set of guidelines for persuasive interfaces: …beck/csc8570/papers/nemery.pdfto present and...

Vol. 9, I ssue 3, May 2014 pp. 105-128

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Set of Guidelines for Persuasive Interfaces: Organization and Validation of the Criteria

Alexandra Némery User Experience Designer Researcher Université de Lorraine–Metz, PErSEUs EA 7312–User Experience Lab. Faculté des Sciences Humaines et Sociales–I le du Saulcy–CS 60228–57045 METZ cedex 01, France SAGE–10, rue Fruct idor–75017 Paris [email protected]

Eric Brangier Full Professor Université de Lorraine–Metz, PErSEUs EA 7312–User Experience Lab. Faculté des Sciences Humaines et Sociales–I le du Saulcy–CS 60228–57045 METZ cedex 01, France Eric.Brangier@univ- lorraine.fr

Abstract This study presents an at tem pt to organize and validate a set of guidelines to assess the persuasive characterist ics of interfaces (web, software, etc.) . Persuasive aspects of interfaces are a fast growing topic of interest ; num erous website and applicat ion designers have understood the im portance of using interfaces to persuade and even to change users’ at t itudes and behaviors. However, research has so far been lim ited by a lack of available tools to m easure interface persuasion. This paper provides a criteria-based approach to ident ify and assess the persuasive power of interfaces.

We selected164 publicat ions in the field of persuasive technology, and we used those publicat ions to define eight cr iter ia: credibilit y, pr ivacy, personalizat ion, at t ract iveness, solicitat ion, prim ing, com mitm ent , and ascendency. Thirty experts in hum an-com puter interact ion (HCI ) were asked to use guidelines to ident ify and classify persuasive elem ents of 15 interfaces. The average percentage of correct ident ificat ion was 78.8% , with Randolph’s kappa coefficient = 0.61. These results confirm that the criteria for interact ive persuasion, in their current form , can be considered as valid, reliable, and usable. This paper provides som e inherent lim itat ions of this m ethod and ident ifies potent ial refinem ents of some definit ions. Finally, this paper dem onst rates how a checklist can be used to inspect the persuasiveness of interfaces.

Keywords Persuasive interface, ergonom ic criter ia, persuasive technology, guidelines

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Journal of Usability Studies Vol. 9, I ssue 3, May 2014

Introduction Usabilit y can be assessed through widely known inspect ion m ethods such as walk- throughs and heurist ic evaluat ions. Historically, while these m ethods were being developed, a set of usabilit y assessm ent m et rics and criteria were defined and resulted in the I nternat ional standards for HCI and usabilit y in 1998 ( i.e., I SO 9241) . I n this context , these criteria have been developed to facilitate the use of technical system s; to m ake interact ion m ore useful, efficient , sim ple, pleasant , etc.; and to m ake life easier for users.

Our research seeks to define criteria and validate a checklist of cr iter ia to assess the persuasive dim ensions of user interfaces. Ergonom ics and HCI have produced num erous recom mendat ions to m easure the ergonom ic quality of products and services, but there is a lack of knowledge and criteria to design and to evaluate persuasive interact ions.

The increase in new technologies and interact ive m edia provides new opportunit ies to influence users. Nowadays, technology offers unprecedented means to change users’ attitude or behavior because technology is an everyday presence for a large port ion of the world’s populat ion and can be accessed easily and quickly. This accessibilit y has created a new type of relat ionship between users and technology; the user ’s experience can be rooted in a m ore em ot ional context . Through persuasion, technological m edia can take users by the hand and lead them to perform target behavior.

Several definit ions of persuasive interfaces can be found in the literature. For the purpose of this study, we define a persuasive interface as a com binat ion of stat ic qualit ies and steps that guide a user through a process to change his or her at t itude or behavior. Many indust r ies and com panies that have an online presence, such as social networks, e-com merce, e-health or e-learning com panies, invest in and produce persuasive interfaces. For instance, Consolvo, Everit t , Sm ith, and Landay (2006) and Adam s et al. (2009) observed a significant increase in healthcare companies’ efforts that aim ed to encourage safe behavior and healthier lifestyles. While this effort provides a way to foster bet ter health habits, which has the potent ial to cut healthcare costs, it also raises som e ethical issues like the m anipulat ion of health behaviors.

With a large variety of websites, software, m obile phones, and other applicat ions, experts need access to effect ive tools to evaluate the persuasiveness of an online interface. These tools can be found in the m ult idisciplinary field of ergonom ics. Ergonom ics has a t radit ion of producing standards, checklists, guidelines, norm s, and recom mendat ions that can be used to evaluate the persuasiveness of interfaces.

To present and validate a checklist to assess persuasion in interfaces, we start by describing the theoret ical basis of our set of guidelines. Then, we classify persuasive criteria into eight categories. Our set of criter ia aim s to define and gather all aspects of persuasion present on the interfaces. We then describe an experim ent with 30 usabilit y experts perform ing an evaluat ion task on the persuasive human-com puter interfaces. The validat ion of the checklist is based on a m ethod of categorizat ion criteria: the m ore the experts categorize ident ical cr iter ia, the bet ter the quality of the guidelines. From this point of view, our m ain quest ion is to understand if the validat ion says som ething about whether part icipants can tell the eight cr it er ia apart and whether the part icipants find that the eight cr iter ia capture im portant aspects of the persuasiveness of the interfaces. Finally, we analyze and discuss the results of the experim ent and provide som e recom mendat ions for the use of this set of guidelines.

Conceptual Framework The following sect ions discuss the theoret ical background for the concept and design of this study, the design of the set of guidelines used for this study, and the select ion of guideline criteria for the checklist .

Theoretical Background Fogg opened the way for the field of persuasive technology for which he coined the term “captology,” an abbreviat ion for “computers as persuasive technologies” (2003, p. 5) . The concept of persuasion can cover a range of meanings, but Fogg defines it as “an attempt to shape, reinforce, or change behaviors, feelings, or thoughts about an issue, object , or act ion.” Fogg presents persuasion technology as a tool to achieve a change in the user’s behavior or

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at t itude, a m edia interact ion that creates an experience between the user and the product , and a social actor. The social actor deals with the technology's use of st rategies for social influence and com pliance. I n short , persuasion technology is a medium to influence and persuade people through HCI . I ndeed, the technology becom es persuasive when people give it qualit ies and attributes that may increase its social influence in order to change users’ behavior. This not ion is at the crossroads between ergonom ics, social psychology, organizat ional m anagem ent , and (obviously) the design of a user interface.

We chose to use and adapt som e t radit ional persuasion techniques used in studies from fields such as advert ising, polit ics, and health (Chaiken, Liberm an, & Eagly, 1989; Greenwald & Banaj i, 1995; McGuire, 1969; Pet ty & Cacioppo, 1981) . We adapted the techniques used in these earlier studies to m atch m odern technological system s capabilit ies and specificit ies (Oinas-Kukkonen, 2010) .

This last decade has m et with a growing interest for technological persuasion, as shown by Tørning and Oinas-Kukkonen (2009) , who invest igated persuasive system s produced between 2006 and 2008. The large body of research on this topic clearly at tests to the im portance of the field of technological persuasion. One of the recom mendat ions for future research is to create m ethods for a bet ter m easurem ent of successful persuasive system s (Lockton, Harrison, & Stanton, 2010; Oinas-Kukkonen & Harjum aa, 2009; Tørning & Oinas-Kukkonen, 2009) . I n the sam e way that Oinas-Kukkonen and Harjum aa (2009) defined four persuasive dim ensions (social support , system credibilit y support , prim ary task support , and dialogue support ) , we consider that designers and ergonom ists need to have a set of guidelines to evaluate persuasion.

I n this paper, we present a checklist that serves as a set of guidelines. We explain the goal of our checklist which is to develop ergonom ic pract ices and to integrate persuasive evaluat ions into software design processes using concepts such as reduct ion, credibilit y, legit im acy, tunneling, tailoring, personalizat ion, self-m onitor ing, sim ulat ion, rehearsal, monitoring, social learning, open persuasion, praise, rewards, rem inders, suggest ion, sim ilarity, at t ract iveness, lik ing, social role, and addict ive characterist ics—all of which originate from different research (Fogg, 2003; Nem ery, Brangier, & Kopp, 2009; Oinas-Kukkonen & Harjumaa, 2008).

Designing the Set of Guidelines A lack of m eans to assess or to design persuasive interfaces is apparent (Tørning & Oinas- Kukkonen, 2009) . Som e of the criteria gathered from earlier studies (Oinas-Kukkonen & Harjum aa, 2009) were not validated with experim ental m ethods that showed the relevance and efficiency of the criter ia.

Another crit icism , which can be addressed against exist ing criteria, is the lack of considerat ion for t im e as a st ructural elem ent of social influence. Persuasion is always linked to a tem poral process that includes a beginning, followed by m odificat ions leading to behavioral change. This process always takes place over several hours, som et im es several m onths. A set of cr iter ia should include the m oment in t im e to understand form s of last ing influence, such as addict ion or dependence. The concept of tem porality em erged as a necessary condit ion to change user behavior. I n order to incur last ing changes of at t itude and behavior, it was observed that accept ing an effort less init ial request predisposes a user in a posit ive way to accept a subsequent request asking for a greater effort ; this social influence is always tem porarily st ructured. For exam ple, the interface displays an invitat ion to play a short videogame, if you choose to play the gam e, you are gradually presented with seem ingly innocuous requests designed to covert ly persuade you, eventually, to give personal inform at ion or to purchase opt ions.

Our checklist is based on an analysis of 164 docum ents linked to technological persuasion ( for details of the collected docum ents, see Ném ery, 2012) . These docum ents helped us ident ify the m ain com ponents of persuasion m ediated by tools, organized those com ponents, and offered a classificat ion fram ework for our guidelines. We developed a total of eight persuasive criteria and 23 sub-criteria. The set of guidelines different iates stat ic aspects from dynam ic aspects. The assum pt ion is that user interfaces need a set of necessary qualit ies and propert ies. These elem ents are conducive to the establishm ent of a com mitm ent loop that will lead a subject from behavior A (or at t itude A) to behavior B (or at t itude B) .

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New devices offer m ore and more interact ive and innovat ive interfaces that use new user interface features and technologies. The potent ial persuasive im pact of these dynam ic interfaces is m uch greater than a stat ic inform at ion display where no interact ion is possible. Given a few enabling condit ions, users are able to dynam ically interact with a device in a short t im e, which facilitates the desired change in users’ behaviors or attitudes towards a product, service, or inform at ional interface. I nterface propert ies are necessary but not sufficient to change behaviors and at t itudes. Change is also a social const ruct that could be planned while taking into account user specificit ies.

To bet ter understand the process of persuasion, let 's com pare a human to human relat ionship. For instance, you would not lend money to st rangers or som eone you do not see as t rustworthy. Along the sam e line of thinking, people will not buy som ething on a website without cr it ical elem ents that inspire quality and security during the interact ion. I f websites are not deem ed t rustworthy, people generally do not buy products from those sites.

A com puter is a social actor (Fogg, 2003) . Through repet it ive interact ions, a user will be m ore and more em ot ionally involved. Com puters can feel like a liv ing ent ity with hum anlike qualit ies. Through the use of formal and polite language em ployed in interfaces—as advice, rewards, m ot ivat ing messages, or praise—com puters can serve as a social support ent ity or as a team m ate (Nass, Fogg, & Moon, 1996) . Depending on the context , a com puter can becom e a coach, a cooperat ive partner, or a sort of a sym biot ic assistant .

Selection of Criteria and Construction of the Checklist Research to develop the list of cr iter ia (Figure 1) is based on a review of 164 art icles on captology and persuasive design. We also used database research (PsychI nfo, Springer-Kluwer, and Science Direct ) and conference proceedings presentat ions and art icles (CHI , HCI I , Persuasive conference) to develop the criteria. These resources provide a r ich collect ion of m any exam ples of persuasive technology. The publicat ion rate in this area has increased over the past ten years.

Figure 1. Const ruct ion of the checklist .

To select the publicat ions for our research, we used the following indicators of relevance:

x We selected papers that included research that dealt with a user’s change of at t itude or behavior through technology. We avoided art icles that did not gather sufficient em pir ical ev idence in their m ethodology.

x We selected papers that focused on topics of persuasive interfaces, i.e., com puter-hum an interact ions that com bined qualit ies and planned interact ions that guide a user through a process to change his or her at t itude or behavior. The papers that did not focus direct ly on these topics or were deem ed less relevant to our area of study were

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excluded ( i.e., papers using persuasive concepts but dealing with the challenge of advert ising on the I nternet and/ or business m odel of e-com merce) .

Two ergonom ists selected or rejected art icles based on reading the t it les and abst racts of those art icles. For an art icle to be accepted for the resource list , at least one of the two ergonom ists needed to select the art icle. I n total, 164 art icles were selected. The general conclusions that em erged from this set of publicat ions were the following:

x The publicat ion rate in this area has clearly increased over the past ten years. From 1994 to 2005, persuasive technology was just em erging (around five papers each year presented recom mendat ions on persuasive interfaces) , but after 2005, publicat ions increased unt il 2009 with 42 papers published on persuasive technologies.

x Our findings were interdisciplinary and take into account related disciplines such as sociology, psychology, com puter science, m arket ing, and com municat ion science.

x The selected publicat ions include the following fields ( the percentage represents the rat io of papers/ fields used in this study) : Health (28.22% ), Educat ion (23.31% ), Service (18.40% ), Business (9.82% ), Ecology (9.20% ), Market ing (6.75% ), and Leisure (4.29% ).

x The following technologies are the m ain technology resources writ ten about in the selected publicat ions: websites (31.90% ), gam es (26.38% ), software (16.56% ), m obile apps (9.20% ), am bient system s (4.29% ), and others (11.67% ).

Previous research defined the init ial checklists (Ném ery, Brangier, & Kopp, 2009, 2010, 2011) . After an iterat ive work of classificat ion and organizat ion, we designed the checklist to include eight elem ents (Figure 2) . After this checklist was finalized, a second checklist was drawn up with the following requirem ents:

x Sim plificat ion: Each elem ent m ust be understood by ergonom ists, HCI specialists, com puters engineers, and interact ive designers.

x Consistency: All cr iter ia ext racted from the 164 art icles m ust be reflected in the checklist (Figure 2) . This consistency gives relevance to the checklist because all item s m ust be present in one form or another.

x Exclusivity: Only one criter ion per persuasive item m ust be applied. Am biguity between criteria should be avoided as m uch as possible. A persuasive item m ust be relevant to the criteria, and vice versa.

Figure 2. Dist r ibut ion of papers (n= 164, published from 1994 to 2011) dealing with the eight cr iter ia developed in the checklist for persuasive interfaces.

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A Set of Guidelines for Persuasive Technology A classificat ion of the 164 papers shows that 53% of papers dealt with the dynam ic aspect of the com puter's influence and 47% were m ore related to stat ic dim ensions of persuasive interfaces (Figure 2) .

Static Criteria Stat ic cr iter ia are all the surface elem ents that are necessary to help launch a dynam ic process of user engagem ent . That is to say, in interfaces, som e prerequisites are necessary to foster acceptance of an engaging process. These criteria are based on the influence content . Most ly not affected by t im e or sequence of interact ions, these criteria are designed to assess the content of the interact ion. Stat ic cr iter ia are opposed to dynam ic criteria, which incorporates st rong tem poral com ponents. We ident ified four stat ic com ponents to foster acceptance and confidence of users: credibilit y, privacy, personalizat ion, and at t ract iveness (Figure 3) .

Figure 3. General architecture of the eight interact ive persuasive criteria.

Credibility is the first general cr iter ion (see Figure 4 for an exam ple) . I t is the abilit y of the interface to inspire confidence and to m ake the user t rust the veracity of its inform at ion. Credibilit y is also based on a company’s or an industry’s reputation. All the people or inst itut ions that provide the com ponents that make up an industry’s or company’s systems (e.g., the different types of technical system s) m ust be recognized as being honest , com petent , fair , and object ive. Credibilit y com prises three com ponents: t rustworthiness, expert ise, and legit im acy. Much has been writ ten about the credibilit y and t rust of web m edia (e.g., Bart , Venkatesh, Fareena, & Urban, 2005; Bergeron & Rajaobelina, 2009; Huang, 2009) .

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Figure 4. Exam ple of credibilit y cr iter ion: This website provides a list of fam ous clients to inspire t rustworthiness and legit im acy in their field (source: www.cooper.com, screenshot captured on 03/ 15/ 2013) .

Privacy m eans the protect ion of personal data, the preservat ion of personal integrity, and the security of the interact ion (Figure 5) . I t covers all aspects of privacy that are used in interact ions. This criter ion also aim s to ensure protect ion against loss, dest ruct ion, or inadvertent disclosure of data (Liu, Marchewka, Lu, & Yu, 2005) . Privacy concerns the expression of perceived safety, the percept ion of r ights, and the protect ion of confident iality of inform at ion. For instance, organizat ions such as Alcoholic Anonym ous and Weight Watchers are successful ent it ies because they are very r igorous in term s of privacy (Khaled, Barr, Noble, & Biddle, 2006) .

Figure 5. Exam ple of privacy criterion: This financial website helps users m anage their budget . Mint provides a lot of reassuring inform at ion about security and associated ent it ies (source: www.m int .com, screenshot captured on 03/ 15/ 2013) .

Personalization refers to the abilit y for a user to adapt the interface to the user ’s preferences (Peppers & Rogers, 1998; Figure 6) . Personalizat ion includes all act ions aim ed at characterizing a greet ing, a prom ot ion, or a context to achieve a closer approach to the user. Personalizat ion m ay include individualizat ion and group m em bership. Personalizat ion requires an analysis of the act ivity beforehand. I ts power is dependent on the quality of data from the user and the degree of personalizat ion. The lat ter im plies that the interface gradually learns the characterist ics of the user and modifies its interact ion with the user towards a higher level of personalizat ion. Personalizat ion offers greater potent ial returns than standardized and im personal products

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(Pine, 1993) . As custom ers, users want services tailored to their needs. E-com merce has produced m any st rategies to custom ize interact ions. Old business pract ices prom oted a hom ogeneous m arket with standardized products; this m odel is not adequate any longer and does not fit each user’s needs. The proliferat ion of inter face products pressures designers to m ass produce custom izable interfaces. They provide personalized inform at ion and synthesized solut ions that m atch the choice of respect ive com munit ies, not necessarily individuals. All these elem ents com bined lead to a custom ized user experience.

Figure 6. Exam ple of personalizat ion criterion: The user is called by his or her f irst nam e throughout the interact ion which creates a sense of fam iliar ity (source: www.facebook.com , screenshot captured on 03/ 15/ 2013) .

Attractiveness is the use of aesthet ics (graphic, art , design) to capture the at tent ion of the user, to support the interact ion, and to create a posit ive em ot ion (Figure 7) . The anim at ion, colors, m enus, drawings, and video film s are designed to catch and maintain the interest of the user. Presentat ion of these persuasive interact ive elem ents m ust consider the cognit ive perceptual characterist ics of the user. Persuasive design could m ot ivate users towards specific act ions (Redst röm , 2006) . At t ract iveness has three components: em ot ional appeal, call to act ion, and tunneling design. Tunneling design is a kind of at t ract ive interact ion that t r ies to lead the user through a predefined succession of act ions to produce a desired result .

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Figure 7. Exam ple of at t ract iveness criter ion: The drawings are used to create a visual ident ity and unique experience that could leverage posit ive em ot ions (source: www.axure.com, screenshot captured on 03/ 15/ 2013) .

Dynamic Criteria To encourage users to change their behavior, it is im portant to take the tem poral aspect into account . Design is an engaging and interact ive act ivity that requires segm ent ing and planning persuasive processes. Here, dynam ics means a way to involve the user in an interact ive process that progressively engages him or her with the interface. There are four dynam ic criteria: solicitat ion, prim ing, com mitm ent , and ascendency.

Solicitation refers to the first stage that aim s to briefly at t ract and challenge the user to init iate a relat ionship (Figure 8) . We can dist inguish three elem ents for this “invitation” stage: allusion, suggest ion, and ent icem ent . The invitat ion sets up the beginning of the relat ionship and the dialogue between the user and elect ronic m edia. When this approach is used often by elect ronic m edia, the probabilit y of init iat ing the desired act ion by the user increases. The interface at tem pts, by words, graphics, or any form of dialogue, to suggest a behavior followed by act ion. Solicitat ion represents the abilit y to induce an act ion by the user with m inim al influence from the interface. Here, the interface suggests ideas or act ions that the user could init iate or could have (Dhamija, Tygar, & Hearst , 2006) .

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Figure 8. Exam ple of solicitat ion criterion: Several advert isem ents are displayed with a short m essage that t r ies to catch a user ’s at tent ion with an at t ract ive or free offer (source: www.awwwards.com, screenshot captured on 03/ 15/ 2013) .

Priming refers to elem ents of the m edia that t r igger the persuasive influence (Figure 9) . These elem ents m ay take the form of pilot ing the first steps. Following requests from the interface, the user's at tent ion is captured. Users are encouraged to realize the first engaging act ion. With prim ing, the first act ion is carr ied out without coercion or awareness. Users are caught in a process that gradually draws them in (Yang, 2005) .

Figure 9. Exam ple of prim ing criterion: The first act ion expected in an e-com merce website is to make the user add an item to the cart. In this interface, the message “Add to cart” on the button is replaced by “It’s perfect” as an invitation and incitation to click. In addition, the m essage is reinforced by the fact that on mouse over, the message is replaced by “I’ll take it” to st rengthen the intent ion (source: ht tp: / / www.thefutureperfect .com , screenshot captured on 03/ 15/ 2013) .

Commitment m eans that the interface cont inues to involve the user through a process (Figure 10) . This process uses act ion sequences or predeterm ined interact ions to gradually involve the user. The successful com plet ion of these queries and act ions is followed by praise,

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encouragem ent , reward, and cont inued interact ion, which induces m ore intensive and regular behavior from the user (Weiksner, Fogg, & Liu, 2008) .

Figure 10. Exam ple of comm itm ent cr iter ion: To use this app, the person m ust follow a wizard assistant that asks for m ore personal inform at ion through a tunneling process. The idea is not to lose the user's at tent ion and also to ask the user to interest his fr iends in the app by (a) making the user’s use public and (b) asking the user to invite fr iends (source: iPhone App—Path, screenshot captured on 03/ 15/ 2013) .

Ascendency is an indicator of the com plet ion of the engaging scenario (Figure 11) . When users enter the ascendency stage, the user has definitely accepted the logic and goals of the elect ronic m edia. The “grip” or hold that the technology has on a user’s attention is the deepest form of technological persuasion. At this stage, the user involvem ent is com plete and he or she runs the r isk of addict ion or at least an over-consum pt ion of the elect ronic m edia. I n these interact ions, the user behaves in such a way as to generate pleasure and maybe relieve negat ive feelings.

At the interface level, the influence is apparent through various elem ents: irrepressible interact ion, tension release, and also consequences beyond the interact ion with the media. I m m ersion is often m ent ioned in the field of video gam ing. Because these gam es were first devoted to a flight sim ulator environm ent , they were realist ic, and they caught the user's full at tent ion. I n the end, the player becom es a part of the environm ent due to the repet it ion and the regularity of interact ion (Baranowski, Buday, Thom son, & Baranowski, 2008) . The em ot ional involvem ent in the story of the gam e could cause a r isk of dependence and ident ificat ion. This does not only concern desktop applicat ions, but also GPSs and Sm artphones. Users who developed an em ot ional at tachm ent to their device cannot im agine liv ing without these products (Nass, Fogg, & Moon, 1996) .

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Figure 11. Exam ple of ascendency criterion: The gam e challenges users throughout their progression and offers them different types of rewards like public badges to show success or new item s to accom plish m ore exploits. Stat ist ics are also displayed to encourage daily use. At this stage, there is a r isk of addict ion or overconsum pt ion (source: Video gam e—Starcraft 2, screenshot captured on 3/ 15/ 2013) .

To sum up, this fram ework presents eight persuasion cr iteria div ided into two dim ensions—stat ic and dynam ic aspects of the interface—that seeks to im prove the evaluat ion of persuasive elem ents in interfaces. These criteria com plem ent the t radit ional ergonom ic criteria that focus on the user's abilit y to be effect ive, efficient , and sat isfied. Persuasion is generally not within the scope of the inspect ion, and criteria can be sum m arized in the following sentences:

x Credibilit y: Give enough inform at ion to users that enable them to ident ify the source of inform at ion as reliable, relevant , expert , and t rustworthy.

x Privacy: Do not persuade users to do something that publicly exposes their pr ivate life and which they would not consent to do.

x Personalizat ion: Consider a user as a person and consequent ly develop a personal relat ionship.

x At t ract iveness: Capture the at tent ion of a user to elicit em ot ion and induce favorable act ion.

x Solicitat ion: I nit iate the relat ionship by first tem ptat ions. x Prim ing: Help users to do what the system wants them to do. x Com mitm ent : I nvolve, engage, and adhere to the object ives of the system . x Ascendency: Provide incent ives to get a user totally involved with the system.

I nterface designers and ergonom ic analyzers m ust be careful to ensure that their interfaces do not contain int rusive aspects that would affect the ethical handling of certain dom est ic or professional interact ions, part icularly as these factors affect the at t itudes of users. By applying knowledge about how hum ans work on the psychosocial level, our criter ia are therefore based on a norm at ive approach of what should or should not be persuasive. At present , it is necessary to validate these guidelines to dem onst rate their relevance and consistency.

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Method to Validate the Set of Guidelines for Persuasive Interfaces The following sect ions discuss the internal validat ion process, the study part icipants, the study m aterials and equipm ent , the tasks and procedures used in the study, and the data collect ion and analysis m ethods.

Internal Validation On the basis of the com pilat ion of recom mendat ions presented in the previous sect ion, we com piled a list of persuasive criteria to serve as a support for the classificat ion and ident ificat ion of persuasive elem ents related to interfaces (exam ples shown in Figures 3 through 12) .

I n agreem ent with Bast ien (1996) , Bast ien and Scapin (1992) , Bach (2004) , Bach and Scapin (2003, 2010) , who have validated ergonom ic criter ia on the pragm at ic aspects of interfaces, we used the sam e m ethod to validate the criteria of persuasion. The validat ion m ethod of the checklist consists of a test , com pleted by experts in HCI (or study part icipants) , ident ifying persuasive elem ents in the interfaces using the proposed criteria. I f the HCI experts ident ify the problem with the r ight cr iter ia, the checklist is relevant . Conversely, if experts m isident ify problem s and/ or cr iter ia, the checklist is ir relevant . Correct ident ificat ion is calculated and shown on the screen interface, and subject scores are broken down into stat ic vs. dynam ic criteria. Correct ident if icat ion (or good assignm ent ) is when the criteria defined by the part icipant m atches the one ident ified by the specialist . A high percentage of correct ident ificat ion reflects the quality of the set of guidelines; a high percentage of m ism atches indicates that the set is not efficient .

The task assignm ent can also ident ify the possible confusion between two or m ore criteria. An overall score is calculated by com paring part icipants' responses and expected responses. This score can include the proport ion of correct ident ificat ion or the proport ion of incorrect ident ificat ion, which could signify that a criter ion could be confused with another criter ion. I n other words, the task of the test part icipants is to find then classify elem ents. Several outcom es can happen during this process. Part icipants can either find an elem ent that was defined as persuasive (hit , correct ) , or they cannot find it (unident ified) . Or they can find an elem ent that they consider persuasive, but that is not real according to the expected criter ia ( false) . Once som ething has been correct ly or incorrect ly ident ified as a persuasive elem ent , it can be classified either correct ly, incorrect ly ( false) , or the test part icipant m ay not know how to classify it (unident ified) . Consequent ly, our hypothesis is that the higher the num ber of correct ident ificat ions (hits) , the m ore the internal validat ion is conclusive. Respect ively, the higher the num ber of m isallocat ions ( false) , the m ore the internal validat ion will be ineffect ive and the quality of the guidelines inferior.

Participants Thirty people part icipated in the study. They were all experts in the HCI field: ergonom ists, engineers, professors, researchers, project m anagers, R&D, and interact ion designers. They were all experienced with user interfaces evaluat ion and/ or design. On average, part icipants had 13 years of experience (SD= 7.9) . Twelve of the experts were from the academ ic field and eighteen em ployed in indust ry, but all had a sim ilar background in evaluat ion and in com puter experience. Ten wom en and twenty m en were gathered for this study.

Materials and Equipment The eight cr iter ia were sum m arized as a set of eight paper cards (Figure 12) . Each criterion card provided a detailed definit ion, a rat ionale based on references, and som e exam ples illust rated from various devices and fields.

Two experts ( the authors) exam ined and selected the interfaces used in this study. They found 84 persuasive item s that included 43 stat ic elem ents and 41 dynam ic elem ents: 12 form s of credibilit y, 10 form s of privacy, 13 form s of personalizat ion, 8 form s of at t ract iveness, 22 form s of solicitat ion, 8 form s of prim ing, 8 form s of com mitm ent and 3 form s of ascendency. On average, there were 10.5 elem ents per criter ia (SD= 5.5) and 5.6 elem ents per inter face (SD= 2.7) . These 15 screen interfaces were chosen because both authors were in total agreem ent on the criteria m ent ioned. Screen interfaces that were disagreed upon were discarded.

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Figure 12. Credibilit y card and the seven others cards (sm all) . Download the French version of the cards used during the experim ent at ht tp: / / www.univ-m etz.fr / ufr/ sha/ 2lp-et ic/ Criteres_Persuasion_I nteract ive-2.pdf.

We chose fifteen interfaces that contained persuasive elem ents for the study (Table 1 and Figure 13) . Twelve of them were websites and three were software applicat ions. Various areas were represented: business (3) , video gam es (3) , social networks (3) , e-com merce (2) , t ransport (1) , com m unicat ion tools (1) , health (1) and ecology (1) . Six interfaces were unknown, whereas nine were fam iliar to the part icipants.

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Table 1. General Character ist ics of the Tested I nterfaces and Stat ist ical Results of Correct I dent ificat ion Percentage of Criteria, Screen by Screen

Screen Source Field Type Renowned Correct identification percentage

1 www.voyages-sncf.com Ut ility Website Yes 77.14% 2 www.qualityst reet .fr Business Website No 67.77% 3 www.meet ic.fr Leisure Website Yes 84.00% 4 www.lecoleagit .fr Educat ion Video Game No 76.00% 5 www.fam icity.com Ut ility Website No 81.53% 6 Explorer Business Software No 81.33% 7 Em ail Yves Rocher Advert ising Em ail Yes 92.50% 8 www.skype.com Ut ility Website Yes 87.62% 9 www.lge.com Advert ising Website Yes 84.00% 10 www.urban-r ivals.com Leisure Video Game No 65.41% 11 m ail. live.com Advert ising Website Yes 78.33% 12 App WeightMan Health Mobile No 76.66% 13 www.monster.fr Business Website Yes 75.00% 14 www.facebook.com Leisure Website Yes 76.66% 15 Dragon Age Origins Leisure Video Game No 52.22%

Figure 13. Set of 15 interfaces.

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Task and Procedure The experim ental task is, in fact , an ident ificat ion task of persuasive criteria from exam ples (classificat ion accuracy) . We individually tested each part icipant . The inst ruct ion began with an int roduct ion to the experim ental procedure and inform at ion about the gathered data and criteria. We asked the part icipants to provide com prehensive com ments about their percept ions of the criteria. At the beginning of the session, we asked part icipants about their background, years of experience, and expert ise in heur ist ic evaluat ion.

I n the first phase ( fam iliar izat ion) , we asked part icipants to read the criteria docum ents very carefully (Figure 12) and to pay part icular at tent ion to the screenshot exam ples that would only be viewed dur ing this first phase ( i.e., the exam ples would not be visible later on in the experim ent ) . The object ive of this phase was for the part icipants to becom e fam iliar with the criteria and its content . The first inst ruct ion was, "I will present you with a list of eight cr iter ia to assess interact ive persuasion. I will ask you to read this list in full; it different iates stat ic cr iter ia and dynam ic criteria of interfaces. I would like you to look carefully at the exam ples because I won't display them again during the ident if icat ion phase. Do not hesitate to ask m e any quest ions."

I n the second phase ( ident ificat ion test ) , the aim of the task was to allow part icipants to m em orize the crit eria and its definit ions. I n this second phase, we gave the part icipants another version of the checklist without screenshot exam ples. This specific point of the m ethod was em phasized by Bast ien (1996) who suggests that subjects should not refer to the exam ples that are anecdotal, but to the criteria that are conceptual. I ndeed, the presence of the screenshot exam ples could have influenced the criteria ident ificat ion during this phase. I n fact , the goal was not to test the abilit y of recognit ion, but to check their understanding of the crit eria. I n addit ion, with only two or three exam ples per sheet , it was not possible to cover all the possible cases for a single criterion. The 30 experts were invited to review the 15 screen interfaces; in each interface, they had to ident ify persuasive elem ents and match them with the set of guidelines. They could freely consult the criteria docum ents. At the end of each session, the interfaces were presented again to allow the part icipants to confirm or not their init ial choice to assign a criterion to an item on the interfaces.

I n a third phase (confirm at ion) , the interfaces were shown again in order to confirm or refute participants’ first choice.

At the end of the session, the researchers perform ed a sem i-st ructured interv iew to obtain qualitat ive data on the st ructure and content of the docum ent containing the experim ental cr iter ia. The quest ions were open and encouraged greater freedom of speech and crit icism from the part icipants.

To sum up, the part icipants were asked to evaluate 15 interfaces with the checklist for persuasive interfaces. For each screen interface, they had to find persuasive elem ents and classify them according to the set of guidelines. For each subject , interfaces were random ly presented, and there were no t im e lim itat ions for each task.

Data Collection and Analysis Two types of data were collected. First , we were interested in recording how m uch t im e part icipants spent reading the criteria. The second m easure was the num ber of persuasive criteria correct ly ident if ied by the part icipants, which was separated into dynam ic vs. stat ic cr iter ia. We also calculated a score for the correct ident ificat ion per interface and per subject . I dent ificat ion was considered as correct if the answer matched with the expected criteria (or theoret ical answer) . I f the answer did not m atch the expected criter ia, it was considered false. Quality of the criteria definit ion was evaluated through this assignm ent task, that is, if a criter ion was defined as correct or false. Proport ions of correct ident if icat ions or false ident ificat ions ( i.e., confusions) were analyzed as discrim inat ive indicators of the relevance of the checklist .

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Results and Analysis The following sect ions present the overall scores, details about the criteria m at rix and Kappa score, and the quality of the criteria definit ions.

Overall Scores The average t im e to read the criteria docum ent was 11 m inutes 45 seconds (SD= 4.2) . The average session t im e was 1 hour 6 m inutes (SD= 13) . The overall scores of the confusion criteria m at rix show a high level of agreem ent am ong part icipants to ident ify persuasive elem ents in interfaces. Table 2 shows the classificat ion criteria according to the proport ion of correct ident if icat ion.

Correct ident ificat ions reached the score of 78.8% on average. This result is encouraging considering the short t im e that part icipants took to learn new criteria and considering that m ost of the part icipants lacked prior knowledge of the technological persuasion theory. With an acceptance of 50% as a threshold to determ ine the quality of definit ion (Bach, 2004) , all the criteria reached a good to a very good level of acceptance. Overall, stat ic cr iter ia were bet ter understood than the dynam ic criteria. Another possible explanat ion for this finding is that stat ic elem ents are easier to ident ify on a stat ic user interface com pared to ident ifying dynam ic elem ents on a stat ic interface. However, ascendency appears to be difficult to assess. This can be explained by the fact that ascendency is a criteria that does not solely depend on the interface itself, but also on behavioral pat terns of the user that are acquired over t im e and that cannot be readily observed in the context of this experim ent .

We calculated a percentage of correct ident ificat ion per screenshot (see Table 1) . Most of the screenshots perform ed very well (between 65% and 85% ) ; however, 2 screenshots stand out : screens 7 and 15.

x Screen 7 is an advert isem ent received by em ail for a birthday prom ot ional offer. All the elem ents on the screen indicate that a user's percept ion is used to associate persuasive at tem pts in the advert ising area. The awareness is developed especially when the delivery m edium is em ail. A score of 92.5% shows a high capacity to detect this part icular form at as a persuasive elem ent .

x Screen 15 is a dashboard screen of a role-playing video gam e. According to the stat ist ics displayed, it indicates that the user played a huge am ount of hours in just few days. I n this case, the persuasive elem ents were not easily found by the part icipants (52.22% ). I t indicates that in the video gam e area, the link with persuasion is not obvious for the part icipants. This m edia is associated with fun and a degree of freedom . Even if the r isk of dependency is quite known.

Criteria Matrix: Agreement and Disagreement of Persuasive Elements Table 2 reports the criter ia m at rix for the interface elem ents classificat ion. Each row of the m at rix represents the instances in a predicted criterion category. For exam ple, we assum e that the 15 interfaces include a total of 12 item s relat ing to credibilit y. These 12 item s were presented to 30 experts, represent ing a total of 360 item s that should be proper ly assigned to the criterion of credibilit y. I n reality, part icipants ident ified 293 item s correct ly, com m it ted 53 errors (2 x privacy, 5 x personalizat ion, 12 x at t ract iveness, 10 x solicitat ion, 10 x prim ing, 2 x com mitm ent , 2 x ascendency) , and provided 14 non-assignm ents because they could not categorize the item . This sam e row also shows how m any part icipants m ade m istakes ( the stat ist ics are placed after the icon; for instance, two part icipants classified two item s of presupposed credibilit y in pr ivacy) . The following is a detailed descript ion of the data in the table:

x The first colum n represents the expected answers based on the am ount of each criterion potent ial for a correct m atch, as determ ined by the researchers. The bot tom num bers in this colum n represent the maxim um possible num ber of correct ident ificat ions.

x The diagonal table cells (boldface) show the percentage of agreem ent between the part icipants for persuasive elem ents for each of the eight cr iter ia.

x The score below the percentage (diagonal cells) represents the num ber, rather than the percentage, of part icipant agreem ents for that cr iter ion.

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x All of the other percentages in the table represent disagreem ent between the participants’ characterization of each criterion.

x The num bers in brackets presented in conjunct ion with the icons represent the num ber of part icipants who cont r ibuted to the disagreem ent score.

x The right colum n (Unident ified) contains elem ents that could not be m atched with any elem ent in the ident ified elem ents or cr iter ia.

The criteria m at rix reveals that m ost of cr iter ia (except ascendancy, see The Quality of Criteria Definit ion sect ion) are generally found in m any interfaces. The 30 experts correct ly ident ified the elem ents of persuasion and were able to categorize persuasive criter ia accurately. I nter-ranking judgm ents were fair ly hom ogeneous; the sam e features of persuasive interfaces were nam ed consistent ly.

Table 2. Criter ia Mat rix: Agreem ent and Disagreem ent of Persuasive Elem ents

Identified criteria by test participant Credibility Privacy Personalization Attractiveness Solicitation Priming Commitment Ascendency Unidentified

Expe

cted

Cri

teri

a

Credibility

81.39*% 0.5% (2)

1.4% ( 3)

3.3% ( 9)

2.8% ( 9)

0.5% ( 2)

0.5% ( 2)

0.5% ( 2)

6.7% (4)

360 293 2 5 12 10 10 2 2 24

Privacy

3.3% ( 8)

90.67%

0% ( 0)

1.7% ( 4)

1.3% ( 3)

1.7% ( 5)

0.7% ( 2)

0% ( 0)

0.7% (2)

300 10 272 0 5 4 5 2 0 2 Personalization

1.5% ( 5)

3.1% ( 10)

82.82%

1.8% ( 7)

1% ( 4)

3.3% ( 8)

1.5% ( 6)

1% ( 3)

3.8% (14)

390 6 12 323 7 4 13 6 4 15 Attractiveness

1.7% ( 4)

0% ( 0)

0.4% ( 1)

79.17%

5.8% ( 12)

5.4% (13)

0.8% ( 2)

0% ( 0)

6.7% (11)

240 4 0 1 190 14 13 2 0 16 Solicitation

2% ( 1)

0.4% (3)

3.8% ( 15)

6.2% ( 17)

75.30%

6.1% (17)

2.3% ( 9)

2.6% ( 10)

1.4% (7)

660 13 3 25 41 497 40 15 17 9 Priming

1.7% ( 4)

2.9% (7)

4.2% ( 9)

3.3% ( 6)

5.8% ( 9)

74.58%

4.2% ( 9)

0.4% ( 1)

2.9% (6)

240 4 7 10 8 14 179 10 1 7 Commitment

1.7% ( 4)

0.8% (2)

3.3% ( 7)

2.1% ( 5)

6.7% ( 12)

2.9% ( 6)

75%

3.7% ( 8)

3.7% (8)

240 4 2 8 5 16 7 180 9 9 Ascendency

0% ( 0)

1.1% (1)

6.7% ( 6)

4.4% ( 4)

4.4% ( 4)

6.7% ( 6)

5.5% ( 5)

57.78%

13.3% (9)

90 0 1 6 4 4 6 5 52 12 * The diagonal table cells (boldface) show the percentage of agreement between the part icipants for persuasive elem ents for each of the eight cr iter ia. The score below the percentage (diagonal cells) represents the number, rather than the percentage, of part icipant agreem ents for that cr iter ion. Kappa Score Kappa is a stat ist ical coefficient for assessing the reliabilit y of agreem ent between a fixed num ber of evaluators when assigning categorical rat ings to a num ber of criter ia. We used Randolph’s kappa (2005) , instead of Cohen's Kappa or Fleiss’ Kappa, because it gives a m ore accurate and precise reliabilit y m easurem ent of the 30 experts' agreem ent . We used a free-m arginal version of kappa that is recom mended when raters are not rest r icted in the num ber of cases that can be assigned to each category, which is often the case in typical agreem ent studies.

Randolph’s kappa is considered to be a good stat ist ical m easure of inter- rater agreem ent ; this m easurem ent tool resulted in a score of 0.61 for our study. An indicat ion of st rong agreem ent

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for a m easure of quality of a classificat ion system , according to Landis and Koch (1977) , is when an overall perform ance score is higher than 60% .

Finally we m ust rem em ber that the kappa value m easures the agreem ent am ong the 30 part icipants, independent of the two experts who determ ined which interfaces had which persuasive elem ents.

The Quality of Criteria Definition Table 2 also presents the classificat ion criteria based on their average frequency of m isident if icat ion and the proport ion of correct ident if icat ion. From this point of view, seven criteria are considered to be well defined. These criteria were privacy, personalizat ion, credibilit y, at t ract iveness, solicitat ion, prim ing, and com mitm ent . Am ong all the elem entary criteria, only one requires an im provem ent in definit ion: ascendency. These results are part icularly interest ing when taking into account the short t im e taken by part icipants during the learning phase (M = 11m in 45s; S.D. = 4.2) and the unfam iliar ity of the subjects with social psychology.

There was som e slight confusion for all of the criteria; however, m isclassificat ions are always very low, usually below 5% . I n our study the m isclassificat ion score was som et im es slight ly higher and in the case of the ascendancy criteria was 13% . Ascendency is the criterion that caused the m ost confusion and that needs the m ost adjustm ents. I n fact , the ascendancy score is linked both to confusion with other criter ia (personalizat ion, 6.7% ; prim ing, 6.7% ; com mitm ent , 5.5% ) and to the lack of at t r ibut ion; experts prefer to classify form s of ascendancy as an unident ified criter ion (13.3% ) . Other criter ia show confusion too, due to the high num ber of persuasive elem ents. Note also that the stat ic cr iter ia, which are direct ly ident ifiable on the screens, cause less confusion than the dynam ic criteria, which involve a t im e aspect of persuasion.

Discussion and Conclusion Com puter ergonom ics have produced a num ber of guidelines to m easure the ergonom ic quality of products, technologies, and services. We seek to establish a tool—a checklist that is sound, reliable, useful, relevant , and easy to use—to focus on the persuasive dim ensions of interfaces and their effects.

While m any criteria appear in the literature, it is very im portant to st ructure them within a set of guidelines or som e other form s. Too many suggested criteria are not supported by further inform at ion on its potent ial use and applicat ion by HCI experts. The fundam ental advantage of this checklist is its rem arkable internal validat ion. The criteria for interact ive persuasion in its current form can be considered valid, reliable, and usable. The use of criter ia enables the ident ificat ion and classificat ion of elem ents at the source of social influence in interfaces. We designed an experim ental test to assess the relevance of our checklist by m easuring confusion scores—the lower the confusion score is, the bet ter the set of guidelines. I n addit ion to im proving the rate of elem ents found, the checklist also covers a larger num ber of elem ents ( regardless of the type of software, website, gam e, or mobile device to be evaluated.)

Let us sum marize our m ain findings during the successive phases of refinem ent of the criteria and the findings from the experim ents. First ly, the criter ia were based on the select ion and classificat ion of 164 publicat ions published between 1994 and 2011, covering many sectors (health, services, business, ecology, educat ion, leisure, and m arket ing) , and based on all types of technologies (website, video gam es, phones, software, and am bient systems) . Secondly, the content analysis of these publicat ions ident if ied two dim ensions of persuasion (stat ic and dynam ic) and eight cr iter ia that we have defined, explained, and exem plified. The cr iteria for interact ive persuasion em phasize the social and em ot ional dim ensions of interfaces and, as such, enhance the usual inspect ion criter ia (clarity, consistency, hom ogeneity, com pat ibilit y, usabilit y, etc.) . Thirdly, an experim entat ion of an internal validat ion of the criteria should show that the criteria can be effect ively ident ified in a realist ic set t ing. I n an allocat ion task, 30 specialists in ergonom ic software correct ly m atched 78.8% of a series of 15 interfaces with 84 form s of persuasion (12 form s of credibilit y, 10 form s of privacy, 13 form s of personalizat ion, 8 form s of at t ract iveness, 22 form s of solicitat ion, 8 form s of prim ing, 8 form s of com mitm ent , and 3 form s of ascendency) . Accuracy m easures are a lit t le bit higher than those found by Bast ien (1996) and Bach (2004) when studying the ergonom ic quality of inspect ion criteria.

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Fourthly, the criterion of ascendency has a good score, but is lower than other criter ia. Not surprisingly ascendency is the last step of the engaging scenario. I n m any cases, ascendency is not visible on the interface as it is the user who reflects the result of the persuasive influence. Ascendency is a hard criterion to allocate because it also has a st rong tem poral incidence.

The influence of technology is a long- term process that does not only depend on the interface but also on the user's personal characterist ics. I n regards to ascendency, the problem is to ident ify the possible effects that go over and beyond interact ions with the media: the user persists in at t itude or behavior, even outside his or her interact ion with technology. The way to ident ify ascendency im plies m aking assum pt ions about links between elem ents of interfaces and opportunit ies for acquir ing and engaging the user. These diagnoses are not obvious to ergonom ists and interact ive designers. For som e of them , psychosocial aspects and not ions were not easily understood. Obviously, ascendency is the one that gets the lowest score in term s of correct assignm ent . I t represents the m ost difficult dynam ic criterion because it is the last of the engaging scenario. I t m arks the final step, the one where we obtain the change in behavior or at t itude expected. However, in m ost cases of persuasive processes, this result is not direct ly observable through the interface, but in the real environm ent of the subject and som et im es a long t im e after interact ions with the media. We can im agine that this cr iter ion could be im proved by com bining it with a m ore clinical analysis. But , the object ive for this checklist is only to be integrated into the pract ice of HCI experts.

Criteria allow rapid ident ificat ion of the m ost im portant problem s; they can also ident ify a wide variety of problem s. That said, the experim ent task im plies that the ident ified cr iteria are correlated with scenarios. This m ay be a drawback of the m ethod that we used. I ndeed, this presupposes the existence of a relat ively generic m odel of part icipant . Nevertheless, the diversity of people and context of use ( tested interfaces cam e from gam es, m obile phones, business software, social networks, e-com merce, health, and t ransport ) belies this narrow concept ion of the hum an. Consequent ly, the support provided by persuasive crit eria can certainly be im proved further by producing com plem entary m ethods. I t seem s im portant that specialists’ inspection methods refine ways to integrate these persuasive criteria in assessment pract ices (Mahatody, Sagar, & Kolski, 2010) .

As with other guidelines, our persuasive set of guidelines could be useful in several effect ive ways. An interface, for exam ple, could receive a score of absence/ presence per crit er ion (0 if the criterion is not present , 1 if the interface m atches the criterion) or a score on a scale (e.g., 0 if the criterion is not present , 5 if the interface totally m atches the criterion) . A general score could be calculated for several interfaces in order to compare them . The checklist could also help the designer to detect st rengths and weaknesses in his or her interface am ong the eight cr iter ia. For exam ple, Brangier and Desm arais (2013, 2014) at tem pted to design a m ore m ot ivat ing and engaging e- learning applicat ions through persuasive guidelines. They conducted a persuasiveness assessm ent with our set of guidelines of an exist ing e- learning applicat ion intended for self- regulated learning of m iddle school m athem at ics (Figure 14) . I t showed that the inspect ion could reveal factors that explained the low engagem ent observed as the e- learning applicat ion was used and usage data was recorded.

Becom ing a social actor, technology is not only shaping inform at ion, but it also reshapes our behaviors, percept ions, and at t itudes towards desired goals, often without the awareness of the user. Thus, persuasion is effect ively insem inat ing the field of HCI and thereby renewing the concepts, m ethods, and tools of interact ions analysis, leading us to develop a new set of criter ia for the design and evaluat ion of persuasive interact ions, which aim to provide user experience designers with more knowledge to develop persuasive interfaces, while respecting users’ ethics.

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Figure 14. Exam ple of a screen inspected with the set of guidelines for persuasiveness in interfaces. (Source: ht tp: / / www.polym t l.ca/ , Brangier and Desm arais 2013, 2014) .

Tips for Usability Practitioners Our approach is to define guidelines to m easure and assess the persuasive dim ensions of user experience. The following t ips are recom mended for those who are studying persuasive system s:

x I n addit ion to accessibilit y, usabilit y, and em ot ional cr iter ia, HCI experts can provide efficient and effect ive guidance to gauge how interfaces could be persuasive. A set of persuasive guidelines should be used by UX designers in interface evaluat ion and design to im prove the psychosocial aspect of the interfaces.

x The set of eight cr iter ia is well ident ified by HCI experts. Consequent ly, it encourages the use of credibilit y, privacy, personalizat ion, at t ract iveness, solicitat ion, prim ing, com mitm ent , and ascendency criteria to understand how persuasive elem ents work in interfaces. The im portance of persuasive elem ents in interface design can be an im portant factor in developing or evaluat ing a study that relies on that technology.

x I n addit ion to the criteria of clarity, consistency, brevity, adaptabilit y, etc., persuasive guidelines should/ could also be included in the t raining of UX designers.

Acknowledgments. The authors would like to thank SAP (Front de Seine - 157-159 rue Anatole France - 92309 Levallois-Perret cedex) and especially Steve Kopp, Christophe Favart , and Chahab Nastar, PhD for their involvem ent and for their num erous insight ful com ments on this research.

Special thanks to the three reviewers for their const ruct ive com ments on this paper.

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References Adam s, M.A., Marshall, S.J., Dillon, L., Caparosa, S., Ram irez, E., Phillips, J., & Norm an, G.J.

(2009) . A theory-based fram ework for evaluat ing exergam es as persuasive technology. Proceedings of the 4th International Conference on Persuasive Technology. New York, NY : ACM.

Bach, C. (2004) . Élaboration et validation de Critères Ergonomiques pour les Interactions Homme-Environnements Virtuels. Thèse de Doctorat , Université Paul Verlaine, Metz.

Bach, C., & Scapin D. L. (2003) . Adaptat ion of ergonom ic criteria to hum an-virtual environm ents interact ions. I n Interact’03 (pp. 880-883) . Zürich, Switzerland) : I OS Press.

Bach, C., & Scapin, D.L. (2010) . Com paring inspect ions and user test ing for the evaluat ion of virtual environm ents. International Journal of Human-Computer Interaction, 26(8) , 786-824.

Baranowski, T., Buday, R., Thom son, D., & Baranowski J. (2008) . Playing for real video gam es and stories for health- related behavior change. Am J Prev Med, 34(1) , 74-82.

Bart , Y., Venkatesh, S., Fareena, S., & Urban, G.L. (2005) . Are the drivers and role of online t rust the sam e for all web sites and consum ers? A large-scale exploratory em pir ical study, Journal of Marketing, 69, 133-52.

Bast ien, J. M. C (1996) . Les critères ergonomiques: Un pas vers une aide méthodologique à l’évaluation des systèmes interactifs. Thèse de Doctorat , Université René Descartes, Paris.

Bast ien, J. M. C., & Scapin, D. L. (1992) . A validat ion of ergonom ic criter ia for the evaluat ion of hum an-com puter interfaces. International Journal of Human-Computer Interaction, 4 (156) , 183-196.

Bergeron, J., & Rajaobelina, L. (2009) . Antecedents and consequences of buyer-seller relat ionship quality in the banking sector. The International Journal of Bank Marketing, 27 (5) , 359-380.

Brangier, E., & Desm arais, M. (2013) . The design and evaluat ion of the persuasiveness of e-learning interfaces. International Journal of Conceptual Structures and Smart Applications. Special issue on Persuasive Technology in Learning and Teaching. 1(2) , 38-47.

Brangier, E., & Desm arais, M. (2014) . Heurist ic inspect ion to assess persuasiveness: a case study of a mathem at ics e- learning program. I n A. Marcus (Ed.) : DUXU 2014, Part I . Springer I nternat ional Publishing. LNCS 8517, pp. 425–436.

Chaiken, S. Liberm an, A., & Eagly, A.H. (1995) . Heurist ic and system at ic processing within and beyond the persuasion context . I n J.S. Ulem an, J.A. & Bargh,(Eds.) , Attitude Strength: Antecedents and Consequences (pp. 387-412) . NJ: Erlbaum .

Consolvo, S., Everit t , K., Sm ith, I . , & Landay, J.A. (2006) . Design requirem ents for technologies that encourage physical act ivity. Proceedings of the ACM Conference on Human Factors in Computing systems , CHI 2006 (pp. 457-466) . New York, NY: ACM.

Dham ija, R., Tygar, J.D., & Hearst , M. (2006) . Why phishing works. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2006 (pp. 581-590) . New York, NY: ACM.

Fogg, B. J. (2003) . Persuasive technology: Using computers to change what we think and do. San Francisco: Morgan Kaufm ann.

Greenwald, A.G., & Banaj i, M.R. (1995) . I m plicit social cognit ion: At t itudes, self-esteem and stereotypes. Psychological Review, 102, 4-27.

Huang, X. (2009) . A review of credibilist ic port folio select ion. Journal Fuzzy Optimization and Decision Making archive, 8(3) , 263-281

Khaled, R., Barr, P., Noble, J., & Biddle, R. (2006) . I nvest igat ing social software as persuasive technology. Proceedings of the First International Conference on Persuasive Technology for Human Well-Being, Persuasive06. Berlin Heidelberg: Spr inger.

127


Landis, J. R., & Koch, G. G. (1977) . The m easurem ent of observer agreem ent for categorical data. Biometrics, 33, 159–174.

Liu, C., Marchewka, J., Lu, J. , & Yu, C. (2005) . Beyond concern: A privacy- t rust -behavioral intent ion m odel of elect ronic com merce, Information and Management, 42, 289-304.

Lockton, D., Harrison, D., & Stanton, N. (2010) . The design with intent m ethod: A design tool for influencing user behaviour. Applied Ergonomics, 41(3) , 382-392.

Mahatody, T., Sagar M., & Kolski C. (2010) . State of the art on the cognit ive walkthrough m ethod, its variants and evolut ions. International Journal of Human-Computer Interaction, 26(8) , 741-785.

McGuire, W.J. (1969) . At t itude and at t itude change. I n G. Lindzey & E. Aronson (Eds.) , Handbook of social psychology (2nd. ed., pp. 136-314) Reading, MA: Addison-Wesley.

Nass, C., Fogg, B.J., & Moon, Y. (1996) . Can com puters be team m ates? International Journal of Human-Computer Studies, 45(6) , 669-678.

Ném ery, A. (2012) . Élaborat ion, validat ion et applicat ion de la grille de cr itères de persuasion interact ive. Thèse de Doctorat , Université de Lorraine, Metz. ht tp: / / tel.archives-ouvertes.fr / tel-00735714

Ném ery, A., Brangier, E., & Kopp, S. (2009) . How cognit ive ergonom ics can deal with the problem of persuasive interfaces: I s a criteria-based approach possible? I n L. Norros, H. Koskinen, L. Salo, & P. Savioja. Designing beyond the product: understanding activity an user experience in ubiquitous environments (pp. 61-64) . ECCE’2009, European Conference on Cognitive Ergonomics. New York, NY: ACM.

Ném ery, A., Brangier, E., & Kopp, S. (2010) . Proposit ion d’une grille de critères d’analyses ergonom iques des form es de persuasion interact ive I n B. David, M. Noirhom m e et A. Tricot (Eds) , Proceedings of IHM 2010, International Conference Proceedings Series (pp. 153-156) . New York, NY: ACM.

Ném ery, A., Brangier, E., & Kopp, S. (2011) . First validat ion of persuasive criteria for designing and evaluat ing the social influence of user interfaces: Just ificat ion of a guideline. I n A. Marcus (Ed) , Design, User Experience, and Usability (pp.616-624) , LNCS 6770.

Oinas-Kukkonen, H. (2010) . Behavior change support system s: Research agenda and future direct ions. Lecture Notes for Com puter Science, Persuasive2010, 6137, 4-14.

Oinas-Kukkonen, H., & Harjum aa, M. (2009) . Persuasive system s design: Key issues , process m odel, and system features. Communications of the Association for Information Systems, 24(1) , 485-500.

Peppers P., & Rogers M. (1998) . Bet ter business one custom er at a t im e. The Journal For Quality and Participation, Cincinnat i, 21(2) , 30-37.

Pet ty, R.E., & Cacioppo, J.T. (1981) . Personal involvem ent as a determ inant of argum ent -based persuasion. Journal of Personality and Social Psychology, 41, 847-855.

Pine I I , B.J. (1993) . Mass Customization. Boston: Harvard Business School Press.

Randolph, J. J. (2005, October 14-15) . Free-m arginal m ult irater kappa: An alternat ive to Fleiss' fixed-m arginal m ult irater kappa. Paper presented at the Joensuu University Learning and Instruction Symposium 2005, Joensuu, Finland.

Redst röm , J. (2006) . Persuasive design: Fringes and foundat ions. Persuasive 2006, LNCS 3962, pp. 112 – 122.

Tørning, K., & Oinas-Kukkonen, H. (2009) . Persuasive system design: State of art and future direct ions. ACM I nternat ional Conference Proceeding Ser ies, Proceedings of the Fourth International Conference on Persuasive Technology, Clarem ont , CA, USA. New York, NY: ACM.

Weiksner, G. M., Fogg, B. J., & Liu, X. (2008) . Six pat terns for persuasion in online social networks. I n Proceedings of the 3rd international conference on Persuasive Technology. Persuasive '08. Berlin Heidelberg: Springer.

128


Yang, K.C.C. (2005) . The influence of hum anlike navigat ion interface on users’ responses to I nternet advert ising. Telematics and Informatics, 23, 38-55.

About the Authors

Alexandra Némery Dr. Némery com pleted a PhD in Ergonom ics of Persuasive Systems. She is working as a User Experience Manager at Sage Com pany in Par is. She’s also teaching Hum an Computer I nteract ion at a Com puter Engineer ing School. She is an expert in hum an-com puter interact ion, web usability, st rategy and m arket ing.

Eric Brangier Pr. Dr. Brangier is Full Professor at the University of Lorraine. His field of expert ise is in the dom ains of Cognit ive Ergonom ics, Hum an-Com puter I nteract ions, Prospect ive Ergonom ics and Psychology of the user. He has also been working as an expert for different inst itut ions in France, Canada and Belgium.

set of guidelines for persuasive interfaces: …beck/csc8570/papers/nemery.pdfto present and...

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