Supriyo Roy

Sustainable tourism has become a growing area of interest globally, particularly in the post‐pandemic era. This study examines the impact of synergising Social Innovation (SI) and Social Entrepreneurship (SE) on Sustainable Development (SD) within the tourism domain. Grounded in a pragmatist research philosophy, the study employs a conceptual framework to connect SI and SE to SD. The originality of this research lies in its empirically validated three‐tier conceptual framework that connects innovation, entrepreneurship, and sustainability in tourism; a perspective that remains underexplored in developing nations. For empirical validation, we use Structural Equation Modelling to analyse primary data collected from 298 stakeholders, including tour operators, community members, and government officials, in the cultural heritage town of Bishnupur, India. The results reveal that SI significantly contributes to both SE and SD, while SE has a moderate, positive effect on SD. These findings highlight the synergistic role of SI, SE, and SD in advancing inclusive, resilient, and sustainable tourism under heritage contexts. The study directs practical implications for policymakers, tourism managers, and community actors, advocating integrated, bottom‐up approaches to sustainable tourism and adding substantial value to this area of research.

Understanding the economic performance of heritage-rich towns requires a systematic evaluation of how heritage-related components collectively contribute to revenue generation. Existing studies often examine heritage assets, socio-cultural factors, physical infrastructure, and local economic conditions independently, resulting in fragmented insights that limit comprehensive planning for local economic development. This study develops and validates an integrated Cultural Heritage Economy Model that quantifies the influence of heritage resources, social, physical, and economic aspects on revenue generation in heritage contexts. The model is conceptualized through a structured synthesis of theoretical literature and domain-specific indicators, followed by construct operationalization, expert validation, and pilot-level assessment. Using Structural Equation Modelling (SEM-PLS), the study demonstrates strong reliability, convergent validity, discriminant validity, and significant structural relationships. The predictive relevance of the final model is further evaluated through PLSpredict, confirming its suitability for future estimation. The findings confirm that revenue generation is a product of the combined and mutually reinforcing effects of heritage, socio-cultural, physical, and economic dimensions, rather than just by the influence of heritage resources. By offering this novel, empirically grounded, multidimensional framework to estimate heritage-driven economic outcomes, this research establishes a foundational model that can guide evidence-based resource allocation, policy formulation, and long-term sustainable urban development planning.

The notion of heritage assessment has evolved from valuing monuments in isolation to recognizing heritage as complex socio-economic cultural systems. While the early approaches emphasized tangibility, later shifting to tourism potential, and economic valuation methods, where the heritage context was seldom considered. Subsequent frameworks have incorporated economic impact and sectoral development while specifically focusing on economic returns or heritage conservation. In recent years, integrated frameworks and management toolkits have broadened scope to include social, governance, and cultural aspects. However, frameworks remained generic or significantly oriented to the cultural heritage, lacking context-sensitive approaches tailored to the heritage ecosystem. This study aims to develop Cultural Heritage Town Development Index (CHTDI), a comprehensive framework that unifies conservation, socio-economic, physical, environmental, economic, and developmental aspects. Formulated using a three-stage integrated Delphi–AHP (Analytic Hierarchy Process) technique and PCA (Principal Component Analysis), this framework integrates six-dimensional criteria comprising parameters ranging from heritage-specific aspects to community participation, tourism, infrastructure, economy, cultural entrepreneurship, development policy, revenue generation, etc. The application of CHTDI to heritage towns in India demonstrates its diagnostic capacity to reveal sectoral gaps and aids evidence-based urban planning, representing the future of heritage assessment, and advancing value-centric models to comprehensive heritage ecosystem evaluation.

This research paper delves into the concept of sustainability by examining the critical indicators for evaluating and understanding the key drivers of socioeconomic development, social innovation, and entrepreneurship in small heritage towns. These towns, often characterised by their rich cultural heritage, unique traditions, and historical significance, present a dual challenge: preserving their cultural identity while promoting economic growth and sustainable development. The study identifies and explores the multifaceted indicators, addressing how they contribute to these communities' overall well-being and resilience. The findings of this study aim to equip researchers, local policymakers, community leaders, and other key stakeholders with a plethora of indicators applicable to small heritage towns seeking sustainable development integrating social innovation, socio-economic development, cultural heritage, heritage entrepreneurship, and community empowerment.

Purpose: The study aims to develop a robust, fuzzy, data-driven ERM model that incorporates the decision-makers’ varied levels of expertise and the relative importance of risk factors. Design/methodology/approach: The study presents a robust multi-criteria fuzzy model that integrates inputs from multiple decision-makers to enhance risk prioritization in supply chain operations. It employs triangular fuzzy numbers to normalize decision-maker weights and uses the fuzzy AHP to determine risk criteria weights. Risks are evaluated using fuzzy linguistic terms, such as fuzzy FMEA, followed by weighted fuzzy aggregation. Finally, defuzzification generates priority numbers for ranking risks. Findings: This approach enhances user-friendliness and promotes greater acceptance, making the model particularly suitable for implementation in typical steel plant settings, which may be extendable to the general industry with suitable modifications of model parameters on a “case-to-case” basis. Research limitations/implications: Due to its advanced calculations and multi-step processes, the framework’s complexity may deter adoption, especially in organizations unfamiliar with fuzzy logic. Implementation demands specialized training or software support, posing challenges for smaller enterprises. Customization to specific industrial contexts requires substantial resources, making adoption difficult for resource-constrained organizations. Practical implications: The proposed fuzzy framework delivers a more nuanced approach to risk management by integrating imprecise information and leveraging diverse expertise. This contribution broadens supply chain knowledge, particularly within the context of complex, multi-tiered risks, advancing beyond traditional linear perspectives in risk management literature. Originality/value: The proposed model is novel in terms of successful validation under a steel plant environment using fuzzy AHP combined with fuzzy FMEA.

Purpose: Conventional risk prioritization methods rely on crisp inputs but struggle with imprecise data and hesitancy, resulting in inaccurate assessments that affect service and information quality and performance monitoring. This study proposes a fuzzy data-driven risk prioritization model for service quality under imprecise information. Design/methodology/approach: Enterprise risk management is crucial for service quality management, ensuring effective identification, assessment and mitigation of risks impacting service delivery and customer satisfaction. This paper proposes a fuzzy data-driven multi-criteria model for risk prioritization involving multiple decision-makers. It introduces a hybrid method combining intuitionistic and hesitant fuzzy group decision-making to assess better and prioritize risks based on decision-maker preferences. Findings: The proposed hybrid fuzzy model improves service quality in business operations by efficiently representing uncertain information in traditional frameworks. It helps identify potential risks in advance and enhances control over business operations, enabling organizations to benchmark service quality and identify best practices. Accordingly, organizations acquire information and background knowledge to benchmark their service quality. This, in turn, improves service quality under performance management. Research limitations/implications: Despite the advantages of fuzzy models in risk prioritization, such as mimicking human reasoning more accurately, their complexity can hinder adoption. The intricate computational steps may deter shop-floor managers who prefer the more straightforward conventional crisp RPN approach, which is easier to understand and implement. However, while developing a hybrid fuzzy risk prioritization model may require more effort, its benefits become apparent over time. Once developed, the model can be integrated into software applications, allowing decision-makers to use it easily. This integration simplifies fuzzy computations and enhances risk prioritization, leading to more informed decision-making and improved risk management in the long term. Practical implications: The proposed robust fuzzy framework improves risk management by integrating uncertain information and multiple decision-makers expertise, leading to more reliable outputs that enhance strategic decisions and operational efficiency. Originality/value: We validate the proposed approach at an integrated steel plant’s risk management process, covering broad areas of the service quality domain. To the best of our knowledge, no study exists in existing literature attempting to explore the efficacy of the proposed hybrid fuzzy approach in risk management practices at prime sectors like steel. The study’s novelty is backed by this validation experiment, which indicates that the effectiveness of the results obtained from the proposed multi-attribute hybrid fuzzy methodology is more practical. The model’s outcome substantially adds value to the current risk assessment and prioritization literature that significantly affects service quality.

Purpose: Traditional risk prioritization methods in Enterprise Risk Management (ERM) rely on precise data, which is often not available in real-world contexts. This study addresses the need for a robust model that can handle uncertain and imprecise information for more accurate risk assessment. Design/methodology/approach: We propose a group decision-making approach using fuzzy numbers to represent risk attributes and preferences. These are converted into fuzzy risk scores through defuzzification, providing a reliable method for risk ranking. Findings: The proposed fuzzy risk prioritization framework improves decision-making and risk awareness in businesses. It offers a more accurate and robust ranking of enterprise risks, enhancing control and performance in supply chain operations by effectively representing uncertainty and accommodating multiple decision-makers. Practical implications: The adoption of this fuzzy risk prioritization framework can lead to significant improvements in enterprise risk management across various industries. By accommodating uncertainty and multiple decision-makers, organizations can achieve more reliable risk assessments, ultimately enhancing operational efficiency and strategic decision-making. This model serves as a guide for firms seeking to refine their risk management processes under conditions of imprecise information. Originality/value: This study introduces a novel weighted fuzzy Risk Priority Number method validated in the risk management process of an integrated steel plant. It is the first to apply this fuzzy approach in the steel industry, demonstrating its practical effectiveness under imprecise information. The results contribute significantly to risk assessment literature and provide a benchmarking tool for improving ERM practices.

Purpose: Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.Design/methodology/approach: An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).Findings: The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.Research limitations/implications: This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.Practical implications: The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.Originality/value: This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

Purpose: Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.Design/methodology/approach: The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.Findings: The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.Research limitations/implications: In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.Practical implications: The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).Originality/value: This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.