Ontological meta-analysis and synthesis

36Citations
Citations of this article
54Readers
Mendeley users who have this article in their library.

Abstract

We present ontological meta-analysis and synthesis as a method for reviewing, mapping, and visualizing the research literature in a domain cumulatively, logically, systematically, and systemically. The method highlights a domain’s bright spots that have been heavily studied, the light spots that have been lightly studied, the blind spots that have been overlooked, and the blank spots that have not been studied. It highlights the biases in a domain’s research; the research can then be realigned to make it stronger and more effective. We illustrate the method using the emerging domain of public health informatics (PHI). We present an ontological framework for the domain, map the literature onto the framework, and highlight its bright, light, and blind/blank spots. We also present detailed analyses using the ontological maps of dyads and triads. We conclude by discussing how (a) the results can be used to realign PHI research, and (b) the method can be used in other information systems domains.

References Powered by Scopus

Toward principles for the design of ontologies used for knowledge sharing

4627Citations
N/AReaders
Get full text

THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.

3687Citations
N/AReaders
Get full text

Strong inference

2329Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Access to healthcare during covid-19

169Citations
N/AReaders
Get full text

Technological innovation for sustainable growth: An ontological perspective

155Citations
N/AReaders
Get full text

A unified definition of a smart city

99Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Ramaprasad, A., & Syn, T. (2015). Ontological meta-analysis and synthesis. Communications of the Association for Information Systems, 37, 138–153. https://doi.org/10.17705/1cais.03707

Readers over time

‘13‘15‘16‘17‘18‘19‘20‘21‘22‘23‘24036912

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 26

68%

Professor / Associate Prof. 6

16%

Lecturer / Post doc 3

8%

Researcher 3

8%

Readers' Discipline

Tooltip

Computer Science 19

48%

Business, Management and Accounting 17

43%

Engineering 2

5%

Psychology 2

5%

Save time finding and organizing research with Mendeley

Sign up for free
0