No abstract

No abstract

No abstract

Socially responsible supplier selection and sustainable supply chain development involve extremely important decision‐making processes in supply chain management encompassing many quantitative and qualitative factors to consider. Given the vagueness of experts' opinions and the complex interrelationships among evaluation criteria, this study proposes and illustrates a hybrid model that combines total interpretive structural modeling (TISM) and fuzzy analytic network process (FANP) to determine the most appropriate supplier from a social responsibility perspective. We use TISM to explain the interrelationships among variables and the FANP to calculate specific scores for each candidate. We illustrate an application of the proposed hybrid model using a case study from the Chinese food industry. The result demonstrates the feasibility and effectiveness of combining TISM and the FANP for socially responsible supplier selection. This research may provide managerial insights for practitioners to evaluate suppliers and develop sustainable supply chain strategies.

PurposeThis study adopts self-determination theory and stimulus-organism-response framework to develop a model that explores the motivations of such donors by considering their self-determination needs and extrinsic and intrinsic motivations.Design/methodology/approachBased on online survey data collected from 436 crowdfunding donors in China, this study follows a structural equation modeling analysis to test hypotheses.FindingsThe results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.Originality/valueThe findings shed light on various extrinsic and intrinsic motivations advancing knowledge of individual fund motivation in donation-based crowdfunding and provide guidelines for the development of donation-based crowdfunding theory and practice.

Abstract. An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity matrix to extract a partition of the data. The performance of EBSC has been studied on real data sets. Results demonstrate the effectiveness of this algorithm in clustering mixed data tasks. Comparisons with other related clustering schemes illustrate the superior performance of this approach.

No abstract

With the popularity of crowdfunding, many small- and medium-sized ventures and startups which have insufficient funds advertise and sell their services or products in reward-based crowdfunding markets. The success of crowdfunding projects for sale purposes is therefore beneficial to the sustainable development of these growing enterprises. Based upon goal attainment theory, a research model based on a cost–benefit framework is proposed to analyze consumer purchase intention in reward-based crowdfunding markets. The research model is empirically tested with data collected from 398 participants in China. A structural equation modeling analysis reveal that perceived benefits (price concession and perceived innovation) exert a significant positive impact on perceived net goal attainment (PNGA), whereas perceived costs (transaction cost and performance risk) have a weak negative effect on PNGA. The results also indicate that satisfaction mediates between PNGA and purchase intention. Furthermore, we use an artificial neural network analysis to weigh the relative importance of the antecedents of PNGA. The results suggest that perceived innovation is more important than price concession, which is consistent with the structural equation modeling analysis. These results might deepen our understanding of how consumers trade off costs and benefits in the purchase of crowdfunding products/services.