-term challenges. Despite several discussions on city sustainability, the literature does not address some of the specific problems of pilgrimage cities. Hence, this study attempts at developing a method to examine the growth pattern and sustainability of pilgrimage cities in southern part of India.

The benchmarking method and the social, economic and environmental dimensions of sustainability are considered to construct the Pilgrimage City Sustainability Index (PCSI). Appropriate variables and categories are identified through a literature review and expert opinion survey. The benchmark values of the variables are derived by contemplating the pilgrimage cities of Tamil Nadu, one of the states with the largest tourist arrivals in India. Subsequently, three prominent pilgrimage cities from Tamil Nadu were chosen for the case study and the method was tested.

The result reveals that the cities investigated are performing above average in the sustainability index, with slight variations in their dimension scores. While the category scores of cities assist in identifying macro-level issues, the variable scores provide an insight into micro-level issues. Furthermore, the gap analysis between the benchmark and the present value of each variable discloses the immediate area of attention in each city. Thus, the cities could set more specific targets, frame strategies and/or collaborate with matching cities to bridge these gaps.

This index assessment provides a comparison of the pros and cons of these pilgrimage cities and helps identify their demand and supply. Policymakers can find appropriate tools and approaches that aid in sustainable urban development and tourism management.

To the best of our knowledge, this is the first study in emphasizing the application of the benchmarking method to assess the sustainability of Indian pilgrimage sites. With appropriate modifications, this method can be used in varied contexts across the globe.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

In this study, a novel framework was proposed to assess the trust in e-government (e-Gov) services under an uncertain environment. The proposed framework was applied in Iranian municipality websites of e-Gov services to evaluate the readiness score of trust in e-Gov services.

A unique hybrid research methodology was proposed. In the first phase, a comprehensive set of indices were determined from an extensive literature review and finalized by employing the fuzzy Delphi method. In the second phase, interval-valued intuitionistic fuzzy set (IVIFS) -was utilized to model the problem's uncertainty with analytic called IVIFS- hierarchy process (AHP) to determine the importance of indices and indicators by assigning the weights. In the third phase, the fuzzy evaluation method (FEM) is followed for assessing the readiness score of indices in case studies.

The findings indicated that “Trust in government” is the most significant index affecting citizen's trust in e-Gov services while “Maintenance and support” has the least impact on user's intention to use e–Gov services.

The study contributes by introducing a unique research methodology that integrates three phases, including fuzzy Delphi, IVIFS AHP and fuzzy evaluation method. Moreover, the fuzzy sets theory helps to reach a more accurate result by modeling the inherent ambiguity of indicators and indices. Interval-valued intuitionistic fuzzy models the ambiguity of experts' judgments in an interval.

The study helps policy makers to monitor wider aspects of trust in e-Gov services as well as understanding their importance. The study enables policy makers to apply the framework to any potential case studies to evaluate the readiness score of indices and recognizing strengths and weakness of trust dimensions as well as recommending advice for improving the situation.

The study is one of the few to indicate significant indices of trust in e-Gov services in developing countries. The study shows the importance of indicators and indices by assigning a weight. Additionally, the framework can assess the readiness score of various case studies.

This work aims to assess the potential of a Bio-District as a model for applying the circular economy concerning the waste scope. It aims to understand the capability of organic farms to manage waste with a circular perspective, starting with the use of indicators that directly or indirectly impact the waste scope.

This study is based on previous work that identified and systematised the circular indicators of the agri-food sector within a dashboard. With this research as a basis, the indicators within the waste scope in the dashboard were extracted. Cross-linked indicators with an indirect connection to the waste scope were also systematised and tested in a case study. Primary and secondary data were used for the study. The primary data came from a semi-structured interview, and the secondary data were from official databases.

The work highlights two important results. The first allows the definition of a subclassification of indicators by product and organisation, extracting those with a cross-linked characteristic concerning the waste scope. Secondly, the indicators' application shows the farm's circular and waste valorisation potential within the Bio-District. The study also made it possible to test a new indicator, the “Potential Energy Biomass Recovery”, to measure the farm's potential to produce energy from waste.

This research proposes a new circular economy approach to evaluate waste management in the agri-food sector.

Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multi-dimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness.

A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization.

The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization.

The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption.

Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.

Sustainable construction deficits are common in developing economies, and resolutions are constrained by the failure to prioritise the plethora of available indicators. This study aims to report on overlapping indicators for benchmarking sustainable construction for construction organisations.

Online survey data were collected from construction professionals, academics and senior managers in government bodies. Pearson chi-squared tests and overlapping analysis were used to determine significant indicators. Kruskal–Wallis tests were used to determine statistically significant differences among the dimensions.

Overlapping analysis determined indicators significant for economic, environmental and social performance. Environmental protection and reporting (pollution and emissions) were significant for all three performance dimensions. The most significant indicators are economic performance (adequate competence of key project staff), environmental performance (environmental protection and reporting – pollution and emissions) and social performance (adequate sustainability expenditure by construction organisations). Significant differences due to dimensions existed for adequate competence of key project staff, sustainable construction and eco-design, adequate governance and organisational excellence of construction projects and satisfactory workers’ morale.

Determining overlapping indicators enables prioritised implementation that ensures sustainable construction. Excluding construction workers was a significant limitation for a holistic interrogation.

To the best of the authors’ knowledge, this is the first study to determine overlapping indicators for sustainable construction performance in Zimbabwe.

This study aims to examine the interrelationship between financial stability and economic growth with a comprehensive analysis.

The panel Granger causality testing approach is carried out to the panels of the Fragile Five (F5) and the Group of Seven (G7) countries for the period 1998–2020. To capture the different aspects of financial stability the authors use eight different indicators.

The findings reveal some important implications: the relationship between financial stability and economic growth is sensitive to the financial stability indicators for both the F5 and G7 countries. The stability indicators related to the credit market contain much more causality relationship with economic growth than the indicators related to the stock market. Z-score and provisions to nonperforming loans (NPLs) are among the two variables with the highest causality relationship with economic growth. The least number of causality link is found for the Regulatory Capital Ratio and Stock Price Volatility in F5 countries and Credit Ratio, NPLs and Stock Price Volatility in G7 countries. Economic growth affects financial stability through credit market stability indicators and mostly for the F5 countries. No causal relationship is found for any of the financial stability indicators of Canada, the UK and the USA from economic growth to financial stability.

Since the linkages between financial stability and economic growth may vary due to country/group specific differences, apart from the previous studies, the authors select two different groups of countries in terms of financial stability and economic size.

This research paper aims to contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking groups and exploring learning opportunities and integration directions.

The study employs a multi-dimensional innovation-driven clustering methodology to analyze data from the 2019 edition of the Global Innovation Index (GII). Hierarchical and K-means Cluster Analysis techniques are applied using various sets of distance matrices to uncover and analyze distinct innovation patterns.

This study classifies 129 countries into four clusters: Specials, Advanced, Intermediates and Primitives. Each cluster exhibits strengths and weaknesses in terms of innovation performance. Specials excel in the areas of institutions and knowledge commercialization, while the Advanced cluster demonstrates strengths in education and ICT-related services but shows weakness in patent commercialization. Intermediates show strengths in venture-capital and labour productivity but display weaknesses in R&D expenditure and the higher education quality. Primitives exhibit strength in creative activities but suffer from weaknesses in digital skills, education and training. Additionally, the study has identified 35 indicators that have negligible variance contributions across countries.

The study contributes to finding the relevant countries’ grouping for the enhancement of communication, integration and learning. To this end, this study highlights the innovation structural differences among countries and provides tailored innovation policies.

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.