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Travel interests of tourists during pandemics and outbreaks are reduced due to the prevalence of fear. It induces lifestyle changes, which may hinder efforts to recover the tourism value chain during post-COVID-19 lockdowns. Subscribing to domestic travel and domestic tourism is deemed to mitigate fear and gradually reopen the tourism industry. Although a crucial initiative, evaluating the perceived degree of exposure of tourists to COVID-19 in tourist sites operating under domestic tourism has not been fully explored in the emerging literature, which forms the main departure of this work.

The problem domain is addressed by adopting multiple criteria sorting method – the VIKORSORT. To demonstrate such application, with 221 survey participants, 35 tourist sites in a province in the central Philippines struggling to revive the tourism industry are evaluated under six attributes that characterize tourists' exposure to COVID-19. To assess its efficacy, the performance of the VIKORSORT is compared to other distance-based multiple criteria sorting methods (i.e. TOPSIS-Sort and CODAS-SORT).

Results show that proximity and volume of tourist arrivals are considered on top of the priority list of attributes. The use of VIKORSORT yields the assignment of 27 sites to the “moderate exposure” class, and eight under the “high exposure” class, with no tourist site assigned to the “low exposure” class. Sorting the tourist sites reveals some observations that tourists prefer sites (1) with open spaces, (2) with activities having limited group dynamics, (3) that are nature-based, and (4) with tourist arrivals that are not relatively high, with enough land area to practice social distancing. In addition, the assignments of the VIKORSORT with TOPSIS-Sort and CODAS-SORT are consistent at least 90% of the time, demonstrating its efficacy in addressing multiple criteria sorting problems.

This work provides an integrative approach in evaluating tourist sites in view of tourism recovery during pandemics. The findings offer crucial insights for the primary stakeholders (i.e. government, tourist operators, and tourists) in planning, resource allocation decisions, and policy formulation. Policy insights are offered, as well as avenues for future works.

The present study aims to provide a practical and robust assessment technique for assessing countries' investability in global supply chains to practitioners. Thus, the proposed approach can help decision-makers evaluate and select appropriate countries in the expansion process of the global supply chains and reduce risks concerning country (market) selection.

The present study proposes a novel decision-making approach, namely the REF-Sort technique. The proposed approach has many valuable contributions to the literature. First, it has an efficient basic algorithm and can be applied to solve highly complicated decision-making problems without requiring advanced mathematical knowledge. Besides, some characteristics differentiate REF-Sort apart from other techniques. REF-Sort employs the value or value range that reflects the most typical characteristic of the relevant class in assignment processes. The reference values in REF-Sort and center profiles are similar in this regard. On the other hand, class references can be defined as ranges in REF-Sort. Secondary values, called successors, can also be employed to assign a value to the appropriate class. REF-Sort can also determine the reference and successor values/ranges independently of the decision matrix. In addition, the proposed model is a maximally stable and consistent decision-making tool, as it is resistant to the rank reversal problem.

The current papers' findings indicate that countries have different features concerning investment. Hence, the current paper pointed out that only 22% of the 95 countries are investable, whereas 19% are risky. Thus, decision-makers should make detailed evaluations using robust, powerful, and practical decision-making tools to make more reasonable and logical decisions concerning country selection.

The current paper proposes a novel decision-making approach to evaluate. According to the authors' information, the proposed model has been applied to evaluate investable countries for the global supply chains for the first time.

The existing three-stage network Data Envelopment Analysis (DEA) models with shared input are self-assessment model that are prone to extreme efficiency scores in pursuit of decision-making units (DMUs) efficiency maximization. This study aims to solve the sorting failure problem of the three-stage network DEA model with shared input and applies the proposed model to evaluate innovation resource allocation efficiency of Chinese industrial enterprises.

A three-stage network cross-DEA model considering shared input is proposed by incorporating the cross-efficiency model into the three-stage network DEA model. An application of the proposed model in the innovation resource allocation of industrial enterprise is implemented in 30 provinces of China during 2015–2019.

The efficiency of DMU would be overestimated if the decision-maker preference is overlooked. Moreover, the innovation resource allocation performance of Chinese industrial enterprises had a different spatial distribution, with high in eastern and central China and low in western China. Eastern China was good at knowledge production and technology development but not good at commercial transformation. Northeast China performed well in technology development and commercial conversion but not in knowledge production. The central China did not perform well in terms of technology development.

A three-stage network DEA model with shared input is proposed for the first time, which makes up for the problem of sorting failure of the general three-stage network model.

A number of studies, most of which have been carried out in the US, examine the appraisal interview by adopting the general research strategy of examining the relationship of one (or more) interview process characteristic(s) to one (or more) interview outcome.

This paper presents an algorithm that can elicitate all or any combination of parameters for the ELECTRE II, III or IV, methods. The algorithm takes some steps of a machine learning ensemble technique, the random forest, and for that, the authors named the approach as Ranking Trees Algorithm.

First, for a given method, the authors generate a set of ELECTRE models, where each model solves a random sample of criteria and actions (alternatives). Second, for each generated model, all actions are projected in a 1D space; in general, the best actions have higher values in a 1D space than the worst ones; therefore, they can be used to guide the genetic algorithm in the final step, the optimization phase. Finally, in the optimization phase, each model has its parameters optimized.

The results can be used in two different ways; the authors can merge all models, to find the elicitated parameters in this way, or the authors can ensemble the models, and the median of all ranks represents the final rank. The numerical examples achieved a Kendall Tau correlation rank over 0.85, and these results could perform as well as the results obtained by a group of specialists.

For the first time, the elicitation of ELECTRE parameters is made by an ensemble technique composed of a set of uncorrelated multicriteria models that can generate robust solutions.

This study aims to evaluate a new fusion technique of visual and textual clusters of objects from a real multimedia data-driven collection to improve the performance of multimedia applications.

The authors focused on using multi-criteria for clustering texts and images. The algorithm consists of these steps: first is text representation using the statistical method of weighting, second is image representation using a bag of words feature descriptors methods and finally application of multi-criteria clustering.

As an application for event detection based on social multimedia data, in particular, Flickr platform. Several experiments were conducted to choose the appropriate parameters for a better scheme of clustering. The new approach achieves better performance when aggregate text clustering is done with image clustering for event detection.

Further researches would be investigated on other social media platforms such as Facebook and Twitter for a generalization of the technique.

This study contributes to multimedia data mining through the new fusion technique of clustering. The technique has its root in such strong field as the field of multi-criteria clustering and decision-making support.

CONSCIOUS OF the problem and convinced of the case for public library involvement the Public Library Research Group decided in May of last year to set up a working party on adult illiteracy. They were encouraged in this by a growing concern about the implications for public librarians of the plans of various national bodies and the evidence of the profession's response to a number of short courses.

The purpose of this paper is to propose a model to guide organizations according to the relative importance of customer's satisfaction about six quality service dimensions: reliability, empathy, assurance, tangibility, responsiveness (Parasuraman et al., 1985) and price.

The present study uses SERVPERF customer requirements and weights of ELECTRE TRI multicriteria method to provide guidance on an appropriated action plan. Subsequently, it was demonstrated how the proposed model applied to the Inn.

SERVPERF is based on the subjective and cognitive perception of customer's service analyses. ELECTRE TRI decreases the deviation of subjectivity and develops manager's energy flow, focussed on enterprise resources.

Measuring quality is hard work and usually the customer perceptions are not well understood, because it changes many times. In addition SERVPERF and ELECTRE TRI weights do not order the quality importance.

Together, SERVPERF and ELECTRE TRI allow managers to prioritize the service level desired by the customer.

The importance of this analysis follows the competitiveness approach: productivity, quality and flexibility (Boljwin and Kumpe, 1990). The paper develops an approach to lead organizational quality performance based on the customer view. It provides a sustainable competitive advantage.

This study aims to quantify and prioritize the financial performance (FP) determinants in Indian small and medium-sized enterprises (SMEs).

Analytic hierarchy process, a multi-criteria decision-making tool, was used. Experts were allowed to express the opinion regarding the relative importance of each factor and sub-factors by making pairwise comparisons through a structured questionnaire based on a nine-point scale.

Market orientation (0.4529) was perceived as the most important FP determinant followed by the entrepreneurial orientation (0.3382) and corporate social responsibility (0.2089) in SMEs.

This study can be considered as a pilot study because it is confined to Indian SMEs. Future research studies can incorporate the opinion or insights of other stakeholders and may target the SMEs situated in different geographical areas.

The inferences drawn in this study would clarify the conceptual and contextual applicability of competitive strategies in SMEs. Indeed, proposed hierarchy and developed framework would guide the SMEs in strategic planning. Moreover, it would help in repositioning and alignment of core strategies duly with business objectives.

The study represents the foremost step and a unique effort in the area of development of hypothetical model (a hierarchal model) with the framework considered to prioritize the FP determinants in SMEs.