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Information Diagnosticity and Purcha...

Gurney, Laura.

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Information Diagnosticity and Purchase Intentions in Ecommerce.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Information Diagnosticity and Purchase Intentions in Ecommerce./
Author:	Gurney, Laura.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	143 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Contained By:	Dissertation Abstracts International80-03B(E).
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10979863
ISBN:	9780438668317

Information Diagnosticity and Purchase Intentions in Ecommerce.
Gurney, Laura.

Information Diagnosticity and Purchase Intentions in Ecommerce. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 143 p.

Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.

Thesis (Ph.D.)--Trident University International, 2018.

Online sales are increasing globally and understanding factors influencing purchase intentions is of direct economic interest to online retailers. Aspects directly impacting purchase intention formation are of economic interest and give further insight into consumer behavior. The adoption of online retail consumerism can only partially be measured by the technology acceptance model (TAM) through perceived ease-of-use and usefulness, and behavioral intentions. Influencing factors in online purchase intention formation include additional diagnostic criteria; therefore, use of Information Diagnosticity building on TAM is proposed. Information Diagnosticity accounts for decision making criteria utilized in option differentiation (Andrews & Allen, 2016; Chau & Banerjee, 2014). The expanded model offers additional measures which accounts for informational categorization utilized in the electronic commerce purchase decision-making process. Diagnostic aspects influencing purchase intentions can be accounted for through the inclusion of diagnostic constructs within the proposed model. Introducing differentiated aspects of trust and valence offers increased explanatory power in identifying influencing factors in electronic the commerce purchase decision-making process. The utilization of text mining's natural language sentiment analysis scoring to quantify valence levels within written consumer reviews clarifies online influences present in electronic commerce. Utilizing Information Diagnosticity in a purchase intentions predictive model, building off of the ecommerce technology acceptance model, extends its application to more accurately fit online commerce methods.

ISBN: 9780438668317Subjects--Topical Terms:

532993
Information technology.

Information Diagnosticity and Purchase Intentions in Ecommerce.
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500 $a Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
500 $a Adviser: John D. Eveland.
502 $a Thesis (Ph.D.)--Trident University International, 2018.
520 $a Online sales are increasing globally and understanding factors influencing purchase intentions is of direct economic interest to online retailers. Aspects directly impacting purchase intention formation are of economic interest and give further insight into consumer behavior. The adoption of online retail consumerism can only partially be measured by the technology acceptance model (TAM) through perceived ease-of-use and usefulness, and behavioral intentions. Influencing factors in online purchase intention formation include additional diagnostic criteria; therefore, use of Information Diagnosticity building on TAM is proposed. Information Diagnosticity accounts for decision making criteria utilized in option differentiation (Andrews & Allen, 2016; Chau & Banerjee, 2014). The expanded model offers additional measures which accounts for informational categorization utilized in the electronic commerce purchase decision-making process. Diagnostic aspects influencing purchase intentions can be accounted for through the inclusion of diagnostic constructs within the proposed model. Introducing differentiated aspects of trust and valence offers increased explanatory power in identifying influencing factors in electronic the commerce purchase decision-making process. The utilization of text mining's natural language sentiment analysis scoring to quantify valence levels within written consumer reviews clarifies online influences present in electronic commerce. Utilizing Information Diagnosticity in a purchase intentions predictive model, building off of the ecommerce technology acceptance model, extends its application to more accurately fit online commerce methods.
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