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The present study aims to construct and compare Composite Environmental Sustainability Index (CESI) for 20 emerging countries for the period 1990–2020.

The study constructs CESI using the principal component analysis (PCA). Furthermore, for the preparation of index weights, varimax rotation is used to get component loadings.

The study finds that the overall CESI values lies between 2 and 4.8 for the 20 emerging countries considered in the study. This study depicts a diverse picture of environmental sustainability among emerging countries. The study also shows the trend of CESI values from 1990 to 2020. The bottom three countries whose CESI is very low compared to others are Iran, South Africa and Saudi Arabia. However, Brazil, Columbia and Chile are top three highest scorers in 2020.

The study contributes to the literature by constructing a composite index comprising of three sub-indices to measure the environmental sustainability of an economy. These sub-indices include seven indicators that are more inclusive and comprehensive. To the authors' knowledge, this is a pioneering attempt in the construction of the index for emerging countries.

Cyclical fluctuations in the level and rate of growth of economic activity have long been a feature of the British economy. The ability to forecast “turning points” (peaks and troughs) in business activity is of crucial importance for changes in companies' stockholding policy, hiring policy, capital budgeting, and many other aspects of corporate appraisal.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

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.

Given the magnitude of the literature, a researcher must be selective of research papers and publications in general. In other words, only papers that meet strict standards of academic integrity and adhere to reliable and credible sources should be referenced. The purpose of this paper is to approach this issue from the prism of scientometrics according to the following research questions: Is it necessary to judge the quality of scientific production? How do we evaluate scientific production? What are the tools to be used in evaluation?

This paper presents a comparative study of scientometric evaluation practices and tools. A systematic literature review is conducted based on articles published in the field of scientometrics between 1951 and 2022. To analyze data, the authors performed three different aspects of analysis: usage analysis based on classification and comparison between the different scientific evaluation practices, type and level analysis based on classifying different scientometric indicators according to their types and application levels and similarity analysis based on studying the correlation between different quantitative metrics to identify similarity between them.

This comparative study leads to classify different scientific evaluation practices into externalist and internalist approaches. The authors categorized the different quantitative metrics according to their types (impact, production and composite indicators), their levels of application (micro, meso and macro) and their use (internalist and externalist). Moreover, the similarity analysis has revealed a high correlation between several scientometric indicators such as author h-index, author publications, citations and journal citations.

The interest in this study lies deeply in identifying the strengths and weaknesses of research groups and guides their actions. This evaluation contributes to the advancement of scientific research and to the motivation of researchers. Moreover, this paper can be applied as a complete in-depth guide to help new researchers select appropriate measurements to evaluate scientific production. The selection of evaluation measures is made according to their types, usage and levels of application. Furthermore, our analysis shows the similarity between the different indicators which can limit the overuse of similar measures.

The aims of this paper are to identify and classify the knowledge resources that shape intellectual capital (IC) within the marketing function, to develop and validate a related scale and to demonstrate the scale's applicability in an empirical context.

A literature-based approach was adopted to identify and classify knowledge assets in the field of marketing. The new scale's content was then tested in a number of companies with different profiles. A subsequent survey of a representative sample of 346 Spanish firms sought to validate the scale and to assess those companies' marketing-related IC.

The literature search provided the basis for a marketing-related IC architecture comprising three main categories, nine subcategories and eighty items whose validity was tested and confirmed. The survey revealed that marketing-specific human capital (HC) is the most developed knowledge resource in Spanish firms, followed by marketing-specific relational capital (RC), while marketing-specific structural capital (SC) is the least developed. Significant differences were also found among companies with different profiles (B2C vs B2B, high-tech vs low-tech and manufacturing vs services).

This study makes a valuable contribution to the IC literature as one of the first to deploy the general IC framework in a specific functional area (here: marketing and sales) for more meaningful and in-depth assessment of firm-specific knowledge resources.

The objective of this paper is to prove that any attempt to implement social justice in its present undefined form is unattainable, and to successfully achieve social justice, the term should be quantified by an appropriate index; accordingly, the first objective of this paper is to make an attempt to construct an appropriate social justice index. The second objective is to quantify this index for a number of developing countries so that a government with low value of social justice index can make policy for achieving an appropriate level of social justice.

The paper constructs a new composite index for social justice for 40 mostly developing countries by selecting six subindicators, each of which represents one aspect or dimension of social justice. The values of the subindicators are then normalized and the final composite index is formed from the weighted average of the subindicators.

The study quantified the levels of social justice in developing countries through a new social justice index and compared the ranking of some developing countries using the new index. The index also helped in identifying areas of social justice that need improvement and hence can improve the developing countries' scores in the social justice index.

The paper is valuable to policy makers in developing countries especially the Arab Spring countries in their pursuit for achieving social justice. Quantifying social justice clarifies exactly where such countries stand and the dimensions of social justice that need urgent action to improve their performance and thus their index scores.

The purpose of this paper is to present and discuss the process and results achieved from the implementation of a design management indicator system in four product development companies.

To this end, instruments and techniques for implementing and collecting composite data were adopted.

The implementation made it possible to test the system metrics, and the analysis of the results enabled the identification of factors that hinder a successful implementation.

Design is being recognized as providing significant economic, social, and environmental benefits, and as it becomes a part of the management process, it can have an impact on business performance. Therefore, information sharing through indicator systems that consider factors that generate reliable and quantifiable information has become fundamental.

The purpose of this paper is to bring together an overview of the basic definitions and functions that indicators and indices have in sustainability and environmental debates. Indicators and indices are widely and increasingly used within environmental and sustainability debates; they provide “evidence” to demonstrate policy effects; they are used for communication of state and condition and to benchmark performance. However, the statistical basis of many indicators and indices is not well defined; so in one sense, they are simple arithmetic rather than inferential tools. This special issue opens up further debate around the creation and utility of indicators and indices and discusses some of the research challenges, including a sound statistical and inferential framework for indicator development.

This short paper brings together an overview of the basic definitions and functions that indicators and indices have in sustainability and environmental debates.

The paper summarises very broadly the rationale for and construction of indicators and indices. It also highlights areas where further work is required to ensure that the indicators are not simply arithmetic summaries but are generalisable.

This paper and the papers of this issue seek to enhance the debate concerning the development of reliable and robust indicators.