ࡱ> qW{| 4v w " a 7 bjbjUU Tt7|7|(lJJJJJJJ$nBBBPBCnrhD P.PPPQ Y|Q\@qqqqqqqs vquJ]QQ]]q_JJPPAr___]*JPJPq_]q_J _/iVp@JJqPD PSn=B^*.q q<Wr0r:qTv_vq_nnJJJJ Why People Hate the Paperclip: Labels, Appearance, Behavior and Social Responses to User Interface Agents Symbolic Systems Program Stanford University Luke Swartz June 12, 2003 I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and quality, as a thesis for the degree of Bachelor of Science with Honors. ____________________________________ Clifford Nass (Principal Advisor) I certify that I have read this thesis and that in my opinion it is fully adequate, in scope and quality, as a thesis for the degree of Bachelor of Science with Honors. ____________________________________ Bonnie A. Nardi (Second Reader) Abstract User interface agents are increasingly used in software products; perhaps the best-known user interface agent is the Microsoft Office Assistant (Clippy the Paperclip). This thesis explores why many people have a negative response to the Office Assistant, using a combination of theoretical, qualitative, and quantitative studies. Among the findings were that labelswhether internal cognitive labels or explicit system-provided labelsof user interface agents can influence users perceptions of those agents. Similarly, specific agent appearance (for example, whether the agent is depicted as a character or not) and behavior (for example, if it obeys standards of social etiquette, or if it tells jokes) can affect users responses, especially in interaction with labels. Acknowledgements While few of us need help from Clippy to write a letter, I needed a lot of help to write this thesis. I would like to thank the following wonderful people: Cliff Nassfor his never-ending enthusiasm and constant support for (wow!) four years. Ive had a great time working on this, and much of that is because of Cliffs great spirit. Bonnie Nardifor graciously agreeing to be my second reader, providing excellent advice on short notice, and for her help thinking through the interviews early on. Also thanks to her and Diane Schiano for continuing my ethnographic education this quarter. Pamela Hindsfor introducing me to ethnography, and her indispensable advice on setting up the interviews and honing the questions Steve Barleyfor his contagious humor and down-to-earth training in ethnographic methods Francis Lee, Amy Huang, and Young Paik, the great I/Me Crew for being awesome colleagues, and for giving me the kick in the pants to actually do this research Tom Wasow and the Symbolic Systems Programfor being a unique program that let me do a unique blend of research; Tom in particular has been a great mentor and friend for many years. Todd Davies also has provided excellent support and guidance for the past two years. Laura Selznik and the Undergraduate Research Program officefor funding my research (thanks to a generous grant from the late Roger Deaton) and for their endless patience in waiting for my receipts The informants and participants (especially the many friends who helped me pre-test the various experiments and interview questions) for their honesty and generosity with their time; everything I learned in this research, I learned from them! My familyfor always being there, and for being my constant inspiration. Contents  TOC \o "1-3" \h \z  HYPERLINK \l "_Toc75140170" Abstract  PAGEREF _Toc75140170 \h iii  HYPERLINK \l "_Toc75140171" Acknowledgements  PAGEREF _Toc75140171 \h iv  HYPERLINK \l "_Toc75140172" Contents  PAGEREF _Toc75140172 \h v  HYPERLINK \l "_Toc75140173" Chapter 1: Introduction  PAGEREF _Toc75140173 \h 1  HYPERLINK \l "_Toc75140174" 1.1 User Interface Agents  PAGEREF _Toc75140174 \h 1  HYPERLINK \l "_Toc75140175" 1.2 Microsoft Office Assistant  PAGEREF _Toc75140175 \h 3  HYPERLINK \l "_Toc75140176" 1.2.1 A Design History of the Office Assistant  PAGEREF _Toc75140176 \h 4  HYPERLINK \l "_Toc75140177" 1.2.2 Behaviors and Roles of the Office Assistant  PAGEREF _Toc75140177 \h 5  HYPERLINK \l "_Toc75140178" Chapter 2: Theoretical Critique of the Office Assistant  PAGEREF _Toc75140178 \h 12  HYPERLINK \l "_Toc75140179" 2.1 Computers As Social Actors  PAGEREF _Toc75140179 \h 12  HYPERLINK \l "_Toc75140180" 2.2 Critique of Agents and Anthropomorphism  PAGEREF _Toc75140180 \h 13  HYPERLINK \l "_Toc75140181" 2.2.1 Direct Manipulation versus Agents  PAGEREF _Toc75140181 \h 18  HYPERLINK \l "_Toc75140182" 2.2.2 The Persona Effect and Empirical Studies of the Effect of Using Agents  PAGEREF _Toc75140182 \h 19  HYPERLINK \l "_Toc75140183" 2.3 Applying CASA  PAGEREF _Toc75140183 \h 21  HYPERLINK \l "_Toc75140184" 2.3.1 Etiquette  PAGEREF _Toc75140184 \h 21  HYPERLINK \l "_Toc75140185" 2.3.2 Appearance  PAGEREF _Toc75140185 \h 22  HYPERLINK \l "_Toc75140186" 2.3.3 Status  PAGEREF _Toc75140186 \h 23  HYPERLINK \l "_Toc75140187" Chapter 3: Qualitative Study of the Office Assistant  PAGEREF _Toc75140187 \h 25  HYPERLINK \l "_Toc75140188" 3.1 Methods and Informants  PAGEREF _Toc75140188 \h 25  HYPERLINK \l "_Toc75140189" 3.1 In-Depth Interviews  PAGEREF _Toc75140189 \h 25  HYPERLINK \l "_Toc75140190" 3.2 Survey Question  PAGEREF _Toc75140190 \h 25  HYPERLINK \l "_Toc75140191" 3.2 Results and Discussion  PAGEREF _Toc75140191 \h 26  HYPERLINK \l "_Toc75140192" 3.2.1 General Response to the Office Assistant  PAGEREF _Toc75140192 \h 26  HYPERLINK \l "_Toc75140193" 3.2.2 Expertise, Help and Learning  PAGEREF _Toc75140193 \h 28  HYPERLINK \l "_Toc75140194" 3.2.3 Mental Model of the Paperclip  PAGEREF _Toc75140194 \h 29  HYPERLINK \l "_Toc75140195" 3.2.4 Character Appearance  PAGEREF _Toc75140195 \h 30  HYPERLINK \l "_Toc75140196" 3.3 Alternative Explanations  PAGEREF _Toc75140196 \h 31  HYPERLINK \l "_Toc75140197" 3.3.1 Attitudes Toward Microsoft  PAGEREF _Toc75140197 \h 31  HYPERLINK \l "_Toc75140198" Chapter 4: Quantitative Study of User Interface Agents  PAGEREF _Toc75140198 \h 32  HYPERLINK \l "_Toc75140199" 4.1 Method  PAGEREF _Toc75140199 \h 32  HYPERLINK \l "_Toc75140200" 4.1.1 Design and Manipulation  PAGEREF _Toc75140200 \h 32  HYPERLINK \l "_Toc75140201" 4.1.2 Participants and Procedure  PAGEREF _Toc75140201 \h 33  HYPERLINK \l "_Toc75140202" 4.1.3 Dependent Measures  PAGEREF _Toc75140202 \h 34  HYPERLINK \l "_Toc75140203" 4.2 Results  PAGEREF _Toc75140203 \h 35  HYPERLINK \l "_Toc75140204" 4.3 Discussion  PAGEREF _Toc75140204 \h 36  HYPERLINK \l "_Toc75140205" Chapter 5: Second Quantitative Study  PAGEREF _Toc75140205 \h 39  HYPERLINK \l "_Toc75140206" 5.1 Method  PAGEREF _Toc75140206 \h 39  HYPERLINK \l "_Toc75140207" 5.1.1 Design and Manipulation  PAGEREF _Toc75140207 \h 39  HYPERLINK \l "_Toc75140208" 5.1.2 Participants and Procedure  PAGEREF _Toc75140208 \h 41  HYPERLINK \l "_Toc75140209" 5.1.3 Dependent Measures  PAGEREF _Toc75140209 \h 41  HYPERLINK \l "_Toc75140210" 5.2 Results  PAGEREF _Toc75140210 \h 42  HYPERLINK \l "_Toc75140211" 5.3 Discussion  PAGEREF _Toc75140211 \h 48  HYPERLINK \l "_Toc75140212" Chapter 6: Conclusions  PAGEREF _Toc75140212 \h 51  HYPERLINK \l "_Toc75140213" References  PAGEREF _Toc75140213 \h 52  HYPERLINK \l "_Toc75140214" Appendix A: Quantitative Questionnaire  PAGEREF _Toc75140214 \h 58  Chapter 1: Introduction I hate that #@$&%#& paperclip! Many people seem to dislike Microsofts Office Assistantwhy? What can one learn from the Office Assistant about how to design user interface agents? This thesis tackles these two questions. Chapter 1 provides an overview of user interface agents and the Office Assistant. Chapter 2 examines the previous literature on user interface agents to critique the Office Assistant. Chapter 3 describes a qualitative study of Microsoft Office users, and Chapters 4 and 5 describe quantitative studies designed to test design issues provoked by the qualitative study. 1.1 User Interface Agents User interface agents are increasingly employed to enhance software products. Websites (e.g. buy.com, extempo.com, ananova.com, mysimon.com) now use characters to guide users through processes or present information, wizards and guides have become standard user interface tools, and a new crop of software that uses Microsoft Agent is beginning to bring anthropomorphic characters to the desktopincluding a Bible-reading character ( REF _Ref40852288 \h Figure 1)!  Figure  SEQ Figure \* ARABIC 1. Agent screenshot from Speakingbible.com While some of the hype around agents has died down, not long ago, Nicholas Negroponte (1995) predicted, The future of computing will be 100% driven by delegating to, rather than manipulating, computers. About the same time, Microsoft chairman Bill Gates (1995) gushed about how the social interface using agents would be the next step beyond the graphical user interface, making it 100 times easier to use than todays VCR is. Agents also feature prominently in science fiction. Stanley Kubricks 2001: A Space Odyssey depicted the interface to the computer system HAL 9000 as an intelligent agent, who not only could interact with astronauts using speech, but could play chess and even read lips. Star Trek: The Next Generation depicted not only a ship computer with a strong personality, but also an android character (Data) who behaved like a normal (albeit humorless) crewmember. The idea of artificial beings designed to assist humans is an ancient oneat least as old as Homers description the god Hephaestus creating golden servants to do his bidding. What is a user interface agent? Unfortunately, agent has come to mean many things in the Computer Science and Human-Computer Interaction literature. On one hand, agent can describe a system designed to mimic human behavior on some levelan interpretation most associated with Artificial Intelligence (AI) and the idea of intelligent agents. In their definitive textbook on agent-centered AI, Russel and Norvig (2003, p. 4) use the related term rational agent to describe a program that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome. On the other hand, agent can describe software that acts on ones behalf to carry out (relatively) independent tasksnot unlike a travel agent. They are often referred to as autonomous agents, or as software agents or, simply, bots. They include most web agents, which scour the web and report back what theyve found. Finally, agent can describe various interface elementsoften involving anthropomorphic charactersdesigned to make a user interface more fun and/or easier to use. Such agents, which I refer to as user interface agents, are related to social, embodied, conversational, life-like, animated, and personified agents (or some combination of these adjectives). I prefer the term user interface, because it does not make a commitment to a particular representation or behaviormerely that the agent is designed to enhance the user interface. An additional difficulty is that often an agent can fall into more than oneeven all threeof these categories. For example, Apple Computers famous Knowledge Navigator concept video (Dubberly & Mitsch, 1987) depicted a bow-tied agent which, while enhancing the user interface of a suped-up Macintosh-like operating system, also seemed to be intelligent (in that it understood natural language, made inferences based on the users input, etc.) and autonomous (in that it carried out activities in the background, such as trying to connect a colleague on the videophone and leaving a message on the users behalf). 1.2 Microsoft Office Assistant Perhaps the most well-known user interface agent is Microsofts Office Assistant, bundled with its Office software suite since 1997. Popularly known as Clippy the Paperclip (the default character, referred to in Microsoft Office itself as Clippit), the agent seems to have attracted widespread negative opinion. The pressparticularly the digerati mediaalmost universally condemned the paperclip. One representative article, Die Clippy, Die, describes how to permanently remove the persistent character (Noteboom 1998). Nearly every website about the paperclip in 2001 (before the introduction of Office XP) showed how to remove or disable it. At least among the technologically elite, Clippy wasand isextremely unpopular. The clamor against the character forced Microsoft to allow users to permanently hide the paperclip in the 2000 version of Office. Later, Microsoft effectively removed the Office Assistant by disabling it by default in Office XP. Microsoft claimed that the new version was so easy to use that the Assistant was no longer necessary, but it also hammed up the idea that Clippy was annoying by having Gilbert Gottfrieds trademark screechy voice play the character during the launch. It also sponsored mocking websites (see  REF _Ref40855639 \h Figure 2) encouraging users to vent their frustrations towards the paperclipand, by extension, to buy the upgrade.  Figure  SEQ Figure \* ARABIC 2. Image from the Microsoft homepage Why did this seemingly innocuous character engender such vituperation? What lessons can we take from the Office Assistant when designing future user interface agents? Let us begin by describing the Office Assistants history and behavior. 1.2.1 A Design History of the Office Assistant The Office Assistant traces its lineage back to Microsoft Bob, a product announced in 1995 as part of the Microsoft Home software line. The software was inspired by Packard Bell Navigators room interface (also common to a number of Hypercard stacks and the Magic Cap system, as noted in Winograd (1996)), as well as then-recent research on social responses to computer technology, in particular, Computers As Social Actors (CASA) theory. CASA theory, first proposed in Nass, Steuer, and Tauber (1994) and later refined and expanded in Reeves and Nass (1996), states that users instinctively treat computers like people. Since CASA showed that computers behave like social actors, it was reasoned, perhaps adding an anthropomorphic character would make a program more natural: one knows instinctively how to respond to people, so one would know instinctively how to respond to the character. Furthermore, it was argued, if Microsoft created a popular character, it could be a commercial success in its own right: They recalled that revenues from California Raisins merchandise exceeded sales of the entire worldwide raisin industry (Cuneo, 1988). Thus, Bob (code named Utopia) included a number of professionally designed cartoon user interface agents, which guided the user through the program. Bob was a commercial failure; pundits disagreed on exactly why: Was the social interface a failed concept, or was it merely a combination of technical difficulties (the program required a then-powerful computer to run, and even then it ran slowly) and poor marketing? In any case, Bobs cartoon user interface agent technology was folded into the next release of Microsoft Office, Office 97 (and Office 98 for the Macintosh). This technology was combined with the Answer Wizard help query system, which had been previously deployed in Office 95 (Heckerman & Horvitz, 1998), as well as the Lumire user goal modeling system (Horvitz, Breese, Heckerman, Hovel & Rommelse, 1998), both of which used Bayesian modeling techniques. 1.2.2 Behaviors and Roles of the Office Assistant What exactly does the Office Assistant do? As a user interface agent, it enhances the user interface; it is also somewhat autonomous (see Proactive Help System below) and intelligent (see Natural Language Help Query, below). The Office Assistant fulfills three major roles: Proactive Help System The Office Assistant gives help proactivelythat is, it suggests ways that it can help the user finish a task better or easier. Perhaps the most famous example of this is that for writing letters: if a user types something resembling a salutation (e.g. Dear John,) into a document, the Office Assistant appears and offers help with writing the letter ( REF _Ref42395540 \h Figure 3).  Figure  SEQ Figure \* ARABIC 3. The infamous letter-writing proactive help feature. Clicking Get help with writing the letter brings up the Letter Wizard, which aids with formatting and layout ( REF _Ref42395682 \h Figure 4). Note that the Letter Wizard can also be invoked on the users initiative (by choosing New from the File menu and selecting Letter Wizard).  Figure  SEQ Figure \* ARABIC 4. The Letter Wizard; what appears if one clicks Get help with writing the letter. Curiously, Office Assistant proactively offers letter-writing help regardless of how many times one has clicked Just type the letter without help. The agent also appears even if it has been hidden (note below that repeated hiding in versions 2000 and above does allow one to turn off the Assistant entirelyalthough not this specific feature). Similar proactive help features are tips triggered by user behaviors, designed to teach users about the programs features. For example, typing a line in all uppercase and pressing return results in a tip explaining the Headings feature ( REF _Ref42396111 \h Figure 5).  Figure  SEQ Figure \* ARABIC 5. Proactive help tip on Headings. Similarly, some tips explain features when triggered by signs that the user might be struggling. For example, clicking repeatedly in the margin (where one generally cant type) causes the Office Assistant to display a light bulb, which, when clicked, explains how to enter text in that area.   Figure  SEQ Figure \* ARABIC 6. Proactive help tip triggered by margin clicks, before and after the user clicks the light bulb One level of proactivity below the light bulb are tips shown only when the user clicks on the agent. For example, if the user has been working with tables and then clicks on the Assistant, the Assistant will guess that the user may want to know more about certain table features, and will show a selection of them ( REF _Ref42478228 \h Figure 7). The help is still proactive in the sense that the agent proactively suggests helpful tips, even though users only see the suggestions if they click on the Office Assistant. This feature is perhaps the closest to the Microsoft prototype Lumire Project described in Horvitz et al. (1998).  Figure  SEQ Figure \* ARABIC 7. Tips displayed after the user clicks on the Office Assistant. Unlike the letter-writing proactive help feature, tips only appear once, and they do not appear unless the Office Assistant is already visible. Users can also configure the Office Assistant to display a random tip when the program startsallowing the agent to be purely pedagogical (rather than proactively helping with a specific need). Natural Language Help Query As noted in section 1.2.1, the Office Assistant incorporates a version of the Answer Wizard feature, developed initially for Office 95 (Heckerman & Horvitz, 1998). The Answer Wizard uses basic Bayesian inference to guess a users goal, given a particular help query. This allows users to ask questions in relatively natural language, and generally provides better results than a mere keyword search. (Note that the original Answer Wizard interface, which does not use an animated character, remains in Office, and can be invoked if the Office Assistant is turned off.) Upon submitting a query, the Office Assistant provides a list of help topics that it thinks would be useful (see  REF _Ref42478570 \h Figure 8); each link displays help on a particular topic in a separate help window.    Figure  SEQ Figure \* ARABIC 8. Natural language help query; the user clicks on the Office Assistant, types a question, and clicks Search. Note that the help displayed is the same as the help available without the Assistant. That is, if one turns off the Assistant, the Answer Wizard provides the same help choices, which lead to the same help screens. Agenthood of the Program Finally, the Office Assistant acts as a locus of agenthood for the program. For example, it is the voice for dialog alert boxes ( REF _Ref42478693 \h Figure 9).  Figure  SEQ Figure \* ARABIC 9. A dialog alert box voiced by the Office Assistant Likewise, certain commands (saving or printing, sending an email) cause the Assistant to display an animation of that action, suggesting that it is, at least in part, responsible for that action. Finally, the agent does small social idle animations (blinking, etc.) when on-screen and nothing is going on (i.e. the user isnt typing or clicking), a feature developed for Bob to make the characters more amusing and natural (Winograd, 1996, p. 149). Office 2000 adds the option of turning off the Assistant entirely (as noted earlier, this is the default in Office XP). The newer versions also allow one to turn each of the roles above on and off (see  REF _Ref41282718 \h Figure 10). For example, unchecking Display alerts disables the dialog box agenthood feature, such that the program returns to displaying standard dialog boxes regardless of whether the Assistant is on screen or not.  Figure  SEQ Figure \* ARABIC 10. Options for Office Assistant (from Office 2000) Office 2000 also adds an automatic disabling feature: If one hides the Assistant several times, it responds with  REF _Ref41286904 \h Figure 11, an option to turn it off permanently (although this merely unchecks the Use the Office Assistant box, and is thus not necessarily permanent).  Figure  SEQ Figure \* ARABIC 11. Automatic disabling (from Office 2000) Chapter 2: Theoretical Critique of the Office Assistant What findings in the existing Human-Computer Interaction literature can help explain why people dislike the Office Assistant? Unfortunately, despite the Office Assistants ubiquity and notoriety, it has received little or no research attention. Nevertheless, there are several theories and debates regarding user interface agents that are relevant to the Assistant. 2.1 Computers As Social Actors As noted in Chapter 1, the Office Assistant was in part inspired by the Computers As Social Actors (CASA) theory that people instinctively treat computers and other media as if they were real people (Nass, Steuer & Tauber, 1994; Reeves & Nass, 1996). The theory is based on a large body of research showing how human psychological phenomena can be replicated on a computer. For example, studies found that people subconsciously use the same standards of politeness (Nass, Moon, & Carney, 1999), gender stereotypes (Nass, Moon, & Green, 1997), teamwork (Nass, Fogg, & Moon, 1996), and reciprocity (Fogg & Nass, 1997) in their interactions with computers as they use with other people. Similarly, there is evidence that people will rate computers with similar personalities to themselves higherjust as people will rate other people with similar personalities higher (Nass, Moon, Fogg, Reeves, & Dryer, 1995; Moon, 1998; Moon & Nass, 1996; Moon & Nass, 1998; Nass & Lee, 2000). It should be stressed that CASA theory refers to unconscious social responsesin fact, many of the participants who are questioned after experiments emphatically deny the very behavior they just exhibited (Reeves & Nass, 1996). Reeves and Nass speculate that humans evolved to assume that objects exhibiting certain human-like traits are actually human. Thus, modern humans presented with interactive media will unconsciously respond to those media in a social wayeven if they know (consciously) that those media are in fact not real humans. What kinds of traits trigger this unconscious response? Nass and Moon (2000) note that exactly what interfaces will trigger what social responses remains a largely unsolved question. Nevertheless, Nass and Steuer (1993) suggest four characteristics that each strongly cue the idea that one is interacting with a social actor: language use, interactivity (defined as how much the system uses prior input to determine its subsequent behavior), playing a social role (e.g. doctor, tutor, parent), and having human-sounding speech. It is also clear that, as Reeves and Nass (1996, p. 25) point out, people dont need much of a cue to respond sociallyindeed, plain text has been found to trigger the same social responses as more vivid technologies such as voice (Ibid.). In fact, Steuer (1994) found that a text-based tutor was perceived as more like the user and likeable than a full-motion video tutor. Nass, Steuer, Henrickson, and Dryer (1994) also emphasize that minimal social cues can induce computer-literate individuals to use social rules. These two aspects of CASA theoryits unconsciousness and the relatively simple ways to trigger social reactionsare often forgotten when applying the theory to design. While the CASA studies show that people unconsciously respond to even simple computer interfaces in social ways, they do not (necessarily) show that people will like or benefit from computer interfaces which consciously try to behave as social agents. It is one thing to take advantage of unconscious social responses, and quite another to make that response explicit by displaying an anthropomorphic character that asserts its agenthood. This distinction implies that the Office Assistant, while inspired by CASA findings, is not in itself justified by those findings. Nevertheless, a number of authors (e.g. Laurel, 1990) suggest that because most computer use is unavoidably social, explicitly social agents make the interface easier to learn and use, because people naturally know how interact socially. 2.2 Critique of Agents and Anthropomorphism Never trust anything that can think for itself if you cant see where it keeps its brain Harry Potter and the Chamber of Secrets, p. 329. Despite enthusiasm from CASA-inspired designers, some authors have criticized the idea of explicitly social, anthropomorphic agents. For example, Ben Shneiderman (1989) refers to anthropomorphism as the humpty dumpty syndrome and implores designers to resist being seduced by this primitive urge. Jaron Lanier (1996) puts it succinctly: Intelligent agents stink. (He has also referred to the idea of such agents as wrong and evil (1995).) Some of the critiques of agents and anthropomorphism are rather easily dismissed: Lazy Programmers and Quirky Interfaces Lanier (1996) claims that the autonomy provided by agents makes programmers lazy because then [the program] has the right to be quirky. While this is a legitimate worryprogrammers and designers may indeed accept unreasonably unpredictable interfaces when designing agentsthis need not apply to all agents. Indeed, Laurel (1991, p. 145) claims that, in fact, caricatured dramatic characters are better suited to the roles of agents than full-blown simulated personalities. That is, the best-designed agents are more predictable than people, because they rely on stock dramatic archetypes rather than quirky, idiosyncratic personalities. Annoying and Distracting Characters Shneiderman (1995) claims, The anthropomorphic styles are cute the first time, silly the second time, and an annoying distraction the third time. Again, this is a legitimate worry, especially for user testingif one merely tests an interface once, annoyances may not present themselves. However, whether a character will be annoying or not largely depends on its behavior: If the anthropomorphic agent almost always presented useful information in an easy-to-understand way, perhaps it would not be annoying or distracting. Indeed, some agents are being designed to reduce the number of distractions one must endure (Markoff, 2000). Moreover, as we will discuss in Chapter 3, some users prefer occasional distraction because it is entertaining. Poor Market Performance Another argument is that various anthropomorphic interfaces, such as Postal Buddy, Microsoft Bob (Shneiderman & Maes, 1997), and anthropomorphic bank terminals such as Tellie the Teller, Harvey Wallbanker, and BOB The Bank of Baltimore (Shneiderman, 1995) have all failed in the marketplace. While one should learn from this history, one cannot infer that all anthropomorphic interfaces are doomed to failure based solely on these products market performance. For instance, it is possible that these failures depended on the specific implementation (e.g. Bobs slowness) or the domain (e.g. ATMs may not be an appropriate place for agents). Furthermore, Microsoft Office was not a poor market performeralthough one might argue that this was in spite of, not because of, the Office Assistant feature. Either way, it offers a useful lesson: one cannot rely solely on market data to determine whether a particular feature is good or not. Other criticisms of agents and anthropomorphism, however, are more substantive and deserve closer study: Anthropomorphic Dissonance: Misleading Expectations and Erroneous Conceptual Models As early as 1980, Sheiderman noted that natural language technology can lead to unrealistic expectations of the computers power (p. 208). Similarly, he argues more recently, appearances and behaviors that attribute autonomy to a computer can deceive, confuse, and mislead users (Shneiderman, 1998). Watt (1998) refers to the gap between expected behavior given an agents appearance and its actual behavior as anthropomorphic dissonance: The bigger the gap, the greater the dissatisfaction with the interface. That is, presented with an agent that putatively understands natural language or acts like a person (encouraged either by marketing or its appearance to believe such things), users will expect more from the interface than it is capable of, leading to inevitable disappointment, frustration, and dissatisfaction. It is possible that the Office Assistant, by presenting itself as having natural language facility, encourages these over-optimistic expectations. Its anthropomorphic appearance might also lead users to over-estimate its abilities as compared with the Answer Wizard, which has the same natural language technologies but no animated character. Moreover, argues Shneiderman (1989), such agents and characters can cause people to form an erroneous model of how computers work and what their capacities are. Perhaps anthropomorphic interfaces encourage people to think about computers in ways that do not reflect how they actually work, thus making using the computer more difficult because of the flawed conceptual model. Chapter 3 discusses how many novice users conceptual models of the Office Assistant were indeed flawed or confusedperhaps encouraged by its anthropomorphic character. However, one must wonder whether a properly presented agent might result in more accurate, useful conceptual models. For example, Shneiderman (1998) cites Resnick and Lammers (1985) to show that [s]ubjects reported being less confused when given constructive (what the authors refer to as neutral) than with human-like or condemning (what the authors refer to as computer-like) feedback. However, the confusion may have been inherent in the manipulation: I dont understand these numbers hardly means the same thing as Use letters only. Perhaps there is a way to communicate a clear conceptual model of the agent while still using human-like dialog (e.g. Id appreciate it if you would only use letters or I only understand letters). While anthropomorphic dissonance presents a real pitfall for agent design, critics sometimes take this argument too far, claiming that anthropomorphic agents will blur the line between humans and computers. Shneiderman (1989) suggests that this is especially important for children, since it is important for children to have a clear sense of their own humanity. Lanier (1995), in view that there is nothing more important to us than our definition of what a person is, claims that agents make people diminish themselves and redefine themselves into lesser beings. Is it true that people cannot distinguish an anthropomorphic computer from a real human being? Laurel (1991, p. 143) argues that [t]here is no evidence to suggest that computer-based characters, no matter what the degree of lifelikeness, lead people to believe that either the machine or the characters themselves are actually alive. Indeed, there does not seem to be any substantive evidence that people consciously believe that computers (or anthropomorphic agents represented on computers) are actually people. The philosophical debate over what constitutes humanity is indeed an interesting and important one, but it is beyond the scope of this thesisand likely does not affect peoples actual responses to user interface agents. The Computer Did It: Lower Feelings of Control and Self-Reliance Shneiderman claims that agents can reduce users feelings of self-reliance and control: I think anthropomorphic representations destroy the users sense of accomplishment; I think users want to have the feeling they did the jobnot some magical agent (Shneiderman & Maes, 1997). Instead, he argues, one should work to create interfaces encouraging an internal locus of control so that users feel that the computer is a tool over which they have mastery, rather than an autonomous agent (Shneiderman, 1989; Shneiderman, 1998). Shneiderman cites two studies to support this claim: Quintanar, Crowell, and Pryor (1982) showed that while students scored higher using an anthropomorphic interface, they felt less responsible for their performance. Gay and Lindwarm (1985) showed that, given an interface using anthropomorphized prompts (e.g. Hi! I am the computer. I am going to ask you some questions.) or neutral prompts (e.g. This is a multiple-choice exercise.), users were more likely to change their opinions to think that computers are harder to use, as compared with those given an interface using you prompts (e.g. You will be answering some questions.). Thus, in both studies, users in anthropomorphic conditions felt a loss of control. While agent proponents might argue that agents necessarily involve giving up control in exchange for substantive benefits (such as not having to worry about a task), when designing help agents such as the Office Assistant, it would seem important to preserve and expand users sense of control and self-reliance. Perhaps a less-anthropomorphic version would make the user feel more in control. In fact, Office XP includes a new task panes feature, which duplicates some of the Office Assistant functionalities without using an animated character: perhaps such non-anthropomorphic panes lead to more feelings of self-control. However, it seems at least possible to design an agent that actually increases users feelings of control and self-reliance: for example, agents could teach users a skill, empowering them to finish a task on their own the next time. To some degree, this is highlighted in the difference between the Office Assistants letter-writing proactive help feature and most other tip help features: the letter-writing feature constantly asks users if they want help (creating or at least suggesting a dependent relationship), while the tips teach users a skill once, leaving them to use that skill on their own the next time (and thus increasing the users feeling of control). 2.2.1 Direct Manipulation versus Agents Coined by Shneiderman (1983), direct manipulation refers to a method of controlling a computer by directly manipulating interface elementssuch as dragging an item around the screen. Some kinds of agentsparticularly autonomous onesseem opposed to this metaphor, as they work using indirect delegation and management rather than direct manipulation. For some direct manipulation enthusiasts, thus, agents represent a step backwards in user interface technology, returning to something akin to pre-GUI command-line dialog interfaces. This has led some people to see the issue as a debate between direct manipulation and agents (e.g. Shneiderman & Maes, 1997). One might argue that this debate was touched off by Maes oft-cited paper on agents (1994), which claims that the direct manipulation metaphor will have to change if untrained users are to make effective use of the computer and networks of tomorrow. Of course, it need not be a simplistic, either-or decision: In response to the frustrated criticism, Why should I have to negotiate with some little dip in a bow tie when I know exactly what I want to do?, Laurel (1990, p 356-357) responds that some tasks are more suited to agents than others: Few of us would hire an agent to push the buttons on our calculator; most of us would hire an agent to scan 5,000 pieces of junk mail. Maes also notes that some tasks are better delegated to agents than othersfor example, she admits that while having someone else fix her car means that she gives up both control and understanding of how the car works, she nevertheless prefers delegating the task (Shneiderman & Maes, 1997). Horvitz (1999) suggests that, rather than choose between direct manipulation and automation, one can seek valuable synergies between the two interface techniques in a mixed-initiative system. Susan Brennan (1990) even argues that agent-like conversation can be a form of direct manipulationand that direct manipulation succeeds partly because it shares features with conversation. Similarly, Rickenberg and Reeves (2000) argue that trying to generalize agents as good or bad is like trying to generalize film or the internet as good or bad: there are a number of both positive and negative factors, each of which depend largely on the situation. Is an agent appropriate to Microsoft Offices situation? Doyle (1999) argues that the Office Assistant is not badly designed but rather chosen for the wrong domain. He notes that [b]uilding a spreadsheet, for example, is essentially a mechanical taskentering numbers and equationsand not one about which a user is likely to want discussion. However, this argument has two problems: First, as well discuss in section 2.3, the Microsoft Office Assistant does have some serious design problems. Second, the argument that spreadsheets are mechanical ignores research (e.g. Nardi & Miller, 1990) showing that spreadsheets actually act as collaborative cognitive artifacts, about which there is a good deal of discussion. Its true that the actual data entry is fairly mechanical and not well-suited to adding an agent, but there may be other tasks involving spreadsheets for which agents might prove useful. (Indeed, one might consider Microsoft Excels AutoFill feature to be a primitive autonomous agent, showing how agents can be useful for even data entry!) While we might not throw out the idea of an agent being useful to office productivity software in general, all these authors have a point: some situations and tasks are more suited to using agents than others. 2.2.2 The Persona Effect and Empirical Studies of the Effect of Using Agents What research, then, has been done to determine the positive and negative effects of agents in various situations? Dehn and van Mulken (2000) present an excellent overview of the bulk of the empirical research that has compared various user interface agents to interfaces without such agents. They note that much of the research is plagued by methodological problems: For example, Lester, Stone, Converse, Kahler, and Barlow (1997) vary not only the presence of an agent but also what advice is given. Similarly, Sproull, Subramani, Kiesler, Walker, and Waters (1996) vary the presence of an agent, but the agent presentation also introduces a one-second delay between the appearance of the agent and hearing the text. Sproull et al.s agent was a three-dimensionally modeled face with an unnatural voice without inflection; several studies at Stanford (e.g. Flannery & Merrill, 2000) suggest that some three-dimensionally modeled faces (such as the Baldi character from the CSLU Toolkit; see Cole et al., 1999) are perceived as being weird and thus serve as a cognitive distractor. McBreen, Shade, Jack, and Wyard (2000) also found that three-dimensional talking heads were perceived badly (compared to video, disembodied voice, and still images), partially because of bad lip synchronization. Despite these methodological difficulties, Dehn and van Mulken attempt to draw some broad conclusions from the research. First, the persona effect posited in Lester et al. (1997)that characters positively influence users responses to a systemfound some support: in particular, it seems clear that adding agents tends to make the system more entertaining. Other effects, they argue, depend on what particular anthropomorphization is chosen andthe domain in which the interaction is set. These trends also bear themselves out in studies not included in Dehn and van Mulkens meta-analysis: For example, Moundriou and Virvou (2002) found that, while instructional agent conditions didnt result in better learning than a non-agent control condition, the system was rated as being more enjoyable and problems were perceived as being less difficult. Wexelblat (1998) also found that anthropomorphic interfaces were rated as being more enjoyable and likeable. Likewise, Dehn and van Mulkens caution that certain kinds of agents fare better than others is borne out in other research: Lee and Nass (1999), like McBreen et al. (2000), found that sometimes less vivid representations of an agentsuch as a text box or disembodied voicewere rated higher than poorly implemented representations (such as a stick figure and a three-dimensionally modeled talking head). How can one apply this research to the Office Assistant? It seems unclear whether an agentand, specifically, an anthropomorphic character agentis appropriate to the office context. However, it seems clear that such an anthropomorphic character agent, if properly designed, could make the program more entertaining, enjoyable and likeable. We shall next question why many users find the Office Assistant annoyingand not enjoyable. 2.3 Applying CASA While CASA theory does not, in itself, justify the use of user interface agents, anthropomorphic or not, if one does use an anthropomorphic interface, that will certainly be enough to trigger an unconsciousand, likely, conscioussocial response to the agent. Thus, the CASA paradigm can be applied to analyze the Office Assistant itself: If people (consciously or unconsciously) treat the Assistant like a person, then how can we predict and explain their responses to it? CASA theory predicts that psychological rules that apply to people will also apply to interactions with a computer (or, in this case, an agent). So, one must ask, What would one want in a human assistant? Many of the Office Assistants behaviors would be outright intolerable in a human, such as continuing to ask the same question over and over. The Office Assistant also breaks more subtle rules of human-human interaction, such as staring at the user and monitoring the users work. For example, Rickenberg and Reeves (2000) found that people who performed a task while an agent monitored them had both higher reported anxiety and lower performance on the task. Perhaps a more successful, less anxiety-creating, Office Assistant would have a desk of its own to work at, minimize itself into an unobtrusive icon, or even turn away from the user when not called into service. 2.3.1 Etiquette Rules of human social interaction can be grouped under the name etiquette, an increasingly important sub-field of user interface agent research. Bickmore (2002) describe etiquette as adhering to prescribed norms in social interactions, or about negotiating and making explicit interactional norms when they do not already exist. To that end, Miller and Funk (2001) propose a short list of etiquette rules, such as Dont make the same mistake twice. Since the Office Assistant continues to persist in displaying its letter-writing proactive help feature despite being dismissed an arbitrary number of times, it obviously breaks this rule! Interestingly, Miller and Funk also encourage agents to talk explicitly about what youre doing and why in a sort of meta-communication; thus, users can be well-informed and develop a more accurate conceptual model of the agent. Bickmore and Cassell (2001) note also that both Microsoft Bob and the Office Assistant use essentially a passive strategy for relationship building. They claim that human etiquette rules demand an active strategy for building, maintaining or changing a relationship with the user: Just as a human assistant would learn ones preferences over time, so to should a user interface agent develop a rich, long-term user model. The Office Assistants letter-writing proactive help feature, thus, breaks every relevant etiquette rule: it ignores social conventions of when to disturb someone, it does not learn from its mistakes, it does not develop a long-term relationship, and (one might argue) it does not even provide a helpful service! Since this feature is the most cited annoyance in the popular press, one cannot help but wonder how much better the Office Assistant would be perceived if this one feature had been fixed or eliminated before its release. 2.3.2 Appearance CASA also suggests that characters that are popular and likeable in the real world would be more popular and likeable on the computer. For example, animators have exploited Konrad Lorenzs Kindchenschema for years, noting that characters with certain baby-like biological triggers, such as large heads, short arms and legs, round skulls, big eyes, and round cheeks are perceived as cute and likeable. Presumably, these traits would also be desirable in a likeable user interface agent character. However, the default Office Assistant character, Clippit the Paperclip, has virtually none of these features; especially in its original version, it features a small (or nonexistent) head, long wire arms, and slanted eyes ( REF _Ref42890964 \h Figure 12).  Figure  SEQ Figure \* ARABIC 12. Clippit (original version from Office 97) Interestingly, in both static and animated tests prior to Offices release (one of which took place in three different countries), several other characters were rated above the paperclip; the paperclip was chosen to be the default mostly because it was associated with an office (Nass & Reeves, 1996). Combined with Kindschenschema data, this suggests that Microsoft made a poor choice in selecting the paperclip as its default assistant character. 2.3.3 Status Sara Neff (2002, pp. 29-32) has suggested applying the concept of status, an especially important one in improvisation theatre, to design. Products, she argues, each assert a different kind of status, and in turn affect the status of their users. It is a universal rule that everybody like his or status raised, so user-raising products will generally be more satisfying. In a way, this attribution is in line with CASA theory: just as people like other people who raise their status, they will also like products (and, presumably, user interface agents) that raise their status. For example, Neff notes that Pull-Up diapers raise the status of toddlers, making them feel older and giving them a feeling of competence and control (their commercial jingle was Im a big kid now). Similarly, Apple Computers Macintosh raised the status of the rest of ususers who were bewildered by the command-line DOS interfaces. In contrast, Neff gives the example of the VCR as a status-lowering object, as it is in effect saying to its user, You are too stupid to understand how to use me. The Office Assistant could help raise the status of beginners, as it would provide a help function close at hand at any timewithout needing to appeal to someone else. However, it can also lower beginners status: For example, a friend told me that she doesnt like the Office Assistant because it reminds me of how much I dont know. For her, the Office Assistant is not unlike the flashing 12:00 on so many VCRs. Regardless of whether the Office Assistant raises or lowers beginners status, it would seem to lower the status of more advanced users. Lanier (1995) calls Microsoft Bob offensively paternalin essence, he thinks it is status-lowering. Many advanced users dont needor at least think they dont needan ever-present help agent, and thus they may perceive the Office Assistant as trying to lower their status, particularly when encountering its proactive help feature. Chapter 3: Qualitative Study of the Office Assistant To gain a holistic view of how people use and view the Office Assistant in context, I conducted a qualitative study of Microsoft Office users. 3.1 Methods and Informants 3.1 In-Depth Interviews In-depth interviews were conducted with 14 informants. The interviews were open-ended, took place in the space where the informants used Microsoft Office the most, and included informant-directed observation of work practices and artifacts (e.g. showing the last few documents used in Word). The informants were found using a variety of means, mostly recruited at a college campus over email with the enticement of a free Jamba Juice gift certificate. Because of the location in which they were recruited, most of the informants work in the education industry. However, special care was taken to recruit administrative support staff and others in less academic roles: No professors and only two current students were part of the informant pool. Informants range in age from early 20s to late 50s, with a good distribution throughout the scale. Four were male, and ten were female; racially, nine were of European descent, three were of Asian descent, and two were of Hispanic descent. Ten informants used Microsoft Office on the PC; one used Microsoft Office on the Mac, and three used Microsoft Office on both a Mac and a PC. All names mentioned are pseudonyms; in some cases, minor details about the informants are changed to protect their anonymity. All quotes, however, are verbatim. 3.2 Survey Question To round out the interview data, the subjects in the quantitative experiments (described in Chapters 4 and 5) were asked, What are your (brief) thoughts on the Microsoft Office Assistant (Clippy the Paperclip)? at the end of an online questionnaire. See Chapters 4 and 5 for more information about how participants were recruited and the nature of the pre-questionnaire task. Their responses were coded and analyzed in a similar fashion to the interview data. 3.2 Results and Discussion 3.2.1 General Response to the Office Assistant Informants were asked what they thought about the Office Assistant, and asked what words they associate with it. Responses varied from I hate that guy! to I love itit makes me laugh! Half of the informantssevenhad an unqualified negative reaction to the Office Assistant. Two of the informants had unqualified positive reactions to the Assistant; the remaining five informants either had mixed responses or were confused about the Assistant (see 3.2.3). Cute Seven of the informants described the paperclip character as cute, although this wasnt always a positive attribute. Three thought it was excessively cute; as one informant put it, Maybe Im just old school, but its too cute for me. Another informant noted that while it is cute and entertainingits annoying when you have to work. Indeed, five informants used the word annoying to describe the paperclip character. Unnecessary Three informants noted that one doesnt need the Office Assistant to get helpthat is, that the already-existing help features can do the same thing as its search box. They called the paperclip stupidneedless and unnecessary. Indeed, it is possible to get the same natural language help query via the Answer Wizardwhose content is taken from the same place as the Contents and Index help features. In the Way Four informants complained about the Office Assistant getting in their way. It takes up space, noted one; It always seemed to be in the way, said another. It is interesting that, even with the advances in Office 2000 (the character appearing on its own, outside of a window, and automatically moving out of the way), the character still obscures ones visual field and serves as an impediment to working. Popping Up Three informants noted that they didnt like the proactive help feature. In the words of one informant, I dont want it to think you need helpI want to ask for it. On reflection, one informant noted that what she finds most annoying about the Office Assistant is that the computer is doing something you havent told it to and that it challenges our authority. Good for Other People Six people noted that it would be useful for beginners, but not themselves. As one informant put it, Its good for a small group of people, like my mom, who are scared of the computerotherwise, its patronizing. However, only one of these six recalled that the Office Assistant had actually been useful when she was a beginner; she noted that it was good to be able to type into this box at any point that she needed help, although now its a hand-holding thing that she no longer needs. Cognitive Labels How can one explain the very different responses to the Office Assistant? Both those who liked and disliked the Assistant noted the qualities listed above. However, those who liked the Assistant more saw the characters animated motions as being a welcome diversion from the tedium of office work. One informant talked at length about how it would amuse her during long work periods, to the point where a friend called it my boyfriend, since it winks at us. That is, both groups saw the Office Assistant as distracting; they differ in whether they saw this distraction as positive or negative. One can explain the dichotomy between those who liked the Office Assistant and those who did not by appealing to the cognitive labels they ascribed to the character. Those who labeled the character as a productivity tool which was supposed to be useful thought that its distracting animations were counter-productive and annoyingthat is, trying to be too cute. Those who labeled the character as an office diversion which was supposed to be fun, by contrast, welcomed the distracting animations. 3.2.2 Expertise, Help and Learning Informants were asked to rate, in their own words, their familiarity and history with Microsoft Office (especially Word) and word processing on a computer. Two were very clearly beginners; as one informant put it, on a scale of one to ten, Im a one! Eight rated themselves as advanced beginner[s] or of intermediate expertise. Four rated themselves as being advanced or having high expertise. Informants were also asked to characterize what they do when they need help with Officein particular, what they did the last time they needed helpand how they learn about new features. Interestingly, answers to this question correlated with informants description of their expertise. Intermediate and high-expertise respondents noted that while they often learn about new features from colleagues, when they need help, they first consult the Microsoft Office help files or the Internet. Blikstein (2000) likewise found that people report learning about new features mostly from other people. Three people said they look at manuals, although two of them noted that many modern software products dont have manuals anymore. If these methods fail, the informants said they would ask around, or perhaps post a question to an email list. The beginners, by contrast, all said that they go straight to other people for help. Take the following example: Interviewer: Think back to the last time you needed help. What happened? Informant: I grabbed [name] next door. Interviewer: Where do you go for help if hes not around? Informant: I go to [name]. Interviewer: And if shes not around? Informant: Id ask someone in [location]. Interviewer: What if nobody is around? Informant: Id wait! On reflection, this behavior makes perfect sense: If one is a novice user, one is surrounded by more-experienced people. Thus, unless there is some impediment to asking help from others, it would make most sense to ask ones co-workers for help when one runs into technological difficulties. Nardi and Miller (1990) likewise found that spreadsheets tend to be used by more than one personthe result of a collaborative effort, in which co-workers taught each other and subcontracted work to each other. If the spreadsheets, reports, letters, and other documents created by Office Assistant are created in a collaborative environment, that environment is likely to be a more natural place for beginning users to get assistance than an online help tool. This suggests that the Office Assistants help features are largely unnecessary for novices. 3.2.3 Mental Model of the Paperclip Informants were asked a number of questions designed to determine their mental model of the paperclipthat is, how they think it works. These questions included, When has it appeared?, What do you think triggers it?, and What is it supposed to do? Again, answers correlated largely with level of computing experience. Four informantstwo beginners and two advanced beginnersseemed confused about what the Office Assistant does. One advanced-beginner informant noted that it tells me Ive done something wrong Its supposed to stop you so you dont continue on to make a mistake. The other confused advanced beginner said, similarly, It tells me when I need help. While the proactive feature does indeed try to step in when the user is attempting to do something that is impossible, this doesnt seem to characterize the Assistants intended or actual role. The two beginner informants were confused as to what the paperclip did. One noted, I dont know what the h*** it was for. Theres no manual that tells you what it does. The only thing Im sure it does is it wiggles when the computers working. The other informants had more accurate mental models of the Office Assistant. They all spoke about being able to type words or questions into its search box. Two people noted that it tends to pop up when one is encountering an unfamiliar feature: It seems to know when I havent done something before. Three informants noted that it offers assistance in writing letters. Two informants associated the Assistant with other automatic tools in Microsoft Word, like AutoComplete and AutoFormat. As one put it, it puts bullets where it thinks the bullets should be. Two interesting points present themselves here: First, beginnersthe people who are supposed to be helped the most by the Office Assistantare at least somewhat confused about what it is supposed to do. Especially given that beginners wont naturally turn to the computer for help (as they seek out people instead, as described in 3.2.2), it may be especially important to introduce such users to what the Assistant does and how to use it effectively. Second, that even relatively experienced users attribute a number of actions (such as automatic formatting) to the Office Assistant suggests that users are so used to the direct-manipulation application-as-tool metaphor, that any amount of independent action will be ascribed to the agent. For these users, the agent has taken on agency for the program itself! 3.2.4 Character Appearance Two informants said that the paperclip character looks stupid. Only two informants had changed the character at any timeboth to the cat. I have a cat, one explained. To get a feel for peoples reactions to different characters, each informant was presented with images of the various Office 2000 characters, as well as Peedy the Parrot from Microsoft Agent, in a printout (see  REF _Ref41115683 \h Figure 13).   Figure  SEQ Figure \* ARABIC 13. Alternative characters presented to informants None of the informants had particularly strong reactions to any character; three preferred the dog and cat characters, while one preferred the Office logo because It has no eyesits not sentient. While it seems clear that Microsoft chose a relatively unpopular look for its character, it seems that the strongest user responses are unrelated to the paperclip character itself. (Indeed, three of the informants use a Macintosh, where the default character is a classic Macintosh box with feet, not the paperclip.) 3.3 Alternative Explanations 3.3.1 Attitudes Toward Microsoft It is possible that people with negative reactions to the Office Assistant actually have negative reactions towards Microsoft or Microsoft products, and use the Assistant as a convenient proxy upon which they project their feelings. Thus, informants were asked about how they felt about Microsoft and Microsoft products. While two informants mentioned that Microsoft is a monopoly and six had somewhat negative feelings about the company, negative feelings about Microsoft did not seem to correlate strongly with feelings toward the Office Assistant. For example, one informant who called Microsoft and its products great said that the paperclip is annoyingI dont like it, while another informant who was very positive towards the paperclip noted that she doesnt like Microsofts monopolistic practices. Chapter 4: Quantitative Study of User Interface Agents The qualitative, ethnographic study (Chapter 3) revealed a number of interesting insights into how users respond to the Microsoft Office Assistant. One surprisingly important insight was that informants cognitive label profoundly influenced the way they perceived the agent. Is this true of other agents in other contextsand can such an effect be shown in a controlled, quantitative study? We set out to test exactly that, varying the explicit label given to users to describe an online user interface agent. Given the interesting comments about agents appearances, we also tested two different kinds of characters. 4.1 Method 4.1.1 Design and Manipulation The experiment was a 2 (label: fun or useful) by 2 (character: human or cartoon) balanced, between-subjects design. The first independent variable, label, corresponded with how the online character was introduced. Those in the useful conditions were told on an introductory webpage that the character is designed to provide useful information and make it easier to find what youre looking for. This should make the site easier to use and help you to get your task done. Those in the fun conditions, by contrast, were told that the character is designed to make your visit more fun and give you a different way to do things. This should liven up the site and make your experience more entertaining. The characters reinforced the label by repeating an excerpt of this text during their self-introductions (see  REF _Ref40817191 \h Figure 14).  EMBED MSPhotoEd.3   EMBED MSPhotoEd.3  Figure  SEQ Figure \* ARABIC 14. Fun and Useful label self-introductions The second independent variable, character, corresponded with what the online character looked like. Those in the human conditions interacted with a photograph of a human (the character Ian from buy.com), while those in the cartoon condition interacted with a cartoon of a person (the The Genius character from Microsoft). (See  REF _Ref40815185 \h Figure 15.)  EMBED MSPhotoEd.3   EMBED MSPhotoEd.3  Figure  SEQ Figure \* ARABIC 15. Ian and The Genius characters 4.1.2 Participants and Procedure Students [N = 48] randomly selected from a large undergraduate lecture course, were randomly assigned to one of the four conditions, with gender balanced across conditions. All participants received class credit for their participation. They completed the experiment on the web, from their residences (mostly on-campus dormitories). Each participant was given a task scenario (from Huang, Lee, Nass, Swartz & Young, 2000): [Y]ou have just graduated from Stanford and are moving to another city. You are going to buy a lot of stuff for your new apartment rather than bringing your old stuff with you. As in Huang et al., this scenario was potentially relevant to all students, regardless of gender. Participants were to told to explore a simulated e-commerce website (abc.com; see  REF _Ref40816063 \h Figure 16), and buy at least two items for their apartment using a provided gift certificate. The agent, in a frame to the left, provided additional guidance and advice, although it provided no additional functionalitythat is, it was possible for participants to complete the assignment without ever referring to the agent. This was intended to mirror the behavior of the Microsoft Office Assistant, which offers guidance which is mostly redundant (that is, the same help facilities can be accessed elsewhere).  Figure  SEQ Figure \* ARABIC 16. Sample web page from experiment 4.1.3 Dependent Measures As a behavioral measure, we recorded which items each participant bought, as well as how long each participant spent on the website. After completing the task, participants completed an online questionnaire with independent, ten-point Likert scale questions. The questions gauged attitudes about the website, the character, the items being sold, and participants own feelings during the experiment. The scales ranged from very poorly (=1) to very well (=10). See Appendix A for a list of the questions asked. The results of these questions were combined into reliable indices, using a combination of theory and factor analysis. All the indices were reliable. Website ease of use was an index composed of two items: whether the participants found the website to be easy to navigate, and whether they found the website to be easy to use ( = 0.93). Feeling good was an index composed of eight adjectives describing the participants feelings while using the website: calm, comfortable, competent, engaged, happy, in control, positive, and relaxed ( = 0.90). All analyses below are based on a 2x2 full-factorial ANOVA. No significant differences were found for gender, major, or year in school, so they are not reported. 4.2 Results Ease of Use Participants in the fun-label condition rated the website as being easier to use than those in the useful-label condition (F(1, 44) = 4.3; p < 0.05) (see  REF _Ref40837906 \h Figure 17). This effect is being driven by the relatively high mean for the human character condition with fun labeling. There were no significant differences based on the appearance of the character, nor were there any significant interaction effects. EMBED MSGraph.Chart.8 \s Figure  SEQ Figure \* ARABIC 17. Website Ease of Use Feeling Good Useful-labeled participants reported feeling better during the interaction than fun-labeled participants (F(1, 44) = 4.4; p < 0.05). There was no significant effect for character appearance. There was a significant cross-over interaction (F(1, 44) = 10.7, p < 0.01) such that participants who saw the cartoon character labeled as useful reported feeling better than those who saw the cartoon character labeled as funwhile participants who saw the human character labeled fun reported feeling better than those who saw the human character labeled useful (see  REF _Ref40837928 \h Figure 18). EMBED MSGraph.Chart.8 \s Figure  SEQ Figure \* ARABIC 18. Feeling Good Time Spent in Store Finally, there was a significant cross-over interaction for the behavioral measure of how much time participants spent in the simulated e-commerce store (F(1, 44) = 4.5; p < 0.05). Participants who saw the cartoon character labeled useful spent more time in the store than those who saw the cartoon character labeled fun, while participants who saw the human character labeled fun spent more time in the store than those who saw the human character labeled useful (and  REF _Ref40837950 \h  \* MERGEFORMAT Figure 19). EMBED MSGraph.Chart.8 \s Figure  SEQ Figure \* ARABIC 19. Time Spent in Online Store 4.3 Discussion Our results confirmed that, indeed, labels do matter, even if provided by the system. Recall that the qualitative study (Chapter 3) found different perceptions based on peoples already-existing cognitive labels, while in this experiment, labels were provided explicitly to participants. Most exciting is that labels not only influenced users attitudes towards the system, but also their behavior (specifically, the amount of time they spent on the website). That users reactions are influenced by explicit labeling mirrors a similar study (Nass, Reeves & Leshner, 1996) in which television programming was perceived differently depending on whether it was viewed on a television labeled Entertainment or News. Entertainment programming was perceived as being funnier when viewed on the Entertainment television, while news programming as perceived as being more informative when viewed on the News television. The interaction between character and label might seem puzzling at first, as traditionally one thinks of cartoons as being more fun than people. However, consistency theory (Nass & Gong, 1999) presents an alternative view, in which more person-like characters are better perceived when they have more person-like characteristics (such as being fun). These results suggest further that the more person-like a character is, the better a fun label will fit that character. Consistency has been shown to be preferred in a number of agents: For example, Najmi (2002) showed that agents exhibiting consistent race (physical appearance) and ethnicity (culture, as defined by accent and greeting style) were rated higher than those with inconsistent race and ethnicity. Similarly, Isbister and Nass (2000) found that agents exhibiting consistent verbal and non-verbal personality cues were not only rated higher but influenced user behavior more. Methodological Concerns The Genius character is available in many versions of Microsoft Office; thus, people might have seen it already and associate it with the product. However, they are far less likely to associate Microsoft products with The Genius than with more familiar characters such as Clippit. Since participants completed the experiment online from their residences, some factors were not controlled (introducing random error) but it also means that it has more external reliability, as the experiment was conducted in close to real-world conditions. Moreover, that our findings were significant despite such random error would seem to highlight their importance. It must be admitted that the two presented characters differ in more than just being a human or cartoon; the photographic human is younger and wears a tee shirt and windbreaker, while the cartoon character, which has been compared to Albert Einstein, is older and dressed in a scholarly suit. It is quite likely that the human character was perceived as being more consistent with the fun label because of its youthful appearanceparticularly given the (relatively) young age of the participants, who are probably unlikely to readily associate someone of their parents or grandparents generation with fun. While this is, to some degree, a methodological flaw (in that more than one variablefor example, cartooniness and agedistinguishes the characters), note that any two characters will have a multitude of differences between them. What is clear, however, is that while explicitly labeling a user interface agent can influence users perceptions and behavior, this decision cannot be made independent of the visual appearance of the agent. Some agents fit some labels better than others; thus, one should strive for consistency between label and appearance. Chapter 5: Second Quantitative Study The first quantitative study (Chapter 4) showed that explicit labeling of agents could affect users attitudes and behaviors, especially in interaction with the kind of character presented. In keeping with the debate over anthropomorphism, what differences occur when no character accompanies the help agent? Also, how would changing the agents behavior to be more fun influence the interaction? 5.1 Method 5.1.1 Design and Manipulation The experiment was a 2 (label: fun or useful) by 2 (presence of character: character or no character) by 2 (joking behavior: jokes or no jokes) balanced, between-subjects design. The first independent variable, label, was the same as in the first quantitative experiment, in which users were introduced to the agent as being either fun or useful. The second independent variable, presence of character, corresponded with whether the online agent had a character associated with it. Those in the character conditions interacted with a character (the same as the human condition in the previous experiment) while those in the no character condition interacted with the same text without any additional pictures. (See  REF _Ref42821258 \h Figure 20.) Note that since there was no character, questionnaires referred to the agent in the no-character condition as a box rather than a character.  EMBED MSPhotoEd.3   EMBED MSPhotoEd.3  Figure  SEQ Figure \* ARABIC 20. Character and no character conditions The third independent variable, joking behavior, describes whether the character told jokes during the interaction. While the no joke conditions had exactly the same text as in the previous quantitative study, the joke conditions interspersed jokes into half of the text boxes. In keeping with Morkes, Kernal, and Nass (2000), care was taken to use silly, innocent humor, which neither deprecated the agent nor the participant. (See  REF _Ref42821232 \h Figure 21 for an example.)  EMBED MSPhotoEd.3   EMBED MSPhotoEd.3  Figure  SEQ Figure \* ARABIC 21. Joke and no joke conditions In addition to the eight conditions in the 2 x 2 x 2 matrix, ten students were placed in a ninth condition with no agent whatsoever. In this control condition, the space where the agent appeared in the other conditions was blank. 5.1.2 Participants and Procedure Students [N = 90] randomly selected from a large undergraduate lecture course (different from the first quantitative study), were randomly assigned to one of the four conditions, with gender balanced across conditions except in three cells (in which there were six males and four females). As with the previous study, all participants received class credit for their participation and completed the experiment on the web. Other than the manipulation, the website was the same as that described in section 4.1.2. 5.1.3 Dependent Measures The behavioral measures (items bought, time on website) and attitudinal measures (questionnaire) were the same as those for the first quantitative study (described in section 4.1.3). Those in the control condition were not asked questions about the agent, as they were not exposed to any agent, and those in the non-character condition were asked about the box on the left, not the character. The results of these questions were combined into reliable indicesboth indices used in the previous study, as well as new indices, using a combination of theory and factor analysis. Tediousness of the agent was an index composed of two items: whether participants thought the agent was boring, and whether participants thought the agent was annoying. This index had marginal reliability ( = 0.53). Likeability of the agent was an index composed of ten adjectives describing the agent: engaging, enjoyable, friendly, fun, genial, intelligent, knowledgeable, personable, pleasant, and sociable ( = 0.92). Shop here again was an index composed of three items: whether participants agreed that the items on the site were of high quality, whether they thought items were worth the cost, and whether they would shop at a similar website ( = 0.82). Website reliability was an index composed of three items: whether participants found the website to be useful, reliable, and well-organized ( = 0.8). Website fun was an index composed of five items: whether the participants found the website to be engaging, enjoyable, fun, interesting, and likeable ( = 0.93). Positive rating of the website was an index composed of ten adjectives describing the website: annoying (reverse-coded), easy to navigate, easy to use, engaging, enjoyable, fun, interesting, likable, useful, and well-organized ( = 0.92). All analyses below are based on a 2x2x2 full-factorial ANOVA, as well as a two-tailed T-test comparing the control condition (no agent) to the other eight conditions (with agents), amplified by Dunnetts test. 5.2 Results Number of Items Bought Useful-labeled participants bought more items than fun-labeled participants (F(1, 44) = 3.6, p < 0.1). (See  REF _Ref42841315 \h Figure 22.) This marginally significant main effect suggests that participants in the useful condition may have been more primed to buy things rather than to be entertained. There were no significant effects for presence of character or joking behavior.  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 22. Number of Items Bought Ease of Use Participants in the various agent conditions rated the website as being easier to use than participants in the agent-less control condition (t(88) = 1.9, p < 0.1 ). (See  REF _Ref42841287 \h Figure 23.) There were no significant effects for label, presence of character, or joking behavior.  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 23. Website Ease of Use Tediousness of the Agent Participants in the various conditions without jokes rated the agent as more tedious than participants in the various conditions with jokes (F(1, 44) = 3.0, p < 0.1). (See  REF _Ref42841261 \h Figure 24.) There were no significant effects for label or presence of character.  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 24. Tediousness of the Agent Likeability of the Agent Participants in the character conditions rated the agent as more likeable than those in the no character conditions (F(1, 44) = 2.8, p < 0.1). This effect is driven by the difference between the two conditions in which fun-labeled agents did not tell jokes: when such agents were not accompanied by a character, they were rated much lower in likeability than fun-labeled, non-joking agents that were accompanied by a character (see  REF _Ref42839078 \h Figure 25).  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 25. Likeability of the Agent Shop Here Again Participants in the joke conditions agreed more that they would shop at the website again than those in the no-joke conditions (F(1, 44) = 3.6, p < 0.1). There was also a crossover interaction with the label ( REF _Ref42841345 \h Figure 26), such that participants whose agents were labeled as being fun and told jokes agreed more that they would shop at the website again than those whose where labeled as being fun but did not tell jokes; meanwhile, participants whose agents were labeled as useful agreed that they would shop at the website again about the same amount, regardless of whether the agent told jokes (F(1, 44) = 5.6, p < 0.05).  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 26. Users' Agreement to Shop Here Again Website Reliability Participants in the joke conditions rated the website as more reliable than those in the no-joke conditions (F(1, 44) = 9.6, p < 0.005). The participants in the control condition also rated the site as being less reliable than those in the agent conditions (t(88) = 2.7, p < 0.01). (See  REF _Ref42841803 \h Figure 27.) There were no significant effects for label or presence of character.  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 27. Website Reliability Website Fun Participants in the joke conditions rated the website as more fun than those in the no-joke conditions (F(1, 44) = 7.8, p < 0.01). There was also a significant interaction with presence of character ( REF _Ref42841765 \h Figure 28), such that of participants who were not presented with a character, those who were in the joke conditions rated the website as much more fun than those in the no-joke conditions, especially compared to participants who were presented with a character (F(1, 44) = 3.4, p < 0.1). This suggests a ceiling effect, in which the presence of a character or a joking agent result in higher ratings for fun, but where the combination is not higher than either one alone.  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 28. Website Fun Positive Rating of Website In a highly significant main effect, participants in the joke conditions rated the website more positively than those in the no-joke conditions (F(1, 44) = 20.1, p < 0.00005). There was also a main effect for presence of character, such that participants in the character conditions rated the website more positively than those in the no character conditions (F(1, 44) = 4.9, p < 0.05). However, this main effect is largely driven by the interaction between presence of character and joking behavior, in which, like the Website Fun index, characters and jokes each raise the positive ratings, but in combination they are no higher than either one alone (F(1, 44) = 4.0, p < 0.1). Finally, there was a significant three-way interaction for peoples positive ratings of the website (F(1, 44) = 5.4; p < 0.05). While participants in the useful conditions rated the website more positively if the character told jokes, there was little difference based on whether a character was present. However, participants in the fun conditions rated the website more positively if the agent either told jokes or was accompanied by a character; participants whose agents were labeled as fun but neither told jokes nor had a character were rated the lowest of any condition. Participants in the agent-less control condition also rated the website less positively than those in the other conditions (t(88) = 2.5, p < 0.05).  EMBED Excel.Chart.8 \s  Figure  SEQ Figure \* ARABIC 29. Positive Rating of the Website 5.3 Discussion Once again, labels made a significant difference in peoples experience of the systemalthough, once again, that difference depended on the kind of character presented. Users tended to buy more items when the agent was labeled as useful, suggesting (as described above) a priming effect that users are prepared for acting rather than being entertained. However, in all other results, labeling affected users reactions in interaction with other factors. Most strikingly, it appears that if an agent is labeled as fun, it is important to have some fun elementeither a character with a fun appearance, or exhibiting fun behavior (such as telling jokes). In short, if one promises fun, one must deliver on that promise. The results also confirm Morkes et al.s (2000) finding that humor can increase peoples attitudes towards a human-computer interaction. Agents which joked were rated as less tedious and more likeable; their websites were rated more reliable, fun, and positive, and users agreed that they would shop at the website again more. Again, this had its most profound effect in the case where there was no character, yet the agent was labeled as fun. The presence and absence of a characteror any agent at allalso had interesting effects. As noted above, the mere presence of a character sufficed to have a successful fun-labeled interaction, regardless of the agents joking behavior. There was also a general trend for agents with characters to be perceived as more likeable than characters without, suggesting that agents with characters will be perceived more positively than those without. Finally, the control condition, without any agent, was rated as less easy to use and less reliable than the various conditions with agents. Even though the agent added no actual features to the site, it seems that, at least in this domain, the mere presence of an agent can positively influence the users experience. Methodological Concerns While the no character condition did not have a characteranthropomorphic or notone could argue that the agents text presentations were, nevertheless, anthropomorphic: For example, the agent refers to itself as I and me. Comparing the agents current text to less personal text (for example, text that avoids first person pronouns) would make for an interesting future experiment; however, this study suggests that anthropomorphic language alone is not enough to classify an agent as funit must also have either a picture of a character, or engage in fun behavior (in this case, telling jokes). Interestingly, the fun behavior gives all the benefits that a character would, suggesting that, with the right behavior, many character-based user interface agents could be changed to simpler, less distracting text boxes. Indeed, Lee and Nass (1999) showed that text boxes were rated higher and were more effective in changing user behaviors than stick-figure characters. (Of course, this depends on the role of the agent: if the agent is required to signal complex conversational turn-taking, it may be difficult or impossible to send these nonverbal cues to the user without resorting to a pictorial character.) It might also be argued that the control condition was somehow odd, in that it had a blank space where the agent appeared in other conditions. While it is indeed unusual to have a large blank space on a website, replacing that blank space with other content would have added confounding variables, making the condition an ineffective control. Nevertheless, it would be interesting to run a study comparing various kinds of agent help to non-agent extraneous user interfaces. Finally, it must be noted that, since users only interacted with the website once, one cannot tell what the reactions would be to the agents behavior and appearance over time. In the joke conditions, this agent always told the same jokes at the same times; for other often-used interfaces, it might be necessary to vary the jokes and timing to preserve the positive effects of humor. Chapter 6: Conclusions The Microsoft Office Assistant, both because of its unpopularity and ubiquity, makes for an interesting lens through which to look at the larger issue of user interface agents. The following are some design conclusions that would apply not only to a redesign of the Office Assistant, but to designing any user interface agent: Consider the agents task in its social element (for example, beginners may want to rely on more experienced users for help and guidancehow can one facilitate this?). Agents should obey human rules of etiquette as much as possible (if one doesnt like a person who disobeys these rules, one will especially dislike a computer agent that disobeys them!). Explore ways to use the agent to teach users skills to make them more self-sufficient (thus allowing users to retain a sense of control over the program). Carefully introduce the agent so as to realistically showcase its best featuresand be sure that the appearance and behavior are consistent with that introduction (for example, if one calls the agent fun, there should be something fun about it!). Study whether it is beneficial to use characters or agents at all (in some cases, a less anthropomorphic agent, or no agent at all, may provide the same benefits with less costs). If one wished to draw a single lesson from this research, it might be that designing effective user interface agents is hard. Many factorstask, situation, behavior, appearance, labelinfluence users responses. However, there seem to be sufficient benefits to using such agents to justify continued research to explore how these factors work. 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M4523  O43" 44% LM3O]Q'44eee > @///7 @  dMbP?_*+%"??wU>@7 @  dMbP?_*+%"??wU>@7 ՜.+,0  PXt | Stanford University Sheet1Sheet2Sheet3Chart2  WorksheetsCharts !FMicrosoft Excel ChartBiff8Excel.Chart.89q_1116567849b!FtSSOle MPRINTadCompObjNb+  o    G ''  Arialw@ ww w0----------"System !w-'- G --  & !!---'--- G --  $``??----'--- C`   `````---'--- G  -  `?`?---'--- G ---'--- & !!---'--- ?a---'--- ?p--  $a?a?a$:?:?:$ff??f$ss??3f- 3f$oo??$HH??$!!??$skk?s?sk---'--- 3f?a---'--- 3fG -  ?????`S?S?S?S?S`?`---'--- G ---'--- G   2 4% 2 m4.4%% 2 m4.8%% 2 m5.2%% 2 m5.6%% 2 t6%---'--- G ---'--- G  2 x^ character"%%%"%2 x no character%%"%%%"%2 x character"%%%"%2 x no character%%"%%%"%??2 "fun"%%2 "useful"%"%%?R?`R`?R---'--- G -   ---'---  ---'---  --  M$ 2 jokes%"%"---'---  ---'---  -3f- 3f  2 }no jokes%%%"%"---'---  ---'--- G ---'--- G --  & !!--' G  '  'ObjInfocfPWorkbook&SummaryInformation(egQDocumentSummaryInformation8U<Oh+'0@HTh SUL Luke SwartzMicrosoft Excel@ӌ@A-՜.+,0  PXt | Stanford University                           ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r  u y z { | } ~  @\p Luke Swartz Ba=%"=kZ-$<X@"1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1dArial1dArial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    `6Chart1Sheet1 #Sheet2$Sheet3`iZR3  @@  ZHKDependent VariableITEMS_BOBOUGHT_FwebfuniWRESPILIKAGENIASSERAGIBADAGENIAGGOODXWEBPOSXWEBRELXWEBEASEXWEBIMPXWEAKXENGAGXOPENEMGIVNOEMTRUECHAR_NOCUSEFUL_FJOKES_NOESTIMATED MARGINAL MEANS 1. CHAR_NOCMean Std. Error95% Confidence Interval Lower Bound Upper Bound 2. USEFUL_F 3. JOKES_NO4. CHAR_NOC * USEFUL_F5. CHAR_NOC * JOKES_NO6. USEFUL_F * JOKES_NO!7. CHAR_NOC * USEFUL_F * JOKES_NO character no characterAgent Likeability THREE WAYALPHA???# Items Bought CHAR x JOKESHow responsible was website?no jokesjokesALPHA? LABEL x JOKESWebsite reliability MAYBE ALPHA?Weakness of CharacterHow engaging was the character?Assertiveness of the AgentAgent Assertiveness'Followed agent's advice (bought futon)?How fun was the website?Positive ratings of the websiteWebsite ease of useWould I shop here again?[same as below& ]"useful""fun"Gave an email address?Gave a *correct* email address?How impersonal was the website?!DON'T USE--XRELIABLE IS THE SAME!How bad was the agent?Assertiveness of Agent 2. CHAR_NOC3. 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CHAR_NOCMean Std. Error95% Confidence Interval Lower Bound Upper Bound 2. USEFUL_F 3. JOKES_NO4. CHAR_NOC * USEFUL_F5. CHAR_NOC * JOKES_NO6. USEFUL_F * JOKES_NO!7. CHAR_NOC * USEFUL_F * JOKES_NO character no characterAgent Likeability THREE WAYALPHA??? CHAR x JOKESHow responsible was website?no jokesjokesALPHA? LABEL x JOKESWebsite reliability MAYBE ALPHA?Weakness of CharacterHow engaging was the character?Assertiveness of the AgentAgent Assertiveness'Followed agent's advice (bought futon)?How fun was the website?Positive ratings of the websiteWebsite ease of useWould I shop here again?[same as below& ]"useful""fun"Gave an email address?Gave a *correct* email address?How impersonal was the website?!DON'T USE--XRELIABLE IS THE SAME!Assertiveness of Agent 2. CHAR_NOC3. USEFUL_F * CHAR_NOCTHIS IS FOR NO JOKE PEOPLE: p = 0.004 p = 0.009F= 9.289 F = 7.5251Positive reaction to website (no joke conditions)controlHow tedious was the agent? 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E0R] `p  @"p??3` >Y` >Y?3d23 M NM4 3Q:N 4How fun was the website?Q ;ORQ ;ORQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA{ X 3O 3 b#M43*.@@#M! M4523  O43" 44% WKM3OQ'44eee xp   6NMM?R _] `   @" ??3` >Y` >Y?3d23 M NM4 3Q:S BPositive ratings of the websiteQ ;TWQ ;TWQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA  3O 3 b#M43*4@$@#M! M4523  O43" 44%  Q}M3OQ'44eee xp   6NMM? RZ_] `   @" ??3` >Y` >Y3d23 M NM4 3Q:X *Website ease of useQ ;Y\Q ;Y\Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAa K 3O 3 b#M43*4@@#M! M4523  O43" 44% MM3OhQ'44eee xp   6NMM?0_Z l] `8   @"8 ??3` >Y` >Y3d23 M NM4 3Q:a 8Assertiveness of the AgentQ ;beQ ;beQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA d 3O 3 b#M43*$@@#M! M4523  O43" 44% PJM3OQ'44eee xp   6NMM?@l y] `   @" ??3` >Y` >Y3d23 M NM4 3Q:f 4Would I shop here again?Q ;gjQ ;gjQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA] 0 3O 3 b#M43* @@#M! M4523  O43" 44% kNM3O~Q'44eee xp  6NMM?`{Z]`h   @"h ??3` >Y` >Y ?3d23 M NM4 3Q:{ BPositive ratings of the websiteQ ;|Q ;|Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAq 3Osg 3 b+MZ43*4@$@#M! M4523  O43" 44% jI4M3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokeseffffffG@楛 A@~j$I@2ҺoH@ñF@p8cA@ 4}'I@@;@e xp  6NMM?pK`]`   @" ??3` >Y` >Y3d23 M NM4 3Q: 0Gave an email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FA}c 3Ou# 3 b+MZ43*ٿ333333??ٿ#M! M4523  O43" 44% GwM3OyQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese????333333??333333?e xp  6NMM?<-]`   @" ??3` >Y` >Y3d23 M NM4 3Q: BGave a *correct* email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAm\ 3Ov3 3 b+MZ43*? #M! M4523  O43" 44% XqpM3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese?ٿɿ????333333?e xp  6NMM? K]`0   @"0 ??3` >Y ` >Y!3d23 M NM4 3Q: BHow impersonal was the website?Q ; Q ; Q3_4E4D $% M 3O& Q4$% M 3O&!Q4FAY * 3O 3 b#M43*@@#M! M4523  O43" 44% &Mc M3OQ'44e character! no charactere33333@*` #@e xp  6NMM? (]`   @" ??3` >Y"` >Y#3d23 M NM4 3Q:# 8How tedious was the agent?Q ;$%Q ;$%Q3_4E4D $% M 3O&"Q4$% M 3O&#Q4FAg 3O$ 3 b#M43*?#M! M4523  O43" 44% RM3OQ'44ejokesno jokese#@1DDD&@e xp  6NMM? ]``  @"`??3` >Y$` >Y%@3d23 M NM4 3Q: 0Assertiveness of AgentQ ;Q ;Q3_4E4D $% M 3O&$Q4$% M 3O&%Q4FAlZ 3OrF 3 b+MZ43*$@4@@#M! M4523  O43" 44% &VTiM3OxQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese-@L0@L1@m` &@*@(@2@g|?U0@e xp  6NMM?` ]`  @"??3` >Y&` >Y'?3d23 M NM4 3Q: f1Positive reaction to website (no joke conditions)Q ;Q ;Q3_4E4D $% M 3O&&Q4$% M 3O&'Q4FAj @3O 3 b+MZ43*4@$@#M! M4523  O43" 44% IP ~M3OQ'44e-character "useful"! no character'character "fun"! no charactere㥛 A@p8cA@2ҺoH@@;@e xp  6NMM?P-]`  @"??3` %(` %)` %*?3d23 M NM4 3Q: "useful"Q ;Q ;Q3_4E4 3Q: "fun"Q ;Q ;Q3_4E4D $% M 3O&*Q4$% M 3O&)Q4FAx  3On m 3 b#M43*??N #M! 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M4523  O43" F g33OF g% M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere(\@ffffff@ /b@cFy@zG@(\@Gz@_b6i@e xp  6NMM?@]`  @"??3` \ -` %5` %63d23 M NM4 3Q:" 8How tedious was the agent?Q ;#$Q ;#$Q3_4E4D $% M 3O&5Q4$% M 3O&6Q4FA z 3Om! 3 b#M&-43*#M&-! M4523  O43" 44% LM3O]Q'44ejokesno jokese@1DDD@e xp  6NMM?&`$]`   @" ??3` %8` %9` oe<?@3d23 M NM4 3Q:! jokesQ ;"%Q ;"%Q3_4E4 3Q:! no jokesQ ;"%Q ;"%Q3_4E4D $% M 3O&8Q4$% M 3O&9Q4FAZ  3O`, 3 b+MZ43*@)@#M! M4523  O43" M G,3OM G% M,3OQ4444% UM3O&<Q 4Tediousness of the Agent'44e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactereffffff%@ffffff%@ @(@$@%@!@$@e xp  6NMM?]`  @"??3` %:` %;3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4D $% M 3O&:Q4$% M 3O&;Q4FA  % 3Or h3 b#M43*??N #M! 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M4523  O43"  ;3O ;% M,3O&?Q44444eee !!>;@mmm7 @  dMbP?_*+%"??U>@7 @  dMbP?_*+%"??U>@7 SUL Luke SwartzMicrosoft Excel@ӌ@-՜.+,0  PXt | Stanford University Sheet1Sheet2Sheet3Chart7  WorksheetsChartsOle gCompObjpshbObjInfojWorkbookrt# yB !FMicrosoft Excel ChartBiff8Excel.Chart.89qOh+'0@HTh SUL Luke SwartzMicrosoft Excel@ӌ@N-՜.+,0  P  @\p Luke Swartz Ba= G 3=xx(9X@"1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1dArial1dArial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1iArial1iArial1Arial1Arial1iArial1iArial1Arial1Arial1Arial1sArial1sArial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    `+Chart6Sheet15Sheet26Sheet3`iZR!3  @@  oNDependent VariableITEMS_BOBOUGHT_FwebfuniWRESPILIKAGENIASSERAGIBADAGENIAGGOODXWEBPOSXWEBRELXWEBEASEXWEBIMPXWEAKXENGAGXOPENEMGIVNOEMTRUECHAR_NOCUSEFUL_FJOKES_NOESTIMATED MARGINAL MEANS 1. CHAR_NOCMean Std. Error95% Confidence Interval Lower Bound Upper Bound 2. USEFUL_F 3. JOKES_NO4. CHAR_NOC * USEFUL_F5. CHAR_NOC * JOKES_NO6. USEFUL_F * JOKES_NO!7. CHAR_NOC * USEFUL_F * JOKES_NO character no characterAgent Likeability THREE WAYALPHA??? CHAR x JOKESHow responsible was website?no jokesjokesALPHA? LABEL x JOKESWebsite reliability MAYBE ALPHA?Weakness of CharacterHow engaging was the character?Assertiveness of the AgentAgent Assertiveness'Followed agent's advice (bought futon)?How fun was the website?Positive ratings of the websiteWebsite ease of useWould I shop here again?[same as below& ]"useful""fun"Gave an email address?Gave a *correct* email address?How impersonal was the website?!DON'T USE--XRELIABLE IS THE SAME!Assertiveness of Agent 2. CHAR_NOC3. USEFUL_F * CHAR_NOCTHIS IS FOR NO JOKE PEOPLE: p = 0.004 p = 0.009F= 9.289 F = 7.5251Positive reaction to website (no joke conditions)controlHow tedious was the agent? 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M4523  O43" 44% L{M3ORQ'44eee xp  6NMM?- 0]`7  @"7??3` >Y` >Y 3d23 M NM4 3Q: 0Number of Items BoughtQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O& Q4FA3 0 3O 3 b#M43*?#M! M4523  O43" 44% NiM3OwQ'44eee xp  6NMM?@(x 5-]`88  @"88??3` >Y ` >Y ?п3d23 M NM4 3Q:( *Website reliabilityQ ;)*Q ;)*Q3_4E4D $% M 3O& Q4$% M 3O& Q4FA[ D 3O 3 b#M43*@@#M! M4523  O43" 44% vMM3OWQ'44eee xp  6NMM?06 C]`8  @"8??3` >Y ` >Y 3d23 M NM4 3Q:: *Agent AssertivenessQ ;;>Q ;;>Q3_4E4D $% M 3O& Q4$% M 3O& Q4FA} K 3O 3 b#M43*$@@?#M! M4523  O43" 44% MrM3OkQ'44eee xp  6NMM?D- 0R]`h9  @"h9??3` >Y` >Y?3d23 M NM4 3Q:I R'Followed agent's advice (bought futon)?Q ;JMQ ;JMQ3_4E4D $% M 3O&Q4$% M 3O&Q4FALn 3O}T 3 b+MZ43*??#M! M4523  O43" 44% T bM3OQ'44e'jokes characterno jokes-jokes no characterno jokese333333?333333???e xp   6NMM? E0R] `:  @":??3` >Y` >Y?3d23 M NM4 3Q:N 4How fun was the website?Q ;ORQ ;ORQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA{ 3O 3 b+MZ43*.@@#M! M4523  O43" 44% WKM3OQ'44e'jokes characterno jokes-jokes no characterno jokese:@^2>)8@l:@0UUUU1@e xp   6NMM?R _] `:  @":??3` >Y` >Y?3d23 M NM4 3Q:S BPositive ratings of the websiteQ ;TWQ ;TWQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA J3O 3 b+MZ43*4@$@#M! M4523  O43" 44%  Q}M3OQ'44e'jokes characterno jokes-jokes no characterno jokese"rhEH@(?E@:jG@v8?@e xp   6NMM? RZ_] `0;  @"0;??3` >Y` >Y3d23 M NM4 3Q:X *Website ease of useQ ;Y\Q ;Y\Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAa 3O 3 b+MZ43*4@@#M! M4523  O43" 44% MM3OhQ'44e'jokes characterno jokes-jokes no characterno jokese5@7@#$s8@Z5@e xp   6NMM?0_Z l] `;  @";??3` >Y` >Y3d23 M NM4 3Q:a 8Assertiveness of the AgentQ ;beQ ;beQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA 3O 3 b+MZ43*$@@#M! M4523  O43" 44% PJM3OQ'44e%jokes "useful"no jokes jokes "fun"no jokesefffff+@̌,@fffff1@H-E+@e xp   6NMM?@l y] ``<  @"`<??3` >Y` >Y3d23 M NM4 3Q:f 4Would I shop here again?Q ;gjQ ;gjQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA] G3O 3 b+MZ43* @@#M! M4523  O43" 44% kNM3O~Q'44e%jokes "useful"no jokes jokes "fun"no jokeseL(@ffffff)@L,@lA"@e xp  6NMM?`{Z]`<  @"<??3` >Y` >Y ?3d23 M NM4 3Q:{ BPositive ratings of the websiteQ ;|Q ;|Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAq 3Osg 3 b+MZ43*4@$@#M! M4523  O43" 44% jI4M3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokeseffffffG@楛 A@~j$I@2ҺoH@ñF@p8cA@ 4}'I@@;@e xp  6NMM?pK`]`=  @"=??3` >Y` >Y3d23 M NM4 3Q: 0Gave an email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FA}c 3Ou# 3 b+MZ43*ٿ333333??ٿ#M! M4523  O43" 44% GwM3OyQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese????333333??333333?e xp  6NMM?<-]`(>  @"(>??3` >Y` >Y3d23 M NM4 3Q: BGave a *correct* email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAm\ 3Ov3 3 b+MZ43*? #M! M4523  O43" 44% XqpM3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese?ٿɿ????333333?e xp  6NMM? K]`>  @">??3` >Y ` >Y!3d23 M NM4 3Q: BHow impersonal was the website?Q ; Q ; Q3_4E4D $% M 3O& Q4$% M 3O&!Q4FAi K 3O 3 b#M43*@@#M! M4523  O43" 44% &Mc M3OQ'44eee xp  6NMM? (]`X?  @"X???3` >Y"` >Y#3d23 M NM4 3Q:# 8How tedious was the agent?Q ;$%Q ;$%Q3_4E4D $% M 3O&"Q4$% M 3O&#Q4FAa 3O$ 3 b#M43*?#M! M4523  O43" 44% RM3OQ'44eee xp  6NMM? ]`?  @"???3` >Y$` >Y%@3d23 M NM4 3Q: 0Assertiveness of AgentQ ;Q ;Q3_4E4D $% M 3O&$Q4$% M 3O&%Q4FAlZ 3OrF 3 b+MZ43*$@4@@#M! M4523  O43" 44% &VTiM3OxQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese-@L0@L1@m` &@*@(@2@g|?U0@e xp  6NMM?` ]`@  @"@??3` >Y&` >Y'?3d23 M NM4 3Q: f1Positive reaction to website (no joke conditions)Q ;Q ;Q3_4E4D $% M 3O&&Q4$% M 3O&'Q4FAj @3O 3 b+MZ43*4@$@#M! M4523  O43" 44% IP ~M3OQ'44e-character "useful"! no character'character "fun"! no charactere㥛 A@p8cA@2ҺoH@@;@e xp  6NMM?P-]` A  @" A??3` %(` %)` %*?3d23 M NM4 3Q: "useful"Q ;Q ;Q3_4E4 3Q: "fun"Q ;Q ;Q3_4E4D $% M 3O&*Q4$% M 3O&)Q4FAx  3On m 3 b#M43*??N #M! M4523  O43"  73O 7% M,3OQ4444% PQM3O&(Q H"Percentage giving an email address'44e'jokes character'jokes characterno jokesno jokes-jokes no character-jokes no characterno jokesno jokese?????333333?333333?e xp  6NMM? @K]`A  @"A??3` %+` %,=?3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4D $% M 3O&+Q4$% M 3O&,Q4FA5 x 3Oa~ 3 b+MZ43*4@K@#M! M4523  O43" I B03OI B% M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere~j$I@2ҺoH@ 4}'I@@;@ffffffG@楛 A@ñF@p8cA@e xp  6NMM?Ki]`PB  @"PB??3` %/` %0@43d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4 3Q: controlQ ;Q ;Q3_4E4D $% M 3O&/Q4$% M 3O&0Q4FAx 3OMd" 3 b+MZ43*4@K@#M! M4523  O43"  ~93O ~% M,3OQ44444e'character "fun"'character "fun"'character "fun"! no character! no character! no character-character "useful"-character "useful"-character "useful"! no character! no character! no charactercontrolcontrolcontrole~j$I@2ҺoH@ 4}'I@@;@ffffffG@楛 A@ñF@p8cA@I +>@e xp  6NMM?$i]`B  @"B??3` %1` %2?@3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4 3Q: controlQ ;Q ;Q3_4E4D $% M 3O&1Q4$% M 3O&2Q4FAI f 3OO 3 b+MZ43*@@#M! M4523  O43"  3O % M,3OQ44444e'character "fun"'character "fun"'character "fun"! no character! no character! no character-character "useful"-character "useful"-character "useful"! no character! no character! no charactercontrolcontrolcontroleec@Z/&A@;A@@Q@k g @L@'} @=U@e xp  6NMM?0<]`C  @"C??3` %3` %4<3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4D $% M 3O&3Q4$% M 3O&4Q4FAT 3Oa{z 3 b#M43*@@?#M! M4523  O43" F 33OF % M,3OQ44444eee xp  6NMM?@]`D  @"D??3` \ -` %5` %63d23 M NM4 3Q:" 8How tedious was the agent?Q ;#$Q ;#$Q3_4E4D $% M 3O&5Q4$% M 3O&6Q4FA l 3Om! 3 b#M&-43*#M&-! M4523  O43" 44% LM3O]Q'44eee xp  6NMM?&`$]`D  @"D??3` %7` %8` oe;?@3d23 M NM4 3Q:! jokesQ ;"%Q ;"%Q3_4E4 3Q:! no jokesQ ;"%Q ;"%Q3_4E4D $% M 3O&7Q4$% M 3O&8Q4FAZ  3O`, 3 b+MZ43*@)@#M! M4523  O43" M G,3OM G% M,3OQ4444% UM3O&;Q 4Tediousness of the Agent'44e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactereffffff%@ffffff%@ @(@$@%@!@$@e xp  6NMM?]`HE  @"HE??3` %9` %:3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4D $% M 3O&9Q4$% M 3O&:Q4FA  % 3Or h3 b#M43*??N #M! M4523  O43"  I3O I% M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere???333333???333333?e xp   6NMM?p 03] `E  @"E??3` .` %<` %=пM3d23 M NM4 3QQ ;-/Q ;-/Q3_4E4D$% M 3O&<Q4$% M 3O&=Q4FAg l 3O3 b#M&.43*@#M&.! M4523  O43" 444eee   >@0007 @  dMbP?_*+%"??U>@7 @  dMbP?_*+%"??U>@7 3 b#M43*??N #M! M4523  O43"  3O % M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere???333333???333333?e xp   6NMM?p 03] `  @"??3` .` %<` %=пM3d23 M NM4 3QQ ;-/Q ;-/Q3_4E4D$% M 3O&<Q4$% M 3O&=Q4FAg 3O3 b#M&.43*@#M&.! M4523  O43" 444eee   >@0007 @  dMbP?_*+%"??U>@7 @  dMbP?_*+%"??U>@7 SummaryInformation(ukDocumentSummaryInformation8o<_1116580347}hx!Fp1NS0oSOle tXt | Stanford University Sheet1Sheet2Sheet3Chart6  WorksheetsCharts !FMicrosoft Excel ChartBiff8Excel.Chart.89qCompObjwzubObjInfowWorkbooky{ -kSummaryInformation(|x                          ! 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CHAR_NOCMean Std. Error95% Confidence Interval Lower Bound Upper Bound 2. USEFUL_F 3. JOKES_NO4. CHAR_NOC * USEFUL_F5. CHAR_NOC * JOKES_NO6. USEFUL_F * JOKES_NO!7. CHAR_NOC * USEFUL_F * JOKES_NO character no characterAgent Likeability THREE WAYALPHA??? CHAR x JOKESHow responsible was website?no jokesjokesALPHA? LABEL x JOKESWebsite reliability MAYBE ALPHA?Weakness of CharacterHow engaging was the character?Assertiveness of the AgentAgent Assertiveness'Followed agent's advice (bought futon)?How fun was the website?Positive ratings of the websiteWebsite ease of useWould I shop here again?[same as below& ]"useful""fun"Gave an email address?Gave a *correct* email address?How impersonal was the website?!DON'T USE--XRELIABLE IS THE SAME!Assertiveness of Agent 2. CHAR_NOC3. USEFUL_F * CHAR_NOCTHIS IS FOR NO JOKE PEOPLE: p = 0.004 p = 0.009F= 9.289 F = 7.5251Positive reaction to website (no joke conditions)controlHow tedious was the agent? EMAILGIVENNumber of Items Boughtagent conditionscontrol (no agent)WEBFUNIR Qk#=h*  @"@BPFBREL??EB3` WK` WL??3d23 M NM4 3Q:M jokesQ ;NOQ ;NOQ3_4E4 3Q:M no jokesQ ;NOQ ;NOQ3_4E4D $% M 3O&KQ4$% M 3O&LQ4FA^dk 3OB <3 b#M43*@@?#M! M4523  O43" ]  3O] % M,3OQ44444e character character! no character! no characterezG@2T@ ߼x@ @e>  @  dMbP?_*+%MrEVSt5PrinterC odXXLetter DINU"4` ݕ: TRJPHSYSTEM BAPH"dXX??U} } } } $} } $} m      !ESTIMATED MARGINAL MEANS 1. CHAR_NOC  Mean Std. Error 95% Confidence Interval Dependent VariableCHAR_NOC  Lower Bound Upper BoundXWEBPOS~ ?V,F@w,ů?4~D@ {H@ THREE WAY~ @`C@q,ů?.A@n!GE@ (How impersonal was the website? XWEBIMP  character~ Ĉ@ @?? C}E@ t!@ ALPHA???   no character *` #@ ;?? 彅"@ ;%@  EMTRUE ?.@ ? R At\?  THREE WAY  @K@ ? X;0;.h|jLuu`>yuR^<<<R!"#$%&'()*+,-./012345789:;<=>?@!WRESPI!jokes!1>5@!%ָN?!N3@!P~ 7@*!!DON'T USE--XRELIABLE IS THE SAME!,!!jokes!no jokes""no jokes"9qq3@"%ָN?"5`i1@"Wey5@ "#"How tedious was the agent? ""BADAGENI""fun"" character"@@ ###How tedious was the agent?$##jokes##@ #@## no character#@@@$BADAGENI$jokes~ $؍@$-t[?$^O @$7Av%@$ALPHA?$$no jokes#$1DDD@# 1DDD&@$$"useful"$ character$@@%%no jokes%1DDD&@%-t[?%o#@%sm`(@,%% no character~ %@%$@&AGGOOD&?@&@U?&+ȧu&@&m#-@& LABEL x JOKES0&'~ '@'>%@'@U?'`"@'c@")@.''jokes'no jokes ((Website reliability&((XWEBREL(agent conditions(v70@((XWEBREL("fun"( character(1@@)XWEBREL)jokes)dq2@)Ow?){ a0@)"/3@"))control (no agent))Oe&@ )) no character~ )3@)z/L(@**no jokes*)ˢ,@*Ow?*h})@* 0@***"useful"* character~ *h@*z/L,@ +(+How impersonal was the website?2++ no character+)` 62@+ m` /@,XWEBIMP,jokes~ ,@,@??,f]@,#ħ!@,ALPHA?",,Website reliability,--no jokes-˂-#@-;??-R!@-MzϠ%@ --XWEBREL-jokes#-^B{ @$ dq2@- ..Weakness of Character(..no jokes#. 25@- )ˢ,@./XWEAK/jokes~ /@/h?/T*@/8oM0@/ MAYBE ALPHA? //control (no agent)#/?ܵ@. Oe&@/00no jokes~ 0@0h?0减0@0D=3@0 1(1How engaging was the character?12XENGAG2jokes2n K@@2SJ?2Qwj=@2RA@233no jokes3Ҩ=@3OJ?3[bJ1:@3B?@34EMGIVNO4?T@4/r?4M;?4{?4 THREE WAY55@?5*r?5whD?5ĽK]?574. CHAR_NOC * USEFUL_F7 8 8Mean8 Std. Error 895% Confidence Interval89Dependent Variable9CHAR_NOC9USEFUL_F 99 Lower Bound9 Upper Bound ::Agent Assertiveness :;ASSERAGI; character;"useful"~ ;`@;?Wԓ?;~ *@;"1@ <<"fun"<Z(}ҧ,@<@Wԓ?<N)(@<[hr0@== no character="useful"~ =@=@Wԓ?=lK$@=T-@ >>"fun">v*1@>@Wԓ?>(--@>"({3@?>@BX-mLwqv`9f@h|hABCDFGHIJKLMNOPQRSTUVWXYZ[\^_`>A>B>C>DF5. CHAR_NOC * JOKES_NO<F G GMeanG Std. Error G95% Confidence Interval2GHDependent VariableHCHAR_NOCHJOKES_NO HH Lower BoundH Upper Bound0H I0I'Followed agent's advice (bought futon)?6IJBOUGHT_FJ characterJjokes~ JN@JDƹ?JOѴ?J?0J KKno jokes~ K@U@KDƹ?Kr§h?KQ?0KLL no characterLjokes~ LT@LDƹ?L(@-?L/2?0L MMno jokes~ M@P@MDƹ?M?M=X%9? MMjokesMno jokesMMjokesMno jokes N!NHow fun was the website?"NNWEBFUNIN character~ Nd@N^2>)8@ NNWEBFUNIN character#NzG@j :@#N2T@O \2>)8@OwebfuniO characterOjokes~ Od@O3kZ@O 6@Ow&",>@OO no characterOl:@O0UUUU1@ OO no character#O ߼x@N l:@#O @N 0UUUU1@ PPno jokesP^2>)8@P 3kZ@P\ )4@P9uT<<@(PQQ no characterQjokesQl:@Q 3kZ@QR6@QH>@"QQjokesQno jokesQ RRno jokesR0UUUU1@R3kZ@Rk*@Rjh5@RRwebfuniR"fun"R character~ R@Rd'}8@R S(SPositive ratings of the website&SS no characterSH-؂<@SAqq1@STXWEBPOST characterTjokesT"rhEH@TB~M@TGME@T@ J@TT"useful"T characterTܤ@\@T UUno jokesU(?E@UB~M@UnOdB@U@G@ UU no character~ U@U!881@UVV no characterVjokesV:jG@VB~M@VʉbE@V\`ZJ@(V WWno jokesWv8?@WB~M@W:Ș9@WY6?B@(W XXWebsite ease of use.XYXWEBEASEY characterYjokes~ Y@YA?YRC3@Y!8@(Y ZZno jokes~ Z @ZA?ZRC4@Z!U9@(Z[[ no character[jokes[#$s8@[A?[l6@[3|k:;@([ \\no jokes\Z5@\A?\wWi^3@\^[7@(\^6. USEFUL_F * JOKES_NO4^ _ _Mean_ Std. Error _95% Confidence Interval*_`Dependent Variable`USEFUL_F`JOKES_NO `` Lower Bound` Upper Bound`@qDBBBBc~+Vb[abcdefghijklmnopqrstuvxyz{|}~ a#aAssertiveness of the Agent"abASSERAGIb"useful"bjokes~ b̕@b?Wԓ?bѱMTE'@b}?]C0@b ccno jokes~ cN@c@Wԓ?c9x'@cr0@cdd"fun"djokes~ d@d@Wԓ?d9x.@dr3@d eeno jokeseH-E+@e@Wԓ?e_02'@eD(0@eejokeseno jokes f!fWould I shop here again?f fAGGOODf"useful"f@ؓ@gAGGOODg"useful"gjokes~ g@g.FR?g#@g,@gg"fun"~ g@glA"@ hhno jokes~ hؓ@h.FR?hqCq~$@h[5[N.@hii"fun"ijokes~ i@i.FR?i'@i`|0@iijokesino jokes jjno jokesjlA"@j.FR?j=C)@jk T&@j jAGGOODj"fun"#j@k L,@#j&:W@k lA"@kXOPENk??@e@k9`ofD'?k6$){?kנu;@)k[same as below& ]kk"useful"#k333333@/ L(@#k@j ffffff)@ ll@V@l?`ofD'?l_q?lftp?lmm@?@`@m?`ofD'?m|)?mGք?m nn@ b@n>`ofD'?njpIR?n3A=v?noEMGIVNOo??V@o,@p1@uk?ue{׍u_"D?u vv@I@vk?vD͉[?vZT?v*x!7. CHAR_NOC * USEFUL_F * JOKES_NOxy yMeany Std. Error y95% Confidence IntervalyzDependent VariablezCHAR_NOCzUSEFUL_FzJOKES_NO zz Lower Boundz Upper Bound{({Positive ratings of the website {|XWEBPOS| character|"useful"|jokes~ |H@|v,ů @| C@|#&K@ }}no jokes}楛 A@}q,ů @}Z}<@}\2}E@ ~~"fun"~jokes~~j$I@~q,ů @~"_sE@~\?L@ no jokes2ҺoH@s,ů @yHM`D@ (K@ no character"useful"jokesñF@q,ů @ O82QPB@| I@(BX] z|zdfd{dfdFhNmzm no jokesp8cA@s,ů @moFcd;@)E@( "fun"jokes 4}'I@s,ů @SѼdKF@Ŗ=@M@( no jokes~ J@s,ů @:$G3@bm\QA@(XOPEN???@-S?ب?#@( @?*S?4KQ0¿&W{) F?( @?T@*S?06?#H?( @@`@+S?LF ?#H?(@??a@*S?y@?G8V@( @@`@+S?LF ?#H?( @?f@+S?uQ†?${k@( @d@+S?<?`x@(Gave an email address? "useful""fun" jokesno jokesEMGIVNO character"useful"jokes~ Y@.r?whD?ƽK]? EMAILGIVEN characterjokesY@D@"fun" characterD@T@ no jokes~ *r?.vݿ.v?no jokesT@  no character?N@ "fun"jokes~ D@*r?<访WHd(? no characterjokesT@?"useful" characterY@ no jokes~ T@+r?!<|?uz*?no jokesN@N@  no characterT@N@ no character"useful"jokes~ T@*r?"<|?uz*?( no jokes~ N@+r?n_?_WAG?( "fun"jokes~ ?+r?vhD?ŽK]?( no jokes~ N@+r?n_?_WAG?((Gave a *correct* email address?.EMTRUE character"useful"jokes~ T@?Z? :H.?( no jokes~ D?tÏ@m7w?( "fun"jokes~ 4?A])Qn ?( no jokes~ D@?Dm7wĿtÏ?( no character"useful"jokes~ D@?Fm7wĿtÏ?( no jokes~ D@?Dm7wĿtÏ?( "fun"jokes~ T@?Z? :H.?( no jokes~ N@?`??(>s0Jp Mean Std. Error 95% Confidence Interval6Dependent VariableCHAR_NOCUSEFUL_FJOKES_NO  Lower Bound Upper Bound4Assertiveness of Agent:ASSERAGI character"useful"jokes~ H@.E?T: @'@/E_,2@4 no jokes~ x@.E?%rڧ *@`Œ3@4 "fun"jokes~ @.E?%rڧ ,@`Œ4@4 no jokesm` &@.E?ڎ* @A-@4 no character"useful"jokes~ P@.E?m@t#@;ɓE0@4 no jokes~ $@.E?m@!@v' /@4 "fun"jokes~  @.E?m@t-@<ɓE5@4 no jokesg|?U0@.E?Y:*@Z\83@jokesno jokes jokesno jokescontrol jokesno jokescontrol" XWEBPOS"fun" character~j$I@2ҺoH@"fun" character~j$I@2ҺoH@ "fun" character#ec@er ~j$I@ #Z/&A@ 1ҺoH@ & no character 4}'I@~ J@  no character 4}'I@~ J@  no character#;A@  4}'I@ #@ @;@ $THIS IS FOR NO JOKE PEOPLE:""useful" character~ H@楛 A@"useful" character~ H@楛 A@ "useful" character#Q@ ffffffG@ #k g @ 㥛 A@  2. CHAR_NOC$ no characterñF@p8cA@  no characterñF@p8cA@  no character#L@ ±F@ #'} @ p8cA@   Mean Std. Error 95% Confidence Interval$control I +>@control #=U@ I +>@ Dependent VariableCHAR_NOC  Lower Bound Upper Bound8XWEBPOS~ ?(?E@g@BjB@v+֖G@  p = 0.0042 ~ @r8?@g@kp9@B@ F= 9.2892 3. USEFUL_F * CHAR_NOCB  Mean Std. Error 95% Confidence Interval8Dependent VariableUSEFUL_FCHAR_NOC  Lower Bound Upper Bound6 :1Positive reaction to website (no joke conditions)<XWEBPOS"useful" character㥛 A@A] @hq6ݬ<@kE@ p = 0.0092   no characterp8cA@B] @ZŇv;@ E@ F = 7.5252 "fun" character2ҺoH@B] @>d%iD@aK@6  no character~ J@B] @%Sk3@1mVJHA@6:sFj+I iX$@(  p  6NMM? ]`  @"??3` >Y` >Y3d23 M NM4 3Q:  &Agent LikeabilityQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAU : 3O 3 b#M43*#M! M4523  O43" 44% L{M3ORQ'44e character! no charactereK@WqH@e xp  6NMM?- 0]`  @"??3` >Y` >Y Pa3d23 M NM4 3Q: 0Number of Items BoughtQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O& Q4FA3 3O 3 b#M43*?#M! M4523  O43" 44% NiM3OwQ'44e"useful""fun"e@@e xp  6NMM?@(x 5-]`p  @"p??3` >Y ` >Y ?п3d23 M NM4 3Q:( *Website reliabilityQ ;)*Q ;)*Q3_4E4D $% M 3O& Q4$% M 3O& Q4FA[ " 3O 3 b#M43*@@#M! M4523  O43" 44% vMM3OWQ'44ejokesno jokesedq2@)ˢ,@e xp  6NMM?06 C]`8  @"8??3` >Y ` >Y 3d23 M NM4 3Q:: *Agent AssertivenessQ ;;>Q ;;>Q3_4E4D $% M 3O& Q4$% M 3O& Q4FA} ]3O 3 b#M43*$@@?#M! M4523  O43" 44% MrM3OkQ'44e-"useful" character"fun"3"useful" no character"fun"e333333/@Z(}ҧ,@@)@v*1@e xp  6NMM?D- 0R]`  @"??3` >Y` >Y?3d23 M NM4 3Q:I R'Followed agent's advice (bought futon)?Q ;JMQ ;JMQ3_4E4D $% M 3O&Q4$% M 3O&Q4FALn O3O}T 3 b#M43*??#M! M4523  O43" 44% T bM3OQ'44e'jokes characterno jokes-jokes no characterno jokese333333?333333???e xp   6NMM? DR] `h   @"h ??3` >Y` >Y?3d23 M NM4 3Q:N 4How fun was the website?Q ;ORQ ;ORQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA{ {3O 3 b#M43*.@@#M! M4523  O43" 44% WKM3OQ'44eee xp   6NMM?R _] `   @" ??3` >Y` >Y?3d23 M NM4 3Q:S BPositive ratings of the websiteQ ;TWQ ;TWQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA 3O 3 b#M43*4@$@#M! M4523  O43" 44%  Q}M3OQ'44e'jokes characterno jokes-jokes no characterno jokese"rhEH@(?E@:jG@v8?@e xp   6NMM? SZ`] `   @" ??3` >Y` >Y3d23 M NM4 3Q:X *Website ease of useQ ;Y\Q ;Y\Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAa ]3O 3 b#M43*4@@#M! M4523  O43" 44% MM3OhQ'44eee xp   6NMM?0_Z l] `0   @"0 ??3` >Y` >Y3d23 M NM4 3Q:a 8Assertiveness of the AgentQ ;beQ ;beQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA 3O 3 b#M43*$@@#M! M4523  O43" 44% PJM3OQ'44e%jokes "useful"no jokes jokes "fun"no jokesefffff+@̌,@fffff1@H-E+@e xp   6NMM?@l y] `   @" ??3` >Y` >Y3d23 M NM4 3Q:f 4Would I shop here again?Q ;gjQ ;gjQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA] 33O 3 b#M43* @@#M! M4523  O43" 44% kNM3O~Q'44e%jokes "useful"no jokes jokes "fun"no jokeseL(@ffffff)@L,@lA"@e xp  6NMM?`{Z]``   @"` ??3` >Y` >Y ?3d23 M NM4 3Q:{ BPositive ratings of the websiteQ ;|Q ;|Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAq 3Osg 3 b#M43*4@$@#M! M4523  O43" 44% jI4M3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokeseffffffG@楛 A@~j$I@2ҺoH@ñF@p8cA@ 4}'I@@;@e xp  6NMM?pK`]`   @" ??3` >Y` >Y3d23 M NM4 3Q: 0Gave an email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FA}c )3Ou# 3 b#M43*ٿ333333??ٿ#M! M4523  O43" 44% GwM3OyQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese????333333??333333?e xp  6NMM?<-]`   @" ??3` >Y` >Y3d23 M NM4 3Q: BGave a *correct* email address?Q ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAm\ X3Ov3 3 b#M43*? #M! M4523  O43" 44% XqpM3OQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese?ٿɿ????333333?e xp  6NMM? K]`(  @"(??3` >Y ` >Y!3d23 M NM4 3Q: BHow impersonal was the website?Q ; Q ; Q3_4E4D $% M 3O& Q4$% M 3O&!Q4FAY * 3O 3 b#M43*@@#M! M4523  O43" 44% &Mc M3OQ'44e character! no charactere33333@*` #@e xp  6NMM? (]`  @"??3` >Y"` >Y#3d23 M NM4 3Q:# 8How tedious was the agent?Q ;$%Q ;$%Q3_4E4D $% M 3O&"Q4$% M 3O&#Q4FAg 3O$ 3 b#M43*?#M! M4523  O43" 44% RM3OQ'44ejokesno jokese#@1DDD&@e xp  6NMM? ]`  @"??3` >Y$` >Y%@3d23 M NM4 3Q: 0Assertiveness of AgentQ ;Q ;Q3_4E4D $% M 3O&$Q4$% M 3O&%Q4FAlZ 3OrF 3 b#M43*$@4@@#M! M4523  O43" 44% &VTiM3OxQ'44e9jokes "useful" characterno jokes jokes "fun"no jokes?jokes "useful" no characterno jokes jokes "fun"no jokese-@L0@L1@m` &@*@(@2@g|?U0@e xp  6NMM?` ]`  @"??3` >Y&` >Y'?3d23 M NM4 3Q: f1Positive reaction to website (no joke conditions)Q ;Q ;Q3_4E4D $% M 3O&&Q4$% M 3O&'Q4FAj 3O 3 b#M43*4@$@#M! 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M4523  O43" F 33OF % `M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere(\@ffffff@ /b@cFy@zG@(\@Gz@_b6i@e xp  6NMM?@]`   @" ??3` \ -` %5` %63d23 M NM4 3Q:" 8How tedious was the agent?Q ;#$Q ;#$Q3_4E4D $% M 3O&5Q4$% M 3O&6Q4FA z 3Om! 3 b#M&-43*#M&-! M4523  O43" 44% LM3O]Q'44ejokesno jokese@1DDD@e xp  6NMM?&`$]`F  @"F??3` %8` %9` oe<?@3d23 M NM4 3Q:! jokesQ ;"%Q ;"%Q3_4E4 3Q:! no jokesQ ;"%Q ;"%Q3_4E4D $% M 3O&8Q4$% M 3O&9Q4FAZ  3O`, 3 b#M43*@)@#M! M4523  O43" M ,3OM % M,3OQ4444% UM3O&<Q 4Tediousness of the Agent'44e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactereffffff%@ffffff%@ @(@$@%@!@$@e xp  6NMM?]`0`  @"0`??3` %:` %;3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4D $% M 3O&:Q4$% M 3O&;Q4FA  3Or h3 b#M43*??N #M! M4523  O43"  3O % M,3OQ44444e'character "fun"'character "fun"! no character! no character-character "useful"-character "useful"! no character! no charactere???333333???333333?e xp   6NMM?p 03] `  @"??3` .` %=` %>пM3d23 M NM4 3QQ ;-/Q ;-/Q3_4E4D$% M 3O&=Q4$% M 3O&>Q4FAg 3O3 b#M&.43*@#M&.! M4523  O43" 444ejokesno jokes-control (no agent)e^B{ @ 25@?ܵ@e xp ! 6NMM?_q]!`@k  @"@k??3` d7` d?` %@` %A'3d23 M NM4 3Q:i jokesQ ;jkQ ;jkQ3_4E4 3Q:i no jokesQ ;jkQ ;jkQ3_4E4D $% M 3O&@Q4$% M 3O&AQ4FA h 3Oh 3 b#M&743*@@#M&7! M4523  O43"  ;3O ;% M,3O&?Q44444eee xp " 6NMM?<0KZ]"`T  @"T??3` t=B` t=C` %D` %Eп3d23 M NM4 3Q:M jokesQ ;NOQ ;NOQ3_4E4 3Q:M no jokesQ ;NOQ ;NOQ3_4E4D $% M 3O&DQ4$% M 3O&EQ4FAI 3Ox W3 b#M&C43*.@>@@#M&C! M4523  O43"  r3O % M,3O&BQ44444eee xp # 6NMM? Q!o]#`(p  @"(p??3` %I` %J@|3d23 M NM4 3Q:Q jokesQ ;RUQ ;RUQ3_4E4 3Q:Q no jokesQ ;RUQ ;RUQ3_4E4D $% M 3O&IQ4$% M 3O&JQ4FA 3O>PW 3 b#M43*#M! 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M4523  O43"  w3O % M,3OQ44444eee $$>;@FFF7 @  dMbP?_*+%"??U>@7 @  dMbP?_*+%"??U>@7 Oh+'0@HTh SUL Luke SwartzMicrosoft Excel@ӌ@,O-՜.+,0  PXt | Stanford University Sheet1Sheet2Sheet3Chart9 DocumentSummaryInformation8|<_1116569613!F SSOle CompObj~b  WorksheetsCharts !FMicrosoft Excel ChartBiff8Excel.Chart.89qOh+'0@HTh SUL Luke SwartzMicrosoft Excel@ӌ@ObjInfoWorkbook s3SummaryInformation(DocumentSummaryInformation8<                          " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~  @Ba=C$=xxU8X1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1dArial1dArial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1Arial1iArial1iArial1Arial1Arial1iArial1iArial1Arial1Arial"$"#,##0_);\("$"#,##0\)!"$"#,##0_);[Red]\("$"#,##0\)""$"#,##0.00_);\("$"#,##0.00\)'""$"#,##0.00_);[Red]\("$"#,##0.00\)7*2_("$"* #,##0_);_("$"* \(#,##0\);_("$"* "-"_);_(@_).))_(* #,##0_);_(* \(#,##0\);_(* "-"_);_(@_)?,:_("$"* #,##0.00_);_("$"* \(#,##0.00\);_("$"* "-"??_);_(@_)6+1_(* #,##0.00_);_(* \(#,##0.00\);_(* "-"??_);_(@_)                + ) , *    fChart5@Sheet11Sheet22Sheet3`iZR3  @@  _MDependent VariableITEMS_BOBOUGHT_FwebfuniWRESPILIKAGENIASSERAGIBADAGENIAGGOODXWEBPOSXWEBRELXWEBEASEXWEBIMPXWEAKXENGAGXOPENEMGIVNOEMTRUECHAR_NOCUSEFUL_FJOKES_NOESTIMATED MARGINAL MEANS 1. CHAR_NOCMean Std. Error95% Confidence Interval Lower Bound Upper Bound 2. USEFUL_F 3. JOKES_NO4. CHAR_NOC * USEFUL_F5. CHAR_NOC * JOKES_NO6. USEFUL_F * JOKES_NO!7. CHAR_NOC * USEFUL_F * JOKES_NO character no characterAgent Likeability THREE WAYALPHA???# Items Bought CHAR x JOKESHow responsible was website?no jokesjokesALPHA? LABEL x JOKESWebsite reliability MAYBE ALPHA?Weakness of CharacterHow engaging was the character?Assertiveness of the AgentAgent Assertiveness'Followed agent's advice (bought futon)?How fun was the website?Positive ratings of the websiteWebsite ease of useWould I shop here again?[same as below& ]"useful""fun"Gave an email address?Gave a *correct* email address?How impersonal was the website?!DON'T USE--XRELIABLE IS THE SAME!Assertiveness of Agent 2. CHAR_NOC3. USEFUL_F * CHAR_NOCTHIS IS FOR NO JOKE PEOPLE: p = 0.004 p = 0.009F= 9.289 F = 7.5251Positive reaction to website (no joke conditions)controlHow tedious was the agent? EMAILGIVENNumber of Items BoughtR k: /?B,z  @"@BPFBREL??EB3` %@` %A?v?3d23 M NM4 3Q: jokesQ ;Q ;Q3_4E4 3Q: no jokesQ ;Q ;Q3_4E4 3Q: controlQ ;Q ;Q3_4E4D $% M 3O&@Q4$% M 3O&AQ4FA 8 3OR| 3 b#M43*@@#M! M4523  O43" (X3O(% M,3OQ44444e'character "fun"'character "fun"'character "fun"! no character! no character! no character-character "useful"-character "useful"-character "useful"! no character! no character! no charactercontrolcontrolcontroleec@Z/&A@;A@@Q@k g @L@'} @=U@e> = @  dMbP?_*+%MrEVSt5PrinterC odXXLetter DINU"4` ݕ: TRJPHSYSTEM BAPH"dXX??U} } } } $} } $} m      !ESTIMATED MARGINAL MEANS 1. CHAR_NOC  Mean Std. Error 95% Confidence Interval Dependent VariableCHAR_NOC  Lower Bound Upper BoundXWEBPOS~ ?V,F@w,ů?4~D@ {H@ THREE WAY~ @`C@q,ů?.A@n!GE@ (How impersonal was the website? XWEBIMP  character~ Ĉ@ @?? C}E@ t!@ ALPHA???   no character *` #@ ;?? 彅"@ ;%@  EMTRUE ?.@ ? R At\?  THREE WAY  @K@ ? X;0;.h|jLuu`>yuR^<<<R!"#$%&'()*+,-./012345789:;<=>?@!WRESPI!jokes!1>5@!%ָN?!N3@!P~ 7@*!!DON'T USE--XRELIABLE IS THE SAME!,!!jokes!no jokes""no jokes"9qq3@"%ָN?"5`i1@"Wey5@ "#"How tedious was the agent? ""BADAGENI""fun"" character"@@ ###How tedious was the agent?$##jokes##@ #@## no character#@@@$BADAGENI$jokes~ $؍@$-t[?$^O @$7Av%@$ALPHA?$$no jokes#$1DDD@# 1DDD&@$$"useful"$ character$@@%%no jokes%1DDD&@%-t[?%o#@%sm`(@,%% no character~ %@%$@&AGGOOD&?@&@U?&+ȧu&@&m#-@& LABEL x JOKES0&'~ '@'>%@'@U?'`"@'c@")@2' ((Website reliability8()XWEBREL)jokes)dq2@)Ow?){ a0@)"/3@2)**no jokes*)ˢ,@*Ow?*h})@* 0@2* +(+How impersonal was the website?8+,XWEBIMP,jokes~ ,@,@??,f]@,#ħ!@,ALPHA?0,--no jokes-˂-#@-;??-R!@-MzϠ%@2- ..Weakness of Character8./XWEAK/jokes~ /@/h?/T*@/8oM0@/ MAYBE ALPHA?0/00no jokes~ 0@0h?0减0@0D=3@0 1(1How engaging was the character?12XENGAG2jokes2n K@@2SJ?2Qwj=@2RA@233no jokes3Ҩ=@3OJ?3[bJ1:@3B?@34EMGIVNO4?T@4/r?4M;?4{?4 THREE WAY55@?5*r?5whD?5ĽK]?574. CHAR_NOC * USEFUL_F7 8 8Mean8 Std. Error 895% Confidence Interval89Dependent Variable9CHAR_NOC9USEFUL_F 99 Lower Bound9 Upper Bound ::Agent Assertiveness :;ASSERAGI; character;"useful"~ ;`@;?Wԓ?;~ *@;"1@ <<"fun"<Z(}ҧ,@<@Wԓ?<N)(@<[hr0@== no character="useful"~ =@=@Wԓ?=lK$@=T-@ >>"fun">v*1@>@Wԓ?>(--@>"({3@?@BX-jvlmLwqv`9f@h|hABCDFGHIJKLMNOPQRSTUVWXYZ[\^_`ABCDF5. CHAR_NOC * JOKES_NOF G GMeanG Std. Error G95% Confidence IntervalGHDependent VariableHCHAR_NOCHJOKES_NO HH Lower BoundH Upper Bound I0I'Followed agent's advice (bought futon)? IJBOUGHT_FJ characterJjokes~ JN@JDƹ?JOѴ?J? KKno jokes~ K@U@KDƹ?Kr§h?KQ?LL no characterLjokes~ LT@LDƹ?L(@-?L/2? MMno jokes~ M@P@MDƹ?M?M=X%9? N!NHow fun was the website? NOwebfuniO characterOjokes~ Od@O3kZ@O 6@Ow&",>@ PPno jokesP^2>)8@P 3kZ@P\ )4@P9uT<<@QQ no characterQjokesQl:@Q 3kZ@QR6@QH>@ RRno jokesR0UUUU1@R3kZ@Rk*@Rjh5@ S(SPositive ratings of the website STXWEBPOST characterTjokesT"rhEH@TB~M@TGME@T@ J@ UUno jokesU(?E@UB~M@UnOdB@U@G@VV no characterVjokesV:jG@VB~M@VʉbE@V\`ZJ@ WWno jokesWv8?@WB~M@W:Ș9@WY6?B@ XXWebsite ease of use XYXWEBEASEY characterYjokes~ Y@YA?YRC3@Y!8@ ZZno jokes~ Z @ZA?ZRC4@Z!U9@[[ no character[jokes[#$s8@[A?[l6@[3|k:;@ \\no jokes\Z5@\A?\wWi^3@\^[7@^6. USEFUL_F * JOKES_NO^ _ _Mean_ Std. Error _95% Confidence Interval_`Dependent Variable`USEFUL_F`JOKES_NO `` Lower Bound` Upper Bound`@u D9fTgygEk}kLk}k@g}k9fabcdefghijklmnopqrstuvxyz{|}~ a#aAssertiveness of the AgentabASSERAGIb"useful"bjokes~ b̕@b?Wԓ?bѱMTE'@b}?]C0@b ccno jokes~ cN@c@Wԓ?c9x'@cr0@cdd"fun"djokes~ d@d@Wԓ?d9x.@dr3@d eeno jokeseH-E+@e@Wԓ?e_02'@eD(0@e f!fWould I shop here again?fgAGGOODg"useful"gjokes~ g@g.FR?g#@g,@g hhno jokes~ hؓ@h.FR?hqCq~$@h[5[N.@hii"fun"ijokes~ i@i.FR?i'@i`|0@i jjno jokesjlA"@j.FR?j=C)@jk T&@jkXOPENk??@e@k9`ofD'?k6$){?kנu;@)k[same as below& ] ll@V@l?`ofD'?l_q?lftp?lmm@?@`@m?`ofD'?m|)?mGք?m nn@ b@n>`ofD'?njpIR?n3A=v?noEMGIVNOo??V@o,@p1@uk?ue{׍u_"D?u vv@I@vk?vD͉[?vZT?v*x!7. CHAR_NOC * USEFUL_F * JOKES_NOxy yMeany Std. Error y95% Confidence IntervalyzDependent VariablezCHAR_NOCzUSEFUL_FzJOKES_NO zz Lower Boundz Upper Bound{({Positive ratings of the website {|XWEBPOS| character|"useful"|jokes~ |H@|v,ů @| C@|#&K@ }}no jokes}楛 A@}q,ů @}Z}<@}\2}E@ ~~"fun"~jokes~~j$I@~q,ů @~"_sE@~\?L@ no jokes2ҺoH@s,ů @yHM`D@ (K@ no character"useful"jokesñF@q,ů @ O82QPB@| I@(B#XIq|uGq|udfd|dfd{dfdFhNmzm no jokesp8cA@s,ů @moFcd;@)E@( "fun"jokes 4}'I@s,ů @SѼdKF@Ŗ=@M@( no jokes~ J@s,ů @:$G3@bm\QA@(XOPEN???@-S?ب?#@( @?*S?4KQ0¿&W{) F?( @?T@*S?06?#H?( @@`@+S?LF ?#H?(@??a@*S?y@?G8V@( @@`@+S?LF ?#H?( @?f@+S?uQ†?${k@( @d@+S?<?`x@(Gave an email address? "useful""fun" jokesno jokesEMGIVNO character"useful"jokes~ Y@.r?whD?ƽK]? EMAILGIVEN characterjokesY@D@"fun" characterD@T@ no jokes~ *r?.vݿ.v?no jokesT@  no character?N@ "fun"jokes~ D@*r?<访WHd(? no characterjokesT@?"useful" characterY@ no jokes~ T@+r?!<|?uz*?no jokesN@N@  no characterT@N@ no character"useful"jokes~ T@*r?"<|?uz*?( no jokes~ N@+r?n_?_WAG?( "fun"jokes~ ?+r?vhD?ŽK]?( no jokes~ N@+r?n_?_WAG?((Gave a *correct* email address?.EMTRUE character"useful"jokes~ T@?Z? :H.?( no jokes~ D?tÏ@m7w?( "fun"jokes~ 4?A])Qn ?( no jokes~ D@?Dm7wĿtÏ?( no character"useful"jokes~ D@?Fm7wĿtÏ?( no jokes~ D@?Dm7wĿtÏ?( "fun"jokes~ T@?Z? :H.?( no jokes~ N@?`??(>s0Jp Mean Std. Error 95% Confidence Interval6Dependent VariableCHAR_NOCUSEFUL_FJOKES_NO  Lower Bound Upper Bound4Assertiveness of Agent:ASSERAGI character"useful"jokes~ H@.E?T: @'@/E_,2@4 no jokes~ x@.E?%rڧ *@`Œ3@4 "fun"jokes~ @.E?%rڧ ,@`Œ4@4 no jokesm` &@.E?ڎ* @A-@4 no character"useful"jokes~ P@.E?m@t#@;ɓE0@4 no jokes~ $@.E?m@!@v' /@4 "fun"jokes~  @.E?m@t-@<ɓE5@4 no jokesg|?U0@.E?Y:*@Z\83@jokesno jokes jokesno jokescontrol jokesno jokescontrol" XWEBPOS"fun" character~j$I@2ҺoH@"fun" character~j$I@2ҺoH@ "fun" character#ec@er ~j$I@ #Z/&A@ 1ҺoH@ & no character 4}'I@~ J@  no character 4}'I@~ J@  no character#;A@  4}'I@ #@ @;@ $THIS IS FOR NO JOKE PEOPLE:""useful" character~ H@楛 A@"useful" character~ H@楛 A@ "useful" character#Q@ ffffffG@ #k g @ 㥛 A@  2. CHAR_NOC$ no characterñF@p8cA@  no characterñF@p8cA@  no character#L@ ±F@ #'} @ p8cA@   Mean Std. Error 95% Confidence Interval$control I +>@control #=U@ I +>@ Dependent VariableCHAR_NOC  Lower Bound Upper Bound8XWEBPOS~ ?(?E@g@BjB@v+֖G@  p = 0.0042 ~ @r8?@g@kp9@B@ F= 9.2892 3. USEFUL_F * CHAR_NOCB  Mean Std. Error 95% Confidence Interval8Dependent VariableUSEFUL_FCHAR_NOC  Lower Bound Upper Bound6 :1Positive reaction to website (no joke conditions)<XWEBPOS"useful" character㥛 A@A] @hq6ݬ<@kE@ p = 0.0092   no characterp8cA@B] @ZŇv;@ E@ F = 7.5252 "fun" character2ҺoH@B] @>d%iD@aK@6  no character~ J@B] @%Sk3@1mVJHA@6:sFj+I ih P (  p  6NMM? ]`  @"??3` >Y` >Y3d23 M NM4 3Q:  &Agent LikeabilityQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAU : 3O 3 b#M43*#M! M4523  O43" 44% L{M3ORQ'44eee xp  6NMM?- 0]`  @"??3` >Y` >Y Pa3d23 M NM4 3Q: 0Number of Items BoughtQ ;Q ;Q3_4E4D $% M 3O&Q4$% M 3O& Q4FA3 3O 3 b#M43*?#M! M4523  O43" 44% NiM3OwQ'44eee xp  6NMM? P(]`p  @"p??3` >Y ` >Y ?п3d23 M NM4 3Q:( *Website reliabilityQ ;)*Q ;)*Q3_4E4D $% M 3O& Q4$% M 3O& Q4FA[ " 3O 3 b#M43*@@#M! M4523  O43" 44% vMM3OWQ'44eee xp  6NMM?`) 6]`  @"??3` >Y ` >Y @?3d23 M NM4 3Q:. .Weakness of CharacterQ ;/0Q ;/0Q3_4E4D $% M 3O& Q4$% M 3O& Q4FAy > 3O 3 b#M43*@@#M! M4523  O43" 44% KM3OwQ'44eee xp  6NMM? )ip6]`  @"??3` >Y` >Y3d23 M NM4 3Q:1 BHow engaging was the character?Q ;23Q ;23Q3_4E4D $% M 3O&Q4$% M 3O&Q4FAp U 3O 3 b#M43*#M! M4523  O43" 44% UJ M3OQ'44eee xp  6NMM?06 C]`8  @"8??3` >Y` >Y3d23 M NM4 3Q:: *Agent AssertivenessQ ;;>Q ;;>Q3_4E4D $% M 3O&Q4$% M 3O&Q4FA} ]3O 3 b#M43*$@@?#M! M4523  O43" 44% MrM3OkQ'44eee xp  6NMM?D- 0R]`  @"??3` >Y` >Y?3d23 M NM4 3Q:I R'Followed agent's advice (bought futon)?Q ;JMQ ;JMQ3_4E4D $% M 3O&Q4$% M 3O&Q4FALn O3O}T 3 b#M43*??#M! M4523  O43" 44% T bM3OQ'44eee xp   6NMM? E0R] `h   @"h ??3` >Y` >Y?3d23 M NM4 3Q:N 4How fun was the website?Q ;ORQ ;ORQ3_4E4D $% M 3O&Q4$% M 3O&Q4FA{ {3O 3 b#M43*.@@#M! 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M4523  O43" 44% jI4M3OQ'44eee xp  6NMM?pK`]`   @" ??3` >Y ` >Y!3d23 M NM4 3Q: 0Gave an email address?Q ;Q ;Q3_4E4D $% M 3O& Q4$% M 3O&!Q4FA}c )3Ou# 3 b#M43*ٿ333333??ٿ#M! M4523  O43" 44% GwM3OyQ'44eee xp  6NMM?<-]`   @" ??3` >Y"` >Y#3d23 M NM4 3Q: BGave a *correct* email address?Q ;Q ;Q3_4E4D $% M 3O&"Q4$% M 3O&#Q4FAm\ X3Ov3 3 b#M43*? #M! M4523  O43" 44% XqpM3OQ'44eee xp  6NMM? K]`(  @"(??3` >Y$` >Y%3d23 M NM4 3Q: BHow impersonal was the website?Q ; Q ; Q3_4E4D $% M 3O&$Q4$% M 3O&%Q4FAY * 3O 3 b#M43*@@#M! M4523  O43" 44% &Mc M3OQ'44eee xp  6NMM? (]`  @"??3` >Y&` >Y'3d23 M NM4 3Q:# 8How tedious was the agent?Q ;$%Q ;$%Q3_4E4D $% M 3O&&Q4$% M 3O&'Q4FAg 3O$ 3 b#M43*?#M! M4523  O43" 44% RM3OQ'44eee xp  6NMM? `$/]`X  @"X??3` >Y(` >Y)3d23 M NM4 3Q:+ BHow impersonal was the website?Q ;,-Q ;,-Q3_4E4D$% M 3O&(Q4$% M 3O&)Q4FA] 3O- 3 b#M43*@@#M! 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thesis - final.docDd*M:$J /[h ^`OJQJo(h ^`OJQJo(oh pp^p`OJQJo(h @ @ ^@ `OJQJo(h ^`OJQJo(oh ^`OJQJo(h ^`OJQJo(h ^`OJQJo(oh PP^P`OJQJo(^`OJPJQJ^Jo(^`OJPJQJ^Jo(- pp^p`OJQJo( @ @ ^@ `OJQJo( ^`OJQJo(o ^`OJQJo( ^`OJQJo( ^`OJQJo(o PP^P`OJQJo(hh^h`o(hh^h`o(.0^`0o(..0^`0o(... 88^8`o( .... 88^8`o( ..... `^``o( ...... `^``o(....... ^`o(........J /M:Dd         bjJ        d  a@p1+1,1-1.1/`@`` `@`2`4`6`8`t@UnknownGz Times New Roman5Symbol3& z Arial5& zaTahoma?5 z Courier New;Wingdings"1hقuffA~<D;i30d 2QWHY PEOPLE HATE THE PAPERCLIP: Luke Swartz Luke Swartz