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As the application of crowdsourcing contests grows, leveraging the participation of superstars (i.e. solvers who have outstanding performance records in a crowdsourcing platform) becomes an emergent approach for managers to solve crowdsourced problems. Although much is known about superstars’ performance implications, it remains unclear whether and how their participation affects the size of a contest crowd for a crowdsourcing contest. Based on social contagion theory, this paper aims to examine the impact of superstars’ participation on the crowd size and studies how this impact varies across solvers with different heterogeneity in terms of skills, exposure and cultural proximity with superstars in crowdsourcing contests.

This paper uses secondary data from one crowdsourcing platform that includes 6,587 innovation contests to examine superstars’ main and contextual effects on the crowd size of a contest.

Our results reveal that superstars’ participation positively affects the crowd size of a contest in general. This finding suggests that social contagion is a fundamental mechanism underlying crowd formation in crowdsourcing contests. Our results also indicate that in contests that involve multiple superstars, superstars’ effect on crowd size becomes negative when we simultaneously consider other solvers’ heterogeneity in terms of skills, exposure and cultural background, and this negative effect will be intensified by increases in the skill gap, extent of exposure and cultural proximity between superstars and other solvers in the same contest.

Our research enhances the understanding of the influence of superstars and the mechanism underlying the emergence of contest crowds in crowdsourcing contests and contributes knowledge to better understand social contagion in a competitive setting. The results are meaningful for sourcing managers and platform supervisors to design contests and supervise crowd size in crowdsourcing contests.

The purpose of this paper is to propose a new yield function for granular materials based on microstructures.

A biaxial compression test on granular materials under different stress paths is numerically simulated by distinct element method. A microstructure parameter S that considers both the arrangement of granular particles and the inter-particle contact forces is proposed. The evolution of the microstructure parameter S under the simulated stress paths is analyzed, from which a yield function for granular materials is derived. The way of determining the two parameters involved in the yield function is proposed.

The new yield function is calibrated using the test data of one sand and two rockfill materials. The shape of the new yield surface is similar to that of the Cam-clay model.

The paper proposes a microstructure parameter S, which considers both the arrangement of granular particles and the inter-particle contact forces. From the evolution of S, a yield function for granular materials is derived. The proposed yield function has a simple structure and the parameters are easy to be determined, leading to a feasible realization of engineering application.

This paper comes to the point from the tax competition of local government in investment promotion and capital introduction. This paper aims to empirically examine the internal mechanism of enterprises obtaining land resources from local government and its resulting equity investment increase and economic consequences of overinvestment.

The data of China’s A-share listed companies from 2007 to 2014 were used to test the relationship between the increase in enterprise equity investment and the acquisition of land resources and overinvestment. The descriptive statistics, correlation analysis and least squares linear regression were used to solve the above question.

One of the reasons for the enterprise equity increase is to obtain scarce land resources. The enterprise acquisition for land resources leads to overinvestment. The equity investment increase from obtaining land resources will further stimulate enterprise group to overinvest.

The authors could not get the actual data of land that subsidiaries have obtained directly. In this research, the authors get the data using consolidated statements and subsidiary statements indirectly.

The results make contributions to the influencing factors and economic consequence of the enterprise investment structural deviation.

It provides reference to optimize the “interaction” relationship between government and enterprises.

It identified the “dual-channel” conduction mechanism between land resource acquisition and enterprise overinvestment.

The purpose of this paper is to further explore the co-citation and bibliographic-coupling relationship among the core authors in the field of Chinese information science (IS), to expose research activity and author impact, and to make induction analyses about Chinese IS research patterns and theme evolution.

The research data include 8,567 papers and 70,947 cited articles in the IS field indexed by Chinese Social Sciences Citation Index from 2000 to 2009. Author co-citation analysis, author bibliographic-coupling analysis, social network analysis, and factor analysis were combined to explore co-citation and bibliographic-coupling relationships and to identify research groups and subjects.

Scholars with greatest impact are different from the most active scholars of Chinese IS; there is no uniform impact pattern forming since authors’ impact subjects are scattered and not steady; while authors’ research activities present higher independence and concentration, there is still no steady research pattern due to no deep research existing. Furthermore, Chinese IS studies can be delineated by: foundation or extension. The research subjects of these two parts, as well as their corresponding/contributing authors, are different under different views. The general research status of core authors is concentrated, while their impact is broad.

The combined use of some related methods could enrich the development and methodology research of the discipline, and the results establish a reference point on the development of IS research.

Large terminologies usually contain a mix of terms that are either generic or domain specific, which makes the use of the terminology itself a difficult task that may limit the positive effects of these systems. The purpose of this paper is to systematically evaluate the degree of domain specificity of the AGROVOC controlled vocabulary terms as a representative of a large terminology in the agricultural domain and discuss the generic/specific boundaries across its hierarchy.

A user-oriented study with domain-experts in conjunction with quantitative and systematic analysis. First an in-depth analysis of AGROVOC was carried out to make a proper selection of terms for the experiment. Then domain-experts were asked to classify the terms according to their domain specificity. An evaluation was conducted to analyse the domain-experts’ results. Finally, the resulting data set was automatically compared with the terms in SUMO, an upper ontology and MILO, a mid-level ontology; to analyse the coincidences.

Results show the existence of a high number of generic terms. The motivation for several of the unclear cases is also depicted. The automatic evaluation showed that there is not a direct way to assess the specificity degree of a term by using SUMO and MILO ontologies, however, it provided additional validation of the results gathered from the domain-experts.

The “domain-analysis” concept has long been discussed and it could be addressed from different perspectives. A resume of these perspectives and an explanation of the approach followed in this experiment is included in the background section.

The authors propose an approach to identify the domain specificity of terms in large domain-specific terminologies and a criterion to measure the overall domain specificity of a knowledge organisation system, based on domain-experts analysis. The authors also provide a first insight about using automated measures to determine the degree to which a given term can be considered domain specific. The resulting data set from the domain-experts’ evaluation can be reused as a gold standard for further research about these automatic measures.

In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure.

Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page.

Purpose — Since a couple of years, we are confronted with the phenomenon of information overload. In particular, the web provides a rich source of a variety of information mainly in textual, i.e. unstructured form. Thus, web search faces new challenges that are how to make the user aware of the variety of content available and how to satisfy users best with such manifold content.

Methodology — This variety of content is considered as diversity, i.e. the reflection of a result set's coverage of multiple interpretations of a query. Diversification within web search aims on the one hand at adapting the ranking in a way that the top results are diverse. Increasingly important becomes on the other hand the organization and classification of content within diversification.

Findings — Various approaches to diversification are available or currently focus on research activities. They range from an adapted ranking by means of similarity measures or diversity scores to a comprehensive diversity analysis which determines topics and classifies text according to opinions etc.

Implications — Given the high diversity of web content, approaches for diversification are extremely important. Web search tries to address this problem from different perspectives. For the future, combination with image search result diversification is important. Further, benchmarks and standard data sets for evaluations need to be established to ensure comparability of results from various approaches.

Originality/value — This chapter provides an overview on diversity in web search from two directions: (a) Diversity is introduced with its notions and dimensions. (b) Methods to assess diversity within web search are presented.