PurposeThe purpose of this study is to clarify theory and identify factors that could explain the level of fintech continuance intentions with an expectation confirmation model that integrates self-efficacy theory.Design/methodology/approachWith data collected from 753 fintech users, this study applies partial least square structural equation modeling to compare and select the research model with the most predictive power.FindingsThe results show that financial self-efficacy, technological self-efficacy and confirmation positively affect perceived usefulness. Among these factors, financial self-efficacy and technological self-efficacy have both direct and indirect effects through confirmation on perceived usefulness. Perceived usefulness and confirmation are positively related to satisfaction. Finally, perceived usefulness and satisfaction positively influence fintech continuance intentions.Originality/valueTo the best of our knowledge, this is one of the earliest studies that investigates the effect of domain-specific self-efficacy on fintech continuance intentions, which enriches the existing research on fintech and deepens our understanding of users' fintech continuance intentions. We distinguish between financial self-efficacy and technological self-efficacy and specify the relationship between self-efficacy and continuance intentions. Moreover, this study highlights the importance of assessing a model's predictive power using the PLSpredict technique and provides a reference for model selection.

Purpose The purpose of this paper is to explore the impact of supplier development (SD) on supplier’s performance by sharing implicit knowledge in mentorship under the influence of supplier’s organizational culture (OC). Design/methodology/approach A survey questionnaire was employed to collect data from 226 employees of participating suppliers after conducting mentorship training at the suppliers’ site. The data were analyzed by the partial least squares structural equation modeling with software SmartPLS Ver. 3.0. Findings The empirical analysis indicates that SD by mentorship partially mediates the total effects of OC – power distance and uncertainty avoidance – on performance. It completely mediates the collaborative culture on performance. Originality/value This study may confirm that the SD program by mentorship is a viable strategy to enhance the performance of supply chain partners and the selection of suppliers.

Human CD38 is a novel multi-functional protein that acts not only as an antigen for B-lymphocyte activation, but also an enzyme catalyzing the synthesis of a Ca2+ messenger molecule, cyclic ADP-ribose, from NAD+. It is well established that this novel Ca2+ signaling enzyme is responsible for regulating a wide range of physiological functions. Based on the crystal structure of the CD38/NAD+ complex, we synthesized a series of simplified N-substituted nicotinamide derivatives (Compound 1–14). A number of these compounds exhibited moderate inhibition of the NAD+ utilizing activity of CD38, with Compound 4 showing the higher potency. The crystal structure of CD38/ Compound 4 complex and computer simulation of Compound 7 docking to CD38 show a significant role of the nicotinamide moiety and the distal aromatic group of the compounds for substrate recognition by the active site of CD38. Biologically, we showed that both Compounds 4 and 7 effectively relaxed the agonist-induced contraction of muscle preparations form rats and guinea pigs. This study is a rational design of inhibitors for CD38 that exhibit important physiological effects, and can serve as a model for future drug development.

Remanufacturing has received increasing attention from researchers over the last decade.While many associated operational issues have been extensively studied, research into the prediction customer demand for, and the market development of, remanufactured products is still in its infancy.The majority of the existing research into remanufactured product demand is largely based on conventional statistical models that fail to capture the non-linear behaviour of customer demand and market factors in real-world business environments, in particular e-marketplaces. Therefore, this paper aims to develop a comprehensible data-mining prediction approach, in order to achieve two objectives: (1) to provide a highly accurate and robust demand prediction model of remanufactured products; and (2) to shed light on the non-linear effect of online market factors as predictors of customer demand. Based on the real-world Amazon dataset, the results suggest that predicting remanufactured product demand is a complex, non-linear problem, and that, by using advanced machine-learning techniques, our proposed approach can predict the product demand with high accuracy. In terms of practical implications, the importance of market factors is ranked according to their predictive powers of demand, while their effects on demand are analysed through their partial dependence plots. Several insights for management are revealed by a thorough comparison of the sales impact of these market factors on remanufactured and new products.

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