Relative Importance Weights Research Articles

An Assessment of Factors Influencing Airline Disruption Management Decisions Through R-SWARA Method

Objective: This study aims to identify the key criteria determining operational decisions to disruptions in airline operations and analyze the importance weights of these criteria.. Method: In this context, a literature review was conducted to identify potential criteria influencing disruption management decisions. Subsequently, semi-structured interviews were held with experts working in airline operations control centers to finalize the criteria. Additionally, the relative importance weights of these factors were determined using the expert-focused R-SWARA (Rough Step-wise Weight Assessment Ratio Analysis) method. Results: The findings reveal that flight safety is the most significant criterion in the decision-making process of airlines' disruption management. Security ranked as the second most important criterion in the current study. The rankings of other criteria also provide valuable scientific insights for further examination. Originality: Research on airline disruption management primarily focuses on optimizing responses to operational disruptions and developing new solution models. These studies have predominantly employed mathematical optimization models to minimize the adverse effects of disruptions. However, no research has been found that utilizes decision-making methods to prioritize the criteria influencing responses to such disruptions. Addressing this topic as a decision-making problem in this study provides a macro perspective on the perspectives influencing the resolution of disruption management challenges.

Supervised and unsupervised machine learning for elemental changes evaluation of torrefied biochars

This study implements a comprehensive analysis of supervised and unsupervised learning to evaluate elemental changes and investigate the merits of the machine learning method for torrefied biochar property analysis. The focus is on data analysis using artificial neural networks (ANNs) and k-means algorithms for analyzing the carbonization index (CI) and deoxygenation index (DI) after biomass torrefaction. The predictive response surfaces for CI and DI are formulated and analyzed accordingly by preprocessing the raw data as either lignocellulosic or microalgal datatype. Based on ANNs, the relative importance weights of the factors of temperature, duration, and biomass type are 45.87 %, 23.19 %, and 30.94 %, respectively, for the CI response, while 35.27 %, 26.62 %, and 38.11 %, respectively for the DI response. For k-means analysis, the optimal number of clusters for lignocellulosic and microalgal datasets are two and three, respectively. The R2 value of ANNs is 0.9565. The distribution and percentage of the dataset within the clusters are influenced by the time for the lignocellulosic datatype, while they are influenced by temperature for the microalgal datatype. The obtained results are conducive to improving the cognition of the machine learning method on torrefaction performance analysis.

How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences.

This study was designed to test hypotheses regarding the path dependence of health-outcome values in the form of linear additivity of health-state utilities and diminishing marginal utility of health outcomes. We employed a discrete-choice experiment to quantify patient treatment preferences for major depressive disorder. In a series of choice questions, participants evaluated seven symptom-improvement sequences and out-of-pocket costs over 6-week durations. Money-equivalent values were derived from a deductive latent-class mixed-logit analysis. The discrete-choice experiment was completed by 751 respondents with self-reported major depressive disorder recruited from an online commercial panel. The class-membership probability was 0.83 for latent-class preferences consistent with supporting relative importance weights for all symptom-improvement sequences in the study design. First, we found strong support for diminishing marginal utility in symptom-improvement sequences. The money-equivalent value of an initial week of normal mood was $147 (95% confidence interval: $128, $166) and a second week of normal mood was $70 ($49, $91). Furthermore, for short treatment durations where conventional discounting was not a factor, equivalent changes in health status were valued more highly for an earlier onset of effect: holding subsequent symptom patterns constant, $338 (211, 454) versus $70 (49, 91) for improvements starting in week 2 versus week 3 and $147 ($128, $166) versus $29 (-$4, $64) for improvements starting in week 3 versus week 4. Our findings imply that conventional quality-adjusted life-year calculations in which health values are assumed to be path independent can understate the value of health improvements that appear earlier in a sequence.

The short-term association between environmental variables and daily pediatric asthma patient visits in Hangzhou, China: A time-series study

BackgroundTo date, a large number of studies have shown correlations between environmental variables and pediatric asthma in short-term lag time. However, their results are inconsistent. Therefore, we aimed to evaluate the short-term impact of environmental variables on daily pediatric asthma patients’ visits (DPAPV) in Hangzhou, China, and find the most important risk factor. MethodsGeneralized additive distribution lag non-linear model (GAM-DLNM) was applied to explore the effect of environmental variables on DPAPV in single- and multi-variable models in Hangzhou, China from 2014 to 2021. Then, risk factors of pediatric asthma were selected (p < 0.05 both in single- and multi-variable models) and used weighted quantile sum (WQS) regression model to evaluate their relative importance. ResultsThere were 313,296 pediatric asthma patient records between 2014 and 2021. Both in single- and multi-variable models, PM2.5, PM10, and NO2 exhibited significant positive correlations in short-term lag time and these correlations reached their maximum in lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), lag day 2 (RR = 1.00, 95%CI:1.00 to 1.01), and lag day 3 (RR = 1.04, 95%CI:1.02 to1.05), respectively. The WQS index showed that NO2 had the greatest relative importance (weight over 70 %). The relative importance of NO2 increased with time passing. Males were more susceptible to the adverse effects of NO2. ConclusionPM2.5, PM10, and NO2 had significant adverse effects on pediatric asthma. Among them, NO2 presented the greatest and most important adverse effect on the disease. Therefore, parents could give priority to paying attention to NO2 to control children's asthma.

Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach

This research proposes a hybrid multi-criteria decision-making (MCDM) framework for workforce recruitment in Taiwan’s electronics manufacturing companies, an area with limited research. First, a comprehensive review of existing literature and interviews with industry experts were conducted to compile a list of decision-making criteria and sub-criteria relevant to workforce selection in Taiwan’s electronics industry. The Fuzzy Delphi Method (FDM) was then applied to identify and retain the most critical criteria while eliminating the less important ones. Next, Interpretive Structural Modelling (ISM) was used to calculate the interdependencies among the identified factors. Finally, based on these relationships, the Fuzzy Analytic Network Process (FANP) was employed to calculate the relative importance weights of the criteria and sub-criteria. These weights were then used to rank the criteria, identifying the most important ones and aiding in decision-making. The findings indicate that the proposed method provides a structured and assessable model for making informed decisions in workforce recruitment, particularly in the challenging environment of Taiwan’s electronics manufacturing industry, which faces a shortage of skilled labor. The research presents three primary contributions: the development of a systematic criteria selection technique using FDM, the establishment of consistent criteria relations for decision-makers using ISM, and the proposal of an application model employing the FANP method to identify and rank appropriate criteria for hiring new employees. The study highlights work attitude, adaptability to the environment, and work ability as major criteria. It also emphasizes the importance of discipline compliance, a positive attitude, and adherence to health and safety protocols as the top sub-criteria for workforce selection.

Topological Spectra and Entropy of Chromatin Loop Networks

The 3D folding of a mammalian gene can be studied by a polymer model, where the chromatin fiber is represented by a semiflexible polymer which interacts with multivalent proteins, representing complexes of DNA-binding transcription factors and RNA polymerases. This physical model leads to the natural emergence of clusters of proteins and binding sites, accompanied by the folding of chromatin into a set of topologies, each associated with a different network of loops. Here, we combine numerics and analytics to first classify these networks and then find their relative importance or statistical weight, when the properties of the underlying polymer are those relevant to chromatin. Unlike polymer networks previously studied, our chromatin networks have finite average distances between successive binding sites, and this leads to giant differences between the weights of topologies with the same number of edges and nodes but different wiring. These weights strongly favor rosettelike structures with a local cloud of loops with respect to more complicated nonlocal topologies. Our results suggest that genes should overwhelmingly fold into a small fraction of all possible 3D topologies, which can be robustly characterized by the framework we propose here. Published by the American Physical Society 2024

An exploration of experts’ views on the relative importance of indoor-environmental quality parameters

ABSTRACT Indoor-environmental quality (IEQ) assessments must consider multiple aspects and domains. In this context, common IEQ building evaluation and rating schemes frequently apply conjoint indicators to aggregate these different aspects via importance or significance ranking formalisms involving, for instance, points, scores and weights. However, the reasoning for the specific selection of variables and the sources of their assigned weights are not necessarily disclosed as a matter of course. In the present contribution, we investigate one of the paths to the provision of such reasoning that leans on experts’ views. Online feedback from a small sample of IEQ experts provided the basis for the illustration of this path and the kinds of principal insights that it can offer, including the degree of consistency among experts and the potential factors that could influence their judgment. Moreover, the study also exemplified the process of deriving relative importance weightings (i.e. coefficients applied to various domain and sub-category variables) and how such processes can be applied toward a total IEQ measure. The study’s findings underscore the need to improve the transparency of the processes through which such schemes and their constitutive ingredients are arrived at.

Study on the Development Strategy of China’s Health Care Industry from the Perspective of Active Ageing: Based on Public Cognition Survey

With an aging population currently posing a challenge, China must accelerate the construction of a health development model with "prevention as the main focus" from the perspective of active aging. This development requirement is exactly met by the growth of health care industry, which is highly significant strategically to cope with the dual challenges of aging populations and public health issues. The study construct an evaluation index system of influencing factors for the development of health care industry, and further empirically analyse the influencing factors of China’s health care industry by using Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM), and calculate the relative importance weights of 18 influencing factors of perceptual indicators under the four levels of economy, environment, products and individuals. Lastly, countermeasures are made for the superior growth of China’s health care industry based on the results of the weighing analysis.

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

The Impact of Indoor Environmental Quality on Occupant Satisfaction in Commercial Buildings: A Comparison of Building Expert Opinions and Residents’ Experiences

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience.

Better decisions with less cognitive load: The Parsimonious BWM

Despite its recent introduction in literature, the Best–Worst Method (BWM) is among the most well-known and applied methods in Multicriteria Decision-Making. The method can be used to elicit the relative importance (weight) of the criteria as well as to get the priorities of the alternatives on the criteria at hand. In this paper, we will present an extension of the method, namely, the parsimonious Best–Worst-Method (P-BWM) permitting to apply the BWM to get the priorities of the alternatives in case they are in a large number. At first, the Decision-Maker (DM) is asked to give a rating to the alternatives under consideration; after, the classical BWM is applied to a set of reference alternatives to get their priorities used to compute, then, the priorities of all the alternatives under consideration. We propose also a procedure to select reference alternatives, possibly in cooperation with the DM, providing a well-distributed coverage of the rating range. The new proposal requires the DM a fewer number of pairwise comparisons than the original BWM. Another contribution of the paper is related to the comparison between BWM, P-BWM, the Analytic Hierarchy Process (AHP), and the parsimonious AHP in terms of the amount of preference information provided by the DM in each method to apply it. In addition to the standard approach, we propose one alternative way of inferring the priority vectors in BWM and P-BWM based on the barycenter of the space of alternatives priorities compatible with the preferences given by the DM. Finally, an experiment with university students has been conducted to test the new proposal. Results of the experiments show that P-BWM performs better than BWM in terms of capability to represent the DM’s preferences and the difference between the results of the two methods is significant from the statistical point of view. The new proposal will permit to use the potentialities of the BWM to get the alternatives’ priorities in real-world decision-making problems where a large number of alternatives must be taken into account.

A hybrid spherical fuzzy AHP-MARCOS model for evaluating the condition of saltwater pipes in Hong Kong

PurposeWater pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.Design/methodology/approach As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.FindingsAnalytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.Originality/valueIt can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

Modeling the Strategic Enablers of Financial Sustainability in Saudi Higher Education Institutions Using an Integrated Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling Approach

Ensuring financial sustainability is paramount for higher education institutions (HEIs) to maintain financial viability and accomplish their strategic objectives by efficiently managing and utilizing resources. Studying strategic enablers’ interconnections and influences on each other is essential to forming a profound understanding and guiding the execution of initiatives linked to achieving financial sustainability plans. The main objective of this research study is to construct a model and analyze the strategic enablers of financial sustainability in HEIs and their associated dimensions. An integrated modeling approach was followed based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods. Input data collected from experts with specialist experience in financial sustainability in HEIs were used in the modeling. The overarching finding is that starting with the development of regulations and legislation-related systems leads to good governance practices, enabling the achievement of financial sustainability in HEIs. The developed model unpacks the complex relationships among the enablers and their dimensions. It reveals their cause-and-effect relationship classifications, ranks based on relative importance weights, dependence and driving powers classifications, relationship directions, and interpretive structure. It provides an overarching view of the interconnected network that acts as a roadmap to achieving financial sustainability in HEIs.

Comparing Discrete Choice Experiment with Swing Weighting to Estimate Attribute Relative Importance: A Case Study in Lung Cancer Patient Preferences.

Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. A total of 307 patients with non-small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen's weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53-0.55). Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability.Most respondents found the DCE and SW tasks very easy or easy to understand and answer.A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.

Evaluating Innovation Capability in Banking Under Uncertainty

The evaluation of innovation capability (IC) plays a key role in an age of keen competition driven by modern technologies, since it enables organizations to review their innovation management process and to adjust their corresponding innovation policies. Moreover, the IC evaluation is, in fact, a multicriteria process with high uncertainty, since the market environments and competitors’ performance are both in a dynamic environment. Therefore, the evaluation of IC under uncertainty is vital to organizations. This study proposes a new integrated method for the evaluation of IC in banking organizations by combining the analytic hierarchy process (AHP) and the evidential reasoning (ER) approach in terms of the Dempster–Shafer theory of evidence. Three Vietnamese banks were used as a case study to demonstrate the applicability and validity of the proposed method. Experts in banking-related fields were invited to determine the relative importance weights of critical innovation management practices (CIMPs) and their sub-CIMPs using the AHP and to score the maturity levels of sub-CIMPs at the evaluated banks. The ER approach was then applied to generate aggregated assessments representing the ICs of banks that were finally used for their ranking in terms of IC.

The Allocation Scheme of Software Development Budget with Minimal Conflict Attributes

During the process of software development, a significant challenge revolves around accurately estimating the associated costs. The primary goal of project managers is to ensure the delivery of a highly trustworthiness product that aligns with the designated budgetary constraints. Nonetheless, the trustworthiness of software hinges upon a range of distinct attributes. When implementing a budget allocation scheme to enhance these attributes, conflicts among them may arise. Thus, it becomes imperative to select an appropriate allocation scheme that effectively mitigates conflict-associated costs. In this paper, we will define the conflict costs and establish costs estimation models. The difficulty coefficient constraint for improving attributes is established. Subsequently, we will analyze the relative importance weights of these attributes. Drawing upon the conflict costs, importance weights, and difficulty coefficient constraint, we present an algorithm to determine an appropriate budget allocation scheme, which can minimize conflict-associated costs. Finally, we provide an illustrative example that demonstrates the practicability of our proposed algorithm. This research offers valuable insights to software managers, aiding them in the reasonable allocation of budgetary resources, thereby maximizing overall benefits.

An integrated SWARA-QFD under Fermatean fuzzy set approach to assess proactive risk mitigation strategies in recycling supply chain: Case study of plastic recycling industry

The role of the recycling supply chain in managing plastic waste problems has been emphasized by both academics and practitioners. Plastic recycling producers face various risks associated with supply chain processes that require proper management and proactive strategies to mitigate negative impacts on business performance. However, quantitative research on risk factors and corresponding proactive risk mitigation strategies in recycling supply chain has been limited. This study proposes a novel integrated approach using Stepwise Weight Assessment Ratio Analysis (SWARA) and Quality Function Deployment (QFD) under the Fermatean fuzzy set framework to fill this research gap. The study identifies eleven risk factors of recycling supply chain and eight corresponding proactive risk mitigation strategies through literature review and expert validation. The SWARA technique is employed to compute the relative importance weights of risk factors, and QFD is used to analyze the relationship between risk factors and corresponding proactive risk mitigation strategies. The proposed framework is applied to a recycling plastic packaging waste manufacturer in Thailand as a case study. The results demonstrate that the proposed framework can assist logistics and supply chain managers in the recycling plastic industry in making rational decisions to implement proactive risk mitigation strategies. Moreover, the framework can be applied to other waste recycling industries in a similar way.

IMPLEMENTATION OF GIS-AHP-OWA FOR LAND SUITABILITY ASSESSMENT IN INFRASTRUCTURE INVESTMENT PROJECTS: A CASE STUDY IN YUCATÁN, MEXICO

This article presents an approach to land suitability assessment (LSA) that accounts for the opposing interests and viewpoints of multiple stakeholders. LSA is a widely used geospatial technique for locating optimal sites for infrastructure investment. The approach described in this paper uses the Analytic Hierarchy Process (AHP) to elicit relative importance weights of a set of geographical attributes, the evaluation of the state of each attribute in spatial units using value functions on a geographic information system (GIS), and the implementation of the Ordered Weighted Average (OWA) as an aggregation operator. The approach is illustrated through the location of potential sites for swine plant investments, taking into consideration environmental conflicts in Yucatán, Mexico. This approach provides a systematic and transparent procedure for analyzing infrastructure projects’ land suitability and developing investment recommendations that promote sustainable development.

An evaluation based on the analytic hierarchy process and GGEbiplot on French fry potato genotypes in Yunnan, China.

A total of 33 potato (Solanum tuberosum L.) cultivars and breeding clones imported from the United States and two local cultivars (Yunshu 401 and Cooperation 88, CK) were planted and evaluated. To determine their suitability for processing into French fries at five locations (e1-e5) in Yunnan Province, China, we developed a comprehensive evaluation system using the analytical hierarchy process (AHP). Eleven evaluation indicators for French fry quality, yield, and agronomic characteristics with a relative importance (weight coefficients) of 0.483, 0.301 and 0.216, respectively, were used to analyze the 35 potato genotypes (designated g1-g35).The genotypes were ranked and the results revealed that (1) on the average, the 33 potato genotypes imported from the United States showed a lower performance compared to the local cultivars. Compared with the CK, they were classified as not vigorous (Mean 5.11 vs CK 7.75), matured earlier (Mean 5.79 vs CK 1.70), and had a low resistance to late blight (Mean 3735.59 vs CK 1418.55), requiring the use of fungicides to control the disease at the five trial locations. (2) The US cultivar 'Defender' (g3) ranked in the top six at all five test locations because it had higher yield (29.56t h m-2), better fry quality (4.64), higher dry matter content (20.41%), better tuber length/width ratio (1.99), longer tubers (13.57cm), stronger plant vigor (7.17) and higher resistance to late blight (AUDPC: 3134.2). (3) By using GGEbiplot analysis, superior genotypes with high and stable yields were g3 and 'Echo Russet' (g33). 'Yunshu 401' (g34) and 'Yukon Gem' (g4) had high but not stable yields. The ideal test environments and hence experimental locations were Luquan (LQ, e2) and Lijiang (LJ, e4) which resulted in the best discrimination between genotypes among the five experimental locations in Yunnan. Overall, the developed evaluation system based on AHP and GGEbiplot analysis including 11 evaluation indicators for French fry quality, yield and agricultural traits can be a model for evaluation and promotion of new French fry cultivars, and evaluating and selecting the test location.

A DEMATEL-ISM Integrated Modeling Approach of Influencing Factors Shaping Destination Image in the Tourism Industry

Tourism is an upcoming industry with a high potential for future growth. Many factors influencing destination image (DI) are affecting tourism development. Therefore, studying factors influencing DI is essential. This research study aims to model the factors influencing DI in the tourism industry. A total of 15 factors were the focus of the study. Data on the factors were collected from 10 tourism industry experts. An integrated modeling approach was adopted using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modelling (ISM). Interrelationships between the factors, causal effects, relative importance weights, ranks, and driving and dependence powers were analyzed to develop causal and interpretive structures. The developed model revealed that political stability is foundational to other aspects of DI. Then, factors related to safety and security, policy rules and regulations, tourism infrastructure, information and communication technologies, environment, economic development and affluence, fairs, exhibits, and festivals in a destination should be considered. These affect others related to health and hygiene, transport infrastructure, natural and cultural resources, human resources, quality of services, prices, hospitality, friendliness, and receptiveness aspects in a destination. The developed model provides a valuable framework for decision-makers in the tourism industry to enhance and shape the DI.