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1 – 10 of 670Zhiwen Pan, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai and Jun Zhang
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society…
Abstract
Purpose
The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets.
Design/methodology/approach
The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis.
Findings
According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other.
Originality/value
By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.
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This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may…
Abstract
Purpose
This paper aims to introduce a crowd-based method for theorizing. The purpose is not to achieve a scientific theory. On the contrary, the purpose is to achieve a model that may challenge current scientific theories or lead research in new phenomena.
Design/methodology/approach
This paper describes a case study of theorizing by using a crowd-based method. The first section of the paper introduces what do the authors know about crowdsourcing, crowd science and the aggregation of non-expert views. The second section details the case study. The third section analyses the aggregation. Finally, the fourth section elaborates the conclusions, limitations and future research.
Findings
This document answers to what extent the crowd-based method produces similar results to theories tested and published by experts.
Research limitations/implications
From a theoretical perspective, this study provides evidence to support the research agenda associated with crowd science. The main limitation of this study is that the crowded research models and the expert research models are compared in terms of the graph. Nevertheless, some academics may argue that theory building is about an academic heritage.
Practical implications
This paper exemplifies how to obtain an expert-level research model by aggregating the views of non-experts.
Social implications
This study is particularly important for institutions with limited access to costly databases, labs and researchers.
Originality/value
Previous research suggested that a collective of individuals may help to conduct all the stages of a research endeavour. Nevertheless, a formal method for theorizing based on the aggregation of non-expert views does not exist. This paper provides the method and evidence of its practical implications.
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Qingyuan Wu, Changchen Zhan, Fu Lee Wang, Siyang Wang and Zeping Tang
The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a…
Abstract
Purpose
The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.
Design/methodology/approach
In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.
Findings
Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.
Originality/value
The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.
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This paper aims to investigate the use of crowdsourcing in the enhancement of an ontology of taxonomic knowledge. The paper proposes a conceptual architecture for the…
Abstract
Purpose
This paper aims to investigate the use of crowdsourcing in the enhancement of an ontology of taxonomic knowledge. The paper proposes a conceptual architecture for the incorporation of crowdsourcing into the creation of ontologies.
Design/methodology/approach
The research adopted the design science research approach characterised by cycles of “build” and “evaluate” until a refined artefact was established.
Findings
Data from a case of a fruit fly platform demonstrates that online crowds can contribute to ontology enhancement if engaged in a structured manner that feeds into a defined ontology model.
Research limitations/implications
The research contributes an architecture to the crowdsourcing body knowledge. The research also makes a methodological contribution for the development of ontologies using crowdsourcing.
Practical implications
Creating ontologies is a demanding task and most ontologies are not exhaustive on the targeted domain knowledge. The proposed architecture provides a guiding structure for the engagement of online crowds in the creation and enhancement of domain ontologies. The research uses a case of taxonomic knowledge ontology.
Originality/value
Crowdsourcing for creation and enhancement of ontologies by non-experts is novel and presents opportunity to build and refine ontologies for different domains by engaging online crowds. The process of ontology creation is also prone to errors and engaging crowds presents opportunity for corrections and enhancements.
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Walaa M. El-Sayed, Hazem M. El-Bakry and Salah M. El-Sayed
Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly…
Abstract
Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.
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Kenning Arlitsch, Jonathan Wheeler, Minh Thi Ngoc Pham and Nikolaus Nova Parulian
This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional…
Abstract
Purpose
This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public.
Design/methodology/approach
The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties.
Findings
The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North.
Research limitations/implications
The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped.
Originality/value
This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0328
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Research has provided accumulative evidence that the willingness of teachers to invest in organizational citizenship behavior (OCB) is a fundamental component for achieving school…
Abstract
Purpose
Research has provided accumulative evidence that the willingness of teachers to invest in organizational citizenship behavior (OCB) is a fundamental component for achieving school effectiveness. However, most studies examined OCB of the individual teacher, while neglecting the fact that such behavior might grow in a context. Furthermore, educational scholars have focused almost solely on OCB of teachers, and have almost completely neglected to address the concept through a managerial prism. By taking a contextual perspective, the purpose of this paper is to postulate a positive link between leader OCB and team OCB, and suggest that organizational justice serves as a moderator in this relationship.
Design/methodology/approach
Data were collected through a survey from multiple sources, to avoid one-source bias. The sample included 82 schools: 82 management teams and their 82 principals, as well as 246 teachers, who were not members of management.
Findings
Results of the hierarchical regression analysis confirmed the hypotheses. The authors found a positive association between leader OCB and team OCB and revealed that this positive relationship was significant under high levels of organizational justice, but non-significant under low levels.
Practical implications
The importance of leader OCB in promoting team OCB can inspire the educational system to learn how to develop organizational mechanisms that encourage principals to perform citizenship behaviors and to take this component into consideration in screening processes and succession planning.
Originality/value
The contribution of the study is in identifying leader OCB as a key instrument that may encourage teams to invest in OCBs. To the best of the authors’ knowledge, this is the first study ever to examine the link between leader OCB and team OCB. The finding that there is a positive association between the two constructs may imply that leader OCBs contribute to the school, not only directly, by exhibiting behaviors of helping and support, but also indirectly, through the leader’s impact on his or her team’s behavior.
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Geraldine Robbins, Breda Sweeney and Miguel Vega
This study examines how an externally imposed management control system (MCS) – hospital accreditation – influences the salience of organisational tensions and consequently…
Abstract
Purpose
This study examines how an externally imposed management control system (MCS) – hospital accreditation – influences the salience of organisational tensions and consequently attitudes of management towards the system.
Design/methodology/approach
Data are collected using a case study of a large public hospital in Spain. In-depth interviews were conducted with 27 senior and middle managers across different functions. Relying on the organisational dualities classification in the literature, tensions are unpacked and analysed.
Findings
Evidence is presented of how hospital accreditation increases the salience of organisational tensions arising from exposition of the organisational dualities of learning, performing, organising and belonging. Salient tensions were evident in the ambivalent attitudes of management towards the hospital accreditation system.
Practical implications
The role of mandatory external control systems in exposing ambivalence and tensions will be of interest to organisational managers.
Originality/value
The study extends the management control literature by identifying an active role for an external MCS (accreditation) in increasing the salience of organisational tensions and triggering ambivalence. Contrary to the prior literature, the embedding of both poles of an organisational duality into the MCS is not a necessary precondition for increased tension salience. The range of attitudes towards MCSs beyond those specified in the previous literature (positive/negative/neutral) is extended to include ambivalence.
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Jakob Thomä, Michael Hayne, Nikolaus Hagedorn, Clare Murray and Rebecca Grattage
To comply with the adopted Paris Agreement, global finance flows must be measured against climate scenarios consistent with possible pathways towards limiting global warming to…
Abstract
Purpose
To comply with the adopted Paris Agreement, global finance flows must be measured against climate scenarios consistent with possible pathways towards limiting global warming to 2°C or less. For this, there must be proven and accepted accounting principles for assessing financial plans of climate relevant actors against climate models. As there are a variety of data sources describing the financial plans of relevant actors, these principles must accommodate a variety of reported information, while still yielding relevant metrics to different stakeholders. The paper aims to discuss these issues.
Design/methodology/approach
A set of accounting principles tested by governments, financial supervisory bodies and both institutional investors and mangers, covering global-listed equity and corporate bond investment is described.
Findings
The application illustrates that a common set of accounting principles can act across both asset classes and provide relevant metrics to multiple stakeholders.
Research limitations/implications
The principles require data of varying quality and are ultimately unverified. Thus, the definitive quality of the output metrics is uncertain and is yet to be characterized. The principles are yet to be applied to the credit market as the information is seldom publicly available, but it too plays an important role in the required market transition and therefore must be incorporated into these guiding principles of analysis.
Practical implications
The principles allow for standardised assessment of financial flows of equity and corporate debt with global climate scenarios.
Originality/value
It illustrates the acceptance of a common set of accounting principles that is relevant across different actors and asset classes and summarizes the principles underlying the first climate finance scenario analyses.
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Qiong Bu, Elena Simperl, Adriane Chapman and Eddy Maddalena
Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to…
Abstract
Purpose
Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.
Design/methodology/approach
The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.
Findings
The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.
Originality/value
Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.
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