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Online transaction has recently benefited from coronavirus; however, the sales of e-commerce in some areas are substantially on the decline. The current study proposes a theoretically constructed and empirically viable way for predicting the relevant factors that may detract or foster e-commerce success. We apply web analytics (one of the big data techniques) to simultaneously, generalizably, and objectively measure the influential factors of e-commerce success. The findings indicate that (1) pageviews is an important key for consumers to make transactions. (2) Bounce rate of the website should not be a member factor of e-commerce success. (3) Adhesion strategy and repeatability strategy can be used to induce consumer online transaction. Several theoretical contributions and practical implications are also provided.

Deploying multiple sensors in the field of AIoTs is inevitable for improving human life to be more intelligent. Unfortunately, most of prior studies pay their attentions merely on "how a single sensor can operate properly in its application scenario" but not on "how multiple sensors can operate coordinately in the same scenario". The current study adopts the concept of Game Theory to propose a sensor interactive model and its respective algorithm design so that the sensors mounted in AIoTs can achieve communicational consensus.

We try to reconfirm whether the previous findings of online consumer behavior research are consistent with the findings of the current study when we apply Big Data technique to evaluate the same online behaviors mentioned in prior studies. The results indicated that different research methods (e.g., web analytics, online survey) do actually lead to different research findings even though the same issue was evaluated.

For the brewing industry, the possibility of beer spoilage by lactic acid bacteria (LAB) or by wild yeasts is always a concern. Contamination by certain LAB, such as Lactobacillus brevis, L. casei,...

Most companies involved in e-commerce use trust-promoting messages to persuade consumers that the websites they visit are safe. Although these messages are common, many consumers still hesitate to use a credit card online. To understand why, we applied social judgment theory and Cialdini's model of persuasion in an experiment exploring the extent to which trust-promoting messages can be used to change consumers' attitudes toward online credit card use. By manipulating message proximity and message source, we found that authority-plus-contrast-plus-scarcity was the most persuasive combination, and there was no significant difference between international and domestic trustmarks. Consumers with positive credit card attitudes were included as a control condition. The results show that the authority-plus-contrast-plus-scarcity combination was essential for consumers holding the same positive attitude, because there was no significant attitude change in this condition. Our findings help shopping websites analyze their existing trust-promoting messages and improve them by adding persuasive elements.

Click-and-mortar is a business model in which companies supplement their physical outlet with an online channel. Channel integration makes it possible for companies to access synergies that may be unavailable to companies that separate these channels into distinct entities. However, a review of previous studies provided no model addressing the underlying dynamics of click-and-mortar behavior. This study proposes a phenomenal model to evaluate the antecedents of customer behavioral processes in online and offline channels, by empirically extending the IS success model to incorporate a tri-component behavioral process as its theoretical foundation. This study draws a number of conclusions. First, information quality does not significantly affect online satisfaction when customers select mortar to click as their involving sequence. Second, environment quality does not affect offline satisfaction when customers choose click to mortar as their involving sequence. Finally, different channels have different advantages and weaknesses.

Streptococcus agalactiae, Streptococcus uberis, and Streptococcus bovis are three of the major pathogens which cause mastitis in dairy herds. Since conventional methods for the detection of these mastitis pathogens are laborious and time-consuming, rapid methods are needed. With an attempt to know if heat shock protein (HSP) genes other than HSP60 gene, could be used for PCR primer designing, in this study, we tried to design PCR primers based on the heat shock protein genes hsp70, hsp40, and hsp10 for the specific detection of S. agalactiae, S. uberis, and S. bovis, respectively. Using these primers, all the randomly selected target strains could be specifically detected. Bacterial species other than the target organisms, including strains of other Streptococcus spp., and strains of non-Streptococcus spp., would not generate any false positive results. As these PCR primers were used for direct detection of mastitis pathogens, the detection limit was N (N=1-9) x 10(3)CFU/ml of cell dilutions. If a 10h pre-enrichment step was performed, the detection limit was N x 10(0)CFU/ml. Thus, these primers could be used for the specific and sensitive detection of bovine mastitis bacteria.