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The purpose of this study is to apply hybrid cluster analysis in classifying PT Pelindo I customers based on the level of customer satisfaction with passenger services of PT Pelindo I.

Hybrid cluster analysis is a combination of hierarchical and non-hierarchical cluster analysis. This hybrid cluster analysis appears to optimize the advantages of hierarchical and non-hierarchical methods simultaneously to obtain optimal grouping. Hybrid cluster analysis itself has high flexibility because it can combine all hierarchical and non-hierarchical methods without any limits in the order of analysis used.

The results showed that 72% of PT Pelindo I customers felt PT Pelindo I service was special, while the remaining 28% felt PT Pelindo I service was good.

In total, 117 customers of PT Pelindo I were involved in a study using the non-probability sampling method.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The term competitive has always been used as a comparison to provide a distinction between two or more things. Southeast Asia handles billions of tonnes of global seaborne trade annually. Thus, there is a necessity to look in detail at the performance indicators of port competitiveness on the basis of port performance.

This study has categorized 18 Malaysian bulk terminals into two different classes based on various performance indicators. The distinctions used a hierarchical cluster analysis by arranging the performance indicators. The technique is among the most popular techniques used to form homogeneous groups of entities or objects.

In this study, it was found that two classes were classified as being competitive from the homogeneous groups created. Based on the performance metrics chosen, Group 1 had the lowest score and Group 2 had the highest score. It was found that the Westport and Northport of Klang Port had the best performance of all.

A major challenge for the study is the lack of variables relevant to other port competitiveness requirements, and a detailed research study is needed to gather information on the satisfaction of terminal customers, the paperwork involved, the accuracy and consistency of tariffs paid, the level of safety at sea and on land and environmental protection around the facility site.

The study on ports has been given less attention among researchers in this particular area. Therefore, this paper focuses on the port terminals in Malaysia and compares port performance metrics between ports to determine their competitiveness.

The purpose of the paper is to present an empirical study that examines the impact of digitalization on informative, strategic and operational marketing activities in manufacturing companies from the entrepreneurial perspective.

A research project was carried out in 205 Italian manufacturing companies by using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis.

The analysis shows the existence of seven clusters of manufacturing companies that differ by the impact of digitalization on marketing activities from the entrepreneurial perspective. Two clusters have a high positive impact of digitalization, primarily on informative and strategic marketing activities. Two clusters are characterized by a low positive impact of digitalization and three clusters perform an intermediate level of digitalization. Furthermore, these groups of clusters differ in terms of the influence of digitalization on customer value.

The small size of the sample and the geographic origin of the companies imply limited generalizability; further research on the topic is thus recommended.

The study suggests that companies should digitalize many key marketing activities to increase marketing effectiveness and customer value. To achieve high levels of digitalization and thus increase their competitiveness, manufacturing companies should consider the importance of relevant technologies and skills.

By focussing on the impact of digitalization on informative, strategic and operational marketing, which has not yet been empirically investigated, the present study reveals many new elements concerning the marketing process in the digital era from the entrepreneur's point of view.

The second Frankfurt Career Study was conducted in 2017 in East and West Germany to analyze the impact of motherhood on female professional advancement in the specific national context of Germany. In addition, this study aims to present a unique perspective of the similarities and dissimilarities between the Western and Eastern parts of the country.

The research is presented as a three-stage statistical approach based on quantitative data generated from a survey conducted among 2,130 working mothers. In the first step, the authors performed a multiple correspondence analysis to explore the relationships between important categorical variables. Using the object scores obtained in the first step, we then ran a hierarchical cluster analysis, followed by the third and last step: using the k-means clustering method to partition the survey respondents into groups.

The authors found that working mothers in Germany are distributed according to four clusters mainly described by demographics and orientation toward work. East Germany has been found as a more egalitarian context than West Germany with respect to family system arrangements. However, the upper bound of the sample in West Germany presented an atypical female breadwinner model in high-performance households.

The authors want to contribute to previous investigations on the topic by providing a more comprehensive view of the phenomenon, especially comparing the two different family systems and social norms from the Eastern and Western parts of the country. The authors ask whether and how career perspectives and female labor supply are influenced by drivers such as work–family conflict determinants, working mothers demographics, partner support and employer support.

This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months.

A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression.

Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results.

Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad.

The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.

The aim of this study was to empirically examine how much traditional attributes and green attributes characterize products within design-oriented firms. Further, we explored how these attributes relate to the perceived level of innovation of the firms.

An exploratory research was carried out in 86 Italian manufacturing companies that are members of the Industrial Design Association. Using the questionnaire method, the entrepreneurs’ perceptions have been analyzed. Data have been treated with hierarchical cluster analysis.

The analysis shows that environmental sustainability is the least important attribute of a design product and four clusters of highly design-oriented firms differ by design-product attributes. Further, the least green firms are also the least innovative in terms of incremental and general innovation.

The small size of the sample and the provenance of firms from a single country imply limited generalizability, and further research on the topic is recommended.

Design-driven innovation based on traditional design attributes provides many competitive advantages to firms. However, given the growing concern about environmental challenges, investing in green attributes in design products allows for remaining competitive and more effective in innovation.

This study, for the first time, reveals the heterogeneity among design-oriented firms, particularly regarding the presence and assortment of traditional design attributes, as well as the incorporation of environmentally friendly attributes in their products. Moreover, the study uncovers the relationship between varying levels of green attributes in the offerings and the perception of the firm’s innovativeness.

The proposed Regional Comprehensive Economic Partnership (RCEP) will become a large trade agreement in Asia, which has brought together the ten members of Association of Southeast Asian Nations (ASEAN) and five of the neighbors’ countries. Under the trend of globalization, the progress of the transportation industry and regional integration will increase the volume of trade, therefore maritime performance is intrinsically linked to trade. In fact, few studies have examined regional integration in the context of seaborne. This paper aims to use the cluster analysis and Poisson quasi-maximum likelihood (PQML) gravity model to investigate the trading bloc phenomenon and relation between trade and marine transportation.

In this paper, hierarchical clustering analysis and tree diagrams are used to identify functional areas characterized by bilateral trade intensity and bilateral liner shipping connectivity indices. Regional reorganizations that have occurred within Asian countries were studied. This study illustrates that these trading blocs have a positive impact on trade when maritime transport, production and trading networks have developed between regions. A gravity model was constructed using worldwide trade data for 2007, 2010 and 2015. The study considered free trade agreement (FTA)/common market (CM) of EU, RCEP and North American Free Trade Agreement (NAFTA) as regional dummies and designed a real trade bloc induction variable. In addition, the study did not use the commonly adopted ordinary least squares (OLS) estimation but used the PQML method to estimate the gravity equation to overcome the problem of a large number of zero trade observations. Preliminary results show that regional integration cannot guarantee the establishment of intraregional trade but depends on the stage of economic development and regional industrial characteristics.

The major findings are summarized as follows. Both liner shipping connectivity and logistics performance have significant advantages with positive coefficients in each regression results. The creation of intraregional trade is not guaranteed, depending on the characteristics of the trade and the stage of economic development of the region. For RCEP, the effect created by intra-regional trade is better than the EU. Instead, the “nominal” intra-RCEP trade was significantly below the “real” trading blocs. For RCEP, the effect created by intra-regional trade is better than that of the EU. Instead, “nominal” intra-RCEP trade is much lower than “real” trading blocs. The real trading bloc between East Asia and Taiwan clearly exists, and the bloc phenomenon is becoming more and more significant. This result shows that Taiwan’s trade flow with East Asia is higher than the normal level relationship implied by its corresponding economic and geographical conditions.

This paper focuses on new empirical work done for this study is on the potential impact on trade. Earlier studies that have discussed and/or provided estimates of the benefits to the RCEP plan from improved transport and supply chain connectivity are cited. Marine transportation performance inherently links to economies of commerce. Few studies have examined regional integration in the context of maritime transportation, which reflects the lack of a mix of trade economists and maritime logistics research in the existing literature. This paper attempts to investigate the trading bloc phenomenon formed by regional integration (such as RCEP) and the relation between trade and marine transportation. With the official entry into force of the RCEP in 2020, it will promote increased trade and demand for logistics and maritime transport services in East Asia.

The purpose of this paper is to explore mechanisms of supply chain inclusion in Base of the Pyramid (BOP) settings. It distinguishes micro-, small- and medium-sized enterprises (MSME)-led local supply chains on the one hand and multinational enterprises (MNEs)-led global supply chains on the other hand. This paper aims to answer the following research question: Which mechanisms of supply chain inclusion are employed empirically by MSMEs and how can these mechanisms influence social impact creation in MNE-led global supply chains?

A large-scale empirical study of MSMEs operating in BOP markets is performed and a cluster analysis conducted to systematically categorize supply chain inclusion. The cluster analysis and current literature yield theory-based implications for MNE-led global supply chains.

The cluster analysis reveals three meaningful clusters of supply chain inclusion in BOP markets and highlights two main aspects. They include direct vs indirect mechanisms of inclusion and diversity in supplier relationships with local organizations aimed at either “sourcing” local capabilities needed for inclusion or “outsourcing” the inclusion. Based on these aspects, two scenarios are proposed and evaluated for local-global supply chain symbiosis.

This study aims to contribute to the existing literature with a more fine-grained understanding of the inclusion of BOP actors in local supply chains and by proposing alternative trajectories for global supply chain inclusion.

The findings outline several important decisions that managers need to make to include BOP actors in supply chain activities.

This paper contributes a novel, combined perspective of local supply chains (MSMEs) and global supply chains (MNEs).