Data quality management, data usage experience and acquisition intention of big data analytics

Kwon, Ohbyung; Lee, Nam Yeon; Shin, Bongsik

doi:10.1016/j.ijinfomgt.2014.02.002

Cited by 576 publications

(397 citation statements)

References 31 publications

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“…Several studies have adopted RBV theory to deepen their understanding of BDA capabilities in achieving organizational performance such as, in manufacturing [4] [18], health care [17] [19], retail [15], and non-specific domains [7] [16] [20] [21]. On the other hand, the adoption of ISSM theory focuses only on data, information and system quality factors towards organizational impact and benefits.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Adrian¹,

Abdullah²,

Atan³

et al. 2018

IJIMAI

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Section: Theoretical Backgroundmentioning

confidence: 99%

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Adrian¹,

Abdullah²,

Atan³

et al. 2018

IJIMAI

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“…Social media was, therefore, described by Carr and Hayes (2015) as internet-based channels that allow users to interact. Big data presents many benefits to businesses; however, new challenges are being presented by its existence, and these challenges are concerned with how information is stored, shared and managed (Kwon, Lee & Shin 2014). Some of the challenges affect both the end users and the technologies that host the social media.…”

Section: Big Data and Social Media In Healthcarementioning

confidence: 99%

Integration of social media with healthcare big data for improved service delivery

Mgudlwa¹,

Iyamu²

2018

S. Afr. j. inf. manag.

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Background: In the last decade, social media users across the world have crossed 1 billion, making it one of the fastest growing sources of big data. Also, people needing healthcare continue to increase in every society. Through accessibility, communication and interaction between health practitioners and patients, this type of ever-growing, social media subscriber–based platform can be of significant use in improving healthcare delivery to society. However, users encounter serious challenges in their attempts to make use of social media and big data for health-related services. The challenges are primarily caused by factors such as integration, complexity, security and privacy. The challenges are mainly owing to the sensitive nature of the healthcare environment, as a result of personalisation and privacy of information. Objectives: The objectives of the study were to examine and gain a better understanding of the complexities that are associated with the use of social media and healthcare big data, through influencing factors, and to develop a framework that can be used to improve health-related services to the patients. Methods: The interpretivist approach was employed, within which qualitative data were collected. This included documents and existing literature in the areas of social media and healthcare big data. To have a good spread of both previous and current state of events within the phenomena being studied, literature published between 2006 and 2016 were gathered. The data were interpretively analysed. Results: Based on the analysis of the data, factors of influence were found, which were used to develop a model. The model illustrates how the factors of influence can enable and at the same time constrain the use of social media for healthcare services. The factors were interpreted from which a framework was developed. The framework is intended to guide integration of social media with healthcare big data through which service delivery to patients can be improved. Conclusion: This study can be used to guide integration of social media with healthcare big data by health facilities in the communities. The study contributes to healthcare workers’ awareness on how social media can possibly be used to improve the services that they provide to the needy. Also, the study will benefit information systems and technologies and academic domains, particularly from the health services’ perspective.

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“…This could be implemented with channels for asynchronous discussions and online facilitation, and with the use of audio and video files [31]. Picciano [9] claims that today's heterogeneous student groups have learners representing different generations, different personality types and different learning styles, motivating the combination of multiple approaches and multiple modalities.…”

Section: Collaboration and Social Presencementioning

confidence: 99%

Redesign and Evaluation of a Technology Enhanced Learning Environment for Thesis Supervision

Ahlin¹,

Mozelius²

2017

ICERI Proceedings

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Thesis supervision is an important activity in higher education with Bachelor's and Master's theses mandatory parts of university programmes. In traditional education, thesis supervision has been a face-to-face activity, but today, both the student groups and the learning environments are blended. This study describes and discusses the design and implementation of a blended learning environment for a student group at Mid Sweden University. The research question that the study aims to answer is: Which are the important factors in the design of a course for undergraduate students, to facilitate supervision of thesis projects in a technology enhanced environment?This study employed a Design science research strategy with three phases for design, implementation and evaluation of a blended learning environment during thesis supervision. For the first phase, design requirements were specified based on document studies and a literature review. In the second phase (first part), the created design requirements were implemented as an instance in the Moodle virtual learning environment. Second phase (second part), the implemented instance was used as the online part of a blended learning course for students writing their Bachelor's thesis. In the third phase an evaluation was carried out with an online questionnaire were students gave their opinions on course design and teaching and learning sessions.Findings show that the learning outcomes were good and that the course aim and the learning objectives were clearly stated. Found important factors for a successful supervision were peer collaboration, multimodality, and technology enhanced interaction. Remaining challenges are to improve course content and to motivate students for research in the field of Informatics. Most surprising positive finding was that peer situation occurred informally, which deepen their knowledge for thesis writing and presentation.

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Data quality management, data usage experience and acquisition intention of big data analytics

Cited by 576 publications

References 31 publications

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Integration of social media with healthcare big data for improved service delivery

Redesign and Evaluation of a Technology Enhanced Learning Environment for Thesis Supervision

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