Theory-driven or process-driven prediction? Epistemological challenges of big data analytics
Journal Of Big DataPublished 23 June 2017Open access
Ahmed Elragal, Ralf Klischewski
Citations90
SJR quartileQ1
SJR score1.98
SNIP4.17
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A lightweight theory-driven approach is proposed in association with these BDA process steps in order to safeguard the analytics process from epistemological pitfalls and serve as a guideline for researchers and practitioners to consider while conducting future big data analytics.
Abstract
Abstract Most scientists are accustomed to make predictions based on consolidated and accepted theories pertaining to the domain of prediction. However, nowadays big data analytics (BDA) is able to deliver predictions based on executing a sequence of data processing while seemingly abstaining from being theoretically informed about the subject matter. This paper discusses how to deal with the shift from theory-driven to process-driven prediction through analyzing the BDA steps and identifying the epistemological challenges and various needs of theoretically informing BDA throughout data acquisition, preprocessing, analysis, and interpretation. We suggest a theory-driven guidance for the BDA process including acquisition, pre-processing, analytics and interpretation. That is, we propose—in association with these BDA process steps—a lightweight theory-driven approach in order to safeguard the analytics process from epistemological pitfalls. This study may serve as a guideline for researchers and practitioners to consider while conducting future big data analytics. Graphical abstract Epistemological challenges of big data analytics.
Keywords
Computer ScienceBusiness, Management and AccountingPhysics and Astronomy
Journal of RetailingConsumer perceived value: The development of a multiple item scale
6,398 Citations2001Jill Sweeney, Geoffrey N. Soutar
MIS QuarterlyThe Nature of Theory in Information Systems1
3,228 Citations2006Gregor
The essay addresses issues of causality, explanation, prediction, and generalization that underlie an understanding of theory, and suggests that the type of theory under development can influence the choice of an epistemological approach.
ScienceQuantitative Analysis of Culture Using Millions of Digitized Books
3,107 Citations2010Jean-Baptiste Michel, Yuan Shen +12 more
This work surveys the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000, and shows how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology and the pursuit of fame.
Academy of Management ReviewWhat Constitutes a Theoretical Contribution?
2,678 Citations1989David A. Whetten
ScienceThe Parable of Google Flu: Traps in Big Data Analysis
2,520 Citations2014David Lazer, Ryan Kennedy +2 more
Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.
Big Data & SocietyBig Data, new epistemologies and paradigm shifts
2,282 Citations2014Rob Kitchin
It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution.
Academy of Management ReviewOrganizational Theories: Some Criteria for Evaluation
1,853 Citations1989Samuel B. Bacharach
American Political Science ReviewAgainst Method: Outline of an Anarchistic Theory of Knowledge. By Paul Feyerabend. (Atlantic Highlands, N.J. and London: Humanities Press, 1975. Pp. 339. $16.50.)
1,339 Citations1977Ernest Nagel
Database Systems: A Practical Approach to Design, Implementation and Management
1,247 Citations1998Thomas Connolly, Carolyn E. Begg
MIS QuarterlyPredictive Analytics in Information Systems Research1
1,239 Citations2011Galit Shmueli, Koppius
Academy of Management JournalTrends in Theory Building and Theory Testing: A Five-Decade Study of the<i>Academy of Management Journal</i>
990 Citations2007Jason A. Colquitt, Cindy P. Muir
Twitter under crisis
943 Citations2010Marcelo Mendoza, Bárbara Poblete +1 more
The behavior of Twitter users under an emergency situation is explored and it is shown that it is posible to detect rumors by using aggregate analysis on tweets, and that the propagation of tweets that correspond to rumors differs from tweets that spread news.
Big Data analytics
762 Citations2012Sachchidanand Singh, Nirmala Singh
This analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business and is critical for success in the age of social media.
DSpace@MIT (Massachusetts Institute of Technology)The Revolutions Were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions
601 Citations2011Gilad Lotan, Erhardt Graeff +4 more
The Revolutions Were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions is published under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd).
Information Systems Research<b>Editorial</b>—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research
599 Citations2014Ritu Agarwal, Vasant Dhar
This work addresses key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science and the strengths that the information systems IS community brings to this discourse.
The Journal of Strategic Information SystemsStrategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of ‘datification’
454 Citations2015Sue Newell, Marco Marabelli
This Viewpoint article aims to shed light on the tension between businesses - that increasingly profile customers and personalize products and services - and individuals, who are often unaware of how the data they produce are being used, and by whom and with what consequences.
Lecture notes in computer scienceBig Data Analytics: A Literature Review Paper
450 Citations2014Nada Elgendy, Ahmed Elragal
The purpose of this paper is to look at some of the various analytics tools and methods that can be used with big data, as well as the opportunities provided by using big data analytics in different decision domains.
European Journal of Information Systems‘Datafication’: making sense of (big) data in a complex world
408 Citations2013Mark Lycett
This is a pre-print of an article published in European Journal of Information Systems, where the author's aim was to demonstrate the power of data-driven decision-making in the rapidly changing environment.
European Journal of Information SystemsUtilizing big data analytics for information systems research: challenges, promises and guidelines
278 Citations2016Oliver Müller, Iris Junglas +2 more
This article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews.
Information Systems JournalEpistemological perspectives on IS research: a framework for analysing and systematizing epistemological assumptions
142 Citations2007Jörg Becker, Björn Niehaves
The aim of this paper is to develop an epistemological framework which can be used for systematically analysing the epistemology assumptions in IS research, and the consensus‐oriented interpretivist approach to conceptual modelling is used as an example.
Data science for business
81 Citations2013Foster Provost, Tom Fawcett
This guide walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect, and helps you understand the many data-mining techniques in use today.