The effect of user characteristics on the intention to use restaurant evaluation application based on big data

2018 ◽  
Vol 27 (1) ◽  
pp. 35-53
Author(s):  
Hong-bumm Kim ◽  
Young-Hee Lim
Keyword(s):  
Big Data ◽  
Intention To Use ◽  
User Characteristics

Understanding the intention to use medical big data processing technique from the perspective of medical data analyst

2017 ◽  
Vol 45 (4) ◽  
pp. 194-201 ◽  
Author(s):  
Shanyong Wang ◽  
Jun Li ◽  
Dingtao Zhao
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Technique ◽  
Medical Data ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Big Data Processing ◽  
Content Type ◽  
Medical Big Data ◽  
Data Processing Technique

Purpose The purpose of this paper is to apply an extended technology acceptance model to examine the medical data analyst’s intention to use medical big data processing technique. Design/methodology/approach Questionnaire survey method was used to collect data from 293 medical data analysts and analyzed with the assistance of structural equation modeling. Findings The results indicate that the perceived usefulness, social influence and attitude are important to the intention to use medical big data processing technique, and the direct effect of perceived usefulness on intention to use is greater than social influence and attitude. The perceived usefulness is influenced by perceived ease of use. Attitude is influenced by perceived usefulness, and attitude acts as a mediator between perceived usefulness and usage intention. Unexpectedly, attitude is not influenced by perceived ease of use and social influence. Originality/value This research examines the medical data analyst’s intention to use medical big data processing technique and provides several implications for using medical big data processing technique.

scholarly journals Potential Biases in Big Data: Omitted Voices on Social Media

2018 ◽  
Vol 38 (1) ◽  
pp. 10-24 ◽  
Author(s):  
Eszter Hargittai
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science Research ◽  
Science Research ◽  
National Sample ◽  
Sociodemographic Factors ◽  
User Characteristics ◽  
Business Interests ◽  
Internet Skills ◽  
Social Media Platforms

While big data offer exciting opportunities to address questions about social behavior, studies must not abandon traditionally important considerations of social science research such as data representativeness and sampling biases. Many big data studies rely on traces of people’s behavior on social media platforms such as opinions expressed through Twitter posts. How representative are such data? Whose voices are most likely to show up on such sites? Analyzing survey data about a national sample of American adults’ social network site usage, this article examines what user characteristics are associated with the adoption of such sites. Findings suggest that several sociodemographic factors relate to who adopts such sites. Those of higher socioeconomic status are more likely to be on several platforms suggesting that big data derived from social media tend to oversample the views of more privileged people. Additionally, Internet skills are related to using such sites, again showing that opinions visible on these sites do not represent all types of people equally. The article cautions against relying on content from such sites as the sole basis of data to avoid disproportionately ignoring the perspectives of the less privileged. Whether business interests or policy considerations, it is important that decisions that concern the whole population are not based on the results of analyses that favor the opinions of those who are already better off.

scholarly journals Students’ Behavioral Intentions Regarding the Future Use of Quantitative Research Methods

2018 ◽  
Vol 64 (2) ◽  
pp. 25-33
Author(s):  
Polona Tominc ◽  
Maruša Krajnc ◽  
Klavdija Vivod ◽  
Monty L. Lynn ◽  
Blaž Frešer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Behavioral Intentions ◽  
Data Analytics ◽  
Quantitative Research ◽  
Business Students ◽  
Intention To Use ◽  
Technological Changes ◽  
Quantitative Research Methods ◽  
The Future

AbstractChanges regarding the importance of graduates’ competences by employers and changes of competences themselves are to a great extend driven by the technological changes, digitalization, and big data. Among these competences, the ability to perform business and data analytics, based on statistical thinking and data mining, is becoming extremely important. In this paper, we study the relationships among several constructs that are related to attitudes of economics and business students regarding quantitative statistical methods and to students’ intention to use them in the future. Findings of our research provide important insights for practitioners, educators, lecturers, and curricular management teams.

scholarly journals Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Muhammad Shahbaz ◽  
Changyuan Gao ◽  
Lili Zhai ◽  
Fakhar Shahzad ◽  
Muhammad Rizwan Arshad
Keyword(s):  
Big Data ◽  
Self Efficacy ◽  
Data Analytics ◽  
Resistance To Change ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Adoption Process ◽  
Intention To Use ◽  
Research Model ◽  
Healthcare Organizations

The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

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