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This study aims to present diverse proposals for the measurement of tourism destination competitiveness that serve as alternatives to the travel and tourism competitiveness index (TTCI).

The proposal includes principal component analysis, the DP2-distance method, goal programming, data envelopment analysis and the Borda count. The study evaluates 17 destinations from Central America and the Caribbean.

These include the feasibility that the methodologies provide reliable competitiveness rankings and the possibility of using less information due to the strength of the statistical methodologies. International tourist arrivals, income from international tourism and travel and tourism contribution to the gross domestic product could be used as approximations of tourism destination competitiveness.

The main limitation is the absence of major destinations from the region that constitutes fierce competitors.

New aggregation methods can build composite indicators for competitiveness measurement and their presentation in a more comprehensible way.

The results serve as an alternative for countries that have yet to be considered in international tourism competitiveness comparisons.

A better explanatory power of the proposed index is given, thanks to their decomposition capacity and the reduction of the limitations of the original TTCI. Moreover, the proposals facilitate the inclusion of external information or the execution of a completely objective methodology.

本研究旨在为衡量旅游目的地竞争力提出多样化的建议, 并作为旅行和旅游竞争力指数的替代方案。

该提案包括主成分分析、DP₂ 距离方法、目标规划、数据包络分析和 Borda 计数。 该研究评估了中美洲和加勒比地区的 17 个目的地。

结果包括这些方法提供可靠的竞争力排名的可行性, 以及由于统计方法的优势而使用较少信息的可能性。 国际旅游人数、国际旅游收入以及旅行和旅游对 GDP 的贡献可以用作旅游目的地竞争力的近似值。

主要局限是该地区没有竞争激烈的主要目的地。

新的聚合方法可以为竞争力测量建立综合指标, 并以更易于理解的方式呈现。

结果可作为国际旅游竞争力比较中, 衡量尚未考虑国家的替代方案。

由于其分解能力和原始 TTCI 限制的减少, 所提出的指数具有更好的解释力。 此外, 这些建议有助于纳入外部信息及执行完全客观的方法。

El presente estudio busca presentar diversas metodologías para medir la competitividad de los destinos turísticos, de modo que sirvan como alternativa al Índice de Competitividad de Viajes y Turismo.

La propuesta incluye Análisis de Componentes Principales, el método de distancia DP₂, Programación por Metas, Análisis Envolvente de Datos y el Recuento de Borda. Se analizan 17 destinos de Centro América y el Caribe.

Estos incluyen la validez de las metodologías para obtener rankings de competitividad fiables y la posibilidad de emplear menor cantidad de información, dadas las fortalezas de los procedimientos estadísticos propuestos. Las Llegadas de Turistas Internacionales, los Ingresos por Turismo Internacional, y la Contribución del Turismo al PIB podrían ser buenas aproximaciones para medir competitividad turística

La principal limitación es la ausencia de destinos importantes de la región, que se consideran importantes competidores.

Novedosos procedimientos de agregación para crear indicadores sintéticos para medir la competitividad turística y su presentación de un modo más comprensible.

Los resultados sirven como alternativa para otros destinos que aún no han sido considerados en comparaciones internacionales de competitividad turística.

Un mejor poder explicativo de los índices propuestos, gracias a su capacidad de descomposición, y la reducción de las limitaciones del índice del WEF. Además, las propuestas facilitan la inclusión de información externa o la ejecución de un método completamente objetivo.

Performance-based contracting (PBC) links pricing with performance objectives in service business relationships. Although interest in PBC has surged recently, there is still great uncertainty about the risks, opportunities and challenges. This paper aims to provide a deeper understanding of the contextual factors of PBC and how providers assess them.

This paper includes conducting a multiple-case study evaluation and analyzes data from 21 cases. Risks, opportunities and contextual factors are identified through interviews, and the case data are analyzed with several methods, including Borda count and cross-tabulation.

The results show that the most important factors of PBC are clear responsibilities, clear performance indicators, transparent measurement, cooperative culture and a precise utilization profile of core assets. Surprisingly, incentives are of minor perceived relevance. The analysis supports the differentiation of PBC into two subcategories: lean (low integrated) and customized (high integrated) PBC.

While many studies stress the uniqueness of PBC in accordance with the “one-size-does-not-fit-all” mantra, this research differentiates the standardized PBC from a customized one. The findings face the limitations of case study research and qualitative data analysis in general.

Practitioners are provided with guidance to develop either a customized or a standardized PBC.

Previously, broader empirical insights have still been rare; thus, this paper contributes to the PBC literature, as it provides data from multiple cross-industry cases. The findings (e.g. the minor relevance of incentives) stand in contrast to parts of the academic literature and contribute also to the wider service management field.

For classification problems of customer relationship management (CRM), the purpose of this paper is to propose a method with interpretability of the classification results that combines multiple decision trees based on a genetic algorithm.

In the proposed method, multiple decision trees are combined in parallel. Subsequently, a genetic algorithm is used to optimize the weight matrix in the combination algorithm.

The method is applied to customer credit rating assessment and customer response behavior pattern recognition. The results demonstrate that compared to a single decision tree, the proposed combination method improves the predictive accuracy and optimizes the classification rules, while maintaining interpretability of the classification results.

The findings of this study contribute to research methodologies in CRM. It specifically focuses on a new method with interpretability by combining multiple decision trees based on genetic algorithms for customer classification.

Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.

The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.

Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.

The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.

Ranking relevant journals is very critical for researchers to choose their publication outlets, which can affect their research performance. In the management information systems (MIS) subject, many related studies conducted surveys as the subjective method for identifying MIS journal rankings. However, very few consider other objective methods, such as journals’ impact factors and h-indexes. The paper aims to discuss these issues.

In this paper, top 50 ranked journals identified by researchers’ perceptions are examined in terms of the correlation to the rankings by their impact factors and h-indexes. Moreover, a hybrid method to combine these different rankings based on Borda count is used to produce new MIS journal rankings.

The results show that there are low correlations between the subjective and objective based MIS journal rankings. In addition, the new MIS journal rankings by the Borda count approach can also be considered for future researches.

The contribution of this paper is to apply the Borda count approach to combine different MIS journal rankings produced by subjective and objective methods. The new MIS journal rankings and previous studies can be complementary to allow researchers to determine the top-ranked journals for their publication outlets.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

Although journal rankings are important for authors, readers, publishers, promotion, and tenure committees, it has been argued that the use of different measures (e.g. the journal impact factor (JIF), and Hirsch’s h-index) often lead to different journal rankings, which render it difficult to make an appropriate decision. A hybrid ranking method based on the Borda count approach, the Standardized Average Index (SA index), was introduced to solve this problem. The paper aims to discuss these issues.

Citations received by the articles published in 85 Health Care Sciences and Services (HCSS) journals in the period of 2009-2013 were analyzed with the use of the JIF, the h-index, and the SA index.

The SA index exhibits a high correlation with the JIF and the h-index (γ > 0.9, p < 0.01) and yields results with higher accuracy than the h-index. The new, comprehensive citation impact analysis of the 85 HCSS journals shows that the SA index can help researchers to find journals with both high JIFs and high h-indices more easily, thereby harvesting references for paper submissions and research directions.

The contribution of this study is the application of the Borda count approach to combine the HCSS journal rankings produced by the two widely accepted indices of the JIF and the h-index. The new HCSS journal rankings can be used by publishers, journal editors, researchers, policymakers, librarians, and practitioners as a reference for journal selection and the establishment of decisions and professional judgment.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

This paper aims to conduct a comprehensive analysis to evaluate the current situation of journals, examine the factors that influence their development, and establish an evaluation index system and model. The objective is to enhance the theory and methodologies used for journal evaluation and provide guidance for their positive development.

This study uses empirical data from economics journals to analyse their evaluation dimensions, methods, index system and evaluation framework. This study then assigns weights to journal data using single and combined evaluations in three dimensions: influence, communication and novelty. It calculates several evaluation metrics, including the explanation rate, information entropy value, difference coefficient and novelty degree. Finally, this study applies the concept of fuzzy mathematics to measure the final results.

The use of affiliation degree and fuzzy Borda number can synthesize ranking and score differences among evaluation methods. It combines internal objective information and improves model accuracy. The novelty of journal topics positively correlates with both the journal impact factor and social media mentions. In addition, journal communication power indicators compensate for the shortcomings of traditional citation analysis. Finally, the three-dimensional representative evaluation index serves as a reminder to academic journals to avoid the vortex of the Matthew effect.

This paper proposes a journal evaluation model comprising academic influence, communication power and novelty dimensions. It uses fuzzy Borda evaluation to address issues related to the weighing of single evaluation methods. This study also analyses the relationship of the three dimensions and offers insights for journal development in the new media era.

The purpose of this paper is to understand Indian tourists’ perceptions of Airbnb compared to other hospitality options, and the factors driving their purchase intentions.

An integrated model for purchase intention was conceptualized based on the theory of planned behavior and social exchange theory. Constructs such as trust, authenticity, travel innovativeness, price sensitivity and effort expectancy were included based on a survey of the literature. Structural equation models were built using survey data. Respondent ranking of different criteria for Airbnb vs its competitors were aggregated using Borda count method.

Price is the most important criteria across hospitality choices, including Airbnb, except high-end hotels. Facilities, home-like feeling, trust and friendly service were important for Airbnb. Consumer expectations from Airbnb are similar to homestays, mid-range and budget hotels and different from resorts and high-range hotels. In the theory of planned behavior model, trust in Airbnb and perceived authenticity had large significant positive effects on purchase intention, mediated by attitude. Social norms and effort expectancy had direct positive effects on behavioral intentions. Price sensitivity had a direct small negative effect on purchase intention. Overall, fit of the model was within acceptable parameters.

Despite being an important emerging market, Airbnb in India has not been covered by studies of consumer behavior. This paper fills that research gap. Airbnb’s main competitors are home-stays and mid-range hotels. Building trust, creating authentic experiences and ensuring price competitiveness will drive adoption.