UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

This study aims to investigate users’ knowledge sharing intention and collaborative stickiness intention towards social network sites (SNS). SNS offer an opportunity for users to interact and form relationships, while knowledge is accrued by integrating user’s information, experience, and practice. However, there have been few systematic studies that ask why people use SNS to share knowledge. We adopt social capital theory, social identity theory, as well as use and gratification theory to explore the determinants of members’ knowledge sharing intention in SNS. The survey was conducted on two education VCs of facebook, while most members were teachers and educators. Data analysis was carried out to validate our research model, and SmartPLS were used to analyze users’ collaborative stickiness intention. The result shows that social capital and social identity have impact on teacher’s knowledge sharing intention, in turn, influence on collaborative stickiness intention toward on SNS. Our findings not only help researchers interpret why members sharing their knowledge in VC, but also assist practitioners in developing better SNS strategy

AN ANALYSIS OF SPECIFIC FlTNESS TESTING IN FEMALE JUNIOR TENNIS PLAYERS

The purpose of the present study was to determine the anthropometry, physical fitness parameters and compare with USTA normative data. Seven-teen female teenage tennis players who have the Taiwan national level were included in the study. The analysis focused on the examination of muscular strength, endurance, power flexibility, speed and agility. Results showed that (a) there were excellent performances in grip strength of the nondominant hand (7.8%) and hexagon test (8.1 %) scored well relative to USTA normative data. (b) there were needs improvement performance in sit and reach (0.5%) and spider test (6.7%) relative to USTA. In conclusion, this study indicated the identification of weaknesses in flexibility, speed and agility parameters and allows designing efficient physical training programs

Innovative Approaches: Leveraging Neuroscience Technologies for Understanding of Consumer Behavior in E-Commerce

The surge in interest surrounding the application of advanced technology in consumer behavior analysis within the e-commerce domain has grown significantly in recent years. Traditional market research methods, constrained by limitations in capturing accurate consumer responses, have paved the way for these sophisticated technologies to provide deeper insights into the intricacies of consumer behavior and decision-making processes. This comprehensive review navigates through various techniques utilized for scrutinizing consumer behavior, delving into the capabilities and limitations of each technology. EEG emerges as a powerful tool capable of measuring brain activity, shedding light on cognitive and emotional responses to marketing stimuli. The review further explores the potential applications of these technologies in the e-commerce landscape. Examples include assessing website design effectiveness using EEG. This review underscores the advantages of deploying advanced technologies in analyzing consumer behavior in e-commerce, showcasing their potential to enhance marketing strategies and user experiences. This article is particularly pertinent to applied science readers interested in the practical implementation of cutting-edge technologies in consumer behavior analysis

Can the Social and Cultural Impacts of Ports be Assessed in Terms of Economic Value?

The aim of the thesis is to study ordering in binary alloys on the basis of first-principles or {\it ab-initio} techniques employing density functional theory (DFT). The ordering phenomena of materials are of crucial importance for technological applications. The results of the thesis are intended to demonstrate the applicability of the first-principles calculations to provide fundamental insight to the true, namely electronic structure, nature of ordering in binary alloys. The main part of the thesis focuses on atomic short- and long-range order phenomena in binary alloys as a function of both temperature and chemical composition in FeCo and NiCr alloys. In particular, the influence of magnetism on atomic ordering in FeCo alloys is investigated using the disordered local moment.A large number of concentration dependent effective cluster interactions, derived without the use of any adjustable parameters, are obtained by the SGPM as it is implemented in the EMTO within the CPA. The SGPM interactions can subsequently be used in thermodynamic Monte-Carlo simulations or mean field approximations to determine the ordering phenomena in binary alloys. First-principles calculations of intrinsic stacking-fault energies (SFE) andanti-phase boundary energies (APBE) in Al$_{3}$Sc and the effects of temperature on SFE and APBE are investigated by using the axial Ising model and supercellapproach. Temperature effects have been taken into consideration byincluding the one-electron thermal excitations in the electronicstructure calculations, and vibrational free energy in the harmonicapproximation as well as by using temperature dependent lattice constants.The latter has been determined within the Debye-Gr{\"u}neisen model,which reproduces well the experimental data. Within the framework of the quasiharmonic approximation, the thermodynamics and elastic properties of B2- FeCo alloy are studied using first-principles calculations. The calculated thermal and elastic properties are found to be in good agreement withthe available measured values when the generalized gradientapproximations is used for the exchange correlation potential.The calculated finite temperature elastic constants show thatthe FeCo alloy is mechanically stable in the ordered phase.Meanwhile, a large elastic anisotropy exhibits a moderate dependence ontemperature.QC 20130927</p

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

Abstract This study aims to investigate users&apos; knowledge sharing intention and collaborative stickiness intention towards social network sites (SN