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The current research prioritizes the dimensions of a higher-order customer engagement construct in the tourism destination context in order of their relative importance as perceived by tourists to provide meaningful insights to the tourism industry player.

Data was collected from 429 tourists' from selected tourist destinations. Exploratory factor analysis (EFA) was used for data analysis using SPSS. The scale items were prioritized through hybrid evidence-based modeling using the RIDIT-GRA (Relative to an Identified Distribution and Grey Relational Analysis, respectively) approach.

The prioritization of customer engagement scale items is done through RIDIT analysis and is verified through GRA. The rankings of two independent methodologies show a 93% correlation, thereby ensuring the ranking's robustness.

Prioritizing customer engagement scale items may assist destination managers in strategy formulation to ensure tourists' high-level loyalty transcending purchase. The findings help develop key response areas (KRA's) for effective destination brand management.

Such precedence analysis of customer engagement scale items has not been done in the tourism studies yet. Also, the present study verified the results by incorporating two independent methodologies, thus providing more valid results.

The pharmaceutical industry faces multiple risks that adversely affect its performance. Within these risks, some dependencies have been observed, which help in streamlining the mitigation efforts. Therefore, the present work identifies and categorizes various risks/sub-risks in cause–effect groups, considering uncertainty in the decision-making process.

An extensive literature review and experts' opinions were utilized to identify and finalize the risks faced by the pharmaceutical industry. For further analysis, data collection was done using a questionnaire focusing on finalized risks. Based on the data, the causal relation under uncertainty between various risks/sub-risks was identified using a multi-criteria decision making (MCDM) technique, i.e. intuitionistic fuzzy DEMATEL, in a pairwise manner.

The results show that the three most prominent risk categories are operational, demand/customer/market and financial. Also, out of the seven main risks, only supplier and operational are categorized within the effect group and the rest, i.e. financial, demand, logistics, political and technology within the cause group. The sub-risks within each category have also been categorized into cause–effect groups. The mitigation of cause group risks will help in economize the financial resources and improve the performance and resilience of the industry.

There is insufficient research on identifying the causality among the pharmaceutical industry risks. Additionally, an extensive discussion on the identified cause–effect groups is also missing in the literature. Therefore, in this work, efforts have been made to determine the prominent risks for the Indian pharmaceutical industry that will be helpful for channelizing the resources to mitigate risks for a resilient industry.

The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically utilizing vague, imprecise, and uncertain data. The press unit of a paper mill situated in a northern part of India, producing 200 tons of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behavior has also been done.

In the proposed approach, two important tools namely traditional Lambda‐Tau technique and genetic algorithm have been hybridized to build genetic algorithms‐based Lambda‐Tau (GABLT) technique to analyze the behavior of complex repairable industrial systems stochastically up to a desired degree of accuracy. This technique has been demonstrated by computing six well‐known reliability indices used for behavior analysis of the considered system in more promising way.

The behavior analysis results computed by GABLT technique have reduced region of prediction in comparison of existing Lambda‐Tau technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. The paper suggested an approach to improve the system's performance.

The paper suggests a hybridized technique for analyzing the stochastic behavior of an industrial subsystem by computing six well‐known reliability indices in the form of fuzzy membership function.

The COVID-19 pandemic forced us not only to think but also to pause and objectively reflect on how to deal with the situation that has arisen and how to develop well-being and resilience strategies for the welfare of humanity at large. We long to be in those vicinities where we want to calm our minds in the quest for a deeper meaning of one existence. It can be a nature-based destination, a pilgrim destination or a part of a religious tour depending on one's faith, belief and orientation towards spirituality and bliss. The abundance of natural resources available in nature is the prime source of overall wellness in all parts of the world, as are the traditional Indian practices of Yoga, Ayurveda and the practices suggested by traditional holy scriptures. This chapter attempts to see the wave of spiritual gurus boosting the wellness sector associated with travelling. Gurus like Sri Sri Ravi Shankar, Sadguru Vasudev Jaggi and HH Dalai Lama are some of the international gurus who have influenced spiritual and wellness tourism to a large extent. The discourse analysis revealed that people long for spirituality for peace and wellness. It also reveals being spiritual has no fixed dimension; it varies from person to person.

This paper aims to analyze the interaction among the major variables of reverse logistics seen in automobile industries.

In this research, interpretive structural modeling (ISM) has been used to understand mutual influences among identified variables of reverse logistics. The advantage of the ISM methodology is that variables can be categorized depending upon their driving power and dependence.

Regulations make it mandatory for automobile companies to own responsibility of products manufactured throughout their life cycle by collecting and reusing products, reducing volume of waste generated, increasing the use of recycled materials, etc. For example, End-of-Life Vehicle Directive had directed manufacturers to take back their vehicles at the end of their usefulness and responsibly dispose them. In this research, regulation has appeared at bottom of the ISM model, indicating that it has high driving power to influence other variables. Also, financial limitations are a significant inhibitor faced by the top management for implementing reverse logistics programs.

The ISM methodology relies upon expert opinions for developing contextual relation among identified variables. Thus, an expert’s knowledge, his familiarity with industry and its operations may have affected the final results of the ISM model. One of research implications of this study is that variables identified in this ISM model are quite generic, and thus, with marginal adjustments, these can be used in the context of any other supply chain for increasing its productivity and performance.

The ISM model reveals that regulations affect a significant number of enabler variables of reverse logistics like support of policy entrepreneurs, green purchasing by companies, information and communication technologies that are seen at the upper level of ISM. This indicates that regulations force companies to be proactive towards product recovery actions that lead to initiation of reverse logistics programs by them.

This research has tried to analyze the interaction among the major variables of reverse logistics typically seen in automobile industries which could be useful to logistics managers for taking strategic-level decisions.

The purpose of this paper is to focus on examining the first day returns of initial public offerings (IPOs) and the role of information on their performance. The study tries to optimize the returns of the new issues during 2005-2012 with risk as a constraint.

The initial returns are measured through the market-adjusted excess return and the risk associated with the new issue is measured through underwriters’ reputation. The returns have been optimized through a mixed integer linear problem using the Maple software.

The previous studies show that various informational variables affect the listing day returns significantly. The results of the present study indicate that the mean of initial returns for IPOs during 2005-2012 is 18.03 and the mean risk for these issues is 0.46. The findings also suggest that the optimal returns are obtained in the pre-recession era (2005-2008) and the value for the same is 50.02 percent.

The current study contributes in the investment decisions for global investors as every investor wants to maximize his/her returns. The optimal returns with risk as a constraint will help the investors in improving their investment decision as a prudent investor does not aim solely at maximizing the expected return of an investment but is also interested in optimizing with the minimization of risk.

This study aims to identify the enablers of supply chain resilience (SCR) through a literature review and expert panel input in the context of Pakistan and the post-pandemic era. This study also aims to categorize and rank the identified enablers using expert panel input.

A review of the extant literature was conducted to investigate and identify the factors that contribute to SCR. The relative ranking of the enablers was carried out by a group of industry and academic experts. The expert panel was convened to compare the main categories and each enabler in pairs and to score the enablers using triangular fuzzy numbers.

This study identified 16 critical SCR enablers. Using the fuzzy analytic hierarchy process (AHP), these enablers were divided into three groups and analyzed. The results show that financial enablers, technology enablers and then social enablers are prioritized when it comes to SCR in emerging markets. The robustness of the ranking of enablers is tested through sensitivity analysis.

The results shall be helpful for policymakers and managers to understand the important enablers and also help allocate resources to important enablers. Managers will be able to formulate strategies to achieve SCR in an uncertain environment.

This is one of the first attempts to identify and rank the enablers of SCR in an emerging economy context.

In today’s uncertain business environment, Industry 4.0 is regarded as a viable strategic plan for addressing a wide range of manufacturing-related challenges. However, it appears that its level of adoption varies across many countries. In the case of a developing economy like India, practitioners are still in the early stages of implementation. The implementation of Industry 4.0 appears to be complex, and it must be investigated holistically in order to gain a better understanding of it. Therefore, an attempt has been made to examine the Industry 4.0 implementation for the Indian manufacturing organization in a detailed way by analyzing the complexities of relevant variables.

SAP-LAP (situation-actor-process and learning-action-performance) and an efficient interpretive ranking process (e-IRP) were used to analyze the various variables influencing Industry 4.0 implementation. The variables were identified, as per SAP-LAP, through a thorough review of the literature and based on the perspectives of various experts. The e-IRP has been used to prioritize the selected elements (i.e. actors with respect to processes and actions with respect to performance) of SAP-LAP.

This study ranked five stakeholders according to their priority in Industry 4.0 implementation: government policymakers, industry associations, research and academic institutions, manufacturers and customers. In addition, the study also prioritized important actions that need to be taken by these stakeholders.

The results of this study would be useful in identifying and managing the various actors and actions related to Industry 4.0 implementation. Accordingly, their prioritized sequence would be useful to the practitioners in preparing the well-defined and comprehensive strategic roadmap for Industry 4.0.

This study has adopted qualitative and quantitative approaches for identifying and prioritizing different variables of Industry 4.0 implementation. This, in turn, helps the stakeholder to comprehend the concept of Industry 4.0 in a much simpler way.

Waste generated from electrical and electronic equipment, collectively known as E-waste, remains a persistent environmental, economic and social problem. Sustainable E-waste management (EWM) has numerous benefits, such as preventing electronic waste from entering landfills, reducing the need for virgin materials by recovering valuable materials from recycling and lowering greenhouse gas emissions. Circular economy (CE) practices are considered the initial steps toward sustainable EWM, but some hurdles have been reported in the adoption of these practices. Therefore, the current study aims to identify the common CE practices, sustainability of the EWM process and the challenges in EWM, and to develop a conceptual framework for effective EWM.

Very few studies have proposed frameworks that acknowledge the challenges and CE practices of EWM. To fill this gap, a systematic literature review (SLR) was performed, and 169 research articles were explored.

A total of seven challenges in the adoption of effective EWM were identified: rules and policy, infrastructure, consumer behaviour, informal sectors, community culture, technology and economy. Eight common CE practices were also found for effective EWM: reuse, recycle, remanufacturing, refurbishment, repair, reduce, recover and repurpose.

A conceptual framework guiding sustainable EWM was proposed, which includes solutions for the identified challenges, and CE practices with sustainable benefits.

This study aims to understand the effect of consumers' national identity (NI) on their willingness to buy (WTB) domestic (Indian) products vis-à-vis foreign (Chinese) products. Secondly, it explores the role of psychological ownership (PO) and consumers' animosity in explaining their WTB domestic products.

In this paper data were collected online from Indian consumers (N = 408) through the survey method, using a structured questionnaire. Data analysis was conducted using SPSS AMOS Version 24 and the PROCESS SPSS macro, using mediations and moderated mediation models.

This study establishes the positive effect of consumers' NI on their WTB domestic products over Chinese ones. With a long history of hostility between India and China in the backdrop, the authors find a significant mediating role of PO and consumer animosity (CA) in the relationship between NI and WTB . The results also demonstrate that while consumer ethnocentrism (CET) positively moderates the mediating path via PO at all levels from low to high, it moderates the path via CA only at the mean and high levels.

This study applies the Psychological Ownership Theory, Social Identity Theory (SIT) and the Attribution Theory to explore the interplay between consumers' NI, PO , CA and ethnocentrism in the Indian context. The study asserts the distinction between these constructs by analyzing the interaction and inter-relationships between these variables. Further, it provides a comprehensive understanding of Indian consumers' preference for domestic products over Chinese ones.