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Information Networks to Derive Value...

Malhotra, Pankhuri,

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Information Networks to Derive Value from Social Media /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information Networks to Derive Value from Social Media // Pankhuri Malhotra.
Author:	Malhotra, Pankhuri,
Description:	1 electronic resource (164 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 82-10, Section: A.
Contained By:	Dissertations Abstracts International82-10A.
Subject:	Information science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28334076
ISBN:	9798557096430

Information Networks to Derive Value from Social Media /
Malhotra, Pankhuri,

Information Networks to Derive Value from Social Media /Pankhuri Malhotra. - 1 electronic resource (164 pages)

Source: Dissertations Abstracts International, Volume: 82-10, Section: A.

The rise in electronic interactions has made information networks ubiquitous. Correspondingly, research across multiple domains has begun to acknowledge the social and economic value of these networks for business decision-making. In this dissertation, we derive brand networks from social media to provide statistical knowledge on online market structures and automatically infer brand associations over time. Compared to extant data mining approaches that rely on substantial human intervention, this unsupervised automated approach lets practitioners study the relative positioning of their brand not only against a set of common competitors but against any other brand in the ecosystem; thus, uncovering a broader picture on both within-industry competition and across-industry complementarities. To investigate the usefulness of our proposed methodology, we validate the findings from our automated approach against external survey ratings and conduct extensive robustness checks to ensure reliability of underlying Twitter data.Large scale data focused methods for brand management are relatively new and present many opportunities for future research. We hope the methods introduced in this dissertation serve as a foundation for researchers interested in leveraging implicit brand networks for gaining insights into consumers and brands.

English

ISBN: 9798557096430Subjects--Topical Terms:

554358
Information science.
Subjects--Index Terms:

Information networks

Information Networks to Derive Value from Social Media /
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020 $a 9798557096430
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035 $a (MiAaPQD)0799vireo2324Malhotra
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040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Malhotra, Pankhuri, $e author. $3 3826711
245 1 0 $a Information Networks to Derive Value from Social Media / $c Pankhuri Malhotra.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 1 electronic resource (164 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 82-10, Section: A.
500 $a Advisors: Bhattacharyya, Siddhartha Committee members: Hu, Yuheng; Cutler, Jennifer; Mehta, Kumar; Kamble, Vijay .
502 $b Ph.D. $c University of Illinois at Chicago $d 2020.
520 $a The rise in electronic interactions has made information networks ubiquitous. Correspondingly, research across multiple domains has begun to acknowledge the social and economic value of these networks for business decision-making. In this dissertation, we derive brand networks from social media to provide statistical knowledge on online market structures and automatically infer brand associations over time. Compared to extant data mining approaches that rely on substantial human intervention, this unsupervised automated approach lets practitioners study the relative positioning of their brand not only against a set of common competitors but against any other brand in the ecosystem; thus, uncovering a broader picture on both within-industry competition and across-industry complementarities. To investigate the usefulness of our proposed methodology, we validate the findings from our automated approach against external survey ratings and conduct extensive robustness checks to ensure reliability of underlying Twitter data.Large scale data focused methods for brand management are relatively new and present many opportunities for future research. We hope the methods introduced in this dissertation serve as a foundation for researchers interested in leveraging implicit brand networks for gaining insights into consumers and brands.
546 $a English
590 $a School code: 0799
650 4 $a Information science. $3 554358
650 4 $a Perceptions. $3 3435328
650 4 $a Decision making. $3 517204
653 $a Information networks
653 $a Social media
653 $a Brand associations
653 $a Brand positioning
653 $a Twitter
690 $a 0723
690 $a 0338
710 2 $a University of Illinois at Chicago. $b Information and Decision Sciences. $e degree granting institution. $3 3826712
720 1 $a Bhattacharyya, Siddhartha $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 82-10A.
790 $a 0799
791 $a Ph.D.
792 $a 2020
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28334076