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Despite the relevance of financial information relating to logistics service providers (LSPs), recent research has paid little attention to the financial analysis of LSPs. The aim of this paper is to examine the balance sheet structure of LSPs in order to find out if there are differences between single providers or defined LSP groups (clusters), respectively. Furthermore, the dependency of asset, capital and liquidity structures on LSPs specific characteristics is pointed out. Finally, we show which financial indicators positively influence profitability.

A total of 150 quoted LSPs from all over the world, allocated to six different clusters depending on scope of service were examined. A detailed balance sheet analysis using contingency theory, complemented by a correlation analysis, provides information about the financial structure, similarities and differences within and in-between the LSP clusters.

It was found that there are many differences regarding the financial structures of LSPs. The asset and liquidity structure of LSPs show significant differences, while the capital structure is mostly homogeneous. Profitability is achieved in various ways: Focusing on high net profit margin or asset turnover rates.

Only quoted LSPs are analyzed. With this broad research approach the authors point out the range of possibilities for financial statement analysis of LSPs and demonstrate the potential for future research.

Financial analysis yields information for making strategic decisions including organic growth, outsourcing, mergers and acquisitions or cooperation between LSPs.

This paper contributes to further performance examinations of LSPs by providing a profound financial statement analysis with potential benefits for logistics executives, analysts and researchers.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.

The co‐word analysis method is commonly used to cluster‐related keywords into the same keyword domain. In other words, traditional co‐word analysis cannot cluster the same keywords into more than one keyword domain, and disregards the multi‐domain property of keywords. The purpose of this paper is to propose an innovative keyword co‐citation approach called “Complete Keyword Pair (CKP) method”, which groups complete keyword sets of reference papers into clusters, and thus finds keywords belonging to more than one keyword domain, namely bridge‐keywords.

The approach regards complete author keywords of a paper as a complete keyword set to compute the relations among keywords. Any two complete keyword sets whose corresponding papers are co‐referenced by the same paper are recorded as a CKP. A clustering method is performed with the correlation matrix computed from the frequency counts of the CKPs, for clustering the complete keyword sets. Since keywords may be involved in more than one complete keyword set, the same keywords may end up appearing in different clusters.

Results of this study show that the CKP method can discover bridge‐keywords with average precision of 80 per cent in the Journal of the Association for Computing Machinery citation bank during 2000‐2006 when compared against the benchmark of Association for Computing Machinery Computing Classification System.

Traditional co‐word analysis focuses on co‐occurrence of keywords, and therefore, cannot cluster the same keywords into more than one keyword domain. The CKP approach considers complete author keyword sets of reference papers to discover bridge‐keywords. Therefore, the keyword recommendation system based on CKP can recommend keywords across multiple keyword domains via the bridge‐keywords.

Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component Analysis (KMPCA) to cluster multivariate time series data which havemultiple dimensions with auto- and cross-correlations. We then check whether this method works well in clustering those data by employing simulation for generalization. Two simulation studies with two different mean structures with nine combinations of auto- and cross-correlations were conducted. The results showed that KMPCA cluster two different mean structure groups over 90% success rates with an appropriate kernel function. We also found that when the mean structures are the same, auto-correlation, the number of temporal points, and the kernel function parameter have the statistically significant effects on clustering performance. The second and third order interaction effects with each of those factors also have effects on clustering success rates. Among the effects of the main factors, the kernel function parameter is the most critical factor to consider for obtaining better performance. A similar error structure may obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and a larger number of temporal points. The paper also discussed some limitations of the KMPCA model and suggested directions for future research that could improve the model.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

The purpose of this paper is to examine the relation between extreme return correlation and return volatility, in the context of US stock indexes, by detecting clusters of extreme returns using return and volatility thresholds based on an algorithm suggested in Laurini.

The daily returns and conditional volatilities estimated using GARCH (1, 1) serve as inputs to the two threshold algorithm that detects extreme return clusters. The analysis of the relation between correlation and volatility is then based on the extent of overlapping extreme return clusters across DJIA, S&P 500 and NASDAQ composite.

It is found that the correlation positive extreme returns within overlapping clusters significantly increases with volatility between DJIA and S&P 500. The authors did not find any significant change in the pair‐wise correlation between the positive extreme returns within overlapping clusters in each of these indexes with those of NASDAQ composite.

Prior researches examine extreme returns by using a return threshold and have found mixed results on the relation between correlation and volatility. This paper examines the relation between correlation and volatility between clusters of extreme returns and provides consistent results that are of vital interest to investors.

The purpose of this study is to explore patterns of importance ratings of managerial competencies in 22 countries in different regions around the globe, to guide specificity in assessing and developing managers in multiple geographies. Additionally, this study examined the utility of clustering countries based on shared culture, as defined by House et al. (2004), to determine whether such clustering aids in interpreting and acting on any differences identified.

The PROFILOR^® for Managers contains 135 behavioral items, grouped into 24 competency scales. The instrument was developed from a review of the management and psychology literatures, exhaustive analysis of a large database (Sevy et al., 1985), job analysis questionnaires and interviews of hundreds of managers representing many functional areas and most major industries.

Results suggest that clustering countries together for the purpose of providing prescriptive guidance for the development of individuals planning expatriate assignments does not clarify such guidance; in fact, it masks unique differences in competency priorities as measured on a country-by-country basis.

The participants for this study come from mid- to large-size organizations in 22 countries around the world. The organizations represented sought out management consulting services from a large, highly respected private-sector consultancy. As such, these findings are likely to be generalizable to managers from similar organizations. No attempt has been made to generalize these findings to entrepreneurial start-ups, small local organizations or organizations not inclined to seek Western-style management consulting services.

This study is one of the first to examine the effectiveness of the GLOBE clusters as they relate to managerial competencies in multicultural workforces.

The purpose of this study is to examine customer orientation and fashion merchandising competencies to learn which strategic option has a stronger relationship with retailer financial performance.

A cross‐sectional survey was used to collect self‐report data from a random sample of 275 small specialty retailers of women's clothing in Bangkok. Retailers offer similar merchandise assortments and customer services in dense, highly competitive, agglomerative environments. The survey form contained multi‐item scales measuring customer orientation, fashion merchandising competencies, and store financial performance. Bivariate correlations, multiple regression coefficients, and hierarchical linear model coefficients describe relationships of interest, controlling for retailer location.

Results show medium to large effect sizes for several fashion merchandising competencies but no substantive effects for the two customer orientation constructs. Effect sizes depend on whether financial performance is measured subjectively or as retailer return on investment or as probability of retailer survival.

Data are restricted in range and reported effect sizes are smaller than true effect sizes. Data also are influenced by common method variance, influencing reported effect sizes in an opposite direction. Effect sizes may or may not describe causal relationships because of the study's cross‐sectional design. Because of the study's setting in Bangkok, results must be extended to similar retail settings with caution. Results indicate that a clustered fashion retailer can improve financial performance by striving for a fashion leadership position, anticipating fashion trends, and offering merchandise assortments in terms of styles and usages. Results indicate that a clustered fashion retailer will have difficulty improving financial performance via customer service and CRM activities.

Few studies in fashion retailing address predictors of financial performance at the individual store level. The authors help fill this knowledge gap by examining relationships between customer service activities, CRM activities, and key merchandising competencies and retailer subjective financial performance, return on investment, and probability of survival. Retailers compete in a spatially confined area, facilitating comparison shopping and heightening rivalries between retailers.

The purpose of this paper is to explore the correlation among health outcomes, and civil and political rights (CPR) and also economic, social and cultural rights.

The study uses cross-sectional data from 161 countries. The authors use health outcomes and human rights variables in the model. In order to combine dimensions of human rights, this paper uses factor analysis and obtains proxy variables that measure economic, social and cultural rights and CPR. The two proxy variables are used as independent variables to explain variations in health in a regression model. The paper then classifies countries by cluster analysis and explores the patterns of different components of human rights and health outcomes across country clusters.

The regression model demonstrates that the economic, social and cultural rights variables explain variations in all health outcomes. The relationship between CPR and health is weaker than that of the economic, social and cultural rights. Cluster analysis further reveals that despite the country’s commitment to CPR, those that highly respect economic, social and cultural rights lead to superior health outcomes. The more respect a country has for economic, social and cultural rights, the better the health outcomes for the citizens of that country.

National policies should consider equal emphasis on all dimensions of human rights for further improvements in health.

The sole promotion of CPR such as democracy and empowerment, absence of adequate support of economic, social and cultural rights such as rights to housing, education, food and work can only contribute partially to health.

This paper is the main section on quantitative data analysis. It explains the concepts at a greater detail to help non-Math/Stat scholars to understand the basics easily. Proper data analysis is critical to any research. If data are not properly analyzed, then it may give results which either cannot be properly interpreted or wrongly interpreted. This section covers univariate, multivariate analysis and then, factor analysis, cluster analysis, conjoint analysis, and multidimensional scaling (MDS) techniques.