Search results

The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

From the commitment–trust theory angle, this study aims to understand why members of social network sites (SNSs) are willing to build a relationship commitment with hospitality SNSs, engage in online word-of-mouth (WOM) and show a willingness to repurchase. This paper proposes a model to express the relationship commitment and gender as a moderator in the relationship.

The interviews of a formal survey were selected by a purposive sampling method, and an online questionnaire survey was conducted in Taiwan. This study used the partial least square method to conduct structural equation modeling analysis.

The findings suggest that trust and perceived playfulness of the hospitality community have positive influences on relationship commitment, and also that the relationship commitment has a positive influence on online WOM and willingness to repurchase. This analysis provides strong support for the view that gender exerts a significant moderating role on our model relationships.

SNSs aspiring to stand out in the highly competitive internet environment must cultivate consumers’ trust and relationship commitment, and develop strategies to retain community members, as well as strengthen the safeguard personal information and the playfulness of activities. SNSs that launch relationship marketing activities should encourage community members to spread positive WOM through various activities.

This study combined the commitment–trust theory and technology acceptance model. It aimed to develop a theory-based model of relationship commitment in the hospitality SNSs’ context. Both trust and perceived playfulness are positively related to commitment; they are essential and important elements of successful hospitality SNSs. The gender difference plays a vital role in determining individuals’ behavior intention in the hospitality SNSs, as females and males have different decision-making processes.

In response to the intense competition in the platform economy, e-commerce platforms are actively introducing value-added services to maintain their competitiveness. However, how effective these value-added services are in fulfilling this purpose remains unclear. This paper explores how value-added services can enhance e-commerce platform competitiveness, measured by both user scale and reputation, considering the effect of network externalities.

A bilateral e-commerce platform with potential high-quality sellers and low-quality sellers on one side and potential buyers on the other side was chosen as research setting. Game theory models are constructed to simultaneously consider the behaviors of all actors (including sellers, buyers and the platform).

On the one hand, to increase the seller scale, basic services play a substituting role in determining the effect of value-added services. On the other hand, to increase the buyer scale and improve platform reputation, basic services play a fundamental role in determining the effect of value-added services. Furthermore, the higher the loss rate of the product value, the bigger the room for providing value-added services. With increasing loss rate of the product value, participating buyers who are attracted by value-added services are the fastest growing indicators; this indicates that the most significant effect of value-added services is its increase in the buyer scale.

Basic services determine the lower limit of platform competitiveness, while value-added services set the upper limit. The results of this paper can instruct different types of platforms to enhance their competitiveness in different ways.

(1) While previous studies on how to enhance platform competitiveness only considered scale or reputation separately, this paper applies a new perspective of platform competitiveness, namely the improvement of both the seller scale/buyer scale and platform reputation. (2) According to the characteristics of bilateral platforms, game theory models are constructed to explore how value-added services can enhance platform competitiveness considering both positive and negative network externalities. (3) The existing literature studies basic services and value-added services in a fragmented state; this paper contributes to research on value-added services by considering the mutual effect between basic and value-added services.

The purpose of this paper is to examine how the Japanese and Taiwanese national quality assurance (QA) agencies, National Institution for Academic Degrees and Quality Enhancement (NIAD-QE) and Higher Education Evaluation and Accreditation Council of Taiwan (HEEACT), transform their respective frameworks in response to social demands, and analyze and compare the respective approaches for the key concepts of autonomy, accountability, improvement and transparency.

Using a qualitative document analysis approach, this paper initially examines the higher education system, major policies and QA developments, after which the methods associated with the QA restructuring transformations are outlined in terms of motivations, expectations and challenges. Finally, the NIAD-QE and HEEACT evaluation policies and frameworks are compared to assess how each has prepared to respond to emerging challenges.

During the QA framework restructuring, both the NIAD-QE and HEEACT struggled to achieve autonomy, accountability, improvements and transparency. While the new internal Japanese QA policy is assured through the external QA, the Taiwanese internal QA, which has a self-accreditation policy, is internally embedded with university autonomy emphasized. The QA policies in both the NIAD-QE and HEEACT have moved from general compliance to overall improvement, and both emphasize that accountability should be achieved through improvements. Finally, both agencies sought transparency through the disclosure of the QA process and/or results to the public and the enhancement of public communication.

This study gives valuable insights into the QA framework in Asian higher education institutions and how QA has been transformed to respond to social needs.

The purpose of this paper is to help China's science and technology (abbr. as S&T) managers and related policy makers to allocate S&T human resources, optimize organizational systems of laboratories, design and plan some grant projects, and manage other S&T‐related work in the field of nanoscience and nanotechnology, by measuring and mapping of technology‐fields correlation, with nanotechnology as an example.

Methodologies such as co‐occurrence analysis, correlation analysis, multidimensional scaling (abbr. as MDS) analysis, dendrogram (tree‐like) analysis, etc. are employed to measure and map technology‐fields correlation.

It is found that the exact relevance degree of any two technology‐fields exists among the top 33 technology‐fields with high frequencies. There are three industrial clusters in Multidimentional Scaling View, that is, nanotechnology used in bio‐medical industry, nanotechnology used in new material industry and nanotechnology used in electronic industry. Hierarchy of any two technology‐fields can be found out in the dendrogram view of the top 33 technology‐fields.

This paper could be of great significance to China's S&T managers and related policy makers, especially in the area of nanotechnology, in selecting and managing generic technology and the findings in this paper can be applied in some other fields of science and technology management in China. Both technology‐fields correlation analysis and MDS and dendrogram view analysis could benefit China's policy makers in managing nanotechnology research and development activities.

Increasing scarcity of natural resources and the adverse effects of unsustainable practices call for more and more efficient management strategies in the energy industry. The quality of the coke plays a significant role in the quality and durability of the output steel which is produced using the energy from the coal. This paper aims to investigate the dynamic coal blending problem under overall cost and coke quality constraints in the steel industry within a periodic cycle of operations.

Considering the variability of the natural properties over a periodic cycle, this study proposes a multi-period mixed-integer non-linear programming formulation to optimize the total blending costs while taking various coke quality constraints into account. Besides, this study applies factorial design to investigate about the significant effect of coal proportions as well as improvement into the overall cost of blending.

In this case study, utilizing real data from a coal blending facility in India, through a factorial design, the authors obtain optimal desirable levels of coal proportions and their criticality levels towards the total cost of blending (TCB) or objective function. This analysis reflects the role of the coke quality constraints in the objective function value while characterizing the price of sustainability for the case study among other critical insights.

Objective function (or TCB) includes basic coal cost, movement cost and environmental costs during the coal and coke processing at a coke-oven and blast furnace of steel industry. The price of sustainability provides managerial insights on that sacrifices the industry has to make in order to become more “sustainable”.