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A Quantitative Assessment of the Fac...

Taylor, Matthew A.

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A Quantitative Assessment of the Factors that Predict Mobile Advertising Technology Acceptance.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Quantitative Assessment of the Factors that Predict Mobile Advertising Technology Acceptance./
作者:	Taylor, Matthew A.
面頁冊數:	217 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
Contained By:	Dissertation Abstracts International77-09B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10106245
ISBN:	9781339696959

A Quantitative Assessment of the Factors that Predict Mobile Advertising Technology Acceptance.
Taylor, Matthew A.

A Quantitative Assessment of the Factors that Predict Mobile Advertising Technology Acceptance. - 217 p.

Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.

Thesis (Ph.D.)--Northcentral University, 2016.

The use of mobile technology has expanded rapidly, but it is still far from reaching its full potential due to the lack of mobile advertising (m-advertising) technology adoption. Hence, there is a need to examine the value of predictive m-advertising adoption factors that are based on the situational privacy-related environment of m-advertising. The purpose of this quantitative, nonexperimental study was to determine the extent to which situation privacy-concerns (SPC), privacy-control (PC), risk-beliefs (RB), perceived ease-of-use (PEOU), and perceived-usefulness (PU) are predictive of users' behavioral intentions (BI) to adopt m-advertising technology, including the extent to which the predictions of PC and RB are mediated by SPC. Implementation success was evaluated in accordance with the integration of SPC, PC, and RB with the technology acceptance model (TAM). The sample size was 397 Americans, age 18--35, who were smartphones users and members of the SurveyMonkeyRTM's consumer panel. A partial least squares path modeling (PLS-PM) analysis with bootstrapping was used for analysis. The findings revealed that PU is a statistically significant predictor of BI with a path coefficient (r) of 0.297, t = 2.445, R2 = .178, Q 2 = 0.150, and p = 0.015. Similarly, statistically significant evidence was found which indicated that RB (r = -0.304, t = 2.884, p = 0.00); PC (r = 0.305, t =4.685, p = .001) and SPC (r = -0.638, t = 9.375, R2 =.816, Q2 = 0.525, p = 0.001) are predictors of BI. However, no statistically significant evidence was found to indicate that PEOU influenced BI (r = .29, t = 0.702, p = 483). The predictions of PEOU, RB, and PC were statistically significantly mediated by PU ( r = 0.422, t = 8.133, p = 0.001), SPC (r = -0.211, p = .001, t =9.183), and SPC (r = -0.762, t =4.599, p = .001) respectively. The findings of this study are significant theoretical and practical contributions to the Information System field. Future researchers could explore the demographics of smartphone users' as potential predictors using a qualitative methodology.

ISBN: 9781339696959Subjects--Topical Terms:

523869
Computer science.

A Quantitative Assessment of the Factors that Predict Mobile Advertising Technology Acceptance.
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