Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams

Bibliographic Details
Main Author:	Hill, Geoffrey
Language:	English
Published:	Kent State University / OhioLINK 2015
Subjects:	Information Systems
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-kent14335973542021-08-03T06:31:26Z Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams Hill, Geoffrey Information Systems Social media networks create vast streams of unstructured big data resulting in a deluge of data, but oftentimes a comparative paucity of insights. This dissertation describes and implements a process model based upon varying dimensions of uncertainty emanating from multiple sources. The intent being to enhance the likelihood of generating actionable knowledge, and therefore returns on investments (ROI), from big data streams. Central to this mechanism is combining strategic analytics and sensemaking (Weick, 1995) into a holistic approach of knowledge discovery. The results of this dissertation support the applicability of this approach across multiple knowledge domains.Following a three essay model, this dissertation begins by describing a process map that aligns organizational knowledge management processes with contemporary definitions of big data. Building upon three characteristics of uncertainty in big data: volume, velocity and variety (Laney, 2001), this research examines varied effects and outcomes of unstructured big data transformations. The process model surfaces varying dimensions of uncertainty across the levels of transformation in the data-information-knowledge (DIK) conversion continuum. This research contributes by using process reengineering to identify and detail a model termed Big Data Re-engineering (BDR) that controls for the effects of various sources of uncertainty at appropriate levels of the DIK continuum.The second essay validates the effectiveness of the BDR process model by investigating technology adoption perceptions towards a consumer product within the context of social media. Focused upon the Apple Watch, this research describes an automated method of content analysis of over 7 million user-generated social media messages from nearly 30,000 unique authors ultimately providing quantitative measures suitable for path analysis of the constructs associated with the Technology Acceptance Model (TAM) (Davis, Bagozzi & Warshaw, 1989). The combination of a-priori requirement assessment, contemporary mining techniques and comprehensive theory contextualizes the generated knowledge and exemplifies the sensemaking process model.The third essay composes a performance index termed SPIN (Sphere of Influence in Networks) that measures influence within social media networks. Referencing ANT (Actor-Network Theory) to identify pertinent characteristics, this index individually assesses 1,296 official campaign Twitter accounts of politicians engaged in the 2014 U.S. general election cycle. The resultant index values are assessed against outcome measures such as victory and number of votes. The SPIN index results are then compared across factors such as party affiliation and incumbency to contextualize the results into the debate surrounding the transformational (Barber, 1998) or normalization (Davis, 1999) theses regarding the effects of the Internet in contemporary politics. The SPIN index exemplifies how strategic analytics and sensemaking can be used to drive organizational decision-making and practice by establishing performance criteria derived from unstructured big data. 2015-06-12 English text Kent State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354 http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Information Systems
spellingShingle	Information Systems Hill, Geoffrey Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
author	Hill, Geoffrey
author_facet	Hill, Geoffrey
author_sort	Hill, Geoffrey
title	Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
title_short	Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
title_full	Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
title_fullStr	Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
title_full_unstemmed	Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams
title_sort	sensemaking in big data: conceptual and empirical approaches to actionable knowledge generation from unstructured text streams
publisher	Kent State University / OhioLINK
publishDate	2015
url	http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354
work_keys_str_mv	AT hillgeoffrey sensemakinginbigdataconceptualandempiricalapproachestoactionableknowledgegenerationfromunstructuredtextstreams
_version_	1719438330824753152

Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams

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