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Effect of Personalization Provider Characteristics on privacy attitudes
and behavioursAn Elaboration Likelihood Model Approach
Rishabh Agarwal
ABSTRACTComputer user have conflict with their desire for privacy,
therefore they tend not to disclose data.
a.) Peripheral route
b.) Client Side
c.) Cloud
Personalization-privacy paradox1.Privacy Calculus view: Central idea is when people have
to decide whether or not to perform a privacy-relevant
action(such as disclosing a personal data).Determine by
benefits against privacy risk.
2. Heuristics Shortcuts View: User choice is influenced
by
Willingness to disclose this information.
The order of sensitivity in which items are being asked.
Overall professionalism of user interface design.
The available choice options to choose from.
What is default and how one ask.
Why to Study Elaboration Likelihood Model?ELM reconcile “rational” privacy calculus view on privacy
decision making with Alternate heuristic view.
ELM is “dual process theory” of attitude formation and
decision making that integrates decision making processes
with different degrees of elaboration.
According to Elaboration Likelihood Model, people uses two
routes:
“Central Route (High Elaboration)” Line with Privacy Calculus
-more effortful process(such as argument quality, superiority of product,
distinctive features of the product)
“Peripheral Route (Low Elaboration)” Perform a heuristics evaluation
-Often relies on mood or general feeling
Research Model and hypothesis DevelopmentPersonalization Providers:
American Personalization
Amazon
The Cloud
Client-side personalization
Hypothesized EFFECTS
Distinguish factor of information disclosureThe current privacy literature commonly treats information
disclosure as a one dimensional, recent studies have revealed
multidimensional construct, in the sense that different type
of information can be hide from different people. There are 2
distinguish factor of Information Disclosure,
“Demographics”(SELF REPORTED PERSONAL INFORMATION)
“Context Disclosure”(system track smartphone usage data)
Information Disclosure BehaviorPeople are likely to withhold information when they are
concerned about privacy but at same time are likely to
disclose personal data and forgo privacy in return for
anticipated benefits from personalization. Defined as 2
factor:
System-specific Privacy Concerns (SPC)
Satisfaction (SAT)
Perceived PRivacy Protection
Perceived Privacy Protection (PPP) is the degree to which
people believe that personalization protect their personal
information.
General Privacy Concern and Privacy self EfficacyGeneral privacy concerns (GPC) moderate the relationship
between provider and perceived privacy protection.
Privacy self efficacy moderate the relationship between
provider and PPP. PPP is likely to be lower for people with
high levels of PSE except for those who use client-side
personalization
Online Experiment People were asked to install a smartphone app
that gives wide variety on personal
recommendation on wide variety of topics.
The experiment started with a online survey at
survey website, which introduced participants
to an android app named “check-it-out”.
The survey asked 15 question and then
participant install the app which would give
better recommendations.
The app subsequently ask participants for
various kind of demographic information,
alternating with requests for permission to
Items requested by check-it-out
Post Experimental QuestionnairesMeasured with 5 subjective factors using 29 statement for
which user were asked to state their agreement or
disagreement on a 7-point scale.
Self-anticipated satisfaction with check-it-out (SAT).
System-specific privacy concerns (SPC).
Perceived privacy protection (PPP).
General online privacy concern(GPC).
Privacy self-efficacy(PSE).
The next table displays the results of confirmatory factor.
Survey ITEMS
Results
StatisticsFor American Personalization, user who predominantly use the
central route feel less protected than user who predominantly
use the peripheral route.
Client-side Personalization is not seen as more protective than
American Personalization in peripheral route. However, in
contrast to American Personalization the privacy protection
afforded by client-side personalization is the same in both
routes. Participants who predominantly use the peripheral route
think that Amazon protects them more, while this effect is
significantly lower than central route.
Participants who predominantly use the peripheral route think that
personalization in the Cloud is marginally worse in terms of
protection than at American Personalization and they assume it’s
even worse when the predominantly use central route.
Referenceshttp://www.ics.uci.edu/~kobsa/papers/201x-JASIST-Kobsa.pdf
http://www.psy.ohio-state.edu/petty/PDF%20Files/1999-
DUAL%20PROCESS-Petty,Wegener.pdf