Investigating the role of utilitarian and hedonic goals in characterizing customer loyalty in E-marketplaces.

Despite significant growth in sales in recent years, retaining customers remains a major challenge for the electronic marketplace (e-marketplace) industry worldwide, including Indonesia. The small basket size of Indonesian customers has created a highly price-sensitive market, making it difficult to nurture customer loyalty. This study investigates the factors affecting consumer behavior and loyalty in Indonesian e-marketplaces by employing a newly proposed E-Marketplace Customer Loyalty Model. Since customers' expectations have extended from mostly utilitarian-oriented goals to incorporate hedonic goals, the proposed model includes both utilitarian and hedonic-based constructs by integrating the Expectation-Confirmation Theory, e-service quality, and the Hedonic Information Systems Model. The proposed model was tested using a comparative approach of machine learning classification algorithms and partial least square, aiming to create a more robust model. The PLS analytical results of ECLM hiearchical component model from 678 customers show that the fulfillment of hedonic values via perceived enjoyment has a greater impact on customer satisfaction than utilitarian values of perceived service quality. Perceived enjoyment, personalization, and customer satisfaction positively affect customer loyalty. The classification results provide further evidence for all hypothesized relationships of the ECLM. The Sequential Minimal Optimization (SMO) algorithm has demonstrated superior performance compared with other classifiers in predicting the dependent variable in most cases. Based on findings, this study offers theoretical and practical implications, and recommendations for sustainable loyalty programs in e-marketplaces.

The significance of long-range correction to the hydroperoxyl radical-scavenging reaction of trans-resveratrol and gnetin C.

Density functional theory has been gaining popularity for studying the radical scavenging activity of antioxidants. However, only a few studies investigate the importance of calculation methods on the radical-scavenging reactions. In this study, we examined the significance of (i) the long-range correction on the coulombic interaction and (ii) the London dispersion correction to the hydroperoxyl radical-scavenging reaction of trans-resveratrol and gnetin C. We employed B3LYP, CAM-B3LYP, M06-2X exchange-correlation functionals and B3LYP with the D3 version of Grimme's dispersion in the calculations. The results showed that long-range correction on the coulombic interaction had a significant effect on the increase of reaction and activation energies. The increase was in line with the change of hydroperoxyl radical's orientation in the transition state structure. Meanwhile, the London dispersion correction only had a minor effect on the transition state structure, reaction energy and activation energy. Overall, long-range correction on the coulombic interaction had a significant impact on the radical-scavenging reaction.

Teaching reaction kinetics through isomerization cases with the basis of density-functional calculations.

The fundamental mechanism of biochemistry lies on the reaction kinetics, which is determined by the reaction pathways. Interestingly, the reaction pathway is a challenging concept for undergraduate students. Experimentally, it is difficult to observe, and theoretically, it requires some degree of physics knowledge, namely statistical and quantum mechanics. However, students can utilize computational methods to study the reaction kinetics without paying too much attention but not wholly neglecting the comprehension of physics. We hereby provided an approach to study the reaction kinetics based on density-functional calculations. We particularized the study of the isomerization case involving five molecules at three different temperatures and emphasized the importance of the transition state in the study of reaction kinetics. The results we presented were in good agreement with the experiments and provided useful insights to assist students in the application of their knowledge into their research.

Drug Abuse Research Trend Investigation with Text Mining.

Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.

A Density-Functional Study of the Conformational Preference of Acetylcholine in the Neutral Hydrolysis.

Acetylcholine, which is associated with Alzheimer's disease, is widely known to have conformers. The preference of each conformer to undergo neutral hydrolysis is yet to be considered. In this study, we employed density-functional calculations to build the conformers and investigated their preference in one-step neutral hydrolysis. The results showed the preference in ten possible hydrolysis pathways involving seven acetylcholine conformers (reactant), four transition state structures, and two choline conformers (product). Three out of the seven acetylcholine conformers predicted from the results confirmed experimental findings on the conformers stability. We suggested that two out of ten possible pathways were observed in the experimental results based on agreement in reaction energy. Eventually, this study will emphasize the importance of considering acetylcholine conformers in its hydrolysis study.

The transition state conformational effect on the activation energy of ethyl acetate neutral hydrolysis.

We report a first-principles study on ethyl acetate neutral hydrolysis in which we focus on the activation energy variation resulting from the conformational effect in the transition state. We use the conformers of ethyl formate, ethyl acetate, ethyl fluoroacetate, and ethyl chloroacetate as the ester models and one water molecule with a one-step reaction mechanism. We also consider the long-range interaction and the surrounding water in the form of PCM. Our results show that the various conformers yield a significant range of activation energy. Moreover, the gauche conformer has lower activation energy than the trans conformer. The activation energy in its own right is lowered by the halogen atoms. Finally, we remark that the long-range correction and PCM stabilize the transition state geometry but raise the activation energy.

Characterizing Consumer Behavior in Leveraging Social Media for E-Patient and Health-Related Activities.

The emergence of e-patients has encouraged consumers, people who are non-medical experts, to be more engaged in healthcare needs by utilizing online sources via social media. However, the nature of social media and regulation issues have caused concerns for the reliability and validity of the shared information. These phenomena shape consumers behavior in leveraging social media for e-patient activities. This study investigates consumer behavior using an integrated model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Protection Motivation Theory (PMT). The data collected from the participants (N = 312) was analyzed using partial least square structural equation modelling. The results showed that behavioral intention to use social media for e-patient activities was significantly affected by performance expectancy, effort expectancy, perceived severity, perceived susceptibility, and response efficacy; and that behavioral intention corresponded positively to usage intention. In addition, the results also indicate that the intention to use social media for health-related purposes is driven by awareness of preventing health problems and attempts to reduce the risk of developing an illness. Based on findings, this study recommends strategies and initiatives to optimize social media for promoting a healthy lifestyle and educating society about public health and healthcare management.

Effects of individual health topic familiarity on activity patterns during health information searches.

BACKGROUND: Non-medical professionals (consumers) are increasingly using the Internet to support their health information needs. However, the cognitive effort required to perform health information searches is affected by the consumer's familiarity with health topics. Consumers may have different levels of familiarity with individual health topics. This variation in familiarity may cause misunderstandings because the information presented by search engines may not be understood correctly by the consumers. OBJECTIVE: As a first step toward the improvement of the health information search process, we aimed to examine the effects of health topic familiarity on health information search behaviors by identifying the common search activity patterns exhibited by groups of consumers with different levels of familiarity. METHODS: Each participant completed a health terminology familiarity questionnaire and health information search tasks. The responses to the familiarity questionnaire were used to grade the familiarity of participants with predefined health topics. The search task data were transcribed into a sequence of search activities using a coding scheme. A computational model was constructed from the sequence data using a Markov chain model to identify the common search patterns in each familiarity group. RESULTS: Forty participants were classified into L1 (not familiar), L2 (somewhat familiar), and L3 (familiar) groups based on their questionnaire responses. They had different levels of familiarity with four health topics. The video data obtained from all of the participants were transcribed into 4595 search activities (mean 28.7, SD 23.27 per session). The most frequent search activities and transitions in all the familiarity groups were related to evaluations of the relevancy of selected web pages in the retrieval results. However, the next most frequent transitions differed in each group and a chi-squared test confirmed this finding (P<.001). Next, according to the results of a perplexity evaluation, the health information search patterns were best represented as a 5-gram sequence pattern. The most common patterns in group L1 were frequent query modifications, with relatively low search efficiency, and accessing and evaluating selected results from a health website. Group L2 performed frequent query modifications, but with better search efficiency, and accessed and evaluated selected results from a health website. Finally, the members of group L3 successfully discovered relevant results from the first query submission, performed verification by accessing several health websites after they discovered relevant results, and directly accessed consumer health information websites. CONCLUSIONS: Familiarity with health topics affects health information search behaviors. Our analysis of state transitions in search activities detected unique behaviors and common search activity patterns in each familiarity group during health information searches.