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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 importance of consumer experience in service industries, particularly healthcare, is widely acknowledged as it captures the intricacies of quality management. In tandem with the emerging research trends that evaluate service excellence through user experience, this study renders a performance analysis of the dimensions of consumer experience that individually or collectively shape healthcare consumers’ perceptions of service quality.

A cross-sectional study was conducted across 13 mid-tier corporate hospitals to collect data from 438 patients. The data was processed through factor analysis in SPSS to confirm sample adequacy and factor extractability. Further, two independent multi-criteria decision-making (MCDM) tools, Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) and Grey Relational Analysis (GRA), were executed to render performance analysis of identified factors.

Using F-TOPSIS, factors such as “information” and “hospital environment” received higher performance ratings, while items related to “communication with doctors” and “humanistic care” received lower rankings. Minor yet anticipated deviations were observed while verifying performance scores using GRA. Nonetheless, both outcomes exhibited a strong correlation coefficient of 97.14%, confirming analytical consistency.

Hitherto, such usages of hybrid MCDM techniques have rarely been executed to convey a clear understanding of consumers’ experiences in healthcare services. Moreover, the findings provide a clear insight into consumers’ key response areas, which can further be translated to maximize consumer gratification, thus assisting healthcare managers in improving service performance and clinical decision-making.

This study identifies antecedents and consequences of destination brand engagement (DBE) that have redefined the tourists' behavior during the COVID-19 outbreak. Specifically, it assesses the impact of social media interaction and destination authenticity on DBE and DBE's effect on tourists' willingness to pay (WTP). Further, it examines the mediating role of affective attitude and the moderating role of perceived risk between the associations of DBE and WTP.

Survey data were collected from 436 respondents through a structured questionnaire from selected tourism destinations in India. Structural equation modeling (CB-SEM) is employed to empirically examine the formulated hypotheses. Besides, hierarchical second-order confirmatory factor analysis is also deployed to analyze certain constructs as higher-order factors.

Social media interaction and destination authenticity elevate DBE, and DBE enhances affective attitude and WTP. Affective attitude mediates the relationship between DBE and WTP. Hence, the association between tourists' engagement and spending behavior becomes crucial under elevated affective attitude conditions. Finally, perceived risk during the pandemic dampens the association between DBE and WTP.

The findings may provide newer insights to the tourism companies, marketers and policymakers to create targeted strategies to minimize the risk perception and improve engagement levels of the tourists, which can yield economic returns in the long run.

Based on the cues from protection motivation theory (PMT), this study develops a DBE framework and identifies the factors affecting its sustenance in the pandemic hit tourism sector. Further, suggesting implications to ensure safety measures in the tourism industry that may assist in establishing economic resilience.

Previous research has indicated that entrepreneurial marketing (EM) positively influences small and medium-sized enterprises’ (SMEs) performance. While most studies have examined the relationship in a stable environment, EMs’ effectiveness during environmental instability remains uncertain. Therefore, the study aims to investigate the influence of EM on Indian manufacturing-based SMEs’ performance during the COVID-19-induced environmental instability. Additionally, it explores the mediating role of innovative performance in the relationship between EM and SME performance.

The data were collected by distributing a structured survey questionnaire to 302 owners/managers of SMEs. Hypotheses were tested using structural equation modeling (SEM).

The result indicates that EM significantly impacts both innovation and SME performance. Furthermore, the innovative performance partially mediates the link between EM and SME performance. The findings suggest that even within severely affected sectors (manufacturing) during the pandemic, SMEs can achieve growth and innovation through effective EM practices.

This study validates the theoretical notion that EM remains effective even in unpredictable environments such as the COVID-19 pandemic. The findings offer valuable insights for SMEs seeking innovative strategies to enhance their performance, particularly those in emerging economies.

Prior studies have relied on a single layer of abstraction to analyze the impact of EM. The present study is the first to extend standard construct (EM) conceptualization. Furthermore, it evaluated the efficiency of EM in situations characterized by instability, which is rare in the EM and SME literature.

Over the years, despite the best efforts to evaluate service quality through multiple techniques, the connotation between service deliverance and consumer expectation remains sporadic. In the quest to quantify service quality from an innovative perspective, the current study has leveraged consumer-perceived experiences to unveil the novel intricacies of healthcare quality.

Anchoring on the dimensions of patient experience (PX), field data were collected from 244 patients at different private hospitals operating across India. Further, this study incorporated a fuzzy analytic hierarchy process (F-AHP) to evaluate consumer preferences and prioritised the dimensions of PX in three categories of Indian hospitals, namely nursing homes (NHs), mid-tier corporate hospitals (MCHs) and top-tier corporate hospitals (TCHs).

The results establish a performance ranking by demonstrating that MCHs outperform the rest alternatives on the grounds of perceived experiences. Tukey's honestly significance difference (HSD) test was executed to confirm the heterogeneity among the participants' preferences across three different hospital categories. The results reveal that for most of the alternatives, the mean scores of the criterion were statistically significantly different.

In healthcare studies, PX dawned as an entity with an ability to propel healthcare quality in a better way than the classical techniques did. The study's findings present a comprehensive picture of a care delivery system by identifying relatively significant dimensions of PX, hence improving the quality quotients by adjusting healthcare offerings in alignment with consumer expectations and organisational strategies.

This research examines the role of a therapist’s attributes, namely, expertise, sociability, likability and mind-set similarity, in building trust, satisfaction and commitment amongst visitors in Indian wellness resorts and hotels.

The text mining approach was adopted to collect a large corpus of 3,94,373 online reviews from TripAdvisor, Google Reviews and hotels.com. Reviews were taken from 1,677 resorts and hotels that deal in spa and wellness care across India. This study uses unsupervised Naïve Bayes classification and n-gram lexical TF-IDF vectorizer method to classify and find the sentiment of the reviews shared by the visitors of the wellness resorts. Additionally, multiple linear regression is performed to understand the impact of the therapist’s identified attributes on the visitor’s relationship quality.

The research found positive sentiment towards the therapist’s likability, and visitors seemed satisfied with the overall wellness service. The sentiment towards trust and commitment is low. The study also found significant links between likability and expertise in building the relationship quality between the therapist and the visitors. The expertise of the therapist enhances visitors’ trust and willingness to return. The therapist’s likability nature helps in increasing visitor satisfaction.

This study helps to understand the service personnel's level of relationship with the customer in hospitality services. Further, the study empirically verifies the important factors that build relationship quality in Indian wellness services.

The present study argues the need for greater clarity in understanding the customer perception of the services provided by wellness therapists in Indian wellness resorts and hotels. The study guides hotel managers to perform training of wellness therapists to improve customer satisfaction. Using the findings of the current study, managers can prioritize therapists’ attributes and realign their core strategies and provide satisfying wellness services to customers.

This study demonstrates the essential qualities a therapist should develop to enhance the relationship with the resort visitors and foster trust, commitment and satisfaction. The study goes a step further by using a vast database of online data for deep insights into the visitor’s view and the use of machine learning for amplifying results.

Green Lean Six Sigma (GLSS) is a comprehensive approach that aims to reduce waste, emissions and non-value added activities, thus mark an effective impact on sustainability of firms. Despite the numerous benefits of GLSS implementation, it is evident that Micro Small and Medium Enterprises (MSMEs) are still struggling to understand, integrate and implement this strategy. This research work provides a comprehensive analysis of GLSS within MSMEs and proposes a conceptual integration framework of GLSS for improving economic and environmental dimensions of sustainability MSMEs.

A systematic literature review (SLR) methodology was planned to assess and analyse the research articles from 2007 to 2022. Different key elements of GLSS such as barriers, enablers and tool sets have been thoroughly reviewed and analysed for MSME organisations to understand their behaviour and effectively adopt the GLSS approach in their operations.

This study provides the analysis of different perspectives of GLSS and this will contribute to improve different metrics related to emissions and quality in MSMEs. It provides MSMEs industrial managers with a comprehensive knowledge base of GLSS elements, enabling effective deployment.

The present study provides a significant knowledge base and know-how of GLSS to researchers that will assist in deploying this sustainable approach in different industrial domains. The study also assists industrial managers by providing a systematic framework of GLSS for MSMEs. Further, the study also supports society by proving pathway to improve the environmental dynamics through the execution GLSS.

The study is of the first kind that review different facets of GLSS related to MSME. The study not only enhances theoretical know how of GLSS but also guides practitioners how to implement comprehensive GLSS program for improved environmental sustainability.

Considering the role of the textile industry in the generation of employment and export in the Indian economy, it is important to comprehend the efficiency level in the operations of the textile units located in different states in India. In this light, the purpose of this paper is to examine the operational efficiency of textile manufacturing units in Haryana, a northern state of India.

The study applies data envelopment analysis (DEA) approach consisting of input-oriented CCR and BCC techniques along with the return to scale technique for the analysis of five years of data from 2015–2016 to 2019–2020.

The results reveal that Haryana’s textile units have significantly underperformed operationally, with an average technical efficiency score of just 0.25 for five years, from 2015–2016 to 2019–2020. The yearly ratings of the overall technical efficiency of the selected textile companies include 0.20, 0.18, 0.18, 0.40 and 0.28; PTE scores are 0.43, 0.43, 0.55, 0.60, 0.62 and scale efficiency scores 0.54, 0.44, 0.29, 0.71, 0.38, respectively, from 2015–2016 to 2019–2020. On the other hand, average of 5.8 units are functioning at the constant return to scale, 10.2 units are at increasing return to scale and average of 45 units are functioning at decreasing return to scale (DRS). It is found that most of the companies are functioning at a DRS; to boost efficiency, these companies must reduce their input size since they are running at a DRS.

The results of the current paper provide key insight into the inefficiency level of the textile manufacturing industry in the context of northern India. Industry professionals can take corrective measures based on these findings. Moreover, for investors and portfolio managers, knowing which companies are efficient and which are not will help them make better decisions. The study helps policymakers to frame appropriate policy guidelines to make the textile units in the state more efficient and competitive.

To the best of the authors’ knowledge, no study has been done so far on the operational performance of the textile industry in Haryana based on the DEA technique. So, it will contribute to the extant literature on the performance of the textile industry.