Analysis of Trust in the E-Commerce Adoption

Understanding user acceptance of the Internet, especially the usage intention of virtual communities, is important in explaining the fact that virtual communities have been growing at an exponential rate in recent years. This paper studies the trust of virtual communities to better understand and manage the activities of E-commerce. A theoretical model proposed in this paper is to clarify the factors as they are related to the Technology Acceptance Model. In particular the relationship between trust and Intentions is hypothesized. Using the Technology Acceptance Model, this research showed that the importance of trust in virtual communities. According to the research, different ways of stimulating the members are necessary in order to facilitate participation in activities of virtual communities. The effect of trust in members on intention to use is stronger than that of trust in service providers. The intention to purchase is more sensitive to trust in service providers than trust in members

Shear behavior of a shear thickening fluid-impregnated aramid fabrics at high shear rate

Shear-thickening fluid-impregnated aramid (STF-im-AR) fabrics have been manufactured for advanced soft body armor applications for which they provide improved ballistic and stab resistances. It is not yet clear whether or not such improvements can be attributed solely to the STF. In this study, the rate-dependent behavior of an STF-im-AR fabric was investigated at the fabric level, using uniaxial tensile, bias-extension, and picture-frame tests. Rate-dependent behavior of the STF-im-AR fabric was observed during uniaxial tensile testing; however, the effect of the STF treatment was slight and consistent with only the inherent effect of the polymeric nature of its constituent fibers. The shear rigidity of the STF-im-AR fabric increased, due to the presence of the STF and the sensitivity of the fabric's shear stiffness to changes in the shear strain rate also increased slightly. This rate-sensitive shear stiffness of STF-im-AR fabrics may contribute to improved ballistic and stab resistances

Exact Algorithm for the Capacitated Team Orienteering Problem with Time Windows

The capacitated team orienteering problem with time windows (CTOPTW) is a problem to determine players' paths that have the maximum rewards while satisfying the constraints. In this paper, we present the exact solution approach for the CTOPTW which has not been done in previous literature. We show that the branch-and-price (B&P) scheme which was originally developed for the team orienteering problem can be applied to the CTOPTW. To solve pricing problems, we used implicit enumeration acceleration techniques, heuristic algorithms, and ng-route relaxations

DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load

Mitotic cell division increases tumour mutation burden and copy number load, predictive markers of the clinical benefit of immunotherapy. Cell division correlates also with genomic demethylation involving methylation loss in late-replicating partial methylation domains. Here we find that immunomodulatory pathway genes are concentrated in these domains and transcriptionally repressed in demethylated tumours with CpG island promoter hypermethylation. Global methylation loss correlated with immune evasion signatures independently of mutation burden and aneuploidy. Methylome data of our cohort (n = 60) and a published cohort (n = 81) in lung cancer and a melanoma cohort (n = 40) consistently demonstrated that genomic methylation alterations counteract the contribution of high mutation burden and increase immunotherapeutic resistance. Higher predictive power was observed for methylation loss than mutation burden. We also found that genomic hypomethylation correlates with the immune escape signatures of aneuploid tumours. Hence, DNA methylation alterations implicate epigenetic modulation in precision immunotherapy

Comparison of risk and protective factors associated with smartphone addiction and Internet addiction

Background and Aims Smartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. This study assessed the risk and protective factors associated with smartphone addiction in college students and compared these factors to those linked to Internet addiction. Methods College students (N = 448) in South Korea completed the Smartphone Addiction Scale, the Young’s Internet Addiction Test, the Alcohol Use Disorders Identification Test, the Beck Depression Inventory I, the State–Trait Anxiety Inventory (Trait Version), the Character Strengths Test, and the Connor–Davidson Resilience Scale. The data were analyzed using multiple linear regression analyses. Results The risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance. In contrast, the risk factors for Internet addiction were male gender, smartphone use, anxiety, and wisdom/knowledge, while the protective factor was courage. Discussion These differences may result from unique features of smartphones, such as high availability and primary use as a tool for interpersonal relationships. Conclusions Our findings will aid clinicians in distinguishing between predictive factors for smartphone and Internet addiction and can consequently be utilized in the prevention and treatment of smartphone addiction

Mechanical Properties of Porcine and Fish Skin-Based Collagen and Conjugated Collagen Fibers

Collagen is a protein that is a major component of animal skins and tendons. It is used in various medical, cosmetic, and food products through extraction and purification. The fibrous products of purified collagen fibers extracted from raw mammal materials have relatively excellent mechanical properties and are used for high-end medical products. In this study, we examined collagen materials produced from porcine and fish skins, which are major sources of collagen raw materials. We examined a method for spinning collagen fibers from fish skin-based collagen and analyzed the physical properties of those collagen fibers. In addition, we examined the characteristics and advantages of conjugated fibers according to their porcine- and/or fish skin-based compositions. The spinnability and mechanical properties of these conjugated fibers were analyzed according to their compositions. The mechanical properties of collagen structure are determined by hydroxyproline content and can be manipulated by the composition of collagen in the conjugated fibers

Factors Affecting Intention to Adopt Cloud-Based ERP from a Comprehensive Approach

To enhance the sustainability of business operations, enterprises have interests in enterprise resource planning (ERP) transitions from an existing on-premise method to a cloud-based system. This study conducts a comprehensive analysis using the technology-organization-environment, diffusion of innovation, and the model of innovation resistance frameworks. The empirical analysis shows that the factors of organizational culture, regulatory environment, relative advantage, trialability, and vendor lock-in all had a significant influence on the intention to adopt cloud-based ERP, while information and communications technology skill, complexity, observability, data security, and customization had no significant influence on the intention to adopt cloud-based ERP. This study’s findings provide meaningful guidance for companies that want to adopt cloud-based ERP, governments that support enterprise digitalization, and vendors who sell cloud-based ERP systems

Not All Churn Customers Are the Same: Investigating the Effect of Customer Churn Heterogeneity on Customer Value in the Financial Sector

This research suggests a way to sustain a firm’s business by focusing on the economic aspects of relationship marketing by managing the heterogeneity of churn customers. In general, firms have regarded churn customers as a homogeneous segment, for they have not been conscious that churn ego can be various. However, customer churn can be divided into voluntary and involuntary, implying that firms should reform the retention strategy by focusing on egos that seem homogenous but are heterogeneous in terms of churn behavior. Using a multiple regression model, this study analyzed customer data from an insurance company to investigate the heterogeneous impacts of churn customers. It measured the impact based on the period and revenue in the second lifetime, comprehensively representing customer satisfaction. Empirical results show that customer churn heterogeneity significantly affects customers’ second-lifetime behavior. The analysis reveals how the firm effectively performed customer regaining initiatives and successfully maintained persistency. This research also concludes that voluntary and involuntary churn occurred by intrinsic and extrinsic motivation. Finally, this research implicates the retention strategy that differs from the heterogeneity to achieve a firm’s high performance and suggests an empirical method of spurious loyalty avoidance by hedging loyal customer selection risk

E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors

This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model