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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.

In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode.

This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool.

It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm³/min and 0.001034 g/min, respectively.

Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

The main objective of this research is to investigate the factors that influence consumer purchase decisions for halal products before and during the COVID-19 pandemic, based on the Engel-Kollat-Blackwell (EKB) theory.

The research was conducted as a survey. The influencing factors were determined based on the grey relational analysis (GRA) approach.

The findings indicate before the COVID-19 pandemic, consumers mainly purchased halal products based on four key factors: purchasing experience, certification label, Internet searches and past consumption experience. However, during the pandemic, the ranking and factors have changed to six indicators, which are past consumption experience, purchasing experience, certification labels, standardized specifications, Internet searches and halal certification labels.

The study was limited by the sample size and geographical area. Nevertheless, the findings could be further explored by expanding related theories toward understand human decisions based on spiritual beliefs.

The findings of this study have important implications for research, practice and society. Understanding the factors influencing halal purchase decisions before and during the pandemic can help businesses, policymakers and halal certification bodies to better cater to consumers' needs and preferences and ensure the continued growth and development of the halal industry.

This study evaluates halal purchasing decisions between periods of certainty and uncertainty by using the GRA. Changes in halal consumption and purchase decisions in response to COVID-19 pandemic have become an emerging topic of discovery. The study addresses the gap in the literature regarding changes in consumer decision pattern.

The manufacturing sector is highly competitive and operationally complex. Therefore, the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive, food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies.

First, determining the critical factors based on literature review and expert opinions, these criteria are weighted, and analytical hierarchy process is run to calculate the weights of these criteria. Afterward, the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study.

As a result of AHP, “Infrastructure for Right Methodology, Techniques and Tools, is in the first place,” Organizational Strategy, is in the second place, while the third highest critical factor is “Capital Investment”. Moreover, based on grey relational analysis (GRA) results, the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies.

This study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world.

Considering the high impacts caused by manufacturers on sustainability, this research aims to analyse how Brazilian manufacturing companies deal with sustainability issues. To do this, sustainability parameters are analysed to verify possible improvement opportunities.

This research uses Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and grey relational analysis (GRA) to analyse data from a survey with Brazilian professionals regarding aspects of sustainability in Brazilian manufacturers. The average score levels and the ranking of these aspects are evaluated.

Through the analysis performed, it was possible to verify that manufacturers in Brazil still have a long path to travel in the search for sustainability. Comparatively, it was observed that practices related to local communities received the lowest scores, on average. In contrast, on average, practices related to productivity and efficiency, occupational accidents and diseases, and compliance with environmental legislation received the highest scores.

The results presented in this paper show that there are several improvement opportunities to be sought by Brazilian manufacturing companies regarding sustainability aspects. Particular attention should be given to local community practices. Besides companies, policymakers can also use this analysis to guide their future actions, encouraging manufacturing companies to better support the local community. Researchers can use the instrument of analysis (TOPSIS and GRA) to analyse other realities and compare them with the findings presented.

The analysis of Brazilian manufacturing companies’ reality regarding sustainability practices and considering a model based on Global Reporting Initiative (GRI) and Brazilian Institute of Corporate Governance (IBGC) is novel in the literature. The use of TOPSIS and GRA, as well as comparing their findings, generated interesting insights for companies, policymakers and researchers. The analysis presented shows the need for more significant concern for local communities and can be used to support further debates and action plans to minimise this gap.

This study aims to present a study on the supply chain process of a Myanmar-based pharmaceutical company (named ABC Pvt. Ltd. in this study) that produces pharmaceutical products across Myanmar and aims of bringing quality medical products and best care for Myanmar people’s health. The study aims to identify the key supply chain challenges and manage the opportunities executed by this pharmaceutical company to improve the supply chain process during the COVID-19 outbreak.

This work used a case study and conducted semistructured interviews with the manager, senior managers and senior staff of the ABC Company to improve the supply chain process and develop a comprehensive structural relationship to rank them to streamline the uncertainties, real-time information and agility in a digital supply chain using grey relational analysis (GRA) method.

From the data analysis and results, “Impact of political factor,” “Delay in import process” and “Weak internet connection,” and “Weak knowledge of the use of digital platform,” “Poor information sharing in online by employees” and “Information flow from top management to operational level” have been identified as top and bottom three key challenges, respectively. “Inventory management,” “Selection of transport method” and “Operational cost”, and “Marketing and brand Innovation,” “Online delivery of products” and “E-commerce enablement (Launching applications, tracking system)” are identified as the top and bottom three managing the opportunities, respectively.

The findings of the study help to supply chain decision-makers of the company in their establishment of key challenges and opportunities during the COVID-19 era. As a leading company, it always tries to add value to its product through a supply chain system, effective management teams and working with skillful decision-making toward satisfying the demand on time and monitoring the supplier performance.

The novelty of this study is to identify the key supply chain challenges and opportunities by the GRA method to rank them, considering the case of Myanmar pharmaceutical manufacturing company as a case-based approach to measuring its performance during the COVID-19 outbreak era. This work will assist managers and practitioners help to the company to provide optimal services to its consumers on time in this critical situation.

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.