scholarly journals Key Factors for Project Crowdfunding Success: An Empirical Study

2020 ◽  
Vol 12 (2) ◽  
pp. 599 ◽  
Author(s):  
Aladino Fernandez-Blanco ◽  
Joaquin Villanueva-Balsera ◽  
Vicente Rodriguez-Montequin ◽  
Henar Moran-Palacios
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Competitive Advantage ◽  
Economic Crisis ◽  
Influential Factors ◽  
Key Factors ◽  
Data Set ◽  
Clustering Techniques ◽  
Banking Institutions ◽  
Financing Problem

Crowdfunding is a response to the financing problem of innovative projects in an environment of severe economic crisis. Its competitive advantage lies in its independence from banking institutions and the distribution of risk among a certain number of funders. Since its inception, the number of successfully completed projects has grown to a point where it has started to suffer a downturn that puts its sustainability at risk. This study concerns this particular period of downturn, in order to identify attributes that characterize it, and to define behavioral stereotypes that may be associated with new projects. On a wide data set from sufficiently contrasted projects, and through the use data mining techniques, we extracted the most influential factors in determining the success or failure of the projects, that will subsequently be grouped together using clustering techniques. Six groups of projects have been identified, each with their own characteristics that define them, two of them clearly guide the projects to success and another one allows the modification its characteristics to move away from failure. This achieved strategy allows us to estimate which potential group would be the result of a new project.

Empirical study on the effect of using synthetic attributes on classification algorithms

2017 ◽  
Vol 10 (2) ◽  
pp. 111-129 ◽  
Author(s):  
Ali Hasan Alsaffar
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Student Performance ◽  
Software Tool ◽  
Real Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Past Performance ◽  
Data Set ◽  
Content Type

Purpose The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts. The attributes represent past performance achievements in a course, which are defined as global performance (GP) and local performance (LP). GP of a course is an aggregated performance achieved by all students who have taken this course, and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course. Design/methodology/approach The paper uses Educational Data Mining techniques to predict student performance in courses, where it identifies the relevant attributes that are the most key influencers for predicting the final grade (performance) and reports the effect of the two suggested attributes on the classification algorithms. As a research paradigm, the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool. Six classification algorithms are experimented: C4.5 and CART Decision Trees, Naive Bayes, k-neighboring, rule-based induction and support vector machines. Findings The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms, and also they have been highly ranked according to their influence to the target variable. Originality/value This paper proposes two synthetic attributes that are integrated into real data set. The key motivation is to improve the quality of the data and make classification algorithms perform better. The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

scholarly journals An Empirical Study of Open Source Software Usability

2011 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Arif Raza ◽  
Luiz Fernando Capretz ◽  
Faheem Ahmed
Keyword(s):  
Empirical Study ◽  
Open Source ◽  
Open Source Software ◽  
Research Model ◽  
Key Factors ◽  
Software Developers ◽  
Data Set ◽  
Software Usability ◽  
Novice Users ◽  
The Relationship

Recent years have seen a sharp increase in the use of open source projects by common novice users; Open Source Software (OSS) is thus no longer a reserved arena for software developers and computer gurus. Although user-centered designs are gaining popularity in OSS, usability is still not considered one of the prime objectives in many design scenarios. This paper analyzes industry users’ perception of usability factors, including understandability, learnability, operability, and attractiveness on OSS usability. The research model of this empirical study establishes the relationship between the key usability factors and OSS usability from industrial perspective. In order to conduct the study, a data set of 105 industry users is included. The results of the empirical investigation indicate the significance of the key factors for OSS usability.

scholarly journals AKNOBAS: A knowledge-based segmentation recommender system based on intelligent data mining techniques

2012 ◽  
Vol 9 (2) ◽  
pp. 713-740 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Javier Torres-Niño ◽  
Enrique Jimenez-Domingo ◽  
Miguel Gomez-Berbis ◽  
Giner Alor-Hernandez
Keyword(s):  
Data Mining ◽  
Competitive Advantage ◽  
Recommender Systems ◽  
Business Model ◽  
Recommender System ◽  
Added Value ◽  
Proof Of Concept ◽  
Clustering Techniques ◽  
Knowledge Based ◽  
Substantial Progress

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

An Empirical Study on Initializing Centroid in K-Means Clustering for Feature Selection

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Amit Saxena ◽  
John Wang ◽  
Wutiphol Sintunavarat
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Empirical Study ◽  
Data Sets ◽  
Data Set ◽  
Unsupervised Feature Selection ◽  
The Impact

One of the main problems in K-means clustering is setting of initial centroids which can cause misclustering of patterns which affects clustering accuracy. Recently, a density and distance-based technique for determining initial centroids has claimed a faster convergence of clusters. Motivated from this key idea, the authors study the impact of initial centroids on clustering accuracy for unsupervised feature selection. Three metrics are used to rank the features of a data set. The centroids of the clusters in the data sets, to be applied in K-means clustering, are initialized randomly as well as by density and distance-based approaches. Extensive experiments are performed on 15 datasets. The main significance of the paper is that the K-means clustering yields higher accuracies in majority of these datasets using proposed density and distance-based approach. As an impact of the paper, with fewer features, a good clustering accuracy can be achieved which can be useful in data mining of data sets with thousands of features.

An Empirical Study of Open Source Software Usability

2013 ◽  
pp. 1-17
Author(s):  
Arif Raza ◽  
Luiz Fernando Capretz ◽  
Faheem Ahmed
Keyword(s):  
Empirical Study ◽  
Open Source ◽  
Open Source Software ◽  
Sharp Increase ◽  
Research Model ◽  
Key Factors ◽  
Data Set ◽  
Software Usability ◽  
Novice Users ◽  
The Relationship

Recent years have seen a sharp increase in the use of open source projects by common novice users; Open Source Software (OSS) is thus no longer a reserved arena for software developers and computer gurus. Although user-centered designs are gaining popularity in OSS, usability is still not considered one of the prime objectives in many design scenarios. This paper analyzes industry users’ perception of usability factors, including understandability, learnability, operability, and attractiveness on OSS usability. The research model of this empirical study establishes the relationship between the key usability factors and OSS usability from industrial perspective. In order to conduct the study, a data set of 105 industry users is included. The results of the empirical investigation indicate the significance of the key factors for OSS usability.

scholarly journals The Determinants to Promote College Students’ Use of Car-Sharing: An Empirical Study at Dalian Maritime University, China

2021 ◽  
Vol 13 (12) ◽  
pp. 6627
Author(s):  
Shichao Sun ◽  
Yuanqian Liu ◽  
Yukun Yao ◽  
Zhengyu Duan ◽  
Xiaokun Wang
Keyword(s):  
College Students ◽  
Empirical Study ◽  
Service Providers ◽  
Positive Impact ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Policy Implications ◽  
Key Factors ◽  
Car Sharing ◽  
Willingness To Use

Sustaining the development of car-sharing is considered an efficient way to counter environmental issues worldwide. Against this background, college students are recognized as a promising customer group of car-sharing service providers in China. However, the determinants that promote students’ willingness to use car-sharing services are rarely studied, and the uniqueness of college students in China in the context of car-sharing is justified. Therefore, this paper examines the key factors that affect Chinese college students’ adoption of car-sharing. An empirical study using samples from Dalian Maritime University was conducted, and survey data were collected via the Internet. Specifically, respondents’ socio-demographics were obtained, and their latent attitudes on car-sharing services were measured in terms of willingness to use car-sharing services, perceived usefulness, perceived ease of use and safety concerns. In addition, nine hypothetical travel scenarios were defined, and regarding each travel scenario, the respondents were asked to state whether they were willing or not to use car-sharing services. On this basis, a hybrid logit model was established to investigate the key factors that influenced the willingness to use car-sharing services. Aside from the common findings in line with previous studies, the results indicate that with the increase in the number of travel fellows, willingness to use car-sharing services went up. Furthermore, college students’ willingness to use car-sharing services was significantly affected by money costs rather than time costs. Additionally, college students in China are more likely to use car-sharing services during workday off-peak hours and weekends. Separately, among the respondents’ latent attitudes, only the perceived usefulness of car-sharing services was found to have a significant and positive impact on students’ willingness to use them. Relevant policy implications with regards to theoretical findings are also offered in this paper to car-sharing service providers in China.

Sign in / Sign up

Export Citation Format

Share Document