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The present study is aimed to develop a firm export performance measurement model. Increased levels of globalization are forcing firms, especially SMEs (i.e. small and medium-sized enterprises) and entrepreneurial firms, to enter new markets and increase their exports. Consequently, export performance measurement has become a vital tool for achieving competitive advantages, and this type of measurement is considered an important topic of research in the field of international marketing. However, the literature lacks a comprehensive framework for measuring export performance, even though many criteria for gauging this performance's parameters have been discovered and applied.

A literature review was conducted, and the most important criteria for export performance measurement was identified using weights obtained by stepwise weight assessment ratio analysis method. Then, the export performance of five firms was evaluated using the additive ratio assessment method with interval-valued triangular fuzzy numbers.

The 15 most significant criteria were identified and categorized into three dimensions: economic, strategic, and other. The strategic dimension was identified as the most important, while the criteria “strategic goals achievement” and “return on investment” were found to be the most relevant.

A comprehensive framework including a definitive set of the most prominent, useful evaluation criteria was established which enables both scholars and practitioners to assess export performance.

The current study attempts to fill the gaps in the literature by presenting a comprehensive framework for export performance measurement.

The purpose of this paper is to quantify and evaluate transport sustainability from an integrated perspective. It is argued that transportation systems being interdisciplinary and complex, the integrated approach towards quantification and evaluation would present its sustainability goals’ attainment in a much better way.

The proposed integrated methodology is divided into five major steps that are sub-divided. It adopts a three-tier classification system i.e. pillar, sub-division and indicator. In total, 116 sustainable transportation indicators (STIs) were classified into 10 subdivisions corresponding to three sustainability pillars. The weighting and aggregation of STIs were done using a combination of a statistical tool called principal component analysis/factor analysis, a machine learning tool called fuzzy logic and equal weighting method. The final output that the proposed integrated methodology generates is a new composite index called the integrated sustainable transportation index (IIST).

To check the credibility of the proposed integrated methodology, it was applied to 26 states and the National Capital Territory (NCT) of Delhi, India. Based on the computed IIST, 27 study zones were ranked and mapped. The results showed that while Tamil Nadu (0.643), Telangana (0.574) and Maharashtra (0.564) were the best-performing states, Goa (0.347), Chhattisgarh (0.436) and Meghalaya (0.439) were the worst-performing states, as far as the relative transportation sustainability is concerned.

The proposed integrated methodology could be useful for transportation evaluators, planners and policymakers who can recognize, track and improve the strengths and overcome the weaknesses within a transportation system.

The novelty of this research work is that it uses a multi-stage multi-tool approach to quantify and evaluate transport sustainability that minimizes the demerits of single-stage individual tools while maximizing their merits.

Given the high importance of information systems for procurement, surprisingly there have been little efforts to analyze the process and the relevant reasons for the procurement decision of such systems from a stakeholder perspective. The purpose of this paper is to explore these aspects in the context of low-value (C-)parts.

Research is based on a case study in a pre-fabricator company in Germany and analyzes the process to procure a system for sourcing low-value parts.

As a finding, the procurement process and decision attributes have been integrated into a framework which supports corporate decision-making considering the procurement reasons of all involved stakeholders (internal departments, external customers and suppliers).

Research is based on case study analysis. Findings are specific to case companies and the environment in which they operate. The framework should be tested further in different contexts.

The developed decision frame supports the evaluation of different sourcing information systems, including clearly measurable criteria but also qualitative or company-strategic decision attributes.

The combination of financial and other perspectives (operations, information technology (IT)-administration, system users, etc.), is used to evolve a robust ex-ante instrument for supporting buying decisions for procurement information systems. The case description also illustrates the findings and develops new insights about stakeholders and buying groups decision-making for information systems.

This paper aims to develop an algorithm to pretest an industrial portfolio on a new scale. Portfolios include complex and uncertain projects at the front-end phase. The study, therefore, proposes a procedure that helps decision-makers to handle various complex projects and defines a common scale applicable to various kinds of industrial projects.

Decision-makers can employ the preference algorithm to reach a common understanding. To this end, the current paper posits the organization of criteria in various project sets. A sexagesimal scale is developed based on project complexity and its ability to achieve broad impact, both these factors being gauged on a five-point scale of user-friendly numberings.

The proposed algorithm shows the equivalence of industrial projects in different fields. Also, the algorithm articulates the status in terms of uncertainty, complexity, risk, and value of projects. The connections between decision-makers and criteria operate on the basis of the foreseen complexity, risk, and value. It can be said that this study exemplifies and visualizes the portfolio and criteria relationship.

The procedure covers contingency exercises at the front-end phase of a portfolio and supports decisions. However, updated information can change support positions.

The paper presents original scoring guidance for portfolio complexity on a new scale. The scaling and scoring are adjustable and calibrated using the proposed sexagesimal system. It presents an original classification of project risk and value. The main contribution is the presented algorithm which can be used to pretest industrial portfolios composed of projects that vary in both size and context.

How resources for social care are allocated to individual service users has long been a concern. There are debates regarding the priority given to certain needs in Resource Allocation Systems (RASs). The purpose of this paper is to compare the views of adults with a learning disability and Directors of Adult Social Care regarding their priorities for resource allocation with priorities arising from observed resource allocation decisions.

In a consultation workshop, 12 adults with learning disabilities were asked to rank the perceived importance of eight needs-related outcomes. Directors of Adult Social Care completed an online questionnaire concerning the distribution of resources across the same eight outcomes. Actual resource allocation data from 11 local authorities were also modelled against these outcomes. A variable importance metric (the percentage contribution of each outcome to predicting costs) was used to rank the importance of these outcomes in terms of determining actual resource allocation. Findings from these data collections were compared.

There were discrepancies between the views of adults with a learning disability, the perspectives of Directors and actual resource allocation data. Whereas adults with a learning disability perceived psychological well-being as most important, Directors and actual resource allocation data stressed the importance of activities of daily living and carer burden.

This analysis will prove useful in understanding the concerns of adults with a learning disability and whether these are adequately addressed by current RASs.

The purpose of this paper is to quantify complexity in translational research. The impact of major operational steps and technical requirements is calculated with respect to their ability to accelerate moving new discoveries into clinical practice.

A three-phase integrated quality function deployment (QFD) and analytic hierarchy process (AHP) method was used to quantify complexity in translational research. A case study in obesity was used to usability.

Generally, the evidence generated was valuable for understanding various components in translational research. Particularly, the authors found that collaboration networks, multidisciplinary team capacity and community engagement are crucial for translating new discoveries into practice.

As the method is mainly based on subjective opinion, some argue that the results may be biased. However, a consistency ratio is calculated and used as a guide to subjectivity. Alternatively, a larger sample may be incorporated to reduce bias.

The integrated QFD-AHP framework provides evidence that could be helpful to generate agreement, develop guidelines, allocate resources wisely, identify benchmarks and enhance collaboration among similar projects.

Current conceptual models in translational research provide little or no clue to assess complexity. The proposed method aimed to fill this gap. Additionally, the literature review includes various features that have not been explored in translational research.

The purpose of this study is to identify the risks that exist in halal meat supply chain activities and to carry out a risk assessment using the fuzzy best-worst method (FBWM) along with mitigating risks using the risk mitigation number (RMN).

The method used is to collect several literature reviews related to the halal meat supply chain, which has information relevant to the risks of the meat industry in Indonesia. Then, a focus group discussion was held with several experts who play a role in the meat industry in Indonesia, and 33 identified risks were identified in halal meat supply chain activities. The proposed methodology uses FBWM and RMN in conducting risk assessment and mitigation in the meat industry in Indonesia.

The analysis reveals that priority risk is obtained by using the global weight value on the FBWM, and then risk mitigation is carried out with RMN. Priority mitigation strategies can mitigate some of the risks to the meat industry in Indonesia. The proposed mitigation strategy is designed to be more effective and efficient in preventing risks that can interfere with product halalness in halal meat supply chain activities in the Indonesian meat industry.

The implications of this study highlight the need for collaboration among stakeholders, improved risk assessment methodologies and the expansion of research into other halal supply chains. By addressing these implications, the halal industry can enhance its integrity, consumer confidence and overall contribution to the global market.

This research provides an integrated approach to identifying, analyzing, assessing and mitigating risks to the meat industry in Indonesia.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

Textile manufacturers worldwide are reformulating their networks, often outsourcing them to maintain a competitive advantage and increase market share. From this perspective, the purpose of this study is support the partnership selection process to develop a sustainable chain that effectively meets customer needs. Brazil has the largest textile and apparel chain in the West and is distinguished by its completeness, from fiber production, spinning, weaving, knitting, finishing and sewing to fashion shows. However, a firm’s relationship, especially in the production stage, is based on informal contracts, which result in a negative operational impact.

A methodological framework was developed based on a stable matching process to determine the optimal supplier network structure. This study presents a model application for the denim apparel chain in northeast Brazil.

In these environments, providing choices and recommending suppliers can be beneficial for effectively attending to demand requests, reducing production costs and improving quality through collaboration with sense relationships in a network. Thus, this study presents a better match from the negotiators’ perspective.

The findings of this research are of primary interest for guiding collaborative network composition in the textile and apparel chain. In particular, apparel domain companies can improve their effectiveness in decision-making by measuring the characteristics and potential of all companies involved in networks.

Churn management is a fundamental process in firms to keep their customers. Therefore, predicting the customer’s churn is essential to facilitate such processes. The literature has introduced data mining approaches for this purpose. On the other hand, results indicate that performance of classification models increases by combining two or more techniques. The purpose of this paper is to propose a combined model based on clustering and ensemble classifiers.

Based on churn data set in Cell2Cell, single baseline classifiers, ensemble classifiers are used for comparisons. Specifically, self-organizing map (SOM) clustering technique, and four other classifier techniques including decision tree, artificial neural networks, support vector machine, and K-nearest neighbors were used. Moreover, for reduced dimensions of the features, principal component analysis (PCA) method was employed.

As results 14 models are compared with each other regarding accuracy, sensitivity, specification, F-measure, and AUC. The results showed that combination of SOM, PCA, and heterogeneous boosting achieved the best performance comparing with other classification models.

This study examined the performance of classifier ensembles in predicting customers churn. In particular, heterogeneous classifier ensembles such as bagging and boosting are compared.