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Dissertation/Thesis Abstract

Quantitative Analysis on Graduate Student Participation in Mobile Crowdsensing Given Privacy Concerns
by Chacon, Rene David, D.I.T., Capella University, 2021, 160; 28319245
Abstract (Summary)

With the increasing data security awareness of mobile crowdsensing (MCS) participants, smartphone app developers are continually developing technical security protections, but with little consideration on human behaviors. Information technology (IT) advancements seen in the introduction of homomorphic encryption and privacy preserving decentralized block chains have allowed for increased data security while considering the negative impacts of MCS operability, given the added burden of encrypting decrypting data and increasing resource consumption. Despite these technical advancements seen in data security protections, researchers are still burdened with choosing maximal security or ensuring maximal MCS operability. Prominent human behavioral theories, such as the protection motivation theory (PMT), have proven successful in rationalizing the influences behind user actions in IT security centric scenarios. Leveraging PMT, specifically the variables of perceived severity, perceived vulnerability, self-efficacy, response efficacy, and response cost, this study proposed a primary research question along with five research sub questions to quantify the statistical significance that PMT had on influencing MCS participation. Following a quantitative, nonexperimental, correlation research design, this study leveraged Survey Monkey to administer an existing survey instrument to gather the necessary data to perform a multiple linear regression analysis in SPSS v26. Usage of Survey Monkey’s audience panel capability in combination with constructed preliminary survey questions allowed for ensuring that sample participants were graduate students within the United States of America, within the age group of 21 to 35 years old and actively participating in MCS via the usage of mobile navigation apps such as Google Maps, Apple Maps, and WAZE. Executing a multiple linear regression analysis in SPSS, precisely that of calculating the F-test for ANOVA, R2, and adjusted R2, in addition to calculating the unstandardized and standardized beta coefficients with t test and corresponding significance values allowed for answering the research questions and corresponding hypotheses. Data analysis concluded that three of PMT’s variables, response cost, self-efficacy, and perceived severity, each exhibited statistical significance in descending order. While the remaining two PMT variables, response efficacy, and perceived vulnerability, did not exhibit statistical significance. As a whole, PMT exhibited statistical significance in rationalizing a graduate student’s behavioral inclination to participate in MCS smartphone apps susceptible to PII theft. MCS smartphone app developers aim to benefit from leveraging PMT as an additional lens when architecting data security protections. The added knowledge of those statistically significant influences that persuade or deter MCS participation given one’s existing awareness of data security threats.

Indexing (document details)
Advisor: Witteman, Pamelyn
Commitee: Lupo, Crystal, Vann, Valerie
School: Capella University
Department: School of Business, Technology and Health Administration
School Location: United States -- Minnesota
Source: DAI-A 82/9(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Information Technology, Behavioral psychology, Information science
Keywords: Mobile Crowd Sensing, Personally identifiable information, Protection Motivation Theory
Publication Number: 28319245
ISBN: 9798582558996
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