Commitment is an act, not a word: Examining members’ commitment to travel-related online communities

Does social media contribute to research impact? An Altmetric study of highly-cited marketing research

Long waiting time to appointment may be a worry for pregnant women, particularly those who need perinatology consultation since it could increase anxiety and, in a worst case scenario, lead to an increase in fetal, infant, and maternal mortality. Treatment costs may also increase since pregnant women with diverse pathologies can develop more severe complications. As a step towards improving this process, we propose a methodological approach to reduce the appointment lead-time in outpatient gynecobstetrics departments. This framework involves combining the Six Sigma method to identify defects in the appointment scheduling process with a discrete-event simulation (DES) to evaluate the potential success of removing such defects in simulation before we resort to changing the real-world healthcare system. To do these, we initially characterize the gynecobstetrics department using a SIPOC diagram. Then, six sigma performance metrics are calculated to evaluate how well the department meets the government target in relation to the appointment lead-time. Afterwards, a cause-and-effect analysis is undertaken to identify potential causes of appointment lead-time variation. These causes are later validated through ANOVA, regression analysis, and DES. Improvement scenarios are next designed and pretested through computer simulation models. Finally, control plans are deployed to maintain the results achieved through the implementation of the DES-Six sigma approach. The aforementioned framework was validated in a public gynecobstetrics outpatient department. The results revealed that mean waiting time decreased from 6.9 days to 4.1 days while variance passed from 2.46 days2 to 1.53 days2.

/pdf/integrating-lean-six-sigma-and-discrete-event-simulation-for-3cbs9in5cb.pdf

Integrating Lean Six Sigma and discrete-event simulation for shortening the appointment lead-time in gynecobstetrics departments: a case study

Based on many past studies regarding Social Media Applications (SMAs) rarely focus on aspects of continuous use and Perceived Privacy (PP). The increasing user awareness issue regarding PP in using SMA is escalating due to various cybersecurity threats, such as malware, software, password and Man-in-the-Middle (MitM) attacks. Accordingly, this study examines the effect of users' PP on System Quality (SQ), Information Quality (IQ), Service Quality (SVQ), User Satisfaction (US) and Usage (USG) based on the DeLone and McLean Information System (IS) Success Model. A total of 345 samples were used for this study using the convenience sampling approach and SmartPLS 3 as the analysis tool. Based on the study's results, it was discovered that SVQ and IQ affect the use of SMA. In addition, it was discovered that PP affects the relationship between SQ, SVQ and the use of SMA. The analysis results related to PP reveal that it is a dominant construct in the study model. Note that the importance of PP should be a solid foundation for developing an SMA application. The PP component in this study can be used as a guide and checklist for software developers to improve security features and SVQ, which is a catalyst for US and ensure that an SMA application is used continuously. 

pdf/a-new-framework-on-success-factors-of-social-media-1r7lnymk8q.pdf

A new framework on success factors of social media applications usage

Abstract The development of informatization has increasingly become the transformation direction of the Civic Education in colleges and universities in the new media era. This paper outlines the path of civic and political information technology, in conjunction with campus networks in general, and presents a series of support mechanisms for information technology. Taking S university as a research case, firstly, Fractal Brownian motion is used to scientifically characterize the traffic of campus network users. The network group characteristics are estimated through the determination of the Hurst coefficient. Secondly, the Apriori algorithm is used to extract the association rules of online behaviors hidden in these traffic features to explore the relationship between online behaviors in time series. Finally, by combining the above features and association rules, we developed subspace clustering techniques for college students’ campus Internet usage behavior. We proposed a path for ideological education based on the clustering results. The results show that the online behavior of different students on campus shows significant differences. Class A students like to stay up late on weekends, and the number of such students online fluctuates slightly from 14:30 to 23:30. Class B students generally prefer to go online during the daytime. Class C students also have some students who stay up late, but the average duration is not long, with a maximum of 766 seconds. Class D students all stay up all night, and the peak of the mean value of the online duration occurs at 4:30. Class D students all stay up all night, and the peak of the mean value of the online duration occurs at 4:30. Category D students need to be given more attention in the management of political education.

Exploring the Path of Informatization Construction of Civic Education in Colleges and Universities in the Era of Melting Media

By integrating the perspectives of the DeLone and McLean information systems success model, media synchronicity theory, and organisational support, this study develops a theoretical model to examin...

Measuring social media brand community success: the roles of media capability and organisational support

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Measuring social media brand community success: the roles of media capability and organisational support