In-Store Personalization A Privacy Perspective

Minh Chau | May 2015 Advisor: Len Schlesinger

BACKGROUND The introduction of Apple’s iBeacon, a location positioning system based on Bluetooth low energy technology, made the use of consumers’ location in companies’ marketing activities more mainstream.i Most documented uses of iBeacon and other similar systems have been focused on pushing marketing and sales promotions to consumers when they are within a certain geographical proximity (e.g. within 10 meters of a store). Luxury retailers, such as Armani and Longchamp, along London’s Regent Street have installed iBeacon within their stores for this particular purpose.ii Other than setting up a system where promotions are pushed to consumers’ mobile devices, retailers have otherwise played a passive role, waiting for consumers to act on the promotional offers. As of this writing, no documented usage could be found where iBeacon was used to push notifications to anyone other than the consumer. In particular interest are luxury retailers’ sales staff, who can use the knowledge that a high value customer is in the store, in combination with data on the customer’s historical shopping behavior, to provide an enhanced in-store experience for him/her. In turn, this could lead to opportunities to cross-sell, up-sell, or simply increase the size of the customer’s purchase basket. However, barriers to realizing this value are numerous, with consumer privacy one of the major considerations. This leads to the research question of this paper – are consumers willing to trade their privacy in return for an “Amazonification” of their in-store experience? The remainder of this section will provide 1) a brief overview of recent events as it relates to consumer privacy 2) a summary of academic literature findings regarding consumers’ tradeoffs between their privacy and personalization of services and 3) a review of how consumers have been responding to retailers’ use of iBeacon. The remaining sections will

describe the methodology, findings, and conclusions of the survey instrument designed to address the research question. Technological innovations in the last few years have enabled companies to collect an increasingly wider breadth and depth of information about consumers.iii Sources of this information vary from mobile phones, in-store shopping histories, online browsing and purchasing behavior, to social network sites. Usage of this information is just as varied, ranging from unsolicited marketing calls to online personalized product and service recommendations. However, this trend has not been without concerns around the privacy of consumers’ data with regard to its protection from unauthorized access and misuse by companies. Recent data breaches at Sony, Target Corp., Home Depot Inc., and other companies, as well as Nordstrom’s surreptitious collection of its customers’ shopping information from their smartphones highlight the costs and risks of consumers’ data being in the hands of companies. It is therefore not a surprise that much research have been conducted on companies’ collection and usage of consumer information and on consumers’ attitude towards it. In their survey of English, French, and Romanian consumers’ perception of privacy in mobile commerce, Gurău and Ranchhod found that while awareness levels varied across consumers, the majority are open to unsolicited advertising so long as the advertising is personalized to their needs.iv This openness, however, appeared to be related to how frequently consumers used mobile communications, with high intensity users more open to the unsolicited advertisements. Similar correlation between intensity of use and willingness to share data with online services was shown in another study.v However, the type of information also has an influence on consumers’ willingness to share. While information related to consumers’ finances and physical location was deemed highly private, consumers indicated that if asked, they would

share information regarding their personal interests, profile information, shopping and purchases, and personal activities.vi Trust has also been shown as an important element in determining consumers’ willingness to provide their personal information to companies during online interactions.vii This has led to the development of privacy policies that seek to reduce consumers’ fear of their information being misused or disclosed.viii Other research have shown similar findings, whereby consumers’ willingness to provide information online increases with their belief that their information will not be misused and that they can choose to have their information be deleted if they so desire.ix Additionally, the building of trust between consumers and companies occur both prior to service usage and during service usage. In particular, consumers may place greater trust in companies with good reputations (prior to usage) and in those whose personalized services provide higher quality recommendations (during service usage).x xi However, past negative experiences with information disclosure increases consumers’ concerns regarding their privacy and the risk of their information being misused.xii Further, the level of trust that consumers place in companies varies depending on the industry. In Scibelli’s survey of consumers “Banking and Financial Institutions” were trusted the most, with “Retail Stores” the least trusted.xiii Various studies have shown that consumers are willing to provide personal information in return for personalized products and/or services (i.e. those that better fit their needs/wants reactively or proactively) so long as they derive in/tangible value.xiv xv xvi xvii xviii xix However, the primary research topic has been focused on the personalization of online services through the provision, by consumers, or the collection, by companies, of personal data. What is of interest with respect to the research question of this paper is consumers’ acceptance of companies

combining their online data with their in-store retailing data (browsing and purchasing history) in order to enhance their in-store retail experience. According to a recent report from BI Intelligence,xx consumers may be receptive to the use of beacons for improving their in-store experience. However, as mentioned in the opening, this is primarily through the pushing of information to consumers, as opposed to the retailer’s staff. Further, not all consumers are responding enthusiastically, with many rejecting mobile shopping alerts due to the lack of relevance of the offers, low value of the offers, and annoyance at the notifications.xxi

METHODOLOGY A survey instrument, with questions based off of previous literature, was developed to test each of the four hypotheses below. Variable Definitions – Independent Variables Hypothesis 1: Consumers’ willingness to provide information for personalized in-store, offline experience is positively correlated with increased use of the internet and in-store shopping.

The following were asked (on a five point Likert scale from Never to Very

Frequently): 

Mobile (or Computer) Email (“Mobile(Computer)Email”): How frequently respondents used their cellphone or computer to check their email.



Mobile

(or

Computer)

Search

(“Mobile(Computer)Search”):

How

frequently

respondents used their cellphone or computer to search for information. 

Mobile (or Computer) Shop (“Mobile(Computer)Shop”): How frequently respondents used their cellphone or computer to shop.



Mobile (or Computer) Location (“Mobile(Computer)Location”): How frequently respondents used their cellphone or computer for location-specific services.



Physical Retail (“Physical”): How frequently respondents go to retailers’ physical stores to make purchases. For subsequent hypotheses and questions, respondents were asked to imagine the

following scenario: You are on your way to your favorite luxury clothing retailer, Burberry, to buy a gift for your significant other’s birthday. As you enter the Burberry store, your smartphone informs the Burberry sales staff that you are in the store via their iPads. Upon notification, the sales staff can see your past Burberry purchases online and in-store and your interests based on your activity on Burberry.com (e.g. Burberry items you viewed, items in your online Burberry shopping cart, etc.). From this information, the sales staff noticed that you were viewing products under “Gifts for Him/Her.” The sales staff member greets you by name, introduces him/herself, and says that based on the information in your Burberry profile, you’re searching for a gift for someone and asks if s/he can help you with recommendations. After selecting the gift for your significant other, the sales staff tells you that the newest version of the scarf you purchased two years ago just arrived and asks if you want to view it. In addition, the sales staff mentions that based on your profile, s/he would like to invite you to an invitational-only Burberry event next Friday. Hypothesis 2: Consumers’ willingness to provide information for personalized in-store, offline experience is positively correlated with increased trust in the luxury clothing retailer and its transparency on data collection. The following were asked (on a seven point Likert scale from Total Agreement to Total Disagreement): 

Familiarity with Retailer (“Familiarity”): Respondent’s familiarity with the retailer.



Previous Purchase at Retailer (“PrevPurch”): Respondent’s previous interaction (i.e. purchase or usage of products/services) with the retailer.



Third Party Security Safeguards (“ThirdParty”): Respondent’s increased willingness to use the personalized service with the presence of third party privacy safeguards.



Retailer’s Data Collection Policies (“Transparency”): Respondent’s increased willingness to use the personalized service if the retailer’s data collection policies were transparent and simple to understand. Hypothesis 3: Consumers’ willingness to provide information for personalized in-store,

offline experience is positively correlated with increased perceived value of the personalized service. The following were asked (on a seven point Likert scale from Total Agreement to Total Disagreement): 

Willingness to Use – Informational Benefits (“WTU - Info”) (an aggregation of six questions): Respondents’ willingness to use the personalized service if it provided informational value (e.g. “honest wardrobe advice”).



Willingness to Use – Intrinsic Benefits (“WTU - Intrinsic”) (an aggregation of three questions): Respondents’ willingness to use the personalized service if it provided intrinsic value (e.g. “improves how I feel about what I wear”).



Willingness to Use – Financial Benefits (“WTU - Fin”): Respondents’ willingness to use the personalized service if it provided intrinsic value (e.g. “customized offers and sales”).



Valuation of Personalization (“ValPer”) (an aggregation of six questions): Outside of the presented Burberry example, respondents’ value of personalized services (e.g. “I value web sites that are personalized for my usage experience preferences”). Hypothesis 4: Consumers’ willingness to provide information for personalized in-store,

offline experience is positively correlated with decreased past negative experience with privacy issues (not specific to the Burberry scenario). The following were asked (on a seven point Likert scale from Total Agreement to Total Disagreement):



Dissatisfied

with

Sending

Info

to

(“DisPrevSharing”):

Firms

Respondents’

dissatisfaction with earlier choice(s) to send their personal information to companies. 

Unsatisfactory

Experience

in

Responding

to

Advertising

(“PrevAdUnsatisf”):

Respondents’ dissatisfactory experience in responding to company advertising. 

Past Decision to Send Personal Info was Unwise (“PrevSharingUnwise”): Respondents’ belief that previous decision to send personal information to companies was unwise.

Variable Definitions – Dependent Variables Six dependent variables across two broad categories were reviewed. The first category measured respondents’ concerns or sensitivity towards giving out their information to Burberry after having read the scenario. Four types of information were looked at: 

Preferences Information (“Concerns - PrefInfo”)



Anonymous Information (“Concerns - AnonyInfo”): “information collected automatically but cannot be used to identify me, such as my computer, operating system, etc.”



Personally Unidentifiable Information (“Concerns - PUID”): “information that I have voluntarily given out but cannot be used to identify me, e.g., postal code, age range, etc.”



Personally Identifiable Information (“Concerns - PID”): “information that I have voluntarily given out AND can be used to identify me as an individual, e.g., name, credit card information, etc.” The second dependent variable category covered two questions and asked respondents

their willingness to 1) “provide further personal information to this service in exchange for better personalized experiences” (“WTP – PersonalInfo”) and 2) “frequently update my personal information to receive better personalized experiences” (“WTU - PersonalInfo”). Both questions were in referenced to the Burberry scenario.

Demographic information was used as control variables, with each converted into dummy variables for analysis. These variables included: Gender, Age, Income, Education, and Race. For response options that were provided to survey takers, please refer to the survey instrument included in the appendix. Research Design Survey respondents were adults drawn from Amazon’s mTurk service, which provides access to a scalable workforce (termed “Workers”). Only Workers whose work had a 97% or higher approval rate were allowed to participate. Workers were provided a link to the Qualtrics survey, where they were shown the Information Sheet and had to agree with the terms to proceed. Multiple attention questions (e.g. “Please answer (5) for this question”) were spread throughout the survey to prevent individuals from simply populating answers and hitting “Next.” Surveys that took less than 2.5 minutes to complete or were not completed fully were discarded. Once Workers completed the survey, they were provided with a randomized code to enter on the mTurk website to verify that they had completed the survey. Once done, Workers were paid $0.15. No personally identifiable information was collected from the Workers either through questions in the survey or through Qualtrics’ or mTurk’s tracking tools. Instrument Validity The survey instrument utilized questions previously validated in other academic research. Sources are noted in the survey in the appendix.

Experimental Results Likert-scaled responses were treated as continuous since each question had the recommended 5-7 response points and a lower p-value (0.03) was used to declare significance.xxii

Further, the purpose of the model is to describe a general trend, not to derive a precise prediction. Responses for the control variables were converted to dummy variables, with Females as the reference point for Gender, White / Caucasian for Race, 18-21 for Age, <$20K for Income, and
anonymous, and personally unidentifiable information in the presence of third party privacy safeguards and / or simple and transparent data collection policies. Respondents’ concerns regarding the sharing of personally identifiable information appeared to be mitigated if they had previously made a purchase at Burberry. These data suggests that actual experience with the retailer is a critical component in convincing consumers to share their most sensitive data. Hypothesis 3: Higher perceived value of the personalized service is correlated with lower concerns in sharing information and higher willingness to provide that information. Across all three types of surveyed personalization value – informational, intrinsic, and financial – respondents who indicated they valued such benefits were more likely to be less concern with sharing their preference, anonymous, personally unidentifiable, and personally identifiable information with the retailer. They were also more likely to share and update their personal information with the retailer. This result is in line with the data from Hypothesis 2 since such value can only be acquired through actual experience with the retailer. The benefit “Provides a positive experience for me” more often than not led to a higher willingness to use the personalized service when compared with the other potential benefits of the service. When asked about their valuation of personalized services in general (i.e. not specific to the Burberry scenario), respondents’ answers revealed similar results. In this general scenario case, and also as suggested in Hypothesis 1, males expressed a higher value for personalized services (male average of 4.3 vs. female average of 3.8, p = 0.004). Hypothesis 4: Past negative experiences with sharing of information is correlated with higher concerns in providing information to retailers. However, even with past negative experiences, the differences between males and females persist, with the average of males’ WTP

– PersonalInfo (3.88 vs. 3.12, p = 0.006) and WTU – PersonalInfo (4.24 vs. 3.19, p = 0.0002) higher than those of females.

DISCUSSION Existing literature on consumers’ privacy sensitivity towards personalized services focuses primarily on the customization of online services. The purpose of this present research is to better understand consumers’ privacy sensitivity towards personalized in-store experiences. In particular, how would consumers respond to the use of their location, historical shopping, and preference information by retailers’ sales staff to provide them with a more personal shopping experience? Further, this paper assumed that value exits for retailers to provide personalization to their customers, as have been shown in previous research.xxiii This paper showed that while there are similarities in the results between consumers’ sensitivity towards on- and off-line privacy, there are also differences that companies and public policy officials should take note of. On the similarities front, survey respondents’ answers replicated previous results in the online realm whereby past negative experiencesxxiv led to lower willingness to share and higher concerns about sharing their information. Furthermore, respondents’ frequency of mobile shopping, retailers’ use of trust-building mechanisms (i.e. transparent data collection policies and third party privacy safeguards), and the perceived value of the personalized services all led to increased comfort with sharing information.xxv xxvi However, unlike the online realm, traditional trust-building mechanisms were not sufficient to mitigate respondents’ concerns in sharing their personally identifiable information. It appears that only personal experience with the retailer (e.g. having made a previous purchase or envisioning receiving value from using the personalized service) decreased respondents’

concerns, and increased their willingness, in sharing their personal information. Male respondents also tended towards having lower concerns and higher willingness to share in return for personalized experiences. While further research needs to be done to validate the results of this survey, the preliminary conclusions suggest several implications for firms and public policy officials. As firms move towards refreshing their POS systems and unifying their commerce platforms xxvii they need to consider the data architecture required to support in-store personalized services and the IT infrastructure needed to ameliorate consumers’ privacy concerns. More importantly, retailers need to understand that not all their customers will share the same level of sensitivity towards their information, and that even within an individual that sensitivity will vary across different types of information. Responsive retailers that develop the flexibility to adapt to each customer’s privacy sensitivity will be better situated to capture and deliver value to customers who want the personalized service and avoid the negative PR backlash from those who do not.xxviii Given respondents’ sensitivity, retailers whose personalized in-store experiences do not require customers’ personally identifiable information are likely to have an easier time in executing their programs. For those that require such information, survey results suggest that retailers need to begin by focusing on generating consumers’ trust through first time purchases or other types of trust-building interactions prior to offering any personalization. From a public policy perspective, as companies seek to gather more personally identifiable information from consumers, it may be necessary for regulators to better define the required protections to ensure the safety of consumers’ information. The U.S. Federal Trade Commission’s principles for fair information practices (notice, choice, access, security, and

enforcement)xxix offer a starting point, but the nearly constant stream of data breaches xxx in the past year alone suggests that industry self-policing may not be sufficient. Going forward, research to quantify the monetary threshold at which the derived value of personalized services must surpass in order for consumers to become willing to share their information can provide further guidelines to retailers as they refine their marketing approach.

LIMITATIONS Several limitations exist to this study. First, indicating that the study relates to privacy (on the survey Information Sheet) may have primed respondents to be more sensitive to privacyrelated questions, which could have in turn biased their responses relative to their true attitudes towards privacy. Second, since the study did not ask respondents for their actual behavior, it is necessary to consider that their expressed preferences (as indicated by their survey responses) may differ from their revealed or true preferences. This would not be unexpected given past literaturexxxi

xxxii xxxiii

. Third, the study’s respondents were sourced primarily from Amazon’s

mTurk service, which makes it difficult to monitor survey quality. This was partially mitigated by using a lower p-value threshold as well as removing potentially errant responses, as indicated in the Methodology section. Fourth, since the mTurk respondents’ demographics do not, in terms of sample size, adequately cover the entire range of the population, the analysis of the results was restricted and the findings in this paper may not apply to those outside of the segments analyzed here.

APPENDIX Survey Information Sheet You are being asked to take part in a research study being done by Minh Chau from Harvard University. If you choose to be in the study, you will complete a survey. This survey will help us learn more about consumers’ tradeoff between privacy and their value of in-store personalized experiences at luxury clothing retailers. The survey will take you about 5-10 minutes. You can skip questions that you do not want to answer or stop the survey at any time. The survey is anonymous, and no one will be able to link your answers back to you. Please do not include your name or other information that could be used to identify you in the survey responses. Please make sure to mark your Amazon Profile as private if you do not want it to be found from your Mechanical Turk Worker ID. You may not be compensated if you fail to answer a question that checked to see if you read and understood the instructions per Amazon Mechanical Turk policy. Being in this study is voluntary. Please close the webpage if you do not want to participate. Questions? Please contact Minh Chau at [email protected]. Whom to contact about your rights in this research, for questions, concerns, suggestions, or complaints that are not being addressed by the researcher, or research-related harm: Committee on the Use of Human Subjects in Research at Harvard University, 1414 Massachusetts avenue, Second Floor, Cambridge, MA 02138. Phone 617-496-CUHS (2847). Email: [email protected]. You may print a copy of this information sheet by using your browser's Print feature. I have read the information sheet above and want to participate in this study I do not want to participate in this study QUESTIONS The following set of questions will ask you about your cellphone usage. Never, Rarely, Somewhat Frequently, Frequently, Very Frequently    

I use my cellphone to check emails. I use my cellphone to search for information on the internet. I use my cellphone to shop. I use my cellphone for location-specific services (e.g. finding directions, nearby restaurants).

The following set of questions will ask you about your computer usage. Never, Rarely, Somewhat Frequently, Frequently, Very Frequently  

I use my computer to check emails. I use my computer to search for information on the internet.

 

I use my computer to shop. I use my computer for location-specific services (e.g. finding directions, nearby restaurants).

The following set of questions will ask you about your physical store usage. Never, Rarely, Somewhat Frequently, Frequently, Very Frequently 

I go to retailers’ physical stores to make my purchases.

For the rest of the survey, please imagine the following scenario: You are on your way to your favorite luxury clothing retailer, Burberry, to buy a gift for your significant other’s birthday. As you enter the Burberry store, your smartphone informs the Burberry sales staff that you are in the store via their iPads. Upon notification, the sales staff can see your past Burberry purchases online and in-store and your interests based on your activity on Burberry.com (e.g. Burberry items you viewed, items in your online Burberry shopping cart, etc.). From this information, the sales staff noticed that you were viewing products under “Gifts for Him/Her.” The sales staff member greets you by name, introduces him/herself, and says that based on the information in your Burberry profile, you’re searching for a gift for someone and asks if s/he can help you with recommendations. After selecting the gift for your significant other, the sales staff tells you that the newest version of the scarf you purchased two years ago just arrived and asks if you want to view it. In addition, the sales staff mentions that based on your profile, s/he would like to invite you to an invitational-only Burberry event next Friday. Please indicate the level to which you agree or disagree with the following statementsxxxiv: 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)    

I am familiar with this luxury clothing retailer. I have previously used or purchased services or products from this luxury clothing retailer. Third-party privacy safeguards (e.g., security logos and features) increase the likelihood that I will use this personalized service. The retailer’s transparent and simple to understand data collection policies will increase the likelihood that I will use this personalized service.

Please indicate the level to which you agree or disagree with the following statementsxxxv: 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)    

I am sensitive about giving out information regarding my preferences to this luxury retailer. I am concerned about anonymous information (i.e. information collected automatically but cannot be used to identify me, such as my computer, operating system, etc.) that is collected about me by this luxury retailer. I am concerned about how my personally unidentifiable information (i.e. information that I have voluntarily given out but cannot be used to identify me, e.g., postal code, age range, etc.) will be used by this luxury retailer. I am concerned about how my personally identifiable information ( i.e information that I have voluntarily given out AND can be used to identify me as an individual, e.g., name, credit card information, etc. ) will be used by this luxury retailer.

Please indicate the level to which you agree or disagree with the following statementsxxxvi: “I am willing to use this service if it…” 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)      

…provides me honest wardrobe advice and suggestions. …provides me with information on the latest trends to stay ahead of the curve. …provides up-to-date fashion trends to me. …provides tailored recommendations based on my needs (e.g. special occasions). …saves me time from having to do my own research. …reduces the work required in assembling outfits from different departments.

  

…improves how I feel about what I wear. …makes the wardrobe shopping experience fun. …provides a positive experience to me.



…provides me customized offers and sales based on my needs, interests, and past purchases.

Please indicate the level to which you agree or disagree with the following statementsxxxvii: 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)  

I am willing to provide further personal information to this service in exchange for better personalized experiences. I am willing to frequently update my personal information to receive better personalized experiences.

Please indicate the level to which you agree or disagree with the following statementsxxxviii: 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)     

I value web sites that are personalized for the device (e.g. computer, mobile phone etc.), browser (e.g. Internet explorer) and operating system (e.g. Windows) that I use. I value web sites that are personalized for my usage experience preferences. I value web sites that acquire my personal preferences and personalize the services and products themselves. I value goods and services that are personalized based on information that is collected automatically (such as IP address, pages viewed, access time) but cannot identify me as an individual. I value goods and services that are personalized on information that I have voluntarily given out (such as age range, salary range, Zip Code) but cannot identify me as an individual. I value goods and services that are personalized on information I have voluntarily given out AND can identify me as an individual (such as name, shipping address, credit card information).

Please indicate the level to which you agree or disagree with the following statementsxxxix: 1 (Total Disagreement) 2 3 4 5 6 7 (Total Agreement)   

I feel dissatisfied with my earlier choice to send my personal information to companies. My experience in responding to company advertising is very unsatisfactory. In the past, my decision to send my personal information to companies has not been a wise one.

What is your gender? Male

Female What is your age group? 18-21 22-29 30-44 45-59 60+ What is your income group? <$20K $20K-$34,999 $35K-$49,999 $50K-$74,999 $75K-$99,999 $100K-$124,999 $125K-$149,999 >$150K What is your educational group?
Endnotes i

Phillips, Amanda. “Use location marketing to enhance brand relevance, but don't become Big Brother.” The Guardian. 2015. March 2015. . ii Regent Street. “New Regent Street App Launches.” Regent Street. 2014. March 2015. . iii The Economist. “Getting to know you.” The Economist. 2014. March 2015. . iv Gurau, Calin, and Ashok Ranchhod. “Consumer privacy issues in mobile commerce: a comparative study of British, French and Romanian consumers.” Journal of Consumer Marketing 26.7 (2009): 496-507. v Scibelli, David. The trade-off: Consumer privacy for technology products and services. Dissertation, Robert Morris University. Ann Arbor: ProQuest/UMI, 2013. (Publication No. 3568700.) vi Ibid. vii Chellappa, Ramnath K., and Raymond G. Sin. “Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma.” Information Technology and Management 6.2-3 (2005): 181-202. viii Wua, Kuang-Wen, et al. “The effect of online privacy policy on consumer privacy concern and trust.” Computers in Human Behavior 28 (2012): 889–897. ix Ibid. x Li, Ting, and Till Unger. “Willing to pay for quality personalization? Trade-off between quality and privacy.” European Journal of Information Systems 21 (2012): 621–642. xi Hagel, John III, and Jeffrey F. Rayport. “The Coming Battle for Customer Information.” Harvard Business Review. 1997. March 2015. < https://hbr.org/1997/01/the-coming-battle-for-customer-information>. xii (“consumer privacy concerns and preference for degree of regulatory control”) xiii Scibelli, David. The trade-off: Consumer privacy for technology products and services. Dissertation, Robert Morris University. Ann Arbor: ProQuest/UMI, 2013. (Publication No. 3568700.) xiv Chellappa, Ramnath K., and Raymond G. Sin. “Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma.” Information Technology and Management 6.2-3 (2005): 181-202. xv Culnan, M.J., and P.K. Armstrong. “Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation.” Organization Science 10.1 (1999): 104–115. xvi Glazer S. “Marketing in an information-intensive environment: Strategic implications of knowledge as an asset.” Journal of Marketing 55.4 (1991): 1–19. xvii Milne G.R., and M.E. Gordon. “Direct mail privacy-efficiency trade-offs within an implied social contract Framework.” Journal of Public Policy & Marketing 12.3 (1993): 206–215. xviii Lubrano, Sophie. “Digital Trust: A Trade-off between Privacy Protection and Usage Value.” Digiworld Economic Journal 88 (2012): 141-148. xix Business Wire. “Annual National Survey Finds More Consumers Willing to Trade off Privacy for Personalization.” Business Wire. 2007. March 2015. < http://search.proquest.com.ezpprod1.hul.harvard.edu/docview/ 445108515?accountid=11311>. xx Danova, Tony. “BEACONS: What They Are, How They Work, And Why Apple's iBeacon Technology Is Ahead Of The Pack.” Business Insider. 2014. March 2015. < http://www.businessinsider.com/beacons-and-ibeaconscreate-a-new-market-2013-12>. xxi Heine, Christopher. “Consumer Study Suggests Apple's iBeacon Could Work for Retailers.” Adweek. 2013. March 2015. < http://www.adweek.com/news/technology/consumer-study-suggests-apples-ibeacon-could-workretailers-154503>. xxii Grace-Martin, Karen. “Can Likert Scale Data ever be Continuous?” The Analysis Factor. March 2015. . xxiii Li, Ting, and Till Unger. “Willing to pay for quality personalization? Trade-off between quality and privacy.” European Journal of Information Systems 21 (2012): 621–642. xxiv Okazaki, Shintaro, Hairong Li, and Morikazu Hirose. “Consumer Privacy Concerns and Preference for Degree of Regulatory Control.” Journal of Advertising 38.4 (2009): 63-77. xxv Li, Ting, and Till Unger. “Willing to pay for quality personalization? Trade-off between quality and privacy.” European Journal of Information Systems 21 (2012): 621–642.

xxvi

Hagel, John III, and Jeffrey F. Rayport. “The Coming Battle for Customer Information.” Harvard Business Review. 1997. March 2015. < https://hbr.org/1997/01/the-coming-battle-for-customer-information>. xxvii National Retail Federation. “Building the Business Case for a Unified Commerce Platform.” National Retail Federation. 2014. March 2015. . xxviii Cohan, Peter. “How Nordstrom Uses WiFi To Spy On Shoppers.” Forbes. 2013. March 2015. . xxix Federal Trade Commission. “Privacy Online: A Report to Congress.” Federal Trade Commission 1998. March 2015. < https://www.ftc.gov/sites/default/files/documents/reports/privacy-online-report-congress/priv-23a.pdf>. xxx Hardekopf, Bill. “The Big Data Breaches of 2014.” Forbes 2015. March 2015. < http://www.forbes.com/sites/ moneybuilder/2015/01/13/the-big-data-breaches-of-2014/>. xxxi Acquisti, Alessandro, L. John, and G. Loewenstein. “What is Privacy Worth?” The Journal of Legal Studies 42.2 (2013): 249-274. xxxii Acquisti, Alessandro. “Privacy in electronic commerce and the economics of immediate gratification.” Proceedings of the 5th ACM conference on Electronic commerce (2004): 21-29. xxxiii Kobsa, Alfred. “Privacy-enhanced personalization.” Communications of the ACM 50.8 (2007): p. 24-33. xxxiv Li, Ting, and Till Unger. “Willing to pay for quality personalization? Trade-off between quality and privacy.” European Journal of Information Systems 21 (2012): 621–642. xxxv Chellappa, Ramnath K., and Raymond G. Sin. “Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma.” Information Technology and Management 6.2-3 (2005): 181-202. xxxvi Li, Ting, and Till Unger. “Willing to pay for quality personalization? Trade-off between quality and privacy.” European Journal of Information Systems 21 (2012): 621–642. xxxvii Ibid. xxxviii Chellappa, Ramnath K., and Raymond G. Sin. “Personalization versus Privacy: An Empirical Examination of the Online Consumer’s Dilemma.” Information Technology and Management 6.2-3 (2005): 181-202. Print. xxxix Okazaki, Shintaro, Hairong Li, and Morikazu Hirose. “Consumer Privacy Concerns and Preference for Degree of Regulatory Control.” Journal of Advertising 38.4 (2009): 63-77.