Mary Foster Tina West Avner Levin Ted Rogers School of Management Ryerson University September 2011

Acknowledgement

This project is supported in part by the Office of the Commissioner. In addition, Ryerson University provided funds through its SIG program to support this research. We would also like to thank our Research Assistants, Colin Rogers and Roman Cezar, for their help in conducting the field work.

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This report investigates the emerging field of targeted online from the experiences of two different sets of stakeholders: advertisers who want to reach consumers, and consumers who may or may not be receptive to receiving advertising messages; and through two lenses: those of the marketer who is trying to find more effective promotion mechanisms and those of the privacy advocate who is concerned about the potential harm of technologically- enabled targeting. This project employed both qualitative and quantitative methods to collect the information required to meet the project goals. The qualitative phase consisted of in-depth interviews with senior executives representing advertisers and publishers of targeted advertisements. In addition, we sought input from consumers through focus groups and town halls. Further, we collected information about consumers’ online behaviour and attitudes toward accessing content and advertisements through an online quantitative survey that yielded 1317 respondents. The consensus among our business respondents is that online capabilities have enhanced companies’ effectiveness in segmenting and targeting their markets. They are able to identify with more surety and clarity the consumers that will be most receptive to their promotional messages. is categorized into types, such as targeted, behavioural, contextual, and social, but for business respondents the only feature that really matters is whether it is relevant. Digital promotion is not only more cost-effective because making changes in the content or format of the message is not as expensive, but also the return on investment in terms of additional and revenues is much higher than with traditional advertisers. Marketers are not likely to give up this tool, and in the future it will be expanded to mobile devices that leverage GPS tracking. The only concerns voiced were those about how the performance data are used for decision-making because the samples may not be representative. In addition, online surveys are not as adept at collecting data on motivations and sentiments, the insights that marketers have traditionally highly valued for making decisions. A main purpose of this project has been to develop a taxonomy or framework for describing the relationship among those engaged in creating advertising content, buying advertising services, disseminating content to the appropriate audiences and measuring the performance of online advertising. What this project contributes to the discussion is the pivotal role of analysers. This group has a role in collecting information, analyzing the results and disseminating their interpretation, not only for decisions related to the goods and services being promoted, which is of interest to the purchasers of online advertising to better support their brand, but also to the creators of advertising content, and the publishers of advertising content who are interested in doing an effective job at targeting their market and focusing their message to better serve their clients. Privacy is not a major concern among our business respondents because they believe they are already compliant. Several view compliance as a competitive advantage because taking the time to discuss these considerations gives every program an extra vetting that may uncover problems that need to be addressed before launch. Our business respondents see no conflict

iii between marketers and privacy officers over the issue of because it is in the interests of both to be compliant. Government regulation is not necessary or preferred in their opinion. Because technology is evolving and capabilities and applications are developing, the attitudes and behaviours of consumers with respect to online advertising do not necessarily present a coherent picture. Consumers purport to ignore and dislike all forms of advertisement, yet a proportion report clicking on online ads, and even more believe that ads are a fact of life and something they are willing to endure to get free content on the Internet. Although they find online ads distasteful, they are not willing to pay to avoid them. Free content trumps all. Another contradiction is seen in their views toward privacy. They firmly believe that privacy is a right and they do not like the fact that companies collect information about them through their Internet habits. However, knowing that they are being tracked, most still would not change their online behaviour or avoid websites that do tracking. They don’t like their privacy invaded, but seem unwilling to change their behaviour to protect their privacy. One possible explanation for this contradiction is that participants do not really believe targeting to be effective, and therefore, do not really believe their privacy is invaded and personal information compromised. Another is that participants expect government to enforce their , and conversely, that participants view advertising that is not challenged by government as legal and therefore not violating their privacy rights. As a result participants do not see a need or room for individual activism in this area. If further analysis supports this tentative conclusion then it is a clear call for action on behalf of the Office of the Privacy Commissioner. Participants expect OPC to protect their privacy, and carry perhaps an unrealistic understanding of the OPC’s enforcement powers. OPC should therefore engage in both enhancing public awareness – to ensure the public has a clear understanding of its privacy rights and the ability of OPC to enforce them – and in calling for legislative reform, to ensure that our privacy legislation includes the necessary tools to protect privacy online.

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Acknowledgement…………………………………………………………………………..ii

Executive Summary……………………………………………………………………..…iii

List of Charts, Figures and Tables…………………………………………………… ....vii

Chapter 1: Overview……………………………………………………………………..…1

Chapter 2: Review of Literature…………………………………………………………...5 Consumers and the Internet……………………………………………………….….5 Business Perspective on Online Advertising………………………………………....8 Privacy Perspective on Online Advertising…………………………………………10

Chapter 3: Project Goals and Research Design………………………………………….12 Summary of Goals…………………………………………………………………...12 Methodological Approach…………………………………………………………...12 Qualitative Phase…………………………………………………………….12 In-depth Interviews…………………………………………………..12 Focus Groups………………………………………………………...12 Town Hall…………………………………………………………....13 Analytic Approach…………………………………………………...13 Quantitative Phase………………………………………………………..….13 Sample Collection……………………………………………………13 Analytic Approach…………………………………………………...14

Chapter 4: Results……………………………………………………………………….…15 Company Perspective……………………………………………………………..…15 Shift to Digital………………………………………………………….……15 Taxonomy for the Online Advertising Space………………………………..18 Types of Online Advertisements…………………………………………….22 Online Advertising and Privacy…………………………………………..…23 Consumer Perspective……………………………………………………………….24 Focus Groups and Town Hall……………………………………………….24 Student Online Behaviours………………………………………….25 The physical world meets the online world……………...….25 Options for connecting the physical and online worlds……..26 Blurred lines of functionality and use……………………….27

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Attitudes and Behaviour Related to e-commerce and Online Advertising………………………………………………………….28 Stage 1: Consideration…………………………………...….28 Stage 2: Evaluation………………………………………….28 Stage 3: Purchase……………………..……………………..31 Stage 4: Enjoyment, advocacy, bonding…………………….31

Online Quantitative Survey…………………………………………………32 Online Behaviour……………………………………………………32 Attitudes about Receiving and Accessing Online Content and Advertisements……………………………………………………...35

Chapter 5: Discussion and Conclusions…………………………………………………..41

Bibliography………………………………………………..………………………………44

Appendices…………………………………………………………………...……………..46 A: In-depth Interview Questions and Topics………………………………………..47 B: Focus Group and Town Hall Discussion Guide………………………………….49 C: Online Survey Questions…………………………………………………………51 D: Analysis of User Group Segments………………………………...61

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CHARTS Chart 1: Respondent Age………………………………………………………………….……13 Chart 2: Respondents by Gender……………………………………………………………….14

FIGURES Figure 1: Relationship Between Various Online Advertising Businesses………………………9 Figure 2: The Crowded Display Ad Tech Landscape………………………………………….18 Figure 3: Relationships in the Digital Space…………………………………….…………….22 Figure 4: Today’s Reality: Physical and Online Communication on Demand…………..…….27

TABLES Table 1: Online Ad Spending as a % of Total Ad Spending in Canada, 2009-2014…………...1 Table 2: US Behaviorally Targeted Online Advertising Spending, 2008-2014……………..…2 Table 3: US Internet Users who are Concerned Websites are Collecting Too Much Personal Information About Them, December 2009…………………………….….….3 Table 4: US Internet Users’ Attitudes Toward Online Tracking for Ads by Age, Dec. 2010……………………………………………………………………………….3 Table 5: US Internet Users Who would Allow Advertising Networks to Target Ads to Them, by Age, Dec. 2010……………………………………………………………4 Table 6: US Shoppers Who Have Used At-Home Digital Shopping Tools by Type, Sept. 2010………………………………………………………………………..……..5 Table 7: US Online Buyers who Use Shopping Sites, Dec. 2010………………………….…..6 Table 8: US Users Who are More Likely to Buy a Product or Visit a Retailer Based on a Positive Facebook Friend Referral, March 2010…………………8 Table 9: Percent Engaging in Online Activities at Least One Hour Per Week……………….32 Table 10:Percent Searching for Product Information Online at Least Sometimes…………....33 Table 11: Percent Taking Action Related to an Online Advertisement…………………...….33 Table 12: Percent Taking Action to Control their Receiving Targeted Online Advertisements…………………………………………………………….………….34 Table 13: Percent Engaging in Other Activities Related to Technical Capabilities……….….34 Table 14: Percent Agreeing with Statements about Privacy and the Internet………………...36 Table 15: Percent Agreeing with Statements about Payment and Online Services…………..36 Table 16: Percent Agreeing with Statements about Increasing the Appeal of Online Advertising…………………………………………………………………………….37 Table 17: Percent Agreeing with Statements about Targeted versus Random Advertising…...38 Table 18: Percent Agreeing with Statements about Online versus Traditional Advertising…..38 Table 19: Percent Agreeing with Statements about the Location of Online Advertisements….39 Table 20: Comparing Privacy/Security Concerns to other Online Shopping Issues…………..39 Table 21: The Price of Privacy………………………………………………………………...40

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Technology is embedded into the lives of most Canadians. According to the latest Canadian Internet Use Survey (2009), 80% of Canadians aged 16+ or 21.7 million people use the Internet for personal reasons. Almost all (96%) access the Internet from home and 92% of users have a high-speed connection. Canadians use the Internet for more than e-mail with 70% reporting searching for health information, 50% ordering goods and services, 67% banking online or doing on-line bill payment, 31% downloading or watching TV or a movie and 27% contributing by writing a , posting photographs or joining discussion groups. A recent report of the Bureau of Canada (2009) suggests that over the past five years, consumers have dramatically increased the time they spend on the Internet. Overall, Internet use now ranks third, just behind TV and radio, but for the important 18 to 34 age group, it is the most accessed medium in terms of total weekly time spent online. It reaches more adults each week than magazines or newspapers and it reaches more 18 to 34 year olds than radio. The habits of this young age group are significant because consumers tend to form lifelong media habits. The challenge for marketers is to make decisions about how to allocate promotion budgets in the midst of these dramatic technological changes when the impact of the recession has both decreased available funds and put pressure on revenue goals.

Table 1:

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The impact of the Internet challenges advertisers to learn to use online media to promote their products effectively. This has been a “ by doing” exercise because although advertisers had about 60 years to learn how to market effectively through television, broadband Internet access has only been available for six years, and technological innovations and applications such as social media and social networking have brought new opportunities and challenges (Bielski, 2008). Previously, most Internet marketers focused on generating a high volume of views; now, their focus has shifted to customization, and targeting as evidenced by the trend data in Table 2.

Table 2:

Behavioural advertising is one form of targeted advertising. According to McDonald and Cranor (2010), this form of advertising involves creating profiles based on data collected from Internet users through mechanisms such as cookies, and drawing inferences about preferences from this information. As marketers are turning their emphasis toward targeting in their online promotions, questions of privacy emerge. The tracking necessary to do effective targeting can be viewed as invasive given that: 1) today’s technology may allow marketers to infer the actual identity of the user; 2) even if a user opts out, “flash cookies” may allow deleted cookies to be restored; 3) browsing history, cache and bookmarks may be part of the profiling information collected; and 4) by Internet service providers resembles many features traditionally associated with wiretapping (Szoka & Thierer, 2008). As Tables 3 through 5 demonstrate, consumers see the privacy threat through targeted advertising, but at the same time, concede that there may be circumstances in which they would allow targeted online advertising. Table 3 shows that a sizable proportion of Internet consumers have privacy concerns about online advertising. However, Tables 4 and 5 seem to point to the existence of a digital divide with younger consumers feeling less negative about targeted advertising than older consumers. This report investigates the emerging field of targeted online advertising from the experiences of two different sets of stakeholders: advertisers who want to reach consumers, and consumers who may or may not be receptive to receiving advertising messages (Evans, 2009);

2 and through two lenses: those of the marketer who is trying to find more effective promotion mechanisms; and those of the privacy advocate who is concerned about the potential harm of technologically-enabled targeting.

Table 3:

Table 4:

3

Table 5:

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Consumers and the Internet The first step in exploring the burgeoning field of online advertising is to understand the usage patterns and motivations of Internet users, particularly those engaged in social media technologies (SMT). One of the strongest growth areas in Internet usage has been the adoption of social networking sites such as Facebook. Currently, this site has more than 500 million active users, half of whom log on to Facebook daily for an average of 55 minutes a day. In addition to individual users, 1.5 million businesses have active pages on Facebook (Facebook Statistics, 2011). Access to the Internet has fundamentally changed how we shop, how we communicate and how we gather information as illustrated in Tables 6 through 8. As Table 6 shows, almost three-quarters of US shoppers report using a shopping tool transmitted electronically. Table 7 indicates the level of interest in group buying sites, such as GroupOn and Wagjag that use personal information gleaned from Internet usage to provide customized product and service deals. Table 8 shows the power of word-of-mouth marketing through social media to influence purchases. With the emergence of these powerful trends, it is not surprising that between 2007 and 2012, marketers expect to see a 10.6% compound annual sales growth in e-commerce sales in Canada (E-marketer, 2010). Likewise, as a result of the increasing adoption of web-enabled smart phones, and commerce will be the defining trend for retail industry in the years to come (E-marketer, 2010).

Table 6:

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Table 7:

Table 8:

Previous understanding of SMT viewed segments as simply users and non-users. More recent research has indicated that SMT behaviour can be conceptualized as a continuum based on a combination of technical task participation, frequency of use and the social component of tasks performed. Foster and her colleagues (forthcoming in 2011) identified four distinct and sizeable user segments (SMUG – social media user groups) based on the need for information and the likelihood to engage in interactive participation.

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The first segment, the SMT Maven group (high on both information needs as well as their likelihood to participate actively, both through social and information-driven networks) participates to a greater extent in all online activities when compared to the other segments. The second segment, the Minimally Involved group is less likely to participate in all online activities compared to the other three profile categories. The third segment, the Info Seekers are more likely than the fourth segment, the Socializers to be involved in more passive, information-search types of online activities such as reading the comments of others, while the Socializers are higher than Info Seekers when it comes to more active social endeavours such as posting comments to the social network pages of others. However, they remain passive when it comes to posting content of an informational nature. This suggests that SMT participants cannot be viewed as a homogenous group in terms of their behaviour. Further research indicated that their motivational profiles are also distinct. Foster and her colleagues (2010) examined variations in motivations for and barriers to participation in terms of established SMUG segments. They found five key motivators that influence participation in SMT: Community Membership, Friendship Connections, Information Value, Participation Confidence and Participation Concerns on which SMUG segments differ. Community Membership represents consumers’ need for belonging to a community with a substantial base. This is very similar to the weaker ties associated with broad social networks discussed by Best and Krueger (2006) and Song and Walden (2007). The Friendship Connections motivator refers to the opportunity to maintain ties with existing and old friends or acquaintances, which is very similar to the “thick” social ties discussed by Granovetter (1973). Prior research has tended to examine social motivations for participation in online social networks as a single construct. Foster’s et al. research suggests this may in fact be too broad an interpretation, since certain individuals are more likely to be motivated by the need for either community membership, that is, the weak ties of the social capital model (Granovetter, 1973) or the stronger ties as exemplified in friendship connectivity, rather than to both. The third motivator is identified as Information Value, which represents an evaluation of the content found through online sources in terms of its accuracy, credibility and importance. This element has been largely overlooked as a key motivator for participation. However, previous studies investigating the development of trust in online organizations suggest that perceptions of information credibility represent an important precursor to e-commerce (for example, Connolly & Bannister, 2008). The final two motivators in the model are Participation Confidence and Participation Concerns, both of which represent potential barriers to participation. Participation Confidence relates to concerns of inadequacy or the potential for damaging one’s image when contributing information in the online environment, while Participation Concerns refer more directly to privacy concerns and the potential for harm as a result of strangers accessing posted information. This appears consistent with the findings of Ardichvili (2008), lending further support to the importance of understanding not only the facilitators, but also the potential barriers of participation in the online space. With respect to online advertising and Internet usage behaviour, Hollis (2005) found that click-through is most likely to occur among respondents who are active information seekers and when the advertisement offers immediate relevance. This result is consistent with the research of Lohtia, Donthu and Herschberger (2003) who found that regardless of content and design elements, active information seekers were significantly more likely than passive receivers to

7 click on an advertisement and that the likelihood of engaging in this behaviour is magnified by relevance to the viewer. For the purposes of this research project, it is also important to understand how consumers use the Internet to make purchases. ATG (2010) recently completed a consumer research study that provides useful insights to increase our understanding of e-commerce behaviour in the digital space. In terms of how consumers are discovering new products and services, 52% report search engines, such as , 42% e-mails from merchants, 31% word of mouth recommendations, and 21% online advertising. Interestingly, almost 30% of respondents in the 18 to 34 age group compared to 4% of those 55+ have discovered a product or service through a social network. About 41% of this younger age group is using mobile devices to complete purchases of products, compared to 9% of the oldest age group. About 18% of the younger consumers look for coupons or special offers on Facebook. This suggests that SMT and mobile are a growing mechanism for sharing information about products and making purchases, which has implications for online advertisers. In terms of the online shopping experience, this same study found that 22% of those in the 18 to 34 age group want more personalized offers compared to 5% of those 35+. Other features identified by respondents as recommended improvements to the online shopping experience include: more detailed product information (45%); better search capabilities (36%); live help options (29%); and better design and navigation (26%). Given that 70% of those who abandoned their online purchases report the reason as unexpected shipping charges, it is not surprising that 26% chose better shipping/pickup options as a way to improve online shopping. The study also identified the type of product recommendations that was most likely to resonate with consumers: for 40% it is those that relate to other items in their shopping cart; for 35% percent it is those that reflect their buying/browsing history on the site; for 24% it is best sellers and other items that other shoppers have purchased; for 21%, it is new products and for 10%, it is gift guides. To summarize, more consumers are using the Internet for a variety of activities many of which have to do with shopping including seeking information, and making the purchase. Younger consumers are displaying different patterns than older consumers as they embrace social media and mobile devices as a pivotal element of their shopping behaviour. Given that the use of these tools continues to evolve, the challenge for marketers is to know how to leverage this technology for what has become an essential component of any promotional strategy.

Business Perspective on Online Advertising The second lens through which we view online advertising is through the businesses that operate in this space. The first thing that is evident is that there is no conventional taxonomy to describe the new landscape of online advertising. For example, Turow and colleagues (2009), in their study of opinions about behavioural marketing, suggest that there are three different kinds of companies operating in this space: websites that can track customer behaviour online; advertising networks, which can also track the movements of users, but across thousands of websites; and offline retailers who can track user preferences and habits through frequent shopper cards. Szoka and Thierer (2008) built their taxonomy based on the forms of advertising in addition to how it is delivered. Targeted advertising may be based on demographics, geography, current context, or patterns over time. Contextual advertisements based on what a person is searching for at the moment may be delivered by search engines such as Google, or by third­

8 party advertising networks. Behavioural advertising is “smarter” than because it targets consumers based on their user profiles as determined by tracking their behaviour over time and over a number of websites. Evans (2009) notes that the features of the online advertising industry are in considerable flux, yet there are certain features of this ecosystem that remain constant. There are three basic groups: advertisers who want to reach consumers with their message, consumers who may or may not want to receive the message, and in between are various intermediaries. Figure 1 (adapted from Evans, 2009) illustrates the nature of the relationship between the various intermediaries, some of whom focus on the advertiser, some on the consumer, and some on both through “interlocking multisided platforms” (pg. 40). As Evans points out, advertising networks such as Google collaborate both with publishers to sell advertising, and with advertisers to deliver targeted viewers. It auctions keywords that appear on participating publishers’ webpages and then provides advertisements that match those keywords or other characteristics of the website.

Figure 1: Relationship between various online advertising businesses

What Who Examples Advertiser Omnicon Group, Producing ads Advertising agencies and WPP Group plc, creative tools Interpublic, Publicus

Managing ad campaigns, Advertiser tools DoubleClick, sending ads to publishers Google, aQuantive

Intermediation, Spiegel’s sale force, Matching ads to inventory direct sales, ad networks, Valueclick, Google, and setting prices ad exchanges Right Media, DoubleClick

Managing publisher DoubleClick, Google, Publisher tools inventory, serving ads aQuantive, into ad space 24/7Real Media

Liberto.it, Spiegel.de, Eyeball Attracting eyeballs with Publishers FT.com, engaget.com content

Source: Evans, 2009, pg. 41

Deane and Pathak (2009) identify three main participants in the online advertising space. First there are the advertisers — companies that have contracts with publishers to promote their products. Second, there are the publishers that produce online advertisements as a way to generate revenue. Finally, there are the consumers who, by virtue of browsing various websites online, are exposed to online advertisements.

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Another view on the taxonomy concerns marketing behaviours — , monitoring and tracking. For example, Google Adworks offers keyword search advertisements that appear beside search results, and site-targeted advertisements, which allow advertisers to choose individual sites for their advertisements. The keyword option uses a cost-per-click payment mechanism, which means advertisers pay each time a user clicks on their link on the search page. The site option uses a cost-per-thousand impressions (CPM) payment method, which means an advertiser pays each time the ad is displayed on a website. Both methods have their pros and cons and there have been only a few studies that attempt to identify the optimal mix in terms of revenue generation (Chen, 2008). Coupled with the different revenue models is the inability of the online advertising industry to provide reliable measures of effectiveness for marketers that have been standard in other forms of media (Anonymous, 2009). These measures are pivotal to making budget allocation decisions. Thus, one key objective of this research is to develop a taxonomy based on parameters that take into account the needs and perspectives of all stakeholder groups and one that can be used by scholars as a framework for future research and for policy-makers as a structure for regulation.

Privacy Perspective on Online Advertising From the perspective of privacy policy makers and practitioners, any framework or taxonomy that is developed needs to include the impact of the method of online advertising on the privacy of Internet users. Such a framework needs to take into account not only the different conceptual aspects of the value of privacy that are relevant to online advertising, but also the structural and organizational roles of privacy officers, the executives who are supposed to manage privacy for their organization. In response to concerns about regulations that govern privacy, many organizations have a position on their senior management team for a person to oversee privacy issues with customers, employees and suppliers. Privacy officers in companies involved in online advertising are in an interesting and potential conflict of interest situation as they execute the terms of their job description. For example, if they protect the privacy of their customers, are they undermining the goals and objectives of the company and ultimately affecting the ability of the company to be profitable? Investigating this aspect of online advertising is another focus of this project. There are two main conceptual aspects of privacy that we explore in the context of online advertising – privacy as anonymity, and privacy as personal information protection. Anonymity is one of the core aspects of privacy, that is, the ability of individuals to hide their identity, and experience freedom of action and expression as a result (Westin, 1967). Common belief holds that anonymity is a key aspect of the online experience. Peter Steiner captures this in his seminal New Yorker cartoon, in which a dog is pictured sitting in front of a PC presumably surfing the Internet. The cartoon’s caption reads, "on the Internet no one knows you’re a dog”. Internet users have gone to court in order to protect their anonymity. In reality, Internet service providers and content providers have access to data that may well conflict with the popular perception of an entitlement to anonymity online. Personal Information Protection, or Data Protection, is a more recent notion that provides individuals with a quasi-proprietary interest in data that identify them. This “property” interest affords individuals with some measure of control over how that data are collected, used, disclosed and retained, and requires data handlers to seek the consent of individuals for such actions (Solove, 2006). Personal information protection regulators have focused on providing individuals with better control over their personal information that may be collected by

10 advertisers. The Privacy Commissioner of Canada’s findings on Facebook’s advertising practices serve as both a recent example and a model for future regulation in this area (OPC, 2009). Further complicating the taxonomy of both advertising and privacy terms is the use and creation of marketing terms by privacy scholars. For example, privacy scholars draw distinctions between “” a term which they use in reference to word-of-mouth recommendations, viral advertising, and similar, supposedly consumer-driven initiatives, and “behavioural advertising”, a term which they use to identify advertising based on a consumer’s prior interests and personal information and preference (McGeveran, 2008). The extent to which marketers use the same taxonomy is not clear. This project establishes a common, mutual frame of reference for both marketers and privacy advocates.

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Summary of Goals The goals of this project are to understand:  The structure of the online advertising industry,  The trends in marketing promotion in the online environment  The current and potential uses of behavioural and contextual advertising  The measurement and accountability gaps, concerns and issues in online advertising  The perceptions of marketing executives and privacy officers about existing protection for private information, consumer knowledge about privacy issues and threats related to online advertising  The role of marketing executives and privacy officers in collecting and protecting consumer information  The structure and relationship of companies involved in buying and supplying online advertising and measuring its impact  The behaviour of consumers on the Internet as it relates to online advertising  The attitudes and perceptions of consumers about online advertising as it relates to their personal information.

Methodological Approach This project employed both qualitative and quantitative research designs to collect the information required to meet the project goals.

Qualitative Phase In-depth interviews. We conducted six in-depth interviews lasting from 60 to 90 minutes with senior executives representing advertisers who market and promote their goods and services in an online environment and with publishers who provide space or act as brokers for space for online advertisements and ad networks. We used a semi-structured interview that covered topics such as trends in marketing promotion, the structure of the online advertising industry, the role of privacy officers and marketing executives, context advertising, behavioural advertising, viral marketing, techniques employed for capture, protection and use of personal information, and mechanisms for obtaining consent and protecting anonymity. See Appendix A for a list of interview questions. Focus Groups. We conducted two focus groups in October 2010 with consumers in the 18 to 30 year old age range to gain insight into their use of the Internet related to shopping behaviour and their attitudes and opinions about targeted online advertising. The discussion outline for the focus groups investigated the consumer perspective on: awareness of online

12 advertising, interaction with online advertising, attitudes toward companies that participate in online advertising, perception of data collection with respect to their personal information, knowledge about cookies and flash cookies, attitudes and beliefs about privacy protection and privacy concerns in online advertising and social media/networking sites. See Appendix B for a list of discussion topics. Town Hall. After each focus group, we opened up the discussion to an audience of 60 observers to add to the comments and opinions expressed by the focus group participants to enhance our understanding of how young consumers operate in the digital space. Analytic Approach. For the qualitative portion of the study, interviews and focus group discussions were transcribed and analysed by independent researchers for trends, issues, themes and concerns.

Quantitative Phase Using the results of the focus group and town hall research as well as items from the questionnaire used by McDonald and Cranor (2010) in their study of Internet users’ understanding of behavioural advertising, we constructed a quantitative survey instrument for online deployment. The resulting 149-item questionnaire contained a series of mainly closed- ended questions relating to demographics, attitudes, beliefs and behaviours related to usage of digital media tools and experiences with online advertising. The questionnaire is attached to this report as Appendix C. Sample Collection. An online survey was launched for a 4-week period from February 16 to March 18, 2011 using the online survey tool “Opinio”. A link to the online instrument was made available to undergraduate students at Ryerson University – a large, culturally diverse urban Canadian university located in Toronto, Canada with an enrolment of approximately 27,000. A total of 1592 respondents consented to participate. Of these, 270 submissions were eliminated because they were either incomplete or identified as duplicates, resulting in a total of 1,317 questionnaires that were used for the final analysis.

Chart 1: Respondent Age 45% 40% 38.2% 35%

30% 25%

20% 16.0% 15% 12.2% 12.5% % of Respondents 10% 7.0% 3.8% 5% 2.4% 2.0% 3.3% 0.4% 0.4% 0.7% 1.1% 0% 18 19 20 21 22 23 24 25 26 27 28 29 30+ Age

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As illustrated in Chart 1, student ages ranged from 18 to 55 years, with 94.5% falling in the 18 to 26 category, and an overall mean age of 21.3 years. Chart 2 confirms a relatively equal representation of males (50.4%) and females (48.4%), with 1.2% of respondents who chose not to reveal their gender.

Chart 2: Respondents by Gender

Not Reported 1.2%

Males Females 50.4% 48.4%

Analytical Approach. We analysed the results of the questions from the quantitative survey using SPSS (version 19). Basic summary statistics included frequency distributions and mean values for scale questions.

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The data collected represent two different perspectives: those of the companies working in the online advertising space and those of consumers who are the targets of these advertisements.

Company Perspective The first set of results presents the views of companies working in online advertising as publishers, advertisers, analysts and privacy officers as captured through in-depth interviews with senior executives.

Shift to Digital The first topic for consideration was the shift to digital as manifested in the reallocation of media dollars. Although this trend, according to R21 “has been more pronounced or spoken about...in the last four or five years”, in his opinion “it’s been happening for quite some time...we saw this trend back in ’95”. We asked respondents first to comment on the advantages and disadvantages of this shift. According to respondent comments, the advantages can be grouped into two main categories: versatility and impact. In terms of versatility, digital provides “more ways to tell a story” with “video and audio [to make ] it more real time” (R1). In addition, because “online is non-linear” (R3), marketers have more leeway to be creative in their presentations, which often “makes the story more compelling” (R1). Thus, the versatility of digital as a medium of communication provides more tools for marketers to develop creative and competitive promotional strategies for their products and services. Likewise, these same tools allow them to use different mechanisms to get feedback from consumers about their attitudes and perceptions of these same products and services. “Online research has grown in popularity to the point now where Internet-based research is ahead of telephone” (R6). Further this research design may actually offer advantages as a number of researchers believe that “online self-administered methodology is actually a better method for reducing social desirability bias” (R6). For companies, using the online space provides researchers with the ability to “convene [focus groups] in different time zones across the globe in one group at one time” (R5), which adds to the impact of the research findings, which in turn may positively affect business decision-making. The comments about the positive influence of the shift to digital in terms of impact can be grouped into three main categories: lower cost, better targeting, and better performance. As R3 points out digital “is a lower risk medium than traditional media, so unlike TV and print and other media that you have to buy upfront”. Further, it is much more cost effective to make

1 We identify verbatim comments from the key informant interviews using respondent numbers only. 15 changes in digital advertising than in traditional advertising. With Internet advertising, “you can run an ad across a network or two ads and see what pulls better....because it is relatively inexpensive to do it. But if you develop a television ad to go back and change it, it’s millions of dollars” (R3). Part of this cost impact has to do with better targeting because digital advertising “has the ability to be more focused in aiming a message at a particular interested group or even individual” (R5). “The Internet allows you to focus on niche markets as opposed to mass” (R3). Although respondents acknowledge that every marketer knows who their target is, with digital the segments get more refined and more focused. This “allows marketers to refine their budget, their message and their offer, as opposed to offering [a product or service] and spending a lot of money and hoping in the net they are going to get these people” (R3). Because of the ability to target more specifically “the return on marketing investment is higher in the case of digital....$20 versus direct mail at $12 versus radio or television at $6 or $7” (R5). Not only is the return on investment better with digital, but the ability to measure that performance, have more confidence in the results, and make better marketing decisions is also better with digital. Using a digital format “gives you actionable statistics of not only what you’re getting out there in the market place....but allows us the option to actually develop creative that can be tested in a number of iterations that can influence what we do in traditional media” (R2). Despite its many advantages, respondents do see some issues with the shift to digital. The most important concern has to do with internal organizational issues. One respondent strongly believes that “it’s actually better to build this from the ground up than it is to take an existing house and reconfigure it” (R1). Because transitioning to digital tools also changes the dynamic of an organization, it presents management “challenges ...getting the right skills in the right places...figuring out who owns what and who is accountable for what” (R1). This can be a very problematical change in that “people are territorial, people are unsure of what they should be doing so therefore get defensive, and that puts pressure on the organization, which is actually difficult unless you are actually wired that way” (R1). Likewise, the shift to digital “can be a little labour-intensive and requires a different structural model of how it’s compensated....and you can get lost in the data” (R2). Planning is also done differently in that “in the old days companies used to have a five year strategy; they ran a five year plan. Now it’s quarter to quarter almost” (R3). The whole value structure of the organization can be turned upside down by moving into the digital space. “It causes us to question the value of the products we’ve been selling for a long-time. It changes the landscape a lot in terms of the value changing for customers and the nature of our partnerships” (R4). Further, the rules of operation change. “We’re finding increasingly that the regulatory framework that exists for traditional media doesn’t fit in the digital media world” (R4). For example, there are many more opportunities for fraud as the marketing research industry has found a dramatic upswing in “online fraud with people joining panels from China under multiple falsified identities” (R6). Although digital offers some marketing research advantages, there are also disadvantages. Although there are data that are collected automatically and passively about sites visited, pages viewed, transactions made, “what is missing is attitudinal and opinion research” (R6), that is the motivations for and significance of the actions taken; data that are not as easily or reliably collected online, but are key elements of strategic decision-making. The most significant disadvantage from the perspective of the companies producing and advertising goods and services is their diminished lack of control in the digital space. “The

16 client no longer controls the consumer anymore” (R3). The consumer decides what to watch, what to click-through, and can choose to opt out of advertising. Traditionally, the way to avoid television ads was to take a trip to the refrigerator during the commercial break; now viewers simply click on a button. Next we asked respondents to describe how marketing success is measured in the digital environment. They agree that success is largely measured quantitatively through “exposure, click-throughs, who actually looked at the items, read it, see it....and actual conversion to sales” (R4). There are advantages to these measures in that “you see real-time ROI, it’s relatively inexpensive compared to what you’re dealing with from the past and it’s trackable” (R3). However, respondents also point out that marketers have not found effective ways to collect data about “what is going on in [consumers’] minds when they interact with your brand” (R1). Further, others comment that “we’re focused way too much on the science, the hard tactical kind of measurements, like clicks or unique visitors or page views...We’re ignoring the relationships we have” (R4). However, this lack of qualitative data is not just an issue for digital research because “there really aren’t very good measures for that...even offline” (R4), and most marketers believe that gaining insight into the reasons for actions is essential for building the strength of a brand. Others have difficulty with the quantitative aspect of digital research because of its lack of rigour. “These newer methods [digital online research] are poor substitutes for our ability as an industry to design scientific samples based on probability theory and conduct an empirical survey, which is then projectable to a particular population” (R6). While the quantitative data provided through digital interfaces is helpful for tracking performance, there remain unresolved measurement issues, which create concerns about the decisions that are being made, including the allocation of dollars. Passive measurements of click­ throughs and sales are reliable, but some question whether these data are strategically informative because there appear to be no reliable ways to measure the motivations behind the actions, something marketers value in decision-making. Even the reliability and representativeness of some of the online marketing research that is being done is questioned because “22% of Canadians are not on the Internet” (R6), and thus are not able to participate in online surveys even if they wanted to. Although clearly some of the current challenges in operating in the digital space have not been resolved to everyone’s satisfaction, we asked for perspectives on the next frontier in . Respondents agree that mobile is the future of the digital space, especially the geo­ targeting capability that comes with the GPS dimension of smartphones. Another future trend is customization, with digital capabilities allowing marketers to provide exactly what customers want when they want it. We also asked for comments about new challenges and opportunities that arise with these future developments. Respondents identify the need for data as a key challenge. “Never before has (sic) data been as important as it is now, as a competitive advantage to a marketer” (R3). They believe that new marketers will need superior data skills, not only collecting appropriate information, but also in bringing insight to numbers, because numbers alone are not sufficient for strategic decision-making.

Taxonomy for the Online Advertising Space

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One of the challenges in understanding this new landscape is the lack of a generally accepted taxonomy. One of the reasons for this is that the features of the digital space continue to evolve as new technologies come on stream, as more competitors enter the market and as consumer tastes shift in response to new opportunities. As a result the landscape is incredibly complex. Figure 3 represents an attempt by the Interactive Advertising Bureau (IAB) to capture the participants in the online advertising industry as well as the relationships between them. Due to the many technological innovations attempting to enhance advertising online as well as analyze customer data, there are many participants in this field, and each entrant creates its own taxonomy, or modifies the existing taxonomy, or even just its terms, to suit its business purposes.

Figure 2:

Source: http://www.iab.net

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What follows is an attempt, based on the interviews we conducted, to distill a common taxonomy that would be useful for academic researchers, regulators and the industry. The basic building blocks of the taxonomy are traditional: advertisers and audience. An advertiser is still a person with a product to sell, and an audience is still the collective of potential customers. Interestingly, the largest online advertisers are not the largest . In the US, mobile phone companies are among the top ten advertisers, as well as credit bureaus and trading houses (Comscore, 2010). Soft drink and fast food manufacturers are nowhere to be found. This report is not the place to analyze why this is so, but certainly one reason is that the online audience does not reflect in full the traditional market. “Let’s face it, in Canada the internet population is roughly 77 – 78%. So 22% of the population is not online. They tend to be older and have lower levels of income and education. So if you’re trying to conduct a survey where you want to project the results to the entire population, I don’t think that online is the best way to go” (R6). The buildings blocks between advertisers and audience are flexible, since the attraction of online advertising for advertisers lies in its flexibility: “So the thing about internet advertising is that it does a couple of things. One is that you can stop and start it very quickly. Two is that you can optimize it. Optimize it means that you can run an ad across a network, or two ads, and see what pulls better. What we mean by pulling is it that it’s a click-through or people are responding to it. Or you refine the messages and so-on, because it’s relatively inexpensive to do it” (R3). There are also many analytical participants, since the other major attraction of online advertising, in addition to its flexibility, is its measurability (R3). In fact, being able to measure the impact of online advertising is what enables it to be flexible. One example of flexibility is the ability to eliminate traditional middlemen. An advertiser can contact audiences directly through its own online media, and can contact online publishers directly through advertising programs created by publishers, such as Google’s AdWords (the feature that will bring up an advertiser’s ad next to a search for certain key words). Therefore, while the following taxonomy lists building blocks, it is important to note that, with the exception of advertisers and audience, these building blocks are not necessary for online advertising. “Before the networks came about, I would say we were dealing with the publishers directly. And we still do…we deal with them directly because they’re big, but I also deal with these networks” (R2). In the event an advertiser does wish to retain an intermediary, the closest one traditionally, and part of our suggested taxonomy, is the agency. The advertising agency is the business charged by the advertisers with all aspects of managing the advertisement of the advertiser’s product (R3). These aspects include buying media for ads, researching the market, analyzing how well ads perform, and the like. All of these functions can be performed by businesses other than the agency, and respondents noted that some agencies in the process of pursuing opportunities, purchasing and owning many of the supporting businesses. “[W]hat they’re doing is, certainly the large multinational research firms are acquiring these upstart companies that are involved in this space… [Agency X] acquired [Researcher Y] a year ago, so [Agency X], in addition to owning a number of advertising agencies, also owns… a number of research agencies… [Agency X] also owns a lot of public relations agencies… and the thinking is with these large marketing and advertising conglomerates… is that you can provide a one-stop shop” (R6). Agencies choose to own many of the supporting businesses because it enables them greater control over sensitive and lucrative customer data. “It’s pretty much internal I would say because maybe in the different agencies different clients may follow that, but I think that in this case that’s the value of the data. Whether it’s anonymized or however that you’re looking at it, because that gives you actionable statistics

19 of not only what you're getting out there in the market place, because right now this allows us the option to actually develop creative that can be tested in a number of iterations that can influence what we do in traditional media, so if at all we outsource or do those things, that’s not going to be – it’s going to just be more, sort of, I would say we would lose a certain amount of control or actionable information on that” (R2). On the other side, the business closest to the audience is the publisher. The publisher is the business that displays the ad to the audience. Publishing ads is often a secondary business online for a traditional media outlet, such as a newspaper, magazine or television station, but it also generates revenue that allows media to remain free online. Publishers are therefore interested in analytic information as well, to support their and ensure that they are in business with the optimal advertisers from their perspective. “[A]t the end of the day the advertisers or the clients want to know who’s buying our content, where they’re buying it, how much time they’re spending with it, what their demographic profile looks like, what their psychographic profile looks like, because they have targets that they have established for segments and people that they want to reach. [W]e have to tell them for smart-phone and the tablet and the e-reader, and our content showing up in other places as well. People are taking it at Google and others are taking it certainly online, so again if you can imagine, you had one channel before and now you’ve got multiple and it’s getting more complex all the time” (R1). Because of the complexity, publishers often turn to other businesses for the analysis, just as the agencies. “[M]uch of it was done by external – much of it is still done by external – syndicated data. It comes in from industry organizations… [F]rom a web standpoint [advertisers] want to know in the next hour who’s going to be online and how they’re using it… [W]e’ve outsourced the actual data piece to a company that does it for a lot of publishers… that tracks all that data for us and provides reports. We spend all of our time analyzing reports, trying to figure out what’s going on” (R1). However, depending on the strength and appeal of the publisher, they may cooperate selectively with companies that participate in online advertising, which are not the actual advertisers themselves. Publishers for which there is less demand may choose a steady, less lucrative revenue stream by turning over some of their publishing availability to other businesses that then re-sell it to advertisers. “A lot of the media buying platforms… are third party groups that take inventory off of our website, or anybody’s website, and consolidate them into packages for media buyers to buy. We feel we have enough traffic that we don't need to sell it in that way… There’s a lot of demand for our space, but also we’re effectively responding to a niche market… So all that ad network stuff in the middle, ad exchanges, they’re all, as you know, there's close to probably a billion websites, they’re all aggregating the inventory and trying to find creative ways to sell it to clients who don’t want to deal with all of the individual sites, and they’re also looking to figure out better ways to target customers, so they're trying to build segments and profiles of audiences based on what they know about the inventory they've got” (R1). Cooperation with intermediaries is an option for agencies as well. “Like we bought a certain section and we make sure that a certain amount of rotation for our clients. So yes, we would not go to the ad networks for that – that’s something that you would deal directly with the sites – and we deal with as many of those big ones in those cases. Now a network example would be perhaps a lot more smaller sites, which I would not get a chance to go and buy individually, because either those individual sites don’t sell it on their own, they may be a part of [a larger] network… where they are not significant or not large enough. So when I go in and buy the ad networks, it allows me to buy a lot more targeted and mainstream. But if at all I’m buying – I would buy the long tail from some of those guys, because the other thing that all these ad

20 networks get is like the bigger sites. [A bigger site] sells on its own – we buy from them directly as we’re buying Canadian eyeballs, but they also sell to the certain networks what they call remnant inventory. So there are chances that I could have bought it [directly] but part of that will show up on your [bigger site] through the ad network, because they have bought the remnant inventory” (R2). And so the emerging taxonomy is of several large building blocks – advertisers, agencies and publishers (with the audience, of course, in the background) supported by a field of smaller players, some of whom are owned by the larger bodies, and some of whom operate independently. One participant described this field as “middle-ware – all these people in here support [agencies and publishers]” (R3). Respondents suggest that the field is too crowded, and that consolidation, through mergers and acquisitions, was inevitable. “Look at the landscape – imagine all the different ways somebody might measure page view or an ad delivery. There are way too many players in that space and it’s all going to consolidate and it’s going to be driven by things like ‘I want to be able to measure the same way across everything that I do’. So I think we’re going to see that consolidation across mobile, online, interactive television, even OTT video. I think that has to happen, that’s inevitable, and a consolidation of the players, all the people trying to get a piece of really the measurements of success. When you look at all these categories here, that’s going to happen soon” (R4). Based on participants’ comments, we suggest several categories that both clarify, and consolidate, some of the existing “middle-ware”. Our first suggested category is that of purchasers. Purchasers are intermediaries that currently purchase advertisements from advertisers and agencies (for example, ad networks and ad exchanges) or intermediaries that currently purchase publishing opportunities from publishers (such as media-buyers). Purchasers facilitate advertising transactions, and optimize opportunities for both advertisers and publishers. Our second suggested category is that of analyzers. Analyzers crunch data and provide information to all the other participants in the online advertising landscape. Current examples are data brokers, data aggregators, data analyzers, ad optimizers and the like. One participant described the operation of an analyzer: “Well there’s tracking software in terms of how many people showed up at my site, how many pages they clicked on… things like that. [W]hat they’ll do is they’ll get it from a whole bunch of different sources, then what they’ll do is they’ll aggregate that data, then what they’ll do is they’ll basically sell it to you, they could sell it to a publisher, they could sell it to a marketer, because that company probably collects different types of data” (R3). The various building blocks in our taxonomy are depicted in the Figure 3. Another, potentially disruptive, participant missing from this picture is a new form of intermediary – the facilitator of collective purchasing. Businesses that deploy collective purchasing power, such as the current market leader Groupon, combine publishing, purchasing and word-of-mouth referrals in ways that may change the this taxonomy and the roles of its participants.

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Figure 3: Relationships in the Digital Space

Agencies

Advertisers Analyzers Publishers

Purchasers

Types of Online Advertisements The literature tends to differentiate among four types of online advertising: a) targeted advertising, which is based on demographics, geography, current context or patterns over time; b) contextual advertising, which is based on current online searches, and is thus is delivered by search engines, such as Google or third party advertising networks; c) behavioural advertising, which targets consumers based on information determined by tracking behaviour over time and over a number of websites; and d) social advertising, which targets consumers based on their network of connections over social media. We asked respondents to comment on these types and to share their understanding of digital advertising. Respondents agree that “targeted advertising is based on demographics, geography, current context and patterns over time” (R2), but make the point that “this was being done a long time ago” (R2) through and target marketing and is not something new that has arisen concurrent with online digital capabilities. There was some disagreement with the definition of contextual advertising as being based on current searches. Respondents prefer to separate out advertising searches or what they call “intent-based advertising” (R4), from contextual advertising, which they see as having to do with specific content. “So if somebody’s looking at an article on mobile phones, I can contextually serve up an ad, which is about mobile phones without having anybody actually search for it” (R2). Behavioural advertising involves tracking and thus tends to be the purview of networks and portals, and has been done for a while. However, social advertising is a bit of a mystery to

22 marketers because of the lack of a business model. “You know we are trying to figure it out as much as they are.... until two weeks ago...didn’t have an ad model. They were burning through venture capital money until they figured out how to make some money...what they call ‘promoted tweets’” (R2). Respondents agree that the only thing that matters is relevance, no matter how you categorize advertising. “If you can be sure what you’re communicating is relevant, if you can increase the probability that what you’re presenting to the consumer is something they’re interested in, it changes. It converts from being annoying clatter to being useful information” (R5). In general, the consensus seems to be that the categorization of types of online advertising is not as important as how relevant the advertisement is to the end-user.

Online Advertising and Privacy Because the essence of this project is to understand where marketing meets privacy in the online advertising space, we asked respondents to comment on any current or future conflict that may occur between these competing objectives. Respondents are clear that marketers have to observe privacy regulations. “I think that responsible marketers and publishers have to put a framework in place on their sites to ensure [privacy regulations are met] and be very transparent to consumers”(R3). Privacy is clearly a more prominent issue in the market place now than it was previously. “In 2010 there’s much greater sensitivity to privacy issues within the business community” (R5). That said, respondents also believe “it is a free market” (R3) and don’t believe it is the role of the government to regulate online advertising. Instead “it is up to the individual and website owners to govern their actions accordingly” (R4). Marketers have to be transparent in that consumers “want to be able to understand how their personal information is being used” (R5), and have to be able “to protect themselves and make choices” (R5). Respondents believe that “people are aware that they are being tracked online” (R6) and may even welcome targeting “as long it is relevant and it is not intrusive, it’s not hindering your experience” (R2). R6 offers a slightly different view that “behavioural advertising offers more relevant ads, but people aren’t comfortable with this idea of being tracked to display those ads. However, they don’t have a problem receiving ads from people they trust, companies or brands that they’ve purchased from before”. This seems to suggest that online advertising is better received when a relationship already exists between the consumer and the company. In terms of responsibility for privacy, respondents believe that the responsibility lies mainly with the individual. “At the end of the day, the best protection is to protect yourself and understand how best to do that” (R1). The responsibility of marketers is “to honour...their right to opt out of getting communications of any kind and be obviously supportive of that” (R1). None of the respondents is keen on government oversight in this area. Further we asked for specifics about their understanding of the role of the privacy officer as it relates to digital advertising. Interestingly, many of our respondents do not have an in-house privacy officer “because we follow standards and all the clients we work with have legal counsel in-house and they do all that for them” (R3). This suggests that the buyers of advertising services are deemed to be responsible for ensuring that the advertising that goes out under their name is privacy compliant and that the creators of those advertisements assume the client has done the necessary due diligence. For those companies with an in-house privacy officer, the role of that person is to “look at our initiatives where we do any kind of behaviourally targeted or interest-based targeted

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[online advertising], and we consult with her on a regular basis to make sure we are aligned and that we’re looking at a standard that would be appropriate for [respondent’s large multi-business line company]” (R4), much the same role that some advertising agencies delegate to their clients. Respondents believe that it is a counterproductive strategy not to be sensitive to privacy issues because it will annoy customers and “you’re going to hurt the company’s reputation” (R5). Being privacy compliant can actually be a competitive advantage. For example, R6 believes that compared to US companies who have less stringent privacy obligations, Canadian companies “add value to marketers and others within our organizations [through the] reputational and public relations risks that you have to mitigate”. For example, a privacy officer’s role involves, “getting marketing and others to think carefully about some of the issues and try to spot every risk and come up with solutions to address them, so that technologies or programs are implemented in a very carefully controlled and responsible manner that ensures that the company is compliant with the law, but also isn’t going to risk annoying customers or prospective customers” (R6). In essence, being privacy compliant becomes a benefit of dealing with a particular company. Finally, we asked respondents to comment on the impact of a consumer base that is increasingly aware of and concerned with online privacy and personal information. Respondents continue to assert that “the biggest responsibility [for privacy protection] has to lie on an individual level with the government providing general protection” (R1). If there is too much legislation or regulation around privacy, “everything comes to a screeching halt and all the benefits of this [targeted advertising] go away as well” (R1). Another issue that respondents are pondering is the dynamic nature of the market and technology. “I think it will become a bigger issue than just talking about privacy...because all these devices...it becomes who owns those [in terms of serving up content]” (R2). We call it real-time marketing...and what is relevant yesterday is not relevant today....It’s not about practice; it’s about evolving practice” (R3). R3 also points out that “direct marketers have been doing this [targeted advertising] for years”. However, now it is “more ubiquitous...there’s more and more marketers asking for more information”. This respondent believes that much of the concern about tracking and profiling comes from the overlay of technology because “technology for the most part scares people” (R3). “Technology changes the game because...you can hack into stuff, you can transfer this and somehow they see it as much more onerous and threatening. But marketers have been collecting stuff on people forever, and [until recently] they [consumers] have never said a word” (R3).

Consumer Perspective We collected information from consumers about their perspective of online advertising using two techniques. The first was qualitative and consisted of focus groups and a town hall. The second was an online survey questionnaire, the contents of which were developed based on some of the insights gained from the qualitative results.

Focus Groups and Town Hall This summary is divided into two sections: The first section provides an overview of the online behaviours of post-secondary students in general. The next section focuses on behaviours and attitudes related to e-commerce and online advertising.

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Student Online Behaviours The physical world meets the online world. It is no longer relevant to ask whether university students are online. They are. Whether by choice or necessity, the Internet and social media technologies in particular, have become an integral part of daily life. However, it would be presumptuous to conclude that online technologies have taken over students’ lives or that they are replacing all physical interaction with online substitutes. Rather, it appears that the two worlds are becoming inextricably intertwined. Students physically attend school, and many hold down jobs as well. While at school and work, they interact face to face with other students, professors, bosses, work colleagues and customers. In their leisure time, they enjoy spending time with friends, pursuing hobbies and other personal interests, listening to music, watching movies and shopping with whatever discretionary income they’re able to amass. At first glance, this sounds like the typical student of decades gone by. But in 2010, there is another layer to this fabric. Today’s students engage in each of these activities both physically and in the online space. It is not that online technology is replacing physical interaction – rather, it complements face-to-face activity. For example, university classrooms still have desks and chairs, but the classroom extends beyond the space contained within its four walls. In addition to attending class, students are often expected to access a course website that may contain a variety of learning resources, including reading materials, tests and discussion forums. Completion of assignments may mean a trip to the library, but could also include a visit to the virtual library’s online databases, not to mention other web search engines. While most professors still hold physical office hours, many also encourage communication through email, Instant Messaging or even via the newer social networking applications such as Facebook, LinkedIn and Twitter. Similarly, the workplace extends beyond its bricks and mortar location. As part-time employees, students may find themselves using social networking applications to communicate with coworkers, check work schedules, log hours or even perform their job function. One student commented that in order to remain current and informed in her job, she “listen[s] to fashion podcasts on YouTube”. When it comes to spending time with friends and family, it is clear that online tools are not viewed as a replacement for the physical encounter. In fact, several students indicate that they would rather “see and talk to friends in person instead of constantly being on Facebook”. Yet, for today’s time-poor and tech-savvy young adults, Facebook, Twitter and to a lessening extent email provide opportunities to maintain a sense of social connectedness by checking in with and checking up on significant others. Social media technologies have also found their way into other aspects of life, including the pursuit of many recreation and leisure activities. Avid sports fans still take advantage of opportunities to join recreational teams or to attend professional games in person. To supplement their interest, they rely on search engines such as Google and Xtorrent, newsfeeds, sports websites and even Twitter to find the latest scores, team standings and other relevant player information. Similarly, many students follow particular celebrities using similar up-to­ the-minute tools, rather than relying on traditional physical media such as newspapers or magazines that don’t offer the same level of currency. Finally, whether as a form of leisure or necessity, students have strong though mixed attitudes towards shopping. Most remain steadfast in their preference for bricks and mortar purchasing over the online option. However, almost all students incorporate web-based applications at some stage of the purchase decision process, particularly for more costly, high

25 involvement items. Among the applications most commonly utilized are group buying sites such as GroupOn, TeamSave and LivingSocial, as well as trusted name brand company websites, product review and customer rating sites. On the other hand, there are certain product categories that students agree are more efficiently bought online. Event tickets and most travel packages are popular online purchases, as are specialty items that are difficult to source in-store. However, items such as clothing, electronics and even books are less likely to be bought online by some in this group, as many still prefer the in-store experience, whether for social reasons, to seek advice from in-store sales associates or for the tactile gratification provided by handling the product prior to purchase. Options for connecting the physical and online worlds. Another important development is the proliferation of options to connect the physical and online worlds in efficient and meaningful ways. While the traditional vehicles, such as desktops and laptops, remain an important, central component in managing one’s interconnected life, newer and more portable options such as iPods, iPads, eReaders, gaming consoles with Internet capability (such as PS3 and XBOX 360), mobile phones and smartphones with wi-fi and/or 3G access provide a more seamless connection between the online and offline environment. These devices are lightweight and easy to transport, thus allowing for greater ease of access anytime and anywhere. While many of these newer portable tools are still in the early stages of development, it is clear that the young, tech-savvy generation has embraced them. A growing number of students own multiple devices with Internet connectivity. Almost all of them own or have access to either a desktop or laptop computer and view it as their primary tool for specific types of ‘important’ activities such as completing school or work-related tasks, or for activities where a large screen is needed such as watching streamed videos or podcasts. However, many students have also recently purchased a smartphone or some other portable device with Internet access and report using it on the go for everything from email and social networking (i.e. Facebook, Twitter) to performing quick searches when conversing with others in a social setting. For some, the portable device has become their principal point of access for social networking in particular, leaving the bulky computer for more ‘serious’ applications. It is also noteworthy that students no longer view a laptop computer as truly portable, due to its heavier weight and larger size when compared with smartphones and iPods that have many similar points of functionality. Overall, the combination of new social media technology applications available through a variety of Internet-enabled devices has created an environment in which today’s post-secondary students are increasingly able to function in a world that integrates physical and online communication on demand. This relationship is shown in Figure 2 below.

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Figure 4: Today’s reality: Physical and online communication on demand.

School Work Family & Friends Recreation Leisure & Entertainment Shopping

Blurred lines of functionality and use. Increasingly, students report they are “always” online, particularly those with multiple points of access to the Internet. In other words, even though their computer may be turned off or out of reach, they are still connected via their smartphone. Many describe their daily routines as constant multitasking across school, work, social and personal activities. One student reports starting the day by “opening four tabs: email, Blackboard [the university’s portal], Facebook and Google – to check on the latest celebrity info – and then flipping back and forth amongst them throughout the day”. Several others confirm that their main social networking site (i.e. Facebook) is “always turned on in the background”. This makes it difficult to describe usage in terms of the number of hours spent, since they may only refer to their Facebook site for a few moments at a time, but the application itself could be on for several hours per day. For some, having multiple applications open is reported to be distracting. As one student notes, “It pulls you away from school or work tasks, like a magnet”. Yet these same students persist in having multiple unrelated applications open on multiple devices most of the time, whether they are actively using another online application at the time, and even when they are offline but engaged in activities like watching television, listening to music or doing homework. Another student who considers himself to be an advanced social media user, deals with the issue of multiple apps by subscribing to Hootsuite.com, a website that allows users to access and manage multiple social media applications (for example, an individual may have accounts with Facebook, Twitter, LinkedIn, Foursquare, etc.) through a single platform. This anecdotal evidence suggests that web-based social media applications and the devices through which they are accessed have become intertwined in students’ lives, both in their online and offline interactions. There is no longer a clear delineation of these two worlds.

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Attitudes and behaviour related to e-commerce and online advertising Virtually all students have made an online purchase in the past, although a few report that they have done so only out of necessity for school related materials (i.e. e-books and other online resources). Those who have not purchased anything aside from required school items give reasons ranging from a lack of purchase power (no access to a credit card) to a lack of interest in searching online for purchase items. However, at the other end of the spectrum, many students have embraced e-commerce and regularly engage in a variety of purchase-related behaviours including searching for products, evaluating options, making purchases, and advocating (or opposing) the products and services they encounter. According to a recent study by McKinsey (2009), today’s consumers have moved away from the traditional ‘funnel’ approach to decision making and purchase whereby the range of available choices is gradually narrowed down until a final selection is made, to a more iterative process involving four stages: initial consideration of ‘top of mind’ purchase options, evaluation of a larger set of options through input from multiple sources; purchase, either online or in-store; and a post-purchase process involving enjoyment, advocacy and bonding with the product/brand. A closer examination of each of these four stages reveals several interesting points. Stage 1: Consideration. In terms of the initial stage of the purchase decision process, many students report having an online routine, whereby they regularly follow “deal” websites such as GroupOn, RedFlagDeals, TeamSave, LivingSocial, DealTicker or WagJag. These represent their top of mind resources for a broad spectrum of purchases. When not deliberately searching for a particular item, these websites provide a steady but uncluttered stream of discounted product options, perhaps similar in nature to the prominent in-store end of aisle displays that feature easy to spot promotions and encourage impulse purchases. However, when it comes to deliberate purchase decisions involving specific products, a number of students identify a particular website as their first stop. For example, for concert or theatre tickets, the site of choice is ticketmaster.com, while for books the top two options are amazon.com and chapters.indigo.ca. One individual identified expedia.ca as the preferred travel website, though others initiated their travel search process with other sites, albeit of a similar format. One of the key features of travel sites like these is their comparison function, whereby users can select and compare a number of different package options across similar criteria. In fact, several online retailers have developed similar product comparison functions within their store websites. Stage 2: Evaluation. Interestingly, two distinct patterns emerge in terms of the initial consideration stage for medium to high involvement purchases: one group who begin the evaluation process by expanding their online search to include search engines, online review sites and multiple company websites and then follow up by seeking advice from trusted friends and family members. This group claimed that online resources provide a broader perspective than what their immediate circle of friends or family could offer. Another group followed the reverse process, beginning their information search with physical contacts and then turning to online resources as a secondary means of information gathering. In the latter case, their reasoning was that friends are more likely to be honest and they tend to have similar likes and dislikes, hence their opinions would have more relevance and value than those of online strangers. In addition, this group saw reviews as extremely subjective and therefore relatively unhelpful in evaluating products or services. One respondent noted

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“people write reactively – they don’t calm down before they write something and are often overly negative”. Regardless of the evaluation process sequence, all respondents agreed that certain online information resources were more trustworthy than others. Several expressed their feeling that Google was a legitimate source of useful information and was consequently their starting point for most searches. Company websites were also seen as credible for basic product information and specs. However, consumer ratings were viewed as more credible when found through independent consumer rating sites than when they were found within a company website. Interestingly, Twitter has become a commonly viewed product information resources, although Twitter feeds originating from the company were less trusted than individual consumer tweets about the company or product. The one exception to this involved an experience from one respondent who contacted a company directly with a question and received a direct tweet response. This was interpreted as highly trustworthy. When asked to describe the factors considered most important in evaluating online purchase options, students identified the following items: 1. Security/Privacy – students expressed the expectation that any online transaction they were involved in would be safe as well as private. One respondent commented “The most important factor on my online purchases is the promise that my data will not be shared”. As well, a valid SSL certificate or some similar means of checking vendor authenticity was seen as a requirement to increase trust and facilitate online purchase. 2. Shipping time – one of the identified advantages of online shopping involved not having to wait in a lineup at a store’s checkout counter. However, others note that while the lineup is avoided, there is still the inevitable problem of delayed gratification – i.e. having to wait until the item is delivered, which would normally take several days or even weeks. Preferred online sellers were noted to have clearly stated estimates of shipping times as well as various shipping options, ranging from slower delivery times coupled with free delivery, to overnight express deliveries offered at a charge. 3. Lower price – another important incentive for online purchases is the ability to purchase items at a lower price than they are offered in-store. The availability of product comparison tools on many websites allows online consumers to evaluate multiple options on a variety of criteria, including price. Also, as indicated earlier, many students actively subscribe to online services that provide coupons or specials on a range of products on an ongoing basis, suggesting there is an important group of deal seekers within this demographic. Interestingly, this is taken to the extreme when it comes to the music industry, as the majority of students prefer to download music for free, whether from legitimate sites or not. Only a small number feel that paying for music downloads is appropriate, citing reasons like “it’s legit”, “I’m passionate about music”, and “it’s the right thing to do”. One student suggested that downloads, in general (whether music, videos, etc.), should be free and that the revenue stream for these items should come from accompanying ads. Others indicate that if they pay for music, they prefer to do so by purchasing a CD or a vintage album through a physical store, despite the higher cost of this alternative: “I like having the actual physical album – it’s more tangible”. 4. Vendor reputation – Most respondents felt that the well-known company branded websites could be trusted to deliver authentic goods. There was disagreement over sites such as e-Bay. Some students reported reluctance to purchase through this site due to concerns that they may end up with illegitimate ‘knock off’ products, while others

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described their past experiences as positive, explaining they rely on e-Bay’s seller rating system and the relative product cost (implying that too low a price likely signals an unauthentic item) when deciding whether to make a purchase through the site. 5. Product selection – Two elements of product selection were identified as advantages for online purchasing. First, several students referred to the wider selection of products available online as compared with traditional retail stores. One individual commented that this was also a potential negative when doing online product searches and then going to stores to make the actual purchase, as often the models, colours, sizes or other specifications were either out of stock or not available in certain store locations. Second, a few liked not just the wider selection, but the often ‘unique’ or exclusive items that are only available through online sites. 6. Customer Service- There were mixed feelings regarding the current level of customer support offered through online e-commerce sites. While some students described positive experiences with real-time chat sales or technical assistance during the purchase decision process, others were less satisfied with the level of service provided, saying “…(they’re) not really helpful. Most of the time you have to go through a bunch of operators.” Many students still prefer the in-store service experience and the presence of a physical sales person over the virtual alternative. 7. Rewards/Loyalty programs – Although this was not mentioned in the focus groups, it was identified by members of the Town Hall as a factor. Finally, when asked about online advertisements, most students seemed to share a feeling of inevitability towards online ads. They have learned to tune out most ads that appear on the outside borders of their Facebook pages, though noting that occasionally they would click through on ones that are of interest to them. Many have noticed ads that appear to be targeted to them, whether through information provided through their Facebook profile, by ‘liking’ particular products, bands, etc. on other sites, or as a result of recent Google searches. Most do not mind these targeted ads, nor do they mind the way their posted information has been used to facilitate the process, for the most part. However, a few report feeling these targeted ads are ‘creepy’, particularly those that are sent through email (), explaining that they view email as a ‘private’ communication venue when compared with the more public tools such as Facebook or Google. While there was agreement that if companies keep track of personal online activities, it is, by definition, an invasion of privacy, most students seemed to passively accept the activity, saying “I’m used to it” or “I’ve trained myself not to pay attention to online ads unless it pertains to me”. However, a few were less comfortable with increasing advertisements appearing as a direct result of their Facebook updates. One individual stopped using the ‘like’ feature in order to stop the resulting advertisements, while another mentioned going a step further and rather than clicking ‘like’, they selected the ‘offensive’ button to ensure they were no longer bothered with unwelcome ads. There is also a distinction between passive acceptance versus either enjoyment or rejection of online advertisements. While acceptance covers the largest category, there are also some online advertisements that students responded to with enthusiasm. For example, several students were able to describe recent ads that they found particularly creative or humorous and as a consequence, clicked through the ad to learn more. In a few instances, this led to a purchase, either online or later on in the bricks and mortar store. In other cases, the advertisement was

30 more about pure entertainment rather than a deliberate enticement to purchase. For example, interactive online ads that function like games (Rogers), or those that link to a variety of social media and are customizable (Old Spice) are particularly well liked. As well, several students described advertisements seen through traditional media sources (i.e. billboards, TTC ads, magazines, television) that prompted them to note the bar code/url and go online to find out more information. On the other hand, when asked to describe at what point ads become annoying, students were quite vocal in their criticism of ads that are overly repetitive (i.e. constantly popping up), or that are overly demanding of bandwidth and consequently slow down their computer’s functionality. The presence of multiple sidebar ads that contain action or music is perceived tooften cause this slowing effect. Also frustrating are ads that open in the middle of the computer screen and block the page that is being viewed, as well as those that automatically connect to another website. In some instances where ads are particularly annoying, one student says “I’ll go out of my way NOT to buy.” Stage 3: Purchase. Many students are very specific about the types of products they purchase online. For example, some purchase movie/concert tickets exclusively online, while others report primarily using the Internet for travel bookings, specialty clothing (such as T-shirts with licensed sports team logos), jewellery, books and/or phone apps. When probed, they appear comfortable making purchases within their ‘tried and tested’ categories, but tend to be less inclined to buy items outside these categories. Acceptance of online buying across a broad spectrum of product/service categories appears to be slow, as most still prefer to make the actual purchase in a physical store location. A uniquely online purchase option is the growing trend of ‘Freemiums’, where basic content (music, news articles, etc.) is provided free of charge, normally accompanied by advertisements. Consumers who wish to access the more detailed version (or an ad free version) can opt to pay for this option. While most students view the free version as sufficient for their needs, they acknowledge that in some circumstances, the upgraded versions may provide value for some individuals. Stage 4: Enjoyment, Advocacy, Bonding. It became clear from the discussion that only a small number of students are able to identify instances in which companies have engaged them in meaningful ways post-purchase. One individual, a regular Amazon customer, indicated a positive acceptance of the online bookseller’s customized post-purchase emails, and would occasionally respond to the emails if something of particular interest was communicated. However, the majority of students continue to view most post-purchase targeted emails as unwelcome and of little relevance to their needs. Moreover, they are unable to identify post- purchase interactions with companies that have added to their enjoyment of the product, nor specific events that would spur them to advocate on behalf of a company or experience a sense of bonding. It appears that this final stage remains the least understood by marketers in terms of how to engage and retain customers in the post-purchase stage.

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Online Quantitative Survey The results from the survey of consumers are divided into two sections: online behaviour and attitudes toward targeted advertising. Appendix D contains more detailed statistical analysis.

Online Behaviour To understand the importance of the Internet in the lives of young adults, we asked them how many hours per week they spend doing a number of online activities. Table 9 presents the percent of respondents who engage in these activities at least one hour per week.

Table 9: Percent Engaging in Online Activities at Least One Hour Per Week (n=1317) % Online Activity participating a minimum one hour/wk a) Creating content for others Posting comments to someone else’s social networking page/account 53 Writing articles or to post online for others to read and comment on 21 Posting to a micro-blogging service such as Twitter 20 Posting original material to content sharing sites such as Tumblr, Digg, Reddit, 16 Technorati or YouTube b) Updating your own content – staying connected with others Visiting social networking sites, such as Facebook, LinkedIn or MySpace 84 Maintaining/updating your profile on a social networking site 44 Publishing or updating your personal webpage (excluding social media sites) 18 Checking in with location-based services such as Facebook Places, Foursquare 16 or Gowalla c) Seeking information or content Using a to find information prior to a product or service purchase 70 Reading or viewing content from content sharing sites such as Tumblr, Digg, 67 Reddit, Technorati or YouTube Reading online forums, blogs and discussion groups written by others 60 Using Smartphone applications to access content 58 Reading customer ratings and/or product/service reviews 37

The data suggest that a high proportion of respondents engage in online activities relating to creating content, searching/reading content/information and socializing for at least an hour each week. The information search capability of the Internet is used by the majority of respondents prior to making a purchase decision (70%). In addition, many spend time reading content sharing sites (67%) or reading online content posted by individuals (60%). A lower proportion report reading consumer reviews (37%). Respondents have expanded the devices on which they seek information to include smartphones (58%). The Internet is clearly used for socializing by a large proportion of respondents. Not surprisingly, 84% report visiting Facebook and other social networking sites, and many others spend one hour or more per week posting comments (53%) and maintaining/updating a profile on these sites (44%).

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When it comes to creating original content outside of social networking sites such as Facebook, only about one in five respondents report being involved in writing and posting content for others to read, which is consistent with previous research on this topic (Foster et al., forthcoming). We next asked respondents how often they engage in activities specifically related to searching for and purchasing goods and services online. Table 10 presents the percent of respondents who search for product information online at least sometimes.

Table 10: Percent Searching for Product Information Online at Least Sometimes (n=1182) % reporting Online Product Information Searching Activities at least sometimes1 Use the Internet to do additional research on a product or service you found in a 81 retail store Use the Internet to do additional research on a product or service recommended 79 to you by family or friends Search for a product or service that you learned about through a television or 63 print ad or billboard Use specific search engines to do comparison shopping before making a 61 purchase of a product or service 1 – Includes all respondents who answered frequently or sometimes on a four point scale.

As the results in Table 10 suggest, the Internet is used by an overwhelming majority of respondents as a tool for getting information, particularly if the search has been triggered by something else, including an in-store retail experience (81%), a recommendation by a family member or a friend (79%), or traditional advertising (63%). An additional useful capability of the Internet is to provide price comparison data (61%), which serves to increase the knowledge of consumers and allows them to make more informed choices. Table 11 focuses on online advertisements in particular and investigates the various actions that can be triggered by this promotion mechanism.

Table 11: Percent Taking Action Related to an Online Advertisement (n=1182) % reporting Action Related to Online Advertisement at least sometimes Click on a link for a product or service that comes up in an online advertisement 32 next to your search results Buy a product or service over the Internet based on an advertisement you saw 30 online Click on a link for a product or service that comes up in an online advertisement 26 on your Facebook page Click on a link for another product or service that comes up in an online 24 advertisement after you have made an online purchase Buy a product or service over the Internet based on an advertisement in an e-mail 21

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About one-third of respondents report clicking on an advertisement that is linked to their search results and even fewer (26%) click on advertisements on their Facebook page. Having just made an online purchase does not appear to motivate respondents to click on an online advertisement with only 24% reporting doing so at least sometimes. While these results suggest that online advertisements are not a powerful tool for motivating further online actions related to product search or purchase, at the same time 37% of respondents agree with the statement that “online advertising can motivate impulse purchases.” Advertisements sent through e-mail are even less likely to trigger a purchase with only 21% reporting buying something at least sometimes as a result of an e-mail advertisement. Table 12 presents information about respondents who take actions to control receiving targeted online advertisements.

Table 12: Percent Taking Action to Control their Receiving Targeted Online Advertisements (n=1182) % reporting Action to Control Receiving Targeted Online Advertisements at least sometimes Click on “like” in order to have access to more information about a particular 34 topic, which may include getting targeted ads about it. Change your Facebook profile information to avoid targeted online 26 advertisements being sent to you. Click on “offensive” as a technique for stopping targeted online advertisements 19 being sent to you.

Interestingly, about one third deliberately seek more information on a topic knowing that it may result in their receiving more targeted ads. On the other hand, approximately one in four have changed their Facebook settings to avoid getting these type of advertisements, and nearly one in five choose to click on “offensive” as a way to ensure that they are not the recipients of particular promotions. Table 13 presents information about respondent uptake of some of the emerging technological trends and capabilities.

Table 13: Percent Engaging in Other Activities Related to Enhanced Technical Capabilities (n=1182) % reporting Other Activities Related to Enhanced Technical Capabilities at least sometimes Refer to team buying services listed above to keep track of the latest deals 29 Buy a product or service through a team buying service such as GroupOn, 25 RedFlagDeals, TeamSave, LivingSocial, DealTicker or WagJag Take a photo of a barcode using your smartphone and view the content online 19 later Pay for content on the web (ie access to information that is not available for free) 17

In the focus groups, respondents identified an interest in group buying sites and we see that mirrored in the quantitative results. Almost 30% of respondents refer to group buying sites

34 at least sometimes to keep apprised of deals related to their interests. One quarter of respondents have bought a product or service through one of these sites, which suggests that it is a key trend for the future. Another technological capability that just recently came on stream is reading barcodes using Smartphones. Only 19% of respondents avail themselves of this service, perhaps because of its two-step nature: first the bar code has to be found (newspaper, retail store) and then stored for later retrieval and search. Given the prevailing attitude of this consumer segment that all information on the Internet should be free, it is not surprising that only 17% of respondents have paid for web content at least sometimes. The findings related to online behaviour suggest that online activities are an integral part of the lives of young Canadians. A small proportion use the Internet to create content for others to view, but a much larger proportion use it stay connected to others and to help them make purchase decisions. As the focus group results indicated, Internet searching for content is an integral part of the consumer journey to eventually make a purchase . Responding to online advertising is a less significant part of the purchase process, but for a small proportion it is a trigger to buy something or at least ponder a product or service purchase. Group buying sites are an emerging trend that seem to have gained some traction among young Canadians.

Attitudes about Receiving and Accessing Online Content and Advertisements

The next set of results focuses not on what respondents do in the online space, but on their attitudes toward receiving and accessing online content including advertisements. A major focus of this study is to understand potential conflicts between marketers who seek the most effective mechanisms for reaching their target markets and privacy experts who are concerned about protecting personal information. Table 14 presents the views of our respondents about privacy, the Internet and targeted online advertising. Because respondents believe that privacy is a right, they are decidedly against paying to keep companies from collecting information about them (73%). Likewise, they feel that companies who do collect that information are invading their privacy (68%). Consistent with these strong views about protecting privacy, only about one quarter of respondents do not care whether marketers collect personal information about their search terms or website activity. Further, nearly half (46%) believe legislation exists to prevent third parties from collecting information about them. Despite negative attitudes toward data that might be collected about them for online advertising, and their view of targeted ads as “creepy” (57%), the majority of respondents seems resigned to the existence of online advertising (60%). Ads, for them, are simply a part of life. They do not hate ads with the same passion that the older generation may have. Likewise, less than half (44%) would change their online behaviour if they knew advertisers were collecting information about them, and even fewer (34%) would consider deliberately avoiding sites that collect data about them to use for targeted ads. In terms of understanding how the technology works, while the majority of respondents (54%) claim to understand the function of cookies, only 40% believe that cookies make you vulnerable to having your password stolen. All but a few (13%) of respondents understand that the anonymity of using a TV is not a feature of using computer technology.

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Table 14: Percent Agreeing with Statements about Privacy and the Internet (n=1317) Attitudes about Privacy and the Internet % agreeing1 Privacy is a right so it is wrong to be asked to pay to keep companies from 73 invading my privacy I feel it is an invasion of privacy for someone to keep track of my online 68 activities Online advertising is just a fact of life 60 Targeted online advertising is creepy when it is based on my online actions 57 I understand the function of cookies on a computer 54 I am protected by law against advertisers collecting data about me. 46 I would watch what I do online more carefully if I knew advertisers were 44 collecting data about me If you have cookies on your computer it makes you more vulnerable to someone 40 stealing your password I would stop using any site that uses behavioural advertising (ie collects data 34 about my online activities in order to target ads to me) I do not care if advertisers collect data about my search terms 27 I do not care if advertisers collect data about which websites I visit 25 Using a computer is just as anonymous as using a TV, since no one really knows 13 what you are doing 1 – Includes all respondents who answered strongly agree or somewhat agree on a five point scale.

Table 15 focuses more specifically on attitudes toward payment for different types of online services, which were described in the table on privacy attitudes and beliefs. Respondents display some interesting attitudes about paying to protect their privacy.

Table 15: Percent Agreeing with Statements about Payment and Online Services (n=1317) Attitudes about Payment and Online Services % agreeing Companies asking me to pay for them to not collect data is like extortion 66 Advertisers will collect data about me whether I pay to stop them from doing so 60 or not, so there is no point in paying Advertisers will collect data about me whether I pay for them to do so or not, so 58 there is no point in paying Online advertising is necessary for the Internet 57 Putting up with online advertising gives me access to sites without having to pay 53 Eventually the really good content on the web is going to cost money 42 I prefer to pay for a song through iTunes than to get it in a free download 20 because I am worried about the quality of free content I hate ads and would pay to avoid them 17 It is worth paying extra to avoid targeted ads 16

First, about two-thirds feel that it is not only wrong, but also similar to extortion for companies to charge consumers in exchange for not collecting their . Further, only 17% would pay to avoid targeted ads, and only 16% think it is worthwhile paying to avoid them. Respondents feel rather powerless when it comes to advertisers collecting online information

36 about them. Whether paying to encourage advertisers to collect their personal preferences or to prevent advertisers from doing so, about 60% believe that advertisers will do what they want, regardless of whether they are paid. Despite these strong views, the majority (57%) believe that online advertising is necessary for the Internet, and that putting up with online advertising allows web content to be free (53%). Perhaps, given these strong views, participants see online ads as a necessary evil. Although some respondents seem to understand that eventually good web content is going to cost money (42%), at the moment few (20%) will opt for quality over free, at least when it comes to downloading music. Marketers see the benefits of enhancing consumers’ perception of the value and impact of online advertising and thus are interested in ways to make it a more appealing experience. Table 16 presents respondent views on some of these options.

Table 16: Percent Agreeing with Statements about Increasing the Appeal of Online Advertising (n=1317) Attitudes about Increasing the Appeal of Online Advertising % agreeing It is more appealing to click through an online advertisement for a brand name 54 product or service than for a no name product or service Online advertisements would get more of my attention if they had a more 32 interactive component

Consistent with some of the qualitative results, consumers are more amenable to online advertising if it is for a good, service or company with which they are familiar and already have a relationship. Over half (54%) agree that it is more appealing to click through an advertisement for a brand name product or service compared to a no name. In the focus groups we asked respondents to describe online advertisements that they remembered and why they remembered them. Respondents were most likely to identify those ads with an interactive component as being superior. About one third of the survey respondents agree with the focus group participants on this observation. Respondents are presenting contradictory views about online advertising and privacy. On the one hand, they want their privacy protected, but on the other hand, they see the necessity for online advertising. Table 17 attempts to provide more clarity on this situation by investigating attitudes toward random online advertisements versus targeted online advertisements. The majority of respondents find targeted ads annoying (62%), and the majority find any type of online advertisement whether it is targeted or random distracting (58%). Respondents want the benefits of relevant advertising (55%) and thus complain about how random ads are annoying (53%). However, their definition of targeted may not be the same as marketers, as 55% note that targeting ads is not very effective because they are getting irrelevant ads directed at them. Over half of respondents report ignoring ads, which may be reflective of social desirability rather than actual behaviour. Focus group participants also reported ignoring ads, but at the same time were able to describe in detail their favourite ad. Interestingly, respondents do not seem to view receipt of the ad itself as harmful – supporting the suggestion that they do not view targeted ads as invading their privacy because the ads were not effective to begin with. For the same reason, perhaps, only a third of respondents (34%) indicated that they would stop surfing due to targeted ads in Table 14 above. Almost half of respondents (48%) believe that advertisements make the Internet slower. About half (48%) believe that random advertising is related to the website it is

37 on, implying some websites are more likely than others to rely on non-targeted advertising. About one third see benefits associated with targeted online advertising, with some noting that it tends to be for products and services that interest them (35%) and 32% believing online advertisements are a beneficial source of information. Less than one quarter (23%) find random online advertising insulting.

Table 17: Percent Agreeing with Statements about Targeted versus Random Advertising (n=1317) Attitudes about Targeted versus Random Advertising % agreeing Targeted online advertising is annoying 62 I ignore ads, so there is no benefit to me if ads are targeted to my interests 58 All online advertising whether targeted or random is distracting 58 Random online advertising has too many unrelated and off topic ads 55 I want the benefits of relevant advertising 55 Targeting users based on their online behaviour doesn’t work because I get ads 55 that are not relevant to me Random advertising (not targeted to my interests) is more annoying than targeted 53 online advertising I ignore ads, so there is no harm to me if ads are targeted to my interests 50 All online advertising whether targeted or random makes the Internet slower 48 Random online advertising tends to be related to the website it appears on 48 Targeted online advertising tends to be for products and services I am interested 35 in Online advertising targeted to me is beneficial because ads are a source of 32 information Random online advertising is insulting 23

According to our in-depth interviews marketers see many benefits of online advertising compared to traditional advertising, mostly in terms of being able to refine market segmentation and to really target their promotion. Table 18 shows respondent views on online versus traditional advertising.

Table 18: Percent Agreeing with Statements about Online Versus Traditional Advertising (n=1317) Attitudes about Online versus Traditional Advertising % agreeing Online advertising does not get my attention the way traditional advertising on 57 television or billboards does Targeted online advertising has more to do with what I want and is less random 48 than other types of advertising like television or magazines Random online advertising is better than television or billboards because it is 43 easier to ignore

More than half (57%) of respondents do not think that online advertising is as effective as traditional advertising in capturing their attention. However, fewer than half believe that it is easier to ignore online advertising compared to television or billboards (43%). Finally about half

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(48%) feel that targeted online advertising is better than television or magazines at presenting ads that relate their interests and needs. In the in-depth interviews, industry respondents predicted that the next iteration of online advertising would be mobile. As Table 19 suggests, almost 60% of consumer respondents are not that keen on mobile and would prefer to receive advertisements on their computer. A similar proportion (62%) prefer a search engine rather than an e-mail targeted advertisement.

Table19: Percent Agreeing with Statements about the Location of Online Advertisements (n=1317) Statements about the location of Online Advertisements % agreeing Online advertising is creepier when it comes through your e-mail rather than 62 through a search engine like Google I would choose to have targeted advertisements on my computer rather than on 59 my Smartphone

Table 20 compares the importance of privacy-related features to other issues that shape the overall shopping experience online.

Table 20: Comparing Privacy/Security Concerns to other Online Shopping Issues (n=1317) Importance of Feature/Concern % reporting at least somewhat Clear information about products 84 Shipping tracking information 81 Improved fraud protection for credit card transactions 81 Information about product in-stock availability 78 No spam policy 77 Availability of product reviews from other customers 76 Assurance your data will not be shared with advertising partners 68 Assurance that your purchase data will be retained for no more than three months 57 Products recommended based on your past purchases 48 Products recommended based on your friend’s past purchases 41

As the data demonstrate, respondents think that some privacy and security features are essential for online shopping. For example, fraud and spam protection both rank highly (81% and 77% respectively) and are comparable to product information, the ability to track shipments and product availability (84%, 81% and 78% respectively). Data retention minimization and non­ disclosure of information are important for respondents, but not as important (57% and 68% respectively) as other features, suggesting that respondents do not place as high a value on the personal information protection principles traditionally a focal point for the OPC. If this finding is supported by further research, one could interpret it as either a call for OPC to change its priorities or to invest more in public awareness and education. Respondents do not care for product recommendations based on their past purchases or their friends’ past purchases, and value product reviews far more. This finding has both marketing and privacy implications, suggesting that it is perhaps not necessary for marketing

39 purposes to track purchase history and potentially invade privacy as a result, since respondents report valuing reviews more in making a purchase decision. Finally, Table 21 attempts to measure the value that privacy has for respondents by providing various monetary options for protecting their private information from online tracking.

Table 21: The Price of Privacy (n=1317) Level of interest in individual privacy protecting techniques % reporting at least somewhat Paying an additional $1 per month to get ads, discount coupons and news items 14 that are tailored to your particular interests Paying an additional $1 per month to ensure that your favourite websites do not 19 collect information about you to share with advertising companies that then target web ads to your interests Your Internet service provider pays you $1 per month to accept targeted 31 advertising from companies that have products and services in which you would be interested, based on your online profile information

Whether respondents are not interest in paying for anything, or whether respondents are not interested in paying for privacy, the results suggest that it is unlikely that respondents will endorse a business model in which they receive higher privacy protection in exchange for payment. In fact, 31% of respondents were more than happy to be paid for receiving ads, not an overall high percentage, but higher relatively than those willing to pay not to receive ads (19). This reluctance to pay for privacy may be explained by the strong view among respondents of privacy as a right, discussed above. Simply put, respondents may not understand why they should pay for what is theirs by right.

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The purpose of this study has been to investigate the emerging field of targeted online advertising from the experiences and perspectives of both businesses and consumers, and through the lens of the marketer who is trying to find more effective promotion mechanisms and the privacy advocate who is concerned about the potential harm of technologically-enabled targeted advertising. From the business perspective, this is a field in continuous development, with practices and norms in a state of flux and evolving as technological capabilities are matched with market needs. The consensus among our business respondents is that online capabilities have enhanced companies’ effectiveness in segmenting and targeting their markets. They are able to identify with more surety and clarity the consumers that will be most receptive to their promotional messages. Previous promotional techniques cast a wide net with the hope that a sufficiently large portion of the would be in that net and would respond to the promotion in large enough numbers to justify the investment. Digital promotion is not only more cost- effective because making changes in the content or format of the message is not as expensive, but also the return on marketing investment in terms of additional sales and revenues is much higher than with traditional advertisers. Technology is now sophisticated enough to do a good job of tracking the online behaviour of consumers, which marketers translate into profiles and can subsequently use for targeted advertising. Likewise the performance measures can provide quite precise data on the impact of this advertising. So not only is the mechanism for finding the target better, but also the measurements of performance are better in the digital space than with traditional advertising. Marketers are not likely to give up this tool, and in the future it will be expanded to mobile devices that leverage GPS tracking. The only concerns voiced were those about how the performance data are used for decision-making. Some executives see issues with the representativeness of online marketing research studies when only three-quarters of Canadians are connected to the Internet. Older and rural consumers are excluded from data collection because they lack technological access. Likewise online surveys are not as adept at collecting data on motivations and sentiments, the insights that marketers have traditionally highly valued for making brand decisions. Online advertising is categorized into types, such as targeted, behavioural, contextual, and social, but for business respondents the only feature that really matters is whether it is relevant. Targeted ads are better than random ads because you have more likelihood of providing an ad that matches the needs and interests of the consumer, which marketers believe means that they are less likely to be perceived as annoying clutter by viewers. Another factor relating to the perception of targeted ads is whether they are promoting goods or services from a company with which the consumer already has a positive relationship and thus trusts more than some random company. This suggests that brand name companies may be able to benefit more from online advertising than unknown companies.

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A main purpose of this project has been to develop a taxonomy or framework for describing the relationship among those engaged in creating advertising content, buying advertising services, disseminating content to the appropriate audiences and measuring the performance of online advertising. To date there have been a number of ways to conceptualize the structure of this digital space. Some view it in terms of the type of company, that is, a set of distinct silos that interact at various points (Deane & Pathak, 2009; Evans, 2009), while others define their “silos” in terms of the type of advertising (Szoka & Thierer, 2008). These different conceptualizations underline how much this sector is changing, as a result of increasing technological capabilities and more creative applications of these tools. What this project contributes to the discussion is the pivotal role of analysers. Our work suggests that most recently the silos have been replaced by a set of overlapping circles with the analyzers as the linchpin. This group has a role in collecting information, analyzing the results and disseminating their interpretation, not only for decisions related to the goods and services being promoted, which is of interest to the purchasers of online advertising to better support their brand, but also to the creators of advertising content, and the publishers of advertising content who are interested in doing an effective job at targeting their market and focusing their message to better serve their clients. Privacy is not a major concern among our business respondents because they believe they are already compliant. Privacy regulations are much more top-of-mind now than they were previously and thus companies have already built in mechanisms to think about the consequences of their actions from a privacy perspective when they launch advertising campaigns online. In terms of expertise, the agencies tend to rely on their clients to do the privacy due diligence because they are more likely to have in-house counsel or privacy officers and these campaigns are associated with their brands as opposed to the agencies. Those companies with in-house expertise rely on these officers to review programs and decisions to ensure they are privacy compliant. For some, it is actually a competitive advantage because taking the time to discuss these considerations gives every program an extra vetting that may uncover problems that need to be addressed before launch. Likewise, not being privacy compliant can result in consumer blow-back and in these competitive times, no company can afford to annoy its customers. Our business respondents see no conflict between marketers and privacy officers over the issue of targeted advertising because it is in the interests of both to be compliant. They also believe that consumers have to take some individual responsibility for protecting their personal information and that website owners have to be transparent and forthcoming about which data are being collected and how it will be used. Government regulation is not necessary or preferred in their opinion. Because technology is evolving and capabilities and applications are developing, the attitudes and behaviours of consumers with respect to online advertising do not necessarily present a coherent picture. Consumers purport to ignore and dislike all forms of advertisement, yet a proportion report clicking on online ads, and even more believe that ads are a fact of life and something they are willing to endure to get free content on the Internet. Although they find online ads distasteful, they are not willing to pay to avoid them. Free content trumps all. Another contradiction is seen in their views toward privacy. They firmly believe that privacy is a right and they do not like the fact that companies collect information about them through their Internet habits. Traditional privacy and security features such as spam and fraud protection are as important to them as are the essential elements of online shopping. However, knowing that they are being tracked, most still would not change their online behaviour or avoid

42 websites that do tracking. They don’t like their privacy invaded, but seem unwilling to change their behaviour to protect their privacy. One possible explanation for this contradiction is that consumers do not really believe targeting to be effective, and therefore, do not really believe their privacy is invaded and personal information compromised. Another is that consumers expect government to enforce their right to privacy, and conversely, that consumers view advertising that is not challenged by government as legal and therefore not violating their privacy rights. As a result consumers do not see a need or room for individual activism in this area, and are in fact reluctant to take private action. If further analysis supports this tentative conclusion then it is a clear call for action on behalf of the Office of the Privacy Commissioner. Participants expect OPC to protect their privacy, and carry perhaps an unrealistic understanding of the OPC’s enforcement powers. OPC should therefore engage in both enhancing public awareness – to ensure the public has a clear understanding of its privacy rights and the ability of OPC to enforce them – and in calling for legislative reform, to ensure that our privacy legislation includes the necessary tools to protect privacy online.

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Anonymous. (Sept. 26, 2009). Great Canadian disconnect. Canadians love the Internet. So why don’t advertisers get it? National Post, FP1. Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice: Motivators, barriers, and enablers. Advances in Developing Human Resources, 10 (4), 541-554. ATG Research Report. (2010). Consumer shopping experiences, preferences and behaviors. Available at: http://www.atg.com/resource-library/white-papers/atg-online-shopping­ study.pdf Bernoff, J, & Lee, C. (2008). Harnessing the power of the oh-so-social web. MIT Sloan Management Review, 49 (3), 36-42. Best, S.J. & Krueger, B.S. (2006). Online interactions and social capital: Distinguishing between new and existing ties. Social Science Computer Review, 24 (4), 395-410. Bielski, L. (2008). The art and science of web marketing. ABA Banking Journal, 100 (9), 40-42. Canadian Internet Use Survey. (2009). Statistics Canada. Available online at: http://www.statcan.gc.ca/daily-quotiden/100510/dq100510a-eng.html Chen, L. (2008). Combining keyword search advertisement and site-targeted advertisement in search engine advertising. Journal of Serv. Sci. & Management, 1, 233-243. Comscore. (2010). The 2010 US digital year in review. Available at http://www.comscore.com/Press_Events/Presentations_Whitepapers/2011/2010_US_Digi tal_Year_in_Review Connolly, R., & Bannister, F. (2008). Factors influencing Irish consumers' trust in internet shopping. Management Research News, 31(5), 339. Deane, J. & Pathak, P. (2009). Ontological analysis of web surf history to maximize the click- through probability of web advertisements. Decision Support Systems, 47, 364-373. Evans, D.S. (2009). The online advertising industry: Economics, evolution and privacy. Journal of Economic Perspectives, 23(3), 37-60. Facebook Statistics. (2011). Available at: http://www.facebook.com/press/info.php?statistics. Foster, M.K. Francescucci, A., & West, B., and Pardo, F. (2011, forthcoming). Exploring social media user segmentation and online brand profiles. Journal of Brand Management. Foster, M.K., Francescucci, A., & West, B. (2010). Why users participate in online social networks. International Journal of e-Business Management, 4 (1), 3-19. Granovetter, M.S. (1973). The strength of weak ties. American Economic Review, 78, 1360­ 1480. Hollis, N. (2005). Ten years of learning on how online advertising builds brands. Journal of , 45, 255-268. Interactive Advertising Bureau of Canada. (2009). 2008 actual + 2009 estimated Canadian online advertising revenue survey detailed report. Toronto ON. Jaffe, J. (2007). Join the conversation: How to engage marketing-weary consumers with the power of community, dialogue and partnership. John Wiley & Sons Inc.: Hoboken, NJ.

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Levin, A., Foster, M.K., Nicholson, M.J., West, T., Hernandez, T. & Cukier, W. (March 2008) The Next Digital Divide: Online Social Network Privacy. Office of the Privacy Commissioner of Canada. Lohtia, R., Donthu, N. & Herschberger, E.K. (2003). The impact of content and design elements on banner advertising click-through rates. Journal of Advertising Research, 43(4), 410­ 18. McGeveran , W. Disclosure, Endorsement, and Identity in Social Marketing, University of Illinois Law Review, (forthcoming). OPC (2009), Report of Findings into the Complaint Filed by the Canadian Internet Policy and Public Interest Clinic (CIPPIC) against Facebook Inc. Under the Personal Information Protection and Electronic Documents Act by Elizabeth Denham Assistant Privacy Commissioner of Canada. Available online at http://priv.gc.ca/cf­ dc/2009/2009_008_0716_e.cfm. Solove, D. (2006) A Taxonomy of Privacy, 154 (3) University of Pennsylvania Law Review, 477. Song, J. & Walden, E. (2007). How consumer perceptions of network size and social interactions influence the intention to adopt peer-to-peer technologies. International Journal of E- Business Research, 3 (4), 49-66. Szoka, B. & Thierer, A. (2008). Online advertising and user privacy: Principles to guide the debate. The Progress & Freedom Foundation, 4 (19). Available online at http://ssrn.com/abstract=1348600. Turow, J., King, J., Hoofnagle, CJ, Bleakley, A., & Hennessy, M. (September, 2009). Americans reject tailored advertising and three activities that enable it. Report sponsored by the Rose Foundation for Communities and the Environment and the Annenberg School for Communication. Weber, L. (2007). Marketing to the social web: How digital communities build your business. John Wiley & Sons Inc: Hoboken, NJ. Westin, A. (1967) Privacy and Freedom 31-32.

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APPENDIX A In-depth Interview Questions and Topics

Speakers at a media conference a few months ago presented data that indicate a shift in how media dollars are being spent. Television, print and radio are decreasing, and digital is increasing. I would like to talk about this trend by focusing on the following:

1. From the perspective of your company, what are the advantages and disadvantages of the shift to digital? 2. How is marketing success measured in the digital environment? 3. What accountability measures are in place to ensure appropriate use of this medium? 4. What are the new frontiers in terms of digital marketing; that is, what can we expect to see in the future? 5. What new challenges and opportunities arise with these future developments?

Some of the appeal of the digital environment appears to be the ability to track in some manner consumer behavior. Through our discussions with others, we identified at least three different types of companies and tracking including:

a) websites that track customer behavior online; b) advertising networks, which can also track the movement of users, but across thousands of websites; and c) offline retailers who can track user preferences through frequent shopper cards.

6. Can you comment on how these types of companies – is it an accurate characterization, would you add to it or change it in any way, to help us refine our understanding?

We also need to understand the types of advertisements that are delivered in this digital space. We have been able to identify at least four distinct types, including:

a) targeted advertising, based on demographics, geography, current context or patterns over time; b) contextual advertising based on what a person may be searching for at the moment, and delivered by search engines, such as Google or by third-party advertising networks; and c) behavioural advertising that targets consumers based on their user profiles as determined by tracking their behavior over time and over a number of websites. d) social advertising that targets consumers based on their network of connections over social media

7. Can you comment on these types and help us refine our understanding of digital advertising? Specifically:

a. What is your understanding of behavioural advertising? b. Why is it appealing to marketers? c. Do you see any issues for marketers in adopting this technique?

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8. One of the overarching themes in our increasing use of technology in all aspects of our life is the issue of privacy of personal information.

a. With respect to the move to digital advertising do you see or foresee any conflict between the needs of marketers and the concerns of privacy officers? b. What is the role of the privacy officer as you understand it with respect to digital advertising? c. Do you see or foresee any tension from a consumer base that is increasingly aware of online privacy and personal information?

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Appendix B Focus Group and Town Hall Discussion Guide

Begin by explaining nature of the study.

Initial screening - Study participants must be:

 Enrolled in a university program  Between 18-30 years of age  A user of SMT (have accessed social media within the last 30 days)

Assignment to specific cluster group, based on subject’s self-identification:

Which of the following statements MOST CLOSELY describes your use of online social media applications:

 I use social media quite a bit, but mostly for keeping in touch with friends, updating my online profile and posting comments on others’ Facebook pages.

 I use social media mostly to look for information, read reviews and follow various types of discussion forums.

 I’m an avid user of and contributor to social media. I socialize with friends, I use it for information search purposes and I also post some of my own informative materials online.

 Although I do use social media, I generally don’t use it as much as other people my age.

Students participating in the focus group must read and sign consent form before proceedings begin.

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Discussion outline:

1. Let’s begin with introductions… tell us your first name & complete the sentence: “When it comes to my Internet use, I would describe myself as...”

2. Tell me about how you spend your time online in an average week… What are your favourite online applications? How much time do you spend on each of them? (Probe for new apps, changes in behaviour, etc.) How do you access (computer, smart phone, etc...)?

3. Have you ever bought anything online? If not, why not? If so, what types of things have you bought/would you consider buying? What do you like best/least about online shopping? (probe for company policies, etc.) Are you ever concerned about privacy/security issues online? Why/why not?

4. How is online buying different from store buying? Where do you get your product information prior to purchase for online vs. instore?

5. Have you ever bought anything as a direct result of an online ad? If yes, tell us about the ad... Where was it? What was it about the ad that enticed you to purchase?

6. What do you think of online ads in general? What types are most/least memorable/informative/annoying? How do they compare on these criteria with traditional media like TV, magazine, etc.?

7. I’d now like to get your reaction to a few scenarios related to online advertising.

a. Let’s say Facebook is allowing companies to post ads on people’s home pages. Would you rather the advertisers used your personal information to show you ads tailored to your needs and interests, OR would you rather they didn’t use your information and just post the same ads as they do for everyone else? Why? b. What about if it was coupons or discount codes for products... custom based on your info or generic and no access to your info?... c. What if you had the choice to PAY (say $1/mo) to get custom ads or discounts versus not pay and get random/generic ads/discounts?... d. What if you had the otion to PAY (say $1/mo) to STOP your information from being shared with online advertisers so that they stopped sending you ads altogether (both customized and generic)?

8. Is there anything else you would like to say in relation to your thoughts and/or experiences with online advertising?

Thank participants for their time. End session.

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Appendix C Online Survey Questions (unformatted)

Think about how you spend your time online in an average week. On average, how many hours per week do you spend doing the following:

10 hrs 5-9 hrs 1-4 hrs Less Never Don’t per wk per wk per wk than 1 Know or more hr /wk Writing articles or blogs to post online for others to read and comment on Visiting social networking sites, such as Facebook, LinkedIn or MySpace Reading online forums, blogs and discussion groups written by others Posting to a micro-blogging service such as Twitter “Checking In” with location based services such as Facebook Places, Foursquare or Gowalla Posting comments to someone else’s social networking page/account Maintaining/updating your profile on a social networking site Publishing or updating your personal web page (excluding social networking sites) Using Smartphone applications to access content Reading or viewing content from content sharing sites such as Tumblr, Digg, Reddit, Technorati or YouTube Posting content to content sharing sites such as Tumblr, Digg, Reddit, Technorati or YouTube Reading customer ratings and/or product/ service reviews Using a search engine to find information prior to a product or service purchase

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This next question set refers to buying products or services over the Internet. If you have never purchased a product or service over the Internet, answer the first item and then skip to the next question set. How often do you do each of the following? Frequently Sometimes Rarely Never Buy a product or service over the Internet Buy a product or service over the Internet based on an advertisement that you saw on the Internet Click on a link for a product or service that comes up in an online advertisement next to your search results Buy a product or service over the Internet based on an advertisement sent unsolicited in an e-mail Click on a link for a product or service that comes up in an online advertisement on your Facebook page Click on a link for another product or service that comes up in an in an online advertisement after you have made an online purchase Search for a product or service that you learned about through a television or print ad or a billboard Use the Internet to do additional research on a product or service you found in a retail store Use specific search engines to do comparison shopping before making a purchase of a product or service Use the Internet to do additional research on a product or service recommended to you by friends or family Buy a product or service through a team buying service such as GroupOn, RedFlagDeals, TeamSave, LivingSocial, DealTicker or WagJag Refer to the team buying services listed above to keep track of the latest deals. Change your Facebook profile information to avoid targeted online advertisements being sent to you Click on “offensive” as a technique for stopping targeted online advertisements being sent to you Click on “like” as a technique to encourage

52 targeted information or advertisements being sent to you Take a photo of a barcode using your smartphone and view the content online later Pay for content on the web (eg. to access information that is not available for free)

Think about the steps you go through to purchase a laptop computer that will cost about $1000. Listed below are a variety of steps that may or may not be a likely part of your purchase process. Please select the step that BEST represents the FIRST thing you would likely do, as well as indicate the likelihood of your using each of the other steps prior to making the purchase.

How likely are you to do each of Not at Not Neutral Somewhat Very This is the following in identifying and all very likely likely the considering an initial set of brands? likely likely FIRST thing I’d do Go to a retail store to talk to a salesperson Conduct a Go to various laptop manufacturer/brand websites Talk with friends and/or family about recommended brands

Once you have the initial set of brands identified for consideration how likely are to do each of the following to either narrow or expand the brands under consideration? Go to a retail store to talk to a salesperson Conduct a Google search Go to various laptop company websites Talk with friends and/or family about recommended brands Read independent product reviews Use a price comparison website How likely are you to purchase the laptop..... Online In a retail store

Think about the steps you go through to purchase a Reading Week holiday that will cost about $1000. Listed below are a variety of steps that may or may not be a likely part of your purchase

53 process. Please select the step that BEST represents the FIRST thing you would likely do, as well as indicate the likelihood of your using each of the other steps prior to making the purchase. How likely are you to do each of Not at Not Neutral Somewhat Very This is the following in identifying and all very likely likely the considering an initial set of likely likely FIRST options? thing I’d do Go to a travel agency to talk to a travel agent Conduct a Google search Go to various destination websites Talk with friends and/or family about recommended destinations Collect and read travel brochures for a variety of destinations

Once you have the initial set of destinations identified for consideration how likely are to do each of the following to either narrow or expand the brands under consideration? Go to a travel agency to talk to a travel agent Conduct a Google search Go to various destination websites Talk with friends and/or family about recommended destinations Read independent reviews on various destinations Use a price comparison website How likely are you to purchase this holiday.... Online In person through a travel agency

On a scale of 1 to 5 where 1 is very unimportant and 5 is very important, please indicate how important each of the following would be in enticing you to purchase more products or services online. Very Somewhat Neutral Somewhat Very important important unimportant unimportant Free shipping Online coupons customized for you Local pickup (e.g. at the nearest

54 retail outlet carrying the product) Web discounts on products or services customized for you No minimum number of items required to place an order Free shipping, even if it means a longer delivery time A higher shipping fee in exchange for overnight delivery A lower online price compared with the retail store price Assurance of at least as broad a selection as in a retail store Assurance of receiving brand name products and services rather than “no name” products and services or imitation ‘knock offs’ Improved fraud protection for credit card transactions Assurance your data will not be shared with advertising partners Easy to use website Clear information about products No hassle return policy Products recommended based on your past purchases Products recommended based on your friend’s past purchases Assurance that your purchase data will be retained for a maximum three months No spam policy Internet-based payment options such as PayPal Availability of product reviews from other customers Information about product in- stock availability Shipping tracking information Real-time chat sales or technical help

On a scale of 1 to 5 where 1 is not at all interested and 5 is very interested, how interested would you be in the following?

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Very Somewhat Neutral Not very Not at all interested interested interested interested Websites you visit to show you ads that are tailored to your specific interests rather than random ads Websites you visit to show you discount coupons that are tailored to your specific interests rather than random discount coupons Websites you visit to show you news items that are tailored to your specific interests rather than random news items Paying an additional $1 per month to get ads, discount coupons and news items that are tailored to your particular interests Paying an additional $1 per month to ensure that your favourite websites do not collect information about you to share with advertising companies that then target web ads to your interests Your Internet service provider pays you $1 per month to accept targeted advertising from companies that have products and services in which you would be interested, based on your online profile information

On a scale of 1 to 5 where 1 is strongly disagree and 5 is strongly agree, please indicate your level of agreement or disagreement with the following statements.

Strongly Somewhat Neutral Somewhat Strongly agree agree disagree disagree I want the benefits of relevant advertising I do not care if advertisers collect data about my search terms In Canada, we are well protected by the Office of the Privacy Commissioner against advertisers collecting data about me Targeting users based on their online behaviour works poorly because I get ads that are not relevant to me

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I would stop using any site that uses behavioural advertising (i.e. that collects data about my online activities in order to target ads to me) I would watch what I do online more carefully if I knew advertisers were collecting data about me I feel that it is an invasion of privacy for someone to keep track of my online activities Loyalty and reward cards do not collect any personal information about me I do not care if advertisers collect data about which websites I visit I ignore ads, so there is no benefit to me if ads are targeted to my interests I ignore ads, so there is no harm to me if ads are targeted to my interests It is worth paying extra to avoid targeted ads Advertisers will collect data about me whether I pay for them to do so or not, so there is no point in paying Advertisers will collect data about me whether I pay to stop them from doing so or not, so there is no point in paying Privacy is a right, so it is wrong to be asked to pay to keep companies from invading my privacy Companies asking me to pay for them to not collect data is like extortion I hate ads and would pay to avoid them Eventually the really good content on the Web is going to cost money I prefer to pay for a song through iTunes than to get it in a free download because I am worried about the quality of free content I understand the function of cookies on a computer If you have cookies on your computer, it makes you more vulnerable to someone stealing your password Using a computer is just as anonymous as using a TV, since no one really knows what you are doing

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On a scale of 1 to 5 where 1 is strongly disagree and 5 is strongly agree, please indicate your level of agreement or disagreement with the following statements about online advertising.

Strongly Somewhat Neutral Somewhat Strongly agree agree disagree disagree Targeted online advertising is annoying Random online advertising (not targeted to my interests) is more annoying than targeted online advertising Online advertising is necessary for the Internet Putting up with online advertising gives me access to sites without having to pay It’s good that deal sites such as Groupon use my personal information to send me relevant deals. Online advertising targeted to me is beneficial because ads are a source of information Random online advertising is better than television or billboards because it is easier to ignore online ads Random online advertising tends to be related to the website it appears on Targeted online advertising tends to be for products or services I am interested in Targeted online advertising has more to do with what I want and is less random than other types of advertising like television or magazines Random online advertising is insulting All online advertising whether targeted or random makes the Internet slower All online advertising whether targeted or random is distracting Random online advertising has too many unrelated and off topic ads Online advertising is creepier when it comes through your e-mail, rather than through a search engine like Google I love how I can get ads on my phone based on my location.

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Targeted online advertising is creepy when it is based on my online actions All online advertising for medical products should be regulated Online advertising is just a fact of life Online advertisements would get more of my attention if they had a more interactive component Online advertising does not get my attention the way traditional advertising on television or through billboards does Online advertising can motivate impulse purchases It is more appealing to click through an online advertisement for a brand name product or service than for a “no name” product or service. I would choose to have targeted advertisements on my computer rather than on my smartphone

How much would you pay per month to belong to a file sharing service that gave you unlimited access to….?

$5 $10 $20 $30 Would not pay breaking news the latest movies the latest videos the latest financial information the newest music the latest sports information the latest celebrity gossip Your favourite TV shows

Briefly describe your favourite online ad.

Why is it your favourite? What are the features that make it good?

Please indicate your level of use of the following technology products to access the Internet.

Frequently Sometimes Rarely Never Desktop computer

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Laptop computer Netbook computer iPod Touch Tablet, such as iPad Smartphone, such as Blackberry or iPhone Game device, such as an XBOX 360, PlayStation 3, or Wii

Are you... Male Female

In what year were you born?

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APPENDIX D Analysis of Social Media User Group Segments

Overview This Appendix provides a detailed investigation of the attitudes and behaviour of segments within the social media user group. It focuses on the following research questions.

1. What are the segments within the broader social media user population?

2. How does purchase behaviour differ by social media user group segment?

3. How does online behaviour differ by social media user group segment?

4. How does the appeal of specific online features differ by social media user group segment?

5. How do attitudes toward privacy differ by social media user group segment?

6. How does interest in new ideas for online interfaces differ by social media user group segment?

7. How do attitudes toward payment options in the online environment differ by social media user group segment?

8. How do attitudes toward the features of online advertising differ by social media user group segment?

9. How do attitudes toward targeted versus online advertising differ by social media user group segment?

Social Media User Group Segments Foster and her colleagues (2010, 2011) identified four distinct and sizeable user segments (SMUG – social media user groups) based on the need for information and the likelihood to engage in interactive participation. We used questionnaire items from these earlier studies updated with additional questions about the use of Smartphones, content sharing sites, such as Tumblr, Digg, Reddit, Technorati and YouTube, microblogging services such as Twitter, and location based services such as Facebook Places, Foursquare and Gowalla to identify the social media user group (SMUG) segments within our sample Using SPSS (version 19), we conducted an exploratory factor analysis (EFA) on a sample of 500 from the original 1037 with varimax rotation on 13 variables from the Foster et al. (2010, 2011) studies and the additional questions noted above. Three indicators from the original model were rejected (Using smartphone applications to access content, Posting to a microblogging service such as Twitter, and Checking in with location-based services such as Facebook Places, Foursquare or Gowalla) because of cross-loadings on multiple factors. This resulted in three first

61 order constructs defined as: 1) Creating 2) Socializing, and 3) Info Seeking. The final EFA results are listed in Table 1. The reliability scores of .860, .747 and .736 are all within acceptable ranges.

Table1: Final Factor Loadings for Social Media User Group Model (n=500) Creating Info Q# Statement Socializing Contributing Seeking α=.860 α=.747 α=.736 Q6G Maintaining/updating your profile on a social .887 networking site Q6F Posting comments to someone else’s social .876 networking page/account Q6B Visiting social networking sites, such as .801 Facebook, LinkedIn or MySpace Q7A Publishing or updating your personal web page .766 (excluding social networking sites) Q6A Writing articles or blogs to post online for others .752 to read and comment on Q7D Posting original material to content sharing sites .701 such as Tumblr, Digg, Reddit, Technorati or YouTube Q7E Reading customer ratings/or product/service .786 reviews Q7F Using a search engine to find information prior .718 to a product or service purchase Q7C Reading or viewing content from content .665 sharing sits such as Tumblr, Digg, Reddit, Technorati or YouTube Q6C Reading online forums, blogs and discussion .635 groups written by others

With the EFA complete, using AMOS (version 19) we constructed a model based on the confirmatory factor analysis (CFA) on the factors and indicators in another sample of 500 from the original 1037. The final model is illustrated in Figure 1.The resulting goodness of fit statistics are: Chi-square= 117.9 (df=32), RMR= .054, GFI= .955, CFI= .944 and RMSEA= .074. Table 2 presents the correlation matrix for the SEM model variables.

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Table 2: Correlation Matrix for Social Media User Group Model Variables (n=500) Q6A Q6B Q6C Q6F Q6G Q7A Q7C Q7D Q7E Q7F Q6A 1 Q6B .162** 1 Q6C .299** .266** 1 Q6F .229** .638** .226** 1 Q6G .269** .569** .201** .745** 1 Q7A .387** .173** .137** .240** .325** 1 Q7C .111* .228** .302** .201** .222** .159** 1 Q7D .348** .168** .197** .237** .312** .500** .291** 1 Q7E .155** .087 .337** .182** .201** .163** .364** .303** 1 Q7F .150* .202** .343** .213** .229** .142** .380** .212** .597** 1 **Correlation is significant at the 0.01 level (two-tailed) *Correlation is significant at the 0.05 level (two-tailed)

K-means cluster analysis was used on the factors in the CFA and illustrated in Figure 1 to identify four discrete social media user group (SMUG) segments, as illustrated in Figure 2.

Figure 2 Typology of Online Technology Users (n=1037)

High Socializers SMT Mavens (n=291) (n=118)

Minimally Involved Info Seekers Low (n=419) (n=209)

Interactive Participation Interactive Low High Information Needs

We replicated the findings of Foster et al. (2010, 2011) by identifying segments within the social media user group based on type of activity and level of usage. SMT Mavens participate to a greater extent in all online activities when compared to the other segments. In contrast, Minimally Involveds are less likely to participate in all online activities compared to the other three profile categories. Info Seekers are focused on more passive, information-search types of online activities such as reading the comments of others, while the Socializers focus on more active social endeavours such as posting comments to the social network pages of others. These segments also have different motivations for being involved online. From a business perspective, this can assist marketers to better target specific customer segments with more directed online strategies. Foster et al. (2010) found that community membership or a sense of

64 belonging is most important to SMT Mavens, followed by Socializers, Information Seekers and least so for Minimally Involveds in terms of motivation to participate online. Friendship Connection, the opportunity to maintain ties with existing and old friends or acquaintances, ranks highest as a motivator for the Socializer segment to participate online. It is interesting that the SMT Maven group, which is highest on all other dimensions, is no more motivated by this factor than is the Minimally Involved segment. While SMT Mavens and Minimally Involveds are both low on friendship connectivity motivations, the underlying reasons for each group may differ. While Minimally Involveds are simply low in overall motivation, the SMT Mavens are more likely to be motivated by factors more important to them than friendship. For example, they may exhibit more individualist rather than collectivist tendencies. In other words, their participation online is less likely due to a desire to stay in touch with friends and more about self expression. The third motivator, identified as Information Value, represents an evaluation of the content found through online sources in terms of its accuracy, credibility and importance. This element has been largely overlooked as a key motivator for participation. While most important to SMT Mavens, it is noteworthy that Socializers and Information Seekers share similar levels of motivation in terms of this factor. It may be that Socializers seek information from members of their own social network because they do not feel confident in their ability to evaluate unknown sources, whereas this social connection is not as relevant for Information Seekers and they are content to seek information from credible third party sources. The final two motivators in the Foster et al. model are Participation Confidence and Participation Concerns, both of which represent potential barriers to participation. Participation Confidence relates to concerns of inadequacy or the potential for damaging one’s image when contributing information in the online environment, while Participation Concerns refer more directly to privacy concerns and the potential for harm as a result of strangers accessing posted information. This appears consistent with the findings of Ardichvili (2008), lending further support to the importance of understanding not only the facilitators but also the potential barriers of participation in the online space. While participation confidence is an important contributing factor for SMT Mavens in particular, there is no difference between Socializers and Information Seekers, who are ranked second on this factor, followed by Minimally Involveds who are lowest. At first glance, it may seem incongruous that the most sophisticated user segment expresses the highest levels of concern with online participation. However, it follows that those who are most actively engaged in all aspects of social media will also have the most to lose in terms of personal image management, while those less engaged give less consideration to these risks. With respect to Participation Concerns, Information Seekers and the Minimally Involved segment are both relatively unconcerned about issues related to privacy protection, while Socializers and SMT Mavens both view such concerns as an important consideration when participating online. Again, this is likely reflective of the varied nature of participation among different SMUG segments, since Socializers and SMT Mavens are more active in their online contributions, while Information Seekers and Minimally Involveds tend to rely on more passive forms of participation. These segments with their differing profiles, behavioural patterns and attitudes are the foundation for further exploration of targeted online advertising.

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Purchase Behaviour Building on the model developed by Court and his colleagues (2009), we asked respondents about their purchase behaviour for two high involvement, high value products: a laptop and a vacation. Table 3 presents the results from an analysis of variance (ANOVA) for purchase behaviour for a laptop by social media user group (SMUG) segments.

Table 3: ANOVA Analysis of Purchase Behaviour for a Laptop by SMUG segments (n=1037) Mean Values for SMUG Segments Sig. Minimally Socializ SMT Info involved ers Mavens Seekers (n=419) (n=291) (n=118) (n=209) Identify and consider an initial set of laptop brands (1= not very likely, 5=very likely) Go to a retail store to talk to a salesperson 3.70 3.94 3.35 3.44 *** Conduct a google search 4.64 4.71 4.65 4.80 ** Go to various laptop/manufacturer/brand 4.16 4.23 4.26 4.38 * websites Talk with friends and/or family about 4.43 4.60 4.36 4.42 *** recommended brands Narrow or expand initial list of laptop brands (1= not very likely, 5=very likely) Go to a retail store to talk to a salesperson 3.63 3.79 3.47 3.30 *** Conduct a google search 4.43 4.57 4.39 4.68 *** Go to various laptop/manufacturer/brand 4.02 4.07 4.10 4.19 websites Talk with friends and/or family about 4.35 4.53 4.36 4.28 *** recommended brands Read independent product reviews 4.16 4.25 4.24 4.61 *** Use a price comparison website 3.68 3.82 4.08 3.98 *** Likelihood of purchasing a laptop (1= not very likely, 5=very likely) Online 2.77 2.70 3.11 3.11 *** In a retail store 4.57 4.76 4.35 4.51 *** *p<.10, **p<.05, ***p<.01

The results show that there are significant differences not only in initial search behaviour, but also in subsequent search refinement and purchase behaviour among the segments. Socializers are significantly more likely than other groups to go to a retail store to seek advice or to ask family and friends about their recommendations in order to develop an initial set of laptop brands. Info Seekers are more likely than others to use online sources for direction in compiling the initial set of brands either through a general Google search or through a more targeted investigation of various brand websites. These behavioural differences are consistent with the social media user profile of these two groups. The same patterns are followed in expanding their initial brand list. Socializers are more likely than others to seek advice from people, either in a retail store or through talking with family and friends. Info Seekers turn again to Google and independent reviews to revise their brand list. Interestingly, SMT Mavens are more likely than others to make use of price comparison lists. Although none of the groups is very likely to purchase a laptop online, the

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SMT Mavens and the Info Seekers are more likely than others. Likewise, the Socializers are more likely than others to make this purchase in a retail store. Table 4 presents the results from an ANOVA for purchase behaviour for a vacation by SMUG segments.

Table 4: ANOVA Analysis of Purchase Behaviour for a Vacation by SMUG segments (n=1037) Mean Values for SMUG Segments Sig. Minimally Socializ SMT Info involved ers Mavens Seekers (n=419) (n=291) (n=118) (n=209) Identify and consider an initial set of holiday options (1= not very likely, 5=very likely) Go to a travel agency to talk to a travel agent 3.17 3.22 3.15 3.08 Conduct a google search 4.48 4.65 4.36 4.63 *** Go to various destination websites 4.42 4.59 4.32 4.53 *** Talk with family and/or friends about 4.42 4.54 4.36 4.47 recommended destinations Collect and read travel brochures for a variety 3.44 3.48 3.57 3.35 of destinations Narrow or expand initial list of holiday options (1= not very likely, 5=very likely) Go to a travel agency to talk to a travel agent 3.38 3.45 3.20 3.35 Conduct a google search 4.37 4.56 4.27 4.47 *** Go to various destination websites 4.37 4.44 4.36 4.47 Talk with family and/or friends about 4.30 4.48 4.29 4.44 *** recommended destinations Read independent product reviews 4.10 4.24 4.20 4.43 *** Use a price comparison website 4.10 4.20 4.09 4.26 Likelihood of purchasing a holiday (1=not very likely, 5 very likely) Online 4.19 4.24 4.25 4.29 Through a Travel Agent/Agency 3.50 3.54 3.40 3.50 *p<.10, **p<.05, ***p<.01

There are not as many significant differences among segments for this service as there are for a tangible product, such as a laptop. Interestingly, both the Socializers and the Info Seekers are more likely than others to do a Google search and to go to various destination websites to identify an initial set of brands. When expanding that initial list, Socializers are more likely than others to conduct a Google search and to talk with family and friends. Consistent with the laptop search, Info Seekers are more likely than others to read independent product reviews to expand their initial brand list. There are no differences in propensity to purchase a vacation online or through a travel agency by SMUG segment. Finally, Table 5 examines differences among the segments in terms of what they do first in their search for an initial set of brands for a laptop and a vacation.

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Table 5: Crosstabulation of “Do First” when Identifying and Considering an Initial Set of Brands by SMUG segment (n=1037) % for each SMUG Segment Minimally Socializ­ SMT Info involved ers Mavens Seekers (n=419) (n=291) (n=118) (n=209) Do First When Identifying and Considering Initial Set of Laptop Brands (chi2 =25.8, df=12, sig.=.011) Conduct a Google search 59 56 60 68 Go to a retail store to talk to a salesperson 4 6 2 3 Go to various laptop/manufacturer/brand websites 12 7 16 8 Talk with friends and/or family about 24 31 21 21 recommended brands Do First When Identifying and Considering Initial Set of Holiday Options (chi2 =20.1, df=15, sig.=.168) Collect and read travel brochures for a variety of 1 1 1 2 destinations Conduct a Google search 50 54 61 53 Go to a travel agency to talk to a travel agent 9 8 9 8 Go to various destination websites 22 16 15 19 Talk with friends and/or family about 20 21 15 18 recommended destinations

There are statistically significant differences among the segments in terms of what is done first to develop an initial set of laptop brands. While most members of all segments do a Google search first, the Info Seekers are more likely to do this as first step than are other segments. The Socializers are more likely than other segments to talk with family and friends. Interestingly, there are no significant differences in initial search behaviour among the SMUG groups for a vacation. This highlights the differences in purchase behaviour for a product and a service. Social media user group profile has more of an impact on the search patterns for a product, particularly a technology product than it does for a service.

Online Behaviour Because this was primarily a study of targeted online advertising, the authors were interested in the types of actions taken by respondents in the online environment. Respondents were provided with a list of options related to participating in the online space and were asked for their frequency of participation. Using SPSS (version 19), we conducted an exploratory factor analysis (EFA) on a sample of 500 from the original 1037 with varimax rotation on 14 items related to online behaviour This resulted in four first order constructs defined as: 1) Taking Action 2) Control, 3) Search, and 4) Innovate. The final EFA results are listed in Table 6. The reliability scores of .828, .677, .674, and .812 are all within acceptable ranges. With the EFA complete, using AMOS (version 19) we constructed a model based on the confirmatory factor analysis (CFA) on the factors and indicators in another sample of 500 from the original 1037. The final model is illustrated in Figure 3. The resulting goodness of fit statistics are: Chi-square= 197.4 (df=71), RMR= .046, GFI= .947, CFI= .942 and RMSEA= .060. Table 2 presents the correlation matrix for the SEM model variables.

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Table 6: Final Factor Loadings for Online Behaviour Model (n=500)

Taking Q# Statement Action Control Search Innovate α=.828 α=.677 α=.674 α=.812 Q9E Click on a link for a product or service that .816 comes up in an online advertisement next to your search results. Q9C Click on a link for a product or service that .749 comes up on an online advertisement on your Facebook page. Q9F Buy a product or service over the Internet .744 based on an advertisement in an e-mail. Q9G Click on a link for another product or .680 service that comes up in an online advertisement after you have made an online purchase. Q9A Buy a product or service over the Internet .671 based on an advertisement that you saw online. Q10A Click on “like” in order to have access to .496 more information about a particular topic, which may include getting targeted ads about it. Q10E Click on “offensive” as a technique for .818 stopping targeted online advertisements sent to you. Q10D Change your Facebook profile information .809 to avoid targeted online advertisements being sent to you. Q10G Take a photo of a barcode using your .589 smartphone and view the content later. Q9D Use the Internet to do additional research on .801 a product or service you found in a retail store. Q10F Use the Internet to do additional research on .758 a product or service recommended to you by friends or family. Q9H Use specific search engines to do .736 comparison shopping before making a purchase of a product or service. Q10B Buy a product or service through a team .882 buying service such as GroupOn, RedFlagDeals, TeamSave, LivingSocial, DealTicker or WagJag. Q10C Refer to the team buying services listed .876 above to keep track of the latest deals

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Table 7: Correlation Matrix for Online Behaviour Model Variables (n=500)

Q9A Q9C Q9D Q9E Q9F Q9G Q9H Q10A Q10B Q10C Q10D Q10E Q10F Q10G Q9A 1 Q9C .385** 1 Q9D .246** .094* 1 Q9E .434** .550** .209** 1 Q9F .451** .414** .158** .432** 1 Q9G .443** .477** .142** .504** .562** 1 Q9H .257** .167** .462** .238** .240** .296** 1 Q10A .322** .415** .116** .357** .315** .403** .168** 1 Q10B .320** .319** .112** .271** .394** .325** .217** .309** 1 Q10C .269** .288** .172** .269** .346** .321** .212** .261** .753** 1 Q10D .176** .250** .011 .166** .257** .221** .141** .270** .279** .254** 1 Q10E .193** .222** .028 .173** .239** .283** .109** .320** .259** .251** .504** 1 Q10F .184** .117** .530** .142** .091* .154** .391** .138** .103* .157** .076 .125** 1 Q10G .336** .203** .125** .249** .286** .273** .193** .326** .292** .265** .270** .314** .199** 1 **Correlation is significant at the 0.01 level (two-tailed) *Correlation is significant at the 0.05 level (two-tailed)

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Taking Action Table 8 presents the results of an ANOVA analysis of the items included in the “Taking Action” construct by SMUG segment. Although none of the segments takes any of these actions frequently, the SMT Maven group is significantly more likely to take action at least sometimes compared to all other segments. Specifically, this group is more likely to click on the link that comes up in an online advertisement next to search results, click on a link in an online advertisement on Facebook, to buy online based on an e-mail advertisement, click on a link in an advertisement that comes up after an online purchase, buy online after an online advertisement, and click on “like” in order to access more information. This is not surprising given that the SMT Maven group is the most sophisticated in terms of range of online activities and frequency of use. Table 8: ANOVA Analysis for Taking Action by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=frequently, 2=sometimes, 3=rarely, 4=never) Click on a link for a product or service that 3.11 3.01 2.38 2.82 *** comes up in an online advertisement next to your search results. Click on a link for a product or service that 3.29 3.05 2.51 3.04 *** comes up in an online advertisement on your Facebook page. Buy a product or service over the Internet 3.42 3.31 2.72 3.22 *** based on an advertisement in an e-mail. Click on a link for another product or 3.29 3.16 2.59 2.95 *** service that comes up in an online advertisement after you have made an online purchase. Buy a product or service over the Internet 3.16 3.00 2.41 2.78 *** based on an advertisement that you saw online. Click on “like” in order to have access to 3.17 2.65 2.24 2.79 *** more information about a particular topic, which may include getting targeted ads about it. ***p<.01

Control The next construct has been labeled “Control” and involves users customizing the online environment to meet their needs. While none of the groups uses these tools frequently, once again the SMT Maven group is significantly more likely than others to use the available tools to take control of the online environment. As Table 9 illustrates, they are more likely to click on “offensive” to stop targeted online advertising, to change their Facebook profile to avoid online

72 advertising, and to take a photo of a barcode to view content later. This is consistent with the profile of the SMT Maven group as more diverse and frequent users of the online environment.

Table 9: ANOVA Analysis for Control by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=frequently, 2=sometimes, 3=rarely, 4=never) Click on “offensive” as a technique for 3.50 3.47 2.90 3.30 *** stopping targeted online advertisements sent to you. Change your Facebook profile information 3.39 3.19 2.74 3.18 *** to avoid targeted online advertisements being sent to you. Take a photo of a barcode using your 3.50 3.46 2.83 3.14 *** smartphone and view the content later. ***p<.01

Search We have already investigated the search patterns by SMUG segment in the previous section on purchase behaviour. The findings in Table 10 reinforce the significant behavioural differences between Info Seekers and others. This group is significantly more likely than others to do additional Internet research after finding a product in a retail store or after getting a recommendation from family or friends. This same group along with the SMT Mavens is significantly more likely than other groups to use specific search engines to do comparison shopping before making a purchase of a product or service.

Table 10: ANOVA Analysis for Search by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved er Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=frequently, 2=sometimes, 3=rarely, 4=never) Use the Internet to do additional research on 2.07 1.78 1.65 1.50 *** a product or service you found in a retail store. Use the Internet to do additional research on 2.12 1.92 1.65 1.59 *** a product or service recommended to you by friends or family. Use specific search engines to do 2.52 2.35 1.98 1.93 *** comparison shopping before making a purchase of a product or service. ***p<.01

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Innovate Technology is constantly changing and thus there are always a new interfaces and applications for users to try. One of the more successful new uses of the Web has been to offer discounts through group buying. A number of companies have been very successful in this space including GroupOn, LivingSocial and WagJag. Table 11 presents the ANOVA analysis for innovation as measured by adoption of group buying by SMUG segments. Although there is not yet widespread adoption of this behaviour, the SMT Maven group is significantly more likely to be engaged in buying a product or service through a team buying service and to keep track of the latest deals on these services.

Table11: ANOVA Analysis for Innovate by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=frequently, 2=sometimes, 3=rarely, 4=never) Buy a product or service through a team 3.38 3.30 2.69 3.00 *** buying service such as GroupOn, RedFlagDeals, TeamSave, LivingSocial, DealTicker or WagJag. Refer to the team buying services listed 3.19 3.24 2.55 2.84 *** above to keep track of the latest deals ***p<.01

Appeal of Online Features Online commerce has becoming increasingly complex in terms of choices and options. Tables 12 through 16 investigate the appeal of various online features for SMUG segments.

Saving Money The first group of features listed in Table 12 relates to the ways a customer can save money by purchasing products and services online. While all of these online options are generally appealing to all segments, they are most appealing to the Info Seekers whose main activity online is searching for information. Info Seekers are more likely than other segments to find free shipping, customized web discounts, lower prices than retail and free shipping with longer delivery as features that will entice them to make more purchases of products and services online. This is consistent with the focus of this SMUG segment on using the online environment as a research/information resource. The outcome of extensive information-seeking is identifying the best possible combination of features available.

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Table 12: ANOVA Analysis of Enticing Purchases Online by Saving Money by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=very important, 2=somewhat important, 3=neutral, 4=somewhat unimportant, 5=very unimportant) Free shipping 1.59 1.63 1.82 1.40 *** Web discounts on products and services 1.99 1.91 2.14 1.76 *** customized for you A lower online price compared with the retail 1.70 1.69 1.86 1.52 ** store price Free shipping (even if it means longer 1.89 1.96 1.98 1.73 * delivery) *p<.10, **p<.05, ***p<.01

Ease of Use There is much research supporting a customer-friendly experience as inspiring brand and retail loyalty and a user-friendly interface as a mechanism for encouraging online use. The results from Table 13 suggest that while these features are somewhat important to all segments, they are most important to Info Seekers and least important to SMT Mavens who are technologically adept. Info Seekers are more likely than other segments to find local pickup, no minimum number of items required, easy to use website and no hassle return policy as features that will entice them to make more purchases of products and services online.

Table 13: ANOVA Analysis of Enticing Purchases Online by Ease of Use by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=very important, 2=somewhat important, 3=neutral, 4=somewhat unimportant, 5=very unimportant) Local pickup (eg. at the nearest retail outlet 2.23 2.10 2.43 1.99 *** carrying the product) No minimum number of items required to 1.95 2.05 2.07 1.72 ** place an order Easy to use website 1.62 1.69 1.88 1.47 *** No hassle return policy 1.67 1.73 1.89 1.56 ** **p<.05, ***p<.01

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Product Features Most products that are available online are also available in traditional retail outlets. Thus, choosing to shop online should provide the same experience in terms of product features. As Table 14 illustrates, these product features are at least somewhat important to all segments, but they are significantly more important to Info Seekers. This segment is more likely to report that assurances of product selection and product quality as evidenced by brand names is important for motivating them to purchase online more often.

Table 14: ANOVA Analysis of Enticing Purchases Online by Product Features by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=very important, 2=somewhat important, 3=neutral, 4=somewhat unimportant, 5=very unimportant) Assurance of at least as broad a selection as 1.91 1.87 2.08 1.69 *** in a retail store Assurance of receiving brand name products 1.87 1.82 1.93 1.64 * and services rather than “no name” products and services or imitation “knock offs” *p<.10, ***p<.01

Online Information

Table 15: ANOVA Analysis of Enticing Purchases Online by Information by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=very important, 2=somewhat important, 3=neutral, 4=somewhat unimportant, 5=very unimportant) Clear information about products 1.58 1.56 1.74 1.35 *** Availability of product reviews from other 1.98 1.95 1.96 1.61 *** customers Information about product in-stock 1.85 1.81 2.05 1.56 *** availability Shipping tracking information 1.75 1.77 1.86 1.51 *** Real time chat sales or technical help 2.49 2.49 2.41 2.19 ** **p<.05, ***p<.01

Consumer decision-making begins with the search for information. However, in the decision-journey there are several instances where additional information is required. Table 15 explores the importance of information at various junctions in the purchase process. All of these

76 are at least somewhat important to all segments, but once again they are significantly more important to the Info Seekers. For this group, clear information about products, product reviews, in-stock availability, shipping tracking and technical help are features that are important for enticing them to make more purchases online.

Privacy and Protection Previous research has highlighted the role of privacy concerns as a barrier to participating in the online environment. Table 16 suggests that while privacy and protection are important for all SMUG segments, they are most important to the Info Seekers, although at a very low level of statistical significance. In terms of enticing more online purchases of products and services, improved fraud protection, protection of transaction data and a no spam policy are important for Info Seekers. Practical solutions for the protection of financial data elicited a more opinionated response from all SMUG segments than the response to the general conceptual privacy statements discussed in the following section.

Table 16: ANOVA Analysis of Enticing Purchases Online by Privacy and Protection by SMUG Segment (n=1037) Mean Values for SMUG Segments Minimally Socializ SMT Info involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) (1=very important, 2=somewhat important, 3=neutral, 4=somewhat unimportant, 5=very unimportant) Improved fraud protection for credit card 1.61 1.62 1.83 1.53 * transactions Assurance that your purchase data will be 2.38 2.49 2.28 2.21 * retained for a maximum of three months No spam policy 1.82 1.82 1.92 1.63 * *p<.10

Attitudes Toward Privacy While Tables 12 through 16 focused on specific features of the online environment, Table 17 looks more generally at attitudes toward privacy among the four segments. The first observation is that very few of these attitudinal items reveal a statistically significant difference among segments. While there is general disagreement that respondents do not care if advertisers collect data about search items, this is most strongly felt among the SMT Mavens. Info Seekers are significantly more likely to agree that they understand the function of cookies, although all segments somewhat agree that they understand. While all segments disagree that using a computer is as anonymous as using a TV, the SMT Mavens are significantly less likely to strongly disagree with that assertion, perhaps because they are technologically adept enough to know how to manipulate the settings on their computer to maximize privacy.

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Table 17: ANOVA Analysis of Attitudes Toward Privacy by SMUG Segment (n=1037) (1=strongly agree, 2=somewhat agree, Mean Values for SMUG Segments 3=neutral, 4=somewhat disagree, 5=strongly Minimally Socializ SMT Info disagree) involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) I do not care if advertisers collect data about 3.47 3.44 3.01 3.36 *** my search items. I am protected by law against advertisers 2.68 2.54 2.53 2.58 collecting data about me. I would stop using any site that uses 2.91 2.96 2.88 2.95 behavioural advertising (ie that collects data about my online activities in order to target ads about me). I feel that it is an invasion of privacy for 2.16 2.06 2.23 2.02 someone to keep track of my online activities I do not care if advertisers collect data about 3.39 3.49 3.21 3.38 which websites I visit I would watch what I do online more 2.84 2.65 2.68 2.72 carefully if I knew advertisers were collecting data about me Privacy is a right, so it is wrong to be asked 1.91 1.79 2.11 1.83 to pay to keep companies from invading my privacy I understand the function of cookies on a 2.58 2.76 2.27 2.13 *** computer. Using a computer is just as anonymous as 4.04 4.03 3.57 4.16 *** using a TV, since no one really knows what you are doing. Targeted online advertising is creepy when it 2.53 2.34 2.46 2.48 * is based on my online actions Online advertising is creepier when it comes 2.24 2.15 2.37 2.18 through your e-mail rather than a search engine like Google *p<.10, ***p<.01

In terms of minimally significant or non-significant differences, there is a trend for the Socializers to be slightly more likely to find online advertising creepy. This is a term that surfaced in previous research on online privacy (Levin, Foster, Nicholson, West, & Hernandez, 2008), and is associated with user concern about unauthorized users being privy to their comments and actions on online social networks, specifically Facebook. The differences among segments are only minimally significant for one item and non-significant for the others, but it appears noteworthy that the Socializers hold the opinion most strongly, given previous research. For the remaining privacy items, respondents display some privacy concerns, but there is no statistical difference among segments on the particular attitudes. This suggests that privacy concerns are not well-developed, in that only one item has a mean value in the strongly agree

78 part of the scale. Three out of four segments believe that privacy is a right and so it is wrong to be asked to pay to keep companies from invading their privacy. The usage level of technology or the variety of activities engaged in or the level of adeptness with technology seem to have no bearing on propensity to be concerned about privacy. Neither those with lots of experience (SMT Mavens) nor those with little experience (Minimally Involved) display strong concerns about their privacy being invaded as a result of online advertising and certainly there is little support for changing online activities in the face of behavioural advertising and the online data gathering and profiling, which is its foundation. This blasé attitude suggests that OPC modify its outreach and education approach to target specific SMUG segments rather than employing a “one-size­ fits-all” approach, and that OPC devote more time to the advocacy of practical protective measures that resonate well with all segments, such as the specific financial data measures discussed above.

Interest in New Ideas

Table 18: ANOVA Analysis of Interest in New Ideas by SMUG Segment (n=1037) (1=very interested, 2=somewhat interested, Mean Values for SMUG Segments 3=neutral, 4=not very interested, 5=not at all Minimally Socializ SMT Info interested) involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) Websites you visit to show you ads that are 2.85 2.81 2.53 2.71 * tailored to your specific interests rather than random ads. Websites you visit to show you discount 2.36 2.29 2.19 2.18 coupons that are tailored to your specific interests rather than random discount coupons Websites you visit to show you news items 2.50 2.54 2.30 2.35 * that are tailored to your specific interests rather than random news items Paying an additional $1 per month to get ads, 4.01 4.03 3.58 4.03 *** discount coupons and news items that are tailored to your particular interests Paying an additional $1 per month to ensure 3.82 3.84 3.50 3.79 * that your favourite websites do not collect information about you to share with advertising companies that then target web ads to your interests Your Internet service provider pays you $1 to 3.28 3.31 3.12 3.36 accept targeted advertising from companies that have products and services in which you would interested based on your online profile *p<.10, ***p<.01

Respondents were asked to react to a variety of options including those designed to protect their online privacy. Table 18 presents the results. Once again, the majority of items tested reveal no significant difference among segments or minimal significant difference.

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Although none of the segments is very interested in paying an additional $1 to get customized ads, coupons and news items, SMT Mavens are less disinterested than others, perhaps because they are online more than the other segments. Respondents show a bit of interest in targeted ads, coupons and news items with SMT Mavens again being marginally more interested than others in ads and news items. None of the groups reports interest in paying to keep personal information out of the hands of advertising companies, with SMT Mavens being marginally less disinterested in paying. Having control over personal information is more than a simple payment issue, in that none of the segments reports interest in being paid $1 to accept targeted advertising. The disinterest displayed by all segments towards these practical measures indicates either a worrisome (to privacy advocates) acceptance of the commercial use of information, or perhaps an expectation for government, rather than individual action, in an area that all segments strongly perceive to be akin to a right.

Payment The next set of questions explores the payment issue more broadly in terms of attitudes. As Table 19 indicates, SMT Mavens are less likely than other segments to disagree with the statements about paying to avoid ads, and its being worth it to pay to avoid targeted ads. This is consistent with their previous views about payment, in that they were less negative than other SMUG segments. The SMT Mavens are also marginally more concerned about the quality of online content as evidenced by their not being as against paying for a song rather than accessing a free download compared to the other segments. Interestingly, it is the Info Seekers who are more accepting of the inevitability of online advertising. They are significantly more likely to believe that putting up with advertising is the price they pay for having access to free sites, that online advertising is necessary for the Internet and is simply a fact of life. This is consistent with their use of the online environment as a research tool for information.

Table 19: ANOVA Analysis of Attitudes Toward Payment by SMUG Segments (n=1037) (1=strongly agree, 2=somewhat agree, Mean Values for SMUG Segments 3=neutral, 4=somewhat disagree, 5=strongly Minimally Socializ SMT Info disagree) involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) I hate ads and would pay to avoid them 3.64 3.71 3.29 3.61 *** It is worth paying extra to avoid targeted ads 3.76 3.87 3.44 3.87 *** I prefer to pay for a song through iTunes than 3.51 3.74 3.39 3.58 ** to get it in a free download because I am worried about the quality of free content Eventually the really good content on the 3.51 3.74 3.39 3.58 Web is going to cost money Putting up with online advertising gives me 2.48 2.42 2.42 2.16 *** access to sites without having to pay Online advertising is necessary for the 2.53 2.47 2.31 2.24 *** Internet Online advertising is just a fact of life 2.41 2.35 2.49 2.17 *** **p<.05, ***p<.01

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Online Advertising Features Table 20 presents the different views held by each segment about specific features of the online environment. SMT Mavens are significantly more likely than other segments to believe that a brand name click-through is more appealing than a “no name” click-through, that online advertising motivates purchases, that including an interactive component in online advertisements would be more attention-getting, that targeted ads are a useful source of information, and that they seek relevant advertising. This is consistent with their heavier involvement in the online environment compared to other segments and the diversity of activities in which they participate. They are more able to speak from experience on these items compared to other SMUG segments.

Table 20: ANOVA Analysis of Appeal of Online Advertising Features by SMUG Segment (n=1037) (1=strongly agree, 2=somewhat agree, Mean Values for SMUG Segments 3=neutral, 4=somewhat disagree, 5=strongly Minimally Socializ SMT Info agree) involved er Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) It is more appealing to click through an 2.33 2.37 2.21 2.38 *** online advertisement for a brand name product or service than for a “no name” product or service Online advertising can motivate impulse 3.08 2.97 2.79 3.02 * purchases Online advertisements would get more of my 3.09 3.00 2.77 3.01 * attention if they had an interactive component Online advertising targeted to me is 3.08 2.97 2.75 2.95 ** beneficial because ads are a source of information I want the benefits of relevant advertising 2.54 2.45 2.28 2.35 ** *p<.10, **p<.05, ***p<.01

Targeted versus Random Advertisements The next set of questions explores the attitudes of respondents toward targeted versus random advertisements. SMT Mavens are significantly more likely than other segments to agree that targeted ads really are targeted, that is they feature products or services of interest, and that random advertising is insulting. Info Seekers are significantly more likely than other segments to agree that random advertising tends to be related to the website rather than to their interests, and that it is more annoying than targeted advertising. There are minimal or no differences among segments in the belief that targeted advertising is less random than TV or magazine advertising, that random online advertising is easier to ignore than TV or billboards, that targeting doesn’t work, that targeting is annoying, that all online advertising is distracting and that random advertising is irrelevant. Targeted online advertising is relatively new compared to other communication and advertising strategies, and is still evolving as technology develops in the online space. For this reason, it may be that respondents are simply not aware of the difference between targeted and random advertisements

81 in the online space or are unable to differentiate because the technical capabilities for profiling are still at a rudimentary level.

Table 21: ANOVA Analysis of Attitudes Toward Targeted versus Random Advertisements by SMUG Segment (n=1037) (1=strongly agree, 2=somewhat agree, Mean Values for SMUG Segments 3=neutral, 4=somewhat disagree, 5=strongly Minimally Socializ SMT Info agree) involved ers Mavens Seekers Sig. (n=419) (n=291) (n=118) (n=209) Targeted online advertising has more to do 2.65 2.57 2.46 2.50 with what I want and is less random then other types of advertising like television or magazines Random online advertising tends to be related 2.50 2.32 2.34 2.22 *** to the website it appears on Targeted online advertising tends to be for 2.98 2.94 2.58 2.96 *** products or services I am interested in Random online advertising is insulting 3.31 3.22 2.88 3.15 *** Random online advertising (not targeted to 2.50 2.32 2.34 2.22 ** my interests) is more annoying than targeted online advertising Random online advertising is better than 2.92 2.83 2.81 2.68 * television or billboards because it is easier to ignore online ads Targeting users based on their online 2.49 2.36 2.53 2.35 * behaviour doesn’t work because I get ads that are not relevant to me Targeted online advertising is annoying 2.35 2.42 2.23 2.33 All online advertising, whether targeted or 2.41 2.38 2.55 2.36 random is distracting Random online advertising has too many 2.33 2.34 2.47 2.29 unrelated and off topic ads *p<.10, **p<.05, ***p<.01

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