scholarly journals User Willingness toward Knowledge Sharing in Social Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 4680 ◽  
Author(s):  
Jie Zhao ◽  
Chanjuan Zhu ◽  
Zhixiang Peng ◽  
Xin Xu ◽  
Yan Liu
Keyword(s):  
Social Networks ◽  
Knowledge Sharing ◽  
Technology Acceptance ◽  
Perceived Value ◽  
Reliability And Validity ◽  
Information Value ◽  
Acceptance Model ◽  
Major Factors ◽  
New Research ◽  
Entertainment Value

Social networks introduce new potential for people to share knowledge with others. However, it is not clear what factors influence user willingness toward knowledge sharing in social networks. Aiming to answer these questions, in this paper we analyze the major factors influencing user willingness toward knowledge sharing in social networks and propose a new research model that is inspired by the technology acceptance model (TAM). In particular, we introduce a new independent variable called perceived value which is described by four aspects: social value, entertainment value, emotion value, and information value. In addition, we introduce a new mediating variable, trust, to reflect the intermediating relationship between perceived value and knowledge-sharing willingness. We conduct an empirical analysis on questionnaire data and present comprehensive results on reliability and validity, factor analysis, correlation analysis, and mediating effects analysis. The results show that perceived value has a significant impact on knowledge-sharing willingness, and trust plays a partial intermediate role between perceived value and knowledge-sharing willingness. Further, we present some research implications for knowledge sharing and learning innovation in social networks, as well as some suggestions for organizations to advance knowledge sharing and learning innovation in the social-network age.

Gender Differences in the Technology’s Classic Models in Social Network Sites

2014 ◽  
pp. 126-142
Author(s):  
Begoña Peral-Peral ◽  
Ángel F. Villarejo-Ramos ◽  
Manuel J. Sánchez-Franco
Keyword(s):  
Gender Differences ◽  
Social Networks ◽  
Social Network ◽  
Technology Acceptance ◽  
Information And Communication Technologies ◽  
Social Network Sites ◽  
Communication Technologies ◽  
Internet Users ◽  
Information And Communication ◽  
Acceptance Model

Social Network Sites (SNS) have very rapidly become part of the daily reality of Internet users in recent years. Firms also use social networks as a two-way communication with their current and potential customers. This exploratory work means to analyze if Internet users’ gender influences the behavior of using social networks. There is a reason for this. Despite Information and Communication Technologies (ICT) acceptance and use being more frequent in men, according to the previous literature, in line with different surveys on the subject, social networks are more used by women. The authors, therefore, analyze in this chapter if there are gender differences in the constructs of technology’s classic models, such as the TAM (Technology Acceptance Model) and the TPB (Theory of Planned Behavior). They use a sample of 1,460 university students.

A Quantitative Study of Factors Affecting Value of Adopting Self-Service Banking Technology (SSBT) Among Customers in Developing and Developed Countries

2018 ◽  
pp. 233-260
Author(s):  
Fouad Omran Elgahwash
Keyword(s):  
Technology Acceptance ◽  
Reliability And Validity ◽  
Developed Countries ◽  
Ease Of Use ◽  
Total Size ◽  
Factors Affecting ◽  
Data Collection Instrument ◽  
Self Service ◽  
Acceptance Model

Self-service banking technology (SSBT) allow customers to perform services on their own without direct assistance from staff. This study focuses on factors affecting the value of adopting self-Service banking technology (SSBT) among customers. It is believed that the successful usage of self-service banking technology will be increasingly advantageous for all (banks & customers). This chapter's purpose is an extension to the technology acceptance model (TAM) and views customer responses to technology as an integrated part of SSBT. The sample used for this study was selected from users of banks in both Libya and Australia, with a total size of 141 respondents. Reliability and validity of the data collection instrument was tested using Cronbach Alpha. Descriptive and regression tests for data analysis were used. The domains in which subjects were tested were “ease of use of SSBT”, “Usefulness of SSBT”, “Quality of SSBT”, “privacy of information” and “Trust of SSBT”.

Knowledge Sharing Adoption Model Based on Artificial Neural Networks

2012 ◽  
pp. 46-58
Author(s):  
Olusegun O. Folorunso ◽  
Rebecca Opeoluwa Vincent ◽  
Adewale Akintayo Ogunde ◽  
Benjamin Agboola
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Sharing ◽  
Technology Acceptance ◽  
Higher Learning ◽  
Perceived Usefulness ◽  
Statistical Tool ◽  
Adoption Model ◽  
Artificial Neural ◽  
Acceptance Model

Knowledge Sharing Adoption Model called (KSAM) was developed in this paper using Artificial Neural Networks (ANN). It investigated students’ Perceived Usefulness and Benefits (PUB) of Knowledge Sharing among students of higher learning in Nigeria. The study was based on the definition as well as on the constucts related to technology acceptance model (TAM). A survey was conducted using structured questionnaire administered among students and analysed with SPSS statistical tool; the results were evaluated using ANN. The KSAM includes six constucts that include Perceived Ease Of Sharing (PEOS), Perceived Usefulness and Benefits (PUB), Perceived Barriers for Sharing (PBS), External Cues to Share (ECS), Attitude Towards Sharing (ATT), and Behavioral Intention to Share (BIS). The result showed that Students’ PUB must be raised in order to effectively increase the adoption of Knowledge Sharing in this domain. The paper also identified a myriad of limitations in knowledge sharing and discovered that the utilization of KSAM using ANN is feasible. Findings from this study may form the bedrock on which further studies can be built.

Construction and empirical research on acceptance model of service robots applied in hotel industry

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lina Zhong ◽  
Xiaoya Zhang ◽  
Jia Rong ◽  
Hing Kai Chan ◽  
Jinyu Xiao ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Technology Acceptance ◽  
Empirical Research ◽  
Hotel Industry ◽  
Perceived Value ◽  
Service Robots ◽  
Content Type ◽  
Hotel Service ◽  
Acceptance Model ◽  
Robot Selection

PurposeRobots, as the crystallization of new artificial intelligence, are being applied in various fields, especially the hotel industry. They are seizing the opportunities, using technology to improve the overall quality and comprehensive competitiveness. However, they also cause many problems due to practical limitations. The purpose of this paper is to study customers' recognition and acceptance of hotel service robots to guide the successful promotion of this technology.Design/methodology/approachThis paper proposed a comprehensive model based on the theory of planned behavior, the technology acceptance model and then the perceived value-based acceptance model. Exploratory factor analysis, confirmatory factor analysis, grouped regression analysis and path analysis was adopted to validate the impacts of each variable to obtain the final reliable model using data collected from hotel guests using a self-designed questionnaire.FindingsThe empirical research based on the theoretical model shows that the constructed conceptual model can thoroughly explain the influencing factors of hotel robot acceptance, enrich the acceptance theory and provide academic support for the use and popularization of hotel service robots. Among all variables, attitude, usefulness and perceived value are the factors that have the greatest impact on acceptance. They have significant differences in the effects of adjustment variables such as gender, educational level, whether hotel robots have been used, and whether other robot services have been experienced on different paths in the model.Practical implicationsThis paper explored the customer acceptance of service robots in hotels, helped to understand the process of decision-making on service robot selection and contributed to the theoretical extension of the hospitality industry. The work guides hotel management to promote better-personalized products and services of robot technology in the hospitality industries.Originality/valueThe acceptance study on hotel service robots provides insight into the hotel industry to understand customers' attitudes and acceptance of emerging technology.

Consequences of XBRL Standardization on Financial Statement Data

2012 ◽  
Vol 26 (1) ◽  
pp. 155-167 ◽  
Author(s):  
Miklos A. Vasarhelyi ◽  
David Y. Chan ◽  
J. P. Krahel
Keyword(s):  
Technology Acceptance ◽  
Financial Reporting ◽  
Current Data ◽  
Third Party ◽  
Financial Statement ◽  
Business Reporting ◽  
Data Fidelity ◽  
Acceptance Model ◽  
New Research ◽  
Five Axes

ABSTRACT XBRL (eXtensible Business Reporting Language) has become an important element of the financial reporting landscape. This paper provides a discussion of the effects of XBRL on the usefulness of financial data. Using the theoretical framework of the technology acceptance model (TAM), we examine XBRL's potential to improve the usefulness of reported financial information. As XBRL gains wider acceptance, we examine five axes along which XBRL standardization will propagate: current data, disclosure format, historical data, data fidelity and assurance, and third-party data. We also call attention to new research opportunities that may arise as XBRL and related technologies mature.

scholarly journals An Empirical Study Of User Acceptance Of Online Social Networks Marketing

2013 ◽  
Vol 50 ◽  
Author(s):  
Olumayowa Mulero ◽  
Michael Adeyeye
Keyword(s):  
Social Networks ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Online Social Networks ◽  
User Acceptance ◽  
Perceived Usefulness ◽  
Western Cape ◽  
User Intentions ◽  
Using Data ◽  
Acceptance Model

The explosion of Internet usage has drawn the attention of researchers towards online Social Networks Marketing (SNM). Research has shown that a number of the Internet users are distrustful and indecisive, when it comes to the use of social networks marketing system. Therefore, there is a need for researchers to identify some of the factors that determine users’ acceptance of social networks marketing using Technology Acceptance Model (TAM). This study extended the Technology Acceptance Model theoretical framework to predict consumer acceptance of social networks marketing within Western Cape Province of South Africa. The research model was tested using data collected from 470 questionnaires and analysed using linear regression. The results showed that user intentions to use SNM are strongly and positively correlated with user acceptance of using SNM systems. Empirical results confirmed that perceived credibility and perceived usefulness are the strongest determinant in predicting user intentions to use SNM system.

scholarly journals ANALISIS TINGKAT KEMANFAATAN KNOWLEDGE SHARING TERHADAP KARYAWAN DENGAN MENGGUNAKAN METODE TECHNOLOGY ACCEPTANCE MODEL (TAM)

2019 ◽  
Vol 5 (1) ◽  
pp. 78
Author(s):  
Nesdi Evrilyan Rozanda ◽  
Rizki Nur Jannah Hasibuan
Keyword(s):  
Knowledge Sharing ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Technology Use ◽  
Best Practice ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Perceived Ease Of Use ◽  
Acceptance Model ◽  
Behavioral Intention To Use

PT. PLN Sektor Pembangkitan Pekanbaru mempunyai sistem knowledge management PLN (KM PLN), berfungsi sebagai media knowledge sharing (KS) seperti pengetahuan terbaru, best practice,inovasi, lessond learned,cara penurun an gangguan penyulang murai (SUTM), upaya penurunan gangguan system rayon, penurunan gangguan penyulang poli o, karbon dan kwarto. Pengetahuan ini berguna untuk meningkatkan kualitas kerja karyawan dalam menyelesaikan peke rjaannya. Penelitian ini menggunakan metode Technology Acceptance Model untuk analisis tingkat kemanfaatan KS ter hadap karyawan, dengan menggunakan 5 konstruk yaitu Perceived Usefulness, Perceived Ease Of Use, Attitude Towar ds Using Technology, Behavioral Intention To Use dan Actual Technology Use. Kuesioner dibagikan kepada seluruh ka ryawan yang berjumlah 57 orang, guna untuk mengetahui konstruk yang paling bermanfaat, Penelitian ini menggunaka n analisis regresi linear berganda. Hasil dari penelitian menunjukkan konstruk  perceived ease of use dalam kategori setuju dengan nilai tertinggi, Sedangkan nilai terendah terdapat pada konstruk perceived usefulness.

Knowledge Sharing Adoption Model Based on Artificial Neural Networks

2010 ◽  
Vol 2 (4) ◽  
pp. 1-14
Author(s):  
Olusegun O. Folorunso ◽  
Rebecca Opeoluwa Vincent ◽  
Adewale Akintayo Ogunde ◽  
Benjamin Agboola
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Sharing ◽  
Technology Acceptance ◽  
Higher Learning ◽  
Perceived Usefulness ◽  
Statistical Tool ◽  
Adoption Model ◽  
Artificial Neural ◽  
Acceptance Model

Knowledge Sharing Adoption Model called (KSAM) was developed in this paper using Artificial Neural Networks (ANN). It investigated students’ Perceived Usefulness and Benefits (PUB) of Knowledge Sharing among students of higher learning in Nigeria. The study was based on the definition as well as on the constucts related to technology acceptance model (TAM). A survey was conducted using structured questionnaire administered among students and analysed with SPSS statistical tool; the results were evaluated using ANN. The KSAM includes six constucts that include Perceived Ease Of Sharing (PEOS), Perceived Usefulness and Benefits (PUB), Perceived Barriers for Sharing (PBS), External Cues to Share (ECS), Attitude Towards Sharing (ATT), and Behavioral Intention to Share (BIS). The result showed that Students’ PUB must be raised in order to effectively increase the adoption of Knowledge Sharing in this domain. The paper also identified a myriad of limitations in knowledge sharing and discovered that the utilization of KSAM using ANN is feasible. Findings from this study may form the bedrock on which further studies can be built.

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