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The purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.

In this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.

A new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.

The implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.

In some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.

Green economy and economic development with high quality have set higher requirements for the development of the urban logistics industry. It can grasp the recent development level of the urban logistics industry by measuring its environmental efficiency to guide its future development direction. The purpose of this study is to improve the environmental efficiency and development level of the urban logistics industry by using a reasonable evaluation method.

This paper uses information entropy to directly aggregate index weights from different models to acquire comprehensive index weights (CIWs) for calculating peer-evaluation efficiency. Then, we weight self and peer-efficiencies to obtain final efficiency. The environmental efficiencies of the urban logistics industry in Anhui Province in 2019 are obtained according to the above method.

Several findings are summarized below. The logistics industry in Anhui is in urgent need of improving environmental efficiency. The environmental efficiency of the logistics industry in North Anhui is the highest one, showing that the logistics industry in North Anhui has achieved a relative balance between economic development and environmental protection. Their final cross-efficiency values based on the CIWs are smaller than those based on the comprehensive efficiency. And the environmental efficiency of almost all urban logistics industries is lower than its economic efficiency. The findings show that the proposed method is feasible and more reasonable. More economic implications and suggestions are proposed.

This paper proposes an extended cross-efficiency evaluation method based on information entropy to measure the environmental efficiency of the urban logistics industry, effectively avoiding the overestimation of efficiency results.

The purpose of this paper is to propose a methodology for assessing thesauri and other controlled vocabularies management tools that can represent content using the Simple Knowledge Organization System (SKOS) data model, and their use in a Linked Open Data (LOD) paradigm. It effectively analyses selected set of tools in order to prove the validity of the method.

A set of 27 criteria grouped in five evaluation indicators is proposed and applied to ten vocabulary management applications which are compliant with the SKOS data model. Previous studies of controlled vocabulary management software are gathered and analyzed, to compare the evaluation parameters used and the results obtained for each tool.

The results indicate that the tool that obtains the highest score in every indicator is Poolparty. The second and third tools are, respectively, TemaTres and Intelligent Theme Manager, but scoring lower in most of the evaluation items. The use of a broad set of criteria to evaluate vocabularies management tools gives satisfactory results. The set of five indicators and 27 criteria proposed here represents a useful evaluation system in the selection of current and future tools to manage vocabularies.

The paper only assesses the ten most important/well know software tools applied for thesaurus and vocabulary management until October 2016. However, the evaluation criteria could be applied to new software that could appear in the future to create/manage SKOS vocabularies in compliance with LOD standards.

The originality of this paper relies on the proposed indicators and criteria to evaluate vocabulary management tools. Those criteria and indicators can be valuable also for future software that might appear. The indicators are also applied to the most exhaustive and qualified list of this kind of tools. The paper will help designers, information architects, metadata librarians, and other staff involved in the design of digital information systems, to choose the right tool to manage their vocabularies in a LOD/vocabulary scenario.

Current business challenges force companies to exchange critical and sensitive data. The data provider pays great attention to the usage of their data and wants to control it by policies. The purpose of this paper is to develop usage control architecture options to enable data sovereignty in business ecosystems.

The architecture options are developed following the design science research process. Based on requirements from an automotive use case, the authors develop architecture options. The different architecture options are demonstrated and evaluated based on the case study with practitioners from the automotive industry.

This paper introduces different architecture options for implementing usage control (UC). The proposed architecture options represent solutions for UC in business ecosystems. The comparison of the architecture options shows the respective advantages and disadvantages for data provider and data consumer.

In this work, the authors address only one case stemming from the German automotive sector.

Technical enforcement of data providers policies instead of relying on trust to support collaborative data exchange between companies.

This research is among the first to introduce architecture options that provide a technical concept for the implementation of data sovereignty in business ecosystems using UC. Consequently, it supports the decision process for the technical implementation of data sovereignty.

In this paper we construct a nonparametric kernel estimator to estimate the joint multivariate cumulative distribution function (CDF) of mixed discrete and continuous variables. We use a data-driven cross-validation method to choose optimal smoothing parameters which asymptotically minimize the mean integrated squared error (MISE). The asymptotic theory of the proposed estimator is derived, and the validity of the cross-validation method is proved. We provide sufficient and necessary conditions for the uniqueness of optimal smoothing parameters when the estimation of CDF degenerates to the case with only continuous variables, and provide a sufficient condition for the general mixed variables case.

The authors review the German voluntary turnover literature and examine how it reflects and extends the overall knowledge of employee turnover. First, the authors describe legal, institutional, and cultural influences specific to Germany that may affect voluntary turnover and its relationships with antecedents and outcomes. The authors then explain how research paradigms, which in German turnover research are primarily embedded in sociology and labor economics and to a lesser degree psychology and management, affect the lens by which voluntary turnover is examined. For instance, the variety of research perspectives leads to a variety of research questions, theories, data, and methodological approaches. Using these diverse perspectives, the authors explain how measurement and data quality concerns may hamper the understanding of turnover in cross-country/cross-cultural comparisons. This review further reveals many similarities with US-based turnover research, regarding the theories, methods, and results. The authors also find that turnover levels are, on average, considerably lower in Germany than in Anglo-Saxon labor markets. The authors suggest that the industry structure in Germany, coined by its strong and traditionally organized “Mittelstand” companies, may partly drive these findings. The authors close by identifying several research opportunities, available through advances in technology to improve the matching process, nonstandard work arrangements (such as in the gig economy), and a broader perspective on institutional peculiarities.

This article aims to analyze strategically optimal maintenance actions for a multi‐component system whose deterioration is observed through a monitoring system set in place to support condition‐based maintenance.

Deterioration of a multi‐component system is modeled by a continuous‐time jump diffusion model which incorporates interaction between the components of the system. A simulation‐based optimization heuristic is developed to obtain strategically optimum maintenance actions. The methodology is applied to an illustrative example.

The article finds that the framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. The optimal solution for the illustrative example recommends a provider to perform a variety of opportunistic maintenance.

In this article, a framework is developed to determine strategically optimal maintenance actions for a multi‐component system whose deterioration is observed in real‐time through embedded monitoring units set in place to support condition‐based maintenance (CBM). The framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. A strategically optimal maintenance policy can then be enhanced to develop a detailed tactical maintenance strategy. This approach is expected to benefit the management of long‐term service agreements, where a service contract is sold bundled with a product, which makes a provider responsible for maintaining the product over a specified contract period.

Besides a tactical approach for performing maintenance, in order to stay profitable in the long‐run, a decision maker needs to assess the strategic performance of maintenance strategies adopted. This framework is a first attempt to facilitate this analysis at a strategic level for a monitoring‐enabled multi‐component system.

The purpose of this paper is to examine how data reuse experience, attitudinal beliefs, social norms, and resource factors influence internet researchers to share data with other researchers outside their teams.

An online survey was conducted to examine the extent to which data reuse experience, attitudinal beliefs, social norms, and resource factors predicted internet researchers’ data sharing intentions and behaviors. The theorized model was tested using a structural equation modeling technique to analyze a total of 201 survey responses from the Association of Internet Researchers mailing list.

Results show that data reuse experience significantly influenced participants’ perception of benefit from data sharing and participants’ norm of data sharing. Belief structures regarding data sharing, including perceived career benefit and risk, and perceived effort, had significant associations with attitude toward data sharing, leading internet researchers to have greater data sharing intentions and behavior. The results also reveal that researchers’ norms for data sharing had a direct effect on data sharing intention. Furthermore, the results indicate that, while the perceived availability of data repository did not yield a positive impact on data sharing intention, it has a significant, direct, positive impact on researchers’ data sharing behaviors.

This study validated its novel theorized model based on the theory of planned behavior (TPB). The study showed a holistic picture of how different data sharing factors, including data reuse experience, attitudinal beliefs, social norms, and data repositories, influence internet researchers’ data sharing intentions and behaviors.

Data reuse experience, attitude toward and norm of data sharing, and the availability of data repository had either direct or indirect influence on internet researchers’ data sharing behaviors. Thus, professional associations, funding agencies, and academic institutions alike should promote academic cultures that value data sharing in order to create a virtuous cycle of reciprocity and encourage researchers to have positive attitudes toward/norms of data sharing; these cultures should be strengthened by the strong support of data repositories.

In line with prior scholarship concerning scientific data sharing, this study of internet researchers offers a map of scientific data sharing intentions and behaviors by examining the impacts of data reuse experience, attitudinal beliefs, social norms, and data repositories together.

This paper utilizes data mining to study the effect of Problem Based Learning (PBL), an innovative pedagogical approach that has been implemented in undergraduate education at a private university in India for teaching Statistics and Operations Research (OR) to techno-management students.

The study follows the assumptions of an in-situ experiment. It employs BBA (IT) and BCA student(s) as a subject and their end of semester GPA as a performance indicator. The pedagogical approach to this study is integrating PBL with classroom teaching. The paper uses a combination of statistics and data mining to analyze the impact of PBL and establish research conclusions.

The study concludes that the introduction of PBL positively results in an improved GPA for students with a math background. PBL is more effective for BBA (IT) male students. Female students seem to be performing equally well irrespective of the inclusion of PBL. Pattern analysis of shape parameters evidences the impact of PBL, and the results are established through the decision tree and test of proportions.

The study is limited to students from a single institute.

This Pattern analysis, as applied in this paper, can be scaled to evaluate the impact of any innovative pedagogical approach agnostic of the field of study. Facilitators can use the process defined in the paper to implement PBL for teaching Statistics and Operations research. Shape parameters of the batch in the previous semester can be utilized by facilitators to plan remedial action for the next semester by classifying students as desirable/non-desirable. Techno-management institutes can alleviate the dread and fear of mathematical subjects by integrating PBL with classroom teaching.

The study utilizes an innovative analytical approach of combining shape parameters with classification. It further provides uniqueness in arriving at a classification of batch performance as desirable/non-desirable and utilizes data mining to emphasize a delineating impact of PBL across both critical parameters of the batch and the student. The study also defines a framework for the implementation of PBL for a techno-management program in Statistics and Operations Research.