transforms.me

TRANSFORMATIONAL SOCIOTECH DESIGN

Prof. AGNIS STIBE

https://www.slideshare.net/leannagingras/session-1-process-interviewing-1

http://blog.earnest-agency.com/blog/5-everyday-terrible-user-experiences-and-how-to-avoid-them

https://echristensen42.com/2016/10/07/creating-your-own-pathways-through-the-cloud/usesidewalks/

https://techcrunch.com/2016/07/22/bad-ux-kills/

https://techcrunch.com/2016/07/22/bad-ux-kills/

NOT change

SELF-CONTAINED

ARE changing

SELF-DRIVEN

WANT to change

JANUARY1st

ATTITUDE

BEHAVIOR

ENVIRONMENT

1202014

€ 30 000 000FROM/VON2010 —

TO/BIS2014

HighlightsRadwege-BauprojekteWichtige Radprojekte, in den Jahren 2010 bis 2014 umgesetzt: 1: Ottakringer Straße, 2: Ring-Rund-Radweg, 3: Radwege rund um den Hauptbahnhof, 4: Landstraßer Gürtel, 5: Zentrum Meidling, 6: Kagraner Platz, 7: fahrradfreundliche Hasnerstraße

Generelle Radverkehrsplanungund StudienAuswahl aus Konzepten und Studien: Radlangstrecken und Lückenschlüsse, befahrbare Haltestellenkaps für RadfahrerInnen, Piktogramme und Pfeile zur Erhöhung der Verkehrssicherheit, Radfahren gegen die Einbahn

1 2

45

6

3

7

Radfahrengegendie Einbahn

+ 16 %

StVO-Novelle umgesetztFahrradstraße: 1.650 mBenutzungspflicht bei Radwegen aufgehoben: 1.970 mBegegnungszonen: 1.200 m

DetailplanungMehr als 600 Einzelmaßnahmen für den (fließenden und ruhenden) Radverkehr pro Jahr, unter breiter interdisziplinärer Beteiligung am Planungs- und Umsetzungsprozess: Dienststellen, Bezirke, Wirtschaftskammer, Polizei etc. (bis zu 30 Beteiligte)

Radfahrnetz

Citybike-Stationen

+ 96 km2010

Budget für die Radinfrastruktur

MillionenEuro

(6 Mio. p.a.)

30

792010

Winterdienst

266 km prioritär geräumte Radwege

Errichtete Radabstellplätze

2010

+ 9.588

27.329 Stück

2014

36.917 Stück

2014 1.270 km

1.174 km

Radinfrastruktur 2010 – 2014

Tempo-30-Zonen in Wien

Befahrbare Haltestellenkaps für

RadfahrerInnen

Radlangstrecken

Piktogrammeund Pfeile

zur Erhöhung der Verkehrs-sicherheit

Radfahrengegen

die Einbahn

Modal Split RadverkehrAnteil des Radverkehrs an den zurückgelegtenWegen der Wienerinnen und Wienern

2010: 1.472 km

2014: 1.657 km

4,6 % 7,1 %

2010

2014

EINBAHN

ausgen.

2010

208.790 m

ausgen.

242.420 mEINBAHN20

14

Impressum: Magistrat der Stadt Wien, Rathaus, A-1082 Wien, www.verkehr.wien.at

ATTITUDE

BEHAVIOR

CITY

http://performancecritical.com/handling-inattention-barrier-effective-communication/

Persuasive Cities for Sustainable Wellbeing

Dr. Agnis Stibe 25

http://inspiration.goreapparel.com/cycling-to-work/

http://www.mobilityweek.eu/

https://www.planning.org/blog/blogpost/9103093/http://willrunformargaritas.com/2011/05/bike-to-work-day.html

http://www.thisiscolossal.com/2014/10/lets-bike-it-bamboo-car-skeletons/

http://levieva.blogspot.fi/2011/06/social-psychology.html

CTCOMPETITION

SLLEARNING

SCCOMPARISON

CRCOOPERATION

NINORMATIVE

SFFACILITATION

RERECOGNITION

© Springer International Publishing Switzerland 2015 T. MacTavish and S. Basapur (Eds.): PERSUASIVE 2015, LNCS 9072, pp. 172–183, 2015. DOI: 10.1007/978-3-319-20306-5_16

Towards a Framework for Socially Influencing Systems: Meta-analysis of Four PLS-SEM Based Studies

Agnis Stibe( )

MIT Media Lab, Cambridge, MA, USA agnis@mit.edu

Abstract. People continuously experience various types of engagement through social media, mobile interaction, location-based applications, and other tech-nologically advanced environments. Often, integral parts of such socio-technical contexts often are information systems designed to change behaviors and attitudes of their users by leveraging powers of social influence, further de-fined as socially influencing systems (SIS). Drawing upon socio-psychological theories, this paper initially reviews and presents a typology of relevant social influence aspects. Following that, it analyzes four partial least squares structural equation modeling (PLS-SEM) based empirical studies to examine the inter-connectedness of their social influence aspects. As a result, the analysis pro-vides grounds for seminal steps towards the development and advancement of a framework for designing and evaluating socially influencing systems. The main findings can also deepen understanding of how to effectively harness social in-fluence for enhanced user engagement in socio-technical environments and guide persuasive engineering of future socially influencing systems.

Keywords: Socially influencing systems · Framework · Persuasive technology

1 Introduction

The dynamic evolution of social media, mobile connectivity, and a digital economy is continuously reshaping how businesses approach and engage customers [1]. Rapidly growing connectedness not only provides new methods for organizations to retain existing relationships with consumers, but also opens new ways to enrich customer engagement experiences and foster innovation [21]. Along the way, businesses and customers tend to naturally follow new market trends and steadily develop an under-standing of the spectrum of opportunities provided by emerging technologies. People seamlessly acquire new habits of interaction and consumption behavior, which then set their expectations about how products and services should be designed [34].

Customers increasingly demand products and services that better match their needs and individual preferences [29]. Therefore, businesses face a need to continuously understand the individual and evolving expectations of their customers [26]. Thus, organizations stand to benefit from systems that are designed to reach customers more proactively and provide convenient ways for interaction [32].

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Persuasive Cities for Sustainable Wellbeing

Dr. Agnis Stibe 36

MatthiasWunsch

SENSITIVEREAD FEEL SENSORS

SMARTCLASSIFY UNDERSTAND BIG DATA

SENSITIVEREAD FEEL SENSORS

PERSUASIVECHANGE CARE SOCIALLY D

INFLUENCING UX

SMARTCLASSIFY UNDERSTAND BIG DATA

SENSITIVEREAD FEEL SENSORS

UX

14COMPANIES

240EMPLOYEES

30000MILES

CT (58%)Competition

RARankings

PDPublic Display

CR (58%)Cooperation

EN (26%)Engagement

SF (21%)Social

facilitation

β = 0.23 ** (.13)

β = 0.27 ** (.14)

β = 0.62 *** (.45)

β = 0.35 *** (.16)

β = 0.23 ** (.10)

β = 0.61 *** (.44)

β = 0.32 ** (.13)

β = 0.24 ** (.08)

Persuasive Cities for Sustainable Wellbeing:Quantified Communities

Agnis Stibe(&) and Kent Larson

MIT Media Lab, Cambridge, USA{agnis,kll}@mit.edu

Abstract. Can you imagine a city that feels, understands, and cares about yourwellbeing? Future cities will reshape human behavior in countless ways. Newstrategies and models are required for future urban spaces to properly respond tohuman activity, environmental conditions, and market dynamics. Persuasiveurban systems will play an important role in making cities more livable andresource-efficient by addressing current environmental challenges and enablinghealthier routines. Persuasive cities research aims at improving wellbeing acrosssocieties through applications of socio-psychological theories and their inte-gration with conceptually new urban designs. This research presents anecosystem of future cities, describes three generic groups of people dependingon their susceptibility to persuasive technology, explains the process of definingbehavior change, and provides tools for social engineering of persuasive cities.Advancing this research is important as it scaffolds scientific knowledge on howto design persuasive cities and refines guidelines for practical applications inachieving their emergence.

Keywords: Persuasive technology ! Socially influencing systems !Wellbeing !Sustainability ! Urban design ! Health behavior change ! Quantifiedcommunities

1 Motivation

Quality of life and the health of the individual and communities are important subjectsthat can be studied and improved through the creation of persuasive cities, streets,buildings, homes, and vehicles [16]. Information technology and computer systems areincreasingly designed to support everyday routines and advance user experience inmultiple ways [6]. Novel computer systems can be also intentionally designed toinfluence how users think and behave. Theories of persuasion [18] and social influence[4] provide various strategies for the developers of such systems to facilitate desiredeffects on users.

Research on persuasive cities seeks to advance urban spaces to facilitate societalchanges. According to social sciences [2], any well-designed environment can become astrong influencer of what people think and do. There is an endlessly dynamic interactionbetween a person, a particular behavior, and an environment in which that behavior isperformed. This initiative leverages this knowledge to engineer persuasive environ-ments and intervention for altering human behavior on individual and societal levels.This research is primarily focused on socially engaging environments for supporting

© Springer International Publishing Switzerland 2016M. Younas et al. (Eds.): MobiWIS 2016, LNCS 9847, pp. 271–282, 2016.DOI: 10.1007/978-3-319-44215-0_22

PEOPLE METRIC

BEHAVIOR TODAY

CHANGE FUTURE

ML PEOPLEON 2/3 FLOORS

# TIMES ELEVATORS GO TO 2/3 FLOORS

USEELEVATORS 100

USESTAIRS 50

THE WHITE MIRROR TOLD THAT IS GOING TO HAPPEN. IT SAID THERE IS NO OTHER WAY. SOMETHING IS ABOUT TO LAND ON THIS PLANET. SOMETHING UNEXPECTEDLY STRONG AND RESILIENT. THE KINGS WONDERED AND COULD NOT BELIEVE THAT. THE KINGS ON THE BRIGHT SIDE OF THE PLANET TRIED TO PULL THEIR HEADS TOGETHER AND FIND A WAY TO PREPARE FOR THE INEVITABLE OCCASION…

ChristianavonHippel

COMPUTER – MEDIATED (CME)

FACE–TO–FACE (FTF)

COMPUTER – HUMAN (CHU)

COMPUTER – MODERATED (CMO)

INT

ERPE

RSO

NA

L

INFL

UEN

CE

USERBEHAVIOR

USERCONTENT

DYNAMICDESIGN

DYNAMICCONTENT

PERSUASIVEDESIGN

© Springer International Publishing Switzerland 2015 T. MacTavish and S. Basapur (Eds.): PERSUASIVE 2015, LNCS 9072, pp. 253–264, 2015. DOI: 10.1007/978-3-319-20306-5_23

Advancing Typology of Computer-Supported Influence: Moderation Effects in Socially Influencing Systems

Agnis Stibe( )

MIT Media Lab, Cambridge, MA, USA agnis@mit.edu

Abstract. Persuasive technologies are commonly engineered to change beha-vior and attitudes of users through persuasion and social influence without us-ing coercion and deception. While earlier research has been extensively focused on exploring the concept of persuasion, the present theory-refining study aims to explain the role of social influence and its distinctive characteristics in the field of persuasive technology. Based on a list of notable differences, this study outlines how both persuasion and social influence can be best supported through computing systems and introduces a notion of computer-moderated in-fluence, thus extending the influence typology. The novel type of influence tends to be more salient for socially influencing systems, which informs design-ers to be mindful when engineering such technologies. The study provides sharper conceptual representation of key terms in persuasive engineering, drafts a structured approach for better understanding of the influence typology, and presents how computers can be moderators of social influence.

Keywords: Influence typology · Computer-moderated · Persuasive technology · Computer-mediated · Computer-human · Socially influencing systems

1 Introduction

Persuasive technologies are commonly engineered to change behavior and attitudes of users through persuasion and social influence without using coercion and deception [9]. Both persuasion [22] and social influence [11] have been studied as concepts in behavioral, cognitive, and social psychology for long time. Evidently, they both exert capacity to alter human attitude and behavior, but each of them employs specific attributes to achieve that through face-to-face communication and presence in the physical world [3], [21].

While computers are becoming ubiquitous as tools, media, and social actors, it is necessary to clarify how the concepts of persuasion and social influence can be engi-neered in computing systems [9], [29]. More importantly, before designing such per-suasive systems, scholars and practitioners should be aware of how each concept can be operationalized and what consequences each design component can bear [28].

According to Fogg [9], people can respond socially to computer products, which opens the door for social influence aspects [29] to exert their powers of motivating and persuading users. Thus, computers can be perceived as social entities or actors

88%PEDALED

Michael Lin

https://vimeo.com/151414235

https://vimeo.com/151414235

ATTITUDE

BEHAVIOR

ENVIRONMENT

SELF-CONTAINED

SELF-DRIVEN

JANUARY1st

SELF-DRIVEN

SELF-CONTAINED

JANUARY1st

POSITIVEOUTCOME

NEGATIVEOUTCOME

INTENDED

UNINTENDED

MAJORSEVERITY

MINORSEVERITY

HIGHLIKELIHOOD

LOWLIKELIHOOD

BACKFIRING

DARK PATTERNS

TARGET BEHAVIOR

SURPRISEBEHAVIOR

MINORSEVERITY

MAJORSEVERITY

LOWLIKELIHOOD

HIGHLIKELIHOOD

Poor Judgment

MistailoringMistargeting

MisdiagnosingMisanticipating

Social Psychology

Anti-ModelingReverse Norming

Personality Responses

Defiance ArousingSelf-Licensing

Fineprint Fallacy

Overemphasizing

Inexperience

Superficializing

Credibility Damage

Self-DiscreditingMessage Hijacking

Persuasive Backfiring: When Behavior ChangeInterventions Trigger Unintended

Negative Outcomes

Agnis Stibe1(&) and Brian Cugelman2,3

1 MIT Media Lab, Cambridge, MA, USAagnis@mit.edu

2 Statistical Cybermetrics Research Group,University of Wolverhampton, Wolverhampton, UK

brian@alterspark.com3 AlterSpark, Toronto, ON, Canada

Abstract. Numerous scholars study how to design evidence-based interven-tions that can improve the lives of individuals, in a way that also brings socialbenefits. However, within the behavioral sciences in general, and the persuasivetechnology field specifically, scholars rarely focus-on, or report the negativeoutcomes of behavior change interventions, and possibly fewer report a specialtype of negative outcome, a backfire. This paper has been authored to start awider discussion within the scientific community on intervention backfiring.Within this paper, we provide tools to aid academics in the study of persuasivebackfiring, present a taxonomy of backfiring causes, and provide an analyticalframework containing the intention-outcome and likelihood-severity matrices.To increase knowledge on how to mitigate the negative impact of interventionbackfiring, we discuss research and practitioner implications.

Keywords: Backfire ! Taxonomy ! Behavior change ! Intention-outcomematrix ! Likelihood-severity matrix ! Persuasive technology ! Interventiondesign

1 Introduction

Scholars have focused on the ways in which technology can produce positive out-comes, such as increasing users’ physical activity [29], reducing binge drinking [10],quitting smoking [26], or managing mood and anxiety disorders [14]. There is con-siderable research on this topic, with several systematic reviews and meta-analyses thatfocus on a wide variety of positive outcomes [9, 38].

However, few papers report negative outcomes, and possibly fewer report a specialclass of negative outcomes, called a backfire, which we define as an intervention thattriggers audiences to adopt the opposite target behavior, rendering the interventionpartially responsible for causing the behavior it was designed to reduce or eliminate.

Examples of backfiring interventions include drug use reduction programs thattrigger drug use by accidently creating a social norm that triggers some youth to feellike everyone else is trying drugs except for them; traffic safety campaigns that use

© Springer International Publishing Switzerland 2016A. Meschtscherjakov et al. (Eds.): PERSUASIVE 2016, LNCS 9638, pp. 65–77, 2016.DOI: 10.1007/978-3-319-31510-2_6

421 3 5 6 87

109 11 12 13 14

Roadmap for Autonomous Cities: Sustainable Transformation of Urban Spaces

Twenty-third Americas Conference on Information Systems, Boston, 2017 1

Roadmap for Autonomous Cities: Sustainable Transformation of Urban Spaces

Full Paper Ariel Noyman MIT Media Lab

noyman@mit.edu

Agnis Stibe MIT Media Lab agnis@mit.edu

Kent Larson MIT Media Lab

kll@mit.edu

Abstract Despite the inherent relationship between cars and their physical urban surroundings, many cities are hesitant to embrace the impact of autonomous mobility on urban design. Industry leaders envision autonomous vehicles soon penetrating global markets, although the relationship between autonomous vehicles and their urban context has been poorly discussed. Witnessing rapid technological advancement and tardiness of city planning and execution, the proposed research diverts discourse from intrinsic technology of autonomous vehicles to their impact on urban design. This paper offers a review of historical cars-oriented design and the global surrender to car-culture in the past century. Then, it elaborates on different autonomous technologies and their potential impact on urban form. Furthermore, it shares plural plausible future perspectives to initiate a discussion on tangible implications of autonomous vehicles on contemporary cities. Ultimately, this research suggests a preliminary roadmap to the way autonomous mobility might be incorporated within new and existing cities.

Keywords

Autonomous vehicles, urban planning, mobility, sustainability, persuasive cities, urban design

Introduction Modern societies continuously require novel ways for sustainability modeling and reporting (Ahmed & Sundaram 2012). The promise of autonomous vehicles (AVs) and gradual shifting towards on-demand transit are leading to a paradigm shift in the way cities accommodate mobility. In many ways, this change has already begun (Stibe & Larson 2016): Recently, after decades of incline, private car ownership went down for the first time in the history of the US. Urban rebirth and ‘back to the city’ movements are defying suburbia; trucks, cars and bicycle-sharing platforms are changing the way people and goods move in cities. While these trends are projected to increase in coming years, contemporary cities are focused primarily on patching holes in old mobility systems or proposing incremental changes to existing infrastructure. As of today, little research has been offered on a compressive vision for the relationship between AVs and cities.

The importance of such debate is tied to the pace by which new mobility technologies are announced and marketed. Both academic and non-academic publishing is seeing an ever-growing discourse on all angles of this subject. Despite this growing interest, predictions concerning the rate and depth of AVs adaptation are varying dramatically. Certain assumptions conclude that market-ready AVs would become a common commodity within less than 5 years. Others are more skeptical and titling these predictions as ‘trends’, ‘science fiction’ or even comparing them to ‘moon colonies’ as fictional technologies that ultimately vanished. Nevertheless, industry leaders, government officials, policy-makers and large percentages of the public all agree that a change is coming: A recent study done by IHS Automotive concludes that more than 54 million self-driving cars are proclaimed to roam the roads by 2035 and by 2050, all cars will be autonomous. AV market is predicted to amount to $42 billion by 2025 and to reach $77 billion by 2035, when AVs will amount to a quarter of all cars. Recently, US government committed to invest $4 billion in

Djurgården 2

Advice Accenture Latvia Magazine | Issue 5 | May 2017

Advice fromAgnis Stibe,Transformational Designer

Nowadays, there are experts in technology, experts in sensors, data analytics, machine learning, and so on – we have this fragmented expertise and we are just refining technology, but there is no one really thinking about and defining the purpose and contribution of these innovations. Is there anyone responsible for painting such a big picture, containing all the emerging innovations and their interplay in the future?

I’d like to address a concern that many people have — that AI is going to be a threat. For thousands of years the mankind had a very simple evolutionary driver — to survive. Now, we have collectively evolved to a different level of intelligence. We have escaped from the worries that we had hundreds of years ago. However, it is very likely that the same mechanism of fear that helped people to survive back then, now triggers us to be afraid of these unknown new technologies. We natu-rally look out for danger whenever we face uncertainty. Today, many don’t know what AI really is, thus are becoming scared of it. Quite commonly, our thoughts shape how we feel. Both positive and negative thinking continuously influence and determine our lives and wellbeing. While fear can be powerful behavioral motivator, it is important to recognize that fear crowds the mind with negative emotional beliefs. Thus, the opportunity today is in building forward-thinking and tech-nology-supported societies by minimizing fear driven aspects and maximizing the behavioral drivers of positive pursuit.

I have a strong opinion that intelligence is something that people have developed over centuries of evolution.

Thus, for machines to be able to resemble anything similar to human intelligence is almost impossible, because people create machines. Consequently, there will always be people at the very inception of any technology, so there will always be traces of human footprints and the biases of creators in AI. Nevertheless, machines have multiple strengths, such as abili-ties to process big data, compute much faster, and store large volumes of fine grain details. Undoubtedly, those are great benefits machines can provide to people. But, for intelligence to emerge, any machine has to become creative, right? So, the question is – if or when a machine can become as creative as a human? It is essentially important to explain to everybody that machines can get only as intelligent as we make them. Com-plete intelligence needs to have high levels of independence, and I don’t believe that machines can reach that state of being independent of human support. We always direct machines to complete certain tasks that we want them to perform.

While working with machines, we also have to understand how technology influences our behavior. New technological advancements may also enable negative behavioral impacts, for example – digital addiction, distracted sense of reality, in-activity, cyber-crimes, etc. Nevertheless, let’s be mindful as, in the end, we are making our final choices. Therefore, let’s design our future environments as ones that orchestrate more posi-tive, encouraging, and fulfilling series of daily interactions. The opportunities – and challenges – are right there in front of us to guide our future in a more purposeful yet rewarding way.

The purpose of innovationsArtificial intelligence (AI) is going to be just another buzzword if we won’t try zooming out and locating it within a bigger picture of our lives. What is AI? What is the purpose of AI? This discussion is currently missing from the discourse around AI. To a certain degree, everyone is interested in talking about AI, many say it is important, but why? How? For whom? How AI will influence our daily routines? Finding answers to these questions is essential already today.

Agnis is a Social Engineer at MIT Media Lab: transforms.me

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